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The Psychophysiology of Real-Time FinancialRisk Processing
Andrew W Lo1 and Dmitry V Repin2
Abstract
amp A longstanding controversy in economics and finance iswhether financial markets are governed by rational forces or byemotional responses We study the importance of emotion inthe decision-making process of professional securities tradersby measuring their physiological characteristics (eg skinconductance blood volume pulse etc) during live tradingsessions while simultaneously capturing real-time prices fromwhich market events can be detected In a sample of 10
traders we find statistically significant differences in meanelectrodermal responses during transient market eventsrelative to no-event control periods and statistically significantmean changes in cardiovascular variables during periods ofheightened market volatility relative to normal-volatility con-trol periods We also observe significant differences in thesephysiological responses across the 10 traders that may besystematically related to the tradersrsquo levels of experience amp
INTRODUCTION
The spectacular rise of US stock market prices in thetechnology sector over the past few years and the evenmore spectacular crash last year has intensified the well-worn controversy surrounding the rationality of invest-ors Most financial economists are advocates of thelsquolsquoEfficient Markets Hypothesisrsquorsquo (Samuelson 1965) inwhich prices are determined by the competitive tradingof many self-interested investors and such trading elim-inates any informational advantages that might existamong any members of the investment communityThe result is a market in which prices lsquolsquofully reflect allavailable informationrsquorsquo and are therefore unforecastable
Critics of the Efficient Markets Hypothesis argue thatinvestors are oftenmdashif not alwaysmdashirrational exhibitingpredictable and financially ruinous biases such as over-confidence (Barber amp Odean 2001 Gervais amp Odean2001 Fischoff amp Slovic 1980) overreaction (DeBond ampThaler 1986) loss aversion (Odean 1998 Shefrin ampStatman 1985 Kahneman amp Tversky 1979) herding(Huberman amp Regev 2001) psychological accounting(Tversky amp Kahneman 1981) miscalibration of proba-bilities (Lichtenstein Fischoff amp Phillips 1982) andregret (Clarke Krase amp Statman 1994 Bell 1982)The sources of these irrationalities are often attributedto psychological factorsmdashfear greed and other emo-tional responses to price fluctuations and dramaticchanges in an investorrsquos wealth Although no clearalternative to the Efficient Markets Hypothesis has yetemerged a growing number of economists psycholo-gists and financial-industry professionals have begun to
use the terms lsquolsquobehavioral economicsrsquorsquo and lsquolsquobehavioralfinancersquorsquo to differentiate themselves from the standardorthodoxy (Shefrin 2001) The fact that the currentvalue of the Nasdaq Composite Index a bellwetherindicator of the technology sector is 164634 (October17 2001)mdashonly 326 of its historical high of 504862(March 10 2000) reached less than 2 years agomdashlendscredence to the critics of market rationality Such criticsargue that either the earlier run-up in the technologysector was driven by unbridled greed and optimism orthat the precipitous drop in value of such a significantportion of the US economy must be due to irrationalfears and pessimism
However recent research in the cognitive sciencesand financial economics suggest an important link be-tween rationality in decision making and emotion (Loe-wenstein 2000 Peters amp Slovic 2000 Lo 1999 Elster1998 Damasio 1994 Grossberg amp Gutowski 1987)implying that the two notions are not antithetical butin fact complementary
In this study we attempt to verify this link experi-mentally by measuring the real-time psychophysiologicalcharacteristicsmdashskin conductance blood volume pulse(BVP) heart rate (HR) electromyographical signalsrespiration and body temperaturemdashof professional se-curities traders during live trading sessions Using port-able biofeedback equipment we are able to measurethese physiological characteristics in a traderrsquos naturalenvironment without disrupting their workflow whilesimultaneously capturing real-time financial pricing datafrom which market events can be defined By matchingthese events with the tradersrsquo psychophysiological re-sponses we are able to determine the relation between1MIT Sloan School of Management 2Boston University
copy 2002 Massachusetts Institu te of Technology Jou rnal of Cogn itive Neuroscience 143 pp 323- 339
financial risk measures and emotional states and dynam-ics In a pilot sample of 10 traders we find statisticallysignificant differences in mean electrodermal responsesduring transient market events as compared with no-event control baselines and statistically significant meanchanges in cardiovascular variables during periods ofheightened market volatility as compared with normal-volatility control baselines We also observe significantdifferences in mean physiological responses among the10 traders that may be systematically related to theamount of trading experience
In studying the link between emotion and rationaldecision making in the face of uncertainty financialsecurities traders are ideal subjects for several reasonsBecause the basic functions of securities trading involvefrequent decisions concerning riskreward trade-offstraders are almost continuously engaged in the activitythat we wish to study This allows us to conduct ourstudy in vivo and with minimal interference to andtherefore minimal contamination of the subjectsrsquo natu-ral motives and behavior Traders are typically providedwith significant economic incentives to avoid many ofthe biases that are often associated with irrational invest-ment practices Moreover they are highly paid profes-sionals that have undergone a variety of trainingexercises apprenticeships and trial periods throughwhich their skills have been finely honed Thereforethey are likely to be among the most rational decisionmakers in the general population hence ideal subjectsfor examining the role of emotion in rational decision-making processes Finally due to the real-time nature ofmost professional trading operations it is possible toconstruct accurate real-time records of the environmentin which traders make their decisions namely thefluctuations of market prices of the securities that theytrade With such real-time records we are able to matchmarket events such as periods of high price-volatilitywith synchronous real-time measurements of physiolog-ical characteristics
To measure the emotional responses of our subjectsduring their trading activities we focus on indirectmanifestations through the responses of the autonomicnervous system (ANS) (Cacioppo Tassinary amp Bernt2000) The ANS innervates the viscera and is responsiblefor the regulation of internal states that are mediated byinternal bodily as well as emotional and cognitiveprocesses ANS responses are relatively easy to measuresince many of them can be measured noninvasively fromexternal body sites without interfering with cognitivetasks performed by the subject ANS responses occur onthe scale of seconds which is essential for investigationof real-time risk processing
Previous studies have focused primarily on time-aver-aged (over hours or tens of minutes) levels of autonomicactivity as a function of task complexity or mental strainFor example some experiments have considered thelink between autonomic activity and driving conditions
and road familiarity for nonprofessional drivers (Brownamp Huffman 1972) and the stage of flight (take-offsteady flight landing) in a jetfighter flight simulator(Lindholm amp Cheatham 1983) Recently the focus ofresearch has started to shift towards finer temporalscales (seconds) of autonomic responses associated withcognitive and emotional processes Perhaps the mostinfluential set of experiments in this area was conductedin the broad context of an investigation of the role ofemotion in decision-making processes (Damasio 1994)In one of these experiments skin conductance re-sponses (SCRs) were measured in subjects involved ina gambling task (Bechara Damasio Tranel amp Damasio1997) The results indicated that the anticipation of themore risky outcomes led to more SCRs than of the lessrisky ones The brain circuitry involved in anticipatingmonetary rewards has also been localized (BreiterAharon Kahneman Dale amp Shizgal 2001) Anotherstudy (Frederikson et al 1998) reported neuroanatom-ical correlates of skin conductance activity with the brainregions that also support anticipation affect and cogni-tively or emotionally mediated motor preparation Re-cent experiments (Critchley Krase amp Statman 1999)support the significance of autonomic responses duringrisk-taking and reward-related behavior They providemore details on brain activation correlates of peripheralautonomic responses and also claim the possibility ofdiscriminating the activity patterns related to changingversus continuing the behavior based on the immediategainloss history in a gambling task
We focus on five types of physiological characteristicsin our study skin conductance cardiovascular data (BVPand HR) electromyographic (EMG) data respirationrate and body temperature
SCR is measured by the voltage drop between twoelectrodes placed on the skin surface a few centimetersapart Changes in skin conductance occur when eccrinesweat glands that are innervated by the sympathetic ANSfibers receive a signal from a certain part of the brainThree distinct lsquolsquobrain information systemsrsquorsquo can poten-tially elicit SCR signals (Boucsein 1992) The affectarousal system is capable of generating a phasic re-sponse (on the scale of seconds) that is associateddirectly with the sensory input indicating attentionfocusing or defense response and the amygdala is theprimary brain region involved The emotional responseto a novel or highly complex task is an example of affectarousal Another system that can initiate a phasic SCRcenters on the basal ganglia and is related to a prepar-atory activation mediated by internal cognitive pro-cesses The body exhibits increased perceptual andmotor readiness and high attention levels Expectationof an event or preparation to an important actionillustrates the operation of this system The third systemoften called the lsquolsquoeffort systemrsquorsquo produces tonic changesin the level of skin conductance and is related to thelong-term changes of a general emotional (hedonic)
324 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
state or attitude indicating a nonspecific increase inattention or arousal Hippocampal information process-ing is believed to be behind this system which isassociated with a higher degree of conscious aware-ness while the first two are mostly attributed tosubconscious processes
The cardiovascular system consists of the heart and allthe blood vessels and the variables of particular interestare BVP and HR BVP is the rate of flow of blood througha particular blood vessel and is related to both bloodpressure and the diameter of the vessel Constriction ordilation of the vessels is controlled by the sympatheticbranch of the ANS and along with electrodermal activityit has been shown to be related to information process-ing and decision making (Papillo amp Shapiro 1990) HRrefers to the frequency of the contractions of the heartmuscle or myocardium Specialized neuronsmdashthe so-called lsquolsquopacemakerrsquorsquo cellsmdashinitiate the contraction ofmyocardium and the output of the pacemakers iscontrolled by both parasympathetic (HR decrease) andsympathetic (HR increase) ANS branches BVP and HRtrack each other closely so that HR deceleration usuallycauses an increase in BVP Compared to SCR arousalindications that refer mostly to cognitive processeschanges in cardiovascular variables register higher levelsof arousal which are often somatically mediated Thesevariables may provide supplemental information forinterpreting high SCR signalsmdashelevated SCR accompa-nied by an increase in HR may be an indication ofextreme significance of the task or stimulus or alter-natively simply indicate that some physical activity isbeing performed simultaneously (Boucsein 1992)
EMG measurements are based on the electrical signalsgenerated by the contraction process of striatedmuscles Muscle action potentials travel along themuscle and some portion of the electrical activity leaksto the skin surface where it can be detected with thehelp of surface electrodes The activation of particularmuscles reflects different types of actions For exampleactivation of the facial muscle (ie the lsquolsquomasseterrsquorsquomuscle) indicates ongoing speech activation of theforearm muscle group (ie the lsquolsquoflexor digitorumrsquorsquogroup) corresponds to finger movements such as typingon a keyboard In addition to their role in motor activitycertain muscle groups exhibit strong correlations withemotional states Most of the research in this literaturehas focused on facial muscles since facial expressionshave been linked to emotional states (Ekman 1982) Forexample an increase in EMG activity over the forehead isassociated with anxiety and tension and an increase inactivity over the brow accompanied by a slight decreasein activity over the cheek often corresponds to unpleas-ant sensory stimuli (Cacioppo et al 2000) ThereforeEMG measurements can capture very subtle changes inmuscle activity that can differentiate otherwise indistin-guishable response patterns However in our currentimplementation the role of EMG measurements is
limited to identifying and eliminating anomalous sensorreadings caused by certain physical motions of a subject
Respiration influences the HR through vascular recep-tors (Lorig amp Schwartz 1990) and although this variableis usually not of primary psychological importance it is areliable indicator of physically demanding activitiesundertaken by the subject for example speaking orcoughing which can often yield anomalous sensor read-ings if not properly taken into account Respiration canbe measured by placing a sensor that monitors chestexpansion and compression
Finally body temperature regulation involves the in-tegration of autonomic motor and endocrine responsesand several studies have related the temperatures ofdifferent parts of the body to certain cognitive and emo-tional contents of the task or stimuli For example fore-head temperature (a proxy for brain temperature)increases while experiencing negative emotions coolingenhances positive affect while warming depresses it(McIntosh Zajonc Vig amp Emerick 1997) Another studyreports that hand skin temperature increases with pos-itive affect and decreases with threatening and unpleas-ant tasks (Rimm-Kaufman amp Kagan 1996)
RESULTS
Our sample of subjects consisted of 10 professionaltraders employed by the foreign-exchange and inter-est-rate derivatives business unit of a major globalfinancial institution based in Boston MA This institu-tion provides banking and other financial services toclients ranging from small regional startups to Fortune500 multinational corporations The foreign-exchangeand interest-rate derivatives business unit employs ap-proximately 90 professionals of which two-thirds spe-cializes in the trading of foreign exchange and relatedinstruments and one-third specializes in the trading ofinterest-rate derivative securities In a typical day thisbusiness unit engages in 1000- 1200 trades with anaverage size of US$3- 5 million per trade Approximately80 of the trades are executed on behalf of the clientsof the financial institution with the remaining 20motivated by the financial institutionrsquos market-makingactivities
For each of the 10 subjects five physiological variableswere monitored in real time during the entire duration ofeach session while the subjects sat at their trading con-soles (see Figure 1) Six sensors were used SCR BVPbody temperature (TMP) respiration (RSP) and twoelectromyographic sensors (facial and forearm EMG)
The benefits of acquiring real-time physiological meas-urements in vivo must be balanced against the cost ofmeasurement error spurious signals and other statisticalartifacts which if left untreated can obscure and con-found any genuine signals in the data To eliminate asmany artifacts as possible while maximizing the informa-tional content of the data each of the five physiological
Lo and Repin 325
variables were preprocessed with filters calibrated toeach variable individually and adaptive time-windowswere used in place of fixed-length windows to matchthe event-driven nature of the market variables
After preprocessing the following seven features wereextracted from SCR BVP temperature and respirationsignals
1 times of onset of SCR responses2 amplitudes of SCR responses3 average HR (every 3 sec)4 BVP signal amplitudes5 respiration rates (every 5 sec)6 respiration amplitudes7 temperature changes (from the 10-sec lag)
These features were selected to reflect the differentproperties and different information contained in theraw physiological variables SCRs were characterized bysmooth lsquolsquobumpsrsquorsquo with a relatively fast onset and slowdecay hence the number of such bumps and theirrelative strength are excellent summary measures ofthe SCR signal Intervals of 3 and 5 sec for heart andrespiration rates were chosen as a compromise betweenthe need for finer time slices to distinguish the onsetand completion of physiological and market events andthe necessity of a sufficient number of heartbeats andrespiration cycles within each interval to compute anaverage Under normal conditions a typical subject willexhibit an average of three to four heartbeats per 3-secinterval and approximately three breaths per 5-sec in-terval Temperature was the slowest-varying physiolog-ical signal and the 10-sec interval to register its changereflected the maximum possible interval in our analysis
For each session real-time market data for key finan-cial instruments actively traded or monitored by thesubject were collected synchronously with the physio-logical data In particular across the 10 subjects a total of15 instruments were consideredmdash13 foreign currencies
and two futures contracts the euro (EUR) the Japaneseyen (JPY) the British pound (GBP) the Canadian dollar(CAD) the Swiss franc (CHF) the Australian dollar(AUD) the New Zealand dollar (NZD) the Mexican peso(MXP) the Brazilian real (BRL) the Argentinian peso(ARS) the Colombian peso (COP) the Venezuelan boli-var (VEB) the Chilean peso (CLP) SampP 500 futures(SPU) and eurodollar futures (EDU) For each securityfour time series were monitored (1) the bid price Pt
b (2)the ask price Pt
a (3) the bidask spread Xt sup2 Ptb iexcl Pt
aand (4) the net return Rt sup2 (Pt iexcl Ptiexcl1) Ptiexcl1 where Pt
denotes the mid-spread price at time t and all priceswere sampled every second
For each of the financial time series we identifiedthree classes of market events deviations trend-rever-sals and volatility events These events are often cited bytraders as significant developments that require height-ened attention potentially signaling a shift in marketdynamics and risk exposures With these definitions andcalibrations in hand we implemented an automaticprocedure for detecting deviations trend-reversals andvolatility events for all the relevant time series in eachsession Table 1 reports event counts for the financialtime series monitored for each subject Feature vectorswere then constructed for all detected market events inall time series for all subjects and a set of control featurevectors was generated by applying the same feature-extraction process to randomly selected windows con-taining no events of any kind One set of control featurevectors was constructed for deviation and trend-reversalevents (10-sec intervals) and a second set was con-structed for volatility-type events (5-min intervals) Atwo-sided t test was applied to each component of eachfeature vector and the corresponding control vector totest the null hypothesis that the feature vectors werestatistically indistinguishable from the control featurevectors
For volatility events which by definition occurred in5-min intervals (event windows) we constructed featurevectors containing the following information for theduration of the event window
1 number of SCR responses2 average SCR amplitude3 average HR4 average ratio of the BVP amplitude to local baseline5 average ratio of the BVP amplitude to global
baseline6 number of temperature changes exceeding 018F7 average respiration rate8 average respiration amplitude
where the local baseline for the BVP signal is the averagelevel during the event window and the global baseline isthe average over the entire recording session for eachtrader Control feature vectors were constructed byapplying the feature-extraction process to randomlyselected 5-min windows containing no events of any
Laptop with data acquisition software
Fiber optic cable Control unit
Market information (Reuters Bloomberg) Order screen etc TIBCO market spreadsheet
eal-time data feed the market data
acquisition computer
Figure 1 Typical set-up for the measurement of real-time physiolo-gical responses of financial traders during live trading sessions Real-time market data used by the traders are recorded synchronously andsubsequently analyzed together with the physiological response data
326 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
kind For the other two types of market eventsmdashdevia-tions and trend-reversalsmdashlsquolsquopre-eventrsquorsquo and lsquolsquopost-eventrsquorsquofeature vectors were constructed before and after eachmarket event using the same variables where featureswere aggregated over 10-sec windows immediately pre-ceding and following the event Control feature vectorswere generated for these cases as well
The motivation for the post-event feature vector wasto capture the subjectrsquos reaction to the event with thepre-event feature vector as a benchmark from which tomeasure the magnitude of the reaction A comparisonbetween the pre-event feature vector and a controlfeature vector may provide an indication of a subjectrsquosanticipation of the event Latencies of the autonomicresponses reported in previous studies were on a timescale of 1 to 10 sec (see Cacioppo et al 2000) hence10-sec event windows were judged to be long enough forevent-related autonomic responses to occur and at thesame time short enough to minimize the likelihood ofoverlaps with other events or anomalies Five-minutewindows were used for volatility events because 10-secintervals were simply insufficient for meaningful volatilitycalculations For all of the financial time series used inthis study and for most financial time series in generalthere are few volatility events that occur in any 10-secinterval except of course under extreme conditions forexample the stock market crash of October 19 1987 (nosuch conditions prevailed during any of our sessions)
The statistical analysis of the physiological and marketdata was motivated by four objectives (1) to identifyparticular classes of events with statistically significantdifferences in mean autonomic responses before or after
an event as compared to the no-event control (2) toidentify particular traders or groups of traders based onexperience or other personal characteristics that dem-onstrate significant mean autonomic responses duringmarket events (3) to identify particular financial instru-ments or groups of instruments that are associated withsignificant mean autonomic responses and (4) to iden-tify particular physiological variables that demonstratesignificant mean response levels immediately followingthe event or during the event-anticipation period ascompared to the no-event control
To address the first objective a total of eight types ofevents were used for each of the time series
1 price deviations2 spread deviations3 return deviations4 price trend-reversals5 spread trend-reversals6 maximum volatility7 price volatility8 return volatility
and for each type of event a two-sided t test wasperformed for the pooled sample of all subjects and alltime series in which mean autonomic responses duringevent periods were compared to mean autonomic re-sponses during no-event control periods
The t statistics corresponding to the two-sided hypoth-esis test are given in the left subpanel of Table 2 labelledlsquolsquoAll Tradersrsquorsquo Statistics that are significant at the 5levelmdashbased on the t distribution with the appropriatedegrees of freedom for each entrymdashare highlighted For
Table 1 Number of Deviation (DEV) Trend-Reversal (TRV) and Volatility (VOL) Events Detected in Real-Time Market Data forEach Trader Over the Course of Each Trading Session
EUR JPYOther MajorCu rrenciesa
Latin Am ericanCu rrenciesb Derivativesc
Trader ID DEV TRV VOL DEV TRV VOL DEV TRV VOL DEV TRV VOL DEV TRV VOL
B32 21 16 15 25 17 15 - - - - - - - - -
B34 18 10 14 17 17 14 - - - - - - - - -
B35 20 16 14 13 17 15 21 9 15 24 16 13 - - -
B36 - - - - - - - - - - - - 28 23 31
B37 19 19 14 18 18 12 18 18 14 105 86 63 - - -
B38 21 20 12 22 18 13 23 20 15 96 61 56 - - -
B39 - - - - - - - - - - - - 40 37 23
B310 10 17 10 18 16 14 116 59 61 - - - - - -
B311 17 15 15 17 16 15 94 61 67 - - - - - -
B312 19 17 15 16 19 13 107 83 71 - - - - - -
aGBP CAD CHF AUD NZDbMXP BRL ARS COP VEB CLPcEDU SPU
Lo and Repin 327
Tab
le2
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vec
tors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
All
Tra
ders
Hig
hEx
peri
ence
Low
orM
oder
ate
Expe
rien
ce
Phys
iolo
gy
Fea
ture
s
DE
V
Pri
ce
DEV
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VOL
Ma
xim
um
DE
V
Pri
ce
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VO
L
Ma
xim
um
DE
V
Pri
ce
DEV
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VO
L
Ma
xim
um
Nu
mbe
ro
fSC
Rre
spon
ses
28
72
060
42
08
81
66
40
636
057
90
561
122
8iexcl
022
1iexcl
126
10
184
20
81
26
96
012
32
75
22
17
0iexcl
066
5iexcl
068
8
Aver
age
SCR
ampl
itud
eiexcl
069
50
887
018
6iexcl
247
81
136
088
00
030
034
40
744
iexcl1
340
114
90
904
075
61
578
iexcl1
265
iexcl1
947
iexcl0
012
iexcl0
615
Aver
age
HR
090
5iexcl
058
8iexcl
059
6iexcl
061
71
032
iexcl0
270
044
91
88
6iexcl
179
7iexcl
163
51
83
6iexcl
201
81
310
005
1iexcl
038
2iexcl
110
6iexcl
031
11
283
Aver
age
BV
Pam
plit
ude
rela
tive
tolo
cal
base
line
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9iexcl
006
1iexcl
074
70
334
001
50
164
26
23
iexcl0
204
iexcl1
775
087
51
79
6iexcl
128
92
70
8
Aver
age
BV
Pam
plit
ude
rela
tive
togl
obal
base
line
141
7iexcl
272
62
32
41
417
iexcl2
211
28
86
iexcl2
529
iexcl1
367
159
01
110
iexcl1
165
23
01
19
17
iexcl2
664
18
84
20
82
iexcl1
759
27
58
Nu
mbe
ro
fte
mp
erat
ure
jum
ps
098
82
82
00
650
17
77
48
68
iexcl2
482
iexcl1
010
iexcl1
435
NA
NA
NA
164
40
837
29
02
114
52
44
52
63
3iexcl
321
3
Aver
age
resp
irat
ion
rate
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4iexcl
155
61
109
012
10
866
iexcl0
102
009
30
079
iexcl0
552
032
3iexcl
063
1iexcl
217
0iexcl
152
8
Aver
age
resp
irat
ion
ampl
itud
e
iexcl1
646
iexcl0
250
013
60
034
iexcl1
487
iexcl3
499
108
70
265
iexcl1
777
iexcl0
657
012
1iexcl
060
7iexcl
274
9iexcl
131
92
14
20
055
iexcl2
832
iexcl4
533
Bo
lded
stat
isti
csar
esi
gnifi
can
tat
the
5le
vel
Th
ele
ftp
anel
give
sag
greg
ate
resu
lts
for
allt
rad
ers
Sim
ilar
ttes
tsfo
rtr
ader
sw
ith
hig
hex
per
ienc
ean
dlo
wo
rm
oder
ate
exp
erie
nce
are
show
nin
the
mid
dle
and
righ
tp
anel
sre
spec
tive
lyE
ntr
ies
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
wer
ep
rese
nt
inei
ther
the
even
tor
cont
rol
feat
ure
vect
or
for
the
par
ticu
lar
mar
ket-
even
tty
pe
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spo
nse
sH
R=
hea
rtra
te
BV
P=
blo
od
volu
me
pul
se
328 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
deviations and trend-reversals both the number of SCRsand the number of temperature jumps reached statisti-cally significant levels for three out of the five event typesBVP amplitude-related features were highly significantfor volatility eventsmdashsignificance levels less than 1 wereobtained for both BVP amplitude features for all threetypes of volatility events Because the results were sosimilar across the three types of volatility events (max-imum volatility price volatility and return volatility) wereport the results only for maximum volatility in Table 2Such patterns of autonomic responses may indicate thepresence of transient emotional responses to deviationsand trend-reversal events that occur within 10-sec win-dows while volatility events defined in 5-min windowselicited a tonic response in BVP
To explore potential differences between experi-enced and less experienced traders a sample of 10traders was divided into two groups each consistingof five traders the first containing highly experiencedtraders and the second containing traders with low ormoderate experience where the levels of experiencewere determined through interviews with the tradersrsquosupervisors The results for each of the two groupsare reported in the two right subpanels of Table 2labelled lsquolsquoHigh Experiencersquorsquo and lsquolsquoLow or ModerateExperiencersquorsquo The high-experience traders generallyexhibited low responses for deviations and trend-re-versal events with only two types of market events(spread deviations and spread trend-reversals) elicitingsignificant mean autonomic responses (average HR)However even for high-experience traders volatilityevents induced statistically significant mean responsesfor both SCR and BVP amplitude Less experiencedtraders showed a much higher number of significantmean responses in the number of SCRs BVP ampli-tude and number of temperature increases for devia-tions and trend-reversal events Table 2 shows thatsessions with low- and moderate-experience tradersyielded 13 t statistics significant at the 5 level whilesessions with high-experience traders yielded only fivet statistics significant at the 5 level For both sets oftraders volatility events were associated with signifi-cant BVP amplitude The differences in responsepatterns for deviations and trend-reversals observedin this case indicate that less experienced traders maybe more sensitive to short-term changes in the marketvariables than their more experienced colleagues
To address the third objective 10-sec intervals imme-diately preceding and immediately following each devia-tion and trend-reversal event were compared to control(ie no-event) time intervals Two separate t tests wereconductedmdashone for pre- and another for post-eventtime intervalsmdashfor each of the three types of deviationsand two types of trend-reversals Because the objectivewas to detect differences in how pre- and post-eventfeature vectors differed from the control we used thesame control feature vectors for both sets of t tests In all
cases the t tests were designed to test the null hypoth-esis that both pre- and post-event feature vectors werestatistically indistinguishable from the control featurevector Surprisingly these two sets of t tests yielded verysimilar results reported in Table 3 implying that noneof the physiological variables were predictors of antici-patory emotional responses These findings may be atleast partly explained by how the events were defined Inparticular the definitions of deviation and trend-reversalevents are those instances where the time seriesachieved a prespecified deviation from the time-seriesmean and its moving average respectively In theabsence of large jumps the values of the time seriesnear an event defined in this way are likely to becomparable to the event itself Therefore the tradersmay be responding to market conditions occurringthroughout the pre- and post-event period not just atthe exact time of the event hence the similarity be-tween the two periods More complex definitions ofevents may allow us to discriminate between pre- andpost-event physiological responses and we are explor-ing several alternatives in ongoing research
Finally to address the fourth objective the followingfour groups of financial instruments were formed on thebasis of similarity in their statistical characteristics
Group 1 EUR JPY
Group 2 GBP CAD CHF AUD NZD (other majorcurrencies)
Group 3 MXP BRL ARS COP VEB CLP (LatinAmerican currencies)
Group 4 SPU EDU (derivatives)
Group 1 is by far the most actively traded set offinancial instruments and accounts for most of thetrading activities of our sample of 10 traders hence itis not surprising that the pattern of statistical signifi-cance for Group 1 in Table 4 is almost identical to thepattern for all traders in Table 2 (the left panel) SCRis significant for price and return deviations temper-ature jumps are significant for spread and price devia-tions and both measures of BVP are significant forvolatility events For Group 2 none of the eightphysiological variables were significant for price orspread deviations although temperature changes andrespiration rates were significant for return deviationsand both BVP measures were significant for pricetrend-reversals and volatility events The financial in-struments in Group 2 were not the primary focus ofany of the traders in our sample which may explainthe different patterns of significance as compared withGroup 1 Group 4 contains the fewest significantresponses and this is consistent with the fact thatthese securities were traded by two of the mostexperienced traders in our sample Derivative secur-ities are considerably more complex instruments thanthe spot currencies requiring more skill and trainingthan the other groups of securities However SCR was
Lo and Repin 329
Tab
le3
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gPr
e-an
dPo
st-e
vent
Tim
eIn
terv
als
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
Pre
-eve
nt
Inte
rva
lP
ost-
even
tIn
terv
al
Phy
sio
logy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mbe
rof
SCR
resp
ons
es3
75
61
543
25
40
21
18
24
01
057
92
87
20
604
20
88
16
64
063
60
579
Aver
age
SCR
amp
litu
de0
700
iexcl0
454
iexcl1
793
051
11
068
088
0iexcl
069
50
887
018
6iexcl
247
81
136
088
0
Aver
age
HR
096
8iexcl
020
5iexcl
055
2iexcl
066
3iexcl
038
6iexcl
027
00
905
iexcl0
588
iexcl0
596
iexcl0
617
103
2iexcl
027
0
Aver
age
BVP
amp
litud
ere
lativ
eto
loca
lba
selin
e0
043
iexcl0
895
006
00
814
iexcl0
045
36
19
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9
Aver
age
BVP
amp
litud
ere
lativ
eto
glob
alba
selin
e1
183
iexcl2
794
23
97
131
7iexcl
225
62
88
61
417
iexcl2
726
23
24
141
7iexcl
221
12
88
6
Nu
mbe
rof
tem
per
atur
eju
mp
s1
110
27
67
050
52
15
83
92
5iexcl
248
20
988
28
20
065
01
77
74
86
8iexcl
248
2
Aver
age
resp
irat
ion
rate
126
1iexcl
165
0iexcl
025
41
303
iexcl1
999
iexcl0
064
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4
Aver
age
resp
irat
ion
amp
litud
eiexcl
162
0iexcl
006
70
561
006
8iexcl
180
7iexcl
349
9iexcl
164
6iexcl
025
00
136
003
4iexcl
148
7iexcl
349
9
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
l
Th
ele
ftp
anel
cont
ain
st
stat
isti
csfo
rp
re-e
vent
feat
ure
vect
ors
and
the
righ
tp
anel
con
tain
st
stat
isti
csfo
rp
ost-
even
tfe
atu
reve
cto
rs
both
test
edag
ain
stth
esa
me
set
of
con
tro
ls
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spon
ses
HR
=h
eart
rate
B
VP
=bl
ood
volu
me
pu
lse
330 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le4
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
oth
esis
Tes
tsof
the
Diff
eren
cein
Mea
nAu
tono
mic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pon
ents
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chTy
pe
ofM
arke
tEv
ent
(Col
umns
)G
roup
edIn
toFo
urD
istin
ctSe
tsof
Fina
ncia
lIn
stru
men
ts
Ma
rket
Eve
nts
Gro
up
1E
UR
an
dJP
YG
rou
p2
Oth
erM
ajo
rC
urr
enci
esa
Gro
up
3La
tin
Am
eric
an
Cu
rren
cies
bG
rou
p4
Der
iva
tive
sc
Phy
siol
ogy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
mD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mb
erof
SCR
resp
on
ses
16
53
iexcl1
902
19
64
iexcl0
554
iexcl2
286
iexcl0
940
033
20
112
054
4iexcl
179
5iexcl
241
7iexcl
110
82
49
82
18
8iexcl
039
72
99
5iexcl
040
50
898
26
82
109
41
66
91
594
iexcl0
239
22
36
Ave
rage
SCR
amp
litu
de
iexcl1
639
062
30
279
iexcl1
756
109
8iexcl
111
2iexcl
050
30
540
113
8iexcl
219
0iexcl
010
60
896
111
61
279
iexcl1
525
iexcl1
802
22
20
iexcl0
986
iexcl0
610
123
60
930
iexcl1
513
024
2iexcl
151
0
Ave
rage
HR
073
1iexcl
000
4iexcl
126
8iexcl
181
5iexcl
069
8iexcl
161
2iexcl
099
1iexcl
145
2iexcl
103
4iexcl
132
6iexcl
030
8iexcl
108
92
92
1iexcl
143
2iexcl
107
0iexcl
100
2iexcl
141
61
183
081
6iexcl
032
20
577
049
20
515
iexcl0
169
Ave
rage
BV
Pam
plit
ude
rela
tive
to
loca
lb
asel
ine
056
2iexcl
136
8iexcl
080
51
273
088
52
58
7iexcl
092
20
220
145
72
32
3iexcl
127
52
30
8iexcl
098
2iexcl
000
12
82
81
454
059
82
19
2iexcl
059
2iexcl
303
2iexcl
395
3iexcl
089
4iexcl
190
4iexcl
143
3
Ave
rage
BV
Pam
plit
ude
rela
tive
tolo
cal
bas
elin
e
059
7iexcl
301
01
71
40
375
iexcl0
252
25
92
052
11
448
097
23
08
7iexcl
149
92
63
3iexcl
192
5iexcl
084
91
482
025
9iexcl
035
1iexcl
012
9iexcl
066
0iexcl
150
1iexcl
327
4iexcl
162
6iexcl
200
4iexcl
241
1
Nu
mb
erof
tem
per
atu
reju
mp
s
068
84
14
1iexcl
144
42
28
30
295
iexcl2
043
107
3iexcl
155
72
40
41
248
37
26
iexcl1
564
iexcl1
603
iexcl1
275
iexcl1
922
iexcl0
512
iexcl1
128
iexcl1
540
iexcl1
367
iexcl1
349
iexcl1
362
iexcl1
367
iexcl1
053
NA
Ave
rage
resp
irat
ion
rate
052
5iexcl
174
1iexcl
079
70
163
iexcl1
884
iexcl0
004
075
6iexcl
056
02
02
3iexcl
109
5iexcl
178
4iexcl
011
60
037
044
6iexcl
112
40
643
iexcl1
392
012
5iexcl
194
6iexcl
029
10
361
041
6iexcl
110
5iexcl
172
5
Ave
rage
resp
irat
ion
amp
litu
de
iexcl2
120
iexcl0
692
034
5iexcl
050
9iexcl
093
5iexcl
207
9iexcl
074
60
560
047
00
905
089
3iexcl
383
90
280
iexcl0
207
142
6iexcl
036
0iexcl
053
4iexcl
043
70
920
iexcl0
955
iexcl1
915
125
71
593
iexcl0
039
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
la M
XP
BR
LA
RS
CO
PV
EB
CLP
bM
XP
BR
LA
RS
CO
PV
EB
C
LP
c ED
U
SPU
Ent
ries
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
was
pre
sen
tin
eith
erth
eev
ent
orco
ntro
lfe
atu
reve
cto
rfo
rth
ep
arti
cula
rm
arke
t-ev
ent
typ
e
DE
V=
dev
iati
on
TR
V=
tren
d-r
ever
sal
VO
L=
vola
tilit
ySC
R=
skin
con
duc
tan
cere
spo
nses
H
R=
hea
rtra
te
BV
P=
bloo
dvo
lum
ep
uls
e
Lo and Repin 331
significant for both price and return deviations inGroup 4 which was not the case for the sample ofhigh-experience traders in Table 2 SCR was alsosignificant for volatility events in Group 4 which isconsistent with the central role that volatility plays inthe pricing and hedging derivative securities (Black ampScholes 1973 Merton 1973) Volatility is a moresophisticated aspect of the trading milieu requiringa higher degree of training to fully appreciate itsimplications for market trends and profit-and-lossprobabilities This may explain the fact that SCR issignificant for volatility events only among the high-experience traders in Table 2 and the derivativestraders in Table 4
DISCUSSION
Our findings suggest that emotional responses are asignificant factor in the real-time processing of financialrisks Contrary to the common belief that emotions haveno place in rational financial decision-making processesphysiological variables associated with the ANS exhibitsignificant changes during market events even for highlyexperienced professional traders Moreover the re-sponse patterns among variables and events differed inimportant ways for less experienced tradersmdashmeanautonomic responses were significantly highermdashsug-gesting the possibility of relating trading skills to certainphysiological characteristics that can be measured
More generally our experiments demonstrate thefeasibility of relating real-time quantitative changes incognitive inputs (financial information) to correspond-ing quantitative changes in physiological responses in acomplex field environment Despite the challenges ofsuch measurements a wealth of information can beobtained regarding high-pressure decision makingunder uncertainty Financial traders operate in a con-trolled environment where the inputs and outputs ofthe decisions are carefully recorded and where thesubjects are highly trained and provided with greateconomic incentives to make rational trading decisionsTherefore in vivo experiments in the securities tradingcontext are likely to become an important part of theempirical analysis of individual risk preferences anddecision-making processes In particular there is con-siderable anecdotal evidence that subjects involved inprofessional trading activities perform very differentlydepending on whether real financial capital is at risk or ifthey are trading with lsquolsquoplayrsquorsquo money Such distinctionshave been documented in other contexts eg it hasbeen shown that different brain regions are activatedduring a subjectrsquos naturally occurring smile and a forcedsmile (Damasio 1994) For this reason measuring sub-jects while they are making decisions in their naturalenvironment is essential for any truly unbiased study offinancial decision-making processes
In capturing relations between cognitive inputs andaffective reactions that are often subconscious and ofwhich subjects are not fully aware our findings may beviewed more broadly as a study of cognitive- emotionalinteractions and the genesis of lsquolsquointuitionrsquorsquo Decisionprocesses based on intuition are characterized by lowlevels of cognitive control low conscious awarenessrapid processing rates and a lack of clear organizingprinciples When intuitive judgments are formed largenumbers of cues are processed simultaneously and thetask is not decomposed into subtasks (HammondHamm Grassia amp Pearson 1987) Expertsrsquo judgmentsare often based on intuition not on explicit analyticalprocessing making it almost impossible to fully explainor replicate the process of how that judgment has beenformed This is particularly germane to financial trad-ersmdashas a group they are unusually heterogeneouswith respect to educational background and formalanalytical skills yet the most successful traders seemto trade based on their intuition about price swingsand market dynamics often without the ability (or theneed) to articulate a precise quantitative algorithm formaking these complex decisions (Niederhoffer 1997Schwager 1989 1992) Their intuitive trading lsquolsquorulesrsquorsquoare based on the associations and relations betweenvarious information tokens that are formed on a sub-conscious level and our findings and those in theextant cognitive sciences literature suggest that deci-sions based on the intuitive judgments require not onlycognitive but also emotional mechanisms A naturalconjecture is that such emotional mechanisms are atleast partly responsible for the ability to form intuitivejudgments and for those judgments to be incorporatedinto a rational decision-making process
Our results may surprise some financial economistsbecause of the apparent inconsistency with marketrationality but a more sophisticated view of the role ofemotion in human cognition (Rolls 1990 1994 1999)can reconcile any contradiction in a complete andintellectually satisfying manner Emotion is the basisfor a reward-and-punishment system that facilitates theselection of advantageous behavioral actions providingthe numeraire for animals to engage in a lsquolsquocost- benefitanalysisrsquorsquo of the various actions open to them (Rolls1999 Chap 103) From an evolutionary perspectiveemotion is a powerful adaptation that dramatically im-proves the efficiency with which animals learn from theirenvironment and their past
These evolutionary underpinnings are more thansimple speculation in the context of financial tradersThe extraordinary degree of competitiveness of globalfinancial markets and the outsize rewards that accrue tothe lsquolsquofittestrsquorsquo traders suggest that Darwinian selectionmdashfinancial selection to be specificmdashis at work in deter-mining the typical profile of the successful trader Afterall unsuccessful traders are generally lsquolsquoeliminatedrsquorsquo fromthe population after suffering a certain level of losses
332 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Our results indicate that emotion is a significant deter-minant of the evolutionary fitness of financial tradersWe hope to investigate this conjecture more formally inthe future in several ways a comparison between tradersand a control group of subjects without trading experi-ence or with unsuccessful trading experiences an anal-ysis of the psychophysiological responses of traders in alaboratory environment a more direct analysis of thecorrelation between autonomic responses and tradingperformance a more fundamental analysis of the neuralbasis of emotion in traders aimed at the function of theamygdala and the orbitofrontal cortex (Rolls 1992 1999Chap 44- 45) and a direct mapping of the neuralcenters for trading activity through functional magneticresonance imaging (Breiter et al 2001)
There are a number of caveats that must be kept inmind in interpreting our findings First because of thesmall sample size of 10 subjects our results are at bestsuggestive and promising not conclusive A more com-prehensive study with a larger and more diverse sampleof traders a more refined set of market events and abroader set of controls are necessary before coming toany firm conclusions regarding the precise mechanismsof the psychophysiology of financial risk processing andthe role of emotion in market efficiency
Second while we have documented the statisticalsignificance of autonomic responses during marketevents relative to control baselines we have not estab-lished specific causal factors for such responses Manycognitive tasks generate autonomic effects hence amarked change in any one psychophysiological indexmay be due to changes in cognitive processing forexample complex numerical computations rather thanmore fundamental affective responses For examplemore experienced traders in our sample may have dis-played lower autonomic response levels because theyrequired less cognitive effort to perform the same func-tions as less experienced traders which may have nothingto do with emotion Without a more targeted set of tasksor additional data on the characteristics of the subjects itis difficult to distinguish between the two One way toaddress this issue is to correlate a subjectrsquos autonomicresponses with his or her trading performance so as todevelop a more complete profile of the relation betweenpsychophysiological indices and the dynamics of thetrading process However because trading performanceis generally summarized by multiple characteristicsmdashtotal profit-and-loss volatility maximum drawdownSharpe ratio and so onmdashestimating a relation betweenautonomic responses and such characteristics may re-quire significantly larger sample sizes and longer obser-vation windows due to the lsquolsquocurse of dimensionalityrsquorsquo Itmay be possible to overcome this challenge to somedegree through a more carefully crafted experimentaldesign in which specific emotional responses are pairedwith specific trading performance measures namelyanxiety levels during consecutive sequences of losing
trades With a larger sample size we can also begin totrack groups of traders over a period of time and throughvarying market conditions By measuring their autonomicresponse profiles at different stages of their careers itmay be possible to determine more directly the role ofemotion in financial risk processing
Finally a much larger set of controls should accompanya larger sample of traders These controls should includepsychophysiological measurements for professional trad-ers taken outside their natural trading environmentmdashideally in a laboratory setting that is identical for alltradersmdashas well as corresponding measurements forsubjects with little or no trading experience in bothtrading and nontrading environments Along with psy-chophysiological data more traditional personality in-ventory data for example MMPI or Myers- Briggs mayprovide additional insights into the psychology of trading
We hope to address each of these issues in ourongoing research program to better understand thecognitive processes underlying financial risk processing
METHODS
Subjects
The 10 subjectsrsquo descriptive characteristics are summar-ized in Table 5 Based on discussions with their super-visors the traders were categorized into three levels ofexperience low moderate and high Five traders spe-cialized in handling client order flow (Retail) threespecialized in trading foreign exchange (FX) and twospecialized in interest-rate derivative securities (Deriva-tives) The durations of the sessions ranged from 49 to83 min and all sessions were held during live tradinghours typically between 8 am and 5 pm easterndaylight time
Physiological Data Collection
A ProComp+ data-acquisition unit and Biograph (Ver-sion 12) biofeedback software from Thought Technol-ogy were used to measure physiological data for allsubjects All six sensors were connected to a smallcontrol unit with a battery power supply which wasplaced on each subjectrsquos belt and from which a fiber-optic connection led to a laptop computer equippedwith real-time data acquisition software (see Figure 2)Each sensor was equipped with a built-in notch filter at60 Hz for automatic elimination of external power linenoise and standard AgCl triode and single electrodeswere used for SCR and EMG sensors respectively Thesampling rate for all data collection was fixed at 32 HzAll physiological data except for respiration and facialEMG were collected from each subjectrsquos nondominantarm SCR electrodes were placed on the palmar sites theBVP photoplesymographic sensor was placed on theinside of the ring or middle finger the arm EMG triodeelectrode was placed on the inside surface of the
Lo and Repin 333
forearm over the flexor digitorum muscle group andthe temperature sensor was inserted between the elasticband placed around the wrist and the skin surface Thefacial EMG electrode was placed on a masseter musclewhich controls jaw movement and is active duringspeech or any other activity involving the jaw Therespiration signal was measured by chest expansionusing a sensor attached to an elastic band placed aroundthe subjectrsquos chest An example of the real-time physio-logical data collected over a 2-min interval for onesubject is given in Figure 3
The entire procedure of outfitting each subject withsensors and connecting the sensors to the laptop re-quired approximately 5 min and was often performedeither before the trading day began or during relativelycalm trading periods Subjects indicated that the pres-
ence of the sensors wires and a control unit did notcompromise or influence their trading in any significantmanner and that their workflow was not impaired in anyway This was verified not only by the subjects but alsoby their supervisors Given the magnitudes of the finan-cial transactions that were being processed and theeconomic and legal responsibilities that the subjectsand their supervisors bore even the slightest interfer-ence with the subjectsrsquo workflow or performance stand-ards would have caused the supervisors or the subjectsto terminate the sessions immediately None of thesessions were terminated prematurely
Physiological Data Feature Extraction
An initial smoothing of the raw EMG signals (sampled at32 Hz) was performed with a moving-average filter oforder 23 If the level of the filtered forearm EMG signalexceeded a threshold of 075 mV both SCR and BVPreadings at this time were discarded because of the highprobability of artifacts for example typing graspingtelephone handsets or inadvertent physical disturban-ces to the sensors Similarly if the level of the filteredfacial EMG signal exceeded 075 mV the respirationsignal at this time was excluded from further processingA very small spatial displacement of the sensor or theelectrode was able to produce a different kind ofartifactmdashan abrupt change in the signal of the orderof 10- 20 standard deviations within 1
32 of a second Suchjumps did not have any physiological meaning hencethey were excluded from further analysis via adaptivethresholding Specifically our adaptive thresholdingprocedure involved marking all observations that
Table 5 Summary Statistics for All Subjects Individual Traderrsquos Characteristics Specialty Type and Number of Market Time-Series Collected During the Session Session Duration and Absolute Time (Eastern Standard Time) of the Start of the Session
TraderID Gender Experience Specialty Market data Available
Nu m ber of MarketTim e-Series Recorded
Session Du ration(m in and sec)
SessionStart Tim e
B33 F high retail major currencies 2 4903000 1320
B34 M high FX major currencies 3 8303200 0856
B35 M high retail major currencies 4 6603000 1102
B36 M high derivatives SampP500 futureseurodollar futures
3 7901600 1255
B37 M moderate FX Latin Americanmajor currencies
9 7000600 0917
B38 M low FX Latin Americanmajor currencies
3 7201200 1102
B39 M high derivatives SampP 500 futureseurodollar futures
9 6200300 0819
B310 M low retail major currencies 7 6000800 0947
B311 M moderate retail major currencies 7 5402500 1132
B312 M low retail major currencies 7 5900000 0910
ProC om p+
Facial EMG Triode electrode measures
activity of the masseter muscle (detects speech)
Respiration sensor Elastic strap measures
chest expansion
Forearm EMG Triode electrode measures activity of the flexor digitorum muscle group (detects finger and arm movement)
Temperature sensor - thermistor
SCR sensor and electrodes
BVP photoplesymographicsensor
Control unit Fiber optic cable to a
data acquisition laptop
Figure 2 Placement of sensors for measuring physiologicalresponses
334 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
financial risk measures and emotional states and dynam-ics In a pilot sample of 10 traders we find statisticallysignificant differences in mean electrodermal responsesduring transient market events as compared with no-event control baselines and statistically significant meanchanges in cardiovascular variables during periods ofheightened market volatility as compared with normal-volatility control baselines We also observe significantdifferences in mean physiological responses among the10 traders that may be systematically related to theamount of trading experience
In studying the link between emotion and rationaldecision making in the face of uncertainty financialsecurities traders are ideal subjects for several reasonsBecause the basic functions of securities trading involvefrequent decisions concerning riskreward trade-offstraders are almost continuously engaged in the activitythat we wish to study This allows us to conduct ourstudy in vivo and with minimal interference to andtherefore minimal contamination of the subjectsrsquo natu-ral motives and behavior Traders are typically providedwith significant economic incentives to avoid many ofthe biases that are often associated with irrational invest-ment practices Moreover they are highly paid profes-sionals that have undergone a variety of trainingexercises apprenticeships and trial periods throughwhich their skills have been finely honed Thereforethey are likely to be among the most rational decisionmakers in the general population hence ideal subjectsfor examining the role of emotion in rational decision-making processes Finally due to the real-time nature ofmost professional trading operations it is possible toconstruct accurate real-time records of the environmentin which traders make their decisions namely thefluctuations of market prices of the securities that theytrade With such real-time records we are able to matchmarket events such as periods of high price-volatilitywith synchronous real-time measurements of physiolog-ical characteristics
To measure the emotional responses of our subjectsduring their trading activities we focus on indirectmanifestations through the responses of the autonomicnervous system (ANS) (Cacioppo Tassinary amp Bernt2000) The ANS innervates the viscera and is responsiblefor the regulation of internal states that are mediated byinternal bodily as well as emotional and cognitiveprocesses ANS responses are relatively easy to measuresince many of them can be measured noninvasively fromexternal body sites without interfering with cognitivetasks performed by the subject ANS responses occur onthe scale of seconds which is essential for investigationof real-time risk processing
Previous studies have focused primarily on time-aver-aged (over hours or tens of minutes) levels of autonomicactivity as a function of task complexity or mental strainFor example some experiments have considered thelink between autonomic activity and driving conditions
and road familiarity for nonprofessional drivers (Brownamp Huffman 1972) and the stage of flight (take-offsteady flight landing) in a jetfighter flight simulator(Lindholm amp Cheatham 1983) Recently the focus ofresearch has started to shift towards finer temporalscales (seconds) of autonomic responses associated withcognitive and emotional processes Perhaps the mostinfluential set of experiments in this area was conductedin the broad context of an investigation of the role ofemotion in decision-making processes (Damasio 1994)In one of these experiments skin conductance re-sponses (SCRs) were measured in subjects involved ina gambling task (Bechara Damasio Tranel amp Damasio1997) The results indicated that the anticipation of themore risky outcomes led to more SCRs than of the lessrisky ones The brain circuitry involved in anticipatingmonetary rewards has also been localized (BreiterAharon Kahneman Dale amp Shizgal 2001) Anotherstudy (Frederikson et al 1998) reported neuroanatom-ical correlates of skin conductance activity with the brainregions that also support anticipation affect and cogni-tively or emotionally mediated motor preparation Re-cent experiments (Critchley Krase amp Statman 1999)support the significance of autonomic responses duringrisk-taking and reward-related behavior They providemore details on brain activation correlates of peripheralautonomic responses and also claim the possibility ofdiscriminating the activity patterns related to changingversus continuing the behavior based on the immediategainloss history in a gambling task
We focus on five types of physiological characteristicsin our study skin conductance cardiovascular data (BVPand HR) electromyographic (EMG) data respirationrate and body temperature
SCR is measured by the voltage drop between twoelectrodes placed on the skin surface a few centimetersapart Changes in skin conductance occur when eccrinesweat glands that are innervated by the sympathetic ANSfibers receive a signal from a certain part of the brainThree distinct lsquolsquobrain information systemsrsquorsquo can poten-tially elicit SCR signals (Boucsein 1992) The affectarousal system is capable of generating a phasic re-sponse (on the scale of seconds) that is associateddirectly with the sensory input indicating attentionfocusing or defense response and the amygdala is theprimary brain region involved The emotional responseto a novel or highly complex task is an example of affectarousal Another system that can initiate a phasic SCRcenters on the basal ganglia and is related to a prepar-atory activation mediated by internal cognitive pro-cesses The body exhibits increased perceptual andmotor readiness and high attention levels Expectationof an event or preparation to an important actionillustrates the operation of this system The third systemoften called the lsquolsquoeffort systemrsquorsquo produces tonic changesin the level of skin conductance and is related to thelong-term changes of a general emotional (hedonic)
324 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
state or attitude indicating a nonspecific increase inattention or arousal Hippocampal information process-ing is believed to be behind this system which isassociated with a higher degree of conscious aware-ness while the first two are mostly attributed tosubconscious processes
The cardiovascular system consists of the heart and allthe blood vessels and the variables of particular interestare BVP and HR BVP is the rate of flow of blood througha particular blood vessel and is related to both bloodpressure and the diameter of the vessel Constriction ordilation of the vessels is controlled by the sympatheticbranch of the ANS and along with electrodermal activityit has been shown to be related to information process-ing and decision making (Papillo amp Shapiro 1990) HRrefers to the frequency of the contractions of the heartmuscle or myocardium Specialized neuronsmdashthe so-called lsquolsquopacemakerrsquorsquo cellsmdashinitiate the contraction ofmyocardium and the output of the pacemakers iscontrolled by both parasympathetic (HR decrease) andsympathetic (HR increase) ANS branches BVP and HRtrack each other closely so that HR deceleration usuallycauses an increase in BVP Compared to SCR arousalindications that refer mostly to cognitive processeschanges in cardiovascular variables register higher levelsof arousal which are often somatically mediated Thesevariables may provide supplemental information forinterpreting high SCR signalsmdashelevated SCR accompa-nied by an increase in HR may be an indication ofextreme significance of the task or stimulus or alter-natively simply indicate that some physical activity isbeing performed simultaneously (Boucsein 1992)
EMG measurements are based on the electrical signalsgenerated by the contraction process of striatedmuscles Muscle action potentials travel along themuscle and some portion of the electrical activity leaksto the skin surface where it can be detected with thehelp of surface electrodes The activation of particularmuscles reflects different types of actions For exampleactivation of the facial muscle (ie the lsquolsquomasseterrsquorsquomuscle) indicates ongoing speech activation of theforearm muscle group (ie the lsquolsquoflexor digitorumrsquorsquogroup) corresponds to finger movements such as typingon a keyboard In addition to their role in motor activitycertain muscle groups exhibit strong correlations withemotional states Most of the research in this literaturehas focused on facial muscles since facial expressionshave been linked to emotional states (Ekman 1982) Forexample an increase in EMG activity over the forehead isassociated with anxiety and tension and an increase inactivity over the brow accompanied by a slight decreasein activity over the cheek often corresponds to unpleas-ant sensory stimuli (Cacioppo et al 2000) ThereforeEMG measurements can capture very subtle changes inmuscle activity that can differentiate otherwise indistin-guishable response patterns However in our currentimplementation the role of EMG measurements is
limited to identifying and eliminating anomalous sensorreadings caused by certain physical motions of a subject
Respiration influences the HR through vascular recep-tors (Lorig amp Schwartz 1990) and although this variableis usually not of primary psychological importance it is areliable indicator of physically demanding activitiesundertaken by the subject for example speaking orcoughing which can often yield anomalous sensor read-ings if not properly taken into account Respiration canbe measured by placing a sensor that monitors chestexpansion and compression
Finally body temperature regulation involves the in-tegration of autonomic motor and endocrine responsesand several studies have related the temperatures ofdifferent parts of the body to certain cognitive and emo-tional contents of the task or stimuli For example fore-head temperature (a proxy for brain temperature)increases while experiencing negative emotions coolingenhances positive affect while warming depresses it(McIntosh Zajonc Vig amp Emerick 1997) Another studyreports that hand skin temperature increases with pos-itive affect and decreases with threatening and unpleas-ant tasks (Rimm-Kaufman amp Kagan 1996)
RESULTS
Our sample of subjects consisted of 10 professionaltraders employed by the foreign-exchange and inter-est-rate derivatives business unit of a major globalfinancial institution based in Boston MA This institu-tion provides banking and other financial services toclients ranging from small regional startups to Fortune500 multinational corporations The foreign-exchangeand interest-rate derivatives business unit employs ap-proximately 90 professionals of which two-thirds spe-cializes in the trading of foreign exchange and relatedinstruments and one-third specializes in the trading ofinterest-rate derivative securities In a typical day thisbusiness unit engages in 1000- 1200 trades with anaverage size of US$3- 5 million per trade Approximately80 of the trades are executed on behalf of the clientsof the financial institution with the remaining 20motivated by the financial institutionrsquos market-makingactivities
For each of the 10 subjects five physiological variableswere monitored in real time during the entire duration ofeach session while the subjects sat at their trading con-soles (see Figure 1) Six sensors were used SCR BVPbody temperature (TMP) respiration (RSP) and twoelectromyographic sensors (facial and forearm EMG)
The benefits of acquiring real-time physiological meas-urements in vivo must be balanced against the cost ofmeasurement error spurious signals and other statisticalartifacts which if left untreated can obscure and con-found any genuine signals in the data To eliminate asmany artifacts as possible while maximizing the informa-tional content of the data each of the five physiological
Lo and Repin 325
variables were preprocessed with filters calibrated toeach variable individually and adaptive time-windowswere used in place of fixed-length windows to matchthe event-driven nature of the market variables
After preprocessing the following seven features wereextracted from SCR BVP temperature and respirationsignals
1 times of onset of SCR responses2 amplitudes of SCR responses3 average HR (every 3 sec)4 BVP signal amplitudes5 respiration rates (every 5 sec)6 respiration amplitudes7 temperature changes (from the 10-sec lag)
These features were selected to reflect the differentproperties and different information contained in theraw physiological variables SCRs were characterized bysmooth lsquolsquobumpsrsquorsquo with a relatively fast onset and slowdecay hence the number of such bumps and theirrelative strength are excellent summary measures ofthe SCR signal Intervals of 3 and 5 sec for heart andrespiration rates were chosen as a compromise betweenthe need for finer time slices to distinguish the onsetand completion of physiological and market events andthe necessity of a sufficient number of heartbeats andrespiration cycles within each interval to compute anaverage Under normal conditions a typical subject willexhibit an average of three to four heartbeats per 3-secinterval and approximately three breaths per 5-sec in-terval Temperature was the slowest-varying physiolog-ical signal and the 10-sec interval to register its changereflected the maximum possible interval in our analysis
For each session real-time market data for key finan-cial instruments actively traded or monitored by thesubject were collected synchronously with the physio-logical data In particular across the 10 subjects a total of15 instruments were consideredmdash13 foreign currencies
and two futures contracts the euro (EUR) the Japaneseyen (JPY) the British pound (GBP) the Canadian dollar(CAD) the Swiss franc (CHF) the Australian dollar(AUD) the New Zealand dollar (NZD) the Mexican peso(MXP) the Brazilian real (BRL) the Argentinian peso(ARS) the Colombian peso (COP) the Venezuelan boli-var (VEB) the Chilean peso (CLP) SampP 500 futures(SPU) and eurodollar futures (EDU) For each securityfour time series were monitored (1) the bid price Pt
b (2)the ask price Pt
a (3) the bidask spread Xt sup2 Ptb iexcl Pt
aand (4) the net return Rt sup2 (Pt iexcl Ptiexcl1) Ptiexcl1 where Pt
denotes the mid-spread price at time t and all priceswere sampled every second
For each of the financial time series we identifiedthree classes of market events deviations trend-rever-sals and volatility events These events are often cited bytraders as significant developments that require height-ened attention potentially signaling a shift in marketdynamics and risk exposures With these definitions andcalibrations in hand we implemented an automaticprocedure for detecting deviations trend-reversals andvolatility events for all the relevant time series in eachsession Table 1 reports event counts for the financialtime series monitored for each subject Feature vectorswere then constructed for all detected market events inall time series for all subjects and a set of control featurevectors was generated by applying the same feature-extraction process to randomly selected windows con-taining no events of any kind One set of control featurevectors was constructed for deviation and trend-reversalevents (10-sec intervals) and a second set was con-structed for volatility-type events (5-min intervals) Atwo-sided t test was applied to each component of eachfeature vector and the corresponding control vector totest the null hypothesis that the feature vectors werestatistically indistinguishable from the control featurevectors
For volatility events which by definition occurred in5-min intervals (event windows) we constructed featurevectors containing the following information for theduration of the event window
1 number of SCR responses2 average SCR amplitude3 average HR4 average ratio of the BVP amplitude to local baseline5 average ratio of the BVP amplitude to global
baseline6 number of temperature changes exceeding 018F7 average respiration rate8 average respiration amplitude
where the local baseline for the BVP signal is the averagelevel during the event window and the global baseline isthe average over the entire recording session for eachtrader Control feature vectors were constructed byapplying the feature-extraction process to randomlyselected 5-min windows containing no events of any
Laptop with data acquisition software
Fiber optic cable Control unit
Market information (Reuters Bloomberg) Order screen etc TIBCO market spreadsheet
eal-time data feed the market data
acquisition computer
Figure 1 Typical set-up for the measurement of real-time physiolo-gical responses of financial traders during live trading sessions Real-time market data used by the traders are recorded synchronously andsubsequently analyzed together with the physiological response data
326 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
kind For the other two types of market eventsmdashdevia-tions and trend-reversalsmdashlsquolsquopre-eventrsquorsquo and lsquolsquopost-eventrsquorsquofeature vectors were constructed before and after eachmarket event using the same variables where featureswere aggregated over 10-sec windows immediately pre-ceding and following the event Control feature vectorswere generated for these cases as well
The motivation for the post-event feature vector wasto capture the subjectrsquos reaction to the event with thepre-event feature vector as a benchmark from which tomeasure the magnitude of the reaction A comparisonbetween the pre-event feature vector and a controlfeature vector may provide an indication of a subjectrsquosanticipation of the event Latencies of the autonomicresponses reported in previous studies were on a timescale of 1 to 10 sec (see Cacioppo et al 2000) hence10-sec event windows were judged to be long enough forevent-related autonomic responses to occur and at thesame time short enough to minimize the likelihood ofoverlaps with other events or anomalies Five-minutewindows were used for volatility events because 10-secintervals were simply insufficient for meaningful volatilitycalculations For all of the financial time series used inthis study and for most financial time series in generalthere are few volatility events that occur in any 10-secinterval except of course under extreme conditions forexample the stock market crash of October 19 1987 (nosuch conditions prevailed during any of our sessions)
The statistical analysis of the physiological and marketdata was motivated by four objectives (1) to identifyparticular classes of events with statistically significantdifferences in mean autonomic responses before or after
an event as compared to the no-event control (2) toidentify particular traders or groups of traders based onexperience or other personal characteristics that dem-onstrate significant mean autonomic responses duringmarket events (3) to identify particular financial instru-ments or groups of instruments that are associated withsignificant mean autonomic responses and (4) to iden-tify particular physiological variables that demonstratesignificant mean response levels immediately followingthe event or during the event-anticipation period ascompared to the no-event control
To address the first objective a total of eight types ofevents were used for each of the time series
1 price deviations2 spread deviations3 return deviations4 price trend-reversals5 spread trend-reversals6 maximum volatility7 price volatility8 return volatility
and for each type of event a two-sided t test wasperformed for the pooled sample of all subjects and alltime series in which mean autonomic responses duringevent periods were compared to mean autonomic re-sponses during no-event control periods
The t statistics corresponding to the two-sided hypoth-esis test are given in the left subpanel of Table 2 labelledlsquolsquoAll Tradersrsquorsquo Statistics that are significant at the 5levelmdashbased on the t distribution with the appropriatedegrees of freedom for each entrymdashare highlighted For
Table 1 Number of Deviation (DEV) Trend-Reversal (TRV) and Volatility (VOL) Events Detected in Real-Time Market Data forEach Trader Over the Course of Each Trading Session
EUR JPYOther MajorCu rrenciesa
Latin Am ericanCu rrenciesb Derivativesc
Trader ID DEV TRV VOL DEV TRV VOL DEV TRV VOL DEV TRV VOL DEV TRV VOL
B32 21 16 15 25 17 15 - - - - - - - - -
B34 18 10 14 17 17 14 - - - - - - - - -
B35 20 16 14 13 17 15 21 9 15 24 16 13 - - -
B36 - - - - - - - - - - - - 28 23 31
B37 19 19 14 18 18 12 18 18 14 105 86 63 - - -
B38 21 20 12 22 18 13 23 20 15 96 61 56 - - -
B39 - - - - - - - - - - - - 40 37 23
B310 10 17 10 18 16 14 116 59 61 - - - - - -
B311 17 15 15 17 16 15 94 61 67 - - - - - -
B312 19 17 15 16 19 13 107 83 71 - - - - - -
aGBP CAD CHF AUD NZDbMXP BRL ARS COP VEB CLPcEDU SPU
Lo and Repin 327
Tab
le2
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vec
tors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
All
Tra
ders
Hig
hEx
peri
ence
Low
orM
oder
ate
Expe
rien
ce
Phys
iolo
gy
Fea
ture
s
DE
V
Pri
ce
DEV
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VOL
Ma
xim
um
DE
V
Pri
ce
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VO
L
Ma
xim
um
DE
V
Pri
ce
DEV
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VO
L
Ma
xim
um
Nu
mbe
ro
fSC
Rre
spon
ses
28
72
060
42
08
81
66
40
636
057
90
561
122
8iexcl
022
1iexcl
126
10
184
20
81
26
96
012
32
75
22
17
0iexcl
066
5iexcl
068
8
Aver
age
SCR
ampl
itud
eiexcl
069
50
887
018
6iexcl
247
81
136
088
00
030
034
40
744
iexcl1
340
114
90
904
075
61
578
iexcl1
265
iexcl1
947
iexcl0
012
iexcl0
615
Aver
age
HR
090
5iexcl
058
8iexcl
059
6iexcl
061
71
032
iexcl0
270
044
91
88
6iexcl
179
7iexcl
163
51
83
6iexcl
201
81
310
005
1iexcl
038
2iexcl
110
6iexcl
031
11
283
Aver
age
BV
Pam
plit
ude
rela
tive
tolo
cal
base
line
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9iexcl
006
1iexcl
074
70
334
001
50
164
26
23
iexcl0
204
iexcl1
775
087
51
79
6iexcl
128
92
70
8
Aver
age
BV
Pam
plit
ude
rela
tive
togl
obal
base
line
141
7iexcl
272
62
32
41
417
iexcl2
211
28
86
iexcl2
529
iexcl1
367
159
01
110
iexcl1
165
23
01
19
17
iexcl2
664
18
84
20
82
iexcl1
759
27
58
Nu
mbe
ro
fte
mp
erat
ure
jum
ps
098
82
82
00
650
17
77
48
68
iexcl2
482
iexcl1
010
iexcl1
435
NA
NA
NA
164
40
837
29
02
114
52
44
52
63
3iexcl
321
3
Aver
age
resp
irat
ion
rate
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4iexcl
155
61
109
012
10
866
iexcl0
102
009
30
079
iexcl0
552
032
3iexcl
063
1iexcl
217
0iexcl
152
8
Aver
age
resp
irat
ion
ampl
itud
e
iexcl1
646
iexcl0
250
013
60
034
iexcl1
487
iexcl3
499
108
70
265
iexcl1
777
iexcl0
657
012
1iexcl
060
7iexcl
274
9iexcl
131
92
14
20
055
iexcl2
832
iexcl4
533
Bo
lded
stat
isti
csar
esi
gnifi
can
tat
the
5le
vel
Th
ele
ftp
anel
give
sag
greg
ate
resu
lts
for
allt
rad
ers
Sim
ilar
ttes
tsfo
rtr
ader
sw
ith
hig
hex
per
ienc
ean
dlo
wo
rm
oder
ate
exp
erie
nce
are
show
nin
the
mid
dle
and
righ
tp
anel
sre
spec
tive
lyE
ntr
ies
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
wer
ep
rese
nt
inei
ther
the
even
tor
cont
rol
feat
ure
vect
or
for
the
par
ticu
lar
mar
ket-
even
tty
pe
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spo
nse
sH
R=
hea
rtra
te
BV
P=
blo
od
volu
me
pul
se
328 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
deviations and trend-reversals both the number of SCRsand the number of temperature jumps reached statisti-cally significant levels for three out of the five event typesBVP amplitude-related features were highly significantfor volatility eventsmdashsignificance levels less than 1 wereobtained for both BVP amplitude features for all threetypes of volatility events Because the results were sosimilar across the three types of volatility events (max-imum volatility price volatility and return volatility) wereport the results only for maximum volatility in Table 2Such patterns of autonomic responses may indicate thepresence of transient emotional responses to deviationsand trend-reversal events that occur within 10-sec win-dows while volatility events defined in 5-min windowselicited a tonic response in BVP
To explore potential differences between experi-enced and less experienced traders a sample of 10traders was divided into two groups each consistingof five traders the first containing highly experiencedtraders and the second containing traders with low ormoderate experience where the levels of experiencewere determined through interviews with the tradersrsquosupervisors The results for each of the two groupsare reported in the two right subpanels of Table 2labelled lsquolsquoHigh Experiencersquorsquo and lsquolsquoLow or ModerateExperiencersquorsquo The high-experience traders generallyexhibited low responses for deviations and trend-re-versal events with only two types of market events(spread deviations and spread trend-reversals) elicitingsignificant mean autonomic responses (average HR)However even for high-experience traders volatilityevents induced statistically significant mean responsesfor both SCR and BVP amplitude Less experiencedtraders showed a much higher number of significantmean responses in the number of SCRs BVP ampli-tude and number of temperature increases for devia-tions and trend-reversal events Table 2 shows thatsessions with low- and moderate-experience tradersyielded 13 t statistics significant at the 5 level whilesessions with high-experience traders yielded only fivet statistics significant at the 5 level For both sets oftraders volatility events were associated with signifi-cant BVP amplitude The differences in responsepatterns for deviations and trend-reversals observedin this case indicate that less experienced traders maybe more sensitive to short-term changes in the marketvariables than their more experienced colleagues
To address the third objective 10-sec intervals imme-diately preceding and immediately following each devia-tion and trend-reversal event were compared to control(ie no-event) time intervals Two separate t tests wereconductedmdashone for pre- and another for post-eventtime intervalsmdashfor each of the three types of deviationsand two types of trend-reversals Because the objectivewas to detect differences in how pre- and post-eventfeature vectors differed from the control we used thesame control feature vectors for both sets of t tests In all
cases the t tests were designed to test the null hypoth-esis that both pre- and post-event feature vectors werestatistically indistinguishable from the control featurevector Surprisingly these two sets of t tests yielded verysimilar results reported in Table 3 implying that noneof the physiological variables were predictors of antici-patory emotional responses These findings may be atleast partly explained by how the events were defined Inparticular the definitions of deviation and trend-reversalevents are those instances where the time seriesachieved a prespecified deviation from the time-seriesmean and its moving average respectively In theabsence of large jumps the values of the time seriesnear an event defined in this way are likely to becomparable to the event itself Therefore the tradersmay be responding to market conditions occurringthroughout the pre- and post-event period not just atthe exact time of the event hence the similarity be-tween the two periods More complex definitions ofevents may allow us to discriminate between pre- andpost-event physiological responses and we are explor-ing several alternatives in ongoing research
Finally to address the fourth objective the followingfour groups of financial instruments were formed on thebasis of similarity in their statistical characteristics
Group 1 EUR JPY
Group 2 GBP CAD CHF AUD NZD (other majorcurrencies)
Group 3 MXP BRL ARS COP VEB CLP (LatinAmerican currencies)
Group 4 SPU EDU (derivatives)
Group 1 is by far the most actively traded set offinancial instruments and accounts for most of thetrading activities of our sample of 10 traders hence itis not surprising that the pattern of statistical signifi-cance for Group 1 in Table 4 is almost identical to thepattern for all traders in Table 2 (the left panel) SCRis significant for price and return deviations temper-ature jumps are significant for spread and price devia-tions and both measures of BVP are significant forvolatility events For Group 2 none of the eightphysiological variables were significant for price orspread deviations although temperature changes andrespiration rates were significant for return deviationsand both BVP measures were significant for pricetrend-reversals and volatility events The financial in-struments in Group 2 were not the primary focus ofany of the traders in our sample which may explainthe different patterns of significance as compared withGroup 1 Group 4 contains the fewest significantresponses and this is consistent with the fact thatthese securities were traded by two of the mostexperienced traders in our sample Derivative secur-ities are considerably more complex instruments thanthe spot currencies requiring more skill and trainingthan the other groups of securities However SCR was
Lo and Repin 329
Tab
le3
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gPr
e-an
dPo
st-e
vent
Tim
eIn
terv
als
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
Pre
-eve
nt
Inte
rva
lP
ost-
even
tIn
terv
al
Phy
sio
logy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mbe
rof
SCR
resp
ons
es3
75
61
543
25
40
21
18
24
01
057
92
87
20
604
20
88
16
64
063
60
579
Aver
age
SCR
amp
litu
de0
700
iexcl0
454
iexcl1
793
051
11
068
088
0iexcl
069
50
887
018
6iexcl
247
81
136
088
0
Aver
age
HR
096
8iexcl
020
5iexcl
055
2iexcl
066
3iexcl
038
6iexcl
027
00
905
iexcl0
588
iexcl0
596
iexcl0
617
103
2iexcl
027
0
Aver
age
BVP
amp
litud
ere
lativ
eto
loca
lba
selin
e0
043
iexcl0
895
006
00
814
iexcl0
045
36
19
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9
Aver
age
BVP
amp
litud
ere
lativ
eto
glob
alba
selin
e1
183
iexcl2
794
23
97
131
7iexcl
225
62
88
61
417
iexcl2
726
23
24
141
7iexcl
221
12
88
6
Nu
mbe
rof
tem
per
atur
eju
mp
s1
110
27
67
050
52
15
83
92
5iexcl
248
20
988
28
20
065
01
77
74
86
8iexcl
248
2
Aver
age
resp
irat
ion
rate
126
1iexcl
165
0iexcl
025
41
303
iexcl1
999
iexcl0
064
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4
Aver
age
resp
irat
ion
amp
litud
eiexcl
162
0iexcl
006
70
561
006
8iexcl
180
7iexcl
349
9iexcl
164
6iexcl
025
00
136
003
4iexcl
148
7iexcl
349
9
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
l
Th
ele
ftp
anel
cont
ain
st
stat
isti
csfo
rp
re-e
vent
feat
ure
vect
ors
and
the
righ
tp
anel
con
tain
st
stat
isti
csfo
rp
ost-
even
tfe
atu
reve
cto
rs
both
test
edag
ain
stth
esa
me
set
of
con
tro
ls
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spon
ses
HR
=h
eart
rate
B
VP
=bl
ood
volu
me
pu
lse
330 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le4
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
oth
esis
Tes
tsof
the
Diff
eren
cein
Mea
nAu
tono
mic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pon
ents
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chTy
pe
ofM
arke
tEv
ent
(Col
umns
)G
roup
edIn
toFo
urD
istin
ctSe
tsof
Fina
ncia
lIn
stru
men
ts
Ma
rket
Eve
nts
Gro
up
1E
UR
an
dJP
YG
rou
p2
Oth
erM
ajo
rC
urr
enci
esa
Gro
up
3La
tin
Am
eric
an
Cu
rren
cies
bG
rou
p4
Der
iva
tive
sc
Phy
siol
ogy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
mD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mb
erof
SCR
resp
on
ses
16
53
iexcl1
902
19
64
iexcl0
554
iexcl2
286
iexcl0
940
033
20
112
054
4iexcl
179
5iexcl
241
7iexcl
110
82
49
82
18
8iexcl
039
72
99
5iexcl
040
50
898
26
82
109
41
66
91
594
iexcl0
239
22
36
Ave
rage
SCR
amp
litu
de
iexcl1
639
062
30
279
iexcl1
756
109
8iexcl
111
2iexcl
050
30
540
113
8iexcl
219
0iexcl
010
60
896
111
61
279
iexcl1
525
iexcl1
802
22
20
iexcl0
986
iexcl0
610
123
60
930
iexcl1
513
024
2iexcl
151
0
Ave
rage
HR
073
1iexcl
000
4iexcl
126
8iexcl
181
5iexcl
069
8iexcl
161
2iexcl
099
1iexcl
145
2iexcl
103
4iexcl
132
6iexcl
030
8iexcl
108
92
92
1iexcl
143
2iexcl
107
0iexcl
100
2iexcl
141
61
183
081
6iexcl
032
20
577
049
20
515
iexcl0
169
Ave
rage
BV
Pam
plit
ude
rela
tive
to
loca
lb
asel
ine
056
2iexcl
136
8iexcl
080
51
273
088
52
58
7iexcl
092
20
220
145
72
32
3iexcl
127
52
30
8iexcl
098
2iexcl
000
12
82
81
454
059
82
19
2iexcl
059
2iexcl
303
2iexcl
395
3iexcl
089
4iexcl
190
4iexcl
143
3
Ave
rage
BV
Pam
plit
ude
rela
tive
tolo
cal
bas
elin
e
059
7iexcl
301
01
71
40
375
iexcl0
252
25
92
052
11
448
097
23
08
7iexcl
149
92
63
3iexcl
192
5iexcl
084
91
482
025
9iexcl
035
1iexcl
012
9iexcl
066
0iexcl
150
1iexcl
327
4iexcl
162
6iexcl
200
4iexcl
241
1
Nu
mb
erof
tem
per
atu
reju
mp
s
068
84
14
1iexcl
144
42
28
30
295
iexcl2
043
107
3iexcl
155
72
40
41
248
37
26
iexcl1
564
iexcl1
603
iexcl1
275
iexcl1
922
iexcl0
512
iexcl1
128
iexcl1
540
iexcl1
367
iexcl1
349
iexcl1
362
iexcl1
367
iexcl1
053
NA
Ave
rage
resp
irat
ion
rate
052
5iexcl
174
1iexcl
079
70
163
iexcl1
884
iexcl0
004
075
6iexcl
056
02
02
3iexcl
109
5iexcl
178
4iexcl
011
60
037
044
6iexcl
112
40
643
iexcl1
392
012
5iexcl
194
6iexcl
029
10
361
041
6iexcl
110
5iexcl
172
5
Ave
rage
resp
irat
ion
amp
litu
de
iexcl2
120
iexcl0
692
034
5iexcl
050
9iexcl
093
5iexcl
207
9iexcl
074
60
560
047
00
905
089
3iexcl
383
90
280
iexcl0
207
142
6iexcl
036
0iexcl
053
4iexcl
043
70
920
iexcl0
955
iexcl1
915
125
71
593
iexcl0
039
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
la M
XP
BR
LA
RS
CO
PV
EB
CLP
bM
XP
BR
LA
RS
CO
PV
EB
C
LP
c ED
U
SPU
Ent
ries
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
was
pre
sen
tin
eith
erth
eev
ent
orco
ntro
lfe
atu
reve
cto
rfo
rth
ep
arti
cula
rm
arke
t-ev
ent
typ
e
DE
V=
dev
iati
on
TR
V=
tren
d-r
ever
sal
VO
L=
vola
tilit
ySC
R=
skin
con
duc
tan
cere
spo
nses
H
R=
hea
rtra
te
BV
P=
bloo
dvo
lum
ep
uls
e
Lo and Repin 331
significant for both price and return deviations inGroup 4 which was not the case for the sample ofhigh-experience traders in Table 2 SCR was alsosignificant for volatility events in Group 4 which isconsistent with the central role that volatility plays inthe pricing and hedging derivative securities (Black ampScholes 1973 Merton 1973) Volatility is a moresophisticated aspect of the trading milieu requiringa higher degree of training to fully appreciate itsimplications for market trends and profit-and-lossprobabilities This may explain the fact that SCR issignificant for volatility events only among the high-experience traders in Table 2 and the derivativestraders in Table 4
DISCUSSION
Our findings suggest that emotional responses are asignificant factor in the real-time processing of financialrisks Contrary to the common belief that emotions haveno place in rational financial decision-making processesphysiological variables associated with the ANS exhibitsignificant changes during market events even for highlyexperienced professional traders Moreover the re-sponse patterns among variables and events differed inimportant ways for less experienced tradersmdashmeanautonomic responses were significantly highermdashsug-gesting the possibility of relating trading skills to certainphysiological characteristics that can be measured
More generally our experiments demonstrate thefeasibility of relating real-time quantitative changes incognitive inputs (financial information) to correspond-ing quantitative changes in physiological responses in acomplex field environment Despite the challenges ofsuch measurements a wealth of information can beobtained regarding high-pressure decision makingunder uncertainty Financial traders operate in a con-trolled environment where the inputs and outputs ofthe decisions are carefully recorded and where thesubjects are highly trained and provided with greateconomic incentives to make rational trading decisionsTherefore in vivo experiments in the securities tradingcontext are likely to become an important part of theempirical analysis of individual risk preferences anddecision-making processes In particular there is con-siderable anecdotal evidence that subjects involved inprofessional trading activities perform very differentlydepending on whether real financial capital is at risk or ifthey are trading with lsquolsquoplayrsquorsquo money Such distinctionshave been documented in other contexts eg it hasbeen shown that different brain regions are activatedduring a subjectrsquos naturally occurring smile and a forcedsmile (Damasio 1994) For this reason measuring sub-jects while they are making decisions in their naturalenvironment is essential for any truly unbiased study offinancial decision-making processes
In capturing relations between cognitive inputs andaffective reactions that are often subconscious and ofwhich subjects are not fully aware our findings may beviewed more broadly as a study of cognitive- emotionalinteractions and the genesis of lsquolsquointuitionrsquorsquo Decisionprocesses based on intuition are characterized by lowlevels of cognitive control low conscious awarenessrapid processing rates and a lack of clear organizingprinciples When intuitive judgments are formed largenumbers of cues are processed simultaneously and thetask is not decomposed into subtasks (HammondHamm Grassia amp Pearson 1987) Expertsrsquo judgmentsare often based on intuition not on explicit analyticalprocessing making it almost impossible to fully explainor replicate the process of how that judgment has beenformed This is particularly germane to financial trad-ersmdashas a group they are unusually heterogeneouswith respect to educational background and formalanalytical skills yet the most successful traders seemto trade based on their intuition about price swingsand market dynamics often without the ability (or theneed) to articulate a precise quantitative algorithm formaking these complex decisions (Niederhoffer 1997Schwager 1989 1992) Their intuitive trading lsquolsquorulesrsquorsquoare based on the associations and relations betweenvarious information tokens that are formed on a sub-conscious level and our findings and those in theextant cognitive sciences literature suggest that deci-sions based on the intuitive judgments require not onlycognitive but also emotional mechanisms A naturalconjecture is that such emotional mechanisms are atleast partly responsible for the ability to form intuitivejudgments and for those judgments to be incorporatedinto a rational decision-making process
Our results may surprise some financial economistsbecause of the apparent inconsistency with marketrationality but a more sophisticated view of the role ofemotion in human cognition (Rolls 1990 1994 1999)can reconcile any contradiction in a complete andintellectually satisfying manner Emotion is the basisfor a reward-and-punishment system that facilitates theselection of advantageous behavioral actions providingthe numeraire for animals to engage in a lsquolsquocost- benefitanalysisrsquorsquo of the various actions open to them (Rolls1999 Chap 103) From an evolutionary perspectiveemotion is a powerful adaptation that dramatically im-proves the efficiency with which animals learn from theirenvironment and their past
These evolutionary underpinnings are more thansimple speculation in the context of financial tradersThe extraordinary degree of competitiveness of globalfinancial markets and the outsize rewards that accrue tothe lsquolsquofittestrsquorsquo traders suggest that Darwinian selectionmdashfinancial selection to be specificmdashis at work in deter-mining the typical profile of the successful trader Afterall unsuccessful traders are generally lsquolsquoeliminatedrsquorsquo fromthe population after suffering a certain level of losses
332 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Our results indicate that emotion is a significant deter-minant of the evolutionary fitness of financial tradersWe hope to investigate this conjecture more formally inthe future in several ways a comparison between tradersand a control group of subjects without trading experi-ence or with unsuccessful trading experiences an anal-ysis of the psychophysiological responses of traders in alaboratory environment a more direct analysis of thecorrelation between autonomic responses and tradingperformance a more fundamental analysis of the neuralbasis of emotion in traders aimed at the function of theamygdala and the orbitofrontal cortex (Rolls 1992 1999Chap 44- 45) and a direct mapping of the neuralcenters for trading activity through functional magneticresonance imaging (Breiter et al 2001)
There are a number of caveats that must be kept inmind in interpreting our findings First because of thesmall sample size of 10 subjects our results are at bestsuggestive and promising not conclusive A more com-prehensive study with a larger and more diverse sampleof traders a more refined set of market events and abroader set of controls are necessary before coming toany firm conclusions regarding the precise mechanismsof the psychophysiology of financial risk processing andthe role of emotion in market efficiency
Second while we have documented the statisticalsignificance of autonomic responses during marketevents relative to control baselines we have not estab-lished specific causal factors for such responses Manycognitive tasks generate autonomic effects hence amarked change in any one psychophysiological indexmay be due to changes in cognitive processing forexample complex numerical computations rather thanmore fundamental affective responses For examplemore experienced traders in our sample may have dis-played lower autonomic response levels because theyrequired less cognitive effort to perform the same func-tions as less experienced traders which may have nothingto do with emotion Without a more targeted set of tasksor additional data on the characteristics of the subjects itis difficult to distinguish between the two One way toaddress this issue is to correlate a subjectrsquos autonomicresponses with his or her trading performance so as todevelop a more complete profile of the relation betweenpsychophysiological indices and the dynamics of thetrading process However because trading performanceis generally summarized by multiple characteristicsmdashtotal profit-and-loss volatility maximum drawdownSharpe ratio and so onmdashestimating a relation betweenautonomic responses and such characteristics may re-quire significantly larger sample sizes and longer obser-vation windows due to the lsquolsquocurse of dimensionalityrsquorsquo Itmay be possible to overcome this challenge to somedegree through a more carefully crafted experimentaldesign in which specific emotional responses are pairedwith specific trading performance measures namelyanxiety levels during consecutive sequences of losing
trades With a larger sample size we can also begin totrack groups of traders over a period of time and throughvarying market conditions By measuring their autonomicresponse profiles at different stages of their careers itmay be possible to determine more directly the role ofemotion in financial risk processing
Finally a much larger set of controls should accompanya larger sample of traders These controls should includepsychophysiological measurements for professional trad-ers taken outside their natural trading environmentmdashideally in a laboratory setting that is identical for alltradersmdashas well as corresponding measurements forsubjects with little or no trading experience in bothtrading and nontrading environments Along with psy-chophysiological data more traditional personality in-ventory data for example MMPI or Myers- Briggs mayprovide additional insights into the psychology of trading
We hope to address each of these issues in ourongoing research program to better understand thecognitive processes underlying financial risk processing
METHODS
Subjects
The 10 subjectsrsquo descriptive characteristics are summar-ized in Table 5 Based on discussions with their super-visors the traders were categorized into three levels ofexperience low moderate and high Five traders spe-cialized in handling client order flow (Retail) threespecialized in trading foreign exchange (FX) and twospecialized in interest-rate derivative securities (Deriva-tives) The durations of the sessions ranged from 49 to83 min and all sessions were held during live tradinghours typically between 8 am and 5 pm easterndaylight time
Physiological Data Collection
A ProComp+ data-acquisition unit and Biograph (Ver-sion 12) biofeedback software from Thought Technol-ogy were used to measure physiological data for allsubjects All six sensors were connected to a smallcontrol unit with a battery power supply which wasplaced on each subjectrsquos belt and from which a fiber-optic connection led to a laptop computer equippedwith real-time data acquisition software (see Figure 2)Each sensor was equipped with a built-in notch filter at60 Hz for automatic elimination of external power linenoise and standard AgCl triode and single electrodeswere used for SCR and EMG sensors respectively Thesampling rate for all data collection was fixed at 32 HzAll physiological data except for respiration and facialEMG were collected from each subjectrsquos nondominantarm SCR electrodes were placed on the palmar sites theBVP photoplesymographic sensor was placed on theinside of the ring or middle finger the arm EMG triodeelectrode was placed on the inside surface of the
Lo and Repin 333
forearm over the flexor digitorum muscle group andthe temperature sensor was inserted between the elasticband placed around the wrist and the skin surface Thefacial EMG electrode was placed on a masseter musclewhich controls jaw movement and is active duringspeech or any other activity involving the jaw Therespiration signal was measured by chest expansionusing a sensor attached to an elastic band placed aroundthe subjectrsquos chest An example of the real-time physio-logical data collected over a 2-min interval for onesubject is given in Figure 3
The entire procedure of outfitting each subject withsensors and connecting the sensors to the laptop re-quired approximately 5 min and was often performedeither before the trading day began or during relativelycalm trading periods Subjects indicated that the pres-
ence of the sensors wires and a control unit did notcompromise or influence their trading in any significantmanner and that their workflow was not impaired in anyway This was verified not only by the subjects but alsoby their supervisors Given the magnitudes of the finan-cial transactions that were being processed and theeconomic and legal responsibilities that the subjectsand their supervisors bore even the slightest interfer-ence with the subjectsrsquo workflow or performance stand-ards would have caused the supervisors or the subjectsto terminate the sessions immediately None of thesessions were terminated prematurely
Physiological Data Feature Extraction
An initial smoothing of the raw EMG signals (sampled at32 Hz) was performed with a moving-average filter oforder 23 If the level of the filtered forearm EMG signalexceeded a threshold of 075 mV both SCR and BVPreadings at this time were discarded because of the highprobability of artifacts for example typing graspingtelephone handsets or inadvertent physical disturban-ces to the sensors Similarly if the level of the filteredfacial EMG signal exceeded 075 mV the respirationsignal at this time was excluded from further processingA very small spatial displacement of the sensor or theelectrode was able to produce a different kind ofartifactmdashan abrupt change in the signal of the orderof 10- 20 standard deviations within 1
32 of a second Suchjumps did not have any physiological meaning hencethey were excluded from further analysis via adaptivethresholding Specifically our adaptive thresholdingprocedure involved marking all observations that
Table 5 Summary Statistics for All Subjects Individual Traderrsquos Characteristics Specialty Type and Number of Market Time-Series Collected During the Session Session Duration and Absolute Time (Eastern Standard Time) of the Start of the Session
TraderID Gender Experience Specialty Market data Available
Nu m ber of MarketTim e-Series Recorded
Session Du ration(m in and sec)
SessionStart Tim e
B33 F high retail major currencies 2 4903000 1320
B34 M high FX major currencies 3 8303200 0856
B35 M high retail major currencies 4 6603000 1102
B36 M high derivatives SampP500 futureseurodollar futures
3 7901600 1255
B37 M moderate FX Latin Americanmajor currencies
9 7000600 0917
B38 M low FX Latin Americanmajor currencies
3 7201200 1102
B39 M high derivatives SampP 500 futureseurodollar futures
9 6200300 0819
B310 M low retail major currencies 7 6000800 0947
B311 M moderate retail major currencies 7 5402500 1132
B312 M low retail major currencies 7 5900000 0910
ProC om p+
Facial EMG Triode electrode measures
activity of the masseter muscle (detects speech)
Respiration sensor Elastic strap measures
chest expansion
Forearm EMG Triode electrode measures activity of the flexor digitorum muscle group (detects finger and arm movement)
Temperature sensor - thermistor
SCR sensor and electrodes
BVP photoplesymographicsensor
Control unit Fiber optic cable to a
data acquisition laptop
Figure 2 Placement of sensors for measuring physiologicalresponses
334 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
state or attitude indicating a nonspecific increase inattention or arousal Hippocampal information process-ing is believed to be behind this system which isassociated with a higher degree of conscious aware-ness while the first two are mostly attributed tosubconscious processes
The cardiovascular system consists of the heart and allthe blood vessels and the variables of particular interestare BVP and HR BVP is the rate of flow of blood througha particular blood vessel and is related to both bloodpressure and the diameter of the vessel Constriction ordilation of the vessels is controlled by the sympatheticbranch of the ANS and along with electrodermal activityit has been shown to be related to information process-ing and decision making (Papillo amp Shapiro 1990) HRrefers to the frequency of the contractions of the heartmuscle or myocardium Specialized neuronsmdashthe so-called lsquolsquopacemakerrsquorsquo cellsmdashinitiate the contraction ofmyocardium and the output of the pacemakers iscontrolled by both parasympathetic (HR decrease) andsympathetic (HR increase) ANS branches BVP and HRtrack each other closely so that HR deceleration usuallycauses an increase in BVP Compared to SCR arousalindications that refer mostly to cognitive processeschanges in cardiovascular variables register higher levelsof arousal which are often somatically mediated Thesevariables may provide supplemental information forinterpreting high SCR signalsmdashelevated SCR accompa-nied by an increase in HR may be an indication ofextreme significance of the task or stimulus or alter-natively simply indicate that some physical activity isbeing performed simultaneously (Boucsein 1992)
EMG measurements are based on the electrical signalsgenerated by the contraction process of striatedmuscles Muscle action potentials travel along themuscle and some portion of the electrical activity leaksto the skin surface where it can be detected with thehelp of surface electrodes The activation of particularmuscles reflects different types of actions For exampleactivation of the facial muscle (ie the lsquolsquomasseterrsquorsquomuscle) indicates ongoing speech activation of theforearm muscle group (ie the lsquolsquoflexor digitorumrsquorsquogroup) corresponds to finger movements such as typingon a keyboard In addition to their role in motor activitycertain muscle groups exhibit strong correlations withemotional states Most of the research in this literaturehas focused on facial muscles since facial expressionshave been linked to emotional states (Ekman 1982) Forexample an increase in EMG activity over the forehead isassociated with anxiety and tension and an increase inactivity over the brow accompanied by a slight decreasein activity over the cheek often corresponds to unpleas-ant sensory stimuli (Cacioppo et al 2000) ThereforeEMG measurements can capture very subtle changes inmuscle activity that can differentiate otherwise indistin-guishable response patterns However in our currentimplementation the role of EMG measurements is
limited to identifying and eliminating anomalous sensorreadings caused by certain physical motions of a subject
Respiration influences the HR through vascular recep-tors (Lorig amp Schwartz 1990) and although this variableis usually not of primary psychological importance it is areliable indicator of physically demanding activitiesundertaken by the subject for example speaking orcoughing which can often yield anomalous sensor read-ings if not properly taken into account Respiration canbe measured by placing a sensor that monitors chestexpansion and compression
Finally body temperature regulation involves the in-tegration of autonomic motor and endocrine responsesand several studies have related the temperatures ofdifferent parts of the body to certain cognitive and emo-tional contents of the task or stimuli For example fore-head temperature (a proxy for brain temperature)increases while experiencing negative emotions coolingenhances positive affect while warming depresses it(McIntosh Zajonc Vig amp Emerick 1997) Another studyreports that hand skin temperature increases with pos-itive affect and decreases with threatening and unpleas-ant tasks (Rimm-Kaufman amp Kagan 1996)
RESULTS
Our sample of subjects consisted of 10 professionaltraders employed by the foreign-exchange and inter-est-rate derivatives business unit of a major globalfinancial institution based in Boston MA This institu-tion provides banking and other financial services toclients ranging from small regional startups to Fortune500 multinational corporations The foreign-exchangeand interest-rate derivatives business unit employs ap-proximately 90 professionals of which two-thirds spe-cializes in the trading of foreign exchange and relatedinstruments and one-third specializes in the trading ofinterest-rate derivative securities In a typical day thisbusiness unit engages in 1000- 1200 trades with anaverage size of US$3- 5 million per trade Approximately80 of the trades are executed on behalf of the clientsof the financial institution with the remaining 20motivated by the financial institutionrsquos market-makingactivities
For each of the 10 subjects five physiological variableswere monitored in real time during the entire duration ofeach session while the subjects sat at their trading con-soles (see Figure 1) Six sensors were used SCR BVPbody temperature (TMP) respiration (RSP) and twoelectromyographic sensors (facial and forearm EMG)
The benefits of acquiring real-time physiological meas-urements in vivo must be balanced against the cost ofmeasurement error spurious signals and other statisticalartifacts which if left untreated can obscure and con-found any genuine signals in the data To eliminate asmany artifacts as possible while maximizing the informa-tional content of the data each of the five physiological
Lo and Repin 325
variables were preprocessed with filters calibrated toeach variable individually and adaptive time-windowswere used in place of fixed-length windows to matchthe event-driven nature of the market variables
After preprocessing the following seven features wereextracted from SCR BVP temperature and respirationsignals
1 times of onset of SCR responses2 amplitudes of SCR responses3 average HR (every 3 sec)4 BVP signal amplitudes5 respiration rates (every 5 sec)6 respiration amplitudes7 temperature changes (from the 10-sec lag)
These features were selected to reflect the differentproperties and different information contained in theraw physiological variables SCRs were characterized bysmooth lsquolsquobumpsrsquorsquo with a relatively fast onset and slowdecay hence the number of such bumps and theirrelative strength are excellent summary measures ofthe SCR signal Intervals of 3 and 5 sec for heart andrespiration rates were chosen as a compromise betweenthe need for finer time slices to distinguish the onsetand completion of physiological and market events andthe necessity of a sufficient number of heartbeats andrespiration cycles within each interval to compute anaverage Under normal conditions a typical subject willexhibit an average of three to four heartbeats per 3-secinterval and approximately three breaths per 5-sec in-terval Temperature was the slowest-varying physiolog-ical signal and the 10-sec interval to register its changereflected the maximum possible interval in our analysis
For each session real-time market data for key finan-cial instruments actively traded or monitored by thesubject were collected synchronously with the physio-logical data In particular across the 10 subjects a total of15 instruments were consideredmdash13 foreign currencies
and two futures contracts the euro (EUR) the Japaneseyen (JPY) the British pound (GBP) the Canadian dollar(CAD) the Swiss franc (CHF) the Australian dollar(AUD) the New Zealand dollar (NZD) the Mexican peso(MXP) the Brazilian real (BRL) the Argentinian peso(ARS) the Colombian peso (COP) the Venezuelan boli-var (VEB) the Chilean peso (CLP) SampP 500 futures(SPU) and eurodollar futures (EDU) For each securityfour time series were monitored (1) the bid price Pt
b (2)the ask price Pt
a (3) the bidask spread Xt sup2 Ptb iexcl Pt
aand (4) the net return Rt sup2 (Pt iexcl Ptiexcl1) Ptiexcl1 where Pt
denotes the mid-spread price at time t and all priceswere sampled every second
For each of the financial time series we identifiedthree classes of market events deviations trend-rever-sals and volatility events These events are often cited bytraders as significant developments that require height-ened attention potentially signaling a shift in marketdynamics and risk exposures With these definitions andcalibrations in hand we implemented an automaticprocedure for detecting deviations trend-reversals andvolatility events for all the relevant time series in eachsession Table 1 reports event counts for the financialtime series monitored for each subject Feature vectorswere then constructed for all detected market events inall time series for all subjects and a set of control featurevectors was generated by applying the same feature-extraction process to randomly selected windows con-taining no events of any kind One set of control featurevectors was constructed for deviation and trend-reversalevents (10-sec intervals) and a second set was con-structed for volatility-type events (5-min intervals) Atwo-sided t test was applied to each component of eachfeature vector and the corresponding control vector totest the null hypothesis that the feature vectors werestatistically indistinguishable from the control featurevectors
For volatility events which by definition occurred in5-min intervals (event windows) we constructed featurevectors containing the following information for theduration of the event window
1 number of SCR responses2 average SCR amplitude3 average HR4 average ratio of the BVP amplitude to local baseline5 average ratio of the BVP amplitude to global
baseline6 number of temperature changes exceeding 018F7 average respiration rate8 average respiration amplitude
where the local baseline for the BVP signal is the averagelevel during the event window and the global baseline isthe average over the entire recording session for eachtrader Control feature vectors were constructed byapplying the feature-extraction process to randomlyselected 5-min windows containing no events of any
Laptop with data acquisition software
Fiber optic cable Control unit
Market information (Reuters Bloomberg) Order screen etc TIBCO market spreadsheet
eal-time data feed the market data
acquisition computer
Figure 1 Typical set-up for the measurement of real-time physiolo-gical responses of financial traders during live trading sessions Real-time market data used by the traders are recorded synchronously andsubsequently analyzed together with the physiological response data
326 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
kind For the other two types of market eventsmdashdevia-tions and trend-reversalsmdashlsquolsquopre-eventrsquorsquo and lsquolsquopost-eventrsquorsquofeature vectors were constructed before and after eachmarket event using the same variables where featureswere aggregated over 10-sec windows immediately pre-ceding and following the event Control feature vectorswere generated for these cases as well
The motivation for the post-event feature vector wasto capture the subjectrsquos reaction to the event with thepre-event feature vector as a benchmark from which tomeasure the magnitude of the reaction A comparisonbetween the pre-event feature vector and a controlfeature vector may provide an indication of a subjectrsquosanticipation of the event Latencies of the autonomicresponses reported in previous studies were on a timescale of 1 to 10 sec (see Cacioppo et al 2000) hence10-sec event windows were judged to be long enough forevent-related autonomic responses to occur and at thesame time short enough to minimize the likelihood ofoverlaps with other events or anomalies Five-minutewindows were used for volatility events because 10-secintervals were simply insufficient for meaningful volatilitycalculations For all of the financial time series used inthis study and for most financial time series in generalthere are few volatility events that occur in any 10-secinterval except of course under extreme conditions forexample the stock market crash of October 19 1987 (nosuch conditions prevailed during any of our sessions)
The statistical analysis of the physiological and marketdata was motivated by four objectives (1) to identifyparticular classes of events with statistically significantdifferences in mean autonomic responses before or after
an event as compared to the no-event control (2) toidentify particular traders or groups of traders based onexperience or other personal characteristics that dem-onstrate significant mean autonomic responses duringmarket events (3) to identify particular financial instru-ments or groups of instruments that are associated withsignificant mean autonomic responses and (4) to iden-tify particular physiological variables that demonstratesignificant mean response levels immediately followingthe event or during the event-anticipation period ascompared to the no-event control
To address the first objective a total of eight types ofevents were used for each of the time series
1 price deviations2 spread deviations3 return deviations4 price trend-reversals5 spread trend-reversals6 maximum volatility7 price volatility8 return volatility
and for each type of event a two-sided t test wasperformed for the pooled sample of all subjects and alltime series in which mean autonomic responses duringevent periods were compared to mean autonomic re-sponses during no-event control periods
The t statistics corresponding to the two-sided hypoth-esis test are given in the left subpanel of Table 2 labelledlsquolsquoAll Tradersrsquorsquo Statistics that are significant at the 5levelmdashbased on the t distribution with the appropriatedegrees of freedom for each entrymdashare highlighted For
Table 1 Number of Deviation (DEV) Trend-Reversal (TRV) and Volatility (VOL) Events Detected in Real-Time Market Data forEach Trader Over the Course of Each Trading Session
EUR JPYOther MajorCu rrenciesa
Latin Am ericanCu rrenciesb Derivativesc
Trader ID DEV TRV VOL DEV TRV VOL DEV TRV VOL DEV TRV VOL DEV TRV VOL
B32 21 16 15 25 17 15 - - - - - - - - -
B34 18 10 14 17 17 14 - - - - - - - - -
B35 20 16 14 13 17 15 21 9 15 24 16 13 - - -
B36 - - - - - - - - - - - - 28 23 31
B37 19 19 14 18 18 12 18 18 14 105 86 63 - - -
B38 21 20 12 22 18 13 23 20 15 96 61 56 - - -
B39 - - - - - - - - - - - - 40 37 23
B310 10 17 10 18 16 14 116 59 61 - - - - - -
B311 17 15 15 17 16 15 94 61 67 - - - - - -
B312 19 17 15 16 19 13 107 83 71 - - - - - -
aGBP CAD CHF AUD NZDbMXP BRL ARS COP VEB CLPcEDU SPU
Lo and Repin 327
Tab
le2
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vec
tors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
All
Tra
ders
Hig
hEx
peri
ence
Low
orM
oder
ate
Expe
rien
ce
Phys
iolo
gy
Fea
ture
s
DE
V
Pri
ce
DEV
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VOL
Ma
xim
um
DE
V
Pri
ce
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VO
L
Ma
xim
um
DE
V
Pri
ce
DEV
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VO
L
Ma
xim
um
Nu
mbe
ro
fSC
Rre
spon
ses
28
72
060
42
08
81
66
40
636
057
90
561
122
8iexcl
022
1iexcl
126
10
184
20
81
26
96
012
32
75
22
17
0iexcl
066
5iexcl
068
8
Aver
age
SCR
ampl
itud
eiexcl
069
50
887
018
6iexcl
247
81
136
088
00
030
034
40
744
iexcl1
340
114
90
904
075
61
578
iexcl1
265
iexcl1
947
iexcl0
012
iexcl0
615
Aver
age
HR
090
5iexcl
058
8iexcl
059
6iexcl
061
71
032
iexcl0
270
044
91
88
6iexcl
179
7iexcl
163
51
83
6iexcl
201
81
310
005
1iexcl
038
2iexcl
110
6iexcl
031
11
283
Aver
age
BV
Pam
plit
ude
rela
tive
tolo
cal
base
line
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9iexcl
006
1iexcl
074
70
334
001
50
164
26
23
iexcl0
204
iexcl1
775
087
51
79
6iexcl
128
92
70
8
Aver
age
BV
Pam
plit
ude
rela
tive
togl
obal
base
line
141
7iexcl
272
62
32
41
417
iexcl2
211
28
86
iexcl2
529
iexcl1
367
159
01
110
iexcl1
165
23
01
19
17
iexcl2
664
18
84
20
82
iexcl1
759
27
58
Nu
mbe
ro
fte
mp
erat
ure
jum
ps
098
82
82
00
650
17
77
48
68
iexcl2
482
iexcl1
010
iexcl1
435
NA
NA
NA
164
40
837
29
02
114
52
44
52
63
3iexcl
321
3
Aver
age
resp
irat
ion
rate
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4iexcl
155
61
109
012
10
866
iexcl0
102
009
30
079
iexcl0
552
032
3iexcl
063
1iexcl
217
0iexcl
152
8
Aver
age
resp
irat
ion
ampl
itud
e
iexcl1
646
iexcl0
250
013
60
034
iexcl1
487
iexcl3
499
108
70
265
iexcl1
777
iexcl0
657
012
1iexcl
060
7iexcl
274
9iexcl
131
92
14
20
055
iexcl2
832
iexcl4
533
Bo
lded
stat
isti
csar
esi
gnifi
can
tat
the
5le
vel
Th
ele
ftp
anel
give
sag
greg
ate
resu
lts
for
allt
rad
ers
Sim
ilar
ttes
tsfo
rtr
ader
sw
ith
hig
hex
per
ienc
ean
dlo
wo
rm
oder
ate
exp
erie
nce
are
show
nin
the
mid
dle
and
righ
tp
anel
sre
spec
tive
lyE
ntr
ies
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
wer
ep
rese
nt
inei
ther
the
even
tor
cont
rol
feat
ure
vect
or
for
the
par
ticu
lar
mar
ket-
even
tty
pe
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spo
nse
sH
R=
hea
rtra
te
BV
P=
blo
od
volu
me
pul
se
328 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
deviations and trend-reversals both the number of SCRsand the number of temperature jumps reached statisti-cally significant levels for three out of the five event typesBVP amplitude-related features were highly significantfor volatility eventsmdashsignificance levels less than 1 wereobtained for both BVP amplitude features for all threetypes of volatility events Because the results were sosimilar across the three types of volatility events (max-imum volatility price volatility and return volatility) wereport the results only for maximum volatility in Table 2Such patterns of autonomic responses may indicate thepresence of transient emotional responses to deviationsand trend-reversal events that occur within 10-sec win-dows while volatility events defined in 5-min windowselicited a tonic response in BVP
To explore potential differences between experi-enced and less experienced traders a sample of 10traders was divided into two groups each consistingof five traders the first containing highly experiencedtraders and the second containing traders with low ormoderate experience where the levels of experiencewere determined through interviews with the tradersrsquosupervisors The results for each of the two groupsare reported in the two right subpanels of Table 2labelled lsquolsquoHigh Experiencersquorsquo and lsquolsquoLow or ModerateExperiencersquorsquo The high-experience traders generallyexhibited low responses for deviations and trend-re-versal events with only two types of market events(spread deviations and spread trend-reversals) elicitingsignificant mean autonomic responses (average HR)However even for high-experience traders volatilityevents induced statistically significant mean responsesfor both SCR and BVP amplitude Less experiencedtraders showed a much higher number of significantmean responses in the number of SCRs BVP ampli-tude and number of temperature increases for devia-tions and trend-reversal events Table 2 shows thatsessions with low- and moderate-experience tradersyielded 13 t statistics significant at the 5 level whilesessions with high-experience traders yielded only fivet statistics significant at the 5 level For both sets oftraders volatility events were associated with signifi-cant BVP amplitude The differences in responsepatterns for deviations and trend-reversals observedin this case indicate that less experienced traders maybe more sensitive to short-term changes in the marketvariables than their more experienced colleagues
To address the third objective 10-sec intervals imme-diately preceding and immediately following each devia-tion and trend-reversal event were compared to control(ie no-event) time intervals Two separate t tests wereconductedmdashone for pre- and another for post-eventtime intervalsmdashfor each of the three types of deviationsand two types of trend-reversals Because the objectivewas to detect differences in how pre- and post-eventfeature vectors differed from the control we used thesame control feature vectors for both sets of t tests In all
cases the t tests were designed to test the null hypoth-esis that both pre- and post-event feature vectors werestatistically indistinguishable from the control featurevector Surprisingly these two sets of t tests yielded verysimilar results reported in Table 3 implying that noneof the physiological variables were predictors of antici-patory emotional responses These findings may be atleast partly explained by how the events were defined Inparticular the definitions of deviation and trend-reversalevents are those instances where the time seriesachieved a prespecified deviation from the time-seriesmean and its moving average respectively In theabsence of large jumps the values of the time seriesnear an event defined in this way are likely to becomparable to the event itself Therefore the tradersmay be responding to market conditions occurringthroughout the pre- and post-event period not just atthe exact time of the event hence the similarity be-tween the two periods More complex definitions ofevents may allow us to discriminate between pre- andpost-event physiological responses and we are explor-ing several alternatives in ongoing research
Finally to address the fourth objective the followingfour groups of financial instruments were formed on thebasis of similarity in their statistical characteristics
Group 1 EUR JPY
Group 2 GBP CAD CHF AUD NZD (other majorcurrencies)
Group 3 MXP BRL ARS COP VEB CLP (LatinAmerican currencies)
Group 4 SPU EDU (derivatives)
Group 1 is by far the most actively traded set offinancial instruments and accounts for most of thetrading activities of our sample of 10 traders hence itis not surprising that the pattern of statistical signifi-cance for Group 1 in Table 4 is almost identical to thepattern for all traders in Table 2 (the left panel) SCRis significant for price and return deviations temper-ature jumps are significant for spread and price devia-tions and both measures of BVP are significant forvolatility events For Group 2 none of the eightphysiological variables were significant for price orspread deviations although temperature changes andrespiration rates were significant for return deviationsand both BVP measures were significant for pricetrend-reversals and volatility events The financial in-struments in Group 2 were not the primary focus ofany of the traders in our sample which may explainthe different patterns of significance as compared withGroup 1 Group 4 contains the fewest significantresponses and this is consistent with the fact thatthese securities were traded by two of the mostexperienced traders in our sample Derivative secur-ities are considerably more complex instruments thanthe spot currencies requiring more skill and trainingthan the other groups of securities However SCR was
Lo and Repin 329
Tab
le3
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gPr
e-an
dPo
st-e
vent
Tim
eIn
terv
als
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
Pre
-eve
nt
Inte
rva
lP
ost-
even
tIn
terv
al
Phy
sio
logy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mbe
rof
SCR
resp
ons
es3
75
61
543
25
40
21
18
24
01
057
92
87
20
604
20
88
16
64
063
60
579
Aver
age
SCR
amp
litu
de0
700
iexcl0
454
iexcl1
793
051
11
068
088
0iexcl
069
50
887
018
6iexcl
247
81
136
088
0
Aver
age
HR
096
8iexcl
020
5iexcl
055
2iexcl
066
3iexcl
038
6iexcl
027
00
905
iexcl0
588
iexcl0
596
iexcl0
617
103
2iexcl
027
0
Aver
age
BVP
amp
litud
ere
lativ
eto
loca
lba
selin
e0
043
iexcl0
895
006
00
814
iexcl0
045
36
19
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9
Aver
age
BVP
amp
litud
ere
lativ
eto
glob
alba
selin
e1
183
iexcl2
794
23
97
131
7iexcl
225
62
88
61
417
iexcl2
726
23
24
141
7iexcl
221
12
88
6
Nu
mbe
rof
tem
per
atur
eju
mp
s1
110
27
67
050
52
15
83
92
5iexcl
248
20
988
28
20
065
01
77
74
86
8iexcl
248
2
Aver
age
resp
irat
ion
rate
126
1iexcl
165
0iexcl
025
41
303
iexcl1
999
iexcl0
064
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4
Aver
age
resp
irat
ion
amp
litud
eiexcl
162
0iexcl
006
70
561
006
8iexcl
180
7iexcl
349
9iexcl
164
6iexcl
025
00
136
003
4iexcl
148
7iexcl
349
9
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
l
Th
ele
ftp
anel
cont
ain
st
stat
isti
csfo
rp
re-e
vent
feat
ure
vect
ors
and
the
righ
tp
anel
con
tain
st
stat
isti
csfo
rp
ost-
even
tfe
atu
reve
cto
rs
both
test
edag
ain
stth
esa
me
set
of
con
tro
ls
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spon
ses
HR
=h
eart
rate
B
VP
=bl
ood
volu
me
pu
lse
330 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le4
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
oth
esis
Tes
tsof
the
Diff
eren
cein
Mea
nAu
tono
mic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pon
ents
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chTy
pe
ofM
arke
tEv
ent
(Col
umns
)G
roup
edIn
toFo
urD
istin
ctSe
tsof
Fina
ncia
lIn
stru
men
ts
Ma
rket
Eve
nts
Gro
up
1E
UR
an
dJP
YG
rou
p2
Oth
erM
ajo
rC
urr
enci
esa
Gro
up
3La
tin
Am
eric
an
Cu
rren
cies
bG
rou
p4
Der
iva
tive
sc
Phy
siol
ogy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
mD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mb
erof
SCR
resp
on
ses
16
53
iexcl1
902
19
64
iexcl0
554
iexcl2
286
iexcl0
940
033
20
112
054
4iexcl
179
5iexcl
241
7iexcl
110
82
49
82
18
8iexcl
039
72
99
5iexcl
040
50
898
26
82
109
41
66
91
594
iexcl0
239
22
36
Ave
rage
SCR
amp
litu
de
iexcl1
639
062
30
279
iexcl1
756
109
8iexcl
111
2iexcl
050
30
540
113
8iexcl
219
0iexcl
010
60
896
111
61
279
iexcl1
525
iexcl1
802
22
20
iexcl0
986
iexcl0
610
123
60
930
iexcl1
513
024
2iexcl
151
0
Ave
rage
HR
073
1iexcl
000
4iexcl
126
8iexcl
181
5iexcl
069
8iexcl
161
2iexcl
099
1iexcl
145
2iexcl
103
4iexcl
132
6iexcl
030
8iexcl
108
92
92
1iexcl
143
2iexcl
107
0iexcl
100
2iexcl
141
61
183
081
6iexcl
032
20
577
049
20
515
iexcl0
169
Ave
rage
BV
Pam
plit
ude
rela
tive
to
loca
lb
asel
ine
056
2iexcl
136
8iexcl
080
51
273
088
52
58
7iexcl
092
20
220
145
72
32
3iexcl
127
52
30
8iexcl
098
2iexcl
000
12
82
81
454
059
82
19
2iexcl
059
2iexcl
303
2iexcl
395
3iexcl
089
4iexcl
190
4iexcl
143
3
Ave
rage
BV
Pam
plit
ude
rela
tive
tolo
cal
bas
elin
e
059
7iexcl
301
01
71
40
375
iexcl0
252
25
92
052
11
448
097
23
08
7iexcl
149
92
63
3iexcl
192
5iexcl
084
91
482
025
9iexcl
035
1iexcl
012
9iexcl
066
0iexcl
150
1iexcl
327
4iexcl
162
6iexcl
200
4iexcl
241
1
Nu
mb
erof
tem
per
atu
reju
mp
s
068
84
14
1iexcl
144
42
28
30
295
iexcl2
043
107
3iexcl
155
72
40
41
248
37
26
iexcl1
564
iexcl1
603
iexcl1
275
iexcl1
922
iexcl0
512
iexcl1
128
iexcl1
540
iexcl1
367
iexcl1
349
iexcl1
362
iexcl1
367
iexcl1
053
NA
Ave
rage
resp
irat
ion
rate
052
5iexcl
174
1iexcl
079
70
163
iexcl1
884
iexcl0
004
075
6iexcl
056
02
02
3iexcl
109
5iexcl
178
4iexcl
011
60
037
044
6iexcl
112
40
643
iexcl1
392
012
5iexcl
194
6iexcl
029
10
361
041
6iexcl
110
5iexcl
172
5
Ave
rage
resp
irat
ion
amp
litu
de
iexcl2
120
iexcl0
692
034
5iexcl
050
9iexcl
093
5iexcl
207
9iexcl
074
60
560
047
00
905
089
3iexcl
383
90
280
iexcl0
207
142
6iexcl
036
0iexcl
053
4iexcl
043
70
920
iexcl0
955
iexcl1
915
125
71
593
iexcl0
039
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
la M
XP
BR
LA
RS
CO
PV
EB
CLP
bM
XP
BR
LA
RS
CO
PV
EB
C
LP
c ED
U
SPU
Ent
ries
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
was
pre
sen
tin
eith
erth
eev
ent
orco
ntro
lfe
atu
reve
cto
rfo
rth
ep
arti
cula
rm
arke
t-ev
ent
typ
e
DE
V=
dev
iati
on
TR
V=
tren
d-r
ever
sal
VO
L=
vola
tilit
ySC
R=
skin
con
duc
tan
cere
spo
nses
H
R=
hea
rtra
te
BV
P=
bloo
dvo
lum
ep
uls
e
Lo and Repin 331
significant for both price and return deviations inGroup 4 which was not the case for the sample ofhigh-experience traders in Table 2 SCR was alsosignificant for volatility events in Group 4 which isconsistent with the central role that volatility plays inthe pricing and hedging derivative securities (Black ampScholes 1973 Merton 1973) Volatility is a moresophisticated aspect of the trading milieu requiringa higher degree of training to fully appreciate itsimplications for market trends and profit-and-lossprobabilities This may explain the fact that SCR issignificant for volatility events only among the high-experience traders in Table 2 and the derivativestraders in Table 4
DISCUSSION
Our findings suggest that emotional responses are asignificant factor in the real-time processing of financialrisks Contrary to the common belief that emotions haveno place in rational financial decision-making processesphysiological variables associated with the ANS exhibitsignificant changes during market events even for highlyexperienced professional traders Moreover the re-sponse patterns among variables and events differed inimportant ways for less experienced tradersmdashmeanautonomic responses were significantly highermdashsug-gesting the possibility of relating trading skills to certainphysiological characteristics that can be measured
More generally our experiments demonstrate thefeasibility of relating real-time quantitative changes incognitive inputs (financial information) to correspond-ing quantitative changes in physiological responses in acomplex field environment Despite the challenges ofsuch measurements a wealth of information can beobtained regarding high-pressure decision makingunder uncertainty Financial traders operate in a con-trolled environment where the inputs and outputs ofthe decisions are carefully recorded and where thesubjects are highly trained and provided with greateconomic incentives to make rational trading decisionsTherefore in vivo experiments in the securities tradingcontext are likely to become an important part of theempirical analysis of individual risk preferences anddecision-making processes In particular there is con-siderable anecdotal evidence that subjects involved inprofessional trading activities perform very differentlydepending on whether real financial capital is at risk or ifthey are trading with lsquolsquoplayrsquorsquo money Such distinctionshave been documented in other contexts eg it hasbeen shown that different brain regions are activatedduring a subjectrsquos naturally occurring smile and a forcedsmile (Damasio 1994) For this reason measuring sub-jects while they are making decisions in their naturalenvironment is essential for any truly unbiased study offinancial decision-making processes
In capturing relations between cognitive inputs andaffective reactions that are often subconscious and ofwhich subjects are not fully aware our findings may beviewed more broadly as a study of cognitive- emotionalinteractions and the genesis of lsquolsquointuitionrsquorsquo Decisionprocesses based on intuition are characterized by lowlevels of cognitive control low conscious awarenessrapid processing rates and a lack of clear organizingprinciples When intuitive judgments are formed largenumbers of cues are processed simultaneously and thetask is not decomposed into subtasks (HammondHamm Grassia amp Pearson 1987) Expertsrsquo judgmentsare often based on intuition not on explicit analyticalprocessing making it almost impossible to fully explainor replicate the process of how that judgment has beenformed This is particularly germane to financial trad-ersmdashas a group they are unusually heterogeneouswith respect to educational background and formalanalytical skills yet the most successful traders seemto trade based on their intuition about price swingsand market dynamics often without the ability (or theneed) to articulate a precise quantitative algorithm formaking these complex decisions (Niederhoffer 1997Schwager 1989 1992) Their intuitive trading lsquolsquorulesrsquorsquoare based on the associations and relations betweenvarious information tokens that are formed on a sub-conscious level and our findings and those in theextant cognitive sciences literature suggest that deci-sions based on the intuitive judgments require not onlycognitive but also emotional mechanisms A naturalconjecture is that such emotional mechanisms are atleast partly responsible for the ability to form intuitivejudgments and for those judgments to be incorporatedinto a rational decision-making process
Our results may surprise some financial economistsbecause of the apparent inconsistency with marketrationality but a more sophisticated view of the role ofemotion in human cognition (Rolls 1990 1994 1999)can reconcile any contradiction in a complete andintellectually satisfying manner Emotion is the basisfor a reward-and-punishment system that facilitates theselection of advantageous behavioral actions providingthe numeraire for animals to engage in a lsquolsquocost- benefitanalysisrsquorsquo of the various actions open to them (Rolls1999 Chap 103) From an evolutionary perspectiveemotion is a powerful adaptation that dramatically im-proves the efficiency with which animals learn from theirenvironment and their past
These evolutionary underpinnings are more thansimple speculation in the context of financial tradersThe extraordinary degree of competitiveness of globalfinancial markets and the outsize rewards that accrue tothe lsquolsquofittestrsquorsquo traders suggest that Darwinian selectionmdashfinancial selection to be specificmdashis at work in deter-mining the typical profile of the successful trader Afterall unsuccessful traders are generally lsquolsquoeliminatedrsquorsquo fromthe population after suffering a certain level of losses
332 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Our results indicate that emotion is a significant deter-minant of the evolutionary fitness of financial tradersWe hope to investigate this conjecture more formally inthe future in several ways a comparison between tradersand a control group of subjects without trading experi-ence or with unsuccessful trading experiences an anal-ysis of the psychophysiological responses of traders in alaboratory environment a more direct analysis of thecorrelation between autonomic responses and tradingperformance a more fundamental analysis of the neuralbasis of emotion in traders aimed at the function of theamygdala and the orbitofrontal cortex (Rolls 1992 1999Chap 44- 45) and a direct mapping of the neuralcenters for trading activity through functional magneticresonance imaging (Breiter et al 2001)
There are a number of caveats that must be kept inmind in interpreting our findings First because of thesmall sample size of 10 subjects our results are at bestsuggestive and promising not conclusive A more com-prehensive study with a larger and more diverse sampleof traders a more refined set of market events and abroader set of controls are necessary before coming toany firm conclusions regarding the precise mechanismsof the psychophysiology of financial risk processing andthe role of emotion in market efficiency
Second while we have documented the statisticalsignificance of autonomic responses during marketevents relative to control baselines we have not estab-lished specific causal factors for such responses Manycognitive tasks generate autonomic effects hence amarked change in any one psychophysiological indexmay be due to changes in cognitive processing forexample complex numerical computations rather thanmore fundamental affective responses For examplemore experienced traders in our sample may have dis-played lower autonomic response levels because theyrequired less cognitive effort to perform the same func-tions as less experienced traders which may have nothingto do with emotion Without a more targeted set of tasksor additional data on the characteristics of the subjects itis difficult to distinguish between the two One way toaddress this issue is to correlate a subjectrsquos autonomicresponses with his or her trading performance so as todevelop a more complete profile of the relation betweenpsychophysiological indices and the dynamics of thetrading process However because trading performanceis generally summarized by multiple characteristicsmdashtotal profit-and-loss volatility maximum drawdownSharpe ratio and so onmdashestimating a relation betweenautonomic responses and such characteristics may re-quire significantly larger sample sizes and longer obser-vation windows due to the lsquolsquocurse of dimensionalityrsquorsquo Itmay be possible to overcome this challenge to somedegree through a more carefully crafted experimentaldesign in which specific emotional responses are pairedwith specific trading performance measures namelyanxiety levels during consecutive sequences of losing
trades With a larger sample size we can also begin totrack groups of traders over a period of time and throughvarying market conditions By measuring their autonomicresponse profiles at different stages of their careers itmay be possible to determine more directly the role ofemotion in financial risk processing
Finally a much larger set of controls should accompanya larger sample of traders These controls should includepsychophysiological measurements for professional trad-ers taken outside their natural trading environmentmdashideally in a laboratory setting that is identical for alltradersmdashas well as corresponding measurements forsubjects with little or no trading experience in bothtrading and nontrading environments Along with psy-chophysiological data more traditional personality in-ventory data for example MMPI or Myers- Briggs mayprovide additional insights into the psychology of trading
We hope to address each of these issues in ourongoing research program to better understand thecognitive processes underlying financial risk processing
METHODS
Subjects
The 10 subjectsrsquo descriptive characteristics are summar-ized in Table 5 Based on discussions with their super-visors the traders were categorized into three levels ofexperience low moderate and high Five traders spe-cialized in handling client order flow (Retail) threespecialized in trading foreign exchange (FX) and twospecialized in interest-rate derivative securities (Deriva-tives) The durations of the sessions ranged from 49 to83 min and all sessions were held during live tradinghours typically between 8 am and 5 pm easterndaylight time
Physiological Data Collection
A ProComp+ data-acquisition unit and Biograph (Ver-sion 12) biofeedback software from Thought Technol-ogy were used to measure physiological data for allsubjects All six sensors were connected to a smallcontrol unit with a battery power supply which wasplaced on each subjectrsquos belt and from which a fiber-optic connection led to a laptop computer equippedwith real-time data acquisition software (see Figure 2)Each sensor was equipped with a built-in notch filter at60 Hz for automatic elimination of external power linenoise and standard AgCl triode and single electrodeswere used for SCR and EMG sensors respectively Thesampling rate for all data collection was fixed at 32 HzAll physiological data except for respiration and facialEMG were collected from each subjectrsquos nondominantarm SCR electrodes were placed on the palmar sites theBVP photoplesymographic sensor was placed on theinside of the ring or middle finger the arm EMG triodeelectrode was placed on the inside surface of the
Lo and Repin 333
forearm over the flexor digitorum muscle group andthe temperature sensor was inserted between the elasticband placed around the wrist and the skin surface Thefacial EMG electrode was placed on a masseter musclewhich controls jaw movement and is active duringspeech or any other activity involving the jaw Therespiration signal was measured by chest expansionusing a sensor attached to an elastic band placed aroundthe subjectrsquos chest An example of the real-time physio-logical data collected over a 2-min interval for onesubject is given in Figure 3
The entire procedure of outfitting each subject withsensors and connecting the sensors to the laptop re-quired approximately 5 min and was often performedeither before the trading day began or during relativelycalm trading periods Subjects indicated that the pres-
ence of the sensors wires and a control unit did notcompromise or influence their trading in any significantmanner and that their workflow was not impaired in anyway This was verified not only by the subjects but alsoby their supervisors Given the magnitudes of the finan-cial transactions that were being processed and theeconomic and legal responsibilities that the subjectsand their supervisors bore even the slightest interfer-ence with the subjectsrsquo workflow or performance stand-ards would have caused the supervisors or the subjectsto terminate the sessions immediately None of thesessions were terminated prematurely
Physiological Data Feature Extraction
An initial smoothing of the raw EMG signals (sampled at32 Hz) was performed with a moving-average filter oforder 23 If the level of the filtered forearm EMG signalexceeded a threshold of 075 mV both SCR and BVPreadings at this time were discarded because of the highprobability of artifacts for example typing graspingtelephone handsets or inadvertent physical disturban-ces to the sensors Similarly if the level of the filteredfacial EMG signal exceeded 075 mV the respirationsignal at this time was excluded from further processingA very small spatial displacement of the sensor or theelectrode was able to produce a different kind ofartifactmdashan abrupt change in the signal of the orderof 10- 20 standard deviations within 1
32 of a second Suchjumps did not have any physiological meaning hencethey were excluded from further analysis via adaptivethresholding Specifically our adaptive thresholdingprocedure involved marking all observations that
Table 5 Summary Statistics for All Subjects Individual Traderrsquos Characteristics Specialty Type and Number of Market Time-Series Collected During the Session Session Duration and Absolute Time (Eastern Standard Time) of the Start of the Session
TraderID Gender Experience Specialty Market data Available
Nu m ber of MarketTim e-Series Recorded
Session Du ration(m in and sec)
SessionStart Tim e
B33 F high retail major currencies 2 4903000 1320
B34 M high FX major currencies 3 8303200 0856
B35 M high retail major currencies 4 6603000 1102
B36 M high derivatives SampP500 futureseurodollar futures
3 7901600 1255
B37 M moderate FX Latin Americanmajor currencies
9 7000600 0917
B38 M low FX Latin Americanmajor currencies
3 7201200 1102
B39 M high derivatives SampP 500 futureseurodollar futures
9 6200300 0819
B310 M low retail major currencies 7 6000800 0947
B311 M moderate retail major currencies 7 5402500 1132
B312 M low retail major currencies 7 5900000 0910
ProC om p+
Facial EMG Triode electrode measures
activity of the masseter muscle (detects speech)
Respiration sensor Elastic strap measures
chest expansion
Forearm EMG Triode electrode measures activity of the flexor digitorum muscle group (detects finger and arm movement)
Temperature sensor - thermistor
SCR sensor and electrodes
BVP photoplesymographicsensor
Control unit Fiber optic cable to a
data acquisition laptop
Figure 2 Placement of sensors for measuring physiologicalresponses
334 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
variables were preprocessed with filters calibrated toeach variable individually and adaptive time-windowswere used in place of fixed-length windows to matchthe event-driven nature of the market variables
After preprocessing the following seven features wereextracted from SCR BVP temperature and respirationsignals
1 times of onset of SCR responses2 amplitudes of SCR responses3 average HR (every 3 sec)4 BVP signal amplitudes5 respiration rates (every 5 sec)6 respiration amplitudes7 temperature changes (from the 10-sec lag)
These features were selected to reflect the differentproperties and different information contained in theraw physiological variables SCRs were characterized bysmooth lsquolsquobumpsrsquorsquo with a relatively fast onset and slowdecay hence the number of such bumps and theirrelative strength are excellent summary measures ofthe SCR signal Intervals of 3 and 5 sec for heart andrespiration rates were chosen as a compromise betweenthe need for finer time slices to distinguish the onsetand completion of physiological and market events andthe necessity of a sufficient number of heartbeats andrespiration cycles within each interval to compute anaverage Under normal conditions a typical subject willexhibit an average of three to four heartbeats per 3-secinterval and approximately three breaths per 5-sec in-terval Temperature was the slowest-varying physiolog-ical signal and the 10-sec interval to register its changereflected the maximum possible interval in our analysis
For each session real-time market data for key finan-cial instruments actively traded or monitored by thesubject were collected synchronously with the physio-logical data In particular across the 10 subjects a total of15 instruments were consideredmdash13 foreign currencies
and two futures contracts the euro (EUR) the Japaneseyen (JPY) the British pound (GBP) the Canadian dollar(CAD) the Swiss franc (CHF) the Australian dollar(AUD) the New Zealand dollar (NZD) the Mexican peso(MXP) the Brazilian real (BRL) the Argentinian peso(ARS) the Colombian peso (COP) the Venezuelan boli-var (VEB) the Chilean peso (CLP) SampP 500 futures(SPU) and eurodollar futures (EDU) For each securityfour time series were monitored (1) the bid price Pt
b (2)the ask price Pt
a (3) the bidask spread Xt sup2 Ptb iexcl Pt
aand (4) the net return Rt sup2 (Pt iexcl Ptiexcl1) Ptiexcl1 where Pt
denotes the mid-spread price at time t and all priceswere sampled every second
For each of the financial time series we identifiedthree classes of market events deviations trend-rever-sals and volatility events These events are often cited bytraders as significant developments that require height-ened attention potentially signaling a shift in marketdynamics and risk exposures With these definitions andcalibrations in hand we implemented an automaticprocedure for detecting deviations trend-reversals andvolatility events for all the relevant time series in eachsession Table 1 reports event counts for the financialtime series monitored for each subject Feature vectorswere then constructed for all detected market events inall time series for all subjects and a set of control featurevectors was generated by applying the same feature-extraction process to randomly selected windows con-taining no events of any kind One set of control featurevectors was constructed for deviation and trend-reversalevents (10-sec intervals) and a second set was con-structed for volatility-type events (5-min intervals) Atwo-sided t test was applied to each component of eachfeature vector and the corresponding control vector totest the null hypothesis that the feature vectors werestatistically indistinguishable from the control featurevectors
For volatility events which by definition occurred in5-min intervals (event windows) we constructed featurevectors containing the following information for theduration of the event window
1 number of SCR responses2 average SCR amplitude3 average HR4 average ratio of the BVP amplitude to local baseline5 average ratio of the BVP amplitude to global
baseline6 number of temperature changes exceeding 018F7 average respiration rate8 average respiration amplitude
where the local baseline for the BVP signal is the averagelevel during the event window and the global baseline isthe average over the entire recording session for eachtrader Control feature vectors were constructed byapplying the feature-extraction process to randomlyselected 5-min windows containing no events of any
Laptop with data acquisition software
Fiber optic cable Control unit
Market information (Reuters Bloomberg) Order screen etc TIBCO market spreadsheet
eal-time data feed the market data
acquisition computer
Figure 1 Typical set-up for the measurement of real-time physiolo-gical responses of financial traders during live trading sessions Real-time market data used by the traders are recorded synchronously andsubsequently analyzed together with the physiological response data
326 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
kind For the other two types of market eventsmdashdevia-tions and trend-reversalsmdashlsquolsquopre-eventrsquorsquo and lsquolsquopost-eventrsquorsquofeature vectors were constructed before and after eachmarket event using the same variables where featureswere aggregated over 10-sec windows immediately pre-ceding and following the event Control feature vectorswere generated for these cases as well
The motivation for the post-event feature vector wasto capture the subjectrsquos reaction to the event with thepre-event feature vector as a benchmark from which tomeasure the magnitude of the reaction A comparisonbetween the pre-event feature vector and a controlfeature vector may provide an indication of a subjectrsquosanticipation of the event Latencies of the autonomicresponses reported in previous studies were on a timescale of 1 to 10 sec (see Cacioppo et al 2000) hence10-sec event windows were judged to be long enough forevent-related autonomic responses to occur and at thesame time short enough to minimize the likelihood ofoverlaps with other events or anomalies Five-minutewindows were used for volatility events because 10-secintervals were simply insufficient for meaningful volatilitycalculations For all of the financial time series used inthis study and for most financial time series in generalthere are few volatility events that occur in any 10-secinterval except of course under extreme conditions forexample the stock market crash of October 19 1987 (nosuch conditions prevailed during any of our sessions)
The statistical analysis of the physiological and marketdata was motivated by four objectives (1) to identifyparticular classes of events with statistically significantdifferences in mean autonomic responses before or after
an event as compared to the no-event control (2) toidentify particular traders or groups of traders based onexperience or other personal characteristics that dem-onstrate significant mean autonomic responses duringmarket events (3) to identify particular financial instru-ments or groups of instruments that are associated withsignificant mean autonomic responses and (4) to iden-tify particular physiological variables that demonstratesignificant mean response levels immediately followingthe event or during the event-anticipation period ascompared to the no-event control
To address the first objective a total of eight types ofevents were used for each of the time series
1 price deviations2 spread deviations3 return deviations4 price trend-reversals5 spread trend-reversals6 maximum volatility7 price volatility8 return volatility
and for each type of event a two-sided t test wasperformed for the pooled sample of all subjects and alltime series in which mean autonomic responses duringevent periods were compared to mean autonomic re-sponses during no-event control periods
The t statistics corresponding to the two-sided hypoth-esis test are given in the left subpanel of Table 2 labelledlsquolsquoAll Tradersrsquorsquo Statistics that are significant at the 5levelmdashbased on the t distribution with the appropriatedegrees of freedom for each entrymdashare highlighted For
Table 1 Number of Deviation (DEV) Trend-Reversal (TRV) and Volatility (VOL) Events Detected in Real-Time Market Data forEach Trader Over the Course of Each Trading Session
EUR JPYOther MajorCu rrenciesa
Latin Am ericanCu rrenciesb Derivativesc
Trader ID DEV TRV VOL DEV TRV VOL DEV TRV VOL DEV TRV VOL DEV TRV VOL
B32 21 16 15 25 17 15 - - - - - - - - -
B34 18 10 14 17 17 14 - - - - - - - - -
B35 20 16 14 13 17 15 21 9 15 24 16 13 - - -
B36 - - - - - - - - - - - - 28 23 31
B37 19 19 14 18 18 12 18 18 14 105 86 63 - - -
B38 21 20 12 22 18 13 23 20 15 96 61 56 - - -
B39 - - - - - - - - - - - - 40 37 23
B310 10 17 10 18 16 14 116 59 61 - - - - - -
B311 17 15 15 17 16 15 94 61 67 - - - - - -
B312 19 17 15 16 19 13 107 83 71 - - - - - -
aGBP CAD CHF AUD NZDbMXP BRL ARS COP VEB CLPcEDU SPU
Lo and Repin 327
Tab
le2
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vec
tors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
All
Tra
ders
Hig
hEx
peri
ence
Low
orM
oder
ate
Expe
rien
ce
Phys
iolo
gy
Fea
ture
s
DE
V
Pri
ce
DEV
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VOL
Ma
xim
um
DE
V
Pri
ce
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VO
L
Ma
xim
um
DE
V
Pri
ce
DEV
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VO
L
Ma
xim
um
Nu
mbe
ro
fSC
Rre
spon
ses
28
72
060
42
08
81
66
40
636
057
90
561
122
8iexcl
022
1iexcl
126
10
184
20
81
26
96
012
32
75
22
17
0iexcl
066
5iexcl
068
8
Aver
age
SCR
ampl
itud
eiexcl
069
50
887
018
6iexcl
247
81
136
088
00
030
034
40
744
iexcl1
340
114
90
904
075
61
578
iexcl1
265
iexcl1
947
iexcl0
012
iexcl0
615
Aver
age
HR
090
5iexcl
058
8iexcl
059
6iexcl
061
71
032
iexcl0
270
044
91
88
6iexcl
179
7iexcl
163
51
83
6iexcl
201
81
310
005
1iexcl
038
2iexcl
110
6iexcl
031
11
283
Aver
age
BV
Pam
plit
ude
rela
tive
tolo
cal
base
line
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9iexcl
006
1iexcl
074
70
334
001
50
164
26
23
iexcl0
204
iexcl1
775
087
51
79
6iexcl
128
92
70
8
Aver
age
BV
Pam
plit
ude
rela
tive
togl
obal
base
line
141
7iexcl
272
62
32
41
417
iexcl2
211
28
86
iexcl2
529
iexcl1
367
159
01
110
iexcl1
165
23
01
19
17
iexcl2
664
18
84
20
82
iexcl1
759
27
58
Nu
mbe
ro
fte
mp
erat
ure
jum
ps
098
82
82
00
650
17
77
48
68
iexcl2
482
iexcl1
010
iexcl1
435
NA
NA
NA
164
40
837
29
02
114
52
44
52
63
3iexcl
321
3
Aver
age
resp
irat
ion
rate
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4iexcl
155
61
109
012
10
866
iexcl0
102
009
30
079
iexcl0
552
032
3iexcl
063
1iexcl
217
0iexcl
152
8
Aver
age
resp
irat
ion
ampl
itud
e
iexcl1
646
iexcl0
250
013
60
034
iexcl1
487
iexcl3
499
108
70
265
iexcl1
777
iexcl0
657
012
1iexcl
060
7iexcl
274
9iexcl
131
92
14
20
055
iexcl2
832
iexcl4
533
Bo
lded
stat
isti
csar
esi
gnifi
can
tat
the
5le
vel
Th
ele
ftp
anel
give
sag
greg
ate
resu
lts
for
allt
rad
ers
Sim
ilar
ttes
tsfo
rtr
ader
sw
ith
hig
hex
per
ienc
ean
dlo
wo
rm
oder
ate
exp
erie
nce
are
show
nin
the
mid
dle
and
righ
tp
anel
sre
spec
tive
lyE
ntr
ies
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
wer
ep
rese
nt
inei
ther
the
even
tor
cont
rol
feat
ure
vect
or
for
the
par
ticu
lar
mar
ket-
even
tty
pe
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spo
nse
sH
R=
hea
rtra
te
BV
P=
blo
od
volu
me
pul
se
328 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
deviations and trend-reversals both the number of SCRsand the number of temperature jumps reached statisti-cally significant levels for three out of the five event typesBVP amplitude-related features were highly significantfor volatility eventsmdashsignificance levels less than 1 wereobtained for both BVP amplitude features for all threetypes of volatility events Because the results were sosimilar across the three types of volatility events (max-imum volatility price volatility and return volatility) wereport the results only for maximum volatility in Table 2Such patterns of autonomic responses may indicate thepresence of transient emotional responses to deviationsand trend-reversal events that occur within 10-sec win-dows while volatility events defined in 5-min windowselicited a tonic response in BVP
To explore potential differences between experi-enced and less experienced traders a sample of 10traders was divided into two groups each consistingof five traders the first containing highly experiencedtraders and the second containing traders with low ormoderate experience where the levels of experiencewere determined through interviews with the tradersrsquosupervisors The results for each of the two groupsare reported in the two right subpanels of Table 2labelled lsquolsquoHigh Experiencersquorsquo and lsquolsquoLow or ModerateExperiencersquorsquo The high-experience traders generallyexhibited low responses for deviations and trend-re-versal events with only two types of market events(spread deviations and spread trend-reversals) elicitingsignificant mean autonomic responses (average HR)However even for high-experience traders volatilityevents induced statistically significant mean responsesfor both SCR and BVP amplitude Less experiencedtraders showed a much higher number of significantmean responses in the number of SCRs BVP ampli-tude and number of temperature increases for devia-tions and trend-reversal events Table 2 shows thatsessions with low- and moderate-experience tradersyielded 13 t statistics significant at the 5 level whilesessions with high-experience traders yielded only fivet statistics significant at the 5 level For both sets oftraders volatility events were associated with signifi-cant BVP amplitude The differences in responsepatterns for deviations and trend-reversals observedin this case indicate that less experienced traders maybe more sensitive to short-term changes in the marketvariables than their more experienced colleagues
To address the third objective 10-sec intervals imme-diately preceding and immediately following each devia-tion and trend-reversal event were compared to control(ie no-event) time intervals Two separate t tests wereconductedmdashone for pre- and another for post-eventtime intervalsmdashfor each of the three types of deviationsand two types of trend-reversals Because the objectivewas to detect differences in how pre- and post-eventfeature vectors differed from the control we used thesame control feature vectors for both sets of t tests In all
cases the t tests were designed to test the null hypoth-esis that both pre- and post-event feature vectors werestatistically indistinguishable from the control featurevector Surprisingly these two sets of t tests yielded verysimilar results reported in Table 3 implying that noneof the physiological variables were predictors of antici-patory emotional responses These findings may be atleast partly explained by how the events were defined Inparticular the definitions of deviation and trend-reversalevents are those instances where the time seriesachieved a prespecified deviation from the time-seriesmean and its moving average respectively In theabsence of large jumps the values of the time seriesnear an event defined in this way are likely to becomparable to the event itself Therefore the tradersmay be responding to market conditions occurringthroughout the pre- and post-event period not just atthe exact time of the event hence the similarity be-tween the two periods More complex definitions ofevents may allow us to discriminate between pre- andpost-event physiological responses and we are explor-ing several alternatives in ongoing research
Finally to address the fourth objective the followingfour groups of financial instruments were formed on thebasis of similarity in their statistical characteristics
Group 1 EUR JPY
Group 2 GBP CAD CHF AUD NZD (other majorcurrencies)
Group 3 MXP BRL ARS COP VEB CLP (LatinAmerican currencies)
Group 4 SPU EDU (derivatives)
Group 1 is by far the most actively traded set offinancial instruments and accounts for most of thetrading activities of our sample of 10 traders hence itis not surprising that the pattern of statistical signifi-cance for Group 1 in Table 4 is almost identical to thepattern for all traders in Table 2 (the left panel) SCRis significant for price and return deviations temper-ature jumps are significant for spread and price devia-tions and both measures of BVP are significant forvolatility events For Group 2 none of the eightphysiological variables were significant for price orspread deviations although temperature changes andrespiration rates were significant for return deviationsand both BVP measures were significant for pricetrend-reversals and volatility events The financial in-struments in Group 2 were not the primary focus ofany of the traders in our sample which may explainthe different patterns of significance as compared withGroup 1 Group 4 contains the fewest significantresponses and this is consistent with the fact thatthese securities were traded by two of the mostexperienced traders in our sample Derivative secur-ities are considerably more complex instruments thanthe spot currencies requiring more skill and trainingthan the other groups of securities However SCR was
Lo and Repin 329
Tab
le3
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gPr
e-an
dPo
st-e
vent
Tim
eIn
terv
als
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
Pre
-eve
nt
Inte
rva
lP
ost-
even
tIn
terv
al
Phy
sio
logy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mbe
rof
SCR
resp
ons
es3
75
61
543
25
40
21
18
24
01
057
92
87
20
604
20
88
16
64
063
60
579
Aver
age
SCR
amp
litu
de0
700
iexcl0
454
iexcl1
793
051
11
068
088
0iexcl
069
50
887
018
6iexcl
247
81
136
088
0
Aver
age
HR
096
8iexcl
020
5iexcl
055
2iexcl
066
3iexcl
038
6iexcl
027
00
905
iexcl0
588
iexcl0
596
iexcl0
617
103
2iexcl
027
0
Aver
age
BVP
amp
litud
ere
lativ
eto
loca
lba
selin
e0
043
iexcl0
895
006
00
814
iexcl0
045
36
19
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9
Aver
age
BVP
amp
litud
ere
lativ
eto
glob
alba
selin
e1
183
iexcl2
794
23
97
131
7iexcl
225
62
88
61
417
iexcl2
726
23
24
141
7iexcl
221
12
88
6
Nu
mbe
rof
tem
per
atur
eju
mp
s1
110
27
67
050
52
15
83
92
5iexcl
248
20
988
28
20
065
01
77
74
86
8iexcl
248
2
Aver
age
resp
irat
ion
rate
126
1iexcl
165
0iexcl
025
41
303
iexcl1
999
iexcl0
064
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4
Aver
age
resp
irat
ion
amp
litud
eiexcl
162
0iexcl
006
70
561
006
8iexcl
180
7iexcl
349
9iexcl
164
6iexcl
025
00
136
003
4iexcl
148
7iexcl
349
9
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
l
Th
ele
ftp
anel
cont
ain
st
stat
isti
csfo
rp
re-e
vent
feat
ure
vect
ors
and
the
righ
tp
anel
con
tain
st
stat
isti
csfo
rp
ost-
even
tfe
atu
reve
cto
rs
both
test
edag
ain
stth
esa
me
set
of
con
tro
ls
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spon
ses
HR
=h
eart
rate
B
VP
=bl
ood
volu
me
pu
lse
330 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le4
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
oth
esis
Tes
tsof
the
Diff
eren
cein
Mea
nAu
tono
mic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pon
ents
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chTy
pe
ofM
arke
tEv
ent
(Col
umns
)G
roup
edIn
toFo
urD
istin
ctSe
tsof
Fina
ncia
lIn
stru
men
ts
Ma
rket
Eve
nts
Gro
up
1E
UR
an
dJP
YG
rou
p2
Oth
erM
ajo
rC
urr
enci
esa
Gro
up
3La
tin
Am
eric
an
Cu
rren
cies
bG
rou
p4
Der
iva
tive
sc
Phy
siol
ogy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
mD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mb
erof
SCR
resp
on
ses
16
53
iexcl1
902
19
64
iexcl0
554
iexcl2
286
iexcl0
940
033
20
112
054
4iexcl
179
5iexcl
241
7iexcl
110
82
49
82
18
8iexcl
039
72
99
5iexcl
040
50
898
26
82
109
41
66
91
594
iexcl0
239
22
36
Ave
rage
SCR
amp
litu
de
iexcl1
639
062
30
279
iexcl1
756
109
8iexcl
111
2iexcl
050
30
540
113
8iexcl
219
0iexcl
010
60
896
111
61
279
iexcl1
525
iexcl1
802
22
20
iexcl0
986
iexcl0
610
123
60
930
iexcl1
513
024
2iexcl
151
0
Ave
rage
HR
073
1iexcl
000
4iexcl
126
8iexcl
181
5iexcl
069
8iexcl
161
2iexcl
099
1iexcl
145
2iexcl
103
4iexcl
132
6iexcl
030
8iexcl
108
92
92
1iexcl
143
2iexcl
107
0iexcl
100
2iexcl
141
61
183
081
6iexcl
032
20
577
049
20
515
iexcl0
169
Ave
rage
BV
Pam
plit
ude
rela
tive
to
loca
lb
asel
ine
056
2iexcl
136
8iexcl
080
51
273
088
52
58
7iexcl
092
20
220
145
72
32
3iexcl
127
52
30
8iexcl
098
2iexcl
000
12
82
81
454
059
82
19
2iexcl
059
2iexcl
303
2iexcl
395
3iexcl
089
4iexcl
190
4iexcl
143
3
Ave
rage
BV
Pam
plit
ude
rela
tive
tolo
cal
bas
elin
e
059
7iexcl
301
01
71
40
375
iexcl0
252
25
92
052
11
448
097
23
08
7iexcl
149
92
63
3iexcl
192
5iexcl
084
91
482
025
9iexcl
035
1iexcl
012
9iexcl
066
0iexcl
150
1iexcl
327
4iexcl
162
6iexcl
200
4iexcl
241
1
Nu
mb
erof
tem
per
atu
reju
mp
s
068
84
14
1iexcl
144
42
28
30
295
iexcl2
043
107
3iexcl
155
72
40
41
248
37
26
iexcl1
564
iexcl1
603
iexcl1
275
iexcl1
922
iexcl0
512
iexcl1
128
iexcl1
540
iexcl1
367
iexcl1
349
iexcl1
362
iexcl1
367
iexcl1
053
NA
Ave
rage
resp
irat
ion
rate
052
5iexcl
174
1iexcl
079
70
163
iexcl1
884
iexcl0
004
075
6iexcl
056
02
02
3iexcl
109
5iexcl
178
4iexcl
011
60
037
044
6iexcl
112
40
643
iexcl1
392
012
5iexcl
194
6iexcl
029
10
361
041
6iexcl
110
5iexcl
172
5
Ave
rage
resp
irat
ion
amp
litu
de
iexcl2
120
iexcl0
692
034
5iexcl
050
9iexcl
093
5iexcl
207
9iexcl
074
60
560
047
00
905
089
3iexcl
383
90
280
iexcl0
207
142
6iexcl
036
0iexcl
053
4iexcl
043
70
920
iexcl0
955
iexcl1
915
125
71
593
iexcl0
039
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
la M
XP
BR
LA
RS
CO
PV
EB
CLP
bM
XP
BR
LA
RS
CO
PV
EB
C
LP
c ED
U
SPU
Ent
ries
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
was
pre
sen
tin
eith
erth
eev
ent
orco
ntro
lfe
atu
reve
cto
rfo
rth
ep
arti
cula
rm
arke
t-ev
ent
typ
e
DE
V=
dev
iati
on
TR
V=
tren
d-r
ever
sal
VO
L=
vola
tilit
ySC
R=
skin
con
duc
tan
cere
spo
nses
H
R=
hea
rtra
te
BV
P=
bloo
dvo
lum
ep
uls
e
Lo and Repin 331
significant for both price and return deviations inGroup 4 which was not the case for the sample ofhigh-experience traders in Table 2 SCR was alsosignificant for volatility events in Group 4 which isconsistent with the central role that volatility plays inthe pricing and hedging derivative securities (Black ampScholes 1973 Merton 1973) Volatility is a moresophisticated aspect of the trading milieu requiringa higher degree of training to fully appreciate itsimplications for market trends and profit-and-lossprobabilities This may explain the fact that SCR issignificant for volatility events only among the high-experience traders in Table 2 and the derivativestraders in Table 4
DISCUSSION
Our findings suggest that emotional responses are asignificant factor in the real-time processing of financialrisks Contrary to the common belief that emotions haveno place in rational financial decision-making processesphysiological variables associated with the ANS exhibitsignificant changes during market events even for highlyexperienced professional traders Moreover the re-sponse patterns among variables and events differed inimportant ways for less experienced tradersmdashmeanautonomic responses were significantly highermdashsug-gesting the possibility of relating trading skills to certainphysiological characteristics that can be measured
More generally our experiments demonstrate thefeasibility of relating real-time quantitative changes incognitive inputs (financial information) to correspond-ing quantitative changes in physiological responses in acomplex field environment Despite the challenges ofsuch measurements a wealth of information can beobtained regarding high-pressure decision makingunder uncertainty Financial traders operate in a con-trolled environment where the inputs and outputs ofthe decisions are carefully recorded and where thesubjects are highly trained and provided with greateconomic incentives to make rational trading decisionsTherefore in vivo experiments in the securities tradingcontext are likely to become an important part of theempirical analysis of individual risk preferences anddecision-making processes In particular there is con-siderable anecdotal evidence that subjects involved inprofessional trading activities perform very differentlydepending on whether real financial capital is at risk or ifthey are trading with lsquolsquoplayrsquorsquo money Such distinctionshave been documented in other contexts eg it hasbeen shown that different brain regions are activatedduring a subjectrsquos naturally occurring smile and a forcedsmile (Damasio 1994) For this reason measuring sub-jects while they are making decisions in their naturalenvironment is essential for any truly unbiased study offinancial decision-making processes
In capturing relations between cognitive inputs andaffective reactions that are often subconscious and ofwhich subjects are not fully aware our findings may beviewed more broadly as a study of cognitive- emotionalinteractions and the genesis of lsquolsquointuitionrsquorsquo Decisionprocesses based on intuition are characterized by lowlevels of cognitive control low conscious awarenessrapid processing rates and a lack of clear organizingprinciples When intuitive judgments are formed largenumbers of cues are processed simultaneously and thetask is not decomposed into subtasks (HammondHamm Grassia amp Pearson 1987) Expertsrsquo judgmentsare often based on intuition not on explicit analyticalprocessing making it almost impossible to fully explainor replicate the process of how that judgment has beenformed This is particularly germane to financial trad-ersmdashas a group they are unusually heterogeneouswith respect to educational background and formalanalytical skills yet the most successful traders seemto trade based on their intuition about price swingsand market dynamics often without the ability (or theneed) to articulate a precise quantitative algorithm formaking these complex decisions (Niederhoffer 1997Schwager 1989 1992) Their intuitive trading lsquolsquorulesrsquorsquoare based on the associations and relations betweenvarious information tokens that are formed on a sub-conscious level and our findings and those in theextant cognitive sciences literature suggest that deci-sions based on the intuitive judgments require not onlycognitive but also emotional mechanisms A naturalconjecture is that such emotional mechanisms are atleast partly responsible for the ability to form intuitivejudgments and for those judgments to be incorporatedinto a rational decision-making process
Our results may surprise some financial economistsbecause of the apparent inconsistency with marketrationality but a more sophisticated view of the role ofemotion in human cognition (Rolls 1990 1994 1999)can reconcile any contradiction in a complete andintellectually satisfying manner Emotion is the basisfor a reward-and-punishment system that facilitates theselection of advantageous behavioral actions providingthe numeraire for animals to engage in a lsquolsquocost- benefitanalysisrsquorsquo of the various actions open to them (Rolls1999 Chap 103) From an evolutionary perspectiveemotion is a powerful adaptation that dramatically im-proves the efficiency with which animals learn from theirenvironment and their past
These evolutionary underpinnings are more thansimple speculation in the context of financial tradersThe extraordinary degree of competitiveness of globalfinancial markets and the outsize rewards that accrue tothe lsquolsquofittestrsquorsquo traders suggest that Darwinian selectionmdashfinancial selection to be specificmdashis at work in deter-mining the typical profile of the successful trader Afterall unsuccessful traders are generally lsquolsquoeliminatedrsquorsquo fromthe population after suffering a certain level of losses
332 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Our results indicate that emotion is a significant deter-minant of the evolutionary fitness of financial tradersWe hope to investigate this conjecture more formally inthe future in several ways a comparison between tradersand a control group of subjects without trading experi-ence or with unsuccessful trading experiences an anal-ysis of the psychophysiological responses of traders in alaboratory environment a more direct analysis of thecorrelation between autonomic responses and tradingperformance a more fundamental analysis of the neuralbasis of emotion in traders aimed at the function of theamygdala and the orbitofrontal cortex (Rolls 1992 1999Chap 44- 45) and a direct mapping of the neuralcenters for trading activity through functional magneticresonance imaging (Breiter et al 2001)
There are a number of caveats that must be kept inmind in interpreting our findings First because of thesmall sample size of 10 subjects our results are at bestsuggestive and promising not conclusive A more com-prehensive study with a larger and more diverse sampleof traders a more refined set of market events and abroader set of controls are necessary before coming toany firm conclusions regarding the precise mechanismsof the psychophysiology of financial risk processing andthe role of emotion in market efficiency
Second while we have documented the statisticalsignificance of autonomic responses during marketevents relative to control baselines we have not estab-lished specific causal factors for such responses Manycognitive tasks generate autonomic effects hence amarked change in any one psychophysiological indexmay be due to changes in cognitive processing forexample complex numerical computations rather thanmore fundamental affective responses For examplemore experienced traders in our sample may have dis-played lower autonomic response levels because theyrequired less cognitive effort to perform the same func-tions as less experienced traders which may have nothingto do with emotion Without a more targeted set of tasksor additional data on the characteristics of the subjects itis difficult to distinguish between the two One way toaddress this issue is to correlate a subjectrsquos autonomicresponses with his or her trading performance so as todevelop a more complete profile of the relation betweenpsychophysiological indices and the dynamics of thetrading process However because trading performanceis generally summarized by multiple characteristicsmdashtotal profit-and-loss volatility maximum drawdownSharpe ratio and so onmdashestimating a relation betweenautonomic responses and such characteristics may re-quire significantly larger sample sizes and longer obser-vation windows due to the lsquolsquocurse of dimensionalityrsquorsquo Itmay be possible to overcome this challenge to somedegree through a more carefully crafted experimentaldesign in which specific emotional responses are pairedwith specific trading performance measures namelyanxiety levels during consecutive sequences of losing
trades With a larger sample size we can also begin totrack groups of traders over a period of time and throughvarying market conditions By measuring their autonomicresponse profiles at different stages of their careers itmay be possible to determine more directly the role ofemotion in financial risk processing
Finally a much larger set of controls should accompanya larger sample of traders These controls should includepsychophysiological measurements for professional trad-ers taken outside their natural trading environmentmdashideally in a laboratory setting that is identical for alltradersmdashas well as corresponding measurements forsubjects with little or no trading experience in bothtrading and nontrading environments Along with psy-chophysiological data more traditional personality in-ventory data for example MMPI or Myers- Briggs mayprovide additional insights into the psychology of trading
We hope to address each of these issues in ourongoing research program to better understand thecognitive processes underlying financial risk processing
METHODS
Subjects
The 10 subjectsrsquo descriptive characteristics are summar-ized in Table 5 Based on discussions with their super-visors the traders were categorized into three levels ofexperience low moderate and high Five traders spe-cialized in handling client order flow (Retail) threespecialized in trading foreign exchange (FX) and twospecialized in interest-rate derivative securities (Deriva-tives) The durations of the sessions ranged from 49 to83 min and all sessions were held during live tradinghours typically between 8 am and 5 pm easterndaylight time
Physiological Data Collection
A ProComp+ data-acquisition unit and Biograph (Ver-sion 12) biofeedback software from Thought Technol-ogy were used to measure physiological data for allsubjects All six sensors were connected to a smallcontrol unit with a battery power supply which wasplaced on each subjectrsquos belt and from which a fiber-optic connection led to a laptop computer equippedwith real-time data acquisition software (see Figure 2)Each sensor was equipped with a built-in notch filter at60 Hz for automatic elimination of external power linenoise and standard AgCl triode and single electrodeswere used for SCR and EMG sensors respectively Thesampling rate for all data collection was fixed at 32 HzAll physiological data except for respiration and facialEMG were collected from each subjectrsquos nondominantarm SCR electrodes were placed on the palmar sites theBVP photoplesymographic sensor was placed on theinside of the ring or middle finger the arm EMG triodeelectrode was placed on the inside surface of the
Lo and Repin 333
forearm over the flexor digitorum muscle group andthe temperature sensor was inserted between the elasticband placed around the wrist and the skin surface Thefacial EMG electrode was placed on a masseter musclewhich controls jaw movement and is active duringspeech or any other activity involving the jaw Therespiration signal was measured by chest expansionusing a sensor attached to an elastic band placed aroundthe subjectrsquos chest An example of the real-time physio-logical data collected over a 2-min interval for onesubject is given in Figure 3
The entire procedure of outfitting each subject withsensors and connecting the sensors to the laptop re-quired approximately 5 min and was often performedeither before the trading day began or during relativelycalm trading periods Subjects indicated that the pres-
ence of the sensors wires and a control unit did notcompromise or influence their trading in any significantmanner and that their workflow was not impaired in anyway This was verified not only by the subjects but alsoby their supervisors Given the magnitudes of the finan-cial transactions that were being processed and theeconomic and legal responsibilities that the subjectsand their supervisors bore even the slightest interfer-ence with the subjectsrsquo workflow or performance stand-ards would have caused the supervisors or the subjectsto terminate the sessions immediately None of thesessions were terminated prematurely
Physiological Data Feature Extraction
An initial smoothing of the raw EMG signals (sampled at32 Hz) was performed with a moving-average filter oforder 23 If the level of the filtered forearm EMG signalexceeded a threshold of 075 mV both SCR and BVPreadings at this time were discarded because of the highprobability of artifacts for example typing graspingtelephone handsets or inadvertent physical disturban-ces to the sensors Similarly if the level of the filteredfacial EMG signal exceeded 075 mV the respirationsignal at this time was excluded from further processingA very small spatial displacement of the sensor or theelectrode was able to produce a different kind ofartifactmdashan abrupt change in the signal of the orderof 10- 20 standard deviations within 1
32 of a second Suchjumps did not have any physiological meaning hencethey were excluded from further analysis via adaptivethresholding Specifically our adaptive thresholdingprocedure involved marking all observations that
Table 5 Summary Statistics for All Subjects Individual Traderrsquos Characteristics Specialty Type and Number of Market Time-Series Collected During the Session Session Duration and Absolute Time (Eastern Standard Time) of the Start of the Session
TraderID Gender Experience Specialty Market data Available
Nu m ber of MarketTim e-Series Recorded
Session Du ration(m in and sec)
SessionStart Tim e
B33 F high retail major currencies 2 4903000 1320
B34 M high FX major currencies 3 8303200 0856
B35 M high retail major currencies 4 6603000 1102
B36 M high derivatives SampP500 futureseurodollar futures
3 7901600 1255
B37 M moderate FX Latin Americanmajor currencies
9 7000600 0917
B38 M low FX Latin Americanmajor currencies
3 7201200 1102
B39 M high derivatives SampP 500 futureseurodollar futures
9 6200300 0819
B310 M low retail major currencies 7 6000800 0947
B311 M moderate retail major currencies 7 5402500 1132
B312 M low retail major currencies 7 5900000 0910
ProC om p+
Facial EMG Triode electrode measures
activity of the masseter muscle (detects speech)
Respiration sensor Elastic strap measures
chest expansion
Forearm EMG Triode electrode measures activity of the flexor digitorum muscle group (detects finger and arm movement)
Temperature sensor - thermistor
SCR sensor and electrodes
BVP photoplesymographicsensor
Control unit Fiber optic cable to a
data acquisition laptop
Figure 2 Placement of sensors for measuring physiologicalresponses
334 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
kind For the other two types of market eventsmdashdevia-tions and trend-reversalsmdashlsquolsquopre-eventrsquorsquo and lsquolsquopost-eventrsquorsquofeature vectors were constructed before and after eachmarket event using the same variables where featureswere aggregated over 10-sec windows immediately pre-ceding and following the event Control feature vectorswere generated for these cases as well
The motivation for the post-event feature vector wasto capture the subjectrsquos reaction to the event with thepre-event feature vector as a benchmark from which tomeasure the magnitude of the reaction A comparisonbetween the pre-event feature vector and a controlfeature vector may provide an indication of a subjectrsquosanticipation of the event Latencies of the autonomicresponses reported in previous studies were on a timescale of 1 to 10 sec (see Cacioppo et al 2000) hence10-sec event windows were judged to be long enough forevent-related autonomic responses to occur and at thesame time short enough to minimize the likelihood ofoverlaps with other events or anomalies Five-minutewindows were used for volatility events because 10-secintervals were simply insufficient for meaningful volatilitycalculations For all of the financial time series used inthis study and for most financial time series in generalthere are few volatility events that occur in any 10-secinterval except of course under extreme conditions forexample the stock market crash of October 19 1987 (nosuch conditions prevailed during any of our sessions)
The statistical analysis of the physiological and marketdata was motivated by four objectives (1) to identifyparticular classes of events with statistically significantdifferences in mean autonomic responses before or after
an event as compared to the no-event control (2) toidentify particular traders or groups of traders based onexperience or other personal characteristics that dem-onstrate significant mean autonomic responses duringmarket events (3) to identify particular financial instru-ments or groups of instruments that are associated withsignificant mean autonomic responses and (4) to iden-tify particular physiological variables that demonstratesignificant mean response levels immediately followingthe event or during the event-anticipation period ascompared to the no-event control
To address the first objective a total of eight types ofevents were used for each of the time series
1 price deviations2 spread deviations3 return deviations4 price trend-reversals5 spread trend-reversals6 maximum volatility7 price volatility8 return volatility
and for each type of event a two-sided t test wasperformed for the pooled sample of all subjects and alltime series in which mean autonomic responses duringevent periods were compared to mean autonomic re-sponses during no-event control periods
The t statistics corresponding to the two-sided hypoth-esis test are given in the left subpanel of Table 2 labelledlsquolsquoAll Tradersrsquorsquo Statistics that are significant at the 5levelmdashbased on the t distribution with the appropriatedegrees of freedom for each entrymdashare highlighted For
Table 1 Number of Deviation (DEV) Trend-Reversal (TRV) and Volatility (VOL) Events Detected in Real-Time Market Data forEach Trader Over the Course of Each Trading Session
EUR JPYOther MajorCu rrenciesa
Latin Am ericanCu rrenciesb Derivativesc
Trader ID DEV TRV VOL DEV TRV VOL DEV TRV VOL DEV TRV VOL DEV TRV VOL
B32 21 16 15 25 17 15 - - - - - - - - -
B34 18 10 14 17 17 14 - - - - - - - - -
B35 20 16 14 13 17 15 21 9 15 24 16 13 - - -
B36 - - - - - - - - - - - - 28 23 31
B37 19 19 14 18 18 12 18 18 14 105 86 63 - - -
B38 21 20 12 22 18 13 23 20 15 96 61 56 - - -
B39 - - - - - - - - - - - - 40 37 23
B310 10 17 10 18 16 14 116 59 61 - - - - - -
B311 17 15 15 17 16 15 94 61 67 - - - - - -
B312 19 17 15 16 19 13 107 83 71 - - - - - -
aGBP CAD CHF AUD NZDbMXP BRL ARS COP VEB CLPcEDU SPU
Lo and Repin 327
Tab
le2
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vec
tors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
All
Tra
ders
Hig
hEx
peri
ence
Low
orM
oder
ate
Expe
rien
ce
Phys
iolo
gy
Fea
ture
s
DE
V
Pri
ce
DEV
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VOL
Ma
xim
um
DE
V
Pri
ce
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VO
L
Ma
xim
um
DE
V
Pri
ce
DEV
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VO
L
Ma
xim
um
Nu
mbe
ro
fSC
Rre
spon
ses
28
72
060
42
08
81
66
40
636
057
90
561
122
8iexcl
022
1iexcl
126
10
184
20
81
26
96
012
32
75
22
17
0iexcl
066
5iexcl
068
8
Aver
age
SCR
ampl
itud
eiexcl
069
50
887
018
6iexcl
247
81
136
088
00
030
034
40
744
iexcl1
340
114
90
904
075
61
578
iexcl1
265
iexcl1
947
iexcl0
012
iexcl0
615
Aver
age
HR
090
5iexcl
058
8iexcl
059
6iexcl
061
71
032
iexcl0
270
044
91
88
6iexcl
179
7iexcl
163
51
83
6iexcl
201
81
310
005
1iexcl
038
2iexcl
110
6iexcl
031
11
283
Aver
age
BV
Pam
plit
ude
rela
tive
tolo
cal
base
line
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9iexcl
006
1iexcl
074
70
334
001
50
164
26
23
iexcl0
204
iexcl1
775
087
51
79
6iexcl
128
92
70
8
Aver
age
BV
Pam
plit
ude
rela
tive
togl
obal
base
line
141
7iexcl
272
62
32
41
417
iexcl2
211
28
86
iexcl2
529
iexcl1
367
159
01
110
iexcl1
165
23
01
19
17
iexcl2
664
18
84
20
82
iexcl1
759
27
58
Nu
mbe
ro
fte
mp
erat
ure
jum
ps
098
82
82
00
650
17
77
48
68
iexcl2
482
iexcl1
010
iexcl1
435
NA
NA
NA
164
40
837
29
02
114
52
44
52
63
3iexcl
321
3
Aver
age
resp
irat
ion
rate
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4iexcl
155
61
109
012
10
866
iexcl0
102
009
30
079
iexcl0
552
032
3iexcl
063
1iexcl
217
0iexcl
152
8
Aver
age
resp
irat
ion
ampl
itud
e
iexcl1
646
iexcl0
250
013
60
034
iexcl1
487
iexcl3
499
108
70
265
iexcl1
777
iexcl0
657
012
1iexcl
060
7iexcl
274
9iexcl
131
92
14
20
055
iexcl2
832
iexcl4
533
Bo
lded
stat
isti
csar
esi
gnifi
can
tat
the
5le
vel
Th
ele
ftp
anel
give
sag
greg
ate
resu
lts
for
allt
rad
ers
Sim
ilar
ttes
tsfo
rtr
ader
sw
ith
hig
hex
per
ienc
ean
dlo
wo
rm
oder
ate
exp
erie
nce
are
show
nin
the
mid
dle
and
righ
tp
anel
sre
spec
tive
lyE
ntr
ies
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
wer
ep
rese
nt
inei
ther
the
even
tor
cont
rol
feat
ure
vect
or
for
the
par
ticu
lar
mar
ket-
even
tty
pe
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spo
nse
sH
R=
hea
rtra
te
BV
P=
blo
od
volu
me
pul
se
328 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
deviations and trend-reversals both the number of SCRsand the number of temperature jumps reached statisti-cally significant levels for three out of the five event typesBVP amplitude-related features were highly significantfor volatility eventsmdashsignificance levels less than 1 wereobtained for both BVP amplitude features for all threetypes of volatility events Because the results were sosimilar across the three types of volatility events (max-imum volatility price volatility and return volatility) wereport the results only for maximum volatility in Table 2Such patterns of autonomic responses may indicate thepresence of transient emotional responses to deviationsand trend-reversal events that occur within 10-sec win-dows while volatility events defined in 5-min windowselicited a tonic response in BVP
To explore potential differences between experi-enced and less experienced traders a sample of 10traders was divided into two groups each consistingof five traders the first containing highly experiencedtraders and the second containing traders with low ormoderate experience where the levels of experiencewere determined through interviews with the tradersrsquosupervisors The results for each of the two groupsare reported in the two right subpanels of Table 2labelled lsquolsquoHigh Experiencersquorsquo and lsquolsquoLow or ModerateExperiencersquorsquo The high-experience traders generallyexhibited low responses for deviations and trend-re-versal events with only two types of market events(spread deviations and spread trend-reversals) elicitingsignificant mean autonomic responses (average HR)However even for high-experience traders volatilityevents induced statistically significant mean responsesfor both SCR and BVP amplitude Less experiencedtraders showed a much higher number of significantmean responses in the number of SCRs BVP ampli-tude and number of temperature increases for devia-tions and trend-reversal events Table 2 shows thatsessions with low- and moderate-experience tradersyielded 13 t statistics significant at the 5 level whilesessions with high-experience traders yielded only fivet statistics significant at the 5 level For both sets oftraders volatility events were associated with signifi-cant BVP amplitude The differences in responsepatterns for deviations and trend-reversals observedin this case indicate that less experienced traders maybe more sensitive to short-term changes in the marketvariables than their more experienced colleagues
To address the third objective 10-sec intervals imme-diately preceding and immediately following each devia-tion and trend-reversal event were compared to control(ie no-event) time intervals Two separate t tests wereconductedmdashone for pre- and another for post-eventtime intervalsmdashfor each of the three types of deviationsand two types of trend-reversals Because the objectivewas to detect differences in how pre- and post-eventfeature vectors differed from the control we used thesame control feature vectors for both sets of t tests In all
cases the t tests were designed to test the null hypoth-esis that both pre- and post-event feature vectors werestatistically indistinguishable from the control featurevector Surprisingly these two sets of t tests yielded verysimilar results reported in Table 3 implying that noneof the physiological variables were predictors of antici-patory emotional responses These findings may be atleast partly explained by how the events were defined Inparticular the definitions of deviation and trend-reversalevents are those instances where the time seriesachieved a prespecified deviation from the time-seriesmean and its moving average respectively In theabsence of large jumps the values of the time seriesnear an event defined in this way are likely to becomparable to the event itself Therefore the tradersmay be responding to market conditions occurringthroughout the pre- and post-event period not just atthe exact time of the event hence the similarity be-tween the two periods More complex definitions ofevents may allow us to discriminate between pre- andpost-event physiological responses and we are explor-ing several alternatives in ongoing research
Finally to address the fourth objective the followingfour groups of financial instruments were formed on thebasis of similarity in their statistical characteristics
Group 1 EUR JPY
Group 2 GBP CAD CHF AUD NZD (other majorcurrencies)
Group 3 MXP BRL ARS COP VEB CLP (LatinAmerican currencies)
Group 4 SPU EDU (derivatives)
Group 1 is by far the most actively traded set offinancial instruments and accounts for most of thetrading activities of our sample of 10 traders hence itis not surprising that the pattern of statistical signifi-cance for Group 1 in Table 4 is almost identical to thepattern for all traders in Table 2 (the left panel) SCRis significant for price and return deviations temper-ature jumps are significant for spread and price devia-tions and both measures of BVP are significant forvolatility events For Group 2 none of the eightphysiological variables were significant for price orspread deviations although temperature changes andrespiration rates were significant for return deviationsand both BVP measures were significant for pricetrend-reversals and volatility events The financial in-struments in Group 2 were not the primary focus ofany of the traders in our sample which may explainthe different patterns of significance as compared withGroup 1 Group 4 contains the fewest significantresponses and this is consistent with the fact thatthese securities were traded by two of the mostexperienced traders in our sample Derivative secur-ities are considerably more complex instruments thanthe spot currencies requiring more skill and trainingthan the other groups of securities However SCR was
Lo and Repin 329
Tab
le3
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gPr
e-an
dPo
st-e
vent
Tim
eIn
terv
als
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
Pre
-eve
nt
Inte
rva
lP
ost-
even
tIn
terv
al
Phy
sio
logy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mbe
rof
SCR
resp
ons
es3
75
61
543
25
40
21
18
24
01
057
92
87
20
604
20
88
16
64
063
60
579
Aver
age
SCR
amp
litu
de0
700
iexcl0
454
iexcl1
793
051
11
068
088
0iexcl
069
50
887
018
6iexcl
247
81
136
088
0
Aver
age
HR
096
8iexcl
020
5iexcl
055
2iexcl
066
3iexcl
038
6iexcl
027
00
905
iexcl0
588
iexcl0
596
iexcl0
617
103
2iexcl
027
0
Aver
age
BVP
amp
litud
ere
lativ
eto
loca
lba
selin
e0
043
iexcl0
895
006
00
814
iexcl0
045
36
19
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9
Aver
age
BVP
amp
litud
ere
lativ
eto
glob
alba
selin
e1
183
iexcl2
794
23
97
131
7iexcl
225
62
88
61
417
iexcl2
726
23
24
141
7iexcl
221
12
88
6
Nu
mbe
rof
tem
per
atur
eju
mp
s1
110
27
67
050
52
15
83
92
5iexcl
248
20
988
28
20
065
01
77
74
86
8iexcl
248
2
Aver
age
resp
irat
ion
rate
126
1iexcl
165
0iexcl
025
41
303
iexcl1
999
iexcl0
064
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4
Aver
age
resp
irat
ion
amp
litud
eiexcl
162
0iexcl
006
70
561
006
8iexcl
180
7iexcl
349
9iexcl
164
6iexcl
025
00
136
003
4iexcl
148
7iexcl
349
9
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
l
Th
ele
ftp
anel
cont
ain
st
stat
isti
csfo
rp
re-e
vent
feat
ure
vect
ors
and
the
righ
tp
anel
con
tain
st
stat
isti
csfo
rp
ost-
even
tfe
atu
reve
cto
rs
both
test
edag
ain
stth
esa
me
set
of
con
tro
ls
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spon
ses
HR
=h
eart
rate
B
VP
=bl
ood
volu
me
pu
lse
330 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le4
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
oth
esis
Tes
tsof
the
Diff
eren
cein
Mea
nAu
tono
mic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pon
ents
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chTy
pe
ofM
arke
tEv
ent
(Col
umns
)G
roup
edIn
toFo
urD
istin
ctSe
tsof
Fina
ncia
lIn
stru
men
ts
Ma
rket
Eve
nts
Gro
up
1E
UR
an
dJP
YG
rou
p2
Oth
erM
ajo
rC
urr
enci
esa
Gro
up
3La
tin
Am
eric
an
Cu
rren
cies
bG
rou
p4
Der
iva
tive
sc
Phy
siol
ogy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
mD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mb
erof
SCR
resp
on
ses
16
53
iexcl1
902
19
64
iexcl0
554
iexcl2
286
iexcl0
940
033
20
112
054
4iexcl
179
5iexcl
241
7iexcl
110
82
49
82
18
8iexcl
039
72
99
5iexcl
040
50
898
26
82
109
41
66
91
594
iexcl0
239
22
36
Ave
rage
SCR
amp
litu
de
iexcl1
639
062
30
279
iexcl1
756
109
8iexcl
111
2iexcl
050
30
540
113
8iexcl
219
0iexcl
010
60
896
111
61
279
iexcl1
525
iexcl1
802
22
20
iexcl0
986
iexcl0
610
123
60
930
iexcl1
513
024
2iexcl
151
0
Ave
rage
HR
073
1iexcl
000
4iexcl
126
8iexcl
181
5iexcl
069
8iexcl
161
2iexcl
099
1iexcl
145
2iexcl
103
4iexcl
132
6iexcl
030
8iexcl
108
92
92
1iexcl
143
2iexcl
107
0iexcl
100
2iexcl
141
61
183
081
6iexcl
032
20
577
049
20
515
iexcl0
169
Ave
rage
BV
Pam
plit
ude
rela
tive
to
loca
lb
asel
ine
056
2iexcl
136
8iexcl
080
51
273
088
52
58
7iexcl
092
20
220
145
72
32
3iexcl
127
52
30
8iexcl
098
2iexcl
000
12
82
81
454
059
82
19
2iexcl
059
2iexcl
303
2iexcl
395
3iexcl
089
4iexcl
190
4iexcl
143
3
Ave
rage
BV
Pam
plit
ude
rela
tive
tolo
cal
bas
elin
e
059
7iexcl
301
01
71
40
375
iexcl0
252
25
92
052
11
448
097
23
08
7iexcl
149
92
63
3iexcl
192
5iexcl
084
91
482
025
9iexcl
035
1iexcl
012
9iexcl
066
0iexcl
150
1iexcl
327
4iexcl
162
6iexcl
200
4iexcl
241
1
Nu
mb
erof
tem
per
atu
reju
mp
s
068
84
14
1iexcl
144
42
28
30
295
iexcl2
043
107
3iexcl
155
72
40
41
248
37
26
iexcl1
564
iexcl1
603
iexcl1
275
iexcl1
922
iexcl0
512
iexcl1
128
iexcl1
540
iexcl1
367
iexcl1
349
iexcl1
362
iexcl1
367
iexcl1
053
NA
Ave
rage
resp
irat
ion
rate
052
5iexcl
174
1iexcl
079
70
163
iexcl1
884
iexcl0
004
075
6iexcl
056
02
02
3iexcl
109
5iexcl
178
4iexcl
011
60
037
044
6iexcl
112
40
643
iexcl1
392
012
5iexcl
194
6iexcl
029
10
361
041
6iexcl
110
5iexcl
172
5
Ave
rage
resp
irat
ion
amp
litu
de
iexcl2
120
iexcl0
692
034
5iexcl
050
9iexcl
093
5iexcl
207
9iexcl
074
60
560
047
00
905
089
3iexcl
383
90
280
iexcl0
207
142
6iexcl
036
0iexcl
053
4iexcl
043
70
920
iexcl0
955
iexcl1
915
125
71
593
iexcl0
039
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
la M
XP
BR
LA
RS
CO
PV
EB
CLP
bM
XP
BR
LA
RS
CO
PV
EB
C
LP
c ED
U
SPU
Ent
ries
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
was
pre
sen
tin
eith
erth
eev
ent
orco
ntro
lfe
atu
reve
cto
rfo
rth
ep
arti
cula
rm
arke
t-ev
ent
typ
e
DE
V=
dev
iati
on
TR
V=
tren
d-r
ever
sal
VO
L=
vola
tilit
ySC
R=
skin
con
duc
tan
cere
spo
nses
H
R=
hea
rtra
te
BV
P=
bloo
dvo
lum
ep
uls
e
Lo and Repin 331
significant for both price and return deviations inGroup 4 which was not the case for the sample ofhigh-experience traders in Table 2 SCR was alsosignificant for volatility events in Group 4 which isconsistent with the central role that volatility plays inthe pricing and hedging derivative securities (Black ampScholes 1973 Merton 1973) Volatility is a moresophisticated aspect of the trading milieu requiringa higher degree of training to fully appreciate itsimplications for market trends and profit-and-lossprobabilities This may explain the fact that SCR issignificant for volatility events only among the high-experience traders in Table 2 and the derivativestraders in Table 4
DISCUSSION
Our findings suggest that emotional responses are asignificant factor in the real-time processing of financialrisks Contrary to the common belief that emotions haveno place in rational financial decision-making processesphysiological variables associated with the ANS exhibitsignificant changes during market events even for highlyexperienced professional traders Moreover the re-sponse patterns among variables and events differed inimportant ways for less experienced tradersmdashmeanautonomic responses were significantly highermdashsug-gesting the possibility of relating trading skills to certainphysiological characteristics that can be measured
More generally our experiments demonstrate thefeasibility of relating real-time quantitative changes incognitive inputs (financial information) to correspond-ing quantitative changes in physiological responses in acomplex field environment Despite the challenges ofsuch measurements a wealth of information can beobtained regarding high-pressure decision makingunder uncertainty Financial traders operate in a con-trolled environment where the inputs and outputs ofthe decisions are carefully recorded and where thesubjects are highly trained and provided with greateconomic incentives to make rational trading decisionsTherefore in vivo experiments in the securities tradingcontext are likely to become an important part of theempirical analysis of individual risk preferences anddecision-making processes In particular there is con-siderable anecdotal evidence that subjects involved inprofessional trading activities perform very differentlydepending on whether real financial capital is at risk or ifthey are trading with lsquolsquoplayrsquorsquo money Such distinctionshave been documented in other contexts eg it hasbeen shown that different brain regions are activatedduring a subjectrsquos naturally occurring smile and a forcedsmile (Damasio 1994) For this reason measuring sub-jects while they are making decisions in their naturalenvironment is essential for any truly unbiased study offinancial decision-making processes
In capturing relations between cognitive inputs andaffective reactions that are often subconscious and ofwhich subjects are not fully aware our findings may beviewed more broadly as a study of cognitive- emotionalinteractions and the genesis of lsquolsquointuitionrsquorsquo Decisionprocesses based on intuition are characterized by lowlevels of cognitive control low conscious awarenessrapid processing rates and a lack of clear organizingprinciples When intuitive judgments are formed largenumbers of cues are processed simultaneously and thetask is not decomposed into subtasks (HammondHamm Grassia amp Pearson 1987) Expertsrsquo judgmentsare often based on intuition not on explicit analyticalprocessing making it almost impossible to fully explainor replicate the process of how that judgment has beenformed This is particularly germane to financial trad-ersmdashas a group they are unusually heterogeneouswith respect to educational background and formalanalytical skills yet the most successful traders seemto trade based on their intuition about price swingsand market dynamics often without the ability (or theneed) to articulate a precise quantitative algorithm formaking these complex decisions (Niederhoffer 1997Schwager 1989 1992) Their intuitive trading lsquolsquorulesrsquorsquoare based on the associations and relations betweenvarious information tokens that are formed on a sub-conscious level and our findings and those in theextant cognitive sciences literature suggest that deci-sions based on the intuitive judgments require not onlycognitive but also emotional mechanisms A naturalconjecture is that such emotional mechanisms are atleast partly responsible for the ability to form intuitivejudgments and for those judgments to be incorporatedinto a rational decision-making process
Our results may surprise some financial economistsbecause of the apparent inconsistency with marketrationality but a more sophisticated view of the role ofemotion in human cognition (Rolls 1990 1994 1999)can reconcile any contradiction in a complete andintellectually satisfying manner Emotion is the basisfor a reward-and-punishment system that facilitates theselection of advantageous behavioral actions providingthe numeraire for animals to engage in a lsquolsquocost- benefitanalysisrsquorsquo of the various actions open to them (Rolls1999 Chap 103) From an evolutionary perspectiveemotion is a powerful adaptation that dramatically im-proves the efficiency with which animals learn from theirenvironment and their past
These evolutionary underpinnings are more thansimple speculation in the context of financial tradersThe extraordinary degree of competitiveness of globalfinancial markets and the outsize rewards that accrue tothe lsquolsquofittestrsquorsquo traders suggest that Darwinian selectionmdashfinancial selection to be specificmdashis at work in deter-mining the typical profile of the successful trader Afterall unsuccessful traders are generally lsquolsquoeliminatedrsquorsquo fromthe population after suffering a certain level of losses
332 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Our results indicate that emotion is a significant deter-minant of the evolutionary fitness of financial tradersWe hope to investigate this conjecture more formally inthe future in several ways a comparison between tradersand a control group of subjects without trading experi-ence or with unsuccessful trading experiences an anal-ysis of the psychophysiological responses of traders in alaboratory environment a more direct analysis of thecorrelation between autonomic responses and tradingperformance a more fundamental analysis of the neuralbasis of emotion in traders aimed at the function of theamygdala and the orbitofrontal cortex (Rolls 1992 1999Chap 44- 45) and a direct mapping of the neuralcenters for trading activity through functional magneticresonance imaging (Breiter et al 2001)
There are a number of caveats that must be kept inmind in interpreting our findings First because of thesmall sample size of 10 subjects our results are at bestsuggestive and promising not conclusive A more com-prehensive study with a larger and more diverse sampleof traders a more refined set of market events and abroader set of controls are necessary before coming toany firm conclusions regarding the precise mechanismsof the psychophysiology of financial risk processing andthe role of emotion in market efficiency
Second while we have documented the statisticalsignificance of autonomic responses during marketevents relative to control baselines we have not estab-lished specific causal factors for such responses Manycognitive tasks generate autonomic effects hence amarked change in any one psychophysiological indexmay be due to changes in cognitive processing forexample complex numerical computations rather thanmore fundamental affective responses For examplemore experienced traders in our sample may have dis-played lower autonomic response levels because theyrequired less cognitive effort to perform the same func-tions as less experienced traders which may have nothingto do with emotion Without a more targeted set of tasksor additional data on the characteristics of the subjects itis difficult to distinguish between the two One way toaddress this issue is to correlate a subjectrsquos autonomicresponses with his or her trading performance so as todevelop a more complete profile of the relation betweenpsychophysiological indices and the dynamics of thetrading process However because trading performanceis generally summarized by multiple characteristicsmdashtotal profit-and-loss volatility maximum drawdownSharpe ratio and so onmdashestimating a relation betweenautonomic responses and such characteristics may re-quire significantly larger sample sizes and longer obser-vation windows due to the lsquolsquocurse of dimensionalityrsquorsquo Itmay be possible to overcome this challenge to somedegree through a more carefully crafted experimentaldesign in which specific emotional responses are pairedwith specific trading performance measures namelyanxiety levels during consecutive sequences of losing
trades With a larger sample size we can also begin totrack groups of traders over a period of time and throughvarying market conditions By measuring their autonomicresponse profiles at different stages of their careers itmay be possible to determine more directly the role ofemotion in financial risk processing
Finally a much larger set of controls should accompanya larger sample of traders These controls should includepsychophysiological measurements for professional trad-ers taken outside their natural trading environmentmdashideally in a laboratory setting that is identical for alltradersmdashas well as corresponding measurements forsubjects with little or no trading experience in bothtrading and nontrading environments Along with psy-chophysiological data more traditional personality in-ventory data for example MMPI or Myers- Briggs mayprovide additional insights into the psychology of trading
We hope to address each of these issues in ourongoing research program to better understand thecognitive processes underlying financial risk processing
METHODS
Subjects
The 10 subjectsrsquo descriptive characteristics are summar-ized in Table 5 Based on discussions with their super-visors the traders were categorized into three levels ofexperience low moderate and high Five traders spe-cialized in handling client order flow (Retail) threespecialized in trading foreign exchange (FX) and twospecialized in interest-rate derivative securities (Deriva-tives) The durations of the sessions ranged from 49 to83 min and all sessions were held during live tradinghours typically between 8 am and 5 pm easterndaylight time
Physiological Data Collection
A ProComp+ data-acquisition unit and Biograph (Ver-sion 12) biofeedback software from Thought Technol-ogy were used to measure physiological data for allsubjects All six sensors were connected to a smallcontrol unit with a battery power supply which wasplaced on each subjectrsquos belt and from which a fiber-optic connection led to a laptop computer equippedwith real-time data acquisition software (see Figure 2)Each sensor was equipped with a built-in notch filter at60 Hz for automatic elimination of external power linenoise and standard AgCl triode and single electrodeswere used for SCR and EMG sensors respectively Thesampling rate for all data collection was fixed at 32 HzAll physiological data except for respiration and facialEMG were collected from each subjectrsquos nondominantarm SCR electrodes were placed on the palmar sites theBVP photoplesymographic sensor was placed on theinside of the ring or middle finger the arm EMG triodeelectrode was placed on the inside surface of the
Lo and Repin 333
forearm over the flexor digitorum muscle group andthe temperature sensor was inserted between the elasticband placed around the wrist and the skin surface Thefacial EMG electrode was placed on a masseter musclewhich controls jaw movement and is active duringspeech or any other activity involving the jaw Therespiration signal was measured by chest expansionusing a sensor attached to an elastic band placed aroundthe subjectrsquos chest An example of the real-time physio-logical data collected over a 2-min interval for onesubject is given in Figure 3
The entire procedure of outfitting each subject withsensors and connecting the sensors to the laptop re-quired approximately 5 min and was often performedeither before the trading day began or during relativelycalm trading periods Subjects indicated that the pres-
ence of the sensors wires and a control unit did notcompromise or influence their trading in any significantmanner and that their workflow was not impaired in anyway This was verified not only by the subjects but alsoby their supervisors Given the magnitudes of the finan-cial transactions that were being processed and theeconomic and legal responsibilities that the subjectsand their supervisors bore even the slightest interfer-ence with the subjectsrsquo workflow or performance stand-ards would have caused the supervisors or the subjectsto terminate the sessions immediately None of thesessions were terminated prematurely
Physiological Data Feature Extraction
An initial smoothing of the raw EMG signals (sampled at32 Hz) was performed with a moving-average filter oforder 23 If the level of the filtered forearm EMG signalexceeded a threshold of 075 mV both SCR and BVPreadings at this time were discarded because of the highprobability of artifacts for example typing graspingtelephone handsets or inadvertent physical disturban-ces to the sensors Similarly if the level of the filteredfacial EMG signal exceeded 075 mV the respirationsignal at this time was excluded from further processingA very small spatial displacement of the sensor or theelectrode was able to produce a different kind ofartifactmdashan abrupt change in the signal of the orderof 10- 20 standard deviations within 1
32 of a second Suchjumps did not have any physiological meaning hencethey were excluded from further analysis via adaptivethresholding Specifically our adaptive thresholdingprocedure involved marking all observations that
Table 5 Summary Statistics for All Subjects Individual Traderrsquos Characteristics Specialty Type and Number of Market Time-Series Collected During the Session Session Duration and Absolute Time (Eastern Standard Time) of the Start of the Session
TraderID Gender Experience Specialty Market data Available
Nu m ber of MarketTim e-Series Recorded
Session Du ration(m in and sec)
SessionStart Tim e
B33 F high retail major currencies 2 4903000 1320
B34 M high FX major currencies 3 8303200 0856
B35 M high retail major currencies 4 6603000 1102
B36 M high derivatives SampP500 futureseurodollar futures
3 7901600 1255
B37 M moderate FX Latin Americanmajor currencies
9 7000600 0917
B38 M low FX Latin Americanmajor currencies
3 7201200 1102
B39 M high derivatives SampP 500 futureseurodollar futures
9 6200300 0819
B310 M low retail major currencies 7 6000800 0947
B311 M moderate retail major currencies 7 5402500 1132
B312 M low retail major currencies 7 5900000 0910
ProC om p+
Facial EMG Triode electrode measures
activity of the masseter muscle (detects speech)
Respiration sensor Elastic strap measures
chest expansion
Forearm EMG Triode electrode measures activity of the flexor digitorum muscle group (detects finger and arm movement)
Temperature sensor - thermistor
SCR sensor and electrodes
BVP photoplesymographicsensor
Control unit Fiber optic cable to a
data acquisition laptop
Figure 2 Placement of sensors for measuring physiologicalresponses
334 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
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Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
Tab
le2
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vec
tors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
All
Tra
ders
Hig
hEx
peri
ence
Low
orM
oder
ate
Expe
rien
ce
Phys
iolo
gy
Fea
ture
s
DE
V
Pri
ce
DEV
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VOL
Ma
xim
um
DE
V
Pri
ce
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VO
L
Ma
xim
um
DE
V
Pri
ce
DEV
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ce
TRV
Spre
ad
VO
L
Ma
xim
um
Nu
mbe
ro
fSC
Rre
spon
ses
28
72
060
42
08
81
66
40
636
057
90
561
122
8iexcl
022
1iexcl
126
10
184
20
81
26
96
012
32
75
22
17
0iexcl
066
5iexcl
068
8
Aver
age
SCR
ampl
itud
eiexcl
069
50
887
018
6iexcl
247
81
136
088
00
030
034
40
744
iexcl1
340
114
90
904
075
61
578
iexcl1
265
iexcl1
947
iexcl0
012
iexcl0
615
Aver
age
HR
090
5iexcl
058
8iexcl
059
6iexcl
061
71
032
iexcl0
270
044
91
88
6iexcl
179
7iexcl
163
51
83
6iexcl
201
81
310
005
1iexcl
038
2iexcl
110
6iexcl
031
11
283
Aver
age
BV
Pam
plit
ude
rela
tive
tolo
cal
base
line
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9iexcl
006
1iexcl
074
70
334
001
50
164
26
23
iexcl0
204
iexcl1
775
087
51
79
6iexcl
128
92
70
8
Aver
age
BV
Pam
plit
ude
rela
tive
togl
obal
base
line
141
7iexcl
272
62
32
41
417
iexcl2
211
28
86
iexcl2
529
iexcl1
367
159
01
110
iexcl1
165
23
01
19
17
iexcl2
664
18
84
20
82
iexcl1
759
27
58
Nu
mbe
ro
fte
mp
erat
ure
jum
ps
098
82
82
00
650
17
77
48
68
iexcl2
482
iexcl1
010
iexcl1
435
NA
NA
NA
164
40
837
29
02
114
52
44
52
63
3iexcl
321
3
Aver
age
resp
irat
ion
rate
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4iexcl
155
61
109
012
10
866
iexcl0
102
009
30
079
iexcl0
552
032
3iexcl
063
1iexcl
217
0iexcl
152
8
Aver
age
resp
irat
ion
ampl
itud
e
iexcl1
646
iexcl0
250
013
60
034
iexcl1
487
iexcl3
499
108
70
265
iexcl1
777
iexcl0
657
012
1iexcl
060
7iexcl
274
9iexcl
131
92
14
20
055
iexcl2
832
iexcl4
533
Bo
lded
stat
isti
csar
esi
gnifi
can
tat
the
5le
vel
Th
ele
ftp
anel
give
sag
greg
ate
resu
lts
for
allt
rad
ers
Sim
ilar
ttes
tsfo
rtr
ader
sw
ith
hig
hex
per
ienc
ean
dlo
wo
rm
oder
ate
exp
erie
nce
are
show
nin
the
mid
dle
and
righ
tp
anel
sre
spec
tive
lyE
ntr
ies
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
wer
ep
rese
nt
inei
ther
the
even
tor
cont
rol
feat
ure
vect
or
for
the
par
ticu
lar
mar
ket-
even
tty
pe
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spo
nse
sH
R=
hea
rtra
te
BV
P=
blo
od
volu
me
pul
se
328 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
deviations and trend-reversals both the number of SCRsand the number of temperature jumps reached statisti-cally significant levels for three out of the five event typesBVP amplitude-related features were highly significantfor volatility eventsmdashsignificance levels less than 1 wereobtained for both BVP amplitude features for all threetypes of volatility events Because the results were sosimilar across the three types of volatility events (max-imum volatility price volatility and return volatility) wereport the results only for maximum volatility in Table 2Such patterns of autonomic responses may indicate thepresence of transient emotional responses to deviationsand trend-reversal events that occur within 10-sec win-dows while volatility events defined in 5-min windowselicited a tonic response in BVP
To explore potential differences between experi-enced and less experienced traders a sample of 10traders was divided into two groups each consistingof five traders the first containing highly experiencedtraders and the second containing traders with low ormoderate experience where the levels of experiencewere determined through interviews with the tradersrsquosupervisors The results for each of the two groupsare reported in the two right subpanels of Table 2labelled lsquolsquoHigh Experiencersquorsquo and lsquolsquoLow or ModerateExperiencersquorsquo The high-experience traders generallyexhibited low responses for deviations and trend-re-versal events with only two types of market events(spread deviations and spread trend-reversals) elicitingsignificant mean autonomic responses (average HR)However even for high-experience traders volatilityevents induced statistically significant mean responsesfor both SCR and BVP amplitude Less experiencedtraders showed a much higher number of significantmean responses in the number of SCRs BVP ampli-tude and number of temperature increases for devia-tions and trend-reversal events Table 2 shows thatsessions with low- and moderate-experience tradersyielded 13 t statistics significant at the 5 level whilesessions with high-experience traders yielded only fivet statistics significant at the 5 level For both sets oftraders volatility events were associated with signifi-cant BVP amplitude The differences in responsepatterns for deviations and trend-reversals observedin this case indicate that less experienced traders maybe more sensitive to short-term changes in the marketvariables than their more experienced colleagues
To address the third objective 10-sec intervals imme-diately preceding and immediately following each devia-tion and trend-reversal event were compared to control(ie no-event) time intervals Two separate t tests wereconductedmdashone for pre- and another for post-eventtime intervalsmdashfor each of the three types of deviationsand two types of trend-reversals Because the objectivewas to detect differences in how pre- and post-eventfeature vectors differed from the control we used thesame control feature vectors for both sets of t tests In all
cases the t tests were designed to test the null hypoth-esis that both pre- and post-event feature vectors werestatistically indistinguishable from the control featurevector Surprisingly these two sets of t tests yielded verysimilar results reported in Table 3 implying that noneof the physiological variables were predictors of antici-patory emotional responses These findings may be atleast partly explained by how the events were defined Inparticular the definitions of deviation and trend-reversalevents are those instances where the time seriesachieved a prespecified deviation from the time-seriesmean and its moving average respectively In theabsence of large jumps the values of the time seriesnear an event defined in this way are likely to becomparable to the event itself Therefore the tradersmay be responding to market conditions occurringthroughout the pre- and post-event period not just atthe exact time of the event hence the similarity be-tween the two periods More complex definitions ofevents may allow us to discriminate between pre- andpost-event physiological responses and we are explor-ing several alternatives in ongoing research
Finally to address the fourth objective the followingfour groups of financial instruments were formed on thebasis of similarity in their statistical characteristics
Group 1 EUR JPY
Group 2 GBP CAD CHF AUD NZD (other majorcurrencies)
Group 3 MXP BRL ARS COP VEB CLP (LatinAmerican currencies)
Group 4 SPU EDU (derivatives)
Group 1 is by far the most actively traded set offinancial instruments and accounts for most of thetrading activities of our sample of 10 traders hence itis not surprising that the pattern of statistical signifi-cance for Group 1 in Table 4 is almost identical to thepattern for all traders in Table 2 (the left panel) SCRis significant for price and return deviations temper-ature jumps are significant for spread and price devia-tions and both measures of BVP are significant forvolatility events For Group 2 none of the eightphysiological variables were significant for price orspread deviations although temperature changes andrespiration rates were significant for return deviationsand both BVP measures were significant for pricetrend-reversals and volatility events The financial in-struments in Group 2 were not the primary focus ofany of the traders in our sample which may explainthe different patterns of significance as compared withGroup 1 Group 4 contains the fewest significantresponses and this is consistent with the fact thatthese securities were traded by two of the mostexperienced traders in our sample Derivative secur-ities are considerably more complex instruments thanthe spot currencies requiring more skill and trainingthan the other groups of securities However SCR was
Lo and Repin 329
Tab
le3
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gPr
e-an
dPo
st-e
vent
Tim
eIn
terv
als
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
Pre
-eve
nt
Inte
rva
lP
ost-
even
tIn
terv
al
Phy
sio
logy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mbe
rof
SCR
resp
ons
es3
75
61
543
25
40
21
18
24
01
057
92
87
20
604
20
88
16
64
063
60
579
Aver
age
SCR
amp
litu
de0
700
iexcl0
454
iexcl1
793
051
11
068
088
0iexcl
069
50
887
018
6iexcl
247
81
136
088
0
Aver
age
HR
096
8iexcl
020
5iexcl
055
2iexcl
066
3iexcl
038
6iexcl
027
00
905
iexcl0
588
iexcl0
596
iexcl0
617
103
2iexcl
027
0
Aver
age
BVP
amp
litud
ere
lativ
eto
loca
lba
selin
e0
043
iexcl0
895
006
00
814
iexcl0
045
36
19
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9
Aver
age
BVP
amp
litud
ere
lativ
eto
glob
alba
selin
e1
183
iexcl2
794
23
97
131
7iexcl
225
62
88
61
417
iexcl2
726
23
24
141
7iexcl
221
12
88
6
Nu
mbe
rof
tem
per
atur
eju
mp
s1
110
27
67
050
52
15
83
92
5iexcl
248
20
988
28
20
065
01
77
74
86
8iexcl
248
2
Aver
age
resp
irat
ion
rate
126
1iexcl
165
0iexcl
025
41
303
iexcl1
999
iexcl0
064
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4
Aver
age
resp
irat
ion
amp
litud
eiexcl
162
0iexcl
006
70
561
006
8iexcl
180
7iexcl
349
9iexcl
164
6iexcl
025
00
136
003
4iexcl
148
7iexcl
349
9
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
l
Th
ele
ftp
anel
cont
ain
st
stat
isti
csfo
rp
re-e
vent
feat
ure
vect
ors
and
the
righ
tp
anel
con
tain
st
stat
isti
csfo
rp
ost-
even
tfe
atu
reve
cto
rs
both
test
edag
ain
stth
esa
me
set
of
con
tro
ls
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spon
ses
HR
=h
eart
rate
B
VP
=bl
ood
volu
me
pu
lse
330 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le4
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
oth
esis
Tes
tsof
the
Diff
eren
cein
Mea
nAu
tono
mic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pon
ents
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chTy
pe
ofM
arke
tEv
ent
(Col
umns
)G
roup
edIn
toFo
urD
istin
ctSe
tsof
Fina
ncia
lIn
stru
men
ts
Ma
rket
Eve
nts
Gro
up
1E
UR
an
dJP
YG
rou
p2
Oth
erM
ajo
rC
urr
enci
esa
Gro
up
3La
tin
Am
eric
an
Cu
rren
cies
bG
rou
p4
Der
iva
tive
sc
Phy
siol
ogy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
mD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mb
erof
SCR
resp
on
ses
16
53
iexcl1
902
19
64
iexcl0
554
iexcl2
286
iexcl0
940
033
20
112
054
4iexcl
179
5iexcl
241
7iexcl
110
82
49
82
18
8iexcl
039
72
99
5iexcl
040
50
898
26
82
109
41
66
91
594
iexcl0
239
22
36
Ave
rage
SCR
amp
litu
de
iexcl1
639
062
30
279
iexcl1
756
109
8iexcl
111
2iexcl
050
30
540
113
8iexcl
219
0iexcl
010
60
896
111
61
279
iexcl1
525
iexcl1
802
22
20
iexcl0
986
iexcl0
610
123
60
930
iexcl1
513
024
2iexcl
151
0
Ave
rage
HR
073
1iexcl
000
4iexcl
126
8iexcl
181
5iexcl
069
8iexcl
161
2iexcl
099
1iexcl
145
2iexcl
103
4iexcl
132
6iexcl
030
8iexcl
108
92
92
1iexcl
143
2iexcl
107
0iexcl
100
2iexcl
141
61
183
081
6iexcl
032
20
577
049
20
515
iexcl0
169
Ave
rage
BV
Pam
plit
ude
rela
tive
to
loca
lb
asel
ine
056
2iexcl
136
8iexcl
080
51
273
088
52
58
7iexcl
092
20
220
145
72
32
3iexcl
127
52
30
8iexcl
098
2iexcl
000
12
82
81
454
059
82
19
2iexcl
059
2iexcl
303
2iexcl
395
3iexcl
089
4iexcl
190
4iexcl
143
3
Ave
rage
BV
Pam
plit
ude
rela
tive
tolo
cal
bas
elin
e
059
7iexcl
301
01
71
40
375
iexcl0
252
25
92
052
11
448
097
23
08
7iexcl
149
92
63
3iexcl
192
5iexcl
084
91
482
025
9iexcl
035
1iexcl
012
9iexcl
066
0iexcl
150
1iexcl
327
4iexcl
162
6iexcl
200
4iexcl
241
1
Nu
mb
erof
tem
per
atu
reju
mp
s
068
84
14
1iexcl
144
42
28
30
295
iexcl2
043
107
3iexcl
155
72
40
41
248
37
26
iexcl1
564
iexcl1
603
iexcl1
275
iexcl1
922
iexcl0
512
iexcl1
128
iexcl1
540
iexcl1
367
iexcl1
349
iexcl1
362
iexcl1
367
iexcl1
053
NA
Ave
rage
resp
irat
ion
rate
052
5iexcl
174
1iexcl
079
70
163
iexcl1
884
iexcl0
004
075
6iexcl
056
02
02
3iexcl
109
5iexcl
178
4iexcl
011
60
037
044
6iexcl
112
40
643
iexcl1
392
012
5iexcl
194
6iexcl
029
10
361
041
6iexcl
110
5iexcl
172
5
Ave
rage
resp
irat
ion
amp
litu
de
iexcl2
120
iexcl0
692
034
5iexcl
050
9iexcl
093
5iexcl
207
9iexcl
074
60
560
047
00
905
089
3iexcl
383
90
280
iexcl0
207
142
6iexcl
036
0iexcl
053
4iexcl
043
70
920
iexcl0
955
iexcl1
915
125
71
593
iexcl0
039
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
la M
XP
BR
LA
RS
CO
PV
EB
CLP
bM
XP
BR
LA
RS
CO
PV
EB
C
LP
c ED
U
SPU
Ent
ries
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
was
pre
sen
tin
eith
erth
eev
ent
orco
ntro
lfe
atu
reve
cto
rfo
rth
ep
arti
cula
rm
arke
t-ev
ent
typ
e
DE
V=
dev
iati
on
TR
V=
tren
d-r
ever
sal
VO
L=
vola
tilit
ySC
R=
skin
con
duc
tan
cere
spo
nses
H
R=
hea
rtra
te
BV
P=
bloo
dvo
lum
ep
uls
e
Lo and Repin 331
significant for both price and return deviations inGroup 4 which was not the case for the sample ofhigh-experience traders in Table 2 SCR was alsosignificant for volatility events in Group 4 which isconsistent with the central role that volatility plays inthe pricing and hedging derivative securities (Black ampScholes 1973 Merton 1973) Volatility is a moresophisticated aspect of the trading milieu requiringa higher degree of training to fully appreciate itsimplications for market trends and profit-and-lossprobabilities This may explain the fact that SCR issignificant for volatility events only among the high-experience traders in Table 2 and the derivativestraders in Table 4
DISCUSSION
Our findings suggest that emotional responses are asignificant factor in the real-time processing of financialrisks Contrary to the common belief that emotions haveno place in rational financial decision-making processesphysiological variables associated with the ANS exhibitsignificant changes during market events even for highlyexperienced professional traders Moreover the re-sponse patterns among variables and events differed inimportant ways for less experienced tradersmdashmeanautonomic responses were significantly highermdashsug-gesting the possibility of relating trading skills to certainphysiological characteristics that can be measured
More generally our experiments demonstrate thefeasibility of relating real-time quantitative changes incognitive inputs (financial information) to correspond-ing quantitative changes in physiological responses in acomplex field environment Despite the challenges ofsuch measurements a wealth of information can beobtained regarding high-pressure decision makingunder uncertainty Financial traders operate in a con-trolled environment where the inputs and outputs ofthe decisions are carefully recorded and where thesubjects are highly trained and provided with greateconomic incentives to make rational trading decisionsTherefore in vivo experiments in the securities tradingcontext are likely to become an important part of theempirical analysis of individual risk preferences anddecision-making processes In particular there is con-siderable anecdotal evidence that subjects involved inprofessional trading activities perform very differentlydepending on whether real financial capital is at risk or ifthey are trading with lsquolsquoplayrsquorsquo money Such distinctionshave been documented in other contexts eg it hasbeen shown that different brain regions are activatedduring a subjectrsquos naturally occurring smile and a forcedsmile (Damasio 1994) For this reason measuring sub-jects while they are making decisions in their naturalenvironment is essential for any truly unbiased study offinancial decision-making processes
In capturing relations between cognitive inputs andaffective reactions that are often subconscious and ofwhich subjects are not fully aware our findings may beviewed more broadly as a study of cognitive- emotionalinteractions and the genesis of lsquolsquointuitionrsquorsquo Decisionprocesses based on intuition are characterized by lowlevels of cognitive control low conscious awarenessrapid processing rates and a lack of clear organizingprinciples When intuitive judgments are formed largenumbers of cues are processed simultaneously and thetask is not decomposed into subtasks (HammondHamm Grassia amp Pearson 1987) Expertsrsquo judgmentsare often based on intuition not on explicit analyticalprocessing making it almost impossible to fully explainor replicate the process of how that judgment has beenformed This is particularly germane to financial trad-ersmdashas a group they are unusually heterogeneouswith respect to educational background and formalanalytical skills yet the most successful traders seemto trade based on their intuition about price swingsand market dynamics often without the ability (or theneed) to articulate a precise quantitative algorithm formaking these complex decisions (Niederhoffer 1997Schwager 1989 1992) Their intuitive trading lsquolsquorulesrsquorsquoare based on the associations and relations betweenvarious information tokens that are formed on a sub-conscious level and our findings and those in theextant cognitive sciences literature suggest that deci-sions based on the intuitive judgments require not onlycognitive but also emotional mechanisms A naturalconjecture is that such emotional mechanisms are atleast partly responsible for the ability to form intuitivejudgments and for those judgments to be incorporatedinto a rational decision-making process
Our results may surprise some financial economistsbecause of the apparent inconsistency with marketrationality but a more sophisticated view of the role ofemotion in human cognition (Rolls 1990 1994 1999)can reconcile any contradiction in a complete andintellectually satisfying manner Emotion is the basisfor a reward-and-punishment system that facilitates theselection of advantageous behavioral actions providingthe numeraire for animals to engage in a lsquolsquocost- benefitanalysisrsquorsquo of the various actions open to them (Rolls1999 Chap 103) From an evolutionary perspectiveemotion is a powerful adaptation that dramatically im-proves the efficiency with which animals learn from theirenvironment and their past
These evolutionary underpinnings are more thansimple speculation in the context of financial tradersThe extraordinary degree of competitiveness of globalfinancial markets and the outsize rewards that accrue tothe lsquolsquofittestrsquorsquo traders suggest that Darwinian selectionmdashfinancial selection to be specificmdashis at work in deter-mining the typical profile of the successful trader Afterall unsuccessful traders are generally lsquolsquoeliminatedrsquorsquo fromthe population after suffering a certain level of losses
332 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Our results indicate that emotion is a significant deter-minant of the evolutionary fitness of financial tradersWe hope to investigate this conjecture more formally inthe future in several ways a comparison between tradersand a control group of subjects without trading experi-ence or with unsuccessful trading experiences an anal-ysis of the psychophysiological responses of traders in alaboratory environment a more direct analysis of thecorrelation between autonomic responses and tradingperformance a more fundamental analysis of the neuralbasis of emotion in traders aimed at the function of theamygdala and the orbitofrontal cortex (Rolls 1992 1999Chap 44- 45) and a direct mapping of the neuralcenters for trading activity through functional magneticresonance imaging (Breiter et al 2001)
There are a number of caveats that must be kept inmind in interpreting our findings First because of thesmall sample size of 10 subjects our results are at bestsuggestive and promising not conclusive A more com-prehensive study with a larger and more diverse sampleof traders a more refined set of market events and abroader set of controls are necessary before coming toany firm conclusions regarding the precise mechanismsof the psychophysiology of financial risk processing andthe role of emotion in market efficiency
Second while we have documented the statisticalsignificance of autonomic responses during marketevents relative to control baselines we have not estab-lished specific causal factors for such responses Manycognitive tasks generate autonomic effects hence amarked change in any one psychophysiological indexmay be due to changes in cognitive processing forexample complex numerical computations rather thanmore fundamental affective responses For examplemore experienced traders in our sample may have dis-played lower autonomic response levels because theyrequired less cognitive effort to perform the same func-tions as less experienced traders which may have nothingto do with emotion Without a more targeted set of tasksor additional data on the characteristics of the subjects itis difficult to distinguish between the two One way toaddress this issue is to correlate a subjectrsquos autonomicresponses with his or her trading performance so as todevelop a more complete profile of the relation betweenpsychophysiological indices and the dynamics of thetrading process However because trading performanceis generally summarized by multiple characteristicsmdashtotal profit-and-loss volatility maximum drawdownSharpe ratio and so onmdashestimating a relation betweenautonomic responses and such characteristics may re-quire significantly larger sample sizes and longer obser-vation windows due to the lsquolsquocurse of dimensionalityrsquorsquo Itmay be possible to overcome this challenge to somedegree through a more carefully crafted experimentaldesign in which specific emotional responses are pairedwith specific trading performance measures namelyanxiety levels during consecutive sequences of losing
trades With a larger sample size we can also begin totrack groups of traders over a period of time and throughvarying market conditions By measuring their autonomicresponse profiles at different stages of their careers itmay be possible to determine more directly the role ofemotion in financial risk processing
Finally a much larger set of controls should accompanya larger sample of traders These controls should includepsychophysiological measurements for professional trad-ers taken outside their natural trading environmentmdashideally in a laboratory setting that is identical for alltradersmdashas well as corresponding measurements forsubjects with little or no trading experience in bothtrading and nontrading environments Along with psy-chophysiological data more traditional personality in-ventory data for example MMPI or Myers- Briggs mayprovide additional insights into the psychology of trading
We hope to address each of these issues in ourongoing research program to better understand thecognitive processes underlying financial risk processing
METHODS
Subjects
The 10 subjectsrsquo descriptive characteristics are summar-ized in Table 5 Based on discussions with their super-visors the traders were categorized into three levels ofexperience low moderate and high Five traders spe-cialized in handling client order flow (Retail) threespecialized in trading foreign exchange (FX) and twospecialized in interest-rate derivative securities (Deriva-tives) The durations of the sessions ranged from 49 to83 min and all sessions were held during live tradinghours typically between 8 am and 5 pm easterndaylight time
Physiological Data Collection
A ProComp+ data-acquisition unit and Biograph (Ver-sion 12) biofeedback software from Thought Technol-ogy were used to measure physiological data for allsubjects All six sensors were connected to a smallcontrol unit with a battery power supply which wasplaced on each subjectrsquos belt and from which a fiber-optic connection led to a laptop computer equippedwith real-time data acquisition software (see Figure 2)Each sensor was equipped with a built-in notch filter at60 Hz for automatic elimination of external power linenoise and standard AgCl triode and single electrodeswere used for SCR and EMG sensors respectively Thesampling rate for all data collection was fixed at 32 HzAll physiological data except for respiration and facialEMG were collected from each subjectrsquos nondominantarm SCR electrodes were placed on the palmar sites theBVP photoplesymographic sensor was placed on theinside of the ring or middle finger the arm EMG triodeelectrode was placed on the inside surface of the
Lo and Repin 333
forearm over the flexor digitorum muscle group andthe temperature sensor was inserted between the elasticband placed around the wrist and the skin surface Thefacial EMG electrode was placed on a masseter musclewhich controls jaw movement and is active duringspeech or any other activity involving the jaw Therespiration signal was measured by chest expansionusing a sensor attached to an elastic band placed aroundthe subjectrsquos chest An example of the real-time physio-logical data collected over a 2-min interval for onesubject is given in Figure 3
The entire procedure of outfitting each subject withsensors and connecting the sensors to the laptop re-quired approximately 5 min and was often performedeither before the trading day began or during relativelycalm trading periods Subjects indicated that the pres-
ence of the sensors wires and a control unit did notcompromise or influence their trading in any significantmanner and that their workflow was not impaired in anyway This was verified not only by the subjects but alsoby their supervisors Given the magnitudes of the finan-cial transactions that were being processed and theeconomic and legal responsibilities that the subjectsand their supervisors bore even the slightest interfer-ence with the subjectsrsquo workflow or performance stand-ards would have caused the supervisors or the subjectsto terminate the sessions immediately None of thesessions were terminated prematurely
Physiological Data Feature Extraction
An initial smoothing of the raw EMG signals (sampled at32 Hz) was performed with a moving-average filter oforder 23 If the level of the filtered forearm EMG signalexceeded a threshold of 075 mV both SCR and BVPreadings at this time were discarded because of the highprobability of artifacts for example typing graspingtelephone handsets or inadvertent physical disturban-ces to the sensors Similarly if the level of the filteredfacial EMG signal exceeded 075 mV the respirationsignal at this time was excluded from further processingA very small spatial displacement of the sensor or theelectrode was able to produce a different kind ofartifactmdashan abrupt change in the signal of the orderof 10- 20 standard deviations within 1
32 of a second Suchjumps did not have any physiological meaning hencethey were excluded from further analysis via adaptivethresholding Specifically our adaptive thresholdingprocedure involved marking all observations that
Table 5 Summary Statistics for All Subjects Individual Traderrsquos Characteristics Specialty Type and Number of Market Time-Series Collected During the Session Session Duration and Absolute Time (Eastern Standard Time) of the Start of the Session
TraderID Gender Experience Specialty Market data Available
Nu m ber of MarketTim e-Series Recorded
Session Du ration(m in and sec)
SessionStart Tim e
B33 F high retail major currencies 2 4903000 1320
B34 M high FX major currencies 3 8303200 0856
B35 M high retail major currencies 4 6603000 1102
B36 M high derivatives SampP500 futureseurodollar futures
3 7901600 1255
B37 M moderate FX Latin Americanmajor currencies
9 7000600 0917
B38 M low FX Latin Americanmajor currencies
3 7201200 1102
B39 M high derivatives SampP 500 futureseurodollar futures
9 6200300 0819
B310 M low retail major currencies 7 6000800 0947
B311 M moderate retail major currencies 7 5402500 1132
B312 M low retail major currencies 7 5900000 0910
ProC om p+
Facial EMG Triode electrode measures
activity of the masseter muscle (detects speech)
Respiration sensor Elastic strap measures
chest expansion
Forearm EMG Triode electrode measures activity of the flexor digitorum muscle group (detects finger and arm movement)
Temperature sensor - thermistor
SCR sensor and electrodes
BVP photoplesymographicsensor
Control unit Fiber optic cable to a
data acquisition laptop
Figure 2 Placement of sensors for measuring physiologicalresponses
334 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
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Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
deviations and trend-reversals both the number of SCRsand the number of temperature jumps reached statisti-cally significant levels for three out of the five event typesBVP amplitude-related features were highly significantfor volatility eventsmdashsignificance levels less than 1 wereobtained for both BVP amplitude features for all threetypes of volatility events Because the results were sosimilar across the three types of volatility events (max-imum volatility price volatility and return volatility) wereport the results only for maximum volatility in Table 2Such patterns of autonomic responses may indicate thepresence of transient emotional responses to deviationsand trend-reversal events that occur within 10-sec win-dows while volatility events defined in 5-min windowselicited a tonic response in BVP
To explore potential differences between experi-enced and less experienced traders a sample of 10traders was divided into two groups each consistingof five traders the first containing highly experiencedtraders and the second containing traders with low ormoderate experience where the levels of experiencewere determined through interviews with the tradersrsquosupervisors The results for each of the two groupsare reported in the two right subpanels of Table 2labelled lsquolsquoHigh Experiencersquorsquo and lsquolsquoLow or ModerateExperiencersquorsquo The high-experience traders generallyexhibited low responses for deviations and trend-re-versal events with only two types of market events(spread deviations and spread trend-reversals) elicitingsignificant mean autonomic responses (average HR)However even for high-experience traders volatilityevents induced statistically significant mean responsesfor both SCR and BVP amplitude Less experiencedtraders showed a much higher number of significantmean responses in the number of SCRs BVP ampli-tude and number of temperature increases for devia-tions and trend-reversal events Table 2 shows thatsessions with low- and moderate-experience tradersyielded 13 t statistics significant at the 5 level whilesessions with high-experience traders yielded only fivet statistics significant at the 5 level For both sets oftraders volatility events were associated with signifi-cant BVP amplitude The differences in responsepatterns for deviations and trend-reversals observedin this case indicate that less experienced traders maybe more sensitive to short-term changes in the marketvariables than their more experienced colleagues
To address the third objective 10-sec intervals imme-diately preceding and immediately following each devia-tion and trend-reversal event were compared to control(ie no-event) time intervals Two separate t tests wereconductedmdashone for pre- and another for post-eventtime intervalsmdashfor each of the three types of deviationsand two types of trend-reversals Because the objectivewas to detect differences in how pre- and post-eventfeature vectors differed from the control we used thesame control feature vectors for both sets of t tests In all
cases the t tests were designed to test the null hypoth-esis that both pre- and post-event feature vectors werestatistically indistinguishable from the control featurevector Surprisingly these two sets of t tests yielded verysimilar results reported in Table 3 implying that noneof the physiological variables were predictors of antici-patory emotional responses These findings may be atleast partly explained by how the events were defined Inparticular the definitions of deviation and trend-reversalevents are those instances where the time seriesachieved a prespecified deviation from the time-seriesmean and its moving average respectively In theabsence of large jumps the values of the time seriesnear an event defined in this way are likely to becomparable to the event itself Therefore the tradersmay be responding to market conditions occurringthroughout the pre- and post-event period not just atthe exact time of the event hence the similarity be-tween the two periods More complex definitions ofevents may allow us to discriminate between pre- andpost-event physiological responses and we are explor-ing several alternatives in ongoing research
Finally to address the fourth objective the followingfour groups of financial instruments were formed on thebasis of similarity in their statistical characteristics
Group 1 EUR JPY
Group 2 GBP CAD CHF AUD NZD (other majorcurrencies)
Group 3 MXP BRL ARS COP VEB CLP (LatinAmerican currencies)
Group 4 SPU EDU (derivatives)
Group 1 is by far the most actively traded set offinancial instruments and accounts for most of thetrading activities of our sample of 10 traders hence itis not surprising that the pattern of statistical signifi-cance for Group 1 in Table 4 is almost identical to thepattern for all traders in Table 2 (the left panel) SCRis significant for price and return deviations temper-ature jumps are significant for spread and price devia-tions and both measures of BVP are significant forvolatility events For Group 2 none of the eightphysiological variables were significant for price orspread deviations although temperature changes andrespiration rates were significant for return deviationsand both BVP measures were significant for pricetrend-reversals and volatility events The financial in-struments in Group 2 were not the primary focus ofany of the traders in our sample which may explainthe different patterns of significance as compared withGroup 1 Group 4 contains the fewest significantresponses and this is consistent with the fact thatthese securities were traded by two of the mostexperienced traders in our sample Derivative secur-ities are considerably more complex instruments thanthe spot currencies requiring more skill and trainingthan the other groups of securities However SCR was
Lo and Repin 329
Tab
le3
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gPr
e-an
dPo
st-e
vent
Tim
eIn
terv
als
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
Pre
-eve
nt
Inte
rva
lP
ost-
even
tIn
terv
al
Phy
sio
logy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mbe
rof
SCR
resp
ons
es3
75
61
543
25
40
21
18
24
01
057
92
87
20
604
20
88
16
64
063
60
579
Aver
age
SCR
amp
litu
de0
700
iexcl0
454
iexcl1
793
051
11
068
088
0iexcl
069
50
887
018
6iexcl
247
81
136
088
0
Aver
age
HR
096
8iexcl
020
5iexcl
055
2iexcl
066
3iexcl
038
6iexcl
027
00
905
iexcl0
588
iexcl0
596
iexcl0
617
103
2iexcl
027
0
Aver
age
BVP
amp
litud
ere
lativ
eto
loca
lba
selin
e0
043
iexcl0
895
006
00
814
iexcl0
045
36
19
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9
Aver
age
BVP
amp
litud
ere
lativ
eto
glob
alba
selin
e1
183
iexcl2
794
23
97
131
7iexcl
225
62
88
61
417
iexcl2
726
23
24
141
7iexcl
221
12
88
6
Nu
mbe
rof
tem
per
atur
eju
mp
s1
110
27
67
050
52
15
83
92
5iexcl
248
20
988
28
20
065
01
77
74
86
8iexcl
248
2
Aver
age
resp
irat
ion
rate
126
1iexcl
165
0iexcl
025
41
303
iexcl1
999
iexcl0
064
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4
Aver
age
resp
irat
ion
amp
litud
eiexcl
162
0iexcl
006
70
561
006
8iexcl
180
7iexcl
349
9iexcl
164
6iexcl
025
00
136
003
4iexcl
148
7iexcl
349
9
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
l
Th
ele
ftp
anel
cont
ain
st
stat
isti
csfo
rp
re-e
vent
feat
ure
vect
ors
and
the
righ
tp
anel
con
tain
st
stat
isti
csfo
rp
ost-
even
tfe
atu
reve
cto
rs
both
test
edag
ain
stth
esa
me
set
of
con
tro
ls
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spon
ses
HR
=h
eart
rate
B
VP
=bl
ood
volu
me
pu
lse
330 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le4
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
oth
esis
Tes
tsof
the
Diff
eren
cein
Mea
nAu
tono
mic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pon
ents
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chTy
pe
ofM
arke
tEv
ent
(Col
umns
)G
roup
edIn
toFo
urD
istin
ctSe
tsof
Fina
ncia
lIn
stru
men
ts
Ma
rket
Eve
nts
Gro
up
1E
UR
an
dJP
YG
rou
p2
Oth
erM
ajo
rC
urr
enci
esa
Gro
up
3La
tin
Am
eric
an
Cu
rren
cies
bG
rou
p4
Der
iva
tive
sc
Phy
siol
ogy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
mD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mb
erof
SCR
resp
on
ses
16
53
iexcl1
902
19
64
iexcl0
554
iexcl2
286
iexcl0
940
033
20
112
054
4iexcl
179
5iexcl
241
7iexcl
110
82
49
82
18
8iexcl
039
72
99
5iexcl
040
50
898
26
82
109
41
66
91
594
iexcl0
239
22
36
Ave
rage
SCR
amp
litu
de
iexcl1
639
062
30
279
iexcl1
756
109
8iexcl
111
2iexcl
050
30
540
113
8iexcl
219
0iexcl
010
60
896
111
61
279
iexcl1
525
iexcl1
802
22
20
iexcl0
986
iexcl0
610
123
60
930
iexcl1
513
024
2iexcl
151
0
Ave
rage
HR
073
1iexcl
000
4iexcl
126
8iexcl
181
5iexcl
069
8iexcl
161
2iexcl
099
1iexcl
145
2iexcl
103
4iexcl
132
6iexcl
030
8iexcl
108
92
92
1iexcl
143
2iexcl
107
0iexcl
100
2iexcl
141
61
183
081
6iexcl
032
20
577
049
20
515
iexcl0
169
Ave
rage
BV
Pam
plit
ude
rela
tive
to
loca
lb
asel
ine
056
2iexcl
136
8iexcl
080
51
273
088
52
58
7iexcl
092
20
220
145
72
32
3iexcl
127
52
30
8iexcl
098
2iexcl
000
12
82
81
454
059
82
19
2iexcl
059
2iexcl
303
2iexcl
395
3iexcl
089
4iexcl
190
4iexcl
143
3
Ave
rage
BV
Pam
plit
ude
rela
tive
tolo
cal
bas
elin
e
059
7iexcl
301
01
71
40
375
iexcl0
252
25
92
052
11
448
097
23
08
7iexcl
149
92
63
3iexcl
192
5iexcl
084
91
482
025
9iexcl
035
1iexcl
012
9iexcl
066
0iexcl
150
1iexcl
327
4iexcl
162
6iexcl
200
4iexcl
241
1
Nu
mb
erof
tem
per
atu
reju
mp
s
068
84
14
1iexcl
144
42
28
30
295
iexcl2
043
107
3iexcl
155
72
40
41
248
37
26
iexcl1
564
iexcl1
603
iexcl1
275
iexcl1
922
iexcl0
512
iexcl1
128
iexcl1
540
iexcl1
367
iexcl1
349
iexcl1
362
iexcl1
367
iexcl1
053
NA
Ave
rage
resp
irat
ion
rate
052
5iexcl
174
1iexcl
079
70
163
iexcl1
884
iexcl0
004
075
6iexcl
056
02
02
3iexcl
109
5iexcl
178
4iexcl
011
60
037
044
6iexcl
112
40
643
iexcl1
392
012
5iexcl
194
6iexcl
029
10
361
041
6iexcl
110
5iexcl
172
5
Ave
rage
resp
irat
ion
amp
litu
de
iexcl2
120
iexcl0
692
034
5iexcl
050
9iexcl
093
5iexcl
207
9iexcl
074
60
560
047
00
905
089
3iexcl
383
90
280
iexcl0
207
142
6iexcl
036
0iexcl
053
4iexcl
043
70
920
iexcl0
955
iexcl1
915
125
71
593
iexcl0
039
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
la M
XP
BR
LA
RS
CO
PV
EB
CLP
bM
XP
BR
LA
RS
CO
PV
EB
C
LP
c ED
U
SPU
Ent
ries
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
was
pre
sen
tin
eith
erth
eev
ent
orco
ntro
lfe
atu
reve
cto
rfo
rth
ep
arti
cula
rm
arke
t-ev
ent
typ
e
DE
V=
dev
iati
on
TR
V=
tren
d-r
ever
sal
VO
L=
vola
tilit
ySC
R=
skin
con
duc
tan
cere
spo
nses
H
R=
hea
rtra
te
BV
P=
bloo
dvo
lum
ep
uls
e
Lo and Repin 331
significant for both price and return deviations inGroup 4 which was not the case for the sample ofhigh-experience traders in Table 2 SCR was alsosignificant for volatility events in Group 4 which isconsistent with the central role that volatility plays inthe pricing and hedging derivative securities (Black ampScholes 1973 Merton 1973) Volatility is a moresophisticated aspect of the trading milieu requiringa higher degree of training to fully appreciate itsimplications for market trends and profit-and-lossprobabilities This may explain the fact that SCR issignificant for volatility events only among the high-experience traders in Table 2 and the derivativestraders in Table 4
DISCUSSION
Our findings suggest that emotional responses are asignificant factor in the real-time processing of financialrisks Contrary to the common belief that emotions haveno place in rational financial decision-making processesphysiological variables associated with the ANS exhibitsignificant changes during market events even for highlyexperienced professional traders Moreover the re-sponse patterns among variables and events differed inimportant ways for less experienced tradersmdashmeanautonomic responses were significantly highermdashsug-gesting the possibility of relating trading skills to certainphysiological characteristics that can be measured
More generally our experiments demonstrate thefeasibility of relating real-time quantitative changes incognitive inputs (financial information) to correspond-ing quantitative changes in physiological responses in acomplex field environment Despite the challenges ofsuch measurements a wealth of information can beobtained regarding high-pressure decision makingunder uncertainty Financial traders operate in a con-trolled environment where the inputs and outputs ofthe decisions are carefully recorded and where thesubjects are highly trained and provided with greateconomic incentives to make rational trading decisionsTherefore in vivo experiments in the securities tradingcontext are likely to become an important part of theempirical analysis of individual risk preferences anddecision-making processes In particular there is con-siderable anecdotal evidence that subjects involved inprofessional trading activities perform very differentlydepending on whether real financial capital is at risk or ifthey are trading with lsquolsquoplayrsquorsquo money Such distinctionshave been documented in other contexts eg it hasbeen shown that different brain regions are activatedduring a subjectrsquos naturally occurring smile and a forcedsmile (Damasio 1994) For this reason measuring sub-jects while they are making decisions in their naturalenvironment is essential for any truly unbiased study offinancial decision-making processes
In capturing relations between cognitive inputs andaffective reactions that are often subconscious and ofwhich subjects are not fully aware our findings may beviewed more broadly as a study of cognitive- emotionalinteractions and the genesis of lsquolsquointuitionrsquorsquo Decisionprocesses based on intuition are characterized by lowlevels of cognitive control low conscious awarenessrapid processing rates and a lack of clear organizingprinciples When intuitive judgments are formed largenumbers of cues are processed simultaneously and thetask is not decomposed into subtasks (HammondHamm Grassia amp Pearson 1987) Expertsrsquo judgmentsare often based on intuition not on explicit analyticalprocessing making it almost impossible to fully explainor replicate the process of how that judgment has beenformed This is particularly germane to financial trad-ersmdashas a group they are unusually heterogeneouswith respect to educational background and formalanalytical skills yet the most successful traders seemto trade based on their intuition about price swingsand market dynamics often without the ability (or theneed) to articulate a precise quantitative algorithm formaking these complex decisions (Niederhoffer 1997Schwager 1989 1992) Their intuitive trading lsquolsquorulesrsquorsquoare based on the associations and relations betweenvarious information tokens that are formed on a sub-conscious level and our findings and those in theextant cognitive sciences literature suggest that deci-sions based on the intuitive judgments require not onlycognitive but also emotional mechanisms A naturalconjecture is that such emotional mechanisms are atleast partly responsible for the ability to form intuitivejudgments and for those judgments to be incorporatedinto a rational decision-making process
Our results may surprise some financial economistsbecause of the apparent inconsistency with marketrationality but a more sophisticated view of the role ofemotion in human cognition (Rolls 1990 1994 1999)can reconcile any contradiction in a complete andintellectually satisfying manner Emotion is the basisfor a reward-and-punishment system that facilitates theselection of advantageous behavioral actions providingthe numeraire for animals to engage in a lsquolsquocost- benefitanalysisrsquorsquo of the various actions open to them (Rolls1999 Chap 103) From an evolutionary perspectiveemotion is a powerful adaptation that dramatically im-proves the efficiency with which animals learn from theirenvironment and their past
These evolutionary underpinnings are more thansimple speculation in the context of financial tradersThe extraordinary degree of competitiveness of globalfinancial markets and the outsize rewards that accrue tothe lsquolsquofittestrsquorsquo traders suggest that Darwinian selectionmdashfinancial selection to be specificmdashis at work in deter-mining the typical profile of the successful trader Afterall unsuccessful traders are generally lsquolsquoeliminatedrsquorsquo fromthe population after suffering a certain level of losses
332 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Our results indicate that emotion is a significant deter-minant of the evolutionary fitness of financial tradersWe hope to investigate this conjecture more formally inthe future in several ways a comparison between tradersand a control group of subjects without trading experi-ence or with unsuccessful trading experiences an anal-ysis of the psychophysiological responses of traders in alaboratory environment a more direct analysis of thecorrelation between autonomic responses and tradingperformance a more fundamental analysis of the neuralbasis of emotion in traders aimed at the function of theamygdala and the orbitofrontal cortex (Rolls 1992 1999Chap 44- 45) and a direct mapping of the neuralcenters for trading activity through functional magneticresonance imaging (Breiter et al 2001)
There are a number of caveats that must be kept inmind in interpreting our findings First because of thesmall sample size of 10 subjects our results are at bestsuggestive and promising not conclusive A more com-prehensive study with a larger and more diverse sampleof traders a more refined set of market events and abroader set of controls are necessary before coming toany firm conclusions regarding the precise mechanismsof the psychophysiology of financial risk processing andthe role of emotion in market efficiency
Second while we have documented the statisticalsignificance of autonomic responses during marketevents relative to control baselines we have not estab-lished specific causal factors for such responses Manycognitive tasks generate autonomic effects hence amarked change in any one psychophysiological indexmay be due to changes in cognitive processing forexample complex numerical computations rather thanmore fundamental affective responses For examplemore experienced traders in our sample may have dis-played lower autonomic response levels because theyrequired less cognitive effort to perform the same func-tions as less experienced traders which may have nothingto do with emotion Without a more targeted set of tasksor additional data on the characteristics of the subjects itis difficult to distinguish between the two One way toaddress this issue is to correlate a subjectrsquos autonomicresponses with his or her trading performance so as todevelop a more complete profile of the relation betweenpsychophysiological indices and the dynamics of thetrading process However because trading performanceis generally summarized by multiple characteristicsmdashtotal profit-and-loss volatility maximum drawdownSharpe ratio and so onmdashestimating a relation betweenautonomic responses and such characteristics may re-quire significantly larger sample sizes and longer obser-vation windows due to the lsquolsquocurse of dimensionalityrsquorsquo Itmay be possible to overcome this challenge to somedegree through a more carefully crafted experimentaldesign in which specific emotional responses are pairedwith specific trading performance measures namelyanxiety levels during consecutive sequences of losing
trades With a larger sample size we can also begin totrack groups of traders over a period of time and throughvarying market conditions By measuring their autonomicresponse profiles at different stages of their careers itmay be possible to determine more directly the role ofemotion in financial risk processing
Finally a much larger set of controls should accompanya larger sample of traders These controls should includepsychophysiological measurements for professional trad-ers taken outside their natural trading environmentmdashideally in a laboratory setting that is identical for alltradersmdashas well as corresponding measurements forsubjects with little or no trading experience in bothtrading and nontrading environments Along with psy-chophysiological data more traditional personality in-ventory data for example MMPI or Myers- Briggs mayprovide additional insights into the psychology of trading
We hope to address each of these issues in ourongoing research program to better understand thecognitive processes underlying financial risk processing
METHODS
Subjects
The 10 subjectsrsquo descriptive characteristics are summar-ized in Table 5 Based on discussions with their super-visors the traders were categorized into three levels ofexperience low moderate and high Five traders spe-cialized in handling client order flow (Retail) threespecialized in trading foreign exchange (FX) and twospecialized in interest-rate derivative securities (Deriva-tives) The durations of the sessions ranged from 49 to83 min and all sessions were held during live tradinghours typically between 8 am and 5 pm easterndaylight time
Physiological Data Collection
A ProComp+ data-acquisition unit and Biograph (Ver-sion 12) biofeedback software from Thought Technol-ogy were used to measure physiological data for allsubjects All six sensors were connected to a smallcontrol unit with a battery power supply which wasplaced on each subjectrsquos belt and from which a fiber-optic connection led to a laptop computer equippedwith real-time data acquisition software (see Figure 2)Each sensor was equipped with a built-in notch filter at60 Hz for automatic elimination of external power linenoise and standard AgCl triode and single electrodeswere used for SCR and EMG sensors respectively Thesampling rate for all data collection was fixed at 32 HzAll physiological data except for respiration and facialEMG were collected from each subjectrsquos nondominantarm SCR electrodes were placed on the palmar sites theBVP photoplesymographic sensor was placed on theinside of the ring or middle finger the arm EMG triodeelectrode was placed on the inside surface of the
Lo and Repin 333
forearm over the flexor digitorum muscle group andthe temperature sensor was inserted between the elasticband placed around the wrist and the skin surface Thefacial EMG electrode was placed on a masseter musclewhich controls jaw movement and is active duringspeech or any other activity involving the jaw Therespiration signal was measured by chest expansionusing a sensor attached to an elastic band placed aroundthe subjectrsquos chest An example of the real-time physio-logical data collected over a 2-min interval for onesubject is given in Figure 3
The entire procedure of outfitting each subject withsensors and connecting the sensors to the laptop re-quired approximately 5 min and was often performedeither before the trading day began or during relativelycalm trading periods Subjects indicated that the pres-
ence of the sensors wires and a control unit did notcompromise or influence their trading in any significantmanner and that their workflow was not impaired in anyway This was verified not only by the subjects but alsoby their supervisors Given the magnitudes of the finan-cial transactions that were being processed and theeconomic and legal responsibilities that the subjectsand their supervisors bore even the slightest interfer-ence with the subjectsrsquo workflow or performance stand-ards would have caused the supervisors or the subjectsto terminate the sessions immediately None of thesessions were terminated prematurely
Physiological Data Feature Extraction
An initial smoothing of the raw EMG signals (sampled at32 Hz) was performed with a moving-average filter oforder 23 If the level of the filtered forearm EMG signalexceeded a threshold of 075 mV both SCR and BVPreadings at this time were discarded because of the highprobability of artifacts for example typing graspingtelephone handsets or inadvertent physical disturban-ces to the sensors Similarly if the level of the filteredfacial EMG signal exceeded 075 mV the respirationsignal at this time was excluded from further processingA very small spatial displacement of the sensor or theelectrode was able to produce a different kind ofartifactmdashan abrupt change in the signal of the orderof 10- 20 standard deviations within 1
32 of a second Suchjumps did not have any physiological meaning hencethey were excluded from further analysis via adaptivethresholding Specifically our adaptive thresholdingprocedure involved marking all observations that
Table 5 Summary Statistics for All Subjects Individual Traderrsquos Characteristics Specialty Type and Number of Market Time-Series Collected During the Session Session Duration and Absolute Time (Eastern Standard Time) of the Start of the Session
TraderID Gender Experience Specialty Market data Available
Nu m ber of MarketTim e-Series Recorded
Session Du ration(m in and sec)
SessionStart Tim e
B33 F high retail major currencies 2 4903000 1320
B34 M high FX major currencies 3 8303200 0856
B35 M high retail major currencies 4 6603000 1102
B36 M high derivatives SampP500 futureseurodollar futures
3 7901600 1255
B37 M moderate FX Latin Americanmajor currencies
9 7000600 0917
B38 M low FX Latin Americanmajor currencies
3 7201200 1102
B39 M high derivatives SampP 500 futureseurodollar futures
9 6200300 0819
B310 M low retail major currencies 7 6000800 0947
B311 M moderate retail major currencies 7 5402500 1132
B312 M low retail major currencies 7 5900000 0910
ProC om p+
Facial EMG Triode electrode measures
activity of the masseter muscle (detects speech)
Respiration sensor Elastic strap measures
chest expansion
Forearm EMG Triode electrode measures activity of the flexor digitorum muscle group (detects finger and arm movement)
Temperature sensor - thermistor
SCR sensor and electrodes
BVP photoplesymographicsensor
Control unit Fiber optic cable to a
data acquisition laptop
Figure 2 Placement of sensors for measuring physiologicalresponses
334 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
Tab
le3
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
othe
sis
Tes
tsof
the
Diff
eren
cein
Mea
nA
uton
omic
Resp
onse
sD
urin
gPr
e-an
dPo
st-e
vent
Tim
eIn
terv
als
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pone
nts
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chT
ype
ofM
arke
tEv
ent
(Col
umns
)
Ma
rket
Eve
nts
Pre
-eve
nt
Inte
rva
lP
ost-
even
tIn
terv
al
Phy
sio
logy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mbe
rof
SCR
resp
ons
es3
75
61
543
25
40
21
18
24
01
057
92
87
20
604
20
88
16
64
063
60
579
Aver
age
SCR
amp
litu
de0
700
iexcl0
454
iexcl1
793
051
11
068
088
0iexcl
069
50
887
018
6iexcl
247
81
136
088
0
Aver
age
HR
096
8iexcl
020
5iexcl
055
2iexcl
066
3iexcl
038
6iexcl
027
00
905
iexcl0
588
iexcl0
596
iexcl0
617
103
2iexcl
027
0
Aver
age
BVP
amp
litud
ere
lativ
eto
loca
lba
selin
e0
043
iexcl0
895
006
00
814
iexcl0
045
36
19
012
8iexcl
085
50
688
140
8iexcl
052
33
61
9
Aver
age
BVP
amp
litud
ere
lativ
eto
glob
alba
selin
e1
183
iexcl2
794
23
97
131
7iexcl
225
62
88
61
417
iexcl2
726
23
24
141
7iexcl
221
12
88
6
Nu
mbe
rof
tem
per
atur
eju
mp
s1
110
27
67
050
52
15
83
92
5iexcl
248
20
988
28
20
065
01
77
74
86
8iexcl
248
2
Aver
age
resp
irat
ion
rate
126
1iexcl
165
0iexcl
025
41
303
iexcl1
999
iexcl0
064
072
9iexcl
117
30
671
115
5iexcl
162
8iexcl
006
4
Aver
age
resp
irat
ion
amp
litud
eiexcl
162
0iexcl
006
70
561
006
8iexcl
180
7iexcl
349
9iexcl
164
6iexcl
025
00
136
003
4iexcl
148
7iexcl
349
9
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
l
Th
ele
ftp
anel
cont
ain
st
stat
isti
csfo
rp
re-e
vent
feat
ure
vect
ors
and
the
righ
tp
anel
con
tain
st
stat
isti
csfo
rp
ost-
even
tfe
atu
reve
cto
rs
both
test
edag
ain
stth
esa
me
set
of
con
tro
ls
DEV
=d
evia
tion
T
RV
=tr
end-
reve
rsal
V
OL
=vo
lati
lity
SCR
=sk
inco
ndu
ctan
cere
spon
ses
HR
=h
eart
rate
B
VP
=bl
ood
volu
me
pu
lse
330 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le4
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
oth
esis
Tes
tsof
the
Diff
eren
cein
Mea
nAu
tono
mic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pon
ents
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chTy
pe
ofM
arke
tEv
ent
(Col
umns
)G
roup
edIn
toFo
urD
istin
ctSe
tsof
Fina
ncia
lIn
stru
men
ts
Ma
rket
Eve
nts
Gro
up
1E
UR
an
dJP
YG
rou
p2
Oth
erM
ajo
rC
urr
enci
esa
Gro
up
3La
tin
Am
eric
an
Cu
rren
cies
bG
rou
p4
Der
iva
tive
sc
Phy
siol
ogy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
mD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mb
erof
SCR
resp
on
ses
16
53
iexcl1
902
19
64
iexcl0
554
iexcl2
286
iexcl0
940
033
20
112
054
4iexcl
179
5iexcl
241
7iexcl
110
82
49
82
18
8iexcl
039
72
99
5iexcl
040
50
898
26
82
109
41
66
91
594
iexcl0
239
22
36
Ave
rage
SCR
amp
litu
de
iexcl1
639
062
30
279
iexcl1
756
109
8iexcl
111
2iexcl
050
30
540
113
8iexcl
219
0iexcl
010
60
896
111
61
279
iexcl1
525
iexcl1
802
22
20
iexcl0
986
iexcl0
610
123
60
930
iexcl1
513
024
2iexcl
151
0
Ave
rage
HR
073
1iexcl
000
4iexcl
126
8iexcl
181
5iexcl
069
8iexcl
161
2iexcl
099
1iexcl
145
2iexcl
103
4iexcl
132
6iexcl
030
8iexcl
108
92
92
1iexcl
143
2iexcl
107
0iexcl
100
2iexcl
141
61
183
081
6iexcl
032
20
577
049
20
515
iexcl0
169
Ave
rage
BV
Pam
plit
ude
rela
tive
to
loca
lb
asel
ine
056
2iexcl
136
8iexcl
080
51
273
088
52
58
7iexcl
092
20
220
145
72
32
3iexcl
127
52
30
8iexcl
098
2iexcl
000
12
82
81
454
059
82
19
2iexcl
059
2iexcl
303
2iexcl
395
3iexcl
089
4iexcl
190
4iexcl
143
3
Ave
rage
BV
Pam
plit
ude
rela
tive
tolo
cal
bas
elin
e
059
7iexcl
301
01
71
40
375
iexcl0
252
25
92
052
11
448
097
23
08
7iexcl
149
92
63
3iexcl
192
5iexcl
084
91
482
025
9iexcl
035
1iexcl
012
9iexcl
066
0iexcl
150
1iexcl
327
4iexcl
162
6iexcl
200
4iexcl
241
1
Nu
mb
erof
tem
per
atu
reju
mp
s
068
84
14
1iexcl
144
42
28
30
295
iexcl2
043
107
3iexcl
155
72
40
41
248
37
26
iexcl1
564
iexcl1
603
iexcl1
275
iexcl1
922
iexcl0
512
iexcl1
128
iexcl1
540
iexcl1
367
iexcl1
349
iexcl1
362
iexcl1
367
iexcl1
053
NA
Ave
rage
resp
irat
ion
rate
052
5iexcl
174
1iexcl
079
70
163
iexcl1
884
iexcl0
004
075
6iexcl
056
02
02
3iexcl
109
5iexcl
178
4iexcl
011
60
037
044
6iexcl
112
40
643
iexcl1
392
012
5iexcl
194
6iexcl
029
10
361
041
6iexcl
110
5iexcl
172
5
Ave
rage
resp
irat
ion
amp
litu
de
iexcl2
120
iexcl0
692
034
5iexcl
050
9iexcl
093
5iexcl
207
9iexcl
074
60
560
047
00
905
089
3iexcl
383
90
280
iexcl0
207
142
6iexcl
036
0iexcl
053
4iexcl
043
70
920
iexcl0
955
iexcl1
915
125
71
593
iexcl0
039
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
la M
XP
BR
LA
RS
CO
PV
EB
CLP
bM
XP
BR
LA
RS
CO
PV
EB
C
LP
c ED
U
SPU
Ent
ries
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
was
pre
sen
tin
eith
erth
eev
ent
orco
ntro
lfe
atu
reve
cto
rfo
rth
ep
arti
cula
rm
arke
t-ev
ent
typ
e
DE
V=
dev
iati
on
TR
V=
tren
d-r
ever
sal
VO
L=
vola
tilit
ySC
R=
skin
con
duc
tan
cere
spo
nses
H
R=
hea
rtra
te
BV
P=
bloo
dvo
lum
ep
uls
e
Lo and Repin 331
significant for both price and return deviations inGroup 4 which was not the case for the sample ofhigh-experience traders in Table 2 SCR was alsosignificant for volatility events in Group 4 which isconsistent with the central role that volatility plays inthe pricing and hedging derivative securities (Black ampScholes 1973 Merton 1973) Volatility is a moresophisticated aspect of the trading milieu requiringa higher degree of training to fully appreciate itsimplications for market trends and profit-and-lossprobabilities This may explain the fact that SCR issignificant for volatility events only among the high-experience traders in Table 2 and the derivativestraders in Table 4
DISCUSSION
Our findings suggest that emotional responses are asignificant factor in the real-time processing of financialrisks Contrary to the common belief that emotions haveno place in rational financial decision-making processesphysiological variables associated with the ANS exhibitsignificant changes during market events even for highlyexperienced professional traders Moreover the re-sponse patterns among variables and events differed inimportant ways for less experienced tradersmdashmeanautonomic responses were significantly highermdashsug-gesting the possibility of relating trading skills to certainphysiological characteristics that can be measured
More generally our experiments demonstrate thefeasibility of relating real-time quantitative changes incognitive inputs (financial information) to correspond-ing quantitative changes in physiological responses in acomplex field environment Despite the challenges ofsuch measurements a wealth of information can beobtained regarding high-pressure decision makingunder uncertainty Financial traders operate in a con-trolled environment where the inputs and outputs ofthe decisions are carefully recorded and where thesubjects are highly trained and provided with greateconomic incentives to make rational trading decisionsTherefore in vivo experiments in the securities tradingcontext are likely to become an important part of theempirical analysis of individual risk preferences anddecision-making processes In particular there is con-siderable anecdotal evidence that subjects involved inprofessional trading activities perform very differentlydepending on whether real financial capital is at risk or ifthey are trading with lsquolsquoplayrsquorsquo money Such distinctionshave been documented in other contexts eg it hasbeen shown that different brain regions are activatedduring a subjectrsquos naturally occurring smile and a forcedsmile (Damasio 1994) For this reason measuring sub-jects while they are making decisions in their naturalenvironment is essential for any truly unbiased study offinancial decision-making processes
In capturing relations between cognitive inputs andaffective reactions that are often subconscious and ofwhich subjects are not fully aware our findings may beviewed more broadly as a study of cognitive- emotionalinteractions and the genesis of lsquolsquointuitionrsquorsquo Decisionprocesses based on intuition are characterized by lowlevels of cognitive control low conscious awarenessrapid processing rates and a lack of clear organizingprinciples When intuitive judgments are formed largenumbers of cues are processed simultaneously and thetask is not decomposed into subtasks (HammondHamm Grassia amp Pearson 1987) Expertsrsquo judgmentsare often based on intuition not on explicit analyticalprocessing making it almost impossible to fully explainor replicate the process of how that judgment has beenformed This is particularly germane to financial trad-ersmdashas a group they are unusually heterogeneouswith respect to educational background and formalanalytical skills yet the most successful traders seemto trade based on their intuition about price swingsand market dynamics often without the ability (or theneed) to articulate a precise quantitative algorithm formaking these complex decisions (Niederhoffer 1997Schwager 1989 1992) Their intuitive trading lsquolsquorulesrsquorsquoare based on the associations and relations betweenvarious information tokens that are formed on a sub-conscious level and our findings and those in theextant cognitive sciences literature suggest that deci-sions based on the intuitive judgments require not onlycognitive but also emotional mechanisms A naturalconjecture is that such emotional mechanisms are atleast partly responsible for the ability to form intuitivejudgments and for those judgments to be incorporatedinto a rational decision-making process
Our results may surprise some financial economistsbecause of the apparent inconsistency with marketrationality but a more sophisticated view of the role ofemotion in human cognition (Rolls 1990 1994 1999)can reconcile any contradiction in a complete andintellectually satisfying manner Emotion is the basisfor a reward-and-punishment system that facilitates theselection of advantageous behavioral actions providingthe numeraire for animals to engage in a lsquolsquocost- benefitanalysisrsquorsquo of the various actions open to them (Rolls1999 Chap 103) From an evolutionary perspectiveemotion is a powerful adaptation that dramatically im-proves the efficiency with which animals learn from theirenvironment and their past
These evolutionary underpinnings are more thansimple speculation in the context of financial tradersThe extraordinary degree of competitiveness of globalfinancial markets and the outsize rewards that accrue tothe lsquolsquofittestrsquorsquo traders suggest that Darwinian selectionmdashfinancial selection to be specificmdashis at work in deter-mining the typical profile of the successful trader Afterall unsuccessful traders are generally lsquolsquoeliminatedrsquorsquo fromthe population after suffering a certain level of losses
332 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Our results indicate that emotion is a significant deter-minant of the evolutionary fitness of financial tradersWe hope to investigate this conjecture more formally inthe future in several ways a comparison between tradersand a control group of subjects without trading experi-ence or with unsuccessful trading experiences an anal-ysis of the psychophysiological responses of traders in alaboratory environment a more direct analysis of thecorrelation between autonomic responses and tradingperformance a more fundamental analysis of the neuralbasis of emotion in traders aimed at the function of theamygdala and the orbitofrontal cortex (Rolls 1992 1999Chap 44- 45) and a direct mapping of the neuralcenters for trading activity through functional magneticresonance imaging (Breiter et al 2001)
There are a number of caveats that must be kept inmind in interpreting our findings First because of thesmall sample size of 10 subjects our results are at bestsuggestive and promising not conclusive A more com-prehensive study with a larger and more diverse sampleof traders a more refined set of market events and abroader set of controls are necessary before coming toany firm conclusions regarding the precise mechanismsof the psychophysiology of financial risk processing andthe role of emotion in market efficiency
Second while we have documented the statisticalsignificance of autonomic responses during marketevents relative to control baselines we have not estab-lished specific causal factors for such responses Manycognitive tasks generate autonomic effects hence amarked change in any one psychophysiological indexmay be due to changes in cognitive processing forexample complex numerical computations rather thanmore fundamental affective responses For examplemore experienced traders in our sample may have dis-played lower autonomic response levels because theyrequired less cognitive effort to perform the same func-tions as less experienced traders which may have nothingto do with emotion Without a more targeted set of tasksor additional data on the characteristics of the subjects itis difficult to distinguish between the two One way toaddress this issue is to correlate a subjectrsquos autonomicresponses with his or her trading performance so as todevelop a more complete profile of the relation betweenpsychophysiological indices and the dynamics of thetrading process However because trading performanceis generally summarized by multiple characteristicsmdashtotal profit-and-loss volatility maximum drawdownSharpe ratio and so onmdashestimating a relation betweenautonomic responses and such characteristics may re-quire significantly larger sample sizes and longer obser-vation windows due to the lsquolsquocurse of dimensionalityrsquorsquo Itmay be possible to overcome this challenge to somedegree through a more carefully crafted experimentaldesign in which specific emotional responses are pairedwith specific trading performance measures namelyanxiety levels during consecutive sequences of losing
trades With a larger sample size we can also begin totrack groups of traders over a period of time and throughvarying market conditions By measuring their autonomicresponse profiles at different stages of their careers itmay be possible to determine more directly the role ofemotion in financial risk processing
Finally a much larger set of controls should accompanya larger sample of traders These controls should includepsychophysiological measurements for professional trad-ers taken outside their natural trading environmentmdashideally in a laboratory setting that is identical for alltradersmdashas well as corresponding measurements forsubjects with little or no trading experience in bothtrading and nontrading environments Along with psy-chophysiological data more traditional personality in-ventory data for example MMPI or Myers- Briggs mayprovide additional insights into the psychology of trading
We hope to address each of these issues in ourongoing research program to better understand thecognitive processes underlying financial risk processing
METHODS
Subjects
The 10 subjectsrsquo descriptive characteristics are summar-ized in Table 5 Based on discussions with their super-visors the traders were categorized into three levels ofexperience low moderate and high Five traders spe-cialized in handling client order flow (Retail) threespecialized in trading foreign exchange (FX) and twospecialized in interest-rate derivative securities (Deriva-tives) The durations of the sessions ranged from 49 to83 min and all sessions were held during live tradinghours typically between 8 am and 5 pm easterndaylight time
Physiological Data Collection
A ProComp+ data-acquisition unit and Biograph (Ver-sion 12) biofeedback software from Thought Technol-ogy were used to measure physiological data for allsubjects All six sensors were connected to a smallcontrol unit with a battery power supply which wasplaced on each subjectrsquos belt and from which a fiber-optic connection led to a laptop computer equippedwith real-time data acquisition software (see Figure 2)Each sensor was equipped with a built-in notch filter at60 Hz for automatic elimination of external power linenoise and standard AgCl triode and single electrodeswere used for SCR and EMG sensors respectively Thesampling rate for all data collection was fixed at 32 HzAll physiological data except for respiration and facialEMG were collected from each subjectrsquos nondominantarm SCR electrodes were placed on the palmar sites theBVP photoplesymographic sensor was placed on theinside of the ring or middle finger the arm EMG triodeelectrode was placed on the inside surface of the
Lo and Repin 333
forearm over the flexor digitorum muscle group andthe temperature sensor was inserted between the elasticband placed around the wrist and the skin surface Thefacial EMG electrode was placed on a masseter musclewhich controls jaw movement and is active duringspeech or any other activity involving the jaw Therespiration signal was measured by chest expansionusing a sensor attached to an elastic band placed aroundthe subjectrsquos chest An example of the real-time physio-logical data collected over a 2-min interval for onesubject is given in Figure 3
The entire procedure of outfitting each subject withsensors and connecting the sensors to the laptop re-quired approximately 5 min and was often performedeither before the trading day began or during relativelycalm trading periods Subjects indicated that the pres-
ence of the sensors wires and a control unit did notcompromise or influence their trading in any significantmanner and that their workflow was not impaired in anyway This was verified not only by the subjects but alsoby their supervisors Given the magnitudes of the finan-cial transactions that were being processed and theeconomic and legal responsibilities that the subjectsand their supervisors bore even the slightest interfer-ence with the subjectsrsquo workflow or performance stand-ards would have caused the supervisors or the subjectsto terminate the sessions immediately None of thesessions were terminated prematurely
Physiological Data Feature Extraction
An initial smoothing of the raw EMG signals (sampled at32 Hz) was performed with a moving-average filter oforder 23 If the level of the filtered forearm EMG signalexceeded a threshold of 075 mV both SCR and BVPreadings at this time were discarded because of the highprobability of artifacts for example typing graspingtelephone handsets or inadvertent physical disturban-ces to the sensors Similarly if the level of the filteredfacial EMG signal exceeded 075 mV the respirationsignal at this time was excluded from further processingA very small spatial displacement of the sensor or theelectrode was able to produce a different kind ofartifactmdashan abrupt change in the signal of the orderof 10- 20 standard deviations within 1
32 of a second Suchjumps did not have any physiological meaning hencethey were excluded from further analysis via adaptivethresholding Specifically our adaptive thresholdingprocedure involved marking all observations that
Table 5 Summary Statistics for All Subjects Individual Traderrsquos Characteristics Specialty Type and Number of Market Time-Series Collected During the Session Session Duration and Absolute Time (Eastern Standard Time) of the Start of the Session
TraderID Gender Experience Specialty Market data Available
Nu m ber of MarketTim e-Series Recorded
Session Du ration(m in and sec)
SessionStart Tim e
B33 F high retail major currencies 2 4903000 1320
B34 M high FX major currencies 3 8303200 0856
B35 M high retail major currencies 4 6603000 1102
B36 M high derivatives SampP500 futureseurodollar futures
3 7901600 1255
B37 M moderate FX Latin Americanmajor currencies
9 7000600 0917
B38 M low FX Latin Americanmajor currencies
3 7201200 1102
B39 M high derivatives SampP 500 futureseurodollar futures
9 6200300 0819
B310 M low retail major currencies 7 6000800 0947
B311 M moderate retail major currencies 7 5402500 1132
B312 M low retail major currencies 7 5900000 0910
ProC om p+
Facial EMG Triode electrode measures
activity of the masseter muscle (detects speech)
Respiration sensor Elastic strap measures
chest expansion
Forearm EMG Triode electrode measures activity of the flexor digitorum muscle group (detects finger and arm movement)
Temperature sensor - thermistor
SCR sensor and electrodes
BVP photoplesymographicsensor
Control unit Fiber optic cable to a
data acquisition laptop
Figure 2 Placement of sensors for measuring physiologicalresponses
334 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
Tab
le4
tSt
atis
tics
for
Tw
o-Si
ded
Hyp
oth
esis
Tes
tsof
the
Diff
eren
cein
Mea
nAu
tono
mic
Resp
onse
sD
urin
gM
arke
tEv
ents
Ver
sus
No-
Even
tC
ontr
ols
for
Each
ofth
eEi
ght
Com
pon
ents
ofth
ePh
ysio
logy
Feat
ure
Vect
ors
(Row
s)fo
rEa
chTy
pe
ofM
arke
tEv
ent
(Col
umns
)G
roup
edIn
toFo
urD
istin
ctSe
tsof
Fina
ncia
lIn
stru
men
ts
Ma
rket
Eve
nts
Gro
up
1E
UR
an
dJP
YG
rou
p2
Oth
erM
ajo
rC
urr
enci
esa
Gro
up
3La
tin
Am
eric
an
Cu
rren
cies
bG
rou
p4
Der
iva
tive
sc
Phy
siol
ogy
Fea
ture
sD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
mD
EV
P
rice
DE
V
Spre
ad
DE
V
Ret
urn
TR
V
Pri
ceT
RV
Sp
rea
dV
OL
Ma
xim
um
DE
V
Pri
ceD
EV
Sp
rea
dD
EV
R
etu
rnT
RV
P
rice
TR
V
Spre
ad
VO
LM
axi
mu
m
Nu
mb
erof
SCR
resp
on
ses
16
53
iexcl1
902
19
64
iexcl0
554
iexcl2
286
iexcl0
940
033
20
112
054
4iexcl
179
5iexcl
241
7iexcl
110
82
49
82
18
8iexcl
039
72
99
5iexcl
040
50
898
26
82
109
41
66
91
594
iexcl0
239
22
36
Ave
rage
SCR
amp
litu
de
iexcl1
639
062
30
279
iexcl1
756
109
8iexcl
111
2iexcl
050
30
540
113
8iexcl
219
0iexcl
010
60
896
111
61
279
iexcl1
525
iexcl1
802
22
20
iexcl0
986
iexcl0
610
123
60
930
iexcl1
513
024
2iexcl
151
0
Ave
rage
HR
073
1iexcl
000
4iexcl
126
8iexcl
181
5iexcl
069
8iexcl
161
2iexcl
099
1iexcl
145
2iexcl
103
4iexcl
132
6iexcl
030
8iexcl
108
92
92
1iexcl
143
2iexcl
107
0iexcl
100
2iexcl
141
61
183
081
6iexcl
032
20
577
049
20
515
iexcl0
169
Ave
rage
BV
Pam
plit
ude
rela
tive
to
loca
lb
asel
ine
056
2iexcl
136
8iexcl
080
51
273
088
52
58
7iexcl
092
20
220
145
72
32
3iexcl
127
52
30
8iexcl
098
2iexcl
000
12
82
81
454
059
82
19
2iexcl
059
2iexcl
303
2iexcl
395
3iexcl
089
4iexcl
190
4iexcl
143
3
Ave
rage
BV
Pam
plit
ude
rela
tive
tolo
cal
bas
elin
e
059
7iexcl
301
01
71
40
375
iexcl0
252
25
92
052
11
448
097
23
08
7iexcl
149
92
63
3iexcl
192
5iexcl
084
91
482
025
9iexcl
035
1iexcl
012
9iexcl
066
0iexcl
150
1iexcl
327
4iexcl
162
6iexcl
200
4iexcl
241
1
Nu
mb
erof
tem
per
atu
reju
mp
s
068
84
14
1iexcl
144
42
28
30
295
iexcl2
043
107
3iexcl
155
72
40
41
248
37
26
iexcl1
564
iexcl1
603
iexcl1
275
iexcl1
922
iexcl0
512
iexcl1
128
iexcl1
540
iexcl1
367
iexcl1
349
iexcl1
362
iexcl1
367
iexcl1
053
NA
Ave
rage
resp
irat
ion
rate
052
5iexcl
174
1iexcl
079
70
163
iexcl1
884
iexcl0
004
075
6iexcl
056
02
02
3iexcl
109
5iexcl
178
4iexcl
011
60
037
044
6iexcl
112
40
643
iexcl1
392
012
5iexcl
194
6iexcl
029
10
361
041
6iexcl
110
5iexcl
172
5
Ave
rage
resp
irat
ion
amp
litu
de
iexcl2
120
iexcl0
692
034
5iexcl
050
9iexcl
093
5iexcl
207
9iexcl
074
60
560
047
00
905
089
3iexcl
383
90
280
iexcl0
207
142
6iexcl
036
0iexcl
053
4iexcl
043
70
920
iexcl0
955
iexcl1
915
125
71
593
iexcl0
039
Bo
lded
stat
isti
csar
esi
gnifi
cant
atth
e5
leve
la M
XP
BR
LA
RS
CO
PV
EB
CLP
bM
XP
BR
LA
RS
CO
PV
EB
C
LP
c ED
U
SPU
Ent
ries
mar
ked
lsquolsquoNA
rsquorsquoin
dic
ate
that
no
valid
data
was
pre
sen
tin
eith
erth
eev
ent
orco
ntro
lfe
atu
reve
cto
rfo
rth
ep
arti
cula
rm
arke
t-ev
ent
typ
e
DE
V=
dev
iati
on
TR
V=
tren
d-r
ever
sal
VO
L=
vola
tilit
ySC
R=
skin
con
duc
tan
cere
spo
nses
H
R=
hea
rtra
te
BV
P=
bloo
dvo
lum
ep
uls
e
Lo and Repin 331
significant for both price and return deviations inGroup 4 which was not the case for the sample ofhigh-experience traders in Table 2 SCR was alsosignificant for volatility events in Group 4 which isconsistent with the central role that volatility plays inthe pricing and hedging derivative securities (Black ampScholes 1973 Merton 1973) Volatility is a moresophisticated aspect of the trading milieu requiringa higher degree of training to fully appreciate itsimplications for market trends and profit-and-lossprobabilities This may explain the fact that SCR issignificant for volatility events only among the high-experience traders in Table 2 and the derivativestraders in Table 4
DISCUSSION
Our findings suggest that emotional responses are asignificant factor in the real-time processing of financialrisks Contrary to the common belief that emotions haveno place in rational financial decision-making processesphysiological variables associated with the ANS exhibitsignificant changes during market events even for highlyexperienced professional traders Moreover the re-sponse patterns among variables and events differed inimportant ways for less experienced tradersmdashmeanautonomic responses were significantly highermdashsug-gesting the possibility of relating trading skills to certainphysiological characteristics that can be measured
More generally our experiments demonstrate thefeasibility of relating real-time quantitative changes incognitive inputs (financial information) to correspond-ing quantitative changes in physiological responses in acomplex field environment Despite the challenges ofsuch measurements a wealth of information can beobtained regarding high-pressure decision makingunder uncertainty Financial traders operate in a con-trolled environment where the inputs and outputs ofthe decisions are carefully recorded and where thesubjects are highly trained and provided with greateconomic incentives to make rational trading decisionsTherefore in vivo experiments in the securities tradingcontext are likely to become an important part of theempirical analysis of individual risk preferences anddecision-making processes In particular there is con-siderable anecdotal evidence that subjects involved inprofessional trading activities perform very differentlydepending on whether real financial capital is at risk or ifthey are trading with lsquolsquoplayrsquorsquo money Such distinctionshave been documented in other contexts eg it hasbeen shown that different brain regions are activatedduring a subjectrsquos naturally occurring smile and a forcedsmile (Damasio 1994) For this reason measuring sub-jects while they are making decisions in their naturalenvironment is essential for any truly unbiased study offinancial decision-making processes
In capturing relations between cognitive inputs andaffective reactions that are often subconscious and ofwhich subjects are not fully aware our findings may beviewed more broadly as a study of cognitive- emotionalinteractions and the genesis of lsquolsquointuitionrsquorsquo Decisionprocesses based on intuition are characterized by lowlevels of cognitive control low conscious awarenessrapid processing rates and a lack of clear organizingprinciples When intuitive judgments are formed largenumbers of cues are processed simultaneously and thetask is not decomposed into subtasks (HammondHamm Grassia amp Pearson 1987) Expertsrsquo judgmentsare often based on intuition not on explicit analyticalprocessing making it almost impossible to fully explainor replicate the process of how that judgment has beenformed This is particularly germane to financial trad-ersmdashas a group they are unusually heterogeneouswith respect to educational background and formalanalytical skills yet the most successful traders seemto trade based on their intuition about price swingsand market dynamics often without the ability (or theneed) to articulate a precise quantitative algorithm formaking these complex decisions (Niederhoffer 1997Schwager 1989 1992) Their intuitive trading lsquolsquorulesrsquorsquoare based on the associations and relations betweenvarious information tokens that are formed on a sub-conscious level and our findings and those in theextant cognitive sciences literature suggest that deci-sions based on the intuitive judgments require not onlycognitive but also emotional mechanisms A naturalconjecture is that such emotional mechanisms are atleast partly responsible for the ability to form intuitivejudgments and for those judgments to be incorporatedinto a rational decision-making process
Our results may surprise some financial economistsbecause of the apparent inconsistency with marketrationality but a more sophisticated view of the role ofemotion in human cognition (Rolls 1990 1994 1999)can reconcile any contradiction in a complete andintellectually satisfying manner Emotion is the basisfor a reward-and-punishment system that facilitates theselection of advantageous behavioral actions providingthe numeraire for animals to engage in a lsquolsquocost- benefitanalysisrsquorsquo of the various actions open to them (Rolls1999 Chap 103) From an evolutionary perspectiveemotion is a powerful adaptation that dramatically im-proves the efficiency with which animals learn from theirenvironment and their past
These evolutionary underpinnings are more thansimple speculation in the context of financial tradersThe extraordinary degree of competitiveness of globalfinancial markets and the outsize rewards that accrue tothe lsquolsquofittestrsquorsquo traders suggest that Darwinian selectionmdashfinancial selection to be specificmdashis at work in deter-mining the typical profile of the successful trader Afterall unsuccessful traders are generally lsquolsquoeliminatedrsquorsquo fromthe population after suffering a certain level of losses
332 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Our results indicate that emotion is a significant deter-minant of the evolutionary fitness of financial tradersWe hope to investigate this conjecture more formally inthe future in several ways a comparison between tradersand a control group of subjects without trading experi-ence or with unsuccessful trading experiences an anal-ysis of the psychophysiological responses of traders in alaboratory environment a more direct analysis of thecorrelation between autonomic responses and tradingperformance a more fundamental analysis of the neuralbasis of emotion in traders aimed at the function of theamygdala and the orbitofrontal cortex (Rolls 1992 1999Chap 44- 45) and a direct mapping of the neuralcenters for trading activity through functional magneticresonance imaging (Breiter et al 2001)
There are a number of caveats that must be kept inmind in interpreting our findings First because of thesmall sample size of 10 subjects our results are at bestsuggestive and promising not conclusive A more com-prehensive study with a larger and more diverse sampleof traders a more refined set of market events and abroader set of controls are necessary before coming toany firm conclusions regarding the precise mechanismsof the psychophysiology of financial risk processing andthe role of emotion in market efficiency
Second while we have documented the statisticalsignificance of autonomic responses during marketevents relative to control baselines we have not estab-lished specific causal factors for such responses Manycognitive tasks generate autonomic effects hence amarked change in any one psychophysiological indexmay be due to changes in cognitive processing forexample complex numerical computations rather thanmore fundamental affective responses For examplemore experienced traders in our sample may have dis-played lower autonomic response levels because theyrequired less cognitive effort to perform the same func-tions as less experienced traders which may have nothingto do with emotion Without a more targeted set of tasksor additional data on the characteristics of the subjects itis difficult to distinguish between the two One way toaddress this issue is to correlate a subjectrsquos autonomicresponses with his or her trading performance so as todevelop a more complete profile of the relation betweenpsychophysiological indices and the dynamics of thetrading process However because trading performanceis generally summarized by multiple characteristicsmdashtotal profit-and-loss volatility maximum drawdownSharpe ratio and so onmdashestimating a relation betweenautonomic responses and such characteristics may re-quire significantly larger sample sizes and longer obser-vation windows due to the lsquolsquocurse of dimensionalityrsquorsquo Itmay be possible to overcome this challenge to somedegree through a more carefully crafted experimentaldesign in which specific emotional responses are pairedwith specific trading performance measures namelyanxiety levels during consecutive sequences of losing
trades With a larger sample size we can also begin totrack groups of traders over a period of time and throughvarying market conditions By measuring their autonomicresponse profiles at different stages of their careers itmay be possible to determine more directly the role ofemotion in financial risk processing
Finally a much larger set of controls should accompanya larger sample of traders These controls should includepsychophysiological measurements for professional trad-ers taken outside their natural trading environmentmdashideally in a laboratory setting that is identical for alltradersmdashas well as corresponding measurements forsubjects with little or no trading experience in bothtrading and nontrading environments Along with psy-chophysiological data more traditional personality in-ventory data for example MMPI or Myers- Briggs mayprovide additional insights into the psychology of trading
We hope to address each of these issues in ourongoing research program to better understand thecognitive processes underlying financial risk processing
METHODS
Subjects
The 10 subjectsrsquo descriptive characteristics are summar-ized in Table 5 Based on discussions with their super-visors the traders were categorized into three levels ofexperience low moderate and high Five traders spe-cialized in handling client order flow (Retail) threespecialized in trading foreign exchange (FX) and twospecialized in interest-rate derivative securities (Deriva-tives) The durations of the sessions ranged from 49 to83 min and all sessions were held during live tradinghours typically between 8 am and 5 pm easterndaylight time
Physiological Data Collection
A ProComp+ data-acquisition unit and Biograph (Ver-sion 12) biofeedback software from Thought Technol-ogy were used to measure physiological data for allsubjects All six sensors were connected to a smallcontrol unit with a battery power supply which wasplaced on each subjectrsquos belt and from which a fiber-optic connection led to a laptop computer equippedwith real-time data acquisition software (see Figure 2)Each sensor was equipped with a built-in notch filter at60 Hz for automatic elimination of external power linenoise and standard AgCl triode and single electrodeswere used for SCR and EMG sensors respectively Thesampling rate for all data collection was fixed at 32 HzAll physiological data except for respiration and facialEMG were collected from each subjectrsquos nondominantarm SCR electrodes were placed on the palmar sites theBVP photoplesymographic sensor was placed on theinside of the ring or middle finger the arm EMG triodeelectrode was placed on the inside surface of the
Lo and Repin 333
forearm over the flexor digitorum muscle group andthe temperature sensor was inserted between the elasticband placed around the wrist and the skin surface Thefacial EMG electrode was placed on a masseter musclewhich controls jaw movement and is active duringspeech or any other activity involving the jaw Therespiration signal was measured by chest expansionusing a sensor attached to an elastic band placed aroundthe subjectrsquos chest An example of the real-time physio-logical data collected over a 2-min interval for onesubject is given in Figure 3
The entire procedure of outfitting each subject withsensors and connecting the sensors to the laptop re-quired approximately 5 min and was often performedeither before the trading day began or during relativelycalm trading periods Subjects indicated that the pres-
ence of the sensors wires and a control unit did notcompromise or influence their trading in any significantmanner and that their workflow was not impaired in anyway This was verified not only by the subjects but alsoby their supervisors Given the magnitudes of the finan-cial transactions that were being processed and theeconomic and legal responsibilities that the subjectsand their supervisors bore even the slightest interfer-ence with the subjectsrsquo workflow or performance stand-ards would have caused the supervisors or the subjectsto terminate the sessions immediately None of thesessions were terminated prematurely
Physiological Data Feature Extraction
An initial smoothing of the raw EMG signals (sampled at32 Hz) was performed with a moving-average filter oforder 23 If the level of the filtered forearm EMG signalexceeded a threshold of 075 mV both SCR and BVPreadings at this time were discarded because of the highprobability of artifacts for example typing graspingtelephone handsets or inadvertent physical disturban-ces to the sensors Similarly if the level of the filteredfacial EMG signal exceeded 075 mV the respirationsignal at this time was excluded from further processingA very small spatial displacement of the sensor or theelectrode was able to produce a different kind ofartifactmdashan abrupt change in the signal of the orderof 10- 20 standard deviations within 1
32 of a second Suchjumps did not have any physiological meaning hencethey were excluded from further analysis via adaptivethresholding Specifically our adaptive thresholdingprocedure involved marking all observations that
Table 5 Summary Statistics for All Subjects Individual Traderrsquos Characteristics Specialty Type and Number of Market Time-Series Collected During the Session Session Duration and Absolute Time (Eastern Standard Time) of the Start of the Session
TraderID Gender Experience Specialty Market data Available
Nu m ber of MarketTim e-Series Recorded
Session Du ration(m in and sec)
SessionStart Tim e
B33 F high retail major currencies 2 4903000 1320
B34 M high FX major currencies 3 8303200 0856
B35 M high retail major currencies 4 6603000 1102
B36 M high derivatives SampP500 futureseurodollar futures
3 7901600 1255
B37 M moderate FX Latin Americanmajor currencies
9 7000600 0917
B38 M low FX Latin Americanmajor currencies
3 7201200 1102
B39 M high derivatives SampP 500 futureseurodollar futures
9 6200300 0819
B310 M low retail major currencies 7 6000800 0947
B311 M moderate retail major currencies 7 5402500 1132
B312 M low retail major currencies 7 5900000 0910
ProC om p+
Facial EMG Triode electrode measures
activity of the masseter muscle (detects speech)
Respiration sensor Elastic strap measures
chest expansion
Forearm EMG Triode electrode measures activity of the flexor digitorum muscle group (detects finger and arm movement)
Temperature sensor - thermistor
SCR sensor and electrodes
BVP photoplesymographicsensor
Control unit Fiber optic cable to a
data acquisition laptop
Figure 2 Placement of sensors for measuring physiologicalresponses
334 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
significant for both price and return deviations inGroup 4 which was not the case for the sample ofhigh-experience traders in Table 2 SCR was alsosignificant for volatility events in Group 4 which isconsistent with the central role that volatility plays inthe pricing and hedging derivative securities (Black ampScholes 1973 Merton 1973) Volatility is a moresophisticated aspect of the trading milieu requiringa higher degree of training to fully appreciate itsimplications for market trends and profit-and-lossprobabilities This may explain the fact that SCR issignificant for volatility events only among the high-experience traders in Table 2 and the derivativestraders in Table 4
DISCUSSION
Our findings suggest that emotional responses are asignificant factor in the real-time processing of financialrisks Contrary to the common belief that emotions haveno place in rational financial decision-making processesphysiological variables associated with the ANS exhibitsignificant changes during market events even for highlyexperienced professional traders Moreover the re-sponse patterns among variables and events differed inimportant ways for less experienced tradersmdashmeanautonomic responses were significantly highermdashsug-gesting the possibility of relating trading skills to certainphysiological characteristics that can be measured
More generally our experiments demonstrate thefeasibility of relating real-time quantitative changes incognitive inputs (financial information) to correspond-ing quantitative changes in physiological responses in acomplex field environment Despite the challenges ofsuch measurements a wealth of information can beobtained regarding high-pressure decision makingunder uncertainty Financial traders operate in a con-trolled environment where the inputs and outputs ofthe decisions are carefully recorded and where thesubjects are highly trained and provided with greateconomic incentives to make rational trading decisionsTherefore in vivo experiments in the securities tradingcontext are likely to become an important part of theempirical analysis of individual risk preferences anddecision-making processes In particular there is con-siderable anecdotal evidence that subjects involved inprofessional trading activities perform very differentlydepending on whether real financial capital is at risk or ifthey are trading with lsquolsquoplayrsquorsquo money Such distinctionshave been documented in other contexts eg it hasbeen shown that different brain regions are activatedduring a subjectrsquos naturally occurring smile and a forcedsmile (Damasio 1994) For this reason measuring sub-jects while they are making decisions in their naturalenvironment is essential for any truly unbiased study offinancial decision-making processes
In capturing relations between cognitive inputs andaffective reactions that are often subconscious and ofwhich subjects are not fully aware our findings may beviewed more broadly as a study of cognitive- emotionalinteractions and the genesis of lsquolsquointuitionrsquorsquo Decisionprocesses based on intuition are characterized by lowlevels of cognitive control low conscious awarenessrapid processing rates and a lack of clear organizingprinciples When intuitive judgments are formed largenumbers of cues are processed simultaneously and thetask is not decomposed into subtasks (HammondHamm Grassia amp Pearson 1987) Expertsrsquo judgmentsare often based on intuition not on explicit analyticalprocessing making it almost impossible to fully explainor replicate the process of how that judgment has beenformed This is particularly germane to financial trad-ersmdashas a group they are unusually heterogeneouswith respect to educational background and formalanalytical skills yet the most successful traders seemto trade based on their intuition about price swingsand market dynamics often without the ability (or theneed) to articulate a precise quantitative algorithm formaking these complex decisions (Niederhoffer 1997Schwager 1989 1992) Their intuitive trading lsquolsquorulesrsquorsquoare based on the associations and relations betweenvarious information tokens that are formed on a sub-conscious level and our findings and those in theextant cognitive sciences literature suggest that deci-sions based on the intuitive judgments require not onlycognitive but also emotional mechanisms A naturalconjecture is that such emotional mechanisms are atleast partly responsible for the ability to form intuitivejudgments and for those judgments to be incorporatedinto a rational decision-making process
Our results may surprise some financial economistsbecause of the apparent inconsistency with marketrationality but a more sophisticated view of the role ofemotion in human cognition (Rolls 1990 1994 1999)can reconcile any contradiction in a complete andintellectually satisfying manner Emotion is the basisfor a reward-and-punishment system that facilitates theselection of advantageous behavioral actions providingthe numeraire for animals to engage in a lsquolsquocost- benefitanalysisrsquorsquo of the various actions open to them (Rolls1999 Chap 103) From an evolutionary perspectiveemotion is a powerful adaptation that dramatically im-proves the efficiency with which animals learn from theirenvironment and their past
These evolutionary underpinnings are more thansimple speculation in the context of financial tradersThe extraordinary degree of competitiveness of globalfinancial markets and the outsize rewards that accrue tothe lsquolsquofittestrsquorsquo traders suggest that Darwinian selectionmdashfinancial selection to be specificmdashis at work in deter-mining the typical profile of the successful trader Afterall unsuccessful traders are generally lsquolsquoeliminatedrsquorsquo fromthe population after suffering a certain level of losses
332 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Our results indicate that emotion is a significant deter-minant of the evolutionary fitness of financial tradersWe hope to investigate this conjecture more formally inthe future in several ways a comparison between tradersand a control group of subjects without trading experi-ence or with unsuccessful trading experiences an anal-ysis of the psychophysiological responses of traders in alaboratory environment a more direct analysis of thecorrelation between autonomic responses and tradingperformance a more fundamental analysis of the neuralbasis of emotion in traders aimed at the function of theamygdala and the orbitofrontal cortex (Rolls 1992 1999Chap 44- 45) and a direct mapping of the neuralcenters for trading activity through functional magneticresonance imaging (Breiter et al 2001)
There are a number of caveats that must be kept inmind in interpreting our findings First because of thesmall sample size of 10 subjects our results are at bestsuggestive and promising not conclusive A more com-prehensive study with a larger and more diverse sampleof traders a more refined set of market events and abroader set of controls are necessary before coming toany firm conclusions regarding the precise mechanismsof the psychophysiology of financial risk processing andthe role of emotion in market efficiency
Second while we have documented the statisticalsignificance of autonomic responses during marketevents relative to control baselines we have not estab-lished specific causal factors for such responses Manycognitive tasks generate autonomic effects hence amarked change in any one psychophysiological indexmay be due to changes in cognitive processing forexample complex numerical computations rather thanmore fundamental affective responses For examplemore experienced traders in our sample may have dis-played lower autonomic response levels because theyrequired less cognitive effort to perform the same func-tions as less experienced traders which may have nothingto do with emotion Without a more targeted set of tasksor additional data on the characteristics of the subjects itis difficult to distinguish between the two One way toaddress this issue is to correlate a subjectrsquos autonomicresponses with his or her trading performance so as todevelop a more complete profile of the relation betweenpsychophysiological indices and the dynamics of thetrading process However because trading performanceis generally summarized by multiple characteristicsmdashtotal profit-and-loss volatility maximum drawdownSharpe ratio and so onmdashestimating a relation betweenautonomic responses and such characteristics may re-quire significantly larger sample sizes and longer obser-vation windows due to the lsquolsquocurse of dimensionalityrsquorsquo Itmay be possible to overcome this challenge to somedegree through a more carefully crafted experimentaldesign in which specific emotional responses are pairedwith specific trading performance measures namelyanxiety levels during consecutive sequences of losing
trades With a larger sample size we can also begin totrack groups of traders over a period of time and throughvarying market conditions By measuring their autonomicresponse profiles at different stages of their careers itmay be possible to determine more directly the role ofemotion in financial risk processing
Finally a much larger set of controls should accompanya larger sample of traders These controls should includepsychophysiological measurements for professional trad-ers taken outside their natural trading environmentmdashideally in a laboratory setting that is identical for alltradersmdashas well as corresponding measurements forsubjects with little or no trading experience in bothtrading and nontrading environments Along with psy-chophysiological data more traditional personality in-ventory data for example MMPI or Myers- Briggs mayprovide additional insights into the psychology of trading
We hope to address each of these issues in ourongoing research program to better understand thecognitive processes underlying financial risk processing
METHODS
Subjects
The 10 subjectsrsquo descriptive characteristics are summar-ized in Table 5 Based on discussions with their super-visors the traders were categorized into three levels ofexperience low moderate and high Five traders spe-cialized in handling client order flow (Retail) threespecialized in trading foreign exchange (FX) and twospecialized in interest-rate derivative securities (Deriva-tives) The durations of the sessions ranged from 49 to83 min and all sessions were held during live tradinghours typically between 8 am and 5 pm easterndaylight time
Physiological Data Collection
A ProComp+ data-acquisition unit and Biograph (Ver-sion 12) biofeedback software from Thought Technol-ogy were used to measure physiological data for allsubjects All six sensors were connected to a smallcontrol unit with a battery power supply which wasplaced on each subjectrsquos belt and from which a fiber-optic connection led to a laptop computer equippedwith real-time data acquisition software (see Figure 2)Each sensor was equipped with a built-in notch filter at60 Hz for automatic elimination of external power linenoise and standard AgCl triode and single electrodeswere used for SCR and EMG sensors respectively Thesampling rate for all data collection was fixed at 32 HzAll physiological data except for respiration and facialEMG were collected from each subjectrsquos nondominantarm SCR electrodes were placed on the palmar sites theBVP photoplesymographic sensor was placed on theinside of the ring or middle finger the arm EMG triodeelectrode was placed on the inside surface of the
Lo and Repin 333
forearm over the flexor digitorum muscle group andthe temperature sensor was inserted between the elasticband placed around the wrist and the skin surface Thefacial EMG electrode was placed on a masseter musclewhich controls jaw movement and is active duringspeech or any other activity involving the jaw Therespiration signal was measured by chest expansionusing a sensor attached to an elastic band placed aroundthe subjectrsquos chest An example of the real-time physio-logical data collected over a 2-min interval for onesubject is given in Figure 3
The entire procedure of outfitting each subject withsensors and connecting the sensors to the laptop re-quired approximately 5 min and was often performedeither before the trading day began or during relativelycalm trading periods Subjects indicated that the pres-
ence of the sensors wires and a control unit did notcompromise or influence their trading in any significantmanner and that their workflow was not impaired in anyway This was verified not only by the subjects but alsoby their supervisors Given the magnitudes of the finan-cial transactions that were being processed and theeconomic and legal responsibilities that the subjectsand their supervisors bore even the slightest interfer-ence with the subjectsrsquo workflow or performance stand-ards would have caused the supervisors or the subjectsto terminate the sessions immediately None of thesessions were terminated prematurely
Physiological Data Feature Extraction
An initial smoothing of the raw EMG signals (sampled at32 Hz) was performed with a moving-average filter oforder 23 If the level of the filtered forearm EMG signalexceeded a threshold of 075 mV both SCR and BVPreadings at this time were discarded because of the highprobability of artifacts for example typing graspingtelephone handsets or inadvertent physical disturban-ces to the sensors Similarly if the level of the filteredfacial EMG signal exceeded 075 mV the respirationsignal at this time was excluded from further processingA very small spatial displacement of the sensor or theelectrode was able to produce a different kind ofartifactmdashan abrupt change in the signal of the orderof 10- 20 standard deviations within 1
32 of a second Suchjumps did not have any physiological meaning hencethey were excluded from further analysis via adaptivethresholding Specifically our adaptive thresholdingprocedure involved marking all observations that
Table 5 Summary Statistics for All Subjects Individual Traderrsquos Characteristics Specialty Type and Number of Market Time-Series Collected During the Session Session Duration and Absolute Time (Eastern Standard Time) of the Start of the Session
TraderID Gender Experience Specialty Market data Available
Nu m ber of MarketTim e-Series Recorded
Session Du ration(m in and sec)
SessionStart Tim e
B33 F high retail major currencies 2 4903000 1320
B34 M high FX major currencies 3 8303200 0856
B35 M high retail major currencies 4 6603000 1102
B36 M high derivatives SampP500 futureseurodollar futures
3 7901600 1255
B37 M moderate FX Latin Americanmajor currencies
9 7000600 0917
B38 M low FX Latin Americanmajor currencies
3 7201200 1102
B39 M high derivatives SampP 500 futureseurodollar futures
9 6200300 0819
B310 M low retail major currencies 7 6000800 0947
B311 M moderate retail major currencies 7 5402500 1132
B312 M low retail major currencies 7 5900000 0910
ProC om p+
Facial EMG Triode electrode measures
activity of the masseter muscle (detects speech)
Respiration sensor Elastic strap measures
chest expansion
Forearm EMG Triode electrode measures activity of the flexor digitorum muscle group (detects finger and arm movement)
Temperature sensor - thermistor
SCR sensor and electrodes
BVP photoplesymographicsensor
Control unit Fiber optic cable to a
data acquisition laptop
Figure 2 Placement of sensors for measuring physiologicalresponses
334 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
Our results indicate that emotion is a significant deter-minant of the evolutionary fitness of financial tradersWe hope to investigate this conjecture more formally inthe future in several ways a comparison between tradersand a control group of subjects without trading experi-ence or with unsuccessful trading experiences an anal-ysis of the psychophysiological responses of traders in alaboratory environment a more direct analysis of thecorrelation between autonomic responses and tradingperformance a more fundamental analysis of the neuralbasis of emotion in traders aimed at the function of theamygdala and the orbitofrontal cortex (Rolls 1992 1999Chap 44- 45) and a direct mapping of the neuralcenters for trading activity through functional magneticresonance imaging (Breiter et al 2001)
There are a number of caveats that must be kept inmind in interpreting our findings First because of thesmall sample size of 10 subjects our results are at bestsuggestive and promising not conclusive A more com-prehensive study with a larger and more diverse sampleof traders a more refined set of market events and abroader set of controls are necessary before coming toany firm conclusions regarding the precise mechanismsof the psychophysiology of financial risk processing andthe role of emotion in market efficiency
Second while we have documented the statisticalsignificance of autonomic responses during marketevents relative to control baselines we have not estab-lished specific causal factors for such responses Manycognitive tasks generate autonomic effects hence amarked change in any one psychophysiological indexmay be due to changes in cognitive processing forexample complex numerical computations rather thanmore fundamental affective responses For examplemore experienced traders in our sample may have dis-played lower autonomic response levels because theyrequired less cognitive effort to perform the same func-tions as less experienced traders which may have nothingto do with emotion Without a more targeted set of tasksor additional data on the characteristics of the subjects itis difficult to distinguish between the two One way toaddress this issue is to correlate a subjectrsquos autonomicresponses with his or her trading performance so as todevelop a more complete profile of the relation betweenpsychophysiological indices and the dynamics of thetrading process However because trading performanceis generally summarized by multiple characteristicsmdashtotal profit-and-loss volatility maximum drawdownSharpe ratio and so onmdashestimating a relation betweenautonomic responses and such characteristics may re-quire significantly larger sample sizes and longer obser-vation windows due to the lsquolsquocurse of dimensionalityrsquorsquo Itmay be possible to overcome this challenge to somedegree through a more carefully crafted experimentaldesign in which specific emotional responses are pairedwith specific trading performance measures namelyanxiety levels during consecutive sequences of losing
trades With a larger sample size we can also begin totrack groups of traders over a period of time and throughvarying market conditions By measuring their autonomicresponse profiles at different stages of their careers itmay be possible to determine more directly the role ofemotion in financial risk processing
Finally a much larger set of controls should accompanya larger sample of traders These controls should includepsychophysiological measurements for professional trad-ers taken outside their natural trading environmentmdashideally in a laboratory setting that is identical for alltradersmdashas well as corresponding measurements forsubjects with little or no trading experience in bothtrading and nontrading environments Along with psy-chophysiological data more traditional personality in-ventory data for example MMPI or Myers- Briggs mayprovide additional insights into the psychology of trading
We hope to address each of these issues in ourongoing research program to better understand thecognitive processes underlying financial risk processing
METHODS
Subjects
The 10 subjectsrsquo descriptive characteristics are summar-ized in Table 5 Based on discussions with their super-visors the traders were categorized into three levels ofexperience low moderate and high Five traders spe-cialized in handling client order flow (Retail) threespecialized in trading foreign exchange (FX) and twospecialized in interest-rate derivative securities (Deriva-tives) The durations of the sessions ranged from 49 to83 min and all sessions were held during live tradinghours typically between 8 am and 5 pm easterndaylight time
Physiological Data Collection
A ProComp+ data-acquisition unit and Biograph (Ver-sion 12) biofeedback software from Thought Technol-ogy were used to measure physiological data for allsubjects All six sensors were connected to a smallcontrol unit with a battery power supply which wasplaced on each subjectrsquos belt and from which a fiber-optic connection led to a laptop computer equippedwith real-time data acquisition software (see Figure 2)Each sensor was equipped with a built-in notch filter at60 Hz for automatic elimination of external power linenoise and standard AgCl triode and single electrodeswere used for SCR and EMG sensors respectively Thesampling rate for all data collection was fixed at 32 HzAll physiological data except for respiration and facialEMG were collected from each subjectrsquos nondominantarm SCR electrodes were placed on the palmar sites theBVP photoplesymographic sensor was placed on theinside of the ring or middle finger the arm EMG triodeelectrode was placed on the inside surface of the
Lo and Repin 333
forearm over the flexor digitorum muscle group andthe temperature sensor was inserted between the elasticband placed around the wrist and the skin surface Thefacial EMG electrode was placed on a masseter musclewhich controls jaw movement and is active duringspeech or any other activity involving the jaw Therespiration signal was measured by chest expansionusing a sensor attached to an elastic band placed aroundthe subjectrsquos chest An example of the real-time physio-logical data collected over a 2-min interval for onesubject is given in Figure 3
The entire procedure of outfitting each subject withsensors and connecting the sensors to the laptop re-quired approximately 5 min and was often performedeither before the trading day began or during relativelycalm trading periods Subjects indicated that the pres-
ence of the sensors wires and a control unit did notcompromise or influence their trading in any significantmanner and that their workflow was not impaired in anyway This was verified not only by the subjects but alsoby their supervisors Given the magnitudes of the finan-cial transactions that were being processed and theeconomic and legal responsibilities that the subjectsand their supervisors bore even the slightest interfer-ence with the subjectsrsquo workflow or performance stand-ards would have caused the supervisors or the subjectsto terminate the sessions immediately None of thesessions were terminated prematurely
Physiological Data Feature Extraction
An initial smoothing of the raw EMG signals (sampled at32 Hz) was performed with a moving-average filter oforder 23 If the level of the filtered forearm EMG signalexceeded a threshold of 075 mV both SCR and BVPreadings at this time were discarded because of the highprobability of artifacts for example typing graspingtelephone handsets or inadvertent physical disturban-ces to the sensors Similarly if the level of the filteredfacial EMG signal exceeded 075 mV the respirationsignal at this time was excluded from further processingA very small spatial displacement of the sensor or theelectrode was able to produce a different kind ofartifactmdashan abrupt change in the signal of the orderof 10- 20 standard deviations within 1
32 of a second Suchjumps did not have any physiological meaning hencethey were excluded from further analysis via adaptivethresholding Specifically our adaptive thresholdingprocedure involved marking all observations that
Table 5 Summary Statistics for All Subjects Individual Traderrsquos Characteristics Specialty Type and Number of Market Time-Series Collected During the Session Session Duration and Absolute Time (Eastern Standard Time) of the Start of the Session
TraderID Gender Experience Specialty Market data Available
Nu m ber of MarketTim e-Series Recorded
Session Du ration(m in and sec)
SessionStart Tim e
B33 F high retail major currencies 2 4903000 1320
B34 M high FX major currencies 3 8303200 0856
B35 M high retail major currencies 4 6603000 1102
B36 M high derivatives SampP500 futureseurodollar futures
3 7901600 1255
B37 M moderate FX Latin Americanmajor currencies
9 7000600 0917
B38 M low FX Latin Americanmajor currencies
3 7201200 1102
B39 M high derivatives SampP 500 futureseurodollar futures
9 6200300 0819
B310 M low retail major currencies 7 6000800 0947
B311 M moderate retail major currencies 7 5402500 1132
B312 M low retail major currencies 7 5900000 0910
ProC om p+
Facial EMG Triode electrode measures
activity of the masseter muscle (detects speech)
Respiration sensor Elastic strap measures
chest expansion
Forearm EMG Triode electrode measures activity of the flexor digitorum muscle group (detects finger and arm movement)
Temperature sensor - thermistor
SCR sensor and electrodes
BVP photoplesymographicsensor
Control unit Fiber optic cable to a
data acquisition laptop
Figure 2 Placement of sensors for measuring physiologicalresponses
334 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
forearm over the flexor digitorum muscle group andthe temperature sensor was inserted between the elasticband placed around the wrist and the skin surface Thefacial EMG electrode was placed on a masseter musclewhich controls jaw movement and is active duringspeech or any other activity involving the jaw Therespiration signal was measured by chest expansionusing a sensor attached to an elastic band placed aroundthe subjectrsquos chest An example of the real-time physio-logical data collected over a 2-min interval for onesubject is given in Figure 3
The entire procedure of outfitting each subject withsensors and connecting the sensors to the laptop re-quired approximately 5 min and was often performedeither before the trading day began or during relativelycalm trading periods Subjects indicated that the pres-
ence of the sensors wires and a control unit did notcompromise or influence their trading in any significantmanner and that their workflow was not impaired in anyway This was verified not only by the subjects but alsoby their supervisors Given the magnitudes of the finan-cial transactions that were being processed and theeconomic and legal responsibilities that the subjectsand their supervisors bore even the slightest interfer-ence with the subjectsrsquo workflow or performance stand-ards would have caused the supervisors or the subjectsto terminate the sessions immediately None of thesessions were terminated prematurely
Physiological Data Feature Extraction
An initial smoothing of the raw EMG signals (sampled at32 Hz) was performed with a moving-average filter oforder 23 If the level of the filtered forearm EMG signalexceeded a threshold of 075 mV both SCR and BVPreadings at this time were discarded because of the highprobability of artifacts for example typing graspingtelephone handsets or inadvertent physical disturban-ces to the sensors Similarly if the level of the filteredfacial EMG signal exceeded 075 mV the respirationsignal at this time was excluded from further processingA very small spatial displacement of the sensor or theelectrode was able to produce a different kind ofartifactmdashan abrupt change in the signal of the orderof 10- 20 standard deviations within 1
32 of a second Suchjumps did not have any physiological meaning hencethey were excluded from further analysis via adaptivethresholding Specifically our adaptive thresholdingprocedure involved marking all observations that
Table 5 Summary Statistics for All Subjects Individual Traderrsquos Characteristics Specialty Type and Number of Market Time-Series Collected During the Session Session Duration and Absolute Time (Eastern Standard Time) of the Start of the Session
TraderID Gender Experience Specialty Market data Available
Nu m ber of MarketTim e-Series Recorded
Session Du ration(m in and sec)
SessionStart Tim e
B33 F high retail major currencies 2 4903000 1320
B34 M high FX major currencies 3 8303200 0856
B35 M high retail major currencies 4 6603000 1102
B36 M high derivatives SampP500 futureseurodollar futures
3 7901600 1255
B37 M moderate FX Latin Americanmajor currencies
9 7000600 0917
B38 M low FX Latin Americanmajor currencies
3 7201200 1102
B39 M high derivatives SampP 500 futureseurodollar futures
9 6200300 0819
B310 M low retail major currencies 7 6000800 0947
B311 M moderate retail major currencies 7 5402500 1132
B312 M low retail major currencies 7 5900000 0910
ProC om p+
Facial EMG Triode electrode measures
activity of the masseter muscle (detects speech)
Respiration sensor Elastic strap measures
chest expansion
Forearm EMG Triode electrode measures activity of the flexor digitorum muscle group (detects finger and arm movement)
Temperature sensor - thermistor
SCR sensor and electrodes
BVP photoplesymographicsensor
Control unit Fiber optic cable to a
data acquisition laptop
Figure 2 Placement of sensors for measuring physiologicalresponses
334 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
differed by more than 10 standard deviations from alocal average (with both standard deviation and localaverage computed over the most recent 30 sec of datawhich at a sampling rate of 32 Hz yields 960 observa-tions) and replacing these outliers with the immediatelypreceding values This procedure was then repeateduntil all such artifacts were eliminated Finally irrelevanthigh-frequency signal components and noise were elim-inated through a low-pass filter that was individuallydesigned for each of the physiological variables Therelatively smooth nature of the SCR signals permittedthe elimination of all harmonics above 15 Hz while theperiodic structure of the BVP signals pushed the cut-offfrequency to 45 Hz Table 6 reports the means andstandard deviations of the signals measured by the six
sensors for each of the 10 subjects After preprocessingfeature vectors were constructed from SCR BVP tem-perature and respiration signals
Financial Data Collection
At the start of each session a common time-marker wasset in the biofeedback unit and in the subjectrsquos tradingconsole (a networked PC or workstation with real-timedatafeeds such as Bloomberg and Reuters) and softwareinstalled on the trading console (MarketSheet by Tibco)stored all market data for the key financial instrumentsin an Excel spreadsheet time-stamped to the nearestsecond The initial time-markers and time-stampedspreadsheets allowed us to align the market and
Figure 3 Typical physiologicalresponse data recorded by sixsensors for a single subject overa 2-min time interval
85
0 05 1 15 2
25575
242628
7980582
04 05 06
12 13
35753653725
15345
Time min
Time min
Time min
See 3ASee 3ASee 3ASee 3ASee 3A
See 3B
Skin Conductance Response
Arm EMG
Facial EM
Respiratio
Temperature
m m
m V
yen F
Table 6 Summary Statistics of Physiological Response Data for All Traders Means and SD for Each of the Six Sensors
Facial EMG ( m V) Forearm EMG ( m V) SCR ( m m ) TMP ( 8C) BVP () RSP ()
Trader ID Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
B32 123 146 884 2569 332 138 307 013 2334 631 3420 169
B34 136 315 088 186 1154 455 314 039 2493 405 3328 115
B35 126 165 331 357 216 271 313 074 2500 660 3557 165
B36 250 406 104 213 1009 216 297 036 2487 380 3345 157
B37 273 1853 366 1884 396 173 287 067 2506 327 3104 243
B38 835 2012 151 193 1224 221 284 025 2509 761 2975 061
B39 196 264 036 185 2509 340 296 015 2510 672 3665 108
B310 126 073 175 275 199 047 322 029 2487 512 3693 153
B311 137 859 185 1022 2887 210 333 055 2521 882 3312 161
B312 164 119 108 822 359 071 292 008 2510 512 3132 079
EMG = electromyographic data SCR = skin conductance responses TMP = body temperature BVP = blood volume pulse RSP = respiration rate
Lo and Repin 335
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
physiological data to within 05 sec of accuracy Figure 4displays an example of the real-time financial datamdashtheeuroUS dollar exchange ratemdashcollected over a 60-mininterval
Financial Data Feature Extraction
Deviations of a time series Zt were defined as thoseobservations that deviated from the series mean by acertain threshold where the threshold was defined asa multiple k of the standard deviation s z of the timeseries Positive deviations were defined as those ob-servations Zt such that Zt gt Z + k s z and negativedeviations were defined as those observations Zt suchthat Zt lt Z iexcl k s z The value of the multiplier k variedwith the particular series and session and it wascalibrated to yield approximately 5- 10 events persession Because the volatility of financial time seriescan vary across instruments and over time a singlevalue of k for all subjects and instruments is clearlyinappropriate However the sole objective in ourcalibration procedure was to maintain an approxi-mately equal number of events for each time seriesin each session Deviation events were defined forprices spreads and returns Table 7 reports theaverage values of k for each of the 15 financialinstruments in our study which range from 010 forARS and BRL (the Argentinian peso and Brazilian real)to 203 for EUR (the euro) for price deviations 010for ARS BRL and MXP (the Argentine peso Brazilianreal and Mexican peso) to 285 for GBP (the Britishpound) for spread deviations and 001 for ARS (theArgentine peso) to 1500 for COP and MXP (theColombian and Mexican peso) for return deviations
Trend-reversal events were defined as instanceswhen a time series Zt intersected its 5-min moving-average MA5 min(Zt) (see eg Figure 4 Panel 4A)Positive and negative trend-reversals were defined asthose observations Zt such that Zt gt (1 + d )MA5 min(Zt)and Zt lt (1 iexcl d )MA5 min(Zt) respectively The param-
eter d also varied with the particular series and session(see Table 7) ranging from an average of 00001 to 01for price series and 0005 to 1575 for spreads Due tothe high-frequency sampling rate (1 sec) prices did notvary often from one observation to the next hencemost of the return values were zero making it difficultto define trends in returns Therefore we definedtrend-reversals only for prices and spreads excludingreturns from this event category
Three types of volatility events were defined for 5-mintime intervals indexed by j based on the followingstatistics
where Ptjdenotes the average price in the interval tj iexcl
300 to tj and Rt2 is the squared return between t iexcl 1
and t We refer to s j1 as lsquolsquomaximum volatilityrsquorsquo since it is
the difference between the maximum and minimumprices as a fraction of their average and s j
2 and s j3 are
the standard deviations of prices and returns respec-tively lsquolsquoPlusrsquorsquo and lsquolsquominusrsquorsquo volatility events were thendefined as those instances when
s lj gt hellip1 Dagger h dagger s l
jiexcl1 hellipPlus Eventdagger hellip4dagger
s lj lt hellip1 iexcl h dagger s l
jiexcl1 hellipMinus Eventdagger hellip5dagger
for l = 1 2 3 The parameter h was calibrated to yieldfive or less volatility events per session and ranged froman average of 010 to 200 (see Table 7) For volatilityevents we calibrated the threshold to yield five or fewerevents to ensure that the combined time intervalscontaining volatility events comprised less than 50 ofthe total session time
Test Statistics
For each of the eight types of events a sample mean ˆm j
was computed for that component Xjt of the featurevector [X1t X2t X8t] and compared to the sample meanmˆ j
c of the corresponding component of the control fea-ture vector [Y1t Y2t Y8t] according to the test statistic
z j ˆˆm j iexcl ˆm c
j2ˆs 2
j =n j
q hellip6dagger
where
ˆm j ˆ 1
n j
Xn j
tˆ1Xjt ˆm c
j ˆ 1
n j
Xn j
tˆ1Yjt hellip7dagger
0 10 20 30 40 50 60
10733
15 16 17 18 19 20
1072
1073
1074Ask
BidTime min
Time min
euro$
See 4A
Panel 4A
Figure 4 Typical real-time market data (euroUS dollar exchangerate) recorded during a 60-min time interval and the corresponding5-min moving-average
s 1j ˆ
maxtjiexcl300lt t micro tj Pt iexcl mintjiexcl300lt t micro tj Pt
12 hellipmaxtjiexcl300lt t micro tj Pt Dagger mintjiexcl300lt t micro tj Pt dagger
hellip1dagger
s 2j ˆ
1
300
X
tjiexcl300lt t microtj
hellipPt iexcl Ptjdagger2
shellip2dagger
s 3j ˆ
1
300
X
tjiexcl300lt t microtj
R2t
shellip3dagger
336 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
Tab
le7
Aver
age
Val
ues
ofth
ePa
ram
eter
sU
sed
toD
efin
eM
arke
tEv
ents
D
evia
tion
s( k
)T
rend
-Rev
ersa
ls(d
)an
dVo
latil
ity
Even
ts(h
)
Secu
rity
Iden
tifi
erM
ean
kfo
rP
rice
Mea
nk
for
Spre
ad
Mea
nk
for
Ret
urn
Mea
nd
for
Pri
ceM
ean
dfo
rSp
rea
dM
ean
dfo
rR
etu
rn
Mea
nh
for
Ma
xim
um
Vol
ati
lity
Mea
nh
for
Ave
rage
Pri
ceV
ola
tili
ty
Mea
nh
for
Ave
rage
Ret
urn
Vol
ati
lity
ARS
010
010
001
010
000
010
0-
010
010
010
AUD
138
147
101
40
0009
00
475
-0
580
430
58
BRL
010
010
050
000
010
000
5-
200
010
00
200
0
CA
D1
020
8410
83
000
058
025
0-
072
072
063
CH
F1
752
129
170
0005
30
610
-0
380
420
38
CLP
040
100
120
00
0002
00
200
-0
600
500
50
CO
P0
500
5015
00
000
070
020
0-
500
300
300
EDU
140
250
110
00
0015
51
575
-0
750
700
43
EUR
203
243
706
000
066
127
4-
045
045
036
GB
P1
542
859
240
0003
90
472
-0
460
510
42
JPY
118
186
856
000
089
080
3-
054
054
041
MX
P1
000
1015
00
000
040
001
0-
100
105
085
NZD
158
130
100
00
0008
50
288
-0
730
650
73
SPU
063
025
140
90
0000
40
002
-0
680
070
03
VEB
190
250
110
00
0006
00
500
-0
300
400
40
See
Equ
atio
ns1-
3in
the
text
Lo and Repin 337
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
McIntosh D N Zajonc R B Vig P S amp Emerick S W(1997) Facial movement breathing temperature and affectImplication of the vascular theory of emotional efferenceCogn ition and Em otion 11 171- 195
Merton R (1973) Theory of rational option pricing BellJou rnal of Econom ics and Managem ent Science 4141- 183
Niederhoffer V (1997) Education of a specu lator New YorkWiley
Odean T (1998) Are investors reluctant to realize their lossesJou rnal of Finance 53 1775- 1798
Papillo J F amp Shapiro D (1990) The cardiovascular systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tial
338 Jou rnal of Cogn itive Neuroscience Volum e 14 Nu m ber 3
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
ˆs 2j ˆ
12n j iexcl 2
Xn j
tˆ1
hellipXjt iexcl ˆm jdagger2 Dagger
Xn j
tˆ1
hellipYjt iexcl ˆm cj dagger
2
Aacute
hellip8dagger
Under the null hypothesis that Xjt and Yjt are inde-pendently and identically distributed normal randomvariables with mean m and variance s 2 the test statisticz j has a t distribution with 2n jiexcl2 degrees of freedom Inthe absence of normality (Riniolo amp Porges 2000) theCentral Limit Theorem implies that under certain regu-larity conditions z j is approximately standard normal inlarge samples (Bickel amp Doksum 1977 Chap 44B) inwhich case the 95 critical region is defined by thecomplement of the interval [iexcl196 196]
Acknowledgments
Research support from the MIT Laboratory for FinancialEngineering and the Boston University Department ofCognitive and Neural Systems is gratefully acknowledged Wethank Jerome Kagan Ken Kosik JC Mercier Brett Steenbarg-er Svetlana Sussman and two reviewers for helpful commentsand discussion
Reprint requests should be sent to Andrew W Lo MIT SloanSchool of Management 50 Memorial Drive E52-432 Cam-bridge MA 02142 USA or via e-mail alomitedu
REFERENCES
Barber B amp Odean T (2001) Boys will be boys Genderoverconfidence and common stock investment Qu arterlyJou rnal of Econom ics 116 261- 292
Bechara A Damasio H Tranel D amp Damasio A R (1997)Deciding advantageously before knowing the advantageousstrategy Science 275 1293- 1294
Bell D (1982) Risk premiums for decision regretManagem ent Science 29 1156- 1166
Bickel P amp Doksum K (1977) Mathem atical statistics Basicideas and selected topics San Francisco Holden-Day
Black F amp Scholes M (1973) Pricing of options and corpo-rate liabilities Jou rnal of Political Econom y 81 637- 654
Boucsein W (1992) Electroderm al activity New YorkPlenum
Breiter H Aharon I Kahneman D Dale A amp Shizgal P(2001) Functional imaging of neural responses toexpectancy and experience of monetary gains and lossesNeuron 30 619- 639
Brown J D amp Huffman W J (1972) Psychophysiologicalmeasures of drivers under the actual driving conditionsJou rnal of Safety Research 4 172- 178
Cacioppo J T Tassinary L G amp Bernt G (Eds) (2000)Handbook of psychophysiology Cambridge UK CambridgeUniversity Press
Cacioppo J T Tassinary L G amp Fridlund A J (1990) Theskeletomotor system In J T Cacioppo amp L G Tassinary(Eds) Principles of psychophysiology Physical socialand in feren tial elem ents (pp 325- 384) Cambridge UKCambridge University Press
Clarke R Krase S amp Statman M (1994) Tracking errorsregret and tactical asset allocation Jou rnal of PortfolioManagem ent 20 16- 24
Critchley H D Elliot R Mathias C J amp Dolan R (1999)
Central correlates of peripheral arousal during a gamblingtask Abstracts of the 21st In ternational Sum m er School ofBrain Research Amsterdam
Damasio A R (1994) Descartesrsquo error Em otion reason andthe hu m an brain New York Avon Books
DeBond W amp Thaler R (1986) Does the stock marketoverreact Jou rnal of Finance 40 793- 807
Ekman P (1982) Methods for measuring facial action InK Scherer amp P Ekman (Eds) Handbook of m ethods innon verbal behavior research Cambridge UK CambridgeUniversity Press
Elster J (1998) Emotions and economic theory Jou rnal ofEconom ic Literature 36 47- 74
Fischoff B amp Slovic P (1980) A little learning Confidencein multicue judgment tasks In R Nickerson (Ed) Attentionand perform ance VIII Hillsdale NJ Erlbaum
Frederikson M Furmark T Olsson M T Fischer HAndersson J amp Langstrom B (1998) Functional neuro-anatomical correlates of electrodermal activity A positronemission tomographic study Psychophysiology 35 179- 185
Gervais S amp Odean T (2001) Learning to be overconfidentReview of Financial Studies 14 1- 27
Grossberg S amp Gutowski W (1987) Neural dynamics ofdecision making under risk Affective balance and cognitive-emotional interactions Psychological Review 94 300- 318
Hammond K R Hamm R M Grassia J amp Pearson T(1987) Direct comparison of the efficacy of intuitive andanalytical cognition in expert judgment IEEE Transactionson System s Man and Cybernetics SMC-17 753- 770
Huberman G amp Regev T (2001) Contagious speculation anda cure for cancer A nonevent that made stock prices soarJou rnal of Finance 56 387- 396
Kahneman D amp Tversky A (1979) Prospect theory Ananalysis of decision making under risk Econom etrica 47263- 291
Lichtenstein S Fischoff B amp Phillips L D (1982)Calibration of probabilities The state of the art to 1980In D Kahneman P Slovic amp A Tversky (Eds) Judgm entunder uncertain ty Heuristics an d biases (pp 306- 334)Cambridge UK Cambridge University Press
Lindholm E amp Cheatham C M (1983) Autonomic activityand workload during learning of a simulated aircraft carrierlanding task Aviation Space and En vironm entalMedicine 54 435- 439
Lo A W (1999) The three Prsquos of total risk managementFinancial An alysts Jou rnal 55 12- 20
Loewenstein G (2000) Emotions in economic theory andeconomic behavior Am erican Econom ic Review 90426- 432
Lorig T S amp Schwartz G E (1990) The pulmonary systemIn J T Cacioppo amp L G Tassinary (Eds) Principles ofpsychophysiology Physical social and in feren tialelements (pp 580- 598) Cambridge UK CambridgeUniversity Press
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Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
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Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339
elements (pp 456- 512) Cambridge UK CambridgeUniversity Press
Peters E amp Slovic P (2000) The springs of action Affectiveand analytical information processing in choice Personalityand Social Psychology Bu lletin 26 1465- 1475
Rimm-Kaufman S E amp Kagan J (1996) The psychologicalsignificance of changes in skin temperature Motivation andEm otion 20 63- 78
Riniolo T amp Porges S (2000) Evaluating group distributionalcharacteristics Why psychophysiologists should beinterested in qualitative departures from the normaldistribution Psychophysiology 37 21- 28
Rolls E (1990) A theory of emotion and its application tounderstanding the neural basis of emotion Cogn ition andEm otion 4 161- 190
Rolls E (1992) Neurophysiology and functions of the primateamygdala In J P Aggelton (Ed) The am ygdalaNeurobiological aspects of em otion m em ory and mentaldysfunction (pp 143- 166) New York Wiley-Liss
Rolls E (1994) A theory of emotion and consciousness and its
application to understanding the neural basis of emotionIn M S Gazzaniga (Ed) The cogn itive neurosciences(pp 1091- 1106) Cambridge MIT Press
Rolls E (1999) The brain and em otion Oxford UK OxfordUniversity Press
Samuelson P A S (1965) Proof that properly anticipatedprices fluctuate randomly Indu strial Managem ent Review6 41- 49
Schwager J D (1989) Market wizards In terviews with toptraders New York New York Institute of Finance
Schwager J D (1992) The new m arket wizardsConversations with Am ericarsquos top traders New YorkHarperBusiness
Shefrin H (2001) Behavioral finan ce Cheltenham UKEdward Elgar Publishing
Shefrin M amp Statman M (1985) The disposition to sellwinners too early and ride losers too long Theory andevidence Jou rnal of Finance 40 777- 790
Tversky A amp Kahneman D (1981) The framing of decisionsand the psychology of choice Science 211 453- 458
Lo and Repin 339