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Online Appendix for
“Market Maturity and Mispricing”
Heiko Jacobs∗
December 2015
Abstract
Table 1 shows computational details for the eleven anomalies un-
derlying the Stambaugh, Yu, and Yuan (2015) mispricing score.
The table additionally provides information about the construction
of 20 additional cross-sectional anomalies relied on in the paper.
For each country separately, Table 2 shows the sample period and monthly
Fama and French (1993) three-factor alphas obtained from the Stambaugh, Yu,
and Yuan (2015) mispricing measure. On a country-by-country basis, Tables
3 to 13 provide sample periods and alphas for each of the eleven anomalies
entering the aggregate Stambaugh, Yu, and Yuan (2015) mispricing score.
Separately for emerging markets and developed markets, Table 14 reports
∗Heiko Jacobs, Finance Department, University of Mannheim, L5,2, 68131 Mannheim, Germany. E-Mail:
[email protected]. Phone: +49 621 181 3453.
1
alphas obtained from an alternative measure of aggregate mispricing based
on 20 further anomalies (as described in Table 1 of this Online Appendix).
Table 15 reports alphas generated by a mispricing measure which takes both
the Stambaugh, Yu, and Yuan (2015) anomalies and the additional anomalies
of Table 14 of this Online Appendix into account. Separately for emerging
markets and developed markets, Table 16 reports alphas generated by three
aggregate mispricing measures computed from different groups of anomalies.
On a country-by-country basis, Figure 1 to 45 illustrate the fraction of
stocks with valid (individual or composite) anomaly rankings over time.
Table 17 compares the fraction of stocks with valid (individual or compos-
ite) anomaly rankings in developed markets relative to emerging markets.
On a country-by-country basis, Figures 46 to 49 compare the average
number of individual anomalies underlying the composite Stambaugh, Yu, and
Yuan (2015) mispricing score, conditional on non-missing values of the score
(i.e., conditional on the availability of at least five individual anomaly ranks).
Table 18 provides descriptive statistics of the data underlying Figures 46 to 49.
Separately for each year, Figure 50 compares the average number of in-
dividual anomalies underlying the composite Stambaugh, Yu, and Yuan (2015)
mispricing score between the average developed market and the average emerg-
ing market. Table 19 more formally tests for differences between developed
markets and emerging markets with respect to the average number of indi-
vidual anomalies underlying the composite Stambaugh, Yu, and Yuan (2015)
mispricing score. Separately for each year, Figure 51 compares the average
2
number of individual anomalies underlying the composite Stambaugh, Yu, and
Yuan (2015) mispricing score between the pooled stock-level observations in
developed markets and the pooled stock-level observations in emerging markets.
Figures 52 to 55 illustrate the distribution of the alpha difference be-
tween developed markets and emerging markets with respect to sim-
ulated composite mispricing based on five randomly selected indi-
vidual anomalies. In this context, Figure 52 and 53 concentrate on
the Stambaugh, Yu, and Yuan (2015) anomalies, whereas Figures 54
and 55 additionally take the 20 alternative anomalies into account.
References are provided on the last six pages.
3
Table
1:Com
putationofanomalies
inthecross-sectionofexpectedequityreturns
Anomaly
Description
Refere
nces
Portfolios
Holding
Data
base
sComputa
tionaldeta
ils
1.Sta
mbaugh,Yu,and
Yuan
(2015)anomalies
Failure
probabil-
ity
Firms
with
low
failure
profitability
outperform
firm
swith
high
failure
probability.
Campbell,
Hilscher,
and
Szilagyi
(2008),
Con-
rad,Kapadia,
and
(2014)
5failure
prob-
ability(1-5)
twelvemonths
CRSP,
Com-
pustat,
Datastream,
Worldscope
Thecomputation
ofthedistressmeasure
closely
follow
sthemethod
outlined
inTable
IVofCampbell,Hilscher,andSzilagyi(2008).
More
specifically,
distressrisk
iscomputed
as-9.164-20.264*NIM
TAAVG
+1.416*TLMTA
-7.129*EXRETAV
+1.411*SIG
MA
-0.045*RSIZE
-2.132*CASHMTA
+0.075*MB
-0.058*PRIC
E.As
common
in
theliterature
and
inorder
toav
oid
look-ahead
biasand
toassure
comparability
across
countries,
Imatch
accounting
data
for
the
fiscal-yearen
dofyeartwithstock
return
data
from
July
ofyeart+
1
untilJuneofyeart+
2.Market
equityis
updatedmonthly.NIM
TA
is
net
income(C
ompustatannualitem
NIforU.S.data,Worldcopeitem
WC01551forinternationaldata)divided
bythesum
ofmarket
equity
andtotalliabilities(LT,W
C03351).NIM
TAAVG
isequalto
(1-γ
3)/(1-
γ12)*[N
IM
TA
t−1+γ3*NIM
TA
t−4+γ6*NIM
TA
t−7+γ9*NIM
TA
t−10].
Inthis
context,
γis
equalto
2−1/3andtrefers
tomonth
t.TLMTA
istotalliabilities
divided
by
the
sum
ofmarket
equity
and
total
liabilities.
EXRET
isthemonthly
log
firm
excess
return
relativeto
theva
lue-weightedcountrymarket
index,andEXRETAV
is(1-γ
3)/(1-
γ12)*[(EXRETt−
1+...+
γ11*EXRETt−
12].
Inthis
context,
γis
equal
to2−1/3andtrefers
tomonth
t.SIG
MA
isthestandard
deviationof
thefirm
’sdailystock
return
over
thepast
3months.RSIZE
isupdated
monthly
andcomputedasthelogratioofthefirm
’sonemonth
lagged
market
capitalization
relative
tothe
country
market
capitalization.
CASHMTA
istheratio
ofcash
and
short-term
investm
ents
(CHE,
WC02001)divided
bythesum
ofmarket
equityand
totalliabilities.
MB
isthemarket-to-book
ratio.Tomitigate
theim
pact
ofoutliers,
Iadd
10%
ofthedifferen
cebetween
market
and
bookequityto
the
book
valueoftotalassets.
PRIC
Eis
thefirm
’slog
price
per
share
(in
USD,onemonth
lagged
),truncated
aboveat$15
and
below
at
$1.NIM
TAAVG,TLMTA,EXRETAV,SIG
MA,RSIZE,CASHMTA,
MB,andPRIC
Eare
winsorizedatthe5%
andthe95%
level.
[Continued
overleaf]
4
Anomaly
Description
Refere
nces
Portfolios
Typ.
Hold-
ing
Data
base
sComputa
tionaldeta
ils
Ohlson’s
O(dis-
tress)
Financially
healthy
firm
s
outperform
firm
sin
finan-
cialdistress.
Ohlson(1980)
5distress
(1-
5)
twelvemonths
Compustat,
Worldscope
Icompute
distress
(“O-score”)
as
-1.32
-0.407*log(T
A/CPI)
+
6.03*TLTA
-1.43*W
CTA
+0.076*CLCA
-1.72*OENEG
-2.37*NIT
A
-1.83*FUTL+
0.285*IN
TW
O-0.521*CHIN
.Imatchaccountingdata
forthefiscal-yearen
dofyeartwith
stock
return
data
from
July
of
yeart+
1untilJuneofyeart+
2.In
thiscontext,TA
istotalassets(A
T,
WC02999)andCPIis
thecountry-specificconsumer
price
index
(ob-
tained
from
theFed
eralReserveBankandDatastream,resp
ectively).
TLTA
istotalliabilities(D
LC+DLTT,W
C03351)divided
bytotalas-
sets.W
CTA
isworkingcapital(A
CT-LCT,W
C02201-W
C03101)di-
vided
bytotalassets.
CLCA
iscu
rren
tliabilities(L
CT,W
C03101)di-
vided
bycu
rren
tassets(item
ACT,W
C02201).
OENEG
isadummy
variable
whichis
oneif
totalliabilitiesexceed
stotalassets,
andzero
otherwise.
NIT
Ais
net
income(N
I,W
C01551)divided
bytotalassets.
FUTL
isthefundprovided
byoperations(P
I,W
C04201)divided
by
totalliabilities.IN
TW
Oisadummyva
riable
equalto
oneifnet
income
isnegativeforthelast
twoyears
andzero
otherwise.
CHIN
is(N
Iin
t
-NIin
t-1)/(|N
int|+|N
int-1|).
Net
stock
issues
Returns
are
negatively
related
toan-
nualnet
stock
issuance.
Ritter(1991),
Loughran
and
Ritter
(1995),
Fama
and
French
(2008),
Pon-
tiff
and
Woodgate
(2008)
5issuance
(1-
5)
onemonth
CRSP,Datas-
tream
Idefi
nenet
stock
issues
asthelogratioofsplit-adjusted
shares(based
oncfacshrandshroutfortheU.S.aswellasonNOSH
andAFforinter-
nationalmarkets)
outstandingin
month
t-1andsplit-adjusted
shares
outstandingin
month
t-13.
Composite
equity
Returns
are
negatively
related
to
composite
equity
is-
suance
(5
yearwindow
).
Ritter(1991),
Loughran
and
Ritter
(1995),Daniel
and
Titman
(2006)
5compos-
ite
equity
issuance
(1-5)
onemonth
CRSP,Datas-
tream
Idefi
necomposite
equityissuance
aslog(m
arket
capitalizationin
month
t-1/market
capitalizationin
month
t-61)minusthecu
mulativelogre-
turn
over
thesametimeperiod.
[Continued
overleaf]
5
Anomaly
Description
Refere
nces
Portfolios
Typ.
Hold-
ing
Data
base
sComputa
tionaldeta
ils
Accruals
Stocks
of
firm
swith
low
accruals
outperform
firm
swith
highaccruals.
Dechow
,
Sloan,
and
Sweeney
(1995),
Sloan
(1996)
5accruals
(1-
5)
twelvemonths
Compustat,
Worldscope
Inspired
by
Dechow
,Sloan,and
Sweeney
(1995),
Leu
z,Nanda,and
Wysock
(2004),
and
Sloan
(1996),
accruals
are
defi
ned
as(annual
changein
curren
tassets(A
CT,W
C02201)-annualchangein
total
cash
andshort-term
investm
ents
(CHE,W
C02001)-annualchangein
curren
tliabilities(L
CT,W
C03101)+
annualchangein
short
term
deb
t
(DLC,W
C03051)-annualdep
reciation,dep
letionandamortizationex-
pen
sein
yeart(D
P,W
C01151))/(0.5
*totalassetsin
yeart+
0.5
*
totalassetsin
yeart-1).Imatchaccountingdata
forthefiscal-yearen
d
ofyeartwithstock
return
data
from
July
ofyeart+
1untilJuneof
yeart+
2.
Net
op-
erating
assets
Net
operating
assets
scaled
by
total
assets
nega-
tively
predict
returns.
Hirshleifer,
Hou,
Teoh,
and
Zhang
(2004)
5net
operat-
ing
assets(1-
5)
twelvemonths
Compustat,
Worldscope
Inspired
byHirshleifer,Hou,Teoh,and
Zhang(2004),
net
operating
assetsare
defi
ned
as(operatingassets-operatingliabilities)/one
yearlagged
totalassets,
whereoperatingassets=
totalassets(A
T,
WC02999)-cash
and
short-term
Investm
ent(C
HE,W
C02001)and
whereoperatingliabilities=
totalassets-short
term
and
longterm
deb
t(D
LC/DLTT,W
C03255)-minority
interest
(MIB
,W
C03426)-
preferred
stock
andcommonequity(U
.S.market:PSTKRV
/PSTKLif
available+
CEQ,internationalmarkets:W
C03995).Imatchaccounting
data
forthefiscal-yearen
dofyeartwithstock
return
data
from
July
ofyeart+
1untilJuneofyeart+
2.
Momen
tum
Winners
of
the
recent
past
outper-
form
losers
ofthe
recent
past.
Asness,
Moskow
itz,
and
Ped
ersen
(2013),
Chui,
Titman,
and
Wei
(2010),
Jegadeesh
and
Tit-
man
(1993),
Jegadeesh
and
Tit-
man
(2001),
Rouwen
horst
(1998)
5past
returns
(5-1)
sixmonths
CRSP,Datas-
tream
Icompute
themomen
tum
form
ationperiodreturn
asthecu
mulative
return
over
themonthst-6to
t-1.In
unreported
robustnessch
ecks,
I
findthatusingaskipped
month
oralonger
form
ationperiod(upto
twelvemonths)
does
notch
angeinferences.
Ascommonin
themomen
-
tum
literature,Iconstruct
overlappingportfoliosin
thateligible
stocks
are
sorted
into
portfoliosatthebeginningofeach
month
andheldin
theseportfoliosforsixmonths.Themomen
tum
return
inagiven
month
istheequallyweightedav
erageoftheov
erlappingportfolioreturnsin
thatmonth.
[Continued
overleaf]
6
Anomaly
Description
Refere
nces
Portfolios
Typ.
Hold-
ing
Data
base
sComputa
tionaldeta
ils
Gross
profitabil-
ity
Profitability
positively
predicts
returns.
Nov
y-M
arx
(2013)
5gross
prof-
itability(5-1)
twelvemonths
Compustat,
Worldscope
Gross
profitabilityis
measuredas(reven
ues
minuscost
ofgoodssold
(REVT-C
OGS,W
C01100))/totalassets.
Asset
growth
Total
asset
growth
nega-
tivelypredicts
returns.
Cooper,
Gulen,
and
Schill
(2008),
Titman,Wei,
and
Xie
(2013)
5asset
growth
(1-5)
twelvemonths
Compustat,
Worldscope
Asset
growth
isestimatedasthepercentagech
angeoftotalassetsof
thefiscalyearen
dingin
calendaryeart-2to
thefiscalyearen
dingin
calendaryeart-1.
Return
on
assets
Return
on
assets
posi-
tivelypredicts
returns.
Chen
,Nov
y-
Marx,
and
Zhang(2011),
Fama
and
French
(2006)
5return
toas-
sets
(5-1)
twelvemonths
Compustat,
Worldscope
FortheU.S.stock
market,return
onassetsisdefi
ned
asyearlyearnings
(IB)/oneyearlagged
totalassets.
Forinternationalmarkets,
return
onassetsis
defi
ned
byWorldscopeitem
WC08326.Imatchaccounting
data
forthefiscal-yearen
dofyeartwithstock
return
data
from
July
ofyeart+
1untilJuneofyeart+
2.
Investm
ent-
to-assets
Scaled
capital
investm
ents
negatively
predict
re-
turns.
Titman,Wei,
and
Xie
(2004),
(2008)
5capital
in-
vestm
ents
(1-
5)
twelvemonths
Compustat,
Worldscope
Inspired
byChen
,Nov
y-M
arx,andZhang(2011)andLyandres,
Sun,
andZhang(2008),
investm
ent-to-assetsare
defi
ned
as(annualch
ange
ingross
property,plant,andequipmen
t(P
PEGT,W
C02301)+
annual
changein
inventories
(INVT,W
C02101))
/oneyearlagged
totalassets.
Imatch
accountingdata
forthefiscal-yearen
dofyeartwith
stock
return
data
from
July
ofyeart+
1untilJuneofyeart+
2.
Furtheranomalies
Low
volatility
anomaly
Firms
with
low
return
volatility
out-
perform
firm
s
with
high
volatility.
Baker,
Bradley,
and
Wurgler
(2011),
Black
(1972),
Hau-
gen
andHeins
(1975)
5volatility
(1-
5)
onemonth
CRSP,Datas-
tream
Imeasure
volatility
asthestandard
deviationofthemonthly
returns
over
thepreviousfiveyears.Irequireatleast
36va
lidmonthly
return
observations.
Portfolio1den
otesfirm
swiththelowestvolatility.
[Continued
overleaf]
7
Anomaly
Description
Refere
nces
Portfolios
Typ.
Hold-
ing
Data
base
sComputa
tionaldeta
ils
Low
beta
anomaly
Firms
with
low
ex-ante
beta
outper-
form
firm
s
with
high
ex-ante
beta.
Frazzini
and
Ped
ersen
(2014),
Hong
and
Sraer
(2015)
5beta(1-5)
onemonth
CRSP,Datas-
tream
Iestimate
betasfrom
rollingregressionsofdailyexcess
stock
returnson
excess
(country)market
returns.
Iuse
dailyreturnsov
ertheprevious
twelvemonthsandrequireatleast
200va
lidobservations.
Portfolio1
den
otesfirm
swiththelowestbeta.In
unreported
robustnessch
ecks,
I
havealsoestimatedlow-frequen
cybetasbasedonmonthly
data
over
thepreviousfiveyears.Inferencesdon’t
change.
Idiosyn-
cratic
risk
anomaly
Firms
with
low
idiosyn-
cratic
volatil-
ity
outper-
form
firm
s
with
high
idiosyncratic
volatility.
Ang,Hodrick,
Xing,
and
Zhang(2006),
Ang,Hodrick,
Xing,
and
Zhang(2009)
5idiosyn-
cratik
risk
(1-5)
onemonth
CRSP,Datas-
tream
Imeasure
idiosyncraticrisk
asthestandard
deviationoftheresidual
obtained
from
regressingastock’s
excess
return
onacountry-specific
Famaand
French
(1993)three-factormodel.Iuse
dailyreturnsov
er
theprevioustw
elvemonthsandrequireatleast
200va
lidobservations.
Portfolio1den
otesfirm
swith
thelowestidiosyncraticrisk.In
unre-
ported
robustnesschecks,
Ihav
ealsoestimatedlow-frequen
cyidiosyn-
craticvolatility
basedonthemarket
model
andonmonthly
data
over
thepreviousfiveyears.Inferencesdon’t
change.
Maxim
um
daily
return
anomaly
The
maxi-
mum
daily
return
over
the
previous
month
nega-
tivelypredicts
returns.
Bali,
Cakici,
andW
hitelaw
(2011)
5daily
max
return
(1-5)
onemonth
CRSP,Datas-
tream
Portfoliosortingsare
basedonthemaxim
um
dailyreturn
(measured
inlocalcu
rren
cy)ov
erthepreviousmonth.Portfolio1den
otesfirm
s
withthelowestmaxim
um
return.In
unreported
robustnessch
ecks,
I
havealsoestimatedthemaxim
um
abnorm
alreturn
basedonthemarket
model.Inferencesdon’t
change.
Lottery-
type
stocks
anomaly
Stocks
with
lottery
fea-
tures
un-
derperform
non-lottery
stocks.
Kumar(2009)
2(lottery
stocks
-
non-lottery
stocks)
onemonth
CRSP,Datas-
tream
Asin
Kumar(2009),
Idefi
nea
(non)-lottery-typestock
asa
stock
withabov
e(below
)med
ianidiosyncraticvolatility,abov
e(below
)me-
dianidiosyncraticskew
nessandbelow
(abov
e)med
iannominalstock
price.Imeasure
idiosyncraticvolatility
asthestandard
deviation
of
theresidualobtained
from
regressingastock’s
dailyexcess
return
on
acountry-specificFamaandFrench
(1993)three-factormodel
over
the
previoussixmonths.Idiosyncraticskew
nessisthethirdmomen
tofthe
residualobtained
by
regressingthedaily
stock
excess
return
on
the
excess
(country)market
return
andthesquaredexcess
market
return
over
theprevioussixmonths.
[Continued
overleaf]
8
Anomaly
Description
Refere
nces
Portfolios
Typ.
Hold-
ing
Data
base
sComputa
tionaldeta
ils
Short-
term
reversal
Firms
with
extrem
ere-
turns
inthe
previous
month
ex-
hibit
return
reversal.
Jegadeesh
(1990),
Leh
mann
(1990)
5past
returns
(1-5)
onemonth
CRSP,Datas-
tream
Irankstocksbasedontheirraw
return
inthepreviousmonth.
Long-term
reversal
Firms
with
extrem
ere-
turns
inthe
previousthree
tofive
years
exhibit
return
reversal.
DeB
ondt
and
Thaler
(1985),
DeB
ondt
and
Thaler
(1987)
5past
returns
(1-5)
sixmonths
CRSP,Datas-
tream
Irankstocksbasedontheircu
mulativereturn
over
monthst-60to
t-13.
Stocksare
sorted
into
portfoliosatthebeginningofeach
month
and
heldin
theseportfoliosforsixmonths.
Thelong-term
reversalreturn
inagiven
month
istheequallyweighted
averageoftheov
erlapping
portfolioreturnsin
thatmonth.
Turnover
anomaly
Firms
with
high
past
turnover
un-
derperform
stocks
with
low
past
turnover.
Datar,
Naik,
and
Radcliffe
(1998),
Lee
and
Swami-
nathan(2000)
5turnov
er(1-
5)
onemonth
CRSP,Datas-
tream
Irely
ontheaveragemonthly
turnov
erov
ertheprevioustw
elvemonths,
defi
ned
asthenumber
ofsharestraded
divided
bythenumber
ofshares
outstanding.FortheU.S.stock
market,Ionly
consider
stockstradingat
NYSEorAmex,asturnov
erforNasdaqstocksisconceptuallydifferen
t
(e.g.,AtkinsandDyl(1997)).
Seasonality
anomaly
Stocks
tend
tohav
ehigh
(low
)returns
every
year
inthe
same
calendar
month.
Heston
and
Sadka
(2008),
Heston
and
Sadka
(2010)
5past
returns
(5-1)
onemonth
CRSP,Datas-
tream
Form
ationperiodreturnsare
computedastheav
eragereturn
inmonths
t-12,t-24,t-36,t-48andt-60.Irequireatleast
threeva
lidreturn
esti-
mates.
[Continued
overleaf]
9
Anomaly
Description
Refere
nces
Portfolios
Typ.
Hold-
ing
Data
base
sComputa
tionaldeta
ils
Inter-
med
iate
momen
-
tum
Interm
ediate
returns
(i.e.,
inmonths
t-12
tot-7)
positively
predictfuture
returns.
Nov
y-M
arx
(2012)
5past
returns
(5-1)
sixmonths
CRSP,Datas-
tream
Form
ation
period
returnsare
computed
asthecu
mulativereturn
in
monthst-12to
t-7.Stocksare
sorted
into
portfoliosatthebeginningof
each
month
andheldin
theseportfoliosforsixmonths.
Theinterm
edi-
ate
momen
tum
return
inagiven
month
istheequallyweightedaverage
oftheov
erlappingportfolioreturnsin
thatmonth.
Continuous
inform
a-
tion
arrival
anomaly
Momentum
is
concentrated
infirm
sfor
which
in-
form
ation
arrives
con-
tinuously
insm
all
amounts.
Da,
Gurun,
andWarachka
(2014)
5past
returns
(5-1)
con-
ditional
on
inform
ation
discreten
ess
sixmonths
CRSP,Datas-
tream
Iclosely
follow
theapproach
inDa,Gurun,andWarachka
(2014).Infor-
mationdiscreten
essisdefi
ned
assign(cumulativereturnsov
ertheprevi-
oustw
elvemonths)*([%neg-%
pos])/[%
neg+%pos]),
wherethefraction
ofday
sduringtheform
ationperiodwithpositiveornegativereturns
(inlocalcu
rren
cy)are
referred
toas%posor%neg,resp
ectively.In
each
month,Ifirstform
fiveequallysizedform
ationperiodreturn
portfolios
basedonthecu
mulativereturn
over
theprevioustw
elvemonths.W
ithin
each
portfolio,Ithen
compute
threeequallysizedportfoliosbasedon
inform
ationdiscreten
ess,whereportfolio1(3)containsfirm
swithcon-
tinuousinform
ationarrival(discreteinform
ationarrival).Ithen
only
keepfirm
sthatare
ininform
ationdiscreten
essportfolio1.Iuse
over-
lappingportfoliosasin
thecase
of(traditional)
momen
tum.
Earnings
announce-
men
t
premium
Stocks
out-
perform
in
months
when
they
are
ex-
pected
to
announce
earnings.
Barb
er,
George,
Leh
avy,
and
Truem
an
(2013),
Beaver
(1968),
Co-
hen
,Dey,
Lys,
andSun-
der
(2007),
Frazzini
and
Lamont
(2007),
Sav
or
and
Wilson
(2015)
2(expected
announcers
-
other
firm
s)
onemonth
CRSP,
Com-
pustat,
Datastream,
Worldscope
Firmsare
expectedto
hav
eanearningsannouncemen
tin
agiven
month
ifthey
announcedearningstw
elvemonthsago.ForU.S.firm
s,Irely
on
Compustatitem
RDQ
toquantify
theannouncemen
tdate,forinterna-
tionalfirm
sonDatastream
Mnem
onic
Codes
WC05901to
WC05904.
[Continued
overleaf]
10
Anomaly
Description
Refere
nces
Portfolios
Typ.
Hold-
ing
Data
base
sComputa
tionaldeta
ils
Dividen
d
month
anomaly
Stocks
out-
perform
in
months
when
they
are
ex-
pected
topay
adividen
d.
Hartzm
ark
and
Solomon
(2013)
2(expected
announcers
-
other
firm
s)
onemonth
CRSP,Datas-
tream,World-
scope
Firmsare
expectedto
hav
eadividen
dannouncemen
tin
agiven
month
ifthey
announced
adividen
dpay
menttw
elvemonthsago.ForU.S.
firm
s,Irely
onCRSP
item
sDISTCD
andDCLRDT,forinternational
firm
sonDatastream
Mnem
onic
Codes
WC05910to
WC05913.
Announce-
men
t-
return
based
PEAD
Stocks
with
positive
earn-
ings
surprises
(as
measured
by
announce-
mentreturns)
outperform
stocks
with
negativeearn-
ingssurprises.
Brandt,
Kishore,
Santa-C
lara,
and
Ven
kat-
achalam
(2008),
Chan,
Jegadeesh,
and
Lakon-
ishok(1996)
5earnings
surprises
(5-1)
threemonths
CRSP,
Com-
pustat,
Datastream,
Worldscope
Earningssurprisesare
basedonthecu
mulativereturn
betweent-2and
t+1,wheretden
otestheday
oftheearningsannouncemen
t.Portfolio
sortingsare
basedonthemost
recentquarterly
earningssurprise
which
Irequireto
hav
etaken
place
inthepreviousmonth
orearlier,
butnot
more
than
100
day
sago.ForU.S.firm
s,Irely
on
Compustatitem
RDQ
toquantify
theannouncemen
tdate,forinternationalfirm
son
Datastream
Mnem
onic
Codes
WC05901to
WC05904.
Analyst
consensus-
based
PEAD
Stocks
with
positive
earn-
ings
surprises
(as
measured
by
deviations
from
analyst
consensus)
outperform
stocks
with
negativeearn-
ingssurprises.
Doy
le,
Lund-
holm
,and
Soliman
(2006),
Hir-
shleifer,
Lim
,
and
Teoh
(2009)
5earnings
surprises
(5-1)
twelvemonths
CRSP,IB
ES,
Datastream
Irely
onearningsannouncemen
tdatesandanalyst
fiscalyear1earn-
ingsestimates,
both
ofwhichIobtain
from
theIB
ESSummary
Tape.
Earningssurprisesare
computedasthedifferen
cebetweenactualearn-
ingsandthemed
iananalyst
forecast,scaledbythestandard
deviation
oftheforecasts.Portfoliosortingsare
basedonthemost
recentearnings
surprise
whichIrequireto
hav
etaken
place
less
thanoneyearago.
[Continued
overleaf]
11
Anomaly
Description
Refere
nces
Portfolios
Typ.
Hold-
ing
Data
base
sComputa
tionaldeta
ils
R&D
in-
tensity
anomaly
Firms
with
high
(low
)
scaled
R&D
outperform
(underper-
form
).
Chan,Lakon-
ishok,
and
Sougiannis
(2001)
5R&D
inten-
sity
(5-1)
twelvemonths
CRSP,
Com-
pustat,
Datastream,
Worldscope
MotivatedbyChan,Lakonishok,andSougiannis
(2001),
researchand
development(X
RD,W
C01201)intensity
isdefi
ned
asresearch
and
development/salesin
yeart+
0.8*researchanddevelopmen
t/salesin
yeart-1+
0.6*researchanddevelopmen
t/salesin
yeart-3+
0.4*re-
searchanddevelopment/salesin
yeart-4+
0.2*researchanddevelop-
men
t/salesin
yeart-5.Imatchaccountingdata
forthefiscal-yearen
d
ofyeartwithstock
return
data
from
July
ofyeart+
1untilJuneof
yeart+
2.
R&D
growth
anomaly
High
R&D
intensity
firm
sthat
unexpect-
edly
increase
their
R&D
outperform
.
Eberhardt,
Maxwell,
and
Siddique
(2004)
2(even
tfirm
s
-non-event
firm
s)
twelvemonths
CRSP,
Com-
pustat,
Datastream,
Worldscope
MotivatedbyEberhardt,
Maxwell,andSiddique(2004),
ahighR&D
intensity
firm
isconsidered
tounexpectedly
increase
itsR&D
(XRD,
WC01201)ifthefollow
ingcriteria
are
met.First,atthebeginningof
theR&D
increase
year,
theratiosofR&D
toassetsandR&D
tosales
are
atleast
5%.Second,thefirm
needsto
increase
both
itsdollarR&D
anditsratioofR&D
toassetsbyatleast
5%
duringtheeventyear.
ImatchIaccountingdata
forthefiscal-yearen
dofyeartwithstock
return
data
from
July
ofyeart+
1untilJuneofyeart+
2.
200
day
mov
ing
average
anomaly
The
ratio
of
the
curren
t
price
and
the
mov
ing
200
day
average
price
posi-
tivelypredicts
returns.
Brock,Lakon-
ishok,
and
LeB
aron
(1992),
Lo,
Mamay
sky,
and
Wang
(2000),
Sulli-
van,Tim
mer-
mann,
and
White(1999)
5 price/mov
ing
average(5-1)
onemonth
CRSP,Datas-
tream
Iform
theratiooftheprice
attheen
dofmonth
t-1andtheav
erage
price
over
theprevious200day
s.Pricesare
adjusted
fordividen
dsand
stock
splits.
[Continued
overleaf]
12
Anomaly
Description
Refere
nces
Portfolios
Typ.
Hold-
ing
Data
base
sComputa
tionaldeta
ils
52
week
high
anomaly
Nearness
to
the
52
week
high
posi-
tivelypredicts
returns.
Brock,Lakon-
ishok,
and
LeB
aron
(1992),
Hud-
dart,
Lang,
and
Yetman
(2009),Liand
Yu
(2012),
Sullivan,
Tim
mer-
mann,
and
White(1999)
5nearnessto
52
week
high
(5-1)
onemonth
CRSP,Datas-
tream
Iform
theratioofthestock
price
attheen
dofthemonth
t-1andthe
maxim
um
dailyprice
over
theprevious52weeks(endingin
month
t-1).
Pricesare
adjusted
fordividen
dsandstock
splits.
Analyst
forecast
dispersion
anomaly
Stocks
with
low
dispersion
inanalysts’
earningsfore-
casts
outper-
form
stocks
with
high
dispersion.
Diether,
Malloy,
and
Scherbina
(2002),
John-
son(2004)
5forecast
dis-
persion(1-5)
onemonth
CRSP,IB
ES,
Datastream
Inspired
byDiether,Malloy,andScherbina(2002),analyst
forecast
dis-
persion
isdefi
ned
asthestandard
deviation
ofanalyst
fiscalyear1
earningsestimates,
scaledbytheabsolute
valueofthemeanestimate.
Imeasure
analyst
forecast
dispersionin
thepreviousmonth
andcondi-
tiononfirm
swithatleast
twoanalystsontheIB
ESsummary
tape.
13
Table 2: Three-factor alphas, Stambaugh, Yu, and Yuan (2015) mispricing, country-level results
The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios of aggregated mispricing (computed as in Stam-baugh, Yu, and Yuan (2015) and explained in detail in Section 2.2. of the paper)on local Fama and French (1993) three-factor models (as explained in Section 2.3.of the paper). All returns are computed in local currency. T-statistics (in parenthe-ses) are based on the heteroskedasticity-consistent standard errors of White (1980).Two-tailed statistical significance at the 10%, 5%, and 1% level is indicated by *, **,and ***, respectively.
Country Sample period Return weightingStart End Equally weighted returns Value weighted returns
Argentina May-00 Dec-13 0.927* (1.75) 0.554 (0.73)Australia Jan-94 Dec-13 1.314*** (5.19) 0.834** (2.43)Austria Jan-94 Dec-13 1.488*** (4.92) 1.208*** (3.13)Belgium Jan-94 Dec-13 1.397*** (5.48) 1.314*** (3.42)Brazil Mar-98 Dec-13 1.413*** (2.71) 1.150* (1.70)Canada Jan-94 Dec-13 1.039*** (3.56) 1.037** (2.39)Chile Jan-94 Dec-13 0.689*** (3.32) -0.049 (-0.16)China Jul-95 Dec-13 0.608** (2.56) 0.594* (1.90)Colombia Jul-07 Dec-13 0.221 (0.32) -1.269** (-2.11)Denmark Jan-94 Dec-13 1.505*** (6.46) 1.499*** (3.57)Egypt Jul-04 Dec-13 0.845 (1.32) 0.640 (1.11)Finland Jan-94 Dec-13 1.235*** (4.37) 1.403** (2.55)France Jan-94 Dec-13 1.622*** (7.49) 1.345*** (4.33)Germany Jan-94 Dec-13 1.563*** (6.81) 0.806** (2.52)Greece Jan-94 Dec-13 1.251*** (3.35) 1.296** (2.32)Hongkong Jan-94 Dec-13 0.415 (1.08) 0.824* (1.71)India Jul-94 Dec-13 0.925*** (3.52) 0.680* (1.71)Indonesia Jan-94 Dec-13 1.565*** (3.47) 2.226*** (4.42)Ireland Feb-96 Dec-13 0.738 (1.26) 1.642** (2.11)Israel Aug-98 Dec-13 1.053*** (2.88) 1.587*** (2.87)Italy Jan-94 Dec-13 1.139*** (4.54) 0.818** (2.20)Japan Jan-94 Dec-13 0.455*** (2.68) 0.543** (2.14)Jordan Jul-07 Dec-13 0.344 (0.77) 0.860* (1.72)Korea Jan-94 Dec-13 1.289*** (3.97) 1.266*** (3.39)Malaysia Jan-94 Dec-13 1.235*** (6.13) 0.998*** (3.93)Mexico Jan-94 Dec-13 0.901*** (3.38) 0.280 (0.77)Morocco Jul-06 Dec-13 0.425 (1.03) -0.197 (-0.44)Netherlands Jan-94 Dec-13 1.528*** (6.22) 0.216 (0.55)New Zealand Jul-97 Dec-13 1.527*** (4.06) 0.931** (2.25)Norway Jan-94 Dec-13 1.859*** (5.70) 1.533*** (3.32)Pakistan Jul-94 Dec-13 1.101*** (2.93) 1.169** (2.39)Philippines Jul-94 Dec-13 0.233 (0.49) 0.004 (0.01)Poland Aug-98 Dec-13 1.043** (2.50) 0.253 (0.46)Portugal Jan-94 Dec-13 2.187*** (5.44) 1.762*** (3.55)Russia Jul-05 Dec-13 0.017 (0.02) 0.913 (1.24)Singapore Jan-94 Dec-13 0.442** (2.02) 0.142 (0.38)South Africa Jan-94 Dec-13 1.149*** (4.06) 0.494 (1.34)Spain Jan-94 Dec-13 0.726*** (3.16) 0.615** (2.09)Sri Lanka Oct-05 Dec-13 0.556 (1.22) 1.039* (1.68)Sweden Jan-94 Dec-13 1.957*** (6.22) 1.475*** (3.64)Switzerland Jan-94 Dec-13 1.543*** (8.11) 1.118*** (3.30)Taiwan Jul-94 Dec-13 0.717*** (2.95) 0.369 (1.14)Thailand Jan-94 Dec-13 1.610*** (5.75) 1.570*** (4.09)Turkey May-94 Dec-13 -0.013 (-0.03) -1.052 (-1.62)UK Jan-94 Dec-13 1.846*** (10.89) 0.866*** (2.89)USA Jan-94 Dec-13 1.362*** (6.66) 1.179*** (6.00)
14
Table 3: Three-factor alphas, failure probability, country-level results
The table reports monthly alphas (in %) obtained from regressing the quintile-based country-level long/short portfolios sorted on failure probability (as quanti-fied by the Campbell, Hilscher, and Szilagyi (2008) measure) on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). All re-turns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Country Sample period Return weightingStart End Equally weighted returns Value weighted returns
Argentina May-00 Dec-13 0.781 (1.53) 1.205* (1.68)Australia Jan-94 Dec-13 0.647*** (2.60) 0.045 (0.11)Austria Jan-94 Dec-13 1.164*** (3.57) 1.212** (2.59)Belgium Jan-94 Dec-13 1.422*** (5.66) 1.344*** (3.56)Brazil Mar-98 Dec-13 0.603 (0.78) -1.063 (-0.81)Canada Jan-94 Dec-13 0.388 (1.30) 0.345 (0.80)Chile Jan-94 Dec-13 1.059*** (4.17) 0.703** (2.12)China May-96 Dec-13 0.069 (0.21) 0.217 (0.64)Colombia Jul-10 Dec-13 1.281* (1.72) -0.420 (-0.50)Denmark Jan-94 Dec-13 0.719** (2.30) 0.080 (0.18)Egypt May-05 Dec-13 1.279* (1.91) 0.930 (1.10)Finland Jan-94 Dec-13 0.950*** (2.98) 1.145** (2.00)France Jan-94 Dec-13 1.117*** (4.89) 0.899* (1.92)Germany Jan-94 Dec-13 1.155*** (5.29) 1.302*** (3.51)Greece Jan-94 Dec-13 0.735* (1.73) 0.876* (1.82)Hongkong Jan-94 Dec-13 0.195 (0.48) 0.714 (1.31)India May-94 Dec-13 0.549** (2.01) 0.640 (1.27)Indonesia Jan-94 Dec-13 0.921** (2.00) 1.580** (2.27)Ireland May-98 Dec-13 0.162 (0.22) 0.652 (0.64)Israel May-99 Dec-13 0.748* (1.96) 0.527 (0.81)Italy Jan-94 Dec-13 0.955*** (3.47) 1.184*** (2.68)Japan Jan-94 Dec-13 0.400** (2.12) 0.679** (2.26)Jordan May-07 Dec-13 0.080 (0.16) -1.102 (-1.41)Korea Jan-94 Dec-13 1.009*** (2.63) 1.868*** (3.18)Malaysia Jan-94 Dec-13 1.208*** (4.96) 1.214*** (4.20)Mexico Jan-94 Dec-13 0.926*** (2.84) 0.553 (1.28)Morocco May-07 Dec-13 1.210*** (2.80) 1.176** (2.20)Netherlands Jan-94 Dec-13 1.406*** (4.20) 0.686 (1.25)New Zealand May-97 Dec-13 1.379*** (3.71) 1.121** (2.20)Norway Jan-94 Dec-13 1.185*** (3.51) 1.023* (1.91)Pakistan May-94 Dec-13 0.434 (0.98) 0.972* (1.67)Philippines Jul-94 Dec-13 0.771 (1.43) 0.763 (1.31)Poland May-99 Dec-13 0.937** (2.23) 0.848 (1.54)Portugal Jan-94 Dec-13 1.062** (2.31) 0.965* (1.83)Russia May-05 Dec-13 -0.527 (-0.51) -0.405 (-0.44)Singapore Jan-94 Dec-13 0.363 (1.15) 1.032*** (3.01)South Africa Jan-94 Dec-13 0.343 (1.08) 0.700* (1.65)Spain Jan-94 Dec-13 0.847*** (2.66) 1.020*** (2.71)Sri Lanka May-07 Dec-13 0.766 (1.37) 2.440*** (3.69)Sweden Jan-94 Dec-13 1.516*** (4.76) 1.144** (2.08)Switzerland Jan-94 Dec-13 1.213*** (5.78) 1.077** (2.52)Taiwan May-94 Dec-13 0.755*** (2.73) 1.322*** (4.09)Thailand Jan-94 Dec-13 0.817** (2.37) 1.288*** (2.65)Turkey May-94 Dec-13 -0.598 (-1.22) -1.590** (-2.10)UK Jan-94 Dec-13 1.047*** (5.62) 0.288 (0.87)USA Jan-94 Dec-13 0.645** (2.50) 1.186*** (3.91)
15
Table 4: Three-factor alphas, Ohlson (1980) score, country-level results
The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on financial distress (as quantified by theOhlson (1980) score) on local Fama and French (1993) three-factor models (as ex-plained in Section 2.3. of the paper). All returns are computed in local currency.T-statistics (in parentheses) are based on the heteroskedasticity-consistent standarderrors of White (1980). Two-tailed statistical significance at the 10%, 5%, and 1%level is indicated by *, **, and ***, respectively.
Country Sample period Return weightingStart End Equally weighted returns Value weighted returns
Argentina Jul-00 Dec-13 0.907* (1.68) 1.746** (2.26)Australia Jan-94 Dec-13 -0.183 (-0.83) -0.204 (-0.75)Austria Jan-94 Dec-13 0.568* (1.68) 0.824** (1.99)Belgium Jan-94 Dec-13 0.995*** (3.34) 0.783* (1.86)Brazil Mar-98 Dec-13 0.389 (0.50) 0.558 (0.67)Canada Jan-94 Dec-13 -0.099 (-0.31) 0.432 (1.07)Chile Jan-94 Dec-13 0.235 (1.09) 0.208 (0.69)China Jul-95 Dec-13 0.127 (0.41) 0.384 (1.33)Colombia Oct-09 Dec-13 -0.096 (-0.14) -1.571 (-1.50)Denmark Jan-94 Dec-13 0.193 (0.66) 0.062 (0.14)Egypt Jul-05 Dec-13 0.117 (0.21) 0.181 (0.19)Finland Jan-94 Dec-13 0.317 (1.15) 0.308 (0.78)France Jan-94 Dec-13 0.746*** (4.48) 0.414 (1.44)Germany Jan-94 Dec-13 0.271 (1.57) -0.274 (-0.76)Greece Jan-94 Dec-13 0.764** (2.19) 0.566 (1.24)Hongkong Jan-94 Dec-13 -0.463 (-1.09) 0.017 (0.03)India Jul-94 Dec-13 -0.093 (-0.41) 0.216 (0.57)Indonesia Jan-94 Dec-13 0.270 (0.70) 0.448 (0.79)Ireland Jul-98 Dec-13 0.497 (0.70) 0.385 (0.43)Israel Jul-99 Dec-13 0.817** (2.03) 0.614 (0.90)Italy Jan-94 Dec-13 1.180*** (4.61) 0.993*** (2.95)Japan Jan-94 Dec-13 0.418*** (3.35) 0.605*** (2.94)Jordan Jul-07 Dec-13 0.480 (1.16) -0.169 (-0.20)Korea Jan-94 Dec-13 0.720** (2.56) 1.528*** (2.95)Malaysia Jan-94 Dec-13 0.673*** (2.91) 0.774*** (2.68)Mexico Jan-94 Dec-13 0.945*** (3.05) 1.102*** (2.72)Morocco Jul-07 Dec-13 1.103*** (2.94) 0.236 (0.37)Netherlands Jan-94 Dec-13 0.629** (2.52) 0.142 (0.34)New Zealand Jul-97 Dec-13 0.346 (0.93) -0.103 (-0.19)Norway Jan-94 Dec-13 0.733** (2.29) 0.994* (1.94)Pakistan Jul-94 Dec-13 0.508 (1.34) 0.970** (2.04)Philippines Jul-94 Dec-13 1.031* (1.82) 0.177 (0.29)Poland Jul-98 Dec-13 0.316 (0.73) 0.037 (0.06)Portugal Jan-94 Dec-13 0.623 (1.31) 0.605 (1.34)Russia Jul-05 Dec-13 -0.816 (-0.96) 1.236* (1.75)Singapore Jan-94 Dec-13 0.504 (1.64) 0.669** (2.13)South Africa Jan-94 Dec-13 -0.359 (-0.90) -0.045 (-0.10)Spain Jan-94 Dec-13 0.272 (1.03) 0.502 (1.47)Sri Lanka Jul-07 Dec-13 0.626 (0.89) -0.270 (-0.32)Sweden Jan-94 Dec-13 0.815*** (2.90) 0.995*** (2.87)Switzerland Jan-94 Dec-13 0.760*** (3.68) 1.367*** (3.70)Taiwan Jul-94 Dec-13 0.352 (1.35) 0.955*** (3.63)Thailand Jan-94 Dec-13 0.141 (0.44) 0.452 (0.84)Turkey Jul-94 Dec-13 -0.205 (-0.42) -0.939 (-1.25)UK Jan-94 Dec-13 0.641*** (3.45) 0.375 (1.45)USA Jan-94 Dec-13 0.508** (2.15) 0.546** (2.15)
16
Table 5: Three-factor alphas, net stock issues, country-level results
The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on net stock issues on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Country Sample period Return weightingStart End Equally weighted returns Value weighted returns
Argentina May-94 Dec-13 0.250 (0.69) -0.474 (-0.95)Australia Jan-94 Dec-13 0.817*** (3.73) 0.688** (2.38)Austria Jan-94 Dec-13 0.755*** (2.85) 0.291 (0.83)Belgium Jan-94 Dec-13 0.672*** (3.20) 0.136 (0.37)Brazil Sep-95 Dec-13 0.701 (1.28) 1.343 (1.53)Canada Jan-94 Dec-13 0.850*** (3.48) 0.549* (1.79)Chile Jan-94 Dec-13 0.514*** (2.97) -0.135 (-0.45)China Jan-94 Dec-13 -0.141 (-1.01) -0.196 (-0.90)Colombia Mar-94 Dec-13 0.605 (1.56) 0.295 (0.61)Denmark Jan-94 Dec-13 0.763*** (3.73) 0.953** (2.42)Egypt Jul-99 Dec-13 0.318 (0.68) -0.499 (-0.81)Finland Jan-94 Dec-13 0.745*** (3.16) -0.194 (-0.38)France Jan-94 Dec-13 0.829*** (5.20) 0.608*** (2.88)Germany Jan-94 Dec-13 0.710*** (5.19) 0.718*** (2.73)Greece Jan-94 Dec-13 0.800*** (3.09) 0.351 (1.01)Hongkong Jan-94 Dec-13 1.386*** (4.12) 0.444 (1.11)India Jan-94 Dec-13 0.509*** (3.84) 0.179 (0.56)Indonesia Jan-94 Dec-13 0.428 (1.41) 0.500 (1.32)Ireland Jan-94 Dec-13 0.102 (0.23) 0.614 (0.95)Israel Jul-94 Dec-13 0.656*** (3.24) 0.616 (1.45)Italy Jan-94 Dec-13 0.584*** (3.54) -0.020 (-0.08)Japan Jan-94 Dec-13 0.160* (1.74) -0.343** (-2.44)Jordan Dec-06 Dec-13 -0.453 (-1.27) -0.483 (-0.84)Korea Jan-94 Dec-13 2.177*** (7.79) 1.531*** (4.45)Malaysia Jan-94 Dec-13 0.563*** (3.01) 0.512*** (2.80)Mexico Jan-94 Dec-13 -0.010 (-0.04) 0.274 (0.91)Morocco Oct-00 Dec-13 -0.556* (-1.75) -0.548* (-1.88)Netherlands Jan-94 Dec-13 1.038*** (4.16) 0.346 (0.95)New Zealand Jan-94 Dec-13 0.648** (2.08) 0.235 (0.66)Norway Jan-94 Dec-13 1.173*** (4.39) 0.772** (1.99)Pakistan Jan-94 Dec-13 0.281 (1.21) 0.593* (1.67)Philippines Jan-94 Dec-13 0.252 (0.78) 0.201 (0.53)Poland Jan-97 Dec-13 0.046 (0.14) -0.171 (-0.44)Portugal Jan-94 Dec-13 0.925*** (2.64) 0.420 (1.03)Russia Dec-02 Dec-13 -0.388 (-0.80) -0.261 (-0.39)Singapore Jan-94 Dec-13 0.546** (2.54) -0.201 (-0.64)South Africa Jan-94 Dec-13 0.937*** (3.82) -0.092 (-0.27)Spain Jan-94 Dec-13 0.718*** (3.84) 0.822*** (2.73)Sri Lanka Jun-95 Dec-13 0.622* (1.82) 0.372 (0.77)Sweden Jan-94 Dec-13 1.211*** (5.23) 0.001 (0.00)Switzerland Jan-94 Dec-13 0.668*** (4.06) 0.179 (0.59)Taiwan Jan-94 Dec-13 0.393** (2.01) 0.127 (0.49)Thailand Jan-94 Dec-13 0.836*** (3.96) 0.867*** (3.00)Turkey Jan-94 Dec-13 0.517 (1.50) -0.633 (-0.93)UK Jan-94 Dec-13 1.180*** (8.26) 0.450* (1.77)USA Jan-94 Dec-13 1.195*** (7.44) 0.670*** (3.97)
17
Table 6: Three-factor alphas, composite equity issues, country-level results
The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on composite equity issues on local Famaand French (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Country Sample period Return weightingStart End Equally weighted returns Value weighted returns
Argentina Sep-98 Dec-13 0.313 (0.79) -0.022 (-0.03)Australia Jan-94 Dec-13 1.254*** (5.12) 0.410 (1.23)Austria Jan-94 Dec-13 1.034*** (3.46) 0.925*** (2.72)Belgium Jan-94 Dec-13 1.026*** (4.85) 0.145 (0.42)Brazil Sep-99 Dec-13 1.273 (1.34) -0.124 (-0.14)Canada Jan-94 Dec-13 1.401*** (4.33) 0.682* (1.91)Chile Sep-94 Dec-13 1.015*** (4.66) 0.099 (0.22)China Oct-97 Dec-13 0.352** (1.98) 0.246 (1.03)Colombia Apr-99 Dec-13 0.103 (0.17) 0.176 (0.29)Denmark Jan-94 Dec-13 0.869*** (3.95) 0.948** (2.40)Egypt Nov-01 Dec-13 1.351** (2.56) 1.541** (2.15)Finland Oct-94 Dec-13 1.154*** (4.57) 0.792 (1.52)France Jan-94 Dec-13 0.787*** (4.62) 0.457* (1.95)Germany Jan-94 Dec-13 0.754*** (4.93) 0.589* (1.92)Greece Jan-94 Dec-13 1.453*** (4.18) 1.870*** (3.47)Hongkong Jan-94 Dec-13 0.848** (2.13) 0.966 (1.61)India Mar-95 Dec-13 1.092*** (5.94) 0.679** (1.97)Indonesia Jun-95 Dec-13 1.393*** (2.93) 0.370 (0.63)Ireland Jan-94 Dec-13 0.047 (0.07) 0.091 (0.11)Israel Jul-94 Dec-13 0.743*** (3.25) 0.486 (1.18)Italy Jan-94 Dec-13 1.086*** (5.65) 0.738*** (2.72)Japan Jan-94 Dec-13 0.290** (2.40) 0.256 (1.33)Jordan Dec-10 Dec-13 1.025* (1.74) 0.871 (1.01)Korea Jan-94 Dec-13 1.796*** (6.60) 1.016*** (2.62)Malaysia Jan-94 Dec-13 1.232*** (6.46) 1.039*** (4.30)Mexico Jan-94 Dec-13 0.732** (2.26) 0.282 (0.77)Morocco Oct-00 Dec-13 -0.032 (-0.09) -0.382 (-0.99)Netherlands Jan-94 Dec-13 1.191*** (4.79) 0.457 (1.25)New Zealand Jan-94 Dec-13 0.683** (2.06) 0.236 (0.57)Norway Jan-94 Dec-13 0.991*** (3.15) 0.601 (1.42)Pakistan Sep-97 Dec-13 1.339*** (4.07) 1.821*** (3.82)Philippines Feb-95 Dec-13 1.454*** (2.75) 0.589 (1.13)Poland May-02 Dec-13 1.116*** (2.70) 1.143** (2.41)Portugal Jan-94 Dec-13 2.035*** (4.46) 1.164** (2.57)Russia Jul-04 Dec-13 0.314 (0.50) -2.017** (-2.07)Singapore Jan-94 Dec-13 0.951*** (3.80) 0.002 (0.00)South Africa Jan-94 Dec-13 0.964*** (4.20) 0.300 (0.98)Spain Jan-94 Dec-13 1.003*** (4.70) 0.677** (2.35)Sri Lanka Jun-95 Dec-13 0.893** (2.49) 1.326*** (2.99)Sweden Jan-94 Dec-13 0.771** (2.59) 0.688 (1.54)Switzerland Jan-94 Dec-13 0.699*** (4.44) 0.182 (0.60)Taiwan Jan-94 Dec-13 0.564** (2.57) -0.036 (-0.11)Thailand Jan-94 Dec-13 0.908*** (3.19) 1.081*** (2.84)Turkey Jan-94 Dec-13 1.111*** (3.07) 0.285 (0.42)UK Jan-94 Dec-13 1.083*** (7.00) 0.512 (1.57)USA Jan-94 Dec-13 0.742*** (4.53) 0.559*** (3.60)
18
Table 7: Three-factor alphas, accruals, country-level results
The table reports monthly alphas (in %) obtained from regressing the quintile-based country-level long/short portfolios sorted on accruals on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based onthe heteroskedasticity-consistent standard errors of White (1980). Two-tailed sta-tistical significance at the 10%, 5%, and 1% level is indicated by *, **, and ***,respectively.
Country Sample period Return weightingStart End Equally weighted returns Value weighted returns
Argentina Jul-01 Dec-13 0.036 (0.07) -1.036 (-1.19)Australia Jan-94 Dec-13 0.360 (1.64) 0.617* (1.86)Austria Jan-94 Dec-13 0.190 (0.55) 0.376 (0.78)Belgium Jan-94 Dec-13 0.374 (1.41) 0.358 (0.99)Brazil Mar-98 Dec-13 0.673 (1.29) 1.736* (1.85)Canada Jan-94 Dec-13 0.288 (0.86) 0.518 (1.45)Chile Jan-94 Dec-13 -0.112 (-0.45) -0.101 (-0.32)China Jul-95 Dec-13 0.359* (1.69) 0.427 (1.54)Colombia Nov-09 Dec-13 -1.769* (-1.79) -4.573 (-1.21)Denmark Jan-94 Dec-13 0.623** (2.50) 1.988*** (3.69)Egypt Jul-05 Dec-13 -0.866 (-1.09) -1.345 (-1.29)Finland Jan-94 Dec-13 0.585** (2.45) 1.153* (1.92)France Jan-94 Dec-13 0.542*** (3.71) 0.391 (1.06)Germany Jan-94 Dec-13 0.339** (2.16) 0.553 (1.57)Greece Jan-94 Dec-13 0.233 (0.69) -0.092 (-0.21)Hongkong Jan-94 Dec-13 0.069 (0.17) 0.961* (1.82)India Jul-94 Dec-13 0.669*** (3.22) 0.888** (2.35)Indonesia Jan-94 Dec-13 0.628* (1.76) 0.499 (0.86)Ireland Jul-98 Dec-13 0.288 (0.46) 1.358* (1.84)Israel Jul-99 Dec-13 0.487 (1.30) 0.125 (0.21)Italy Jan-94 Dec-13 0.564*** (2.71) 0.551 (1.64)Japan Jan-94 Dec-13 0.074 (0.81) 0.081 (0.46)Jordan Jul-07 Dec-13 -0.110 (-0.21) 0.813 (1.30)Korea Jan-94 Dec-13 0.355 (1.59) 1.246** (2.42)Malaysia Jan-94 Dec-13 0.118 (0.65) 0.280 (0.92)Mexico Jan-94 Dec-13 0.339 (1.07) 0.356 (0.83)Morocco Jul-07 Dec-13 0.810* (1.67) -0.180 (-0.27)Netherlands Jan-94 Dec-13 -0.136 (-0.57) -0.843 (-1.52)New Zealand Jul-97 Dec-13 0.792** (2.10) 0.601 (1.33)Norway Jan-94 Dec-13 0.301 (0.98) 0.522 (1.02)Pakistan Jul-94 Dec-13 -0.025 (-0.07) 0.033 (0.07)Philippines Jul-95 Dec-13 0.143 (0.29) -0.401 (-0.81)Poland Jul-03 Dec-13 0.265 (0.72) 0.184 (0.37)Portugal Jan-94 Dec-13 0.812** (2.08) 1.330*** (3.05)Russia Feb-06 Dec-13 0.890 (1.11) 0.145 (0.20)Singapore Jan-94 Dec-13 0.111 (0.57) 0.416 (1.04)South Africa Jan-94 Dec-13 0.700** (2.47) 0.648* (1.67)Spain Jan-94 Dec-13 -0.013 (-0.05) 0.176 (0.49)Sri Lanka Jul-07 Dec-13 0.495 (0.79) -1.243** (-1.99)Sweden Jan-94 Dec-13 0.291 (1.20) 0.666 (1.48)Switzerland Jan-94 Dec-13 0.376** (2.04) 0.967** (2.55)Taiwan Jul-94 Dec-13 0.169 (0.75) 0.055 (0.16)Thailand Jan-94 Dec-13 1.131*** (3.85) 1.144** (2.57)Turkey Nov-96 Dec-13 -0.372 (-0.98) -0.394 (-0.67)UK Jan-94 Dec-13 0.379*** (3.05) 0.633* (1.71)USA Jan-94 Dec-13 0.263** (2.51) 0.168 (0.85)
19
Table 8: Three-factor alphas, net operating assets, country-level results
The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on net operating assets on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Country Sample period Return weightingStart End Equally weighted returns Value weighted returns
Argentina Jul-00 Dec-13 0.218 (0.39) 0.057 (0.08)Australia Jan-94 Dec-13 0.736*** (3.42) 0.475 (1.38)Austria Jan-94 Dec-13 0.723* (1.95) 0.778* (1.87)Belgium Jan-94 Dec-13 0.263 (1.15) 0.226 (0.78)Brazil Mar-98 Dec-13 0.247 (0.34) 0.312 (0.53)Canada Jan-94 Dec-13 0.212 (0.68) 0.490 (1.45)Chile Jan-94 Dec-13 -0.357 (-1.55) -0.636* (-1.83)China Jul-95 Dec-13 0.480** (2.19) 0.597** (2.24)Colombia Jul-07 Dec-13 -1.463* (-1.96) -1.298* (-1.81)Denmark Jan-94 Dec-13 0.669*** (2.72) 1.072** (2.25)Egypt Jul-05 Dec-13 0.319 (0.51) 0.176 (0.26)Finland Jan-94 Dec-13 0.097 (0.45) 0.523 (0.95)France Jan-94 Dec-13 0.626*** (4.61) 0.030 (0.11)Germany Jan-94 Dec-13 0.706*** (4.19) 0.281 (0.84)Greece Jan-94 Dec-13 0.658** (2.11) 0.430 (1.09)Hongkong Jan-94 Dec-13 0.347 (0.98) 1.056** (2.07)India Jul-94 Dec-13 0.875*** (3.94) 0.764** (2.26)Indonesia Jan-94 Dec-13 1.431*** (3.89) 1.458*** (2.91)Ireland Jul-98 Dec-13 0.585 (0.81) 1.392* (1.77)Israel Jul-98 Dec-13 0.532 (1.59) 0.164 (0.27)Italy Jan-94 Dec-13 0.218 (0.99) -0.101 (-0.32)Japan Jan-94 Dec-13 0.178* (1.92) 0.003 (0.02)Jordan Jul-07 Dec-13 0.348 (0.74) 0.326 (0.49)Korea Jan-94 Dec-13 0.350 (1.50) 0.293 (0.71)Malaysia Jan-94 Dec-13 0.419** (2.04) 0.761*** (2.96)Mexico Jan-94 Dec-13 0.309 (1.09) 0.713* (1.70)Morocco Jul-07 Dec-13 0.640 (1.49) 1.434** (2.11)Netherlands Jan-94 Dec-13 0.237 (0.91) 0.472 (1.18)New Zealand Jul-97 Dec-13 0.137 (0.37) -0.267 (-0.63)Norway Jan-94 Dec-13 1.460*** (4.08) 0.953** (2.10)Pakistan Jul-94 Dec-13 0.977** (2.28) 1.350*** (2.97)Philippines Jul-94 Dec-13 -0.269 (-0.53) 0.426 (0.71)Poland Aug-98 Dec-13 0.059 (0.11) 0.259 (0.33)Portugal Jan-94 Dec-13 1.011** (2.16) -0.296 (-0.63)Russia Jul-05 Dec-13 0.430 (0.54) 1.212 (1.49)Singapore Jan-94 Dec-13 0.087 (0.44) -0.162 (-0.47)South Africa Jan-94 Dec-13 0.811*** (2.94) 0.295 (0.76)Spain Jan-94 Dec-13 0.590*** (2.87) -0.128 (-0.37)Sri Lanka Jul-07 Dec-13 1.323** (2.16) 2.009** (2.39)Sweden Jan-94 Dec-13 0.720*** (2.83) 0.258 (0.51)Switzerland Jan-94 Dec-13 0.510*** (2.85) 0.110 (0.28)Taiwan Jul-94 Dec-13 0.170 (0.73) -0.243 (-0.80)Thailand Jan-94 Dec-13 0.817*** (2.89) 0.568 (1.45)Turkey Jul-94 Dec-13 0.514 (1.21) 0.215 (0.33)UK Jan-94 Dec-13 0.669*** (4.00) 0.406 (1.39)USA Jan-94 Dec-13 0.802*** (4.68) 0.454** (2.53)
20
Table 9: Three-factor alphas, momentum, country-level results
The table reports monthly alphas (in %) obtained from regressing the quintile-based country-level long/short portfolios sorted on momentum on local Fama andFrench (1993) three-factor models (as explained in Section 2.3 of the paper). All re-turns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Country Sample period Return weightingStart End Equally weighted returns Value weighted returns
Argentina Jan-94 Dec-13 -0.030 (-0.07) -0.231 (-0.38)Australia Jan-94 Dec-13 1.880*** (7.37) 2.008*** (6.35)Austria Jan-94 Dec-13 1.169*** (4.12) 0.934*** (3.17)Belgium Jan-94 Dec-13 1.590*** (6.06) 1.477*** (4.06)Brazil Mar-95 Dec-13 0.496 (0.79) 0.833 (0.68)Canada Jan-94 Dec-13 1.079*** (3.21) 1.419*** (3.55)Chile Jan-94 Dec-13 1.155*** (6.37) 0.922*** (3.30)China Jan-94 Dec-13 0.381* (1.71) 0.610** (2.06)Colombia Jan-94 Dec-13 1.058*** (2.75) 1.077* (1.95)Denmark Jan-94 Dec-13 1.654*** (7.01) 1.434*** (3.85)Egypt Jul-99 Dec-13 0.415 (0.92) 0.882 (1.49)Finland Jan-94 Dec-13 1.272*** (4.14) 1.611*** (2.92)France Jan-94 Dec-13 1.513*** (5.71) 0.957*** (2.89)Germany Jan-94 Dec-13 1.445*** (5.16) 1.455*** (3.95)Greece Jan-94 Dec-13 0.972** (2.38) 1.517*** (2.81)Hongkong Jan-94 Dec-13 0.703* (1.77) 1.413*** (2.63)India Jan-94 Dec-13 1.333*** (3.94) 1.449*** (3.14)Indonesia Jan-94 Dec-13 0.378 (0.99) 0.991** (2.26)Ireland Jan-94 Dec-13 1.825*** (4.20) 1.625*** (2.89)Israel Jul-94 Dec-13 1.036*** (3.86) 1.679*** (3.86)Italy Jan-94 Dec-13 1.239*** (4.47) 1.119*** (3.02)Japan Jan-94 Dec-13 0.465** (2.02) 1.010*** (3.25)Jordan Jul-06 Dec-13 0.579 (1.28) 1.335* (1.90)Korea Jan-94 Dec-13 0.821* (1.97) 1.229** (2.59)Malaysia Jan-94 Dec-13 0.685*** (2.70) 0.721** (2.57)Mexico Jan-94 Dec-13 1.020*** (3.35) 0.883*** (2.87)Morocco Oct-00 Dec-13 0.815*** (2.79) 0.735** (2.21)Netherlands Jan-94 Dec-13 1.839*** (5.87) 0.594 (1.33)New Zealand Jan-94 Dec-13 1.784*** (5.96) 1.115*** (3.34)Norway Jan-94 Dec-13 1.488*** (4.86) 1.322*** (3.10)Pakistan Jan-94 Dec-13 0.754* (1.84) 0.780* (1.68)Philippines Jan-94 Dec-13 0.323 (0.77) 0.537 (1.27)Poland Jul-96 Dec-13 1.071*** (2.95) 0.702 (1.53)Portugal Jan-94 Dec-13 1.536*** (3.67) 1.256*** (2.71)Russia Dec-02 Dec-13 0.277 (0.61) 1.649** (2.49)Singapore Jan-94 Dec-13 0.631* (1.91) 0.507 (1.41)South Africa Jan-94 Dec-13 2.405*** (9.95) 2.097*** (5.59)Spain Jan-94 Dec-13 0.916*** (3.52) 1.074*** (3.01)Sri Lanka Jun-95 Dec-13 0.415 (1.24) 1.020*** (2.61)Sweden Jan-94 Dec-13 1.474*** (4.06) 0.756 (1.61)Switzerland Jan-94 Dec-13 1.611*** (5.96) 1.022** (2.39)Taiwan Jan-94 Dec-13 0.500* (1.87) 0.325 (0.89)Thailand Jan-94 Dec-13 1.124*** (3.06) 1.583*** (3.75)Turkey Jan-94 Dec-13 -0.988*** (-2.60) -0.784 (-1.53)UK Jan-94 Dec-13 1.622*** (7.62) 1.257*** (3.87)USA Jan-94 Dec-13 1.211*** (3.67) 1.268*** (3.93)
21
Table 10: Three-factor alphas, gross profitability, country-level results
The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on gross profitability on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Country Sample period Return weightingStart End Equally weighted returns Value weighted returns
Argentina Jul-99 Dec-13 0.132 (0.27) 0.463 (0.76)Australia Jan-94 Dec-13 0.098 (0.34) 0.123 (0.38)Austria Jan-94 Dec-13 -0.164 (-0.52) 0.358 (0.91)Belgium Jan-94 Dec-13 -0.644** (-2.43) -0.369 (-0.84)Brazil Jul-97 Dec-13 0.767 (0.92) 1.932*** (2.75)Canada Jan-94 Dec-13 0.851** (2.37) 0.062 (0.11)Chile Jan-94 Dec-13 0.114 (0.55) -0.050 (-0.14)China Jul-94 Dec-13 0.242 (0.76) 0.077 (0.22)Colombia Jul-10 Dec-13 -1.079 (-0.96) -2.004 (-1.00)Denmark Jan-94 Dec-13 0.801*** (2.83) 1.287*** (2.64)Egypt Jul-04 Dec-13 1.119* (1.84) 1.586* (1.94)Finland Jan-94 Dec-13 0.403* (1.70) 0.223 (0.48)France Jan-94 Dec-13 0.668*** (4.49) 0.468** (2.14)Germany Jan-94 Dec-13 0.775*** (4.81) 0.795** (2.48)Greece Jan-94 Dec-13 0.931*** (3.15) 0.720* (1.90)Hongkong Jan-94 Dec-13 0.244 (0.61) -0.083 (-0.13)India Jan-94 Dec-13 0.272 (1.15) 0.780** (2.23)Indonesia Jan-94 Dec-13 1.209** (2.42) 1.352** (2.52)Ireland Jul-99 Dec-13 -0.894 (-1.07) 0.005 (0.01)Israel Feb-98 Dec-13 0.593 (1.61) 0.264 (0.57)Italy Jan-94 Dec-13 0.403* (1.79) 0.383 (0.98)Japan Jan-94 Dec-13 0.682*** (4.59) 0.660*** (3.23)JordanKorea Jan-94 Dec-13 1.391*** (4.79) 1.206** (2.29)Malaysia Jan-94 Dec-13 0.895*** (4.54) 0.810*** (2.86)Mexico Jan-94 Dec-13 0.331 (1.05) 0.092 (0.24)Morocco Jul-06 Dec-13 0.185 (0.53) 0.071 (0.16)Netherlands Jan-94 Dec-13 0.483** (2.11) 0.128 (0.34)New Zealand Jul-95 Dec-13 0.419 (1.12) -0.300 (-0.57)Norway Jan-94 Dec-13 1.132*** (3.31) 0.755 (1.63)Pakistan Jul-94 Dec-13 0.980** (2.24) 1.125** (2.28)Philippines Jan-94 Dec-13 0.938** (2.03) 1.374*** (2.77)Poland Jul-97 Dec-13 1.283*** (3.27) 1.369** (2.48)Portugal Jan-94 Dec-13 1.433*** (3.02) 2.075*** (3.96)Russia Jul-04 Dec-13 0.820 (0.86) 1.965* (1.98)Singapore Jan-94 Dec-13 0.523** (1.99) 0.712** (2.18)South Africa Jan-94 Dec-13 0.278 (0.99) 0.228 (0.59)Spain Jan-94 Dec-13 0.252 (1.06) 0.760* (1.95)Sri Lanka Oct-05 Dec-13 1.803** (2.45) 2.166*** (2.82)Sweden Jan-94 Dec-13 1.079*** (3.65) 1.061*** (2.92)Switzerland Jan-94 Dec-13 0.650*** (3.30) 0.662** (2.47)Taiwan Jan-94 Dec-13 0.691*** (2.80) 1.315*** (4.03)Thailand Jan-94 Dec-13 0.844*** (3.11) 0.531 (1.31)Turkey Jan-94 Dec-13 0.406 (0.88) 0.413 (0.52)UK Jan-94 Dec-13 0.957*** (4.86) 0.347 (1.02)USA Jan-94 Dec-13 0.616*** (3.71) 0.955*** (4.51)
22
Table 11: Three-factor alphas, asset growth, country-level results
The table reports monthly alphas (in %) obtained from regressing the quintile-based country-level long/short portfolios sorted on asset growth on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). All re-turns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Country Sample period Return weightingStart End Equally weighted returns Value weighted returns
Argentina Jul-00 Dec-13 0.024 (0.05) -0.309 (-0.42)Australia Jan-94 Dec-13 1.142*** (4.99) 0.955*** (2.83)Austria Jan-94 Dec-13 0.373 (1.14) 0.593 (1.57)Belgium Jan-94 Dec-13 0.123 (0.48) -0.116 (-0.33)Brazil Jul-97 Dec-13 0.549 (0.77) 1.211 (1.26)Canada Jan-94 Dec-13 0.338 (0.97) 0.632* (1.77)Chile Jan-94 Dec-13 -0.542** (-2.44) -0.290 (-0.98)China Jul-95 Dec-13 0.435 (1.34) 0.056 (0.14)Colombia Jul-04 Dec-13 0.158 (0.30) 1.304* (1.73)Denmark Jan-94 Dec-13 0.648*** (3.01) 0.856** (2.46)Egypt Jul-04 Dec-13 0.161 (0.30) -0.371 (-0.61)Finland Jan-94 Dec-13 0.207 (0.71) 0.556 (1.26)France Jan-94 Dec-13 0.326* (1.72) 0.581* (1.96)Germany Jan-94 Dec-13 0.311 (1.40) 0.588 (1.52)Greece Jan-94 Dec-13 0.066 (0.19) -1.108** (-2.13)Hongkong Jan-94 Dec-13 -0.020 (-0.05) -0.415 (-0.79)India Jul-94 Dec-13 0.770*** (3.19) 0.620 (1.55)Indonesia Jan-94 Dec-13 0.511 (1.24) 0.302 (0.58)Ireland Jan-94 Dec-13 0.067 (0.12) 0.122 (0.14)Israel Jul-96 Dec-13 0.315 (0.85) -0.489 (-0.75)Italy Jan-94 Dec-13 -0.166 (-0.76) -0.228 (-0.76)Japan Jan-94 Dec-13 -0.119 (-0.83) -0.227 (-1.10)Jordan Jul-06 Dec-13 0.020 (0.04) -0.732 (-0.86)Korea Jan-94 Dec-13 0.219 (0.86) -0.383 (-0.85)Malaysia Jan-94 Dec-13 -0.015 (-0.08) 0.372 (1.45)Mexico Jan-94 Dec-13 -0.283 (-1.05) 0.003 (0.01)Morocco Jul-05 Dec-13 0.028 (0.06) -0.174 (-0.29)Netherlands Jan-94 Dec-13 0.138 (0.50) 0.110 (0.28)New Zealand Jul-97 Dec-13 0.283 (0.74) -0.519 (-1.06)Norway Jan-94 Dec-13 0.308 (0.96) 0.534 (1.29)Pakistan Jul-94 Dec-13 0.347 (0.88) 0.481 (0.98)Philippines Jan-94 Dec-13 -0.331 (-0.80) -0.480 (-1.00)Poland Jul-97 Dec-13 0.117 (0.31) -0.482 (-1.05)Portugal Jan-94 Dec-13 -0.064 (-0.15) 0.118 (0.24)Russia Jul-05 Dec-13 -0.718 (-0.86) 0.978 (1.26)Singapore Jan-94 Dec-13 -0.250 (-1.13) 0.237 (0.57)South Africa Jan-94 Dec-13 0.952*** (3.64) 0.052 (0.15)Spain Jan-94 Dec-13 -0.165 (-0.62) -0.138 (-0.41)Sri Lanka Jul-05 Dec-13 -0.032 (-0.07) 0.402 (0.77)Sweden Jan-94 Dec-13 0.197 (0.74) 0.234 (0.60)Switzerland Jan-94 Dec-13 0.339* (1.76) -0.179 (-0.47)Taiwan Jul-94 Dec-13 -0.238 (-0.99) -0.871*** (-2.83)Thailand Jan-94 Dec-13 0.678** (2.58) 0.364 (0.94)Turkey Jan-94 Dec-13 -0.224 (-0.58) 0.484 (0.78)UK Jan-94 Dec-13 0.473*** (3.44) 0.200 (0.61)USA Jan-94 Dec-13 0.856*** (4.68) 0.288* (1.65)
23
Table 12: Three-factor alphas, return on assets, country-level results
The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on return on assets on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Country Sample period Return weightingStart End Equally weighted returns Value weighted returns
Argentina Jul-00 Dec-13 0.413 (0.70) 1.138* (1.77)Australia Jan-94 Dec-13 0.281 (1.22) -0.083 (-0.26)Austria Jan-94 Dec-13 0.466 (1.52) 0.242 (0.60)Belgium Jan-94 Dec-13 0.960*** (3.59) 0.914** (2.16)Brazil Jul-97 Dec-13 1.039 (1.62) 1.165 (1.64)Canada Jan-94 Dec-13 0.219 (0.69) 0.650* (1.69)Chile Jan-94 Dec-13 0.522** (2.43) 0.120 (0.39)China Jul-95 Dec-13 -0.00 (-0.00) -0.086 (-0.22)Colombia Jul-04 Dec-13 0.581 (0.99) -0.064 (-0.08)Denmark Jan-94 Dec-13 0.314 (1.23) -0.057 (-0.14)Egypt Jul-04 Dec-13 0.061 (0.10) -0.175 (-0.26)Finland Jan-94 Dec-13 0.260 (0.90) 0.126 (0.27)France Jan-94 Dec-13 0.742*** (4.34) 0.537* (1.87)Germany Jan-94 Dec-13 0.860*** (4.39) 0.835** (2.34)Greece Jan-94 Dec-13 0.432 (1.14) 0.939* (1.79)Hongkong Jan-94 Dec-13 -0.003 (-0.01) 0.536 (0.93)India Jul-94 Dec-13 -0.230 (-1.01) 0.017 (0.05)Indonesia Jan-94 Dec-13 0.897** (2.16) 1.602*** (2.95)Ireland Jan-94 Dec-13 0.100 (0.16) 0.350 (0.51)Israel Jul-96 Dec-13 0.237 (0.58) 0.932 (1.34)Italy Jan-94 Dec-13 0.676*** (2.95) 0.540 (1.60)Japan Jan-94 Dec-13 0.361** (2.52) 0.681*** (2.60)Jordan Jul-06 Dec-13 0.285 (0.69) 0.719 (1.09)Korea Jan-94 Dec-13 0.520* (1.80) 1.058** (2.34)Malaysia Jan-94 Dec-13 0.829*** (3.99) 0.731*** (2.80)Mexico Jan-94 Dec-13 0.093 (0.30) -0.886** (-2.08)Morocco Jul-05 Dec-13 0.232 (0.58) -0.360 (-1.05)Netherlands Jan-94 Dec-13 0.698** (2.57) 0.035 (0.09)New Zealand Jul-97 Dec-13 0.606* (1.67) -0.168 (-0.30)Norway Jan-94 Dec-13 0.856*** (2.70) 1.078** (2.04)Pakistan Jul-94 Dec-13 0.478 (1.05) 0.975** (2.05)Philippines Jan-94 Dec-13 0.467 (0.89) 0.541 (1.02)Poland Jul-97 Dec-13 0.244 (0.56) -0.045 (-0.09)Portugal Jan-94 Dec-13 0.693* (1.70) 0.897* (1.95)Russia Jul-05 Dec-13 -0.430 (-0.46) -0.286 (-0.38)Singapore Jan-94 Dec-13 0.716** (2.56) 0.737* (1.92)South Africa Jan-94 Dec-13 -0.225 (-0.96) 0.074 (0.18)Spain Jan-94 Dec-13 0.472* (1.90) 0.640 (1.62)Sri Lanka Jul-05 Dec-13 0.276 (0.64) 1.105* (1.97)Sweden Jan-94 Dec-13 0.859*** (2.72) 1.078*** (2.83)Switzerland Jan-94 Dec-13 0.656*** (3.62) 1.075*** (3.21)Taiwan Jul-94 Dec-13 0.590** (1.98) 1.108*** (2.89)Thailand Jan-94 Dec-13 0.583** (2.15) 1.028*** (2.64)Turkey Jan-94 Dec-13 -0.377 (-0.90) -0.916 (-1.42)UK Jan-94 Dec-13 0.951*** (5.67) 0.638** (2.16)USA Jan-94 Dec-13 0.330 (1.42) 0.618*** (2.84)
24
Table 13: Three-factor alphas, investment-to-assets, country-level results
The table reports monthly alphas (in %) obtained from regressing the quintile-basedcountry-level long/short portfolios sorted on investment-to-assets on local Fama andFrench (1993) three-factor models (as explained in Section 2.3. of the paper). Allreturns are computed in local currency. T-statistics (in parentheses) are based on theheteroskedasticity-consistent standard errors of White (1980). Two-tailed statisticalsignificance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.
Country Sample period Return weightingStart End Equally weighted returns Value weighted returns
Argentina Dec-03 Dec-13 0.588 (1.07) 0.252 (0.26)Australia Jan-94 Dec-13 0.538** (2.30) -0.092 (-0.27)Austria Jun-95 Dec-13 0.151 (0.37) 0.561 (1.14)Belgium Jan-94 Dec-13 0.307 (1.31) 0.356 (0.87)Brazil Jun-98 Dec-13 0.621 (0.73) 0.559 (0.77)Canada Jan-94 Dec-13 0.630** (2.10) 0.845** (2.07)Chile Jan-94 Dec-13 -0.357* (-1.68) -0.775** (-2.42)China Jul-95 Dec-13 0.442 (1.49) 0.469 (1.36)ColombiaDenmark Jan-94 Dec-13 0.835*** (3.15) 0.553 (1.20)Egypt Jul-05 Dec-13 -0.308 (-0.57) 0.238 (0.30)Finland Jul-96 Dec-13 0.259 (0.92) 0.783 (1.38)France Jan-94 Dec-13 0.409** (2.41) 0.065 (0.22)Germany Jan-94 Dec-13 0.493*** (2.60) 0.423 (1.40)Greece Jan-94 Dec-13 -0.117 (-0.35) 0.455 (1.15)Hongkong Jan-94 Dec-13 -0.388 (-1.16) -0.969* (-1.97)India Jul-94 Dec-13 0.569*** (2.60) -0.154 (-0.41)Indonesia Jan-94 Dec-13 0.492 (1.18) -0.153 (-0.34)Ireland Oct-99 Dec-13 -0.838 (-1.02) -1.592* (-1.81)Israel Jul-99 Dec-13 0.053 (0.16) -0.245 (-0.40)Italy Jan-94 Dec-13 -0.129 (-0.53) -0.084 (-0.22)Japan Jan-94 Dec-13 0.032 (0.26) 0.172 (0.84)Jordan Jul-07 Dec-13 0.463 (0.74) 0.498 (0.37)Korea Jan-94 Dec-13 0.311 (1.39) -0.220 (-0.47)Malaysia Jan-94 Dec-13 -0.067 (-0.38) -0.102 (-0.40)Mexico Jan-94 Dec-13 0.073 (0.25) 0.269 (0.80)Morocco Jul-07 Dec-13 0.116 (0.24) -0.196 (-0.31)Netherlands Jan-94 Dec-13 0.256 (1.11) -0.159 (-0.37)New Zealand Jul-97 Dec-13 0.223 (0.66) -0.396 (-0.90)Norway Jan-94 Dec-13 0.735** (2.36) 0.378 (0.91)Pakistan Jul-94 Dec-13 0.736* (1.74) -0.009 (-0.02)Philippines Jul-94 Dec-13 -0.616 (-1.34) -0.388 (-0.70)Poland Jul-03 Dec-13 1.364*** (3.87) -0.004 (-0.01)Portugal Jul-94 Dec-13 0.001 (0.00) -0.379 (-0.68)Russia Aug-05 Dec-13 -0.238 (-0.33) 0.836 (1.05)Singapore Jan-94 Dec-13 0.010 (0.05) -0.006 (-0.02)South Africa Jan-94 Dec-13 0.699** (2.52) -0.054 (-0.17)Spain Jan-94 Dec-13 -0.173 (-0.72) 0.288 (1.00)Sri Lanka Jul-07 Dec-13 0.759 (1.07) 1.814** (2.44)Sweden Jan-94 Dec-13 0.718*** (3.18) 0.915* (1.79)Switzerland Jan-94 Dec-13 0.537*** (2.60) 0.688* (1.85)Taiwan Jul-94 Dec-13 -0.007 (-0.03) -0.333 (-1.04)Thailand Jan-94 Dec-13 0.512 (1.60) 0.445 (1.01)Turkey Jul-95 Dec-13 0.548 (1.50) 0.442 (0.77)UK Jan-94 Dec-13 0.457*** (3.25) -0.047 (-0.17)USA Jan-94 Dec-13 0.623*** (4.41) 0.171 (1.02)
25
Tab
le14:Mispricingbased
onalternativesetof
anomalies:Long/short
returnsandthree-factoralphasin
develop
edvs.
emergingmarkets
Thetable
reportsmonthly
raw
returns(in
%)and
monthly
alphas(in
%)obtained
from
regressingquintile-based
long/short
portfoliosof
agg
regated
mispricingonaglobalFamaandFrench
(1993)three-factormodel
(asexplained
inSection
2.3.
ofthe
pap
er).
Agg
regatemispricingis
computedfrom
thefollow
ing20individualanomalies:low
volatility
anom
aly,low
betaan
omaly,
idiosyncratic
risk
anom
aly,maxim
um
daily
return
anomaly,lottery-typestock
anomaly,short-term
return
reversal,
long-term
return
reversal,turnover
anomaly,
return
seasonality
anomaly,interm
ediate
momentum,continuou
sinform
ationarrivalan
omaly,
earningsannou
ncementpremium
anom
aly,dividend
month
anomaly,PEAD
basedon
announcementreturns,
PEAD
based
on
analyst
consensus,
R&D
intensity
anom
aly,R&D
growth
anomaly,200day
mov
ingaverageanom
aly,52weekhighan
omaly,
and
analyst
forecast
dispersionan
omaly.
Com
putationaldetailsfortheseanomalies
are
provided
inTable
1ofthis
OnlineAppendix.
Themechan
ism
tocompute
agg
regatecross-sectionalmispricingfollow
sStambaugh,Yu,andYuan(2015
)andisexplained
indetail
inSection
2.2.of
thepaper.Moreprecisely,foreach
individualanomaly-m
onth-countrycombination,Ifirstrankstocksin
away
that
thepresumably
mostunderpriced(overpriced)stock
receives
thelowest(highest)
rank.Ranksarestan
dardized
tobeuniformly
distributedover
theinterval(0,1]in
aeach
countrymonth.A
few
anomalies
(e.g.,theearnings
annou
ncementpremium
anom
aly)
arebased
onindicator
variables(e.g.,expectedannouncementin
agiven
month
vs.
noexpectedevent).Tomak
etheranking
procedure
forthesereturn
phenom
enacomparable
totheapproach
fortheother
anomalies,Iim
plementthefollow
ingmethod.Let
x%
oftheeligible
firm
sin
agiven
countrymonth
haveanexpectedearningsannouncement(oranother
eventexpectedto
yield
positiveabnormalreturns).Theseeventfirm
sare
then
assigned
arelativerankof0.5*x,andthenon
-eventfirm
sareassign
eda
rankof
0.5+0.5*x.Asin
Stambau
gh,Yu,an
dYuan(2015),
astock’s
composite
mispricingrankis
computedas
thearithmetic
averageofits(upto
20)
individualan
omalyranks.
Toobtain
acomposite
rankin
agiven
month,thestock
under
consideration
has
tohaveatleasteightvalidrelativeranksonindividualanomalies.Ithen
form
long/short
portfoliosbased
onquintiles,
and
presenttheresultsseparately
fordeveloped
marketsandem
ergingmarkets.
InPanel
A,long/short
returnsin
agiven
mon
thare
computedas
thearithmetic
averag
eof
alleligible
country-level
return
estimates.
InPanel
B,alleligible
stocksfrom
alleligible
countriesare
pooled
beforeacountry-neutraltime-series
oflong/short
returnsis
constructed.Thesample
periodis
Jan
uary1994
toDecem
ber
2013
.T-statistics(inparentheses)are
basedontheheteroskedasticity-consistentstan
dard
errors
ofW
hite(1980).
Two-tailed
statisticalsignificance
atthe10%
,5%,and1%
level
isindicatedby*,**,and***,
respectively.
[Continued
overleaf]
26
Develop
edMarkets
EmergingMarkets
Difference
Developed
Markets
EmergingMarkets
Difference
Equally
weightedreturns
Valueweightedreturns
Panel
A:Countryaverage
Raw
returns
1.561*
**0.644**
0.917***
1.361***
0.687
**0.674***
(4.99)
(2.47)
(4.34)
(3.88)
(2.59)
(2.66)
Three-factoralphas
2.001*
**1.195***
0.806***
1.940***
1.229*
**0.710***
(10.60)
(8.37)
(4.05)
(8.50)
(7.30)
(3.06)
Panel
B:Countrycomposite
Raw
returns
1.418*
**1.191***
0.227
1.110***
0.528*
*0.582*
(3.79)
(4.60)
(0.88)
(3.02)
(2.22)
(1.77)
Three-factoralphas
1.696*
**1.487***
0.209
1.491***
0.799
***
0.693**
(8.08)
(8.08)
(0.98)
(6.10)
(4.08)
(2.41)
27
Tab
le15:Mispricingbasedon
extended
setofanomalies:Long/short
returnsandthree-factoralphas
indeveloped
vs.
emergingmarkets
Thetable
reportsmonthly
raw
returns(in
%)and
monthly
alphas(in
%)obtained
from
regressingquintile-based
long/short
portfoliosof
agg
regated
mispricingonaglobalFamaandFrench
(1993)three-factormodel
(asexplained
inSection
2.3.
ofthe
pap
er).Aggregate
mispricingiscomputedfrom
intotal31individualanomalies.More
precisely,Iconsider
the11Stambau
gh,Yu,
andYuan(2015
)an
omaliesas
inthepap
er:failure
probability,
financialdistress,net
stock
issues,compositeequityissues,accruals,
net
operatingassets,mom
entum,gross
profitability,
asset
growth,return
onassets,
andinvestment-to-assets.
Additionally,Itake
thefollow
ing20
individualan
omaliesinto
account:low
volatility
anomaly,low
betaanomaly,idiosyncraticrisk
anom
aly,
max
imum
daily
return
anom
aly,lottery-typestock
anom
aly,short-term
return
reversal,long-term
return
reversal,turnover
anom
aly,
return
season
ality
anomaly,
interm
ediate
mom
entum,continuousinform
ationarrivalanomaly,earningsan
nouncementpremium
anom
aly,
dividen
dmonth
anomaly,PEAD
based
onannouncementreturns,PEAD
basedonanalyst
consensus,R&D
intensity
anom
aly,R&D
grow
thanom
aly,20
0day
mov
ingaverage
anomaly,52weekhighanomaly,andanalyst
forecast
dispersion
anomaly.
Com
putation
al
detailsforalltheseanom
alies
are
provided
inTable1ofthisOnlineAppendix.Themechanism
tocompute
agg
regatecross-sectional
mispricingfollow
sStambau
gh,Yu,an
dYuan
(2015)andisexplained
indetailin
Section2.2.of
thepap
er.More
precisely,foreach
individual
anom
aly-m
onth-countrycombination,Ifirstrankstocksin
away
thatthepresumably
mostunderpriced(overpriced)
stock
receives
thelowest(highest)
rank.Ranksare
standardized
tobeuniform
lydistributedover
theinterval
(0,1]in
aeach
country
mon
th.A
few
anom
alies
(e.g.,
theearnings
announcementpremium
anomaly)are
based
onindicator
variables(e.g.,
expected
annou
ncementin
agiven
month
vs.
noexpectedevent).Tomaketherankingprocedure
forthesereturn
phenomenacomparab
le
totheap
proachfortheother
anomalies,
Iim
plementthefollow
ingmethod.Let
x%
oftheeligible
firm
sin
agiven
countrymon
th
havean
expectedearningsan
nouncement(oranother
eventexpectedto
yield
positiveabnormalreturns).Theseeventfirm
sare
then
assigned
arelativerankof
0.5*x
,andthenon-eventfirm
sare
assigned
arankof0.5+0.5*x.Asin
Stambau
gh,Yu,an
dYuan
(2015),astock’s
compositemispricingrankis
computedasthearithmetic
averageofits(upto
31)individualan
omalyranks.
To
obtain
acompositerankin
agiven
mon
th,thestock
under
considerationhasto
haveatleastfivevalidrankson
theStambau
gh,
Yu,an
dYuan(201
5)anom
alies
andeigh
tvalidrelativeranksontheremaining20anomalies.Ithen
form
long/shortportfolios
based
onquintiles,an
dpresenttheresultsseparately
fordeveloped
marketsandem
ergingmarkets.In
Panel
A,long/shortreturns
inagiven
mon
tharecomputedasthearithmetic
averageofalleligible
country-level
return
estimates.In
Pan
elB,alleligible
stocks
from
alleligible
countriesare
pooledbefore
acountry-neutraltime-series
oflong/short
returnsis
constructed
.Thesample
period
isJan
uary19
94to
Decem
ber
2013
.T-statistics(inparentheses)are
basedontheheterosked
asticity-con
sistentstan
darderrors
of
White(198
0).Two-tailed
statisticalsignificance
atthe10%,5%,and1%
level
isindicatedby*,**
,an
d**
*,respectively.
[Continued
overleaf]
28
Develop
edMarkets
EmergingMarkets
Difference
Developed
Markets
EmergingMarkets
Difference
Equally
weightedreturns
Valueweightedreturns
Panel
A:Countryaverage
Raw
returns
1.519*
**0.842***
0.677***
1.277***
0.862*
**0.414*
(5.35)
(3.19)
(3.28)
(3.83)
(3.17)
(1.66)
Threefactor
alphas
1.959*
**1.344***
0.615***
1.978***
1.379*
**0.598***
(11.46)
(8.06)
(3.04)
(9.87)
(7.42)
(2.65)
Panel
B:Countrycomposite
Raw
returns
1.346*
**1.135***
0.211
1.071***
0.668
***
0.403
(3.95)
(4.36)
(0.79)
(2.97)
(2.68)
(1.25)
Threefactor
alphas
1.629*
**1.405***
0.224
1.542***
0.917
***
0.625**
(8.26)
(6.88)
(0.96)
(6.44)
(4.33)
(2.09)
29
Tab
le16
:Mispricingbasedon
differentgroupsofanomalies:Three-factoralphasin
develop
edvs.
emergingmarkets
Thetable
reports
mon
thly
alphas
(in%)ob
tained
from
regressingquintile-basedlong/short
portfolios
ofag
gregatedmispricingon
aglob
alFam
aandFrench
(1993
)three-factor
model.Aggregate
mispricingisbasedonthreedifferentgroupsof
anom
alies.In
pan
el
A,Iconsider
anom
alies
based
primarilyonaccountingdata:failure
probability,
financialdistress,
accruals,net
operatingassets,
grossprofitability,assetgrowth,return
onassets,investm
ent-to-assets,R&D
intensity
anomaly,R&D
growth
anom
aly.In
pan
elB,I
consider
anomaliesprimarily
basedonmarket
data:momentum,net
stock
issues
anomaly,composite
equityan
omaly,
low
volatility
anom
aly,
low
betaan
omaly,
idiosyncraticrisk
anomaly,maxim
um
dailyreturn
anomaly,lottery-typestock
anom
aly,
short-term
return
reversal,
long-term
return
reversal,
turnover
anomaly,return
seasonality
anomaly,interm
ediate
momentum,continuou
s
inform
ationarrivalanom
aly,200
day
mov
ingaverageanomaly,52weekhighanomaly.In
pan
elC,Iconsider
other
anom
alies:
earningsannou
ncementpremium
anom
aly,dividend
month
anomaly,PEAD
basedon
announcementreturns,
PEAD
based
on
analyst
consensus,
andanalyst
forecast
dispersionanomaly.Computationaldetailsforallthesean
omaliesare
provided
inTab
le
1of
this
OnlineAppendix.In
allpan
els,
themechanism
tocompute
aggregate
cross-sectionalmispricingfollow
sStambau
gh,Yu,
andYuan
(2015
)andisexplained
indetailin
Section2.2.ofthepaper.More
precisely,foreach
individual
anom
aly-m
onth-cou
ntry
combination,Ifirstrankstocksin
away
thatthepresumably
most
underpriced(overpriced)stock
receives
thelowest(highest)
rank.Ran
ksarestan
dardized
tobeuniformly
distributedover
theinterval(0,1]in
aeach
countrymon
th.A
few
anom
alies(e.g.,the
earningsannou
ncementpremium
anomaly)are
basedonindicatorvariables(e.g.,expectedannou
ncementin
agiven
mon
thvs.no
expectedevent).Tomaketherankingprocedure
forthesereturn
phenomenacomparable
totheapproach
fortheother
anom
alies,
Iim
plementthefollow
ingmethod.Let
x%
oftheeligible
firm
sin
agiven
countrymonth
haveanexpectedearnings
annou
ncement
(oran
other
eventexpectedto
yield
positiveabnorm
alreturns).Theseeventfirm
sare
then
assigned
arelativerankof
0.5*x,an
d
thenon
-eventfirm
sare
assigned
arankof0.5+0.5*x.Asin
Stambaugh,Yu,andYuan(2015),astock’scompositemispricingrank
iscomputedas
thearithmetic
averag
eof
itsindividualanomaly
ranks.
Toobtain
acomposite
rankin
agiven
mon
th,thestock
under
considerationhas
tohaveat
least
fivevalidranksonindividualanomalies
inpanel
AorB
orthreevalidranks(dueto
the
lower
number
ofunderlyingan
omalies)
inpanel
C.Ithen
form
long/short
portfoliosbasedon
quintiles,
andpresenttheresults
separatelyfordeveloped
marketsan
dem
ergingmarkets.
“Countryaverage”
meansthatlong/short
returnsin
agiven
mon
thare
computedasthearithmeticaverage
ofalleligiblecountry-levelreturn
estimates.“Countrycomposite”meansthat
alleligiblestocks
from
alleligible
countriesare
pooledbefore
acountry-neutraltime-series
oflong/short
returnsis
constructed
.Thesample
period
isJan
uary19
94to
Decem
ber
2013
.T-statistics(inparentheses)are
basedontheheterosked
asticity-con
sistentstan
darderrors
of
White(198
0).Two-tailed
statisticalsignificance
atthe10%,5%,and1%
level
isindicatedby*,**
,an
d**
*,respectively.
[Continued
overleaf]
30
Developed
Markets
EmergingMarkets
Differen
ceDeveloped
Markets
EmergingMarkets
Differen
ce
Equallyweightedreturns
Valueweightedreturns
Panel
A:Aggregate
mispricingcomputedfrom
anomalies
(primarily)basedonaccountingdata
Countryav
erage
Threefactoralphas
1.027***
0.765***
-0.263**
0.966***
0.779***
-0.187
(10.92)
(7.13)
(-2.02)
(7.80)
(5.62)
(-1.03)
Countrycomposite
Threefactoralphas
1.078***
0.765***
-0.313**
0.938***
0.752***
-0.186
(11.18)
(6.44)
(-2.32)
(6.37)
(4.34)
(-0.83)
Panel
B:Aggregate
mispricingcomputedfrom
anomalies
(primarily)basedonmarket
data
Countryav
erage
Threefactoralphas
1.895***
1.382***
-0.513**
1.927***
1.290***
-0.637***
(10.19)
(7.67)
(-2.45)
(8.60)
(6.12)
(-2.64)
Countrycomposite
Threefactoralphas
1.423***
1.419***
-0.003
1.393***
0.890***
-0.504
(6.58)
(6.53)
(-0.01)
(5.77)
(3.75)
(-1.64)
Panel
C:Aggregate
mispricingcomputedfrom
other
anomalies
Countryav
erage
Threefactoralphas
1.289***
0.755***
-0.534***
1.041***
0.631***
-0.411**
(14.05)
(5.88)
(-3.89)
(8.46)
(3.89)
(-2.30)
Countrycomposite
Threefactoralphas
1.034***
0.641***
-0.393***
0.866***
0.439**
-0.428*
(10.21)
(5.22)
(-3.04)
(5.70)
(2.42)
(-1.89)
31
Figure
1:Fractionofstockswithvalidanomaly
rankings:
Argentina
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
32
Figure
2:Fractionofstockswithvalidanomaly
rankings:
Australia
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
33
Figure
3:Fractionofstockswithvalidanomaly
rankings:
Austria
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
34
Figure
4:Fractionofstockswithvalidanomaly
rankings:
Belgium
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
35
Figure
5:Fractionofstockswithvalidanomaly
rankings:
Brazil
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
36
Figure
6:Fractionofstockswithvalidanomaly
rankings:
Can
ada
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
37
Figure
7:Fractionofstockswithvalidanomaly
rankings:
Chile
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
38
Figure
8:Fractionofstockswithvalidanomaly
rankings:
China
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
39
Figure
9:Fractionofstockswithvalidanomaly
rankings:
Colombia
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
40
Figure
10:Fractionofstockswithvalidanomaly
rankings:
Denmark
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
41
Figure
11:
Fractionofstockswithvalidanomaly
rankings:
Egypt
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
42
Figure
12:Fractionofstockswithvalidanomaly
rankings:
Finland
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
43
Figure
13:
Fractionofstockswithvalidanomaly
rankings:
France
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
44
Figure
14:
Fractionofstockswithvalidanomaly
rankings:
German
y
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
45
Figure
15:Fractionofstockswithvalidanomaly
rankings:
Greece
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
46
Figure
16:
Fractionofstockswithvalidanomaly
rankings:
HongKong
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
47
Figure
17:
Fractionofstockswithvalidanomaly
rankings:
India
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
48
Figure
18:
Fractionofstockswithvalidanomaly
rankings:
Indon
esia
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
49
Figure
19:Fractionofstockswithvalidanomaly
rankings:
Ireland
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
50
Figure
20:Fractionofstockswithvalidanomaly
rankings:
Israel
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
51
Figure
21:
Fractionofstockswithvalidanomaly
rankings:
Italy
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
52
Figure
22:Fractionofstockswithvalidanomaly
rankings:
Japan
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
53
Figure
23:
Fractionofstockswithvalidanomaly
rankings:
Jordan
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
54
Figure
24:
Fractionofstockswithvalidanomaly
rankings:
Korea
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
55
Figure
25:
Fractionofstockswithvalidanomaly
rankings:
Malay
sia
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
56
Figure
26:
Fractionofstockswithvalidanomaly
rankings:
Mexico
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
57
Figure
27:Fractionofstockswithvalidanomaly
rankings:
Morocco
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
58
Figure
28:Fractionofstockswithvalidanomaly
rankings:
Netherlands
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
59
Figure
29:Fractionofstockswithvalidanomaly
rankings:
New
Zealand
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
60
Figure
30:Fractionofstockswithvalidanomaly
rankings:
Norway
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
61
Figure
31:
Fractionofstockswithvalidanomaly
rankings:
Pak
istan
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
62
Figure
32:
Fractionofstockswithvalidanomaly
rankings:
Philippines
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
63
Figure
33:
Fractionofstockswithvalidanomaly
rankings:
Poland
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
64
Figure
34:Fractionofstockswithvalidanomaly
rankings:
Portuga
l
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
65
Figure
35:
Fractionofstockswithvalidanomaly
rankings:
Russia
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
66
Figure
36:
Fractionofstockswithvalidanomaly
rankings:
Singap
ore
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
67
Figure
37:Fractionofstockswithvalidanomaly
rankings:
South
Africa
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
68
Figure
38:Fractionofstockswithvalidanomaly
rankings:
Spain
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
69
Figure
39:
Fractionofstockswithvalidanomaly
rankings:
Sweden
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
70
Figure
40:
Fractionofstockswithvalidanomaly
rankings:
Switzerlan
d
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
71
Figure
41:
Fractionofstockswithvalidanomaly
rankings:
Taiwan
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
72
Figure
42:Fractionofstockswithvalidanomaly
rankings:
Thailand
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
73
Figure
43:Fractionofstockswithvalidanomaly
rankings:
Turkey
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
74
Figure
44:Fractionofstockswithvalidanomaly
rankings:
UK
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
75
Figure
45:
Fractionofstockswithvalidanomaly
rankings:
USA
Iconsider
stocksthatsurvivethebasicdata
screen
soutlined
inSection2.1
ofthepaper.Thesample
periodcorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Agg
rega
te m
ispr
icin
g sc
ore
Fai
lure
pro
babi
lity
Ohl
son’
s O
(di
stre
ss)
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
sto
ck is
sues
Com
posi
te e
quity
Acc
rual
s
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Net
ope
ratin
g as
sets
Mom
entu
mG
ross
pro
fitab
ility
0.2.4.6.81 1994
1998
2002
2006
2010
2014
year
Ass
et g
row
thR
etur
n on
ass
ets
Inve
stm
ent−
to−
asse
ts
76
Table 17: Developed vs. emerging markets: Fraction of stocks with valid anomaly rankings
The table compares the fraction of stocks with valid (individual or composite)anomaly rankings in developed markets relative to emerging markets. For each coun-try, I consider stocks that survive the basic data screens outlined in Section 2.1. ofthe paper. The sample period for each country corresponds to the sample periodshown in panel B of Table 1 in the paper. I pool all valid country years and regressthem on a developed market dummy which is one (zero) if a country year is classifiedas a developed (emerging) market. Panel A shows results for the full sample period(1994-2013), panel B and C show results for the first and second half of the sampleperiod, respectively. In all panels, standard errors are double-clustered by countryand year. Two-tailed statistical significance at the 10%, 5%, and 1% level is indicatedby *, **, and ***, respectively.
Panel A: Full sample period (1994-2014)
Developed market dummy t-statistic Constant N R2
Mispricing score 0.129*** (3.11) 0.69 803 0.087
Failure probability 0.122*** (3.40) 0.62 803 0.079Ohlson’s O (distress) 0.101*** (2.75) 0.60 803 0.057Net stock issues -0.008 (-0.57) 0.94 803 0.004Composite equity 0.094*** (2.60) 0.63 803 0.064Accruals 0.111*** (3.06) 0.56 803 0.065Net operating assets 0.112*** (2.92) 0.64 803 0.066Momentum -0.018 (-1.39) 0.98 803 0.030Gross profitability 0.079** (2.33) 0.64 803 0.036Asset growth 0.126*** (2.82) 0.71 803 0.078Return on assets 0.125*** (2.84) 0.70 803 0.081Investment-to-assets 0.088** (2.52) 0.55 803 0.036
Panel B: First half of sample period (1994-2003)
Developed market dummy t-statistic Constant N R2
Mispricing score 0.254*** (5.43) 0.49 376 0.309
Failure probability 0.212*** (5.09) 0.45 376 0.241Ohlson’s O (distress) 0.200*** (5.08) 0.42 376 0.240Net stock issues -0.007 (-0.41) 0.92 376 0.002Composite equity 0.131** (2.45) 0.53 376 0.108Accruals 0.211*** (5.38) 0.38 376 0.259Net operating assets 0.219*** (5.18) 0.45 376 0.259Momentum -0.016 (-1.21) 0.97 376 0.023Gross profitability 0.150*** (3.67) 0.48 376 0.154Asset growth 0.253*** (5.10) 0.51 376 0.289Return on assets 0.256*** (5.23) 0.50 376 0.303Investment-to-assets 0.163*** (4.38) 0.36 376 0.167
Panel C: Second half of sample period (2004-2013)
Developed market dummy t-statistic Constant N R2
Mispricing score 0.030 (0.84) 0.86 427 0.012
Failure probability 0.053 (1.41) 0.76 427 0.027Ohlson’s O (distress) 0.024 (0.63) 0.75 427 0.006Net stock issues -0.008 (-0.53) 0.95 427 0.004Composite equity 0.068* (1.92) 0.71 427 0.056Accruals 0.034 (0.83) 0.71 427 0.011Net operating assets 0.030 (0.76) 0.79 427 0.010Momentum -0.019 (-1.38) 0.99 427 0.037Gross profitability 0.026 (0.71) 0.78 427 0.007Asset growth 0.024 (0.63) 0.88 427 0.007Return on assets 0.021 (0.57) 0.87 427 0.006Investment-to-assets 0.034 (0.76) 0.70 427 0.009
77
Figure
46:Number
ofindividualanomalies
underlyingthecomposite
mispricingscore
(1/4
)
Onacountry-by-countrybasis,thegraphscompare
theav
eragenumber
ofindividualanomalies
underlyingthecomposite
Stambaugh,Yu,andYuan(2015)
mispricingscore,conditionalontheavailabilityofatleast
fiveindividualanomaly
ranks.
Thesample
periodforeach
countrycorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
567891011 1994
1998
2002
2006
2010
2014
year
Arg
entin
aA
ustr
alia
Aus
tria
567891011 1994
1998
2002
2006
2010
2014
year
Bel
gium
Bra
zil
Can
ada
567891011 1994
1998
2002
2006
2010
2014
year
Chi
leC
hina
Col
ombi
a
567891011 1994
1998
2002
2006
2010
2014
year
Den
mar
kE
gypt
Fin
land
78
Figure
47:Number
ofindividualanomalies
underlyingthecomposite
mispricingscore
(2/4
)
Onacountry-by-countrybasis,thegraphscompare
theav
eragenumber
ofindividualanomalies
underlyingthecomposite
Stambaugh,Yu,andYuan(2015)
mispricingscore,conditionalontheavailabilityofatleast
fiveindividualanomaly
ranks.
Thesample
periodforeach
countrycorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
567891011 1994
1998
2002
2006
2010
2014
year
Fra
nce
Ger
man
yG
reec
e
567891011 1994
1998
2002
2006
2010
2014
year
Hon
g K
ong
Indi
aIn
done
sia
567891011 1994
1998
2002
2006
2010
2014
year
Irel
and
Isra
elIta
ly
567891011 1994
1998
2002
2006
2010
2014
year
Japa
nJo
rdan
Kor
ea
79
Figure
48:Number
ofindividualanomalies
underlyingthecomposite
mispricingscore
(3/4
)
Onacountry-by-countrybasis,thegraphscompare
theav
eragenumber
ofindividualanomalies
underlyingthecomposite
Stambaugh,Yu,andYuan(2015)
mispricingscore,conditionalontheavailabilityofatleast
fiveindividualanomaly
ranks.
Thesample
periodforeach
countrycorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
567891011 1994
1998
2002
2006
2010
2014
year
Mal
aysi
aM
exic
oM
oroc
co
567891011 1994
1998
2002
2006
2010
2014
year
Net
herla
nds
New
Zea
land
Nor
way
567891011 1994
1998
2002
2006
2010
2014
year
Pak
ista
nP
hilip
pine
sP
olan
d
567891011 1994
1998
2002
2006
2010
2014
year
Por
tuga
lR
ussi
aS
inga
pore
80
Figure
49:Number
ofindividualanomalies
underlyingthecomposite
mispricingscore
(4/4
)
Onacountry-by-countrybasis,thegraphscompare
theav
eragenumber
ofindividualanomalies
underlyingthecomposite
Stambaugh,Yu,andYuan(2015)
mispricingscore,conditionalontheavailabilityofatleast
fiveindividualanomaly
ranks.
Thesample
periodforeach
countrycorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.
567891011 1994
1998
2002
2006
2010
2014
year
Sou
th A
fric
aS
pain
Sw
eden
567891011 1994
1998
2002
2006
2010
2014
year
Sw
itzer
land
Tai
wan
Tha
iland
567891011 1994
1998
2002
2006
2010
2014
year
Tur
key
UK
US
A
81
Table 18: Number of individual anomalies underlying the composite mispricing score: Descriptivestatistics
On a country-by-country basis, the table compares the number of individual anoma-lies underlying the composite Stambaugh, Yu, and Yuan (2015) mispricing score,conditional on non-missing values of the score (i.e., conditional on the availability ofat least five individual anomaly ranks). The unit of observation is the average num-ber of individual anomalies in a country year. The sample period for each countrycorresponds to the sample period shown in panel B of Table 1 in the paper.
Country Sample Start Sample End N Mean SD Min Min
Argentina 2000 2009 10 9.73 0.88 8.06 10.41Australia 1994 2013 20 10.17 0.16 9.96 10.48Austria 1994 2013 20 9.30 0.33 8.35 9.78Belgium 1994 2013 20 9.59 0.31 9.08 10.10Brazil 1998 2013 16 9.75 0.35 8.98 10.18Canada 1994 2013 20 9.93 0.22 9.65 10.32Chile 1994 2013 20 10.01 0.13 9.71 10.16China 1995 2013 19 10.24 0.48 8.81 10.70Colombia 2007 2013 7 7.53 1.65 5.62 9.37Denmark 1994 2013 20 9.35 0.23 8.87 9.66Egypt 2004 2013 10 9.05 1.34 5.63 9.78Finland 1994 2013 20 9.90 0.68 8.58 10.63France 1994 2013 20 9.87 0.43 8.94 10.32Germany 1994 2013 20 9.81 0.41 8.77 10.29Greece 1994 2013 20 9.77 0.54 8.80 10.51Hongkong 1994 2013 20 9.79 0.13 9.56 10.04India 1994 2013 20 10.31 0.25 9.73 10.72Indonesia 1994 2013 20 9.78 0.14 9.57 9.96Ireland 1996 2013 18 8.95 1.63 5.00 10.06Israel 1998 2013 16 9.39 1.00 6.40 10.29Italy 1994 2013 20 9.27 0.37 8.65 9.86Japan 1994 2013 20 10.38 0.15 10.14 10.63Jordan 2007 2008 2 8.42 0.05 8.39 8.46Korea 1994 2013 20 10.07 0.28 9.45 10.46Malaysia 1994 2013 20 10.26 0.20 10.02 10.57Mexico 1994 2013 20 9.95 0.15 9.68 10.19Morocco 2006 2013 8 8.65 1.32 5.68 9.44Netherlands 1994 2013 20 10.26 0.29 9.67 10.58New Zealand 1997 2013 17 10.30 0.20 9.93 10.66Norway 1994 2013 20 9.65 0.37 9.02 10.15Pakistan 1994 2998 15 9.59 0.42 8.62 10.00Philippines 1994 2013 20 9.40 0.39 8.27 9.84Poland 1998 2013 16 8.81 1.07 6.54 10.30Portugal 1994 2013 20 10.03 0.28 9.38 10.31Russia 2005 2013 9 9.36 0.55 8.45 10.06Singapore 1994 2013 20 10.23 0.17 9.92 10.51South Africa 1994 2013 20 9.80 0.28 9.23 10.18Spain 1994 2013 20 9.76 0.22 9.42 10.15Sweden 1994 2013 20 9.98 0.42 9.13 10.47Switzerland 1994 2013 20 9.57 0.17 9.27 9.79Taiwan 1994 2013 20 10.31 0.22 9.92 10.60Thailand 1994 2013 20 9.81 0.26 9.40 10.26Turkey 1994 2013 20 9.72 0.66 7.75 10.17UK 1994 2013 20 10.19 0.16 9.94 10.40USA 1994 2013 20 10.12 0.09 9.99 10.29
82
Figure
50:Number
ofindividual
anomaliesunderlyingthecomposite
mispricingscore:Averagedeveloped
vs.
average
emergingmarket
Sep
arately
foreach
year,
thegraphcomparestheav
eragenumber
ofindividualanomalies
underlyingava
lidcomposite
Stambaugh,Yu,andYuan(2015)
mispricing
score
intheav
eragedeveloped
market
with
theresp
ectivenumber
intheav
erageem
erging
market.Thesample
period
foreach
country
correspondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.Please
note
thatthis
figure
correspondsto
the“countryav
erage”
weighting
schem
erelied
onin
thepaper,whileFigure
51relies
onthe“countrycomposite”weightingschem
e.567891011 19
9419
9820
0220
0620
1020
14
Em
ergi
ng m
arke
tsD
evel
oped
mar
kets
83
Table 19: Number of individual anomalies underlying the composite mispricing score: Developed
vs. emerging markets
Separately for each year, panel A compares the average number of individual anoma-
lies underlying a valid composite Stambaugh, Yu, and Yuan (2015) mispricing score in
the average developed market with the respective number of anomalies in the average
emerging market. More precisely, I regress the average number of individual anoma-
lies on a developed market dummy which is one (zero) if a country year is classified
as a developed (emerging) market. T-statistics are based on the heteroskedasticity-
consistent standard errors of White (1980). Panel B shows results from a panel
regression covering all developed and emerging markets as well as a sample period
from 1994 to 2013. Standard errors are double-clustered by country and year. In both
panels, two-tailed statistical significance at the 10%, 5%, and 1% level is indicated
by *, **, and ***, respectively.
Panel A: Year-by-year comparison
Year Developed market dummy t-statistic Constant N R2
1994 0.18 (0.78) 9.50 33 0.020
1995 0.15 (0.72) 9.57 34 0.017
1996 -0.27 (-0.97) 9.79 35 0.022
1997 -0.25 (-0.93) 9.85 36 0.019
1998 0.18 (0.53) 9.38 39 0.008
1999 -0.12 (-0.54) 9.63 39 0.009
2000 -0.15 (-0.73) 9.64 40 0.015
2001 0.05 (0.29) 9.51 40 0.002
2002 0.11 (0.65) 9.58 40 0.012
2003 0.04 (0.32) 9.79 40 0.003
2004 0.25 (0.94) 9.68 41 0.027
2005 0.23 (1.31) 9.77 42 0.046
2006 0.28 (1.16) 9.71 43 0.035
2007 0.42* (1.83) 9.53 45 0.075
2008 0.40* (1.70) 9.61 45 0.065
2009 0.28 (1.28) 9.82 43 0.043
2010 0.26 (1.46) 9.85 42 0.061
2011 0.16 (1.47) 9.98 42 0.054
2012 0.10 (0.84) 10.04 42 0.016
2013 0.13 (1.10) 10.06 42 0.030
Panel B: Panel regression
Developed market dummy t-statistic Constant N R2
1994-2013 0.13 (1.02) 9.72 803 0.009
84
Figure
51:Number
ofindividual
anom
alies
underlyingthecomposite
mispricingscore:Composite
develop
edvs.
compositeem
ergingmarket
Sep
arately
foreach
year,
thegraphcomparestheav
eragenumber
ofindividualanomalies
underlyingava
lidcomposite
Stambaugh,Yu,andYuan(2015)
mispricingscore
inthepooledstock-level
observationsin
developed
marketswiththepooledstock-level
observationsin
developed
markets.
Thesample
periodforeach
countrycorrespondsto
thesample
periodshow
nin
panel
BofTable
1in
thepaper.Please
note
thatthisfigure
correspondsto
the“country
composite”weightingschem
erelied
onin
thepaper,whileFigure
50relies
onthe“countryav
erage”
weightingschem
e.567891011 19
9419
9820
0220
0620
1020
14
Em
ergi
ng m
arke
tsD
evel
oped
mar
kets
85
Figure 52: Simulated composite mispricing based on five randomly selected individual anomalies
(universe: 11 Stambaugh, Yu, and Yuan (2015) anomalies, equally weighted returns): Alpha
difference between developed markets and emerging markets
The upper figure on the following page shows the distribution of the difference between the monthly
alpha (in %, equally weighted returns) obtained from exploiting aggregate cross-sectional mispricing
in developed markets and the alpha obtained in emerging markets. The lower figure shows the
distribution of the respective two-tailed t-statistic. The distribution is based on 10,000 simulations.
For each simulation, I randomly select five individual anomalies from the 11 individual anomalies
considered in Stambaugh, Yu, and Yuan (2015) as well as in the paper (failure probability, financial
distress, net stock issues, composite equity, total accruals, net operating assets, momentum, gross
profitability, asset growth, return on assets, investments-to-assets). I allow for the overweighting
of specific anomalies in that a specific anomaly can be drawn several times within one simulation.
The mechanism to compute aggregate cross-sectional mispricing based on these randomly selected
individual anomalies follows Stambaugh, Yu, and Yuan (2015) and is explained in detail in Section
2.2. of the paper as well as in Tables 14 and 15 of this Online Appendix. In each country month, the
portfolio goes long (short) stocks in the bottom (top) quintile of mispricing. Long/short returns for
developed or emerging markets in a given month are computed as the arithmetic average of all eligible
country-level return estimates. Alphas are then computed relative to a global Fama and French
(1993) three-factor model (as explained in Section 2.3. of the paper). The sample period is January
1994 to December 2013. The graphs focus on the difference of the alpha (and the corresponding
two-tailed t-statistic) obtained in developed and emerging markets. The average alpha difference is
26 bp. The difference is positive in 92.71% of the cases. The corresponding t-statistic is larger than
2 (smaller than -2) in 43.03% (0.00%) of the 10,000 simulations.
86
020
040
060
0F
requ
ency
−.5 0 .5 1alpha in developed markets − alpha in emerging markets
020
040
060
0F
requ
ency
−2 0 2 4 6T−statistic of the difference in alpha
87
Figure 53: Simulated composite mispricing based on five randomly selected individual anoma-
lies (universe: 11 Stambaugh, Yu, and Yuan (2015) anomalies, value weighted returns): Alpha
difference between developed markets and emerging markets
The upper figure on the following page shows the distribution of the difference between the monthly
alpha (in %, value weighted returns) obtained from exploiting aggregate cross-sectional mispricing
in developed markets and the alpha obtained in emerging markets. The lower figure shows the
distribution of the respective two-tailed t-statistic. The distribution is based on 10,000 simulations.
For each simulation, I randomly select five individual anomalies from the 11 individual anomalies
considered in Stambaugh, Yu, and Yuan (2015) as well as in the paper (failure probability, financial
distress, net stock issues, composite equity, total accruals, net operating assets, momentum, gross
profitability, asset growth, return on assets, investments-to-assets). I allow for the overweighting
of specific anomalies in that a specific anomaly can be drawn several times within one simulation.
The mechanism to compute aggregate cross-sectional mispricing based on these randomly selected
individual anomalies follows Stambaugh, Yu, and Yuan (2015) and is explained in detail in Section
2.2. of the paper as well as in Tables 14 and 15 of this Online Appendix. In each country month, the
portfolio goes long (short) stocks in the bottom (top) quintile of mispricing. Long/short returns for
developed or emerging markets in a given month are computed as the arithmetic average of all eligible
country-level return estimates. Alphas are then computed relative to a global Fama and French
(1993) three-factor model (as explained in Section 2.3. of the paper). The sample period is January
1994 to December 2013. The graphs focus on the difference of the alpha (and the corresponding
two-tailed t-statistic) obtained in developed and emerging markets. The average alpha difference is
26 bp. The difference is positive in 88.03% of the cases. The corresponding t-statistic is larger than
2 (smaller than -2) in 29.01% (0.00%) of the 10,000 simulations.
88
020
040
060
0F
requ
ency
−.5 0 .5 1alpha in developed markets − alpha in emerging markets
020
040
060
0F
requ
ency
−2 0 2 4 6T−statistic of the difference in alpha
89
Figure 54: Simulated composite mispricing based on five randomly selected individual anomalies
(universe: 31 anomalies, equally weighted returns): Alpha difference between developed markets
and emerging markets
The upper figure on the following page shows the distribution of the difference between the monthly
alpha (in %, equally weighted returns) obtained from exploiting aggregate cross-sectional mispric-
ing in developed markets and the alpha obtained in emerging markets. The lower figure shows the
distribution of the respective two-tailed t-statistic. The distribution is based on 10,000 simulations.
For each simulation, I randomly select five individual anomalies from the 31 individual anomalies
described in detail in Table 1 of this Online Appendix (failure probability, financial distress, net
stock issues, composite equity, total accruals, net operating assets, momentum, gross profitability,
asset growth, return on assets, investments-to-assets, low volatility anomaly, low beta anomaly, id-
iosyncratic risk anomaly, maximum daily return anomaly, lottery-type stock anomaly, short-term
return reversal, long-term return reversal, turnover anomaly, return seasonality anomaly, intermedi-
ate momentum, continuous information arrival anomaly, earnings announcement premium anomaly,
dividend month anomaly, PEAD based on announcement returns, PEAD based on analyst consen-
sus, R&D intensity anomaly, R&D growth anomaly, 200 day moving average anomaly, 52 week high
anomaly, and analyst forecast dispersion anomaly). I allow for the overweighting of specific anoma-
lies in that a specific anomaly can be drawn several times within one simulation. The mechanism to
compute aggregate cross-sectional mispricing based on these randomly selected individual anomalies
follows Stambaugh, Yu, and Yuan (2015) and is explained in detail in Section 2.2. of the paper as
well as in Tables 14 and 15 of this Online Appendix. In each country month, the portfolio goes long
(short) stocks in the bottom (top) quintile of mispricing. Long/short returns for developed or emerg-
ing markets in a given month are computed as the arithmetic average of all eligible country-level
return estimates. Alphas are then computed relative to a global Fama and French (1993) three-factor
model (as explained in Section 2.3. of the paper). The sample period is January 1994 to December
2013. The graphs focus on the difference of the alpha (and the corresponding two-tailed t-statistic)
obtained in developed and emerging markets. The average alpha difference is 37 bp. The difference
is positive in 92.03% of the cases. The corresponding t-statistic is larger than 2 (smaller than -2) in
56.59% (0.44%) of the 10,000 simulations.
90
020
040
060
0F
requ
ency
−.5 0 .5 1 1.5alpha in developed markets − alpha in emerging markets
020
040
060
0F
requ
ency
−5 0 5 10T−statistic of the difference in alpha
91
Figure 55: Simulated composite mispricing based on five randomly selected individual anomalies
(universe: 31 anomalies, value weighted returns): Alpha difference between developed markets
and emerging markets
The upper figure on the following page shows the distribution of the difference between the monthly
alpha (in %, value weighted returns) obtained from exploiting aggregate cross-sectional mispricing in
developed markets and the alpha obtained in emerging markets. The lower figure shows the distribu-
tion of the respective two-tailed t-statistic. The distribution is based on 10,000 simulations. For each
simulation, I randomly select five individual anomalies from the 31 individual anomalies described
in detail in Table 1 of this Online Appendix (failure probability, financial distress, net stock issues,
composite equity, total accruals, net operating assets, momentum, gross profitability, asset growth,
return on assets, investments-to-assets, low volatility anomaly, low beta anomaly, idiosyncratic risk
anomaly, maximum daily return anomaly, lottery-type stock anomaly, short-term return reversal,
long-term return reversal, turnover anomaly, return seasonality anomaly, intermediate momentum,
continuous information arrival anomaly, earnings announcement premium anomaly, dividend month
anomaly, PEAD based on announcement returns, PEAD based on analyst consensus, R&D inten-
sity anomaly, R&D growth anomaly, 200 day moving average anomaly, 52 week high anomaly, and
analyst forecast dispersion anomaly). I allow for the overweighting of specific anomalies in that a
specific anomaly can be drawn several times within one simulation. The mechanism to compute
aggregate cross-sectional mispricing based on these randomly selected individual anomalies follows
Stambaugh, Yu, and Yuan (2015) and is explained in detail in Section 2.2. of the paper as well as
in Tables 14 and 15 of this Online Appendix. In each country month, the portfolio goes long (short)
stocks in the bottom (top) quintile of mispricing. Long/short returns for developed or emerging
markets in a given month are computed as the arithmetic average of all eligible country-level re-
turn estimates. Alphas are then computed relative to a global Fama and French (1993) three-factor
model (as explained in Section 2.3. of the paper). The sample period is January 1994 to December
2013. The graphs focus on the difference of the alpha (and the corresponding two-tailed t-statistic)
obtained in developed and emerging markets. The average alpha difference is 30 bp. The difference
is positive in 84.97% of the cases. The corresponding t-statistic is larger than 2 (smaller than -2) in
36.15% (0.56%) of the 10,000 simulations.
92
010
020
030
040
050
0F
requ
ency
−.5 0 .5 1 1.5alpha in developed markets − alpha in emerging markets
010
020
030
040
050
0F
requ
ency
−4 −2 0 2 4 6T−statistic of the difference in alpha
93
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