1-s2.0-S1058330004000151-main

Information flow between the stock and option markets:

Where do informed traders trade?

Carl R. Chen, Peter P. Lung*, Nicholas S.P. Tay

School of Business Administration, Department of Economics and Finance, University of Dayton,

Dayton, OH 45469-2251, USA

Received 22 January 2003; received in revised form 27 January 2004; accepted 23 March 2004Available online 6 July 2004

Abstract

This paper investigates the flow of information between the equity and options markets. We argue that informed

traders, in deciding where to place their trades, are not entirely indifferent to option moneyness, degree of

information asymmetry, and option liquidity. Unlike some previous studies that find information to flow

unilaterally from equity to options markets, we control for the above factors and discover feedback relations

between trades in out-of-the-money (OTM) options and the underlying equities. The finding is consistent with the

pooling equilibrium hypothesis, which asserts that informed traders trade in both the equity and options markets.

Some informed traders are probably attracted to the out-of-the money options because of their higher liquidity,

lower premiums, and higher delta-to-premium ratios, hence, lending support to the liquidity and leverage

hypothesis.

D 2004 Elsevier Inc. All rights reserved.

JEL classification: G12; G13; G14

Keywords: Information flow; Options markets; Informed trading

1. Introduction

The question concerning the informational role of transactions in the stock and option markets

www.elsevier.com/locate/econbase

Review of Financial Economics 14 (2005) 123and the direction of information flow between these markets has continued to attract much attention

because there is still no clear consensus among previous studies on the direction of information flow

between the stock and option markets. Our objective is to shed light on this issue by controlling for

several factors that may have confounded the results in some previous studies. Unlike earlier work,

1058-3300/$ - see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.rfe.2004.03.001

* Corresponding author. Tel.: +1-937-229-3095; fax: +1-937-229-2477.

E-mail address: [email protected] (P.P. Lung).

C.R. Chen et al. / Review of Financial Economics 14 (2005) 1232we argue that in deciding where to place their trades, informed traders are not indifferent to the

option moneyness, the degree of information asymmetry (between managers and investors), and the

relative liquidity of the option markets. Failing to control for these factors could lead to evidence

that is biased towards rejecting the hypothesis that informed traders also trade in option markets.

How would informed traders trade to capitalize their private information? The liquidity hypothesis

asserts that informed traders who are motivated by their desire to minimize their transaction costs

and hide their trades would choose to transact in the most liquid securities. Because equity markets

are generally more liquid than option markets are, informed traders should prefer to trade in equity

markets, holding all other factors constant. Supporting this hypothesis, Vijh (1990) finds the bid-ask

spread in option markets to be larger than that in the stock markets. The larger bid-ask spread

should deter informed traders from trading in option markets unless the potential gain from their

private information exceeds the cost imposed by the larger bid-ask spread. On the other hand, the

leverage hypothesis argues that informed traders who are motivated by their desire to magnify the

potential gains from their private information would choose to trade securities that offer them the

most leverage. Because option markets offer informed investors the opportunity to engage in highly

leverage trades, according to the leverage hypothesis, informed traders should prefer to trade in

option markets, holding all other factors constant. Ultimately, the decision concerning what trading

strategy to adopt will be driven by the size of the potential gains that a particular trading strategy

can offer. To an informed trader, an ideal scenario is one where the most liquid securities in the

market are options with high delta-to-premium ratio. It is beneficial to transact in high delta-to-

premium ratio options because they offer informed traders the highest leverage per unit cost. While

stock markets, in general, are more liquid than option markets are, there certainly had been episodes

when some types of options were as liquid as equities. In addition to liquidity, these options offer

the added benefit of leverage. Hence, the liquidity and leverage hypotheses need not be mutually

exclusive.

The objective of this paper is to verify the abovementioned hypotheses and to identify conditions that

will lead the informed traders to prefer transacting in certain types of options over equities and vice

versa.

This study differs from previous studies in two important aspects. First, we control for the effect of

option moneyness on option liquidity and leverage. This is crucial because options with different

degree of moneyness have different levels of liquidity and different degrees of leverage, as measured

by the delta-to-premium ratio. In general, we find OTM options to be the most liquid and offer the

highest delta-to-premium ratios among all the options. If the objective of informed traders is to extract

as much value as possible from their private information, these traders should prefer transacting in

options with the highest delta-to-premium ratio to get the most bang out of their private

information. Any failure to control for option moneyness, as in previous studies, will inevitably

confound the empirical results.

Second, we isolate a subsample of firms that have a high level of information asymmetry to filter

out data that are more likely to exhibit information-driven trades. The intention is to obtain a cleaner

sample to study information-driven trades. Other things held constant, we conjecture that more private

information is available for a company that has a higher level of information asymmetry between

manager and investors. If investors have private information about firms with high level of

information asymmetry, the potential gains from such private information are likely to be greater,making it worthwhile for the informed traders to consider transactions in options because of the

leverage effect. To this end of the analysis, we study a subsample of the S&P500 firms that have the

highest research and development to sales ratios. Presumably, the information effect should be the

strongest for this group of firms.

C.R. Chen et al. / Review of Financial Economics 14 (2005) 123 3To test these hypotheses, we employ a data set that spans a longer time frame than were

previously used. For example, Anthony (1988) uses 1.5 years of daily data and Easley, OHara, and

Srinivas (1998) use 2 months of intraday data. Our study uses daily equity and option data for the

S&P 500 component stocks from November 1995 to December 2002. To our knowledge, our data

set is the most recent and the largest one that has been employed.1 Employing such a data set

allows us to partition the option sample according to moneyness and firms characteristics. An

additional advantage of using a more recent data set is it allows us to verify the results derived from

an older data set. This is important because recent advances in informational technology and changes

in the regulatory environment may affect the informational efficiency of the markets (Fargher &

Weigand, 1998).

Based upon this expanded data set, we investigate the causality and feedback relations between stock

returns and option trading value ratios (VRs) for the 500 firms included in the S&P 500 index while

controlling for option moneyness, information asymmetry, and liquidity. Option trading VR is defined as

(call volume call premium)/(put volume put premium). A model we developed later in this papersuggests that this is a credible measure for discerning information embedded in option trades, and it

makes more economic sense. Our model is consistent with the suggestions found in Chen and Goodhart

(1998), where they argue that volume alone may not be able to tell the whole story about market

expectations. This is because if trades contain no information, increased demand (supply) can be simply

offset by increased supply (demand) and leave option premiums unchanged. However, if the increased

demand (supply) reflects new information, option dealers will adjust option premiums upward

(downward).

Our findings can be summarized as the following: (1) Stock returns are positively related to option

trading VR. (2) Stock returns lead option trading VRs, suggesting that informed traders prefer to trade

equities over options. This finding is consistent with the liquidity hypothesis, which posits that lower

transaction cost and better liquidity in the equity markets prompt the informed traders to trade in the

equity markets. (3) As options are partitioned according to three different moneynesses, we find

feedback relation with equity returns and option trading value for OTM options, but we do not see

similar phenomenon for other options. Informed traders, therefore, trade both stocks and OTM

options. This feedback relation is stronger for stocks with higher informational asymmetry. More

interestingly, for the more liquid options, the causality relation between stocks to options reverses: We

find that out-of-the money option trading value leads stock returns. This finding is consistent with

both the liquidity and the leverage hypotheses because OTM options have higher liquidity, lower

premiums, and higher delta-to-premium ratios.

The rest of the paper is organized as follows: Section 2 reviews the literature. In Section 3, we provide

a simple model of the relationship between stock returns and callput option trading VR. Section 4

describes the data sample. Section 5 presents test methodologies. Section 6 discusses the empirical

results, and Section 7 concludes.

1 Berkeley Option Database, where all intraday studies were based upon has been discontinued and is no longer availablefor subscription.

Verrecchia (1987), and Mayhew, Sarin, and Shastri (1995), among others, argue that lower transaction

costs and greater financial leverage provided by option trades, coupled with the absence of short sale

C.R. Chen et al. / Review of Financial Economics 14 (2005) 1234restriction in the option markets, offer advantages over stock transactions. Furthermore, Back (1993) and

Cherian (1993) point out that investors can only bet on volatility in the option markets if they have

private information about the underlying assets volatility.

Supporting the argument of Black (1975), Manaster and Rendleman (1982) find that information

content in option prices leads stock prices up to 24 h. Jennings and Starks (1986) also report that stock

prices adjust to new information more rapidly for stocks with options listed on the Chicago Board Options

Exchange (CBOE). Instead of studying the price relation, Anthony (1988) examines the linkage between

option and stock volumes and finds that option volume leads stock volume. These studies therefore support

the contention that option markets contain information beyond what is present in the stock markets.

While these arguments favor the hypothesis that option markets may contain information that has yet

to be compounded in the underlying asset market, some have argued to the contrary that the lower

liquidity experienced in the option markets may be sufficient to dissuade informed traders from

executing their trades in the option markets. Using transaction-by-transaction data, Stephen and Whaley

(1990) report that stock markets lead the option markets by more than 15 min. Vijh (1990) also finds that

the price effect of large option trades is small, therefore suggesting that option trades are not informative.

However, Chan, Chung, and Johnson (1993) counterargue that the results of Stephen and Whaley are

biased due to different price discreteness rules in the stock and option markets. Srinivas (1993) also

provides evidence of sample selection bias in Vijh (1990) and presents evidence that option trades are

informative.

Using trade and quote data, two more recent studies also report conflicting findings. Easley et al.

(1998) present evidence consistent with a pooling equilibrium, where informed investors trade in both

the option and stock markets, and positive/negative option volume predicts future stock price movement.

What is also interesting is that their finding of an asymmetry between the negative- and positive-position

effects, which suggest traders acting on bad news, may find the options markets more attractive possibly

because of the short-sales constraint in the equity markets. On the other hand, Chan, Chung, and Fong

(2002), also using trade and quote data, find that stock net trade volume predicts contemporaneous and

subsequent stock and quote revisions, but option net trade volume does not predict stock quote revision.

Their results therefore suggest that informed traders prefer to initiate trades in the stock market,

consistent with a separating equilibrium argument.

Clearly, there is no consensus among these studies on the direction of information flow between the

stock and option markets. Likewise, it is not clear where the informed traders choose to place their

trades. Our objective is to shed light on this issue by controlling for several factors that may have

confounded the results in some of these studies.

3. Relations between stock returns and option trading value

In this section, we seek to establish the relationship between stock returns and option trading activities2. Literature review

Why might informed traders prefer to trade in the option markets? Black (1975), Diamond andand identify a measure for discerning good news versus bad news information embedded in option

L pUlnQCSU XC pDlnQPXP SD kQCPC QPPP W0 3

Solving the first order conditions of Eq. (3) leads to

pU kPCQC 4pD kPPQP 5

C.R. Chen et al. / Review of Financial Economics 14 (2005) 123 5Dividing Eq. (4) by Eq. (5) gives us

pU=pD QCPC=QPPP 6

Eq. (6) indicates that the ratio of the probability of a price increase (pU) to the probability of a pricedecrease (pD) is equal to the ratio of the call trading value (QCPC) to the put trading value (QPPP).According to Eq. (6), when the probabilities are unobservable, it is possible for the market participantstrades. We consider a two-state world where informed traders have information about the probabilities of

the up and down states. The informed traders are risk adverse, share the same log utility function, and

have homogenous expectations about market movements and price changes. We are interested in

understanding how the informed traders would trade to maximize their expected utilities if they choose

to trade on their information in the option markets. Within this framework, informed traders cash flows

may be summarized as follows.

Cash Flows of Informed Traders

Where S stands for stock price and QC(QP) is the call (put) trading volume. Prob(S= SU) and

Prob(S = SD) are the probabilities that the stock price will increase/decrease to SU/SD at some time T in

the future, respectively. The magnitudes of price up-movement and down-movement are assumed to

be the same. pU (pD) denotes the probability that stock price increases (decreases). XC (XP) is theexercise price for call (put) option and PC (PP) refers to the call (put) premium.

Given their private information and subject to an initial wealth constraint of W0, informed traders will

select optimal quantities of call and put to purchase to maximize their expected utilities. Therefore, their

objective function and constraints can be specified as:

Maximize pUUQCSU XC pDUQPXP SD 1Subject to : QCPC QPPP W0: 2

where U is a log utility function. The same optimization problem may be represented as the following

Lagrange Multiplier Equation:

Trade call Trade put

Cash flow at Time 0 QCPC QPPPPayoff at time T

Prob(S = SU) = pU QC (SUXC) 0Prob(S = SD) = pD 0 QP (XP SD)to infer the relative magnitude of the unobserved probabilities by watching the ratio of the trading

values. When the callput option trading VR, (QCPC)/(QPPP), is greater (smaller) than unity, stockreturn is more likely to be positive (negative). Thus, the VR is a credible measure for discerning good

and bad news information embedded in option trades because it considers information that are

reflected in both option trading volumes and premiums. Eq. (6) confirms that option volume alone

does not fully reflect the market expectations and implies that stock returns should be positively

(SEBL), and for the LIA firms, they range from 0% (ACV) to 0.55% (PD). We are interested in

studying the HIA firms because these firms are more likely to exhibit information-driven trades.

C.R. Chen et al. / Review of Financial Economics 14 (2005) 1236Moreover, if investors have private information about the HIA firms, the potential gains from their

2 Our classification of option moneyness is not without precedence. We follow Rubinstein (1985) to categorize the options

into OTM, ATM, and ITM groups. Deep-in-the-money and deep-out-of-the-money options are excluded from the study due to

thin trading.3 The classification of HIA firms is subjective. We try various cut-off points, such as R & D/sales ratio > = 10%, and findrelated to VR.

4. Data and descriptive statistics

We obtain the daily option data from the Prophet Financial Systems (PFS). PFS compiles their data

from the daily stock option transaction records provided by the CBOE. The database, starting from 1995,

consists of option root symbol, trading date, option type, strike price, code for strike price, code for

expiration date, option premium (open, high, low, and close), trading volume, and open interest. Our

sample consists of all the firms included in the S&P500 index. The daily data spans a period of

approximately 7 years beginning on November 1st of 1995 and ending on December 31st of 2002. In an

effort to avoid possible measurement errors arising from thin trading, we limit our sample according to

the following criteria. First, we restrict our sample to options that have at least 30 days, but no more than

90 days, to expiration. Second, we exclude options with daily trading volume less than 20 contracts.

Finally, we exclude options with strike prices that are either less than 80% or greater than 120% of the

prevailing underlying asset prices.

To examine the informational role of options across different moneynesses, we define OTM call (put)

options as options with strike price ranging from 105 (80)% to 120 (95)% of the underlying asset price,

at-the-money (ATM) options as options with strike price ranging from 95% to 105% of the underlying

asset price, and in-the-money (ITM) call (put) options as options with strike price ranging from 80

(105)% to 95 (120)% of the underlying asset price.2 The corresponding underlying stock returns are

obtained from the CRSP database.

To classify firms with high/low level of information asymmetry, we use the research and development

expenses to sales ratio (R&D/sales ratio) as a proxy of information asymmetry. In the spirit of Shleifer

and Vishny (1989), R&D expenditures represent a managers specific knowledge within the firms core

operation. Therefore, high-R&D firms have higher information asymmetry between the managers and

the outsiders. Our high information asymmetry (HIA) sample comprises 50 S&P 500 firms that have the

highest R&D/sales ratio. On the other hand, the low information asymmetry (LIA) firms consist of 50

stocks that have the lowest R&D/sales ratio. The HIA and LIA firms are listed in Panels A and B of

Table 1, respectively. The R&D/sales ratios for the HIA firms vary from 6.21% (STJ) to 386.69%3the results to be qualitatively similar.

Table 1

List of the HIA firms

Ticker symbol Full name RD/sales ratio (%)

(A) List of high information asymmetry (HIA) firms

A AGILENT TECH 7.82

ABI APPLIED BIOSYS 167.83

AMCC APPLD MICRO 57.34

MCO MOODYS 7.01

MXIM MAXIM INTEGRATED 7.71

AV AVAYA 291.75

BGEN BIOGEN 15.05

BMC BMC SOFTWARE 11.46

BMY BRISTOL MYERS SQ 10.48

BRCM BROADCOM 51.83

BSX BOSTON SCIEN CP 22.01

CA COMPUTER ASSOC 32.25

CHIR CHIRON 9.37

CIEN CIENA 14.31

CSCO CISCO SYSTEMS 6.76

CMVT COMVERSE TECH 7.33

CNXT CONEXANT SYSTEMS 16.66

DD DU PONT 11.18

EBAY EBAY 8.26

FRX FOREST LABS 6.85

GENZ GENZYME GEN 14.74

HI HOUSEHOLD INTERNL 7.08

IMNX IMMUNEX 19.45

INTU INTUIT 15.52

JDSU JDS UNIPHASE 56.95

LSI LSI LOGIC 11.61

MEDI MEDIMMUNE 75.41

MERQ MERCURY INTRACT 9.59

MIL MILLIPORE CP 14.06

MMM 3M 10.12

NOVL NOVELL 11.34

NSM NATL SEMICONDUCT 7.24

NVDA NVIDIA 9.24

NVLS NOVELLUS SYS 19.78

PHA PHARMACIA 24.60

PMCS PMC-SIERRA 27.20

PWER POWER ONE 11.56

RATL RATIONAL SOFTWARE 13.65

MU MICRON TECHNOLOGY 6.76

SEBL SIEBEL SYSTEMS 368.69

STJ ST JUDE MEDICAL 6.21

TER TERADYNE 7.16

TLAB TELLABS 7.93

TRW T R W 9.33

TWX TIME WARNER 11.41

VRTS VERITAS SOFTW 16.90

(continued on next page)

C.R. Chen et al. / Review of Financial Economics 14 (2005) 123 7


(A) List of high information asymmetry (HIA) firms

VTSS VITESSE 32.83

WAT WATERS 10.21

WPI WATSON PHARM 13.06

YHOO YAHOO 7.19

(B) List of low information asymmetry (LIA) firms

AA ALCOA 0.21

ACV ALBERTO CULVER 0.00

ADM ARCHER-DANIELS 0.12

APD AIR PRODS & CHEM 0.25

ASH ASHLAND 0.37

AVP AVON PRODS 0.28

AVY AVERY DENNISON 0.13

BCC BOISE CASCADE 0.31

BJS BJ SERVICES 0.22

BMS BEMIS 0.17

CAH CARDINAL HLTH 0.18

CAT CATERPILLAR 0.44

CD CENDANT CP 0.13

CL COLGATE PALMOLIV 0.18

CLX CLOROX 0.32

COC CONOCO 0.42

CPB CAMPBELL SOUP 0.23

G GILLETTE 0.44

GE GENERAL ELECTRIC 0.52

GIS GENERAL MILLS 0.14

HAL HALLIBURTON 0.51

HSY HERSHEY FOODS CP 0.12

IP INTL PAPER 0.44

ITW ILLINOIS TOOL WK 0.51

JBL JABIL CIRCUIT 0.38

KMB KIMBERLY-CLARK 0.25

LEG LEGGET & PLATT 0.26

LXK LEXMARK INTL 0.53

MCK MCKESSON 0.15

MO ALTRIA GROUP 0.21

NWL NEWELL RUBBERMD 0.49

OXY OCCIDENTAL PETE 0.27

PBI PITNEY BOWES 0.45

PD PHELPS DODGE CP 0.55

PH PARKER-HANNIFIN 0.53

PPG PPG IND 0.49

PX PRAXAIR 0.23

RSH RADIOSHACK 0.03

RTN RAYTHEON 0.36

SBUX STARBUCKS 0.27

Table 1 (continued )

C.R. Chen et al. / Review of Financial Economics 14 (2005) 1238


(B) List of low information asymmetry (LIA) firms

SEE SEALED AIR CP 0.53

SHW SHERWIN-WILLAMS 0.52

SIAL SIGMA ALDRICH 0.21

SLR SOLECTRON 0.13

SUN SUNOCO 0.13

TNB THOMAS & BETTS 0.47

UTX UNITED TECH CP 0.52

WY WEYERHAEUSER 0.08

XOM EXXON MOBIL 0.36

YUM YUM! BRANDS 0.26

Ticker symbol Full name Option volume/stock volume ratio (%)

(C) List of high option liquidity (HL) firms

ABS ALBERTSONS 21.90

ABX BARRICK GOLD 65.99

ADBE ADOBE SYS 41.76

ADI ANALOG DEVICES 40.09

AGN ALLERGAN 287.26

AMGN AMGEN 23.58

BC BRUNSWICK 299.57

BHI BAKER HUGHES 45.92

BSC Bear Stearns 32.41

C CITIGROUP 23.86

CTB COOPER TIRE & RB 204.26

CTL CENTURYTEL 132.21

DAL DELTA AIR LINES 25.21

EDS Electronic Data Systems 23.82

ETR ENTERGY CP 87.47

FCX FRPRT-MCM GD 48.76

G GILLETTE 426.56

GM GENERAL MOTORS 28.75

HCA HCA 22.95

HSY HERSHEY FOODS 452.47

IBM INTL BUS MACHINE 37.03

IGT INTL GAME TECH 222.04

JCI JOHNSON CONTROLS 92.86

KRB MBNA 57.40

LEH LEHMAN BROTHER HD 31.55

LMT LOCKHEED MARTIN 126.89

LUV SW AIRLINES 81.45

MEL MELLON FINL 82.68

MER MERRILL LYNCH 22.55

MMM 3M 23.59

NKE NIKE CL B 128.50

NUE NUCOR 21.43

NVLS NOVELLUS SYS 43.06

NWL NEWELL RUBBERMD 121.60


(continued on next page)



(C) List of high option liquidity (HL) firms

OXY OCCIDENTAL PETE 48.91

PDG PLACER DOME 59.29

PTV PACTIV 177.39

S SEARS, ROEBUCK & 59.46

SBC SBC COMMS 37.13

SCH CHARLES SCHWAB 55.67

SEE SEALED AIR CP 30.99

SPC ST PAUL COS 48.23

SWY SAFEWAY 88.00

TER TERADYNE 41.61

TRW TRW 29.15

UNH UNITED HEALTH 45.36

UNP UNION PACIFIC CP 155.66

UTX UNITED TECH CP 78.88

WAG WALGREEN 105.75

WIN WINN-DIXIE STRS 29.76

(D) List of low option liquidity (LL) firms

AEE AMEREN 0.46

AL ALCAN 1.14

ALTR ALTERA 0.71

APD AIR PRODS & CHEM 0.82

AVY AVERY DENNISON 0.82

BLI BIG LOTS 0.85

BMET BIOMET 0.98

CEG CONSTELL ENERGY 0.93

CF CHARTER ONE 1.07

CIN CINERGY 0.73

CINF CINCINNATI FIN 0.91

CZN CITIZENS COMMS 0.68

DNY DONNELLEY RR & SONS 0.83

DOV DOVER 0.77

DPH DELPHI 0.82

DTE DTE ENERGY 0.65

EIX EDISON INTL 1.13

FDO FAMILY DLR STRS 1.10

FE FIRSTENERGY 0.97

FISV FISERV 0.86

FO FORTUNE BRANDS 0.92

FPL FPL GROUP 1.01

GAS NICOR 0.40

GWW W W GRAINGER 1.13

HBAN HUNTINGTON BANC 1.07

KSE KEYSPAN 1.03

LEG LEGGET & PLATT 0.97

MAS MASCO 1.07

MET METLIFE 1.02





C.R. Chen et al. / Review of Financial Economics 14 (2005) 123 11(D) List of low option liquidity (LL) firms

MOLX MOLEX 1.07

NCC NATIONAL CITY 0.92

NI NISOURCE 1.16

NTAP NETWK APPLIANCE 0.59

PBG PEPSI BOTTLING 1.20

PEG PUBL SVC ENTER 0.89

PGL PEOPLES ENERGY 0.56

PH PARKER-HANNIFIN 0.98

PNW PINNACL WEST 0.72

PPL PPL 0.14

RHI ROB HALF INTL 1.18

ROH ROHM & HAAS 0.77

SAFC SAFECO 0.85

SNA SNAP-ON 1.10

SO SOUTHERN 0.96private information are likely to be greater, making it worthwhile for informed traders to trade in options

despite the higher bid-ask spread in the option markets.

To segregate options according to the liquidity factor, we classify high and low option liquidity firms

based on the ratio of daily average option trading volume (scaled up by a factor of 100 shares) to the

stock trading volume, that is, (100 option volume)/stock volume. Again, the high option liquidity (HL)group is composed of 50 S&P500 companies that have the highest option-to-stock-volume ratio. The

bottom 50 firms are classified into the low option liquidity (LL) group. The HL and LL firms are

reported in Panels C and D of Table 1, respectively. The ratios are from 452.47% (HSY) to 21.43%

(NUE) for the HL group and 1.20% (PBG) to 0.40% (GAS) for the LL group.

Table 2 summarizes the daily average option trading volume per firm. For all the component stocks in

the S&P500, Panel A shows that over the entire sample period, the average daily volumes for call and

put per firm are 1623 and 1058, respectively. OTM calls (puts) account for 64.57% (56.51%) of overall

trading, hence indicating that OTM options are more liquid than the other options.4 Because OTM

SOTR SOUTHTRUST CP 1.06

SRE SEMPRA ENERGY 0.73

TE TECO ENERGY 0.85

TMK TORCHMARK 1.11

TNB THOMAS & BETTS 1.15

X US STEEL 0.82

This table lists firms with HIA, LIA, HL, and LL for firms in the S&P 500 index. HIA and LIA are firms in the top and bottom

10 percentile of the R&D/sales ratio in the S&P500 index, respectively. R&D/sales ratio is calculated based upon the average of

R&D/sales ratios over the sample period (from 1995 to 2002) according to Compustat. HL and LL firms are firms in the top and

bottom 10 percentile of the option volume/stock volume ratio in the S&P500 index, respectively. Option volume/stock volume

ratio is calculated based upon the average of option volume/stock volume ratios over the sample period (from 1995 to 2002)

according to the PFS and CRSP.

4 In our OTM group, there are relatively few options with exercise prices that are near the limit of either 20% above or

below the prevailing underlying price. For instance, options that have exercise prices that are either 15% greater or lesser than

the prevailing underlying price account for a mere 8.85% of the sample of options in the OTM group.

C.R. Chen et al. / Review of Financial Economics 14 (2005) 12312Table 2

Summary of average daily stock option trading volume

Average call Average put

(A) S&P500 component firms

ALL 1623 1058

ATM 625 351

ITM 609 554

OTM 1048 595

(B) HIA firmsoptions are more heavily traded than the other options are, we expect trades in OTM options to be more

informative.

Panel B in Table 2 presents the average daily option volumes per firm for the HIA group. In general,

the average call trading volume for the HIA firms is higher than the average overall call trading volume in

Panel A, while the average put trading volume for HIA firms is lower than the average overall put trading

volume. In other words, on average, more calls were traded for the HIA sample. Within the HIA sample,

OTM options are more heavily traded than the ITM and ATM options. The average trading volume for the

OTM options is more than half the average trading volume for all the options combined. In fact, this ratio

is even more pronounced for the put options. In summary, over 50% of the trading volume in options

arises from trades in the OTM options, making OTM options the most heavily traded options and, hence,

ALL 2309 825

ATM 622 292

ITM 939 296

OTM 1318 530

(C) LIA firms

ALL 1888 956

ATM 920 219

ITM 496 893

OTM 1382 306

(D) HL firms

ALL 6891 5223

ATM 2679 1440

ITM 2704 2688

OTM 4961 3811

(E) LL firms

ALL 96 69

ATM 62 53

ITM 82 48

OTM 63 54

This table summarizes the average daily option trading volume for all the component firms in the S&P500 index, HIA, LIA,

HL, and LL firms. The sample period is from November 1995 to December 2002. Options are categorized into ATM, ITM, and

OTM groups. OTM call (put) options are options with strike price ranging from 105 (80)% to 120 (95)% of the underlying asset

price, ATM options are options with strike price ranging from 95% to 105% of the underlying asset price, and ITM call (put)

options are options with strike price ranging from 80 (105)% to 95 (120)% of the underlying asset price.

C.R. Chen et al. / Review of Financial Economics 14 (2005) 123 13the most liquid among all the options. The option volumes for the LIA firms are reported in Panel C,

which shows a similar pattern as shown in Panel B. Option volumes for the HL and LL firms are presented

in Panels D and E, respectively. The average daily call trading volume per firm for the HL firms is more

than 71 (6891/96) times of that for the LL firms. In Panel D, it also shows that OTM options are the most

heavily traded. These statistics reveal tremendous differences in option liquidity among stock options. It

would only make sense, therefore, to examine options in the disaggregated sample.

Finally, in addition to liquidity measures, OTM options also have the most leverage effect, in the

Fig. 1. Relationship between option delta-to-premium ratio and option moneyness.sense that these options have the highest delta-to-premium ratios. In Fig. 1, we show simulations of the

relationship between option moneyness and the delta-to-premium ratio using the BlackScholes Option

Pricing Model assuming a risk-free rate of 2.5%, a volatility of 30%, and a time-to-expiration of 1

month. Fig. 1 clearly shows an increasing delta-to-premium ratio as the option becomes more OTM.

5. Test methodology

To examine the contemporaneous relation between stock returns and callput option trading VR,

following our theoretical framework presented in Section 2, we use:

Rt a bVRt ut 7

Rt is the underlying stock return, and VR measures the callput trading VR as specified in Eq. (6). A

significant and positive b would support the view that VR and stock returns are positively related.5

5 For a robust test on the contemporary relation between VRs and returns, we also include stock trading volume (Blume,

Easley, and O Hara, 1994; Gallant, Rossi, & Tauchen, 1992; Karpoff, 1987) and open interest (Bessembinder & Seguin, 1993),

and regress returns on VR, stock volume, and open interest. The test results are qualitatively similar.

Following Easley et al. (1998) and Chan et al. (2002), we standardize all variables (R and VR) in Eq.

To study where informed traders are more likely to trade, we examine the leadlag relationship

C.R. Chen et al. / Review of Financial Economics 14 (2005) 12314between option trading VR and stock returns for the whole sample and for various subsamples. Bivariate

vector autoregression (VAR) models allow us to examine whether current option markets (stock markets)

contain useful information to predict future stock markets (option markets) behavior. To analyze the

direction of information flow, we estimate the coefficients in the following strict VAR models:

VRt XL

i1giVRti

XL

i1diRti nt 8

Rt XL

i1aiRti

XL

i1biVRti pt 9

Eq. (8) tests if lagged stock returns contain information regarding future VR behavior. Lagged VRs are

also included in the equation to account for possible serial correlation in VR. On the other hand, Eq. (9)

examines if lagged VRs contain information regarding future stock return behavior. We determine the

optimal number of lags in Eqs. (8) and (9) using the Akaike Information Criterion.6

If informed investors initiate trades in the stock market, information will be impounded in the stock

market first. In this case, stock returns may contain useful information for predicting future transactions in

the option markets. Therefore, at least some of the d coefficients in Eq. (8) will be positive and statisticallysignificant. Conversely, if informed investors prefer to trade in the option markets, information will travel

from the option to the stock markets, and as such, VR may have predictive power for future stock returns

movement. If VR has predictive power for future stock returns, some of the b coefficients in Eq. (9) will bepositive and statistically significant. If both the ds and bs are statistically significant, we have a feedbackrelation between the two markets, which is consistent with the pooling equilibrium.

6. Empirical results

6.1. Contemporaneous relation between stock returns and option trading value

The regression results of the contemporary relation between returns and VR for Eq. (7) are reported in

Table 3. Panel A presents the results based on all the 500 firms that are included in the S&P 500 index.

6 Following the practice of prior studies (e.g., Anthony, 1988) for a robust test, we also set all lag length at six. The results(7) to control for cross-sectional variations across different stocks and options. To standardize a variable

for each trading day, we first calculate the mean and standard deviation of that variable across all the

firms. We then standardize the variable by first subtracting the mean and then dividing the demean value

by the standard deviation. In the regression analysis, we pool all the firms to reduce the impact of any

heteroskedastic error terms. Because it is well documented that stock returns are highly autocorrelated

and heteroskedastic (Conrad & Kaul, 1988; Lo & MacKinlay, 1988; Schwert & Seguin, 1990), Eq. (7) is

estimated using the generalized method of moments regression (GMM).are qualitatively similar.

Table 3

Contemporary relation between stock returns and callput option trading VR

Rt a bVRt e 7

Obs. b t(b) R2 (%)

(A) S&P500 component stocks

All 1573 .0396 7.66*** 3.60

ATM 1565 .0564 7.95*** 3.89

ITM 1568 .0322 5.93*** 2.20

OTM 1570 .0136 2.72*** 0.47

(B) HIA stocks

All 1567 .0369 4.21*** 1.12

ATM 1560 .0806 6.51*** 2.65

ITM 1562 .0486 4.86*** 1.49

OTM 1562 .016 1.74* 0.19

(C) LIA stocks

All 1570 .2135 3.23*** 0.66

ATM 1560 .2055 4.62*** 1.35

ITM 1561 .2865 5.36*** 1.81

OTM 1569 .177 2.97*** 0.56

(D) HL stocks

All 1566 .1246 2.46*** 0.39

ATM 1561 .1803 3.62*** 0.03

ITM 1563 .1984 2.94*** 0.34

OTM 1564 .1005 1.76* 7.94

(E) LL stocks

All 1544 .2321 3.73*** 0.02

ATM 1287 .035 2.84*** 40.37

ITM 1288 .1014 1.7* 8.88

OTM 1482 .0538 0.9 36.63This table shows the test results of the contemporary relation between stock returns and stock option trading VR for all

component stocks in the S&P500, HIA, LIA, HL, and LL firms. Option trading VR is defined as the overall call option trading

value to put option trading VR. HIA and LIA are firms in the top and bottom 10 percentile of the R&D/sales ratio in the

S&P500 index, respectively. R&D/sales ratio is calculated based upon the average of R&D/sales ratios over the sample period

(from 1995 to 2002) according to Compustat. HL and LL are firms in the top and bottom 10 percentile of the option volume/

stock volume ratio in the S&P500 index, respectively. Option volume/stock volume ratio is calculated based upon the average

of option volume/stock volume ratios over the sample period (from 1995 to 2002) according to the PFS and CRSP. Options are

categorized into ATM, ITM, and OTM groups. OTM call (put) options are options with strike price ranging from 105 (80)% to

120 (95)% of the underlying asset price, ATM options are options with strike price ranging from 95% to 105% of the underlying

asset price, and ITM call (put) options are options with strike price ranging from 80 (105)% to 95 (120)% of the underlying

asset price.

*Significant at the 10% level.

***Significant at the 1% level.


For the sample that includes all the options, we generally find that VRs are significantly, positively

related to stock returns. Results for the ATM and ITM options are similar as well. Panels B through E

present results for the HIA, LIA, HL, and LL groups, respectively, and the empirical results are

qualitatively the same as the results for the whole sample. This finding supports the positively

contemporary relation between equity returns and VR, as specified in Eq. (6).

However, we also notice an intriguing contemporaneous relation between stock returns and VRs for

C.R. Chen et al. / Review of Financial Economics 14 (2005) 12316the OTM options. In the case of OTM options, the VRs are significantly, negatively related to the stock

returns in Panels B through D, which is different from the results for the ALL, ATM, and ITM options.

Because this relation is a contemporaneous one, we interpret this as evidence that traders simultaneously

hedge their bets in the equity market with long positions in the OTM options because OTM options are

generally more liquid and have lower premiums. Such hedging activities will result in a negative

contemporaneous relation between stock returns and VR.7 Nevertheless, the OTM VR for the LL group

reported in Panel E is insignificant. This result is not surprising because informed traders will avert from

trading the illiquid options in this group.

6.2. Granger causality analysis

To examine the causeeffect relation between stock returns and VR, we first apply Eqs. (8) and (9) to

the whole data set (ALL), and we subsequently repeat the analysis for subsamples partitioned based

upon the option moneyness, namely, ATM, ITM, and OTM. The results are reported in Table 4. Our

results for ALL, ATM, ITM, and OTM options show clearly that stock returns lead VR, as the dcoefficient for the first lag are all statistically significant at the 1% level. We interpret this as suggesting

that informed trades are more likely to be executed in the equity market. On the other hand, none of the bcoefficients are significant, except for the OTM option, suggesting that the OTM option is the only

option that exhibits informed trades. This preliminary result is consistent with the liquidity hypothesis,

which argues that informed traders are more likely to trade in the equity market because of its higher

liquidity. The finding for the OTM group is also supportive of the liquidity hypothesis because OTM

options are the most liquid among all options. If informed traders trade in the option markets, OTM

options would be preferred over other options because of its liquidity. Furthermore, the fact that only the

OTM group exhibits a feedback relation between stock returns and option trading VR is supportive of

the leverage hypothesis because OTM options offer the highest leverage due to its high delta-to-premium

ratio. Our results therefore suggest a pooling equilibrium in the market. However, the pooling

equilibrium does not apply to all options, it applies to the OTM options, which constitute the most

liquid segment of the options markets.

In Table 5, we report results partitioned based upon information asymmetry. Since we reason that

informed trades are more likely to occur in firms with higher information asymmetry, we partition

firms based upon their R&D/sales ratio, which is what we have used as a proxy for information

7 To further address the impact of hedging activity on the relation between returns and OTM VR, we look into the

differences in open interest between calls and puts (hereafter OI) for the OTM options. Since hedgers are more likely to leave

their positions overnight than speculators do, OI should be relatively more stable for contracts where hedgers dominate.

Specifically, stock returns should be negatively related to the lagged OI of the OTM options. We run a bivariate VAR between

stock returns and the changes in OI for the OTM options. As expected, OI for OTM is negatively related to returns, which is

consistent with the explanation for the inverse contemporary relation between returns and OTM VR. We thank the reviewer forthis suggestion.

C.R. Chen et al. / Review of Financial Economics 14 (2005) 123 17Table 4

The causeeffect relation between returns and option trading VR for component stocks in the S&P500

VRt XL

i1giVRti

XL

i1diRti nt 8

Rt XL

aiRti XL

biVRti pt 9asymmetry. If our reasoning is correct, we will observe more statistically significant relations

between stock returns and option trading VR in the HIA sample, but weaker relations in the LIA

sample. Panel A of Table 5 reports results for the HIA firms. These results are consistent with the

findings reported in Table 4. That is, for ALL, ATM, and ITM options, stock returns lead VR

unilaterally, meaning informed trades occurred only in the equity market. For the OTM group,

however, we observe a feedback relation, suggesting that informed trades also occurred in the OTM

segment of the option market in addition to the equity market, and this is consistent with the

i1 i1

Explanatory variable

Dependent

variable

Lag

variable

Lag 1 Lag 2 Lag 3 Lag 4 Lag

5

Lag

6

Lag

7

Obs. F

statistic

P value

(%)

ALL VR Lags for

returns

0.0238 0.0024 0.0078 0.0011 1430 9.33*** 0.00

t Statistic 5.73*** 0.56 1.23 0.26Returns Lags for VR 0.2817 0.2372 0.2277 0.0451 1430 1.32 25.91

t Statistic 1.51 1.30 1.25 0.27ATM VR Lags for

returns

0.0356 0.0054 0.0167 1445 11.66*** 0.00

t Statistic 5.23*** 0.78 2.44***Returns Lags for VR 0.1792 0.0740 0.1130 1445 1.25 29.05

t Statistic 1.27 0.69 1.10ITM VR Lags for

returns

0.0278 0.0014 0.0011 1452 10.43*** 0.00

t Statistic 5.56*** 0.28 0.21Returns Lags for VR 0.1292 0.0118 0.1631 1452 1.23 29.59

t Statistic 0.92 0.08 1.17OTM VR Lags for

returns

0.0138 0.0080 0.0003 1454 2.42* 7.42

t Statistic 3.06*** 1.75* 0.06

Returns Lags for VR 0.5146 0.0707 0.0335 1454 2.30* 8.50t Statistic 3.35*** 0.46 0.22

This table presents the test results of the causeeffect relation between stock returns and option trading VRs based on all of the

component stocks in the S&P500 index. Option trading VR is defined as the overall call option trading value to put option

trading VR. Options are categorized into ATM, ITM, and OTM groups. OTM call (put) options are options with strike price

ranging from 105 (80)% to 120 (95)% of the underlying asset price, ATM options are options with strike price ranging from

95% to 105% of the underlying asset price, and ITM call (put) options are options with strike price ranging from 80 (105)% to

95 (120)% of the underlying asset price.



Table 5

The causeeffect relation between returns and option trading VR for high information asymmetry (HIA) and low information

asymmetry (LIA) S&P500 firms

VRt XL

i1giVRti

XL

i1diRti nt 8

Rt XL

i1aiRti

XL

i1biVRti pt 9


Dependent

variable

Lag

variable

Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag

6

Lag

7

Obs. F

statistic

P value

(%)

(A) HIA firms

ALL VR Lags for

returns

0.0298 0.0178 0.0016 0.0042 0.0087 1377 3.62*** 0.29

t Statistic 3.42*** 2.06** 0.18 0.49 1.01Returns Lags for

VR

0.0354 0.0221 0.0399 0.0292 0.0104 1377 0.13 98.64

t Statistic 0.42 0.26 0.46 0.33 0.12ATM VR Lags for

returns

0.0459 0.0129 0.0029 1436 4.73*** 0.27

t Statistic 3.61*** 1.02 0.23

Returns Lags for

VR

0.0250 0.0450 0.0392 1436 0.39 75.93

t Statistic 0.44 0.80 0.71ITM VR Lags for

returns

0.0368 0.0002 0.0070 1440 4.85*** 0.23

t Statistic 3.80*** 0.02 0.72

Returns Lags for

VR

0.0385 0.0026 0.0113 1440 0.10 96.04

t Statistic 0.54 0.04 0.16OTM VR Lags for

returns

0.0232 0.0228 0.01295 1478 4.26** 1.83

t Statistic 2.51*** 2..30** 1.0421Returns Lags for

VR

0.1539 0.0946 0.0146 1478 3.07* 2.66

t Statistic 2.11** 2.23** 1.07

(B) LIA firms

ALL VR Lags for

returns

0.0347 0.0006 1488 7.08*** 0.09

t Statistic 3.75*** 0.06

Returns Lags for

VR

0.0534 0.0247 1488 0.29 74.58

t Statistic 0.74 0.35ATM VR Lags for

returns

0.0196 0.0026 1474 2.02* 6.07

t Statistic 1.82* 0.18


C.R. Chen et al. / Review of Financial Economics 14 (2005) 123 19Explanatory variable

Dependent

variable

Lag

variable


6

Lag

7

Obs. F

statistic

P value

(%)

(B) LIA firms

ATM Returns Lags for

VR

0.0163 0.0730 1474 1.24 29.09

t Statistic 0.35 1.57ITM VR Lags for

returns

0.0186 0.0026 0.0198 0.0227 0.0162 0.0231 0.0179 1303 2.18** 3.34

t Statistic 1.72* 0.21 1.68* 1.80* 1.28 1.64 1.45

Returns Lags for

VR

0.0736 0.0230 0.0501 0.0126 0.0224 0.0026 0.1295 1303 0.97 45.48

t Statistic 1.15 0.36 0.82 0.21 0.37 0.04 1.13OTM VR Lags for

returns

0.0129 0.0063 0.0041 0.0090 0.0011 1382 0.55 74.03

Table 5 (continued)argument of the liquidity and leverage hypotheses. In Panel B of Table 5, we report the results of the

LIA firms. While the first lag coefficients of stock returns are still significant for the ALL, ATM,

and ITM options, they are much weaker than the results reported in Panel A, and none of the dcoefficients for the OTM option are significant, suggesting that informed traders are less likely to

trade in the firms with LIA. Of particular interest is the lack of significant b coefficients, even forthe OTM option, suggesting that informed traders do not trade options of LIA firms.

In Table 6, we show the results partitioned based upon the ratio of the trading volume of option to the

trading volume of the underlying asset (for simplicity, we will call this the option-to-stock-volume ratio).

High-liquidity firms are defined as firms belonging to the top 10 percentile of the sample, sorted by the

option-to-stock-volume ratio, while low-liquidity firms are defined as firms belonging to the bottom 10

percentile of the same sorted sample. Panel A presents the results for the HL firms. Surprisingly, equity

returns lead option VR in ALL, ATM, and ITM subsamples, while option VR leads equity returns in the

OTM subsample. One would expect to see a stronger leading role played by the option VR because, after

all, this is the subsample that has the highest option-to-stock-volume ratio. However, when we examine

the option-to-stock-volume ratios for options with various moneyness, we find that the options with high

t Statistic 1.22 0.58 0.38 0.83 0.11Returns Lags for

VR

0.0381 0.0107 0.0735 0.0141 0.0710 1382 0.42 83.39

t Statistic 0.53 0.15 1.03 0.20 1.04This table presents the test results of the causeeffect relation between stock returns and option trading VRs based on HIA and

LIA firms. Option trading VR is defined as the overall call option trading value to put option trading VR. HIA and LIA are

firms in the top and bottom 10 percentile of the R&D/sales ratio in the S&P500 index, respectively. R&D/sales ratio is

calculated based upon the average of R&D/sales ratios over the sample period (from 1995 to 2002) according to Compustat.

Options are categorized into ATM, ITM, and OTM groups. OTM call (put) options are options with strike price ranging from

105 (80)% to 120 (95)% of the underlying asset price, ATM options are options with strike price ranging from 95% to 105% of

the underlying asset price, and ITM call (put) options are options with strike price ranging from 80 (105)% to 95 (120)% of the

underlying asset price.


**Significant at the 5% level.


Table 6

The causeeffect relation between returns and option trading VR for component stocks in the S&P500 index classified into HL

and LL firms

VRt XL

i1giVRti

XL

i1diRti nt 8

Rt XL

i1aiRti

XL

i1biVRti pt 9


Dependent

variable

Lag

variable


6

Lag

7

Obs. F

statistic

P

value

(%)

(A) HIA firms

ALL VR Lags for

returns

0.0244 0.0016 0.0045 0.0032 0.0181 0.0125 0.0051 1315 1.75* 9.46

t Statistic 2.53** 0.17 0.46 0.33 1.85* 1.27 0.53Returns Lags for

VR

0.0918 0.1351 0.0593 0.0660 0.0178 0.0353 0.0404 1315 1.14 33.77

t Statistic 1.14 1.59 0.70 0.80 0.22 0.43 0.55ATM VR Lags for

returns

0.0320 0.0004 0.0036 0.0094 0.0278 0.0173 0.0006 1305 1.90* 6.67

t Statistic 2.49** 0.03 0.27 0.72 2.14** 1.33 0.05Returns Lags for

VR

0.0616 0.1404 0.0335 0.0363 0.0341 0.0681 0.0220 1305 1.53 15.43

t Statistic 1.03 1.31 0.56 0.61 0.58 1.20 0.40ITM VR Lags for

returns

0.0458 0.0094 1481 1.99*** 6.18

t Statistic 2.24** 1.00

Returns Lags for

VR

0.0359 0.0121 1481 0.17 84.58

t Statistic 0.49 0.17OTM VR Lags for

returns

0.0068 0.0155 1445 1.41 24.36

t Statistic 0.66 1.50

Returns Lags for

VR

0.1435 0.1138 1445 3.36** 3.51

t Statistic 1.99* 1.78*

(B) LL firms

ALL VR Lags for

returns

0.0491 1492 2.14* 8.10

t Statistic 1.81*

Returns Lags for

VR

0.0705 1492 1.21 27.07

t Statistic 1.10ATM VR Lags for

returns

0.0249 0.0253 939 2.01* 7.61

t Statistic 1.73** 1.18


(%)

ATM Returns Lags for 0.0557 0.0076 939 0.69 50.13

C.R. Chen et al. / Review of Financial Economics 14 (2005) 123 21VR

t Statistic 1.17 0.16ITM VR Lags for

returns

0.0925 0.036 0.0031 0.0098 0.0012 672 1.98 8.27

t Statistic 1.77* 1.58 0.17 0.54 0.07Returns Lags for

VR

0.1281 0.0041 0.0944 0.0582 0.0771 672 0.99 42.02

t Statistic 1.35 0.04 0.99 0.63 0.86OTM VR Lags for

returns

0.0270 1386 5.51** 1.91Explanatory variable

Dependent

variable

Lag

variable


6

Lag

7

Obs. F

statistic

P

value

Table 6 (continued)liquidity ratios are primarily OTM options. While the average option-to-stock-volume ratio is 66.08%

for the OTM options, the same statistics are merely 14.9% and 14.05% for the ATM and ITM options,

respectively. Therefore, the results partitioned based upon the relative trading volume of options and

stocks again demonstrate the dominant role of the OTM options. Panel B reports the results of the low-

liquidity firms. For this subsample, equity returns lead option VRs in across all option moneyness and

none of the VRs lead the equity returns, consistent with our contention that informed trades occur only in

the high-liquidity market. The option-to-stock-volume ratio for the ATM, ITM, and OTM options in this

LL subsample are miniscule; they are 0.52%, 0.23%, and 0.44%, respectively.

7. Concluding remarks

This study analyzes the informational role of stocks and options across different option moneyness for

firms with different degrees of information asymmetry. We begin our analysis with a theoretical

framework to derive a more meaningful measure for discerning good and bad news information

t Statistic 2.35***

Returns Lags for

VR

0.0087 1386 0.02 88.89

t Statistic 0.14

This table presents the test results of the causeeffect relation between stock returns and option trading VRs based on all of the

component stocks in SP500, HIA, LIA, HL, and LL firms. Option trading VR is defined as the overall call option trading value

to put option trading VR. HL and LL are firms in the top and bottom 10 percentile of the option volume/stock volume ratio in

the S&P500 index, respectively. Option volume/stock volume ratio is calculated based upon the average of option volume/stock

volume ratios over the sample period (from 1995 to 2002) according to the PFS and CRSP. Options are categorized into ATM,

ITM, and OTM groups. OTM call (put) options are options with strike price ranging from 105 (80)% to 120 (95)% of the

underlying asset price, ATM options are options with strike price ranging from 95% to 105% of the underlying asset price, and

ITM call (put) options are options with strike price ranging from 80 (105)% to 95 (120)% of the underlying asset price.


**Significant at the 5% level.


theoretical framework we developed in this paper. That is, stock returns are related to the callput

trading VRs. The callput trading VR makes more economic sense because it considers not only option

show a feedback relation with stock returns, suggesting that informed traders also transact in OTM

options to capitalize their private information. Indeed, OTM options differ from other options in liquidity

C.R. Chen et al. / Review of Financial Economics 14 (2005) 12322and delta-to-premium ratio. Moreover, results from subsamples of high/low information asymmetry

firms indicate an even stronger feedback relation between stock returns and OTM trading value ratios in

the HIA sample and the lack of such a relation in the LIA sample. Finally, results based upon option-to-

stock-volume ratio find that the ratios for OTM option lead the stock returns in the high liquidity option

sample, while such results are not observed in the low liquidity option sample. Our results suggest a

separating equilibrium for the low-liquidity and low-leverage segment of the option market, but a

pooling equilibrium for the high-liquidity and high-leverage segment of the option market that is

primarily made up of OTM options. These findings are therefore consistent with the liquidity and the

leverage hypotheses.

Acknowledgements

The authors thank the reviewer for his/her comments.

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Information flow between the stock and option markets: Where do informed traders trade?IntroductionLiterature reviewRelations between stock returns and option trading valueData and descriptive statisticsTest methodologyEmpirical resultsContemporaneous relation between stock returns and option trading valueGranger causality analysis

Concluding remarksAcknowledgementsReferences

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