Intraday Option to Stock Volume Ratios

and Stock Return Predictability

Kelley Bergsma

Andy Fodor

Vijay Singal

Jitendra Tayal

April 2018

Abstract

We use novel intraday signed option volume data to explore stock return predictability. Our evidence

suggests signed option to stock volume ratios in the first 30 minutes of the market open predict stock

returns during the rest of the trading day. Specifically, bearish option to stock volume ratios have

higher predictability than bullish options to stock volume ratios. These results remain robust after

accounting for intraday shorting activity. Moreover, a composite options trading score (OTS)

incorporating all signed option volume in the first half hour of trading predicts lower stock returns

during the remainder of the trading day. Small trades are the most informative, suggesting

sophisticated traders’ order splitting. Overall, our results demonstrate intraday signed option to stock

volume ratios have significant stock return predictability.

JEL Classification: G12, G13, G14

Keywords: Intraday option trading volume; stock return predictability; short selling.

Kelley Bergsma is an Assistant Professor in Finance at the College of Business, Ohio University, Athens, OH. Andy Fodor is the Leona Hughes Associate Professor in Finance at the College of Business, Ohio University, Athens, OH. Vijay Singal is the J. Gray Ferguson Professor of Finance at Pamplin College of Business, Virginia Tech, Blacksburg, VA. Jitendra Tayal is an Assistant Professor in Finance at the College of Business, Ohio University, Athens, OH. We appreciate helpful comments from Yashar Heydari Barardehi, Travis Davidson, and Sinan Gokkaya as well as seminar participants at Ohio University.

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I. Introduction

Options trading is of great importance to the efficient functioning of financial markets. It not

only incorporates newly released information into prices but also signals the direction of subsequent

stock price movements. Easley, O’Hara, and Srivinas (1998) highlight the significance of option

volume and provide the foundation for subsequent research through their multimarket equilibrium

model in which privately informed investors choose to trade in equity or options markets. They find

empirically that bearish option volume (buying puts and selling calls) has greater predictive power for

future intraday stock returns than bullish option volume (buying calls and selling puts). Pan and

Poteshman (2006) document that daily put-call ratios that open new option positions exhibit negative

stock return predictability, and they conclude their findings are driven by stock prices gradually

adjusting to private information in options trading. Furthermore, Roll, Schwartz, and Subrahmanyam

(2010) introduce a daily option to stock trading volume ratio (O/S), and Johnson and So (2012)

develop a model linking O/S to stock prices in which more informed options trading reflects bad

news than good news. Supporting their model, they find firms in the highest decile of the option to

stock volume ratio (O/S) underperform the lowest decile by 34 bps per week. Notably, Ge, Lin, and

Pearson (2016) (hereafter GLP) find certain components of signed option volume have stronger

weekly stock return predictability. Yet, these important O/S studies examine daily or weekly volume,

rather than intraday volume as in Easley, O’Hara, and Srivinas (1998). Our study seeks to fill this gap

by investigating signed O/S ratios at the intraday level.

Intraday market activity is distinct from daily or weekly activity for two main reasons. First,

Berkman et al. (2012) document that stocks on average exhibit positive overnight returns and negative

intraday returns, where reversals are greatest among stocks that retail investors buy just after market

open. In fact, Berkman et al. recommend postponing stock purchases until later in the day due to

inflated prices driven by retail investors at market open. Second, Lou, Polk, and Skouras (2018) report

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that institutional investors trade throughout the day and especially at market close, while non-

institutional investors are more likely to trade around market open. Mutual fund investors in particular

only trade at market close. Therefore, informed investors in the stock market trade at different times

of the day, consistent with infrequent portfolio rebalancing (Bogousslavsky 2016). In contrast, mutual

funds do not invest in options, so there are no option traders who must wait until the end of the day

to trade. Also, many stocks that retail investors prefer do not have traded options, so we expect smaller

mispricing at market open driven by retail traders in option markets. For these reasons, relative to

stock traders, options traders have less incentive to wait until later in the day to trade. Since many

announcements are released overnight (deHaan, Shevlin, and Thornock 2015; Lyle et al. 2017), the

first half hour of trading reflects the digestion of new information (Gao et al. 2017). We expect more

options trading based on information to occur in the first half hour than stock trading, due to more

stock investors trading throughout the day. As information is gradually incorporated in stock prices

while option trading reacts quickly to information, we predict that option trading at the beginning of

the day is informative for stock returns during the remainder of the day.

Therefore, we explore individual stock return predictability of intraday signed option volume

in a comprehensive analysis of 1,915 unique firms from 2012 to 2014. Specifically, we construct GLP’s

(2016) signed O/S ratios from a new intraday International Securities Exchange (ISE) dataset. We

combine this data with intraday short selling volume from NYSE, AMEX, and Nasdaq and examine

stock return predictability using Trade and Quote (TAQ) data. Consistent with the notion that

informed options trading volume is strong in the first 30 minutes of a trading day, we focus on the

signed O/S measures in the first half hour of trading. We find that the first half hour’s put purchases

that open new positions, call sales that close existing positions, and call purchases that open new

positions significantly predict stock returns during the remainder of the trading day. These O/S ratios’

predictability is in the same direction as documented by GLP at the weekly level, but bearish O/S

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ratios show the strongest predictability as in Easley, O’Hara, and Srivinas (1998). These results are

robust to controlling for short selling activity as well as firm characteristics. This study is the first to

demonstrate that signed option to stock volume ratios in the first 30 minutes of trading have stock

return predictability for the remainder of the trading day.

Our study may be useful for practitioners seeking to incorporate options trading into their

models. In 2017, NASDAQ proposed the “Intellicator Analytic Tool,” which will use real-time option

market activity to gauge if investors are bullish or bearish. This tool was unveiled in an SEC filing, but

later withdrawn as NASDAQ indicated it needed more time to educate the audience and refine the

product. Our evidence suggests that NASDAQ might benefit from incorporating intraday signed

option volume into their tool. We understand that a single indicator derived from the eight signed

option volume categories might be the most practical. As such, we construct an option trading score

(OTS) where option volume with bearish signals are added to the score and option volume with bullish

signals are subtracted from the score. Building on our prior results, we examine whether the first 30

minutes’ OTS predicts abnormal returns during the remainder of the day. In the first 30 minutes, we

rank each stock’s OTS into deciles, so a score of 10 is the most bearish and a score of 1 is the most

bullish. An increase in OTS during the first half hour of trading predicts a significantly lower average

cumulative abnormal return from 10:00 am to 4:00 pm.

Furthermore, OTS constructed using small customer option trading volume shows the

strongest predictability. We analyze the predictability of signed option volume from different ISE

trader classes: Small customers, medium customers, large customers, and firm proprietary traders.

Similar to GLP, we find that option volumes from customer trades have the strongest return

predictability. Small customer predictability is likely driven by order splitting by sophisticated traders.1

1 Pan and Poteshman (2006) identify hedge funds as falling into the customer category. Firm proprietary trade volume is not informative. Pan and Poteshman and GLP point out that firm traders may execute complicated option trading

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For instance, Anand and Chakravarty (2007) document that informed option traders fragment their

orders into smaller trades (less than 100 contracts), suggesting stealth options trading.

The rest of the paper is organized as follows. Section 2 outlines the motivation. Section 3

describes the data and descriptive statistics. Section 4 presents our main results on intraday option to

stock volume predictability. Section 5 concludes.

2. Motivation

This paper serves as a bridge between strands of literature on option volume’s stock return

predictability and intraday trading activity. The main inspiration for our work is Easley, O’Hara, and

Srivinas’ (1998) seminal study of intraday option volume’s predictability for intraday stock returns.

They develop a multimarket equilibrium model in which informed investors can choose to trade in

option or equity markets based on their private information. Testing the model’s predictions using

intraday data demonstrates that negative/bearish option volume contains greater predictive power

than positive/bullish option volume. Bearish option trades are buying puts and selling calls, while

bullish option trades are buying calls and selling puts. They find bearish option volume predicts

significantly lower stock return during the next 15 to 20 minutes, but they find mixed results for bullish

option volume (i.e. the predictability is negative in sign albeit insignificant). Their sample includes

option trading on Chicago Board Options Exchange (CBOE) for 50 firms from October through

November 1990. Based on their study, Easley, O’Hara, and Srivinas (1998) conclude: “If volume per

se is informative, then how volume is correlated with information and what this implies for price

movements is surely important. Our work here provides a first step toward recognizing this role in

option markets...” (p. 464).

strategies, so their trading volume is less likely to contain directional information. Lee and Yi (2001) find that informed option trading is concentrated in small trades, while large trades show little information-motivated trading.

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Not all work, however, supports Easley, O’Hara, and Srivinas (1998). Using a sample of

actively traded options on 14 actively traded NYSE stocks from January through March 1995, Chan,

Chung, and Fong (2002) find that intraday option volume has no stock return predictability. Yet,

Chakravarty, Gulen, and Mayhew (2004) estimate that option markets contribute about 17% to stock

price discovery based on five years of intraday stock and options data for 60 firms, corroborating

Easley, O’Hara, and Srivinas’ assertion that option volume is informative for future stock returns.

Later studies expand the number of firms studied, but examine option volume at the daily or

weekly level. Using proprietary CBOE data from 1990 to 2001, Pan and Poteshman (2006) document

that daily open buy put-call ratios show negative and significant stock return predictability of about

40 bps per day, with the strongest impact occurring in the next day’s stock return. Since their data is

not publicly available, they conclude that that their documented predictability reflects stock prices

gradually adjusting to private information imbedded in options trading. Roll, Schwartz, and

Subrahmanyam (2010) introduce a daily option to stock trading volume ratio (O/S) and explore its

time-series properties and determinants. Johnson and So (2012) develop a theoretical model linking

O/S to stock prices, where short sale constraints mean that options markets are comparatively cheaper

to capitalize on bearish private information. Consistent with option volume reflecting more informed

trading on bad news than good news, Johnson and So report that firms in the highest decile of the

option to stock volume ratio (O/S) underperform the lowest decile by 34 bps per week using

OptionsMetrics data from 1996 to 2010. GLP reconcile the findings of Pan and Poteshman (2006)

and Johnson and So (2012) in their study of signed option volume – specifically, open and close

volume for option buyers and option writers for calls and puts traded on the ISE from 2005 to 2012.

GLP find option trades in synthetic long positions have more stock return predictability than those

related to synthetic short positions, as open buy call to stock volume shows the strongest weekly stock

return predictability. Yet, none of these later studies examines intraday O/S ratios, so their findings

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cannot be directly compared with earlier studies on intraday option volume (Easley, O’Hara, and

Srivinas 1998; Chan, Chung, and Fong 2002; Chakravarty, Gulen, and Mayhew 2004).

Why would one expect intraday option volume to have different stock return predictability

than weekly option volume? Cooper, Cliff, and Gulen (2008) and Berkman et al. (2012) find positive

overnight returns and negative intraday reversals. Intraday stock volume tends is U-shaped, where the

heaviest trading occurs at market open and close (Jain and Joh 1988; Admati and Pfleiderer 1988).2

Since recent studies examine daily or weekly stock return predictability of option to stock volume,

these papers miss the heterogeneity in overnight and intraday stock returns as well as the substantial

variation in trading throughout the day.

Specifically, different types of equity investors trade at different times of day. Lou, Polk, and

Skouras (2018) document that institutional investors tend to trade throughout the day and particularly

at market close, while non-institutional investors are more likely to trade around market open.3 Easley,

O’Hara, and Srivinas (1998) point that high volume around market open and close may allow informed

traders to “hide” more easily, yet some advise against trading at market open. Berkman et al. (2012)

report that retail investors are particularly active just after market open and conclude that negative

intraday stock returns are driven by high opening prices that are inflated by retail investors. Thus, they

suggest postponing purchases of stocks (especially those heavily traded by retail investors) until later

in the trading day. This behavior is consistent with streetlore that investors in the stock market should

not trade before 10:00 am.4 Yet, most news is released before the market opens; for example, between

90-95% of earnings announcements occur overnight (deHaan, Shevlin, and Thornock 2015; Lyle et

al. 2017). Gao et al. (2017) assert that the high volume in the first half hour of trading reflects the

2 Option markets demonstrate positive overnight and negative intraday returns (Murayev and Ni 2017) and U-shaped intraday volume (Easley, O’Hara, and Srivinas 1998). 3 Lou, Polk, and Skouras (2018) find that the tug of war between retail investors and institutional investors drives certain anomalies to accrue solely intraday or overnight. Bogousslavsky (2017) also documents significant cross-sectional variation in average stock returns over the trading day and overnight. 4 “Why Morning is the Worst Time to Trade Stocks” (Strumpf and Driebusch, Wall Street Journal, Sept. 14, 2015).

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digestion of new information and they find that S&P 500 ETF’s first half hour return positively

predicts the last half hour return. This evidence is consistent with infrequent portfolio rebalancing

(Bogousslavsky 2016) in that informed stock investors transact at different times of the day.5

Informed investors’ incentive to trade later in the day in the stock market (Berkman et al. 2012;

Gao et al. 2017) may not hold in the option markets. Many stocks heavily traded by retail investors,

such as low price lottery-like stocks, do not have traded options (Han and Kumar 2013; Bergsma and

Tayal 2018). Thus, although it is optimal in some cases for stock traders to postpone purchases until

later in the day due to retail investor-induced mispricing at market open, there is no clear reason why

option traders should not take advantage of higher liquidity in the first 30 minutes of trading (Easley,

O’Hara, and Srivinas 1998). Moreover, mutual fund investors can only trade at market close, which

explains why the last half hour of trading is important in the stock market (Gao et al. 2017). However,

mutual funds do not invest in options, so there are no option investors who are constrained to waiting

until the end of the day to trade. Thus, we expect that option traders have more incentive to trade at

market open on information released overnight, as compared with stock traders who have more

reasons to trade later in the day (Lou et al. 2018). Therefore, we expect information released overnight

to be quickly realized in options trading just after market open, but gradually incorporated into stock

prices throughout the rest of the day. In this way, we predict option trading at the beginning of the

day to be informative for stock returns during the remainder of the day.

Specifically, we focus on the first 30 minutes of signed O/S ratios and associated stock return

predictability during the rest of the day. Since some stock investors choose not to trade between 9:30

to 10:00 am, we expect option trading to be most predictive during this time about the direction of

stock returns for the day. Considering intraday stock returns are on average negative, we predict that

the first 30 minutes’ bearish option to stock volume ratios (buying puts and selling calls) will have

5 Another relevant intraday seasonality study is Heston, Korajczyk, and Sadka (2010).

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greater stock return predictability than bullish ratios, consistent with Easley, O’Hara, and Srivinas

(1998). We will use GLP’s O/S ratios for our tests with the knowledge that results may differ at the

intraday level as compared with GLP’s findings at the weekly level. 6 In particular, the fact that intraday

stock returns are on average negative suggests bearish option volume may play a greater role than bullish

volume in intraday stock return predictability. In weekly returns, GLP find that open buy call to stock

volume (OBC/S) has positive predictability, while close sell call to stock volume (CSC/S) has negative

predictability. GLP also document open buy put to stock volume (OBP/S) and open sell call to stock

volume (OSC/S) have negative predictability, whereas open sell put to stock volume (OSP/S) has

positive predictability. OBC and OBP represent long positions, while OSC and OSP are short

positions (written options). Based on the importance of the first half hour of trading, we expect signed

O/S ratios constructed from the first 30 minutes of trading will predict stock returns during the

remainder of the day, with the strongest results coming from the bearish O/S ratios.

Furthermore, exploring the connection between intraday O/S ratios and stock returns may

yield helpful insights for practitioners. For instance, NASDAQ recently proposed a new options data

service that will gauge if option traders are bullish or bearish. In September 2017, NASDAQ filed with

the SEC a proposed rule change to introduce the Intellicator Analytic Tool, “a new, optional market

data product available for a corresponding fee that is designed to analyze options market transactions

and synthetize that analysis to assist investors in assessing the equities underlying those transactions”

(p. 2, Release No. 34-81754, File No. SR-Phlx-2017-74). NASDAQ subsequently withdrew the

6 The following description explains the signals of four types of signed put volume. Open buy put volume captures the volume of traders initiating long put positions. Opening a long put position is a strong bearish strategy. Close sell put volume captures the volume of traders exiting long put positions. Exiting an existing long put position is a bullish signal because it indicates the trader is no longer bearish. Open sell put volume captures the volume of traders initiating short put positions (writing put options). Opening a short put position is a bullish strategy. Close buy put volume captures the volume of traders exiting short put positions. To exit a short put position, the trader buys a put. Close buy put volume is a bearish signal because the trader is no longer bullish – the put writer no longer wants to be exposed to the risk that the stock price could rise. The four types of signed put volume have the exact opposite signals as call volume, i.e. open buy put volume is bearish while open buy call volume is bullish.

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proposed rule change in December 2017 so that NASDAQ has more time to educate and address

concerns as well as evaluate modifications to the Tool, according to an open letter from NASDAQ

to the SEC. Although NASDAQ proposed using a put-call ratio only rather than O/S ratios, it is

possible that signed O/S ratios may provide rich information to market participants. We understand,

however, that eight categories of signed O/S ratios might be not be as practical as a single indicator.

Therefore, we combine signed O/S ratios to construct a single bullish/bearish metric to capture

option traders’ expectations about future stock price movements. Our option trading score (OTS)

variable subtracts bullish option volume (buying calls and selling puts) from bearish signed option

volume (buying puts and selling calls). A low OTS indicates more option traders hold bullish opinions,

whereas a high OTS suggests option traders have a bearish outlook on a particular stock. Since the

first half hour of trading is very active for option traders, we predict that the first half hour’s OTS

will have a positive relationship with abnormal returns during the remainder of the day.

Our OTS variable is related to prior work in the following way. OTS is similar to other high

frequency measures of market and stock-level sentiment.7 In the options market, the most famous

measure of market expectations is the volatility index (VIX), which tracks the 30-day implied volatility

of near-term at-the-money S&P 500 index options. Flemming, Ostdiek, and Whaley (1995) find that

changes in VIX are negatively correlated with the S&P 100 index, while Durand, Lim, and Zumwalt

(2011) document that the VIX influences stock returns through its effect on the Fama-French factors.

In contrast to the VIX, OTS is a measure of stock-specific investor expectations constructed with

signed option volumes. Two other studies combine or aggregate option volume as we do, but their

methods and purposes are distinct from ours. GLP combine signed opening and closing O/S

7 These measures include Baker and Wurgler’s (2007) monthly investor sentiment, the bull-bear spread (Brown and Cliff 2004, 2005; Han 2008), Google Search Volume Index measures (Da, Engleberg, and Gao 2011; Da, Engleberg, and Gao 2015), and sentiment proxies derived from Internet message boards and social media (Antweiler and Frank 2004; Chen et al. 2014; Ranco et al. 2015; Bartov et al. 2017). At the intraday level, Sun, Najand, and Shen (2016) find textual sentiment of social media, Internet news sources, and news wires predicts S&P 500 index returns, while Renault (2017) documents the first half hour sentiment derived from StockTwits predicts the last half hour’s S&P 500 ETF return.

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measures to construct an options disagreement variable (OCDisagmt), but their purpose is to show

that the signed O/S measures’ significance remains robust after controlling for OCDisagmt. Johnson

and So (2018) create a multimarket measure of information asymmetry (MIA) by calculating a daily

unsigned O/S ratio relative to the level of O/S that occurs in the absence of private information.

MIA predicts future volatility. Neither study examines intraday stock return predictability.

Moreover, prior studies establish that informed option traders split their orders into small

trades, so we expect OTS based on small customer option volume to contain the strongest stock

return predictability. Using CBOE call option data on NYSE stocks from 1989-1990, Lee and Yi

(2001) demonstrate that information-motivated trading is strong only among small trades, not large

trades. They attribute their findings to the fact that small traders remain anonymous, while large traders

are publicly identified by the CBOE. Informed traders prefer anonymity, so they prefer to make small

trades. Supporting this notion, Anand and Chakravarty (2007) report that 81% of option price

discovery comes from options trading volume of less than 100 contracts each (small customer trades

in the ISE dataset).8 They show that informed traders fragment their orders to smaller trades using

transaction data on all options from 100 firms from July-October 1999.9 Therefore, based on prior

work, we expect small customer options trades to be the most informative for stock return

predictability.

The next section describes our data and methodology to test our predictions.

3. Data and descriptive statistics

8 Anand and Chakravarty (2007) classify small trades as less than 5 contracts, medium trades as between 5 and 99 contracts, and large trades as more than 100 contracts. They find small trades account for 40% of price discovery and medium trades contribute 41% to price discovery. ISE classifies small customer trades as less than 100 contracts, so small customer trades include both small and medium trades according to Anand and Chakravarty’s classification. 9 Their data are from Options Price Reporting Authority (OPRA).

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We gather intraday signed option volume from the International Securities Exchange (ISE)

from 2012 to 2014. Other studies, such as Doran, Fodor, and Jiang (2013) and GLP, have used a daily

ISE Open/Close Trade Profile dataset which provides daily open buy, close buy, open sell, and close

sell volume by trader class for calls and puts. Our ISE dataset, however, is unique in that data are at

the intraday level. Individual equity options data are available every ten minutes, so data with a 9:40

am time stamp give option volume aggregated from 9:30-9:40 am. The option volume cumulates

throughout the day, so to obtain the option volume from 9:40-9:50 am, we subtract the 9:40 am

volume from the 9:50 am volume. Our study is the first to our knowledge to use intraday ISE data.

Following Gao et al. (2017), we aggregate volumes into thirteen 30-minute intervals. Gao et

al. (2017) use TAQ data only, but we adopt their methodology to intraday option data. As in GLP, we

have eight categories of option volume: open buy put (OBP), close buy put (CBP), open sell call

(OSC), close sell call (CSC), open buy call (OBC), close buy call (CBC), open sell put (OSP), and close

sell put (CSP). We merge our option volume data with stock data from TAQ and short selling volume

from NYSE, AMEX, and NASDAQ exchanges. We drop all days in which a stock split occurs on

that day. Only common stocks are included in our sample.

Figure 1 plots average volume every 30 minutes from 9:30-10:00 am to 3:30-4:00 pm. Figure

1A presents bearish options volume – OBP, CBP, OSC, and CSC – and Figure 1B presents bullish

options volume – OBC, CBC, OSP, and CSP. On average, 18.33% of options volume occurs in the

first half hour of trading on a typical day for an average stock in our sample.10 Volume declines

throughout the day, but slightly rises towards the close of the day (10.26% of options volume takes

place in the last half hour). The U-shaped option volume throughout the day is consistent with the U-

shape documented in the stock volume (Jain and Joh 1988; Admati and Pfleiderer 1988). However,

10 For each date and 30-minute time interval, average volumes are calculated across all stocks. For each date, average volumes are summed across the time periods and divided by the total average volume for the whole day to calculate percentages every 30 minutes.

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option volume is more heavily weighted towards the start of the day rather than the end of the day,

while stock volume is almost equal in both the first and last half hour. Using TAQ data, we present

the average stock volume every 30 minutes in Figure 1C. On average, 14.15% of stock volume takes

place in the first 30 minutes and 14.51% occurs in the last 30 minutes in our sample.11

<< Figure 1 >>

Next, we construct signed option to stock volume (O/S) ratios in accordance with GLP every

30 minutes by combining ISE option volume with stock volume from TAQ data. For instance, for

OBP/S from 9:30-10:00 am, we divide open buy put volume by stock volume during the first half

hour. We follow a similar method to construct short selling-to-stock volume (SS/S).12 Every 30

minutes, we divide aggregate short selling volume by stock volume. To minimize the influence of

outliers, we truncate all O/S or SS/S measures at the 1% and 99% levels.

Figures 2A and 2B plot average bearish and bullish O/S ratios, respectively, for each half hour

of the trading day. In every case, the ratios are the greatest during the 9:30-10:00 am period and

decrease throughout the day. Thus, O/S ratios demonstrate a downward-sloping pattern throughout

the day.

<< Figure 2 >>

Next, we generate intraday cumulative abnormal returns for our main tests. Our dependent

variable is CAR(10:00-16:00), the cumulative abnormal return from 10:00 am to 4:00 pm (16:00). One

control variable is CAR(9:30-10:00), the cumulative abnormal return from 9:30 to 10:00 am. Abnormal

returns are calculated using the market-adjusted model where the market is the CRSP US Total Market

Index – Intraday.13 We also control for short activity using SS/S (previously defined) since puts can

11 14.69% and 16.63% of shorting volume takes place in the first and last half hours, respectively. 12 Our comprehensive sample includes all stocks on these exchanges from 2012 to 2014. As a comparison, Comerton-Forde, Jones, and Zhang (2016) study a stratified sample of 350 stocks from all short sales executed on NYSE and Nasdaq from January through August 2008. In contrast, our sample includes 1,915 unique firms for a more recent three-year period. 13 We calculate the returns using prices, which are available during our entire time period.

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be substitutes for short selling and put writers hedge their exposure using short sales (Figlewski and

Webb 1993; Chen and Singal 2003). Other control variables include stock-level characteristics

gathered from CRSP and Compustat as defined in Appendix A: Turnover of the first half hour

(TO(9:30-10:30)), Amihud’s (2002) illiquidity measure (Amihud), option implied volatility of the prior

day (Impl_vol), book-to-market ratio (B/M), the natural logarithm of market capitalization (Size),

momentum (Momen), and historical skewness (Skewness). For our tests, only stocks with positive

options trading volume in the first 30 minutes are included.14 Our final sample of 1,915 stocks is much

larger than prior intraday stock and option studies in the U.S. market.15 For instance, Chakravarty,

Gulen, and Mayhew’s sample (2004) contains 60 firms, Anand and Chakravarty (2007) examine

options on 100 firms, and Murayev (2016) studies options trading in 39 stocks. Yet, one caveat is that

our data come from one major options exchange, the International Securities Exchange. GLP state

that the daily ISE dataset represents about 30% of total trading volume in individual equity options

during the 2005 to 2012 period.

First, we examine whether the first 30 minutes of signed O/S ratios predicts the cumulative

abnormal returns during the rest of the day, CAR(10:00-16:00). Panel A of Table 1 presents summary

statistics for the first half hour’s O/S ratios,16 CAR(10:00-16:00), and control variables. Similar to

GLP, open trading volumes are more than twice as large as close trading volumes, yet the first half

hour’s option volumes are slightly smaller in magnitude than in their study. For instance, OBP/S is

0.7% on average in GLP’s weekly sample, while OBP/S is about 0.4% in our sample.17 Also, our

14 Conditional on a stock having positive option trading volume in the first 30 minutes, 60.82% of option volume occurs from 9:30-10:00 am. 15 Chang et al. (2013) and Chang et al. (2015) study large samples of options trading in the Taiwanese market. Hao, Lee, and Piquiera (2013) examine options on 60 Taiwanese stocks. 16 Ratios are calculated each day and then averaged across all days. 17 Moreover, our average SS/S measure during the first half hour of trading is 17.03% on average (standard deviation = 7.30%). The SS/S measure is closest in spirit to Diether, Lee, and Werner’s (2009) daily measure, which is 23.89% for NYSE stocks and 31.22% for Nasdaq stocks.

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sample firms are more liquid and larger than those in GLP.18 The differences between our sample and

those of prior work is not surprising given that the screening procedure stipulates firms must have

positive options trading volume in the first 30 minutes. Panel B of Table 1 reports correlations among

signed O/S ratios and SS/S. The correlation among our signed O/S ratios are smaller than those

reported in GLP; yet, consistent with their sample, open buy volumes are correlated with open sell

volumes within the same option type (calls or puts).19 Like Comerton-Forde, Jones, and Zhang’s

(2016) intraday short selling study, our approach takes a magnifying glass to intraday options trading

dynamics and focuses on the first half hour’s trading impact on future stock returns.

<< Table 1 >>

Following GLP, we partition the O/S and SS/S ratios of stocks into deciles, where decile 1 is

the smallest decile and decile 10 is the largest decile. Decile variables are indicated by appending “Dec”

to the variable name, i.e. DecOBP/S denote the decile ranks for OBP/S. We use decile variables –

ordinal ranks from 0 to 9 – for our main tests.

Options trading score (OTS) is equal to stock-level bearish O/S measures minus bullish O/S

ratios as described in equation (1).

OTS = (OSC/S + CSC/S + OBP/S + CBP/S) – (OBC/S + CBC/S + OSP/S + CSP/S) (1)

Put purchases (open or close buy) and call sales (open or close sell) are bearish indicators. Conversely,

call purchases (whether open or close buy) and put sales (open or close sell) are bullish indicators.

Since OTS is constructed as bearish minus bullish ratios, a higher OTS can be interpreted as a bearish

18 GLP report size and B/M in natural logarithm in their Table 1, while we report the raw values. 19 The correlations between SS/S and O/S ratios is close to zero. SS/S’s correlation is slightly negative with all O/S ratios except OBP/S, where the correlation is slightly positive. This slightly positive correlation is not surprising given that the buying of puts and short selling both indicate bearish sentiment.

15

signal, while a lower OTS can be viewed as a bullish signal. To minimize the influence of outliers, we

rank stocks’ OTSs into deciles to construct DecOTS.

4. Results

4.1 Signed O/S ratios

We begin by examining whether signed O/S ratios during the first half hour predict future

stock returns during the remainder of the day. We use a similar methodology to GLP, but our results

may differ from GLP because our study is at the intraday rather than weekly level. Easley, O’Hara,

and Srivinas (1998) find intraday bearish option volume predicts lower future returns and bullish

option volume has no mixed predictability, but they do not use signed O/S ratios. Our innovation is

to test whether GLP’s signed O/S ratios demonstrate intraday predictability documented in Easley,

O’Hara, and Srivinas (1998). To test our predictions, we regress CAR(10:00-16:00) on O/S measures

from 9:30-10:00 am and control variables. The specification is similar to GLP’s (2016) equation (2) on

page 609:

CAR(10:00-16:00) = α + β1 DecOBP/S + β2 DecCBP/S + β3 DecOSC/S + β4 DecCSC/S

+ β5 DecOBC/S + β6 DecCBC/S + β7 DecOSP/S + β8 DecCSP/S

+ γ1 CAR(9:30-10:00) + γ2 TO(9:30-10:00) + γ3 Amihud + γ4 Impl_vol

+ γ5 B/M + γ6 Size + γ6 Momen + γ9 Skewness + ε (2)

The independent variables of interest are the decile O/S ratios computed from signed option volume.

The first four O/S ratios – OBP/S, CBP/S, OSC/S, and CSC/S – represent bearish signals, while the

next four O/S ratios – OBC/S, CBC/S, OSP/S, and CSP/S – represent bullish signals. We estimate

a cross-sectional regression each day and report the means of the daily regression coefficients using

the Fama and MacBeth (1973) method. Following GLP, Panel A of Table 2 presents the results

16

excluding option expiration weeks, while Panel B presents the results for expiration weeks only.

Coefficient estimates are in basis points.

<< Table 2 >>

We begin with the bearish O/S ratios, which we expect have negative predictability. In Model

1 of Panel A, DecOBP/S has an estimated coefficient of –1.143 (t = –3.62). Since short selling

represents an alternative mechanism for trading on bearish views (Figlewski and Webb 1993), we

control for SS/S in Model 2. In Model 2, DecOBP/S remains negative and highly significant with a

coefficient estimate of –1.077 (t = –3.33). Model 3 indicates that controlling for GLP’s option

disagreement measure (DecOCDisagmt) does not affect the predictability of DecOBP/S. This finding

support the notion that put purchases in the first 30 minutes that open new long positions predict

lower future stock returns during the rest of the day. In economic terms, an increase from the bottom

to top decile of OBP/S in the first 30 minutes generates about a 10 bps lower cumulative abnormal

return during the rest of the day.20 Similarly, the closing of long call positions in the first half hour has

negative predictability for the remainder of the day: DecCSC/S is negative and significant across all

three models, with an estimated coefficient –0.784 (t = –2.76) in Model 2. Thus, an increase from the

bottom to top decile of CSC/S in the first half hour predicts about a 7 bps lower cumulative abnormal

return throughout the remainder of the day. Thus, similar to GLP, OBP/S and CSC/S show

significant negative predictability. This evidence also consistent with Easley, O’Hara, and Srinivas’

(1998) intraday option volume analysis as well as Johnson and So’s (2012) assertion that informed

options trading is more likely to reflect negative news.21 However, while OSC/S is a significant

predictor in GLP, it has no intraday predictability (there is no comparison here, however, with Easley,

20 An increase from the bottom to top decile is a nine decile move from 1 to 10. Therefore, –1.077 times 9 is about 10 bps. 21 It is notable that SS/S is insignificant in the first half hour of trading, whereas OBP/S is negative and significant. Thus, on average, put traders, rather than short sellers, in the first 30 minutes are more likely to act on negative information, supporting Johnson and So’s (2012) theoretical model at the intraday level.

17

O’Hara, and Srinivas (1998) since they do not examine OSC volume separately). Since the two O/S

measures that represent written option positions – CBP/S and OSC/S – show no predictability, this

evidence indicating that only entering and exiting long option positions (opening long put positions

or closing existing long call positions) demonstrate significant intraday stock return predictability.

Easley, O’Hara, and Srivinas (1998) find that volume of buying puts and selling calls predicts

negative future returns over the next 15 to 20 minutes. However, they do not differentiate between

opening or closing long option positions or written options positions. Option writers’ profits are

capped at the premium, while those who have long positions typically have higher potential gains.22

Thus, we would expect long option trader volume to have more predictability than option volume for

written options positions. Pan and Poteshman (2006) make a similar argument to explain why open

sell option volume has weaker predictability as compared with open buy option volume. We find only

buying puts to open long positions and selling calls to close long positions in the first 30 minutes has

negative predictability for the remainder of the day, while put purchases and call sales related to written

options positions do not show predictability. These results underscore why examining signed O/S

ratios is important.

We also investigate whether option volume that has a positive signal in the first half hour

predicts significantly higher returns the rest of the day. Table 2 indicates that DecOBC/S is positive

and significant across all three models, with an estimated coefficient of 0.719 (t = 2.78) in Model 2.

The positive DecOBC/S coefficient estimate supports our prediction that purchases of calls that open

new long positions predict higher future stock returns. In economic terms, a bottom to top decile

increase in OBC/S in the first 30 minutes predicts about a 6 bps higher return the remainder of the

day. Yet, surprisingly, the DecCBC/S coefficient is negative and marginally significant with a

coefficient estimate of –0.590 (t = –1.82), suggesting that call purchases that close written positions

22 Long call traders’ profits are unlimited and long put traders’ profits are equal to the stock price minus the premium.

18

have negative predictability. This result is surprising as call purchases are expected to have positive

predictability; yet, our findings indicate that only call purchases that open long positions in the first 30

minutes predict higher future stock returns, while purchases that close existing written call positions

predict marginally lower stock returns the rest of the day. Put sales (OSP/S and CSP/S) show no

predictability. In contrast, GLP document that both OBC/S and OSP/S predict higher weekly stock

returns and CBC/S is insignificant. Easley, O’Hara, and Srivinas (1998) combine buying calls and

selling puts that open and close positions into one positive option volume measure and report that

bullish option volume has mixed intraday predictability. Although they do not focus on the first 30

minutes’ predictability for the rest of the day, it is possible that their result can be explained by the

conflicting signs of OBC/S and CBC/S.

In Panel B of Table 2, we find almost no predictability for O/S measures during option

expiration weeks, corroborating GLP. In Model 3, OBP/S and CSC/S are marginally significant and

negative in sign, yet both O/S ratios are insignificant in our main specification (Model 2).

In sum, consistent with Easley, O’Hara, and Srinivas (1998), option volume with a negative

signal demonstrates strong intraday predictability, yet our innovation is to investigate this notion using

signed O/S ratios as in GLP.23 Similar to GLP, OBP and CSC shows negative stock predictability: An

increase in these O/S ratios during the first 30 minutes predicts significantly lower cumulative

abnormal returns during the remainder of the day. This result supports Easley, O’Hara, and Srivinas.

Unlike GLP, OSC/S has no intraday stock return predictability, but is consistent with Pan and

Poteshman’s (2006) finding that open sell option volume has weaker predictability than open buy

volume. Moreover, among bullish signed O/S ratios, only OBC/S has significant positive intraday

stock return predictability, while CBC/S has marginally significant negative predictability. GLP report

23 GLP’s Internet Appendix shows their main results hold using a two-day interval, but they do not examine intraday predictability.

19

OBC/S has significantly positive weekly stock return predictability, whereas CBC/S is insignificant.

The opposite signs on OBC and CBC could shed light on why Easley, O’Hara, and Srivinas find that

bullish option volume has no clear predictability. Our results in the next section will further explore

this idea.

4.2 Options trading score

In this section, we construct a new firm-level intraday options trading score (OTS) that

captures option traders’ bullish or bearish sentiment regarding a particular stock. OTS is inspired by

NASDAQ’s proposed Intellicator Analytic Tool, a new data product that will use options market

transactions to quantify stock-level sentiment. Although we find in subsection 4.1 that only OBC/S,

CSC/S, and OBP/S in the first 30 minutes show significant predictability, we include all O/S measures

in our OTS measure to capture all option traders’ activity. The calculation of OTS is described in

equation (1) in Section 3. As in our prior O/S ratios, we use a decile ranked variable, DecOTS, to

minimize the influence of outliers.

In Table 3, we test whether OTS in the first 30 minutes of market open has predictive power

for cumulative abnormal returns during the rest of the day. In Model 1, the DecOTS coefficient is –

0.814 (t = –4.37), after controlling for short selling activity (DecSS/S) and other control variables as

in Table 2. This evidence suggests that on a typical day, a move from the most bullish OTS (Decile 1)

to the most bearish OTS (Decile 10) in the first 30 minutes predicts about a 7 bps average decrease in

cumulative abnormal return from 10:00 am to 4:00 pm.

<< Table 3 >>

Next, we decompose OTS into its bearish and bullish components. OTS-Bearish consists of

OBP/S, CBP/S, OSC/S, and CSC/S. OTS-Bullish is comprised of OBC/S, CBC/S, OSP/S, and

CSP/S. Based on Easley, O’Hara, and Srivinas (1998) and Johnson and So (2012), we expect OTS-

20

Bearish have significantly negative predictability and OTS-Bullish to have insignificant predictability.

In Model 2 of Table 3, the DecOTS-Bearish coefficient estimate is –0.964 (t = –4.75). Thus, on

average, a bottom to top decile increase in OTS in the first half hour is associated with about a 9 bps

lower cumulative abnormal return during the remainder of the day. In Model 3, the DecOTS-Bullish

coefficient is insignificant, supporting Easley, O’Hara, and Srivinas who find insignificant (negative in

sign) predictability for bullish option volume. When we combine both DecOTS-Bearish and DecOTS-

Bullish into one specification, the DecOTS-Bearish coefficient remains negative and significant (–

0.964, t = –4.65) and the DecOTS-Bullish is still insignificant. Thus, OTS’s predictability is mostly

driven by the bearish side (Easley, O’Hara, and Srivinas 1998). Given that Berkman et al. (2012) find

that negative intraday stock returns on average, it is not surprising that OTS-Bearish has the strongest

predictability at the intraday level.

Overall, the evidence indicates that our stock-level sentiment measure based on signed O/S

ratios demonstrates significant intraday stock return predictability. An increase in OTS in the first 30

minutes predicts negative and significant cumulative abnormal returns during the rest of the day.

OTS’s predictability is driven by bearish option volume.

4.3 Trader category

Our main results focus on aggregate signed option volume from all traders. In this subsection,

we examine whether the return predictability of OTS and signed O/S ratios differs across the ISE

trader categories: small customer, medium customer, large customer, and firm. Traders are classified

as firm proprietary traders or public customers, where small customers trade less than 100 contracts,

medium customers trade between 101 and 199 contracts, and large customers trade more than 200

contracts. First, we generate OTS using trading volume from small customers, medium customers,

large customers, and firms separately (DecOTS-Small, DecOTS-Medium, DecOTS-Large, and

21

DecOTS-Firm, respectively). Next, we calculate the signed O/S ratios using trading volume from each

trader category.24 We regress CAR(10:00-16:00) on these measures using the same control variables as

in Table 3.

Table 4 presents the results. In Model 1 of Panel A, the DecOTS-Small coefficient is –0.771

(t = –4.13), indicating that OTS based on small customer trades shows robust predictability. In

Model 2, the DecOTS-Medium coefficient is insignificant, whereas the DecOTS-Large coefficient is

negative and marginally significant. The DecOTS-Firm coefficient is insignificant. Pan and

Poteshman (2006) report that open buy put-call ratios using firm volume does not show daily stock

return predictability, while customer volume shows significant predictability.25 They point out that

hedge funds fall under the customer category in option volume. GLP also find weekly signed O/S

ratios constructed from firm proprietary traders is not informative and they suggest that these

traders may be using more complicated options trading strategies than simply buying or selling an

option. Model 5 includes all OTS generated from each trader category. In Model 5, only the

DecOTS-Small coefficient is significant with an estimate of –0.765 (t = –5.03). One caveat, however,

is that most options volume stems from small customers, so the number of trades in the other trader

categories may be too low to detect informed options trading.26 Nevertheless, as GLP argue, a

sophisticated trader may chose to establish a large options position using multiple small trades,

splitting the order to hide his/her identity.

<< Table 4 >>

24 Within our sample and within the first 30 minutes of positive option volume, about 86% is attributed to small customers, 1% to medium customers, 1% to large customers, and 12% to firm traders. 25 We construct Pan and Poteshman’s (2006) open buy put-call ratio and find that it has negative stock predictability at the intraday level as in their study at the daily level. 26 We are also limited by the data in analyzing intraday ISE options volume by moneyness. The paucity of observations in at-the-money options prevents us from making valid comparisons between OTS constructed by different moneyness categories.

22

In Panel B of Table 4, we examine signed O/S ratios constructed by trader category. Our

results must be interpreted with caution, however, because of the limited number of observations for

all but the small customer category. Following GLP, Panel B repeats the analysis in Model 2 of Table

2 for each trader category individually. Consistent with GLP, the strongest predictability generally

stems from small customer trading volume. For small customers, all of the signed O/S ratios in Table

2 are still significant: DecOBP/S is negative, DecCSC/S is negative, DecOBC/S is positive, and

DecCBC/S is positive. Only DecOBC/S is significant for medium customers, whereas OBP/S and

OSP.S is significant for large customers. None of the signed O/S measures using firm volume are

significant, consistent with GLP. Although the trader category analysis has limitations, the robust

predictability of small customer O/S ratios supports the notion that sophisticated traders split their

orders and hence small customers trading volume is the most informative.

We explore option volume by trader category more fully in Figure 3 using the entire sample

as in Figures 1 and 2. Interestingly, all signed O/S ratios constructed by trader category have the

highest values during the 9:30 to 10:00 am time period. However, this trend is most pronounced for

small customer volume.27 This figure suggests that small customers are the most active in the first half

hour of market open, underscoring why we focus on how the first 30 minutes of options trading

predicts stock returns during the rest of the day.

<< Figure 3 >>

Overall, the results in Table 4 suggest that our earlier findings are largely driven by small

customer traders, suggesting stealth options trading by informed investors (Lee and Yi 2001; Anand

and Chakravarty 2007).

5. Conclusion

27 The first 30 minutes represents an average 18.53% of the small customers’ total options volume in a day.

23

According to Roll, Schwartz, and Subrahmanyam (2010), “Volume is an integral part of

financial markets and deserves a full understanding by finance scholars” (p. 16). We could not agree

more, yet we would add that intraday volume in particular is of paramount importance to grasping

today’s financial markets. Previous studies that investigate how intraday signed options trading volume

is related to stock returns mostly focus on short time periods due to prior data limitations (Easley,

O’Hara, and Srivinas 1998; Chakravarty, Gulen, and Mayhew 2004). Our study, however, examines

this question using a comprehensive sample of intraday signed option volume at 30-minute intervals

for 1,915 firms from 2012 to 2014. Given that the first half hour of trading attracts the highest option

volume relative to stock volume (O/S), we explore how signed O/S measures in the first 30 minutes

after market open affects cumulative abnormal returns during the rest of the trading day. We find that

put purchases that open new long positions (OBP/S) and call sales that close out long positions

(CSC/S) predict lower future returns, whereas call purchases that open new long positions (OBC/S)

predict higher future stock returns. This evidence demonstrates that GLP’s (2016) main findings hold

at the intraday level, even after controlling for short selling activity. However, we confirm Easley,

O’Hara, and Srivinas’s result that bearish option volume has stronger predictability than bullish option

volume at the intraday level.

Moreover, we construct an option trading score (OTS) that subtracts bullish from bearish

option volume. OTS is a stock-specific indicator of options traders’ bullish/bearish sentiment, where

a higher OTS suggests more bearish views. We find that an increase in OTS in the first 30 minutes of

a trading day predicts significantly lower cumulative abnormal returns during the remainder of the

day. When we calculate OTS by option trader category, OTS constructed with small customer option

volume is the most robust indicator. This significant predictability for small customer option volume

suggests order splitting by sophisticated investors. Overall, our findings demonstrate that intraday

signed option to stock volume ratios have strong stock return predictability.

24

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27

Figure 1: Intraday Signed Option Volume and Stock Volume

This figure displays averages every 30 minutes from 9:30-10:00 am (9:30-10:00) to 3:30-4:00 pm (15:30-16:00)

for bearish option volume (1A), bullish option volume (1B), and stock volume (1C). Bearish option volume

consists of open buy put (OBP), close buy put (CBP), open sell call (OSC), and close sell call (CSC) volume.

Bullish option volume is comprised of open buy call (OBC), close buy call (CBC), open sell put (OSP), and

close sell put (CSP) volume. The sample period is from 2012 to 2014.

0

5

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25

30

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40

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OBP CBP OSC CSC

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29

Figure 2: Intraday Signed Option to Stock Volume Ratios

This figure displays the average call or put volume as a ratio of stock volume every 30 minutes from 9:30-10:00

am (9:30-10:00) to 3:30-4:00 pm (15:30-16:00). Bearish O/S ratios (OBP/S, CBP/S, OSC/S, and CSC/S) and

bullish O/S ratios (OBC/S, CBC/S, OSP/S, and CSP/S) are presented in Figures 2A and 2B, respectively.

O/S ratios are defined in the Data Appendix. The sample period is from 2012 to 2014.

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

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OBP/S CBP/S OSC/S CSC/S

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

0.70%

9:30 -10:00

10:00 -10:30

10:30 -11:00

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2B: Bullish O/S Ratios

OBC/S CBC/S OSP/S CSP/S

30

Figure 3: Intraday OTS-Bearish and OTS-Bullish by Trader Category

This figure displays the average OTS-Bearish and OTS-Bullish by trader category every 30 minutes from 9:30-

10:00 am (9:30-10:00) to 3:30-4:00 pm (15:30-16:00). Trader categories include small customer (Small), medium

customer (Medium), large customer (Large), and firm as defined in Section 3. O/S ratios are combined by their

directional signal for future stock returns. OTS-Bearish is comprised of the sum of OBP/S, CBP/S, OSC/S,

and CSC/S and is presented in Figure 3A. OTS-Bullish consists of the sum of OBC/S, CBC/S, OSP/S, and

CSC/P and is presented in Figure 3B. Option to stock (O/S) ratios are defined in Appendix A. The sample

period is from 2012 to 2014.

0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

1.20%

1.40%

9:30 -10:00

10:00 -10:30

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3A: OTS-Bearish by Trader Category

Small Medium Large Firm

0.00%

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0.40%

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0.80%

1.00%

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3B: OTS-Bullish by Trade Category

Small Medium Large Firm

31

Table 1: Summary Statistics and Correlation Matrix

This table reports summary statistics for O/S ratios, SS/S ratio, and other firm characteristics in Panel A and

presents correlations between various O/S measures and SS/S in Panel B. Our sample consists of all firms with

positive option volume in the first half hour of the market open. Full variable definitions are in Appendix A.

The sample period is from 2012 to 2014. The statistics reported are for the “raw” (not decile) values of the

various O/S measures and SS/S variable.

No. obs. Mean Std. dev. Quartile 1 Median Quartile 3

OBP/S 233,648 0.383% 1.303% 0.000% 0.000% 0.077%

CBP/S 233,648 0.156% 0.519% 0.000% 0.000% 0.016%

OSC/S 233,648 0.647% 1.747% 0.000% 0.019% 0.422%

CSC/S 233,648 0.269% 0.782% 0.000% 0.000% 0.115%

OBC/S 233,648 0.638% 1.824% 0.000% 0.000% 0.338%

CBC/S 233,648 0.237% 0.716% 0.000% 0.000% 0.101%

OSP/S 233,648 0.452% 1.368% 0.000% 0.000% 0.195%

CSP/S 233,648 0.121% 0.441% 0.000% 0.000% 0.000%

SS/S 233,269 17.027% 7.298% 11.818% 16.292% 21.550%

CAR(10:00-16:00) 233,648 -0.031% 2.180% -0.626% -0.026% 0.573%

CAR(9:30-10:00) 233,648 -0.019% 1.179% -0.546% -0.036% 0.482%

TO(9:30-10:00) 233,648 0.011% 0.095% 0.001% 0.002% 0.006%

Amihud (x10^9) 233,524 0.829 5.517 0.049 0.121 0.399

Impl_vol 223,844 37.37% 19.02% 24.82% 32.58% 44.53%

B/M 218,065 0.507 0.477 0.213 0.381 0.654

Size (in $ millions) 233,648 26,330 50,306 2,668 8,714 25,964

Momen 232,237 12.81% 34.78% -3.52% 9.59% 23.97%

Skewness 228,292 0.733 0.608 0.356 0.644 1.024

Panel A: Summary statistics

Panel B: Correlation matrix

OBP/S CBP/S OSC/S CSC/S OBC/S CBC/S OSP/S CSP/S SS/S

OBP/S 1.000 0.067 0.069 0.036 0.096 0.029 0.396 0.121 0.001

CBP/S 0.067 1.000 0.023 0.073 0.024 0.099 0.162 0.334 -0.007

OSC/S 0.069 0.023 1.000 0.023 0.309 0.146 0.087 0.031 -0.023

CSC/S 0.036 0.073 0.023 1.000 0.100 0.249 0.028 0.108 -0.025

OBC/S 0.096 0.024 0.309 0.100 1.000 0.032 0.072 0.037 -0.026

CBC/S 0.029 0.099 0.146 0.249 0.032 1.000 0.028 0.089 -0.017

OSP/S 0.396 0.162 0.087 0.028 0.072 0.028 1.000 0.072 -0.010

CSP/S 0.121 0.334 0.031 0.108 0.037 0.089 0.072 1.000 -0.015

SS/S 0.001 -0.007 -0.023 -0.025 -0.026 -0.017 -0.010 -0.015 1.000

32

Table 2: First Half Hour Predictability of Signed O/S Ratios

This table presents the results of daily Fama and MacBeth (1973) regressions of CAR(10:00-16:00) on deciles

of signed option to stock measures (OBP/S, CBP/S, OSC/S, CSC/S, OBC/S, CBC/S, OSP/S, and CSP/S)

and short selling to stock volume (SS/S). The specifications follow equation (2). All O/S and SS/S measures

are constructed using the first half hour of trading. For each stock, we first sum the first half hour’s option

volumes from ISE to obtain the volume for each category of option trades, and sum the first 30 minutes’ stock

volume from TAQ. Then, we rank option to stock volume ratios into deciles. The dependent variable,

CAR(10:00-16:00), cumulates abnormal returns from 10:00 am to 4:00 pm. All control variables are defined in

Appendix A. We estimate a cross-sectional regression for each day, and then report the time-series means of

the regression coefficients. Panel A presents the results of regressions for non-expiration weeks, where the

expiration week is defined as the week containing the third Friday of each month. Panel B presents the results

of regressions for expiration weeks. N is the number of firm-days. ***, **, and * indicate statistical significance

at the 1%, 5%, and 10% levels, respectively. The sample period is from 2012 to 2014.

33

Model 1 2 3

DecOBP/S -1.143*** -1.077*** -1.042***

(-3.62) (-3.33) (-2.97)

DecCBP/S -0.249 -0.220 -0.044

(-0.75) (-0.66) (-0.12)

DecOSC/S -0.219 -0.252 -0.186

(-0.90) (-1.05) (-0.64)

DecCSC/S -0.808*** -0.784*** -0.776**

(-2.87) (-2.76) (-2.53)

DecOBC/S 0.745*** 0.719*** 0.803**

(2.88) (2.78) (2.49)

DecCBC/S -0.549* -0.590* -0.567*

(-1.70) (-1.82) (-1.75)

DecOSP/S 0.316 0.227 0.347

(1.08) (0.77) (1.03)

DecCSP/S 0.464 0.485 0.594

(1.44) (1.51) (1.64)

DecSS/S -0.258 -0.242

(-1.29) (-1.21)

DecOCDisagmt -0.363

(-0.94)

CAR(9:30-10:00) -0.004 -0.004 -0.004

(-0.44) (-0.43) (-0.45)

TO(9:30-10:00) -2.819*** -3.057*** -3.018***

(-2.72) (-2.84) (-2.79)

Amihud 0.639 1.063 1.059

(0.89) (1.36) (1.36)

Impl_vol 0.142 0.139 0.151

(1.42) (1.38) (1.51)

B/M -5.627** -5.399** -5.740***

(-2.58) (-2.47) (-2.63)

Size 0.101 0.231 0.259

(0.12) (0.28) (0.32)

Momen -0.096*** -0.104*** -0.100***

(-2.62) (-2.84) (-2.72)

Skewness -1.191 -1.694 -2.170

(-0.70) (-0.99) (-1.26)

Intercept 3.418 2.957 1.301

(0.25) (0.22) (0.10)

Adj. R2 12.71% 12.64% 12.53%

N 173,861 173,861 173,861

Panel A: Non-expiration weeks

34

Model 1 2 3

DecOBP/S -0.631 -0.778 -1.227*

(-1.11) (-1.33) (-1.87)

DecCBP/S -0.366 -0.491 -0.880

(-0.61) (-0.80) (-1.53)

DecOSC/S 0.905 0.850 0.390

(1.42) (1.30) (0.47)

DecCSC/S -0.684 -0.691 -1.017*

(-1.38) (-1.36) (-1.79)

DecOBC/S -0.376 -0.398 -1.030

(-0.69) (-0.71) (-1.65)

DecCBC/S -0.305 -0.112 -0.394

(-0.41) (-0.15) (-0.55)

DecOSP/S 0.462 0.596 0.064

(0.36) (0.47) (0.06)

DecCSP/S 1.124 1.300 0.955

(0.99) (1.14) (0.86)

DecSS/S -0.696 -0.696

(-1.29) (-1.30)

DecOCDisagmt 0.522

(0.71)

CAR(9:30-10:00) 0.000 0.000 0.004

(-0.01) (-0.02) (0.17)

TO(9:30-10:00) 1.725 1.686 1.289

(0.45) (0.44) (0.38)

Amihud -0.968 -1.074 -1.131

(-0.66) (-0.67) (-0.69)

Impl_vol 0.097 0.100 0.100

(0.35) (0.37) (0.36)

B/M -1.401 -1.500 -2.190

(-0.26) (-0.29) (-0.43)

Size 1.935 1.766 1.676

(0.95) (0.89) (0.79)

Momen -0.090 -0.092 -0.082

(-1.05) (-1.05) (-0.96)

Skewness 0.563 -0.138 -0.222

(0.13) (-0.03) (-0.05)

Intercept -32.594 -26.846 -11.850

(-0.89) (-0.77) (-0.32)

Adj. R2 12.24% 12.29% 12.40%

N 59,757 59,757 59,757

Panel B: Expiration weeks

35

Table 3: Option Trading Score

This table presents the results of daily Fama and MacBeth (1973) regressions of CAR(10:00-16:00) on options

trading score (OTS), as well as the bearish and bullish components of OTS (OTS-Bearish and OTS-Bullish,

respectively). OTS is option trading score, defined as bullish O/S ratios minus bearish O/S ratios as described

in equation (1). The dependent variable is CAR(10:00-16:00), defined as the cumulative abnormal return from

10:00 am to 4:00 pm the same day. The sample is from 2012 to 2014. Regression results are for non-expiration

weeks, where expiration weeks are defined as weeks containing the third Friday of each month. N is the number

of firm-days. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Model 1 2 3 4

DecOTS -0.814***

(-4.37)

DecOTS-Bearish -0.964*** -0.969***

(-4.75) (-4.65)

DecOTS-Bullish -0.102 -0.048

(-0.53) (-0.24)

DecSS/S -0.175 -0.215 -0.200 -0.222

(-0.87) (-1.06) (-0.98) (-1.08)

CAR(9:30-10:00) -0.006 -0.005 -0.007 -0.005

(-0.67) (-0.59) (-0.71) (-0.56)

TO(9:30-10:00) -2.909*** -3.074*** -2.979*** -3.084***

(-2.84) (-2.97) (-2.86) (-2.94)

Amihud 1.045 1.051 1.077 1.045

(1.35) (1.34) (1.40) (1.34)

Impl_vol 0.091 0.117 0.096 0.118

(0.92) (1.18) (0.97) (1.18)

B/M -5.562*** -5.900*** -5.578*** -5.901***

(-2.60) (-2.76) (-2.61) (-2.74)

Size -0.690 -0.413 -0.583 -0.340

(-0.93) (-0.56) (-0.79) (-0.46)

Momen -0.090** -0.095** -0.097*** -0.101***

(-2.43) (-2.57) (-2.63) (-2.73)

Skewness -1.166 -1.332 -1.184 -1.315

(-0.71) (-0.81) (-0.72) (-0.79)

Intercept 8.568 11.775 11.006 10.889

(0.64) (0.88) (0.82) (0.81)

Adj. R2

12.76% 12.79% 12.73% 12.80%

N 173,861 173,861 173,861 173,861

36

Table 4: Trader Category

This table presents the results of daily Fama and MacBeth (1973) regressions of CAR(10:00-16:00) on OTS by

trader category (Panel A) and signed option to stock measures (OBC/S, OSC/S, CBC/S, CSC/S, OBP/S,

OSP/S, CBP/S, and CSP/S) by trader category (Panel B). The specifications in Panel B follow equation (2).

Results are for different trader categories: small customer, medium customer, large customer, and firm as

defined in Section 3. We calculate stock-level signed O/S measures using trading volumes from trader

categories from the first half hour of each trading day. The dependent variable CAR(10:00-16:00) cumulates

abnormal returns from 10:00 am to 4:00 pm. All control variables are defined in Appendix A. We estimate a

cross-sectional regression for each day, and then report the time-series means of the regression coefficients.

Regression results are for non-expiration weeks, where expiration weeks are defined as weeks containing the

third Friday of each month. N is the number of firm-days. ***, **, and * indicate statistical significance at the

1%, 5%, and 10% levels, respectively.

Model 1 2 3 4 5

DecOTS-Small -0.771*** -0.765***

(-4.13) (-3.86)

DecOTS-Medium -1.471 -0.832

(-1.51) (-0.82)

DecOTS-Large -1.416* -0.890

(-1.71) (-1.03)

DecOTS-Firm -0.014 -0.327

(-0.05) (-1.13)

DecSS/S -0.179 -0.167 -0.169 -0.174 -0.150

(-0.90) (-0.82) (-0.83) (-0.86) (-0.73)

CAR(9:30-10:00) -0.005 -0.009 -0.007 -0.007 -0.008

(-0.57) (-0.95) (-0.74) (-0.72) (-0.81)

TO(9:30-10:00) -2.965*** -3.126*** -3.023*** -2.871*** -3.123***

(-2.86) (-3.02) (-2.94) (-2.80) (-3.00)

Amihud 1.058 1.159 1.102 1.069 1.147

(1.37) (1.50) (1.42) (1.38) (1.49)

Impl_vol 0.089 0.096 0.094 0.088 0.096

(0.90) (0.97) (0.95) (0.89) (0.96)

B/M -5.574*** -5.597*** -5.631*** -5.655*** -5.679***

(-2.60) (-2.63) (-2.62) (-2.65) (-2.63)

Size -0.697 -0.577 -0.695 -0.718 -0.583

(-0.93) (-0.78) (-0.92) (-0.97) (-0.77)

Momen -0.090** -0.091** -0.091** -0.088** -0.090**

(-2.44) (-2.49) (-2.46) (-2.39) (-2.45)

Skewness -1.173 -1.150 -1.327 -1.105 -1.316

(-0.72) (-0.71) (-0.80) (-0.67) (-0.80)

Intercept 8.990 3.492 5.869 12.700 -2.379

(0.67) (0.25) (0.41) (0.96) (-0.15)

Adj. R2

12.76% 12.82% 12.77% 12.67% 12.82%

N 173,861 173,861 173,861 173,861 173,861

Panel A: OTS by trader category

37

Small

Customer

Medium

Customer

Large

Customer Firm

Model 1 2 3 4

DecOBP/S -1.316*** -0.144 -2.216** -2.126

(-3.90) (-0.16) (-2.26) (-0.83)

DecCBP/S -0.060 3.303 0.165 -0.220

(-0.16) (1.10) (0.25) (-0.26)

DecOSC/S -0.387 2.392 0.233 0.108

(-1.58) (1.22) (0.16) (0.15)

DecCSC/S -0.879*** -0.295 1.125 -0.009

(-3.02) (-0.26) (1.03) (-0.01)

DecOBC/S 0.610** 4.501*** 1.650 0.168

(2.28) (2.79) (1.21) (0.33)

DecCBC/S -0.754** -3.186 1.603 -0.782

(-2.15) (-1.21) (0.75) (-0.86)

DecOSP/S 0.305 -1.568 2.576*** 0.110

(1.03) (-0.89) (2.60) (0.14)

DecCSP/S 0.349 0.260 0.742 1.724

(0.98) (0.27) (0.95) (1.64)

DecSS/S -0.292 -0.131 -0.139 -0.225

(-1.46) (-0.63) (-0.67) (-1.08)

CAR(9:30-10:00) -0.002 -0.009 -0.007 -0.007

(-0.24) (-0.99) (-0.74) (-0.70)

TO(9:30-10:00) -2.861*** -3.121*** -3.354*** -2.789***

(-2.63) (-3.04) (-3.24) (-2.62)

Amihud 1.139 1.184 1.107 1.168

(1.48) (1.53) (1.43) (1.48)

Impl_vol 0.137 0.086 0.099 0.093

(1.37) (0.88) (0.99) (0.91)

B/M -5.367** -5.722*** -5.793*** -5.388***

(-2.48) (-2.59) (-2.61) (-2.56)

Size 0.295 -0.690 -0.903 -0.569

(0.36) (-0.90) (-1.17) (-0.68)

Momen -0.099*** -0.089** -0.093** -0.103***

(-2.67) (-2.48) (-2.52) (-2.79)

Skewness -1.662 -1.161 -1.521 -1.330

(-0.96) (-0.72) (-0.92) (-0.80)

Intercept 5.120 -8.794 -7.854 14.556

(0.37) (-0.50) (-0.50) (0.89)

Adj. R2

12.48% 13.06% 12.92% 12.73%

N 173,861 173,861 173,861 173,861

Panel B: Signed O/S ratios from each trader category

38

Appendix A: Variable Definitions

Variable name Definition

OBP/S Ratio of ISE open buy put volume to stock volume

CBP/S Ratio of ISE close buy put volume to stock volume

OSC/S Ratio of ISE open buy sell volume to stock volume

CSC/S Ratio of ISE close buy sell volume to stock volume

OBC/S Ratio of ISE open buy call volume to stock volume

CBC/S Ratio of ISE close buy call volume to stock volume

OSP/S Ratio of ISE open buy sell volume to stock volume

CSP/S Ratio of ISE close buy sell volume to stock volume

SS/S Ratio of short selling to stock volume

All of the regressions use decile versions of the above variables.

CAR(10:00-16:00) The underlying stock's cumulative abnormal return from 10:00 am to 4:00 pm using

the market-adjusted model. The CRSP US Total Market Index – Intraday is used as

the market.

CAR(9:30-10:00) The underlying stock's cumulative abnormal return from close to 10:00 am using the

market-adjusted model. The CRSP US Total Market Index – Intraday is used as the

market.

TO(9:30-10:00) Stock volume from 9:30 to 10:00 am divided by shares outstanding.

Amihud Amihud (2002) illiquidity measure of the prior month. We multiply by 1 billion for

ease of presentation.

Impl_vol The underlying stock's average daily implied volatility during the prior day.

B/M

Book-to-market ratio measured at the firm's last quarterly financial statement report

date

Size

Market capitalization measured at the firm's last quarterly financial statement report

date

Momen

Momentum is calculated as the underlying stock's cumulative abnormal return over

the past six months.

Skewness The historical skewness is calculated using daily returns of the prior month.