Available online at www.sciencedirect.com

Journal of Financial Markets 16 (2013) 308330

Short sales and put options: Where is the bad newsrst traded?$

Xiaoting Haoa, Eunju Leeb, Natalia Piqueirac,n

a

& 2012 Elsevier B.V. All rights reserved.

www.elsevier.com/locate/nmar

and suggestions. We thank the C.T. Bauer College of Business for providing access to the BAuer Research

DataSet (BARDS), and Yadira Taylor for her assistance with this database.nCorresponding author.1386-4181/$ - see front matter & 2012 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.nmar.2012.09.005

E-mail addresses: haox@uwm.edu (X. Hao), elee@bauer.uh.edu (E. Lee),

Natalia.Piqueira@brattle.com (N. Piqueira).JEL classification: G14

Keywords: Put option; Short sales; Informed trading; Earnings announcements

$We thank the Editor Amit Goyal, an anonymous referee, Alex Boulatov, Tom George, Kris Jacobs, Archana

Jain, Praveen Kumar, Bruce N. Lehmann, Stuart Turnbull, and participants at the 2010 Financial Management

Association Annual Meeting and the 2011 Midwest Finance Association Annual Meeting for helpful commentsSheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USAbC.T. Bauer College of Business, University of Houston, Houston, TX 77204, USA

cThe Brattle Group, Washington, DC 20036, USA.

Received 9 March 2012; received in revised form 21 August 2012; accepted 12 September 2012

Available online 2 November 2012

Abstract

Although the literature provides strong evidence supporting the presence of informed trading in

both the option and the short equity markets, it is not clear which market attracts more informed

trading. Using a unique dataset that covers intraday transaction data in the option and short equity

markets, we investigate informed trading in a cross-market environment by explicitly studying the

leadlag relationship between the put net trade volume and short sales of the underlying stock. Our

high frequency analysis shows that in general short sales contain more information. However, put

options become more informative before the release of negative earnings announcements.

1. Introduction

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330 309Accumulating evidence in the literature supports the presence of informed trading inoption markets. Black (1975) suggests that informed investors may choose to trade in theoption market since it is typically characterized by lower capital requirements and tradingrestrictions, and by higher leverage. A formal model developed by Easley, OHara, andSrinivas (1998) suggests the existence of a pooling equilibrium in which informed investorschoose to trade in both equity and option markets. Subsequent empirical research furthersupports the presence of informed trading in the option market, by showing that optiontrading volume predicts subsequent returns of the underlying stock (Pan and Poteshman,2006; Chakravarty, Gulen, and Mayhew, 2004; Cao, Chen, and Grifn, 2005, etc.).At the same time, extensive recent research focuses on the role of short sellers in

conveying valuable information about the stocks they short. Most of the empiricalevidence suggests that short sellers are indeed informed traders and therefore they play animportant role in the price discovery process (e.g. Boehmer, Jones, and Zhang, 2008;Diether, Lee, and Werner, 2009; Boehmer and Wu, in press, among others).Although the literature provides evidence supporting the presence of informed trading in

both the option and the short equity markets, it is not clear which market attracts moreinformed trading. The purpose of this paper is to compare the information content of shortsales and put options trading, thus helping to understand which market is more conduciveto price discovery and information incorporation. The practical implications of thisresearch are also relevant given recent temporary changes in short selling regulation andthe possibility of further changes in the near future.1 If indeed short sellers play the mostimportant role in contributing to price efciency, what would be the impact for marketefciency of a more strict regulatory system?In this paper, we investigate the role of short sales and put option trading in revealing

negative information of the underlying stock, by explicitly studying the effects of shortsales and put option trading imbalance on subsequent quote revisions and trading volumein the short equity and put option markets. We use a unique dataset that covers intradaytransaction data in the option and equity markets for a sample of NYSE stocks fromMarch 2005 to June 2007 to estimate, using 5-minute intervals, a structural model based onHasbrouck (1991). We extend his bivariate VAR model of stock market trades and quoterevisions to also include the option and short equity markets. By doing so, we can directlyobserve the price impact and the leadlag relationships of put option trading and shortsales, which allows us to compare the information content between put option trading andshort sales.Our high frequency analysis shows that during the sample period, short sales appear to

contain more information since they predict subsequent stock and option returns and thatthe predictive power of short sales on subsequent put net trade volume is larger than thatof put net trade volume on subsequent short sales. This result suggests more informationcan be learnt from the short equity market than from the put option market. We alsoperform subgroup analysis using the information shares measurement (Hasbrouck, 1995;Chakravarty, Gulen, and Mayhew, 2004) and the relative liquidity in the put option andshort equity markets. Consistent with our expectation, put option trading is more

1See, for example, Boehmer, Jones, and Zhang (2011) for an analysis of the effects of the September 2008shorting ban for nancial stocks.

informative for stocks with higher information shares in the option market. Meanwhile,short sales have predictive power over subsequent put net trade volume in spite of therelative liquidity levels across the put option and short equity markets. In all, our resultsprovide strong support for the idea that informed investors will choose to trade in the shortequity market rst when they are pessimistic about the stocks performance. The onlydifferent results are documented in our sub-period analysis, in which we study the tradingin multiple markets within 3 days before the release of an unexpected negative earningsannouncement on the underlying stocks. With a pending event that will drive down thestocks fundamental value, more informed trading is observed in the put option market.This result suggests that the nding of Cao, Chen, and Grifn (2005) that the call optionmarket plays a more important price discovery role before a takeover announcement issimilarly observed in the put option market.To our knowledge, this paper serves as the rst attempt to use intraday data to

investigate and compare, in a multi-market setting, the informed trading in the put optionand the short equity markets, contributing to our knowledge of how negative informationis incorporated into prices. Our results show that, while the short equity market seems tobe in general more informative, the option market is more critical in the price discoveryprocess before negative events, suggesting that both markets have their different roles inconveying information.The remainder of this paper is organized as follows. Section 2 provides a brief review of

related literature and our contribution. Section 3 describes the methodology. Section 4describes the data and presents summary statistics. In Section 5 we discuss the main results.In Section 6 we present sub-sample results and in Section 7 we discuss our analysis relatedto negative earnings announcements. In Section 8 we conduct robustness tests. Section 9concludes.

2. Related literature

Our paper is related to two main elds of literature. First, we contribute to the literatureinvestigating informed trading across different markets, in particular the studiescomparing informed trading in the option and stock markets. Second, we contribute tothe literature on informed short selling, in particular the studies linking returnpredictability and short selling activity. In this section, we briey summarize the mostrelevant ndings in each of these two elds and present our contribution to the literature.Many empirical studies investigate the presence of informed trading in the option

market and the role of option trading in revealing valuable information about theunderlying stock. Blacks (1975) argument that informed investors would prefer to tradeoptions due to the higher leverage has been tested by several subsequent studies. Forexample, Easley, OHara, and Srinivas (1998) show that informed trading is also observedin option markets. They propose and test a model that allows investors to choose whetherthey want to trade in stock or option markets, showing that a pooling equilibrium i.e.an equilibrium in which informed traders trade in both option and stock markets isassociated with high leverage in the option market, low liquidity in the stock market, or ahigh proportion of overall informed investors. Their empirical results show that the optionnet trade volume predicts future stock price movement, rejecting the separatingequilibrium in which informed investors only trade in the stock market. Pan and

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330310Poteshman (2006) also provide evidence that option trading volume predicts stock returns.

More specically, they nd that stocks with the lowest put-call ratios (positive signal)

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330 311outperform those with the highest put-call ratios (negative signal) in the near future.Chakravarty, Gulen, and Mayhew (2004) measure the price discovery in the stock andoption markets by applying Hasbroucks (1995) methodology to a sample of 60 rms,using intraday data. Their results provide evidence in favor of informed trading occurringin both equity and option markets since they nd signicant price discovery in the optionmarket. Using intraday data, Cao, Chen, and Grifn (2005) study the information contentof call option trading imbalance and underlying stock trading imbalance before takeovers.They nd that during the pre-announcement period, call option trading imbalance hashigher predictability over the next-day stock returns (takeover premiums) than stocktrading imbalance, providing further evidence on the presence of informed trading in theoption market.The results, however, are mixed when the focus is on which market leads the other, i.e.

where informed investors would trade rst. Anthony (1988), using total (not signed) dailyvolume, studies the relationship between equity and call option markets. His results showthat call option trading volume predicts trading in the underlying stock on the next day,i.e. options seem to lead stocks. On the other hand, Chan, Chung, and Fong (2002), using asample of 14 stocks, intraday data and a similar methodology used in our paper, ndevidence that the stock market leads the option market, i.e. informed investors prefer totrade in the stock market.2

Overall, the literature nds strong evidence supporting the presence of informedtrading in options markets but fails to form a consensus on which market containsmore information. Although most of the studies use signed volume in both markets tocompare their relative informativeness, none of them attempt to explicitly incorporate thetrading volume from another market with strong informed investor presencetheshort equity market. Our paper aims to investigate and compare informed trading betweenthe equity and option markets, by explicitly distinguishing short sellers in the equitymarket.Our paper is also related to the literature on informed short selling, in particular the

studies linking return predictability and short selling activity. Many empirical studies testthe predictions of the theoretical model proposed by Diamond and Verrecchia (1987), inparticular the implication that short sellers are informed. Most of the empirical evidencerelated to return predictability with monthly short interest data or intraday short trading(ow) data suggests that short sellers are informed traders and, by having valuable badnews, are able to predict negative returns.3 For example, Boehmer, Jones, and Zhang(2008) show that short sellers are well informed, by using proprietary daily short tradingdata from NYSE from 2000 to 2004. In particular they show that stocks that are heavilyshorted underperform lightly shorted stocks by 1.16% (in risk-adjusted terms) in thesubsequent 20 trading days. Diether, Lee, and Werner (2009) nd that portfolios formedby buying lightly shorted stocks and selling heavily shorted stocks achieve positiveabnormal returns, by using short sales intraday data in 2005. This is again, evidence thatshort sellers have valuable information regarding the stock. Boehmer and Wu (in press)also use daily short trading data to show that short sellers increase the informationalefciency of stock prices, according to several efciency measures.

2This is consistent with separating equilibrium in Easley, OHara, and Srinivas (1998).

3In general, intraday shorting ow data are used to construct a daily series of short trading.

Using monthly short interest data, Asquith, Pathak, and Ritter (2005) show that stockswith high short interest underperform stocks with low short interest for equally-weightedportfolios. Desai, Ramesh, Thiagarajan, and Balachandran (2002) also show thatabnormal negative returns are observed for stocks with high monthly short interest.Although the empirical evidence summarized above suggests that short sellers are

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330312informed traders, these studies do not attempt to investigate short selling in a multi-marketsetting, in particular in an environment where informed traders might also choose to tradeoptions.4 We contribute to this strand of literature by investigating the effects of shortselling in a multi-market environment, aiming to shed light on how information owsacross markets. Our unique dataset, which covers high frequency trading data in optionsand short equity markets, allows us to achieve this goal. To our knowledge, this is the rstpaper that uses intraday data to investigate the effects of both short selling and optiontrading on stock returns and subsequent trading volume.

3. Methodology and empirical predictions

3.1. Systems of regression equations for multiple markets

The model we use to describe the dynamic relationship between trades and quoterevisions in the stock, call, put and short markets is based on Hasbrouck (1991) and Chan,Chung, and Fong (2002). In Hasbrouck (1991), a bivariate VAR model of trades andquote revisions for the stock market is used to study the information content of stocktrades, while in Chan, Chung, and Fong (2002), in order to compare the information rolesof stock and option trades and quote revisions, this model is extended to include the optionmarket. We further extend the structural model proposed by Chan, Chung, and Fong(2002) to also include trades in the short equity market. This is done by assuming that theinformation in short transactions cannot be fully conveyed from the imbalance of stocktrading volume.The basic bivariate VAR model for a single market is specied as follows:5

rt ALrt B0zt BLzt e1,t 1

zt CLrt DLzt e2,t 2where rt is the quote revision at transaction time t, which is calculated as the change of bidask midpoint from these quotes following transaction t1 to the quotes followingtransaction t, and zt is the total trading imbalance (positive if buy-initiated, and negative ifsell-initiated) between transaction time t1 and t. By assumption, the error terms in thetwo equations have zero means and are independent from each other.Chan, Chung, and Fong (2002) extend Eqs. (1) and (2) to include trades and quotes in

multiple (stock, call and put) markets. We follow the same line of reasoning to further

4In a recent paper, Grundy, Lim, and Verwijmeren (2012) study the effects of the 2008 short-sale ban on the

option market trading, and nd that price discovery is more likely to occur in the short equity market instead of

the options market. Our study differs from this study in several ways. First, our study uses intraday data, while

Grundy, Lim, and Verwijmeren (2012) use daily data. Second, Grundy, Lim, and Verwijmeren (2012) focus on a

special period of time, i.e., the period around the 2008 short-sale ban, while our study uses 2.5 years of data and

reveals the relationship between short sales and put options before the 2008 nancial crisis.

5See Hasbrouck (1991) for details on this specication (Eqs. (2) and (4), pp. 183184).

include the trades in the short equity market into our structural model, and by doing so, weassume that it conveys additional information compared to the net stock trading volume.More specically, we dene rt rs,t rc,t rp,t0 and zt zs,t zc,t zp,t zss,t0 in Eqs. (1)

and (2), where rs,t, rc,t and rp,t represent quote revisions in the stock, call, and put marketduring time interval t, and zs,t ,zc,t ,zp,t and zss,t represent net trade volume in the stock,call option, put option markets and the total short trading volume during time interval t.Three lags of each explanatory variable are included in the structural model.AL3 3, B0 3 4, BL3 4, CL4 3, and DL4 4 (L1, 2, 3) arecoefcients to be estimated. Consistent with the methodology used in prior literature,lagged values of dependent variables on the right hand side are used to capture serialcorrelation effects, so that the disturbances can be assumed to be serially independent fromeach other. Compared to the system of two equations in Hasbrouck (1991) and the systemof six equations in Chan, Chung, and Fong (2002), we have a system of seven equations intotal. We believe that by explicitly distinguishing the short trading effects, we will be ableto further investigate the role of short equity trading and put option trading in revealinginformation about the stock.

3.2. Empirical predictions

Since the purpose of this paper is to compare the informational role of put option andshort selling volume, we focus on studying the effects of put net trade volume and shortsize on the subsequent return and trading volume in the put and short equity markets.First, with respect to the effects of net trade volume on subsequent quote revisions, we

expect that put net trade volume (short size) should be more signicant in predicting thesubsequent stock and put market returns if there is more informed trading in the put(short) market. This methodology has been utilized in Chan, Chung, and Fong (2002), inwhich they compare the informational role of stock and option volume by studying theireffects on subsequent returns in the market. In similar research, Cao, Chen, and Grifn(2005) study the information content of call options by relating the option tradingimbalance to the next day stock return. In the analysis of whether short sellers havevaluable information about the stock, Diether, Lee, and Werner (2009) study thecorrelations between short sales and the stocks future returns.Second, we study the effects of net trade volume on subsequent net trade volume, across

markets. We expect that if there is more informed trading in the short market (put optionmarket), we should be able to observe an information ow from the short (put) market to theput option (short) market, thus short sales (put net trade volume) should lead the subsequentput net trade volume (short sales). The leadlag relationship of trading volume in differentmarkets has been studied in related research. For example, Anthony (1988) nds that tradingin the call option market leads the trading of underlying shares with one-day lag. He reasonsthat this represents an information ow from the option market to the stock market. It is alsowidely documented in the literature that option underwriters hedge their positions in the stockmarket (Grundy, Lim, and Verwijmeren, 2012; Battalio and Schultz, 2011). In this case, optiontrading is more informative and leads the stock trading.We also perform subsample analyses. Specically, we divide our sample according to

their stock market and option market characteristics, and test whether these characteristicsaffect the informational role of short and put option trading. Since it is documented in the

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330 313prior literature that call option trading tends to be more informative before corporate

events (Cao, Chen, and Grifn, 2005), we also conduct a sub-period analysis during 3 daysbefore the release of negative unexpected earnings to see whether the same effect holds forput options.

4. Data and summary statistics

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330314The empirical analysis in this paper employs several different databases. The time-stamped option quotes and trades data are retrieved from the BAuer Research DataSet(BARDS) database. This dataset tracks through Reuters the nearest-maturity optionswritten on over 60 stocks starting from March 2005 to June 2009.6 Not all near-maturityoptions for a given stock are tracked; only those whose strike prices fall within a band ofplus and minus $10 of the underlying stock price on the Friday when options are identiedfor tracking. Non-missing observations in BARDS are exactly as reported by Reuters toensure that the data are representative of what traders see in real time. Each intradayobservation contains the stock symbol, option symbol, option type, strike price, expirationdate, transaction date, transaction time, ask price, bid price, trade size, transaction price,quoted ask depth, and quote bid depth. Intraday data are recorded only when atransaction occurs or the ask price, bid price, quote ask depth or quote bid depth changes.To describe main characteristics of the BARDS database, we compare the summary

statistics of the stocks in BARDS with the statistics of all the NYSE and NASDAQ stockswith options in the OptionMetrics database.7 Appendix B presents the summary statisticsof daily turnover and daily trading volume for NYSE/NASDAQ stocks (Panel A) andonly NYSE stocks (Panel B) in the two databases at the end of 2002, when the BARDSdatabase was constructed. As shown in Panels A and B, the cross-sectional distributions ofdaily turnover for the stocks in BARDS and OptionMetrics suggest that, based on theturnover ratio, the samples are similar: the average of daily turnover for the NYSE stocksin BARDS is 0.73%, which is close to the average of daily turnover for the NYSE stocks inOptionMetrics (0.76%). Meanwhile, the summary statistics of trading volume between thetwo groups show that the stocks in the BARDS database are more actively tradedcompared to the stocks in OptionMetrics.Intraday stock trading data are obtained from the Trade and Quote (TAQ) database of

NYSE, which provides a complete history of time-stamped quotes and trades data for allthe underlying stocks whose options are tracked by BARDS. Intraday short sales data forthese stocks are also obtained from the TAQ database and are made available by NYSE aspart of the requirements under Regulation SHO (January 2005).8 For each short-saletransaction, this dataset contains its transaction time, trade size, and an indicator thatidenties short sales that are exempt from the Uptick Rule.9 We maximize the sampleperiod, and study the intraday transaction data in the option and stock markets betweenMarch 21, 2005 and June 8, 2007.10

6The options were tracked for 1 month preceding their expiration. See Appendix A for the list of stocks tracked

in BARDS between 2005 and 2009.7OptionMetrics contains data on all US exchange-listed equities and market indices, as well as all US listed

index and equity options.8For more information on Regulation SHO, see http://www.sec.gov/spotlight/shopilot.htm.9The Uptick Rule is a former rule established by the SEC that requires that every short sale transaction be

entered at a price that is higher than the price of the previous trade.

10The Regulation SHO dataset is available until July 6, 2007.

Since the Regulation SHO dataset only covers stocks listed on NYSE, we are left with 45stocks whose options are also continuously tracked during our sample period in theBARDS dataset. Following Chan, Chung, and Fong (2002), each day the most activelytraded put and call options are selected for each stock. When the most active option has 5

stock, the most active call, the most active put, or the short-sale are deleted. After that,

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330 315we are left with 8,520 option days in total.In order to classify the trading direction for each transaction on each stock and option in

our sample, we use Lee and Ready (1991) algorithm. Specically, if a trade is executed at aprice above (below) the quote midpoint, it is classied as buy-initiated (sell-initiated).Trades at the quote midpoint are classied using the tick test, which determines thedirection by comparing the trade price to the price of preceding trades.To calculate the summary statistics of our sample, we obtain daily prices, returns, trading

volumes of the underlying stocks from the CRSP database and book values of the rms fromCOMPUSTAT. Earnings announcement data are collected from I/B/E/S for the pre-eventperiod analysis. Unexpected earnings are dened as the difference between the actual earningsand the last estimated earnings for the same quarter. All the options days that are within threedays before negative unexpected earnings announcements are chosen for our pre-event studies.Table 1 provides summary statistics of the most active call and put options and

underlying stocks in our sample.12 Consistent with prior literature, the average dailyvolume of the underlying stocks is larger than the daily volumes of the most active put andcall options. The number of trades and daily volume for the most active call options arelarger than those for the most active put options, which suggests that the call optionmarket is more active than the put option market. For the stocks included in the sample,short volume on average accounts for 14.11% of total trading volume over the sample.13

5. Main results

Five-minute intervals are used to estimate the structural model (1)-(2). Each option day ispartitioned into 78 successive 5-minute intervals during the time period when both the optionand the stock markets are open (from 9:30 AM to 4:00 PM EST). For each of these intervals, wegenerate 5-minute quote revisions using the midpoint of the last bid and ask quotes for each ofthe stocks, the most active calls, and the most active puts. If no quote is available for an interval,there is no quote change for that interval, and we keep the quote from the previous interval. The

11We also change the lter with fewer than 15 trades as a robustness test, and the results are overall similar. See

Section 8 for more details.12The statistics are based on the entire sample that includes all trading days.13This is lower than the reported number (19.75%) in Boehmer and Wu (in press) possibly due to the sampledays or less to maturity, it is deleted from our sample since abnormal trading in the optionmarket on the days near expiration is documented by prior literature.As for stock transaction data, only the trades and quotes originated from NYSE are

included in our analysis since it is shown in Hasbrouck (1995) that the price discovery forNYSE stocks is more likely to take place on the NYSE rather than on other exchanges. Atthe same time, since we need trading volume measurements over short (5-minute) intervals,option days with thin trading are deleted from our sample. More specically, followingChan, Chung, and Fong (2002), option trading days with fewer than 20 trades for the

11difference.

Table 1

Summary statistics.

Mean Median Std. Dev. Min. Max.

Call options

Option price 1.089 0.750 1.299 0 25.950

Number of trades 52.331 29 73.653 1 1,859

Option volume 1,985 754 3,854 0 100,779

Put options

Option price 0.927 0.625 1.213 0 34.500

Number of trades 31.103 17 44.008 1 869

Option volume 1,362 462 2,737 0 56,518

Underlying stocks

Price 41.549 37.500 19.052 13.365 86.161

Volume (in thousands) 7,905 5,454 8,567 21 226,983

Short volume (in thousands) 1,116 775 1,103 1 18,448

Market cap (in millions) 87,892 58,283 89,939 954 396,325

Book value (in millions) 29,716 17,074 32,798 252 120,076

Turnover (volume/total shares) 0.011 0.006 0.011 0.002 0.048

Market-adjusted returns(value-weighted) (%) 0.014 0.017 0.052 0.166 0.100

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330316return of each interval is calculated as the log of the ratio of quote midpoints in successiveintervals. We also calculate the net trade volume of the stocks and the most active calls and puts,and the total volume of short-sales of these stocks for every 5-minute interval. Following Easley,OHara, and Srinivas (1998) and Chan, Chung, and Fong (2002), we use the standardized returnand net trade volume variables to control for cross-sectional variations across different stocksand options. For each option day we rst calculate the mean and the standard deviation of thereturns and net trade volume for each variable. The variable is then standardized by subtractingthe mean and dividing by the standard deviation. By doing this, we control for stock xedeffects, and this allows us to pool the 8,520 option days for later analyses.Pooled regression is used to estimate the structural model (1)-(2). Since we use

standardized returns and net trade volume for each of variable in this model, we canassume that the error terms are homoscedastic. Furthermore, since we include laggedvalues of the dependent variables to control for serial correlation, we can also assume thatthe error terms are serially independent. The seven regression equations are estimatedtogether using structural models, and thus we control for the correlations between theequations. We include three lags for each explanatory variable, but for simplicity, we onlyreport the rst two lags in our results. The results of the main model are presented inTable 2 and we will discuss them in detail in the next two sub-sections.

Table 1 reports summary statistics across all option trading days for the most actively traded call/put options and

for the underlying stock trading volume and short volume, and cross sectional summary statistics of time series

means for the remaining variables related to the 45 NYSE stocks during March 2005June 2007. Option price is

calculated as the midpoint of the best closing bid and ask prices, Number of trades is the sum of the number of

daily trades, and option volume is the daily trading volume for the most active calls and puts. Price is the daily

close price in dollars, Volume is the daily number of shares traded, and short volume is the daily number of shares

shorted. Market cap is the market value of equity in millions of dollars, and book value in millions of dollars is

calculated as book value of equity plus balance sheet deferred taxes minus the book value of preferred stock.

Turnover is daily trading volume divided by total shares outstanding, and market-adjusted returns, denoted in

percent, are daily returns minus value-weighted market returns.

Table 2

Regression analysis of the relationship between standardized 5-minutes returns and standardized 5-minutes net trade volume of stocks, calls, puts and short sales.

Explanatory variables

Lagged stock

return

Lagged call

return

Lagged put

return

Lagged stock net trade

volume

Lagged call net trade

volume

Lagged put net trade volume Lagged short size

Dependent

variable

Lag1 Lag2 Lag1 Lag2 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2

Stock returns 0.003 0.021n 0.001 0.002 0.002 0.002 0.042n 0.000 0.001 0.003n 0.001 0.001 0.003n 0.001 0.001 0.01n 0.01n 0.00n0.59 4.68 0.61 0.82 0.92 0.82 34.79 0.02 0.77 2.49 0.72 0.73 2.83 1.09 0.55 6.05 6.74 2.36

Call returns 0.231n 0.086n 0.16n 0.06n 0.08n 0.03n 0.054n 0.060n 0.022n 0.066n 0.020n 0.009n 0.04n 0.000 0.003n 0.029n 0.011n 0.01n48.94 18.43 82.6 32.63 42.85 16.94 42.72 45.75 16.5 54.56 16.49 7.43 28.57 0.18 2.83 19.21 6.73 6.17

Put returns 0.22n 0.08n 0.09n 0.04n 0.15n 0.07n 0.06n 0.06n 0.019n 0.04n 0.000 0.005n 0.061n 0.018n 0.006n 0.006n 0.001 0.011n45.32 16.58 44.06 21.24 76.7 34.92 43.27 42.26 14.53 31.13 0.38 4.31 50.2 14.82 4.94 3.67 0.86 6.64

Stock net trade

volume

0.01n 0.005 0.005n 0.004 0.002 0.000 0.036n 0.018n 0.001 0.000 0.001 0.002 0.060n 0.025n

2.1 1.03 2.64 1.92 0.87 0.15 26.8 13.3 1.1 0.14 1.16 1.27 36.43 14.36Call net trade

volume

0.062n 0.011n 0.01n 0.01n 0.03n 0.01n 0.017n 0.007n 0.027n 0.005n 0.003n 0.001 0.006n 0.000

12.47 2.24 4.34 3.37 13.82 4.64 12.12 4.87 21.13 4.05 2.48 1.15 3.36 0.22Put net trade

volume

0.04n 0.02n 0.03n 0.01n 0.02n 0.01n 0.02n 0.008n 0.003n 0.000 0.032n 0.008n 0.008n 0.002

7.99 4.9 12.96 5.99 7.82 6.05 12.98 5.45 2.33 0.09 24.94 6.39 4.86 1.32Short size 0.02n 0.005 0.003 0.001 0.005n 0.002 0.01n 0.003n 0.001 0.001 0.003n 0.000 0.354n 0.144n

5.19 1.19 1.96 0.5 2.62 1.27 11.43 2.46 1.17 1.29 2.39 0.36 255.71 99.43

This table presents the regression results of the following multivariate VAR model:

rt ALrt B0zt BLzt e1,tzt CLrt DLzt e2,t

where rt rs,t rc,t rp,t

and zt zs,t zc,t zp,t zss,t. rs,t,rc,t and rp,t represent quote revisions in the stock, call, and put market during time interval t, andzs,t ,zc,t ,zp,t and zss,t represent net trade volume in the stock, call, put and short market during time interval t. AL3 3, B0 3 4, BL3 4, CL4 3, andDL 4 4 (L1, 2, 3) are coefcients to be estimated. Each variable is then standardized by subtracting the mean and dividing by the standard deviation of the day.Sample days are deleted where there are less than 20 trades in the put option, call option, or stock market on that day. We use contemporaneous and three lags for the

explanatory variables, and report the regression coefcients for the contemporaneous and rst two lags with nindicating signicance at the 5% level.

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5.1. Comparison with Chan, Chung, and Fong (2002)

subsequent quote revisions in these two markets, Table 2 suggests that while put net trade

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330318volume only predicts option returns, short sales have predictive power over both futurestock and option returns. We show in Table 2 that short sales have predictive power overstock market returns in subsequent 5-minute intervals. For example, the coefcient for therst lagged short size is 0.01 with a t-statistic of 6.74. This evidence indicates again thatshort selling possibly conveys important negative information regarding the stock, which isnot necessarily revealed by looking only at the stock net trading volume. Notice that it hasbeen well documented in the literature that short sellers are able to spot overvalued stocksand their trading predicts negative future stock returns (e.g. Diether, Lee, and Werner,2009; Asquith, Pathak, and Ritter, 2005; Boehmer and Wu, in press). Our nding,however, is different from previous literature since it is the rst study to use intraday data(with 5-minute intervals) to examine the information content of short sales in a multi-market setting. The same results cannot be found for the put net trade volume. Asdiscussed earlier, put net trade volume is only signicant in explaining its own subsequentquote revisions. This result suggests that there is more information contained in short salesin the equity market, compared to put option trading.Second, with respect to the relationship between the net trading volume of short and put

markets, we predict that if the put market (short equity market) has leading information,put net trade volume (short sales) should have predictive power over subsequent short sales

14For contemporaneous (subsequent) option returns, the coefcient estimates on stock net trading volume are

0.05 (0.06) and 0.06 (0.06) for calls and puts. Meanwhile, for contemporaneous stock returns, the coefcientIn this sub-section we compare our results in Table 2 relating options and the stockmarket with Chan, Chung, and Fong (2002). First, we conrm their result that the stockmarket net trading volume has predictive power over contemporaneous and subsequentoption returns, while the option net trade volume is only signicant in explainingcontemporaneous stock returns.14 This is consistent with Chan, Chung, and Fong (2002)interpretation that stock market trading conveys more information than option markettrading.Second, in terms of the relationship among returns we also nd that stock market

returns have predictive power over subsequent quote returns in the option markets, whichis consistent with Chan, Chung, and Fong (2002): for example, the coefcients on laggedstock returns are 0.231 and 0.22 for call and put returns, respectively. Within the optionmarkets, a negative relationship between returns and their own lags is observed as in Chan,Chung, and Fong (2002).The main tests of our paper are (1) comparing the effect of short sales and put net trade

volume on the subsequent quote revisions in the two markets, and (2) studying the leadlagrelationship between short sales and put net trade volume. The purpose of these analyses isto compare the relative price discovery role of the short and put markets. We will discussthem in detail in the next sub-section.

5.2. The informational role of short sales and put option trading

First, with respect to the effects of short sales and put net trade volume on theestimates on option net trade volume are 0.003 and 0.003 for calls and puts, respectively.

lag and it is statistically signicant. Given that all the dependent and independent variablesBoth the analyses of the price impacts of put and short trading and the leadlagrelationship between the two markets indicate that compared to put options, short sellingvolume may be more informative. Notice that prior literature suggests that informedinvestors will prefer to trade options because of the higher leverage offered by thisinstrument (Black, 1975). However, our results suggest that without controlling for rm-specic events, short sales volume contains more information compared to the put optiontrade volume.

6. Subsample analysis

To further compare the information role of put option trading and short sales, we dividethe 45 stocks into two groups according to the information shares measurement(Hasbrouck, 1995) and the relative liquidity in the put option and equity market, sincethese characteristics are likely to be correlated with informed investors decision as towhich market to trade in. We apply the structural model to each group and compare theresults from the subsample analysis.

6.1. Information shares

For stocks traded in multiple markets, Hasbrouck (1995) develops an econometricapproach to study each markets proportional contribution to the price discovery process.Using a modied information share approach, Chakravarty, Gulen, and Mayhew (2004)study the contribution of option markets to price discovery. They rst calculate the impliedstock price using option trading data, and then apply the methodology in Hasbrouck(1995) to the implied and the actual stock price processes. In this paper, we follow themethodology in Chakravarty, Gulen, and Mayhew (2004) and measure the informationare standardized, this result suggests that a one standard deviation increase in lagged shortsize results in a 0.008 standard deviation increase in put net trade volume for the next5-minute interval. Meanwhile, the coefcient on lagged put net trade volume for short sales(0.003 for the rst lag) suggests that a one standard deviation increase in lagged put nettrade volume leads to a 0.003 standard deviation increase in short selling. Although theeffect of put net trade volume on subsequent short selling is statistically signicant, it issmaller in magnitude than the effect of short selling on subsequent put trade volume. Thisindicates that there is more informed trading in the short equity market, and thereforeinformation ows from the short equity market to the put option market.(put net trade volume). If the trades in both markets contain different information and areunrelated with each other, we should not observe any leadlag relationship between thetrading in the two markets. Table 2 provides the main results regarding the leadlagrelationship between the trading in different markets. We notice that stock net tradevolume predicts subsequent stock and option net trade volume. Meanwhile, option nettrade volume predicts subsequent option volume, but does not predict stock net tradevolume. When we include short sales in the regression, we observe signicant explanatorypower of short sales on subsequent stock net trading volume and subsequent put net tradevolume. The coefcient on lagged short size for put net trade volume is 0.008 for the rst

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330 319shares in the option market for the 45 sample stocks between February 20, 2007 and April

20, 2007.15 In particular, using a 1-second interval, we estimate the implied stock intradayprice using the put-call parity and the intraday put and call option prices, and then apply

predicting stock returns, while short sales predict subsequent stock returns in stocks with high

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330320put-short ratio. In terms of trading volume, put net trade volume does not predict subsequentshort sales regardless of the magnitude of the put-short ratio, while short sales are signicant inpredicting put net trade volume for stocks with both high and low put-short ratios. Moreover,the predictability of short sales is more pronounced than the predictability of put volume evenin stocks with high put-short ratio. Thus, we conclude that higher turnover in the put optionmarket cannot indicate more informed trading in this market.The second liquidity measure we use is the bid-ask spread. We construct a put-short

spread ratio by dividing the average bid-ask spread in the option market by the averagebid-ask spread in the short equity market, hence a higher put-short spread ratio representslower liquidity in the put option market compared to the short equity market. Since higherinformation asymmetry also leads to a higher bid-ask spread, it is not clear whether putoption trading contains more information for stocks with high or low put-short spread

15We thank the referee for suggesting the analysis using information shares.16For example, Roll, Schwartz, and Subrahmanyam (2010) nd that lower delta, which implies high hedgeHasbrouck (1995) to estimate the option market information shares.We then apply our main methodology to the stocks with high and low information

shares. The results are reported in Table 3 (Panel A). We expect put options to be moreinformative for stocks with higher option market information shares, since by denition,higher price discovery should be observed in the option markets for these stocks. Theresults conrm our expectations. During the subsample period, short sales are notsignicant in predicting subsequent put net trade volume, but for stocks with higher optionmarket information shares, put net trade volume leads short sales volume. In all, theseresults suggest that our main methodology is supported by and complements theinformation shares methodology, since by considering explicitly the impact of short salesvolume, it allows us to study the informational role of both put option trading and shortsales in the equity market.

6.2. Liquidity

The relative liquidity between the equity and option markets may be an important factorwhen investors decide which market to trade in. As discussed above, one of the drawbacksof choosing to trade in the option market is its illiquidity. In order to address this issue, wegroup the options and underlying stocks in our sample according to the relative liquidity inthe put option and short equity markets.First we calculate the turnover put-short ratio for each stock by dividing the put option

turnover by total short sales volume during our sample period (as in Roll, Schwartz, andSubrahmanyam, 2010). Higher put-short ratio indicates higher liquidity in the put optionmarket relative to the short equity market. It is not clear how the relative liquidity in the shortand put option markets affects the informed trading in these two markets, since on one hand,in a more liquid option market, investors face less transaction costs related with illiquidity; onthe other hand, a liquid option market may also indicate more hedging-related or uninformedtrading.16 Table 3 (Panel B) suggests that rst, put option trading is not signicant inratios, is correlated with higher trading volume in the option market.

Table 3

Subgroup analysis based on stock and option characteristics.

Explanatory variables

Lagged put net trade volume Lagged short size Lagged put net trade volume Lagged short size Lagged put net trade volume Lagged short size

Dependent

variable

Lag0 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2

Panel A Panel B Panel C

Low information shares from option markets Low put/short volume ratio Low put/short bidask spread ratio

Stock returns 0.005 0.001 0.001 0.008 0.02n 0.001 0.001 0.001 0.002 0.02n 0.000 0.01n 0.01n 0.002 0.000 0.11n 0.03n 0.01n0.76 0.13 0.21 0.98 2.14 0.07 0.74 0.8 1.17 12.79 0.12 4.06 3.42 1.23 0.22 56.34 14.9 3.95

Put returns 0.06n 0.03n 0.009 0.004 0.02n 0.03n 0.06n 0.02n 0.01n 0.01n 0.000 0.01n 0.06n 0.02n 0.00n 0.01n 0.000 0.01n9.26 4.19 1.28 0.46 2.21 2.94 35.82 10.99 4.81 2.35 0.08 5.02 39.07 10.51 2.78 5.21 0 5.72

Put net trade volume 0.02n 0.02n 0.006 0.008 0.03n 0.01n 0.01n 0.01n 0.03n 0.01n 0.01n 0.0012.66 2.98 0.56 0.78 16.45 5.72 2.25 2.46 18.04 3.26 3.86 0.24

Short size 0.003 0.006 0.38n 0.13n 0.002 0.001 0.35n 0.15n 0.001 0.001 0.37n 0.14n0.47 1.09 46.72 14.99 1.68 0.68 194.15 76.94 0.59 0.82 204.46 73.09

High information shares from option markets High put/short volume ratio High put/short bid-ask ratio

Stock returns 0.010 0.004 0.003 0.05n 0.02n 0.003 0.0n 0.002 0.00 0.06n 0.03n 0.02n 0.001 0.001 0.000 0.13n 0.02n 0.01n1.88 0.82 0.56 8.13 2.6 0.48 3.46 0.92 0.47 24.98 10.61 8.81 0.37 0.36 0.09 57.33 10.01 2.39

Put returns 0.06n 0.01n 0.010 0.04n 0.04n 0.03n 0.065 0.018 0.003 0.01n 0.004 0.01n 0.06n 0.02n 0.01n 0.001 0.003 0.01n10.51 2.06 1.76 4.97 5.52 3.81 35.25 9.75 1.9 2.93 1.39 4.1 31.41 10.31 4.39 0.49 1.33 3.46

Put net trade volume 0.03n 0.000 0.004 0.008 0.036 0.01n 0.01n 0.003 0.03n 0.01n 0.01n 0.01n4.66 0.05 0.45 0.97 18.75 3.04 4.89 0.97 16.91 5.98 3.08 2.06

Short size 0.01n 0.005 0.39n 0.13n 0.003 0.002 0.36n 0.14n 0.01n 0.003 0.33n 0.15n

2.05 1.01 59.87 18.21 1.69 1.39 166.51 62.79 4.32 1.65 154.69 66.67

This table presents the subgroup panel regression results of the last two equations (with dependent variables put net trade volume and short size) of the following

multivariate VAR model:

rt ALrt B0zt BLzt e1,t zt CLrt DLzt e2,twhere rt rs,t rc,t rp,t0 and zt zs,t zc,t zp,t zss,t0. rs,t,rc,t and rp,t represent quote revisions in the stock, call, and put market during time interval t, and zs,t,zc,t,zp,tand zss,t represent net trade volume in the stock, call, put and short market during time interval t. AL 3 3 , B0 3 4 , BL 3 4 , CL 4 3 , and DL4 4(L1, 2, 3) are coefcients to be estimated. Each variable is then standardized by subtracting the mean and dividing by the standard deviation of the day. Sample days aredeleted when there are less than 20 trades in the call option, put option, or stock market on that day. We use contemporaneous and three lags for the explanatory variables, and

report the regression coefcients for the contemporaneous and rst two lags with n indicating signicance at the 5% level.

We divide all the 45 NYSE sample stocks according to their stock-market related characteristics and apply the VAR model for each of the subgroups. The sample period of

Panel B and Panel C is from March 2005 to June 2007, while the sample period of Panel A is from February 2007 to April 2007.

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signicant in predicting subsequent short sales, while short sales are no longer signicant in

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330322predicting subsequent returns and put net trade volume, suggesting that in a pre-eventperiod, put option trading contains more information since it is a better predictor forsubsequent stock market returns.Our results indicate that the effect documented in Cao, Chen, and Grifn (2005) may

also be similarly found in the put option market. Cao, Chen, and Grifn (2005) show thatwith a pending extreme informational event (takeover), the option market plays animportant role in the price discovery process, while stock market trading imbalances arepredictors of next day stock returns and option volume is uninformative in normal times.Our results suggest that while over the entire sample period and without controlling forrm-specic events there seems to be more informed trading in the short equity market,ratio. According to Table 3 (Panel C), put option trading is signicant at predicting subsequentshort sales for stocks with higher put-short spread ratio. However, for stocks with both highand low put-short spread ratio, short sales are signicant in predicting subsequent put optiontrading, suggesting that short sales are more informative compared to put option tradingregardless of the relative bid-ask spreads in the short equity and the put option market.In all, to further understand the different informational roles of short sales and put

option trading, we perform the subgroup analysis using the information sharesmeasurement (Hasbrouck, 1995) and the relative liquidity in the short equity and putoption markets. Our results suggest that short sales play a more important price discoveryrole than put option trading.

7. Sub-period analysis

Prior literature (Cao, Chen, and Grifn, 2005) nds that call option trading containsmore information before company takeovers in the sense that during the pre-announcement period, call option trading imbalance has greater predictability over thenext-day stock returns (takeover premiums) than stock trading imbalance, while the sameeffect cannot be found during non-takeover period. In particular, they nd that prior toannouncements, buying activity is highest in the short-term out-of-the-money call options(with the highest leverage). This suggests that the leverage advantage in option markets ismore signicant and may attract informed traders when there is a pending event that willchange the companys fundamental value. Thus, in this section, we also conduct pre-eventanalysis to test whether the same effect can be found between put option trading imbalanceand short sales before a companys negative earnings announcement is released.We check all the quarterly earnings announcements of our 45 stocks during the sample

period and calculate their unexpected earnings (the difference between actual and predictedearnings). There are 89 negative earnings announcements in total. For each of them, wekeep three days before the announcement day and apply our structural model to the 267option days. We expect to nd that during this pre-announcement period, put optiontrading should be more informative and lead short sales volume.The results in Table 4 are consistent with the argument above. During the three days

prior to negative earnings announcements, put net trade volume is signicant in predictingsubsequent stock market returns.17 Moreover, we nd that put net trade volume is17The coefcient is 0.01, with a t-statistic of 1.96 for the second lag put net trade volume.

Table 4

Regression analysis of the relationship between standardized 5-minutes returns and standardized 5-minutes net trade volume of stocks, calls, puts and short sales

(3 days before earnings announcements).

Explanatory variables

Lagged stock

return

Lagged call

return

Lagged put

return

Lagged stock net trade

volume

Lagged call net trade

volume

Lagged put net trade

volume

Lagged short size

Dependent

variable

Lag1 Lag2 Lag1 Lag2 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2

Stock returns 0.008 0.022 0.014 0.011 0.006 0.002 0.06n 0.02n 0.005 0.007 0.001 0.007 0.02n 0.011 0.01n 0.05n 0.015 0.0130.4 1.19 1.34 1.01 0.52 0.16 8.53 2.43 0.7 1.05 0.12 1.06 2.16 1.63 1.96 5.96 1.68 1.48

Call returns 0.15n 0.08n 0.16n 0.05n 0.08n 0.03n 0.05n 0.05n 0.02n 0.05n 0.02n 0.001 0.03n 0.002 0.011 0.008 0.02n 0.0047.58 3.96 14.46 4.76 6.97 2.27 6.84 6.83 2.76 7.27 2.85 0.08 3.5 0.28 1.55 1 2.44 0.41

Put returns 0.14n 0.12n 0.06n 0.04n 0.13n 0.04n 0.07n 0.03n 0.007 0.005 0.009 0.008 0.06n 0.02n 0.010 0.003 0.009 0.0126.9 6.2 5.73 3.48 11.52 4.04 9.16 3.3 0.95 0.7 1.29 1.07 8.59 2.59 1.47 0.38 0.99 1.33

Stock net

trade volume

0.013 0.030 0.013 0.004 0.018 0.003 0.009 0.004 0.02n 0.011 0.007 0.06n 0.0100.61 1.43 1.09 0.35 1.55 0.27 1.13 0.47 2.61 1.43 0.89 6.15 1.02

Call net trade

volume

0.009 0.05n 0.008 0.020 0.002 0.003 0.013 0.008 0.03n 0.006 0.009 0.009 0.03n0.4 2.45 0.64 1.73 0.15 0.3 1.58 1.04 4.13 0.83 1.21 0.94 2.66

Put net trade

volume

0.006 0.06n 0.023 0.001 0.020 0.03n 0.02n 0.006 0.007 0.03n 0.011 0.001 0.0090.28 2.67 1.89 0.05 1.72 2.9 2.1 0.73 0.94 3.75 1.48 0.15 0.94

Short size 0.019 0.013 0.003 0.006 0.013 0.001 0.02n 0.001 0.005 0.01n 0.001 0.35n 0.13n1.06 0.75 0.28 0.55 1.26 0.15 3.21 0.12 0.76 2.24 0.12 43.84 14.86

This table presents the panel regression results of the following multivariate VAR model during 3 days before the sample stocks negative earnings announcements:

rt ALrt B0zt BLzt e1,tzt CLrt DLzt e2,t

where rt rs,t rc,t rp,t0 and zt zs,t zc,t zp,t zss,t0. rs,t,rc,t and rp,t represent quote revisions in the stock, call, and put market during time interval t, andzs,t,zc,t,zp,t and zss,t represent net trade volume in the stock, call, put and short market during time interval t. AL3 3, B0 3 4, BL3 4, CL4 3, andDL4 4 (L1, 2, 3) are coefcients to be estimated. Each variable is then standardized by subtracting the mean and dividing by the standard deviation of the day.We use contemporaneous and three lags for the explanatory variables, and report the regression coefcients for the contemporaneous and rst two lags with n

indicating signicance at the 5% level.

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during the pre-event period, informed investors will turn to put option markets and the putoption trading becomes more informative.

18As mentioned in Chordia, Roll, and Subrahmanyam (2005), this method does the opposite of the Fama and

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330324MacBeth (1973) methodology in which coefcients from cross-sectional regressions are averaged over time.19As stated by Easley, OHara, and Srinivas (1998), the lack of trading dictates that the price-volume relation

for these series may not be reliable. (p. 450).8. Robustness tests

In the main analysis, we apply the structural model dened by Eqs. (1) and (2) to theentire sample in a pooled regression. As a robustness test, we rst apply time-seriesregressions to estimate the coefcients of the structural model for each individual stock,and then cross-sectionally average the estimated coefcients.18

Table 5 reports the results of this robustness test. The results are similar to those in themain analysis (Table 2). The results show that short sales are signicant in predictingsubsequent put option trading, but not vice versa, suggesting that short sales are indeedmore informative and lead the put option trading.In the main analysis, we apply the 20-trade lter following Chan, Chung, and Fong

(2002), and delete option trading days with fewer than 20 trades for the stock, the mostactive call, the most active put, or the short sales. After applying the lter, there are 8,520out of 20,427 sample days left for the main analysis. In Table 6, we present the comparisonstatistics of some main variables on sample and discarded option days. As expected,trading volume and number of trades are both higher in all the markets on sample days,since the discarded option days are deleted due to the thin trading problem. In particular,there are on average 56 put option trades on sample days, while there are only 13 putoption trades on discarded days. Since we use 5-minute interval for our main analysis,deleting thin trading days helps to validate the high-frequency methodology.19 However,we acknowledge that by applying the 20-trade lter, we keep only 41.7% of the totalavailable option days, which may bring questions to the robustness of our test results. Toaddress this issue, we conduct several robustness tests.20

First, since we focus on the leadlag relationship between the put option and shortequity market, we drop all the variables related with call options from the VAR model as arobustness test. By doing this, we avoid applying the 20-trade lter to the call optionmarket. After applying the 20-trade lter to the put option market only, we retain 9,441option days for the robustness test, i.e., 46.2% of the total available option days are kept.Table 7 presents the results of panel regressions using the 9,441 option days. The results aresimilar to those in Table 2. More specically, we nd that a one standard deviationincrease in lagged short size results in a 0.008 standard deviation increase in put net tradevolume for the next 5-minute interval, while a one standard deviation increase in laggedput net trade volume only results in a 0.003 standard deviation increase in the subsequentshort size, suggesting that there is more informed trading in the short equity market.Second, we replace the 20-trade lter with the 15-trade lter and widen the 5-minute

interval to the 10-minute interval, and then repeat the main analysis. By doing this, 10,639option days (52.1% of the total available trading days) are kept for the robustness test. Inunreported results, we conrm that short sales contain more information compared to the20We thank the referee for this suggestion.

Table 5

Regression analysis of the relationship between standardized 5-minutes returns and standardized 5-minutes net trade volume of stocks, calls, puts and short sales

(cross-sectional averages of coefcients from individual time-series regressions).

Explanatory variables

Lagged stock

return

Lagged call

return

Lagged put

return

Lagged stock net trade

volume

Lagged call net trade

volume

Lagged put net trade

volume

Lagged short size

Dependent variable Lag1 Lag2 Lag1 Lag2 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2

Stock returns 0.186 0.056 0.004 0.001 0.003 0.001 0.040 0.000 0.001 0.005 0.001 0.002 0.003 0.001 0.001 0.010 0.005 0.0040.82 0.65 1.24 0.19 1.16 0.26 3.24 0.04 0.3 1.96 0.68 1.04 1.55 0.47 0.46 0.29 0.54 0.84

Call returns 4.40n 2.74n 0.18n 0.08n 0.05n 0.02n 0.05n 0.03n 0.01n 0.06n 0.02n 0.01n 0.03n 0.001 0.01n 0.03n 0.004 0.01n5.25 5.21 18.92 12.12 10.04 5.15 12.66 5.65 2.83 20.99 7.69 5.78 12.06 0.71 3.34 4.79 1.15 4.2

Put returns 4.45n 2.75n 0.05n 0.03n 0.17n 0.08n 0.05n 0.03n 0.01n 0.03n 0.01n 0.01n 0.05n 0.02n 0.01n 0.005 0.006 0.01n5.46 5.27 10.41 7.23 26.12 23.42 14.14 6.23 2.31 8.4 3.25 3.7 15.17 7.95 3.45 0.78 1.28 4.7

Stock net trade

volume

0.337 0.081 0.001 0.001 0.005 0.000 0.03n 0.01n 0.003 0.001 0.003 0.002 0.06n 0.02n1.88 0.64 0.23 0.45 1.81 0.12 13.11 5.58 1 0.6 1.74 0.93 9.13 5.48

Call net trade volume 0.84n 0.57n 0.02n 0.004 0.02n 0.004 0.01n 0.01n 0.03n 0.01n 0.00n 0.002 0.002 0.0015.34 3.83 3.84 0.95 7.41 1.01 4.33 2.37 10.09 2.93 2.15 1.42 0.72 0.19

Put net trade volume 1.13n 0.59n 0.02n 0.003 0.03n 0.01n 0.01n 0.01n 0.01n 0.002 0.04n 0.01n 0.01n 0.0034.82 4.49 3.41 0.66 5.65 3.04 5.11 2.98 2.26 0.9 12.36 3.74 4.34 1.3

Short size 0.375 0.27n 0.001 0.001 0.004 0.003 0.01n 0.003 0.002 0.002 0.002 0.002 0.35n 0.14n1.59 2.47 0.31 0.43 1.5 0.73 3.31 1.71 1.01 1.31 1.36 1.13 64.07 43.29

This table reports the cross-sectional average of coefcients from individual time-series regressions. We rst estimate the following multivariate VAR model for each

stock and then average the estimated coefcients over 45 NYSE stocks:

rt ALrt B0zt BLzt e1,tzt CLrt DLzt e2,t

where rt rs,t rc,t rp,t0 and zt zs,t zc,t zp,t zss,t0. rs,t,rc,t and rp,t represent quote revisions in the stock, call, and put market during time interval t, andzs,t,zc,t,zp,t and zss,t represent net trade volume in the stock, call, put and short market during time interval t. AL3 3, B0 3 4, BL3 4, CL4 3, andDL4 4 (L1, 2, 3) are coefcients to be estimated. Each variable is then standardized by subtracting the mean and dividing by the standard deviation of the day.Sample days are deleted where there are less than 20 trades in the call option, put option, or stock market on that day. Stocks are deleted when there are less than 10

sample days. We use contemporaneous and three lags for the explanatory variables, and report the mean and the t-statistics of the regression coefcients for the

contemporaneous and rst two lags with n indicating signicance at the 5% level.

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X. Hao et al. / Journal of Financial Markets 16 (2013) 308330326put option trading, since the predictive power of short sales on subsequent put optiontrade volume (0.008 with a t-statistic of 3.69) is robustly larger in magnitude than the

Table 6

Comparison of sample days and discarded days.

Sample days (8,520) Discarded days (11,907)

Mean Std. Dev. Mean Std. Dev.

Call options

Daily trading volume 3,415 5,333 903 2,227

Daily number of trades 88 84 24 26

Put options

Daily trading volume 2,557 3,584 495 1,653

Daily number of trades 56 51 13 13

Underlying stock

Daily trading volume 10,176,307 9,696,026 6,279,569 7,232,700

Daily number of trades 8,544 3,940 5,299 2,699

Short sales of the underlying stock

Daily trading volume 1,451,010 1,250,084 875,987 912,246

Daily number of trades 1,814 1,291 1,089 764

This table compares the daily trading volume and number of trades of the call options, put options, underlying

stocks, and short sales on sample days and discarded days. The overall sample includes 45 NYSE stocks from

March 2005 to June 2007. Sample days are deleted when there are less than 20 trades in the call option, put option,

or stock market on that day.predictive power of put option on subsequent short sales (0.004 with a t-statistic of 2.98)with these alternative lter and high-frequency intervals.Finally, we repeat the analysis after subtracting the shorting series from the signed stock

volume series. In our main analysis (Table 2), we argue that short sales contain moreinformation than put options, since we observe that short sales can predict subsequentstock returns and stock trading volume, while put option trading cannot. However, onemay argue that the predictive power of short sales over future stock returns and tradingvolume might be due to the persistence of short sales and the fact that about 20% of stocktrades include short sales.21 To rule this out, we subtract the shorting series from the signedstock volume series. We rst match trades data from TAQ with short sales data by symbol,price, size, date, and time. After we identify the trading direction of each trade using theLee and Ready algorithm, we subtract the shorting volume from the total stock volume,generating the adjusted signed stock volume series. The unreported results suggest that theeffect of short sales on subsequent stock returns (0.01 with a t-statistic of 6.97) is verysimilar to our main results (Table 2). Additionally, short sales can still predict subsequentstock trading volume (0.01 with a t-statistic of 4.24), although the effect is smaller inmagnitude when compared to our main analysis. This result suggests that the persistence ofshort sales helps to but cannot explain the predictive power of short sales on subsequentstock trading volume.Overall, our main result that short sales contain more negative information still holds,

after performing several robustness checks.

21We thank the reviewer for this suggestion.

Table 7

Regression analysis of the relationship between standardized 5-minutes returns and standardized 5-minutes net trade volume of stocks, puts and shorts (the equations

and variables related with call options are dropped).

Explanatory variables

Lagged stock return Lagged put return Lagged stock net trade volume Lagged put net trade volume Lagged short size

Dependent variable Lag1 Lag2 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2 Lag0 Lag1 Lag2

Stock returns 0.002 0.021n 0.001 0.001 0.042n 0.000 0.001 0.003n 0.001 0.001 0.008n 0.01n 0.004n0.48 4.71 0.77 0.5 34.81 0.06 0.77 2.82 1.1 0.59 5.96 6.75 2.35

Put returns 0.223n 0.091n 0.109n 0.044n 0.056n 0.059n 0.023n 0.062n 0.019n 0.005n 0.006n 0.002 0.011n46.97 19.4 61.99 25.35 43.85 44.94 17.69 50.57 15.21 4.39 3.66 1.45 6.37

Stock net trade volume 0.010n 0.004 0.004n 0.003 0.037n 0.019n 0.001 0.002 0.060n 0.025n2.05 0.88 2.43 1.48 26.95 13.55 1.09 1.28 36.46 14.4

Put net trade volume 0.041n 0.028n 0.004n 0.005n 0.019n 0.009nn 0.032n 0.008n 0.008n 0.0028.35 5.7 2 2.85 13.65 6.33 25.15 6.23 4.71 1.29

Short size 0.022n 0.005 0.006n 0.002 0.013n 0.003n 0.003n 0.000 0.354n 0.144n5.26 1.32 3.82 1.53 11.6 2.54 2.46 0.39 255.8 99.52

This table presents the panel regression results of the following multivariate VAR model using 45 NYSE stocks from March 2005 to June 2007:

rt ALrt B0zt BLzt e1,t zt CLrt DLzt e2,twhere rt rs,t rc,t rp,t0 and zt zs,t zc,t zp,t zss,t0. rs,t,rc,t and rp,t represent quote revisions in the stock, call, and put market during time interval t, andzs,t,zc,t,zp,t and zss,t represent net trade volume in the stock, call, put and short market during time interval t. AL3 3, B0 3 4, BL3 4, CL4 3, andDL4 4 (L1, 2, 3) are coefcients to be estimated. Each variable is then standardized by subtracting the mean and dividing by the standard deviation of the day.Sample days are deleted where there are less than 20 trades in the call option, put option, or stock market on that day. Stocks are deleted when there are less than 10

sample days. We use contemporaneous and three lags for the explanatory variables, and report the mean and the t-statistics of the regression coefcients for the

contemporaneous and rst two lags with n indicating signicance at the 5% level.

X.Haoet

al./JournalofFinancia

lMarkets

16(2013)308330

327

9. Conclusion

This study provides an empirical comparison of the price discovery roles between put nettrade volume (buyer-initiated trading volume minus seller-initiated trading volume) and shortsales of the underlying stocks for a group of actively traded NYSE stocks and theircorresponding put options. It has been documented in the literature that both put option tradingand short sales convey valuable negative information about the underlying stocks. However, it is

Table A1

The companies stocks and near-maturity options tracked in BARDS from March 2005 to June 2009.

Stock

symbol

Stock name Stock

symbol

Stock name

AIG AMERICAN INTERNATIONAL

GROUP INC

JNJ JOHNSON & JOHNSON

ALL ALLSTATE CORP JPM JPMORGAN CHASE & CO

AMAT APPLIED MATERIALS INC KLAC KLA-TENCOR CORP

AMD ADVANCED MICRO DEVICES KO COCA COLA CO

AMR A M R CORP DEL LU LUCENT TECHNOLOGIE

AMZN AMAZON COM INC LXK LEXMARK INTERNATIONAL INC

NEW

BAC BANK OF AMERICA CORP MER MERRILL LYNCH & CO INC

BCGI BOSTON COMMUNICATION GROUP

INC

MMM 3M CO

BEAS BEA SYSTEMS INC MO ALTRIA GROUP INC

BRCM BROADCOM CORP MRK MERCK & CO INC

BSX BOSTON SCIENTIFIC CO MSFT MICROSOFT CORP

CA COMPUTER ASSOCIATES INTL INC MWD MORGAN STANLEY

CHTR CHARTER COMMUNICATIONS INC MXO MAXTOR CORP

CMCSK COMCAST CORP NEW NEM NEWMONT MINING CORP

COF CAPITAL ONE FINANCIAL CORP NOK NOKIA CORP

CSCO CISCO SYSTEMS INC NXTL NEXTEL COMMUNICATIONS INC

DELL DELL INC ORCL ORACLE CORP

DNA GENENTECH INC OVTI OMNIVISION TECHNOLOGIES

DUK DUKE ENERGY CORP NEW PFE PFIZER INC

EBAY EBAY INC PG PROCTER & GAMBLE CO

EMC E M C CORP MA QCOM QUALCOMM INC

ETFC E TRADE FINANCIAL CORP S SPRINT NEXTEL CORP

FNM FEDERAL NATIONAL MORTGAGE

ASSN

SEBL SIEBEL SYSTEMS INC

FRE FEDERAL HOME LOAN MORTGAGE

CORP

SNDK SANDISK CORP

FRX FOREST LABS INC SUNW SUN MICROSYSTEMS INC

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330328GE GENERAL ELECTRIC CO TWX TIME WARNER INC

GM GENERAL MOTORS CORP TXN TEXAS INSTRUMENTS INC

GP GEORGIA-PACIFIC CORP TYC TYCO INTERNATIONAL LTD NEW

HAL HALLIBURTON COMPANY UPS UNITED PARCEL SERVICE INC

HDI HARLEY DAVIDSON INC USG U S G CORP

HPQ HEWLETT PACKARD CO VZ VERIZON COMMUNICATIONS INC

IBM INTERNATIONAL BUSINESS MACHS

COR

WFC WELLS FARGO & CO NEW

IMCL IMCLONE SYSTEMS INC XOM EXXON MOBIL CORP

INTC INTEL CORP YHOO YAHOO INC

not clear which market attracts more informed investors. Our results show that short salespredict subsequent stock and put option returns, while put option imbalance only predicts itsown future returns, suggesting that short sales convey more negative information about thestocks than put option trading. The analysis of the leadlag relationship between put optionimbalance and short sales also conrms the above argument since the predictive power of shortsales on subsequent put option is larger in magnitude than the predictive power of put option onsubsequent short sales. Subsample tests are also conducted to provide further support for theleading informational role of short sales. The only different results are found in the analysisbefore a companys negative unexpected earnings are released. With a pending event that candrive down stocks fundamental value, more informed trading is observed in the put optionmarket, since in this case put option imbalance is signicant in predicting subsequent short sales,and short sales lose the predictive power. Our results suggest that the short market seems to bemore important in conveying negative information, while the put option market may be morecritical and contain more information before certain corporate events. Overall, this papercontributes to our understanding of the price discovery roles of the equity and options markets,and their different functions in improving nancial market efciency.

Appendix A

See Table A1.

Table B1

Summary statistics of stocks in BARDS and OptionMetrics.

BARDS 67 1.124 2.369 0 0.361 0.625 1.169 307.204

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330 329OptionMetrics 1,399 0.891 4.910 0 0.275 0.491 0.907 901.933

Volume (in thousands)

BARDS 67 11,561 17,133 0 2,692 5,939 12,979 267,041

OptionMetrics 1,399 1,398 4,517 0 141 361 1,049 267,041

Panel B. NYSE stocks

No. Mean Std. Dev. Min. 25th Median 75th Max.

Turnover (%)

BARDS 45 0.727 0.816 0.052 0.321 0.510 0.828 19.424

OptionMetrics 822 0.757 4.869 0 0.273 0.459 0.786 901.933

Volume (in thousands)

BARDS 45 8,830 12,933 23 2,626 5,299 10,042 223,848

OptionMetrics 822 1,400 3,986 0 168 425 1,187 244,746

Panels A (Panel B) reports the cross-sectional averages of trading volume and turnover for all the NYSE/

NASDAQ (NYSE) stocks with options in BARDS and OptionMetrics as of the end of 2002, when the BARDS

database was constructed. Turnover is daily trading volume divided by total shares outstanding, and Volume is the

daily number of shares traded in thousands of dollars. The summary statistics include the number of stocks (No.),

mean values (Mean), standard deviation (Std. Dev.), minimum and maximum (Min. and Max.) and quartilesPanel A. NYSE and NASDAQ stocks

No. Mean Std. Dev. Min. 25th Median 75th Max.

Turnover (%)(25th, Median, and 75th) of the two variables for each group.

Appendix B

X. Hao et al. / Journal of Financial Markets 16 (2013) 308330330SeeTable B1.

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Short sales and put options: Where is the bad news first traded?IntroductionRelated literatureMethodology and empirical predictionsSystems of regression equations for multiple marketsEmpirical predictions

Data and summary statisticsMain resultsComparison with Chan, Chung, and Fong (2002)The informational role of short sales and put option trading

Subsample analysisInformation sharesLiquidity

Sub-period analysisRobustness testsConclusionAppendix AAppendix BReferences