Short-Sale Strategies and Return
`Predictability
`
`Karl B. Diether
`Fisher College of Business, The Ohio State University
`
`Kuan-Hui Lee
`Rutgers Business School, Rutgers University, and Korea University Business
`School
`
`Ingrid M. Werner
`Fisher College of Business, The Ohio State University
`
`We examine short selling in US stocks based on new SEC-mandated data for 2005. There is
`a tremendous amount of short selling in our sample: short sales represent 24% of NYSE and
`31% of Nasdaq share volume. Short sellers increase their trading following positive returns
`and they correctly predict future negative abnormal returns. These patterns are robust to
`controlling for voluntary liquidity provision and for opportunistic risk-bearing by short
`sellers. The results are consistent with short sellers trading on short-term overreaction of
`stock prices. A trading strategy based on daily short-selling activity generates significant
`positive returns during the sample period. (JEL G12, G14)
`
`There is currently tremendous interest in short selling not only from academics,
`but also from issuers, media representatives, the Securities and Exchange Com-
`mission (SEC), and Congress. Academics generally share the view that short
`sellers help markets correct short-term deviations of stock prices from funda-
`mental value. This view is by no means universally held, and many issuers
`and media representatives instead characterize short sellers as immoral, uneth-
`ical, and downright un-American.1 In an attempt to evaluate the efficacy of
`
`We are grateful for comments from Leslie Boni, Frank DeJong, Rudi Fahlenbrach, Frank Hatheway, David Musto,
`Alessio Saretti, Lakshmanan Shivakumar, Ren´e Stulz, and seminar participants at the Ohio State University, the
`University of Georgia, the Institute for International Economic Studies, the Darden School at the University
`of Virginia, and Rotman School at the University of Toronto, as well as participants at the NBER Market
`Microstructure Group, the 2006 Swedish Institute for Financial Research Conference, the Conference in Memory
`of Jan Mossin at the Norwegian School of Business, the 2006 European Finance Association meetings, and the
`2006 Centre for Analytical Finance conference at the Indian School of Business. We are also grateful for
`assistance in computing order imbalance measures from Laura Tuttle. We thank Nasdaq Economic Research
`for data, and the Dice Center for Financial Research at the Fisher College of Business for financial support.
`Finally, we thank the editor and the anonymous referee for extremely helpful comments and suggestions. All
`errors are our own. Address correspondence to Karl B. Diether, Fisher College of Business, The Ohio State
`University, 2100 Neil Avenue, Columbus, OH 43210; telephone: (614) 272-6182, fax: (614) 292-2418; e-mail:
`diether 1@fisher.osu.edu.
`
`1 For example, John Rothchild (1998) in the Bear Book said, “Known short sellers suffer the same reputation as
`the detested bat. They are reviled as odious pests, smudges on Wall Street, pecuniary vampires.”
`C(cid:1) The Author 2008. Published by Oxford University Press on behalf of The Society for Financial Studies.
`All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.
`doi:10.1093/rfs/hhn047
`Advance Access publication May 9, 2008
`
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`short-sale rules, the SEC introduced new regulation governing short sales in
`US markets on 2 January 2005. Washington is also interested in short selling,
`and the Congressional Committee of Financial Services (22 May 2003) and the
`Senate Judiciary Committee (28 June 2006) have recently heard testimonies
`about short sellers and hedge funds.
`Despite this interest, there is relatively little evidence in the academic litera-
`ture on what short sellers actually do. In this paper, we study trading strategies
`used by short sellers of NYSE- and Nasdaq-listed stocks. Specifically, we ex-
`amine the short-horizon relationship between short selling and previous and
`subsequent returns. We find that short-selling activity is strongly positively
`related to past returns. A five-day return of 10% results in an increase in short
`selling as a fraction of daily share volume of 3.71 (2.15) percentage points
`for NYSE (Nasdaq) stocks. We also find that short selling intensifies on days
`preceding negative returns. An increase in short-selling activity by 10% of
`share volume is associated with a future decline in returns by 0.94% (0.72%)
`per month on the NYSE (Nasdaq). A trading strategy that buys stocks with
`low short-selling activity and sells short stocks with high short-selling activity
`generates an abnormal return of roughly 1.39% (1.41%) per month for NYSE
`(Nasdaq) stocks. In sum, the results show that short sellers time their trades
`extremely well relative to short-term price trends.
`How should we interpret the fact that short sellers as a group seem to be able
`to predict short-horizon abnormal returns? Does it mean that they have inside
`information about future fundamental values or are they capable of detecting
`when the current price deviates from the current fundamental value? The first
`alternative suggests that short sellers are either corporate insiders or are privy
`to advance release of material nonpublic information from the corporation. We
`find this hard to believe given how many restrictions are levied on trading by
`corporate insiders. Moreover, Regulation Fair Disclosure (Reg FD) is in effect
`during our sample period, which should limit the ability of outsiders to get
`advance access to material nonpublic information.
`The second alternative suggests that market frictions (Miller, 1977; Harrison
`and Kreps, 1978; Diamond and Verrecchia, 1987; and Scheinkman and Xiong,
`2003) or behavioral biases (DeBondt and Thaler, 1985; Barberis, Shleifer,
`and Vishny, 1998; Daniel, Hirshleifer, and Subrahmanyam, 1998; and Hong
`and Stein, 1999) may cause price to deviate from fundamental value in the
`short run, and that short sellers are exploiting these situations to their benefit.
`However, this interpretation requires that short sellers are more sophisticated
`than the average investor. Given the cost of short selling, short sellers are likely
`to be predominantly institutional traders. For example, Boehmer, Jones, and
`Zhang (2008) find that about 75% of all short sales are executed by institutions,
`while individuals represent less than 2% (the rest are specialists and others).
`Since many institutions are prevented from shorting (e.g., many mutual funds),
`the ones that may use short selling as part of their strategy tend to be more
`sophisticated. Thus, we conjecture that short sellers as a group are likely to be
`sophisticated traders.
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`Short-Sale Strategies and Return Predictability
`
`A third alternative is that short sellers act as voluntary liquidity providers.
`According to this story, short sellers step in and trade when there is a significant
`and temporary buy-order imbalance in the market. As the buying pressure
`subsides, prices should revert to fundamental value and the short sellers can
`cover their positions at a profit. Under this interpretation, the trading patterns
`and predictability we observe are the direct result of short sellers receiving
`compensation for providing immediacy (e.g., Stoll, 1978; Grossman and Miller,
`1988; and Campbell, Grossman, and Wang, 1993). This interpretation suggests
`that elevated levels of short selling should coincide with contemporaneous buy-
`order imbalances and be followed by reduced-order imbalances in the future.
`A fourth explanation is that short sellers step in to provide additional risk-
`bearing capacity in periods of elevated uncertainty. If the uncertainty is caused
`by short-lived asymmetric information (e.g., Copeland and Galai, 1983; and
`Glosten and Milgrom, 1985) or if market makers require compensation for
`inventory risk (e.g., Ho and Stoll, 1981; Biais, 1993), then the elevated short
`selling should coincide with high intraday volatility and wide spreads. As the
`information becomes public, volatility and spreads should fall. By contrast, if
`the uncertainty is associated with differences of opinion (e.g., Varian, 1985;
`and Harris and Raviv, 1993), the elevated short selling should coincide with
`high intraday volatility and low spreads. In a market with wide dispersion in
`reservations values, limit orders posted by (nonstrategic) competing liquidity
`providers result in narrower spreads. As opinions converge, volatility should
`fall and spreads should widen.
`While we find evidence that suggests short sellers use all the strategies
`mentioned above, past returns remain significant predictors of short-selling
`activity after controlling for order imbalances, volatility, and spreads. Perhaps
`more important is that higher short-selling activity predicts negative future
`abnormal returns after controlling for these same variables. In other words, we
`find evidence of informed trading by US short sellers.
`It is worth pointing out that short sellers are not all alike. In our stock-level
`aggregate data on short sales, we clearly have some traders that speculate on
`prices reverting to fundamentals. However, we also have traders that use short
`sales to hedge a long position in the same stock, to conduct convertible or index
`arbitrage, traders who seek to hedge their option positions, etc. Many of the
`trading strategies involving short sales are based on relative valuations of secu-
`rities (e.g., merger arbitrage), which reduces the likelihood that predictability
`will be found in a regression framework. These traders may or may not be
`selling short because they think the shorted stock is overvalued relative to cur-
`rent fundamentals. Their presence in the data will work against us finding that
`stock-level aggregate short sales predict abnormal negative returns. Yet, we do
`find predictability both in the regression analysis and in the portfolio analysis.
`We are not the first to investigate whether short sellers are informed
`traders. There is a rather extensive literature studying the relationship between
`short-selling activity measured as a stock variable (short interest) and stock
`returns. While the earlier literature provided mixed evidence, there is growing
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`consensus that short sellers are informed.2 For example, researchers find that
`high short interest predicts negative abnormal returns for NYSE/AMEX stocks
`(Asquith and Meulbroek, 1995) and for Nasdaq stocks (Desai et al., 2002), that
`predictability is strongest in stocks with low institutional ownership (Asquith,
`Pathak, and Ritter, 2005), that short sellers target companies that are over-
`priced based on fundamental ratios (Dechow et al., 2001), that short sellers
`targets firms with earnings restatements and high accruals (Efendi, Kinney,
`and Swanson, 2005; and Desai, Krishnamurthy, and Venkataramaran, 2006),
`anticipate downward analyst forecast revisions and negative earnings surprises
`(Francis, Venkatachalam, and Zhang, 2006), and that short sellers exploit both
`postearnings announcement drift and the accrual anomaly (Cao, Dhaliwal, and
`Kolasinski, 2006).
`These studies use monthly stock-specific short interest data. These data are
`disclosed by exchanges around the middle of each month and consist of the
`number of shares sold short (a stock variable) at a particular point in time.
`There are two main problems with using monthly short interest data. The
`first problem is that monthly short interest data do not permit a researcher
`to discern whether or not a high level of short interest means that short sell-
`ing is more expensive, which is the prerequisite for the overreaction story as
`proposed by Miller (1977). To remedy this shortcoming of the literature, sev-
`eral authors have relied on proxies for short-sale constraints or demand (Chen,
`Hong, and Stein (2002)—breadth of ownership, Diether, Malloy, and Scherbina
`(2002)—analyst disagreement, Nagel (2005)—institutional ownership, and
`Lamont (2004)—firm’s actions to impede short selling), and even the actual cost
`of borrowing stock (D’Avolio, 2002; Geczy, Musto, and Reed, 2002; Jones and
`Lamont, 2002; Mitchell, Pulvino, and Stafford, 2002; Ofek and Richardson,
`2003; Ofek, Richardson, and Whitelaw, 2004; Cohen, Diether, and Malloy,
`2007; and Reed, 2007) to investigate if short-sale constraints contribute to
`short-term overreaction in stock prices, and if short sellers are informed. The
`general conclusion reached by this literature is that short-sale costs are higher
`and short-sale constraints are more binding among stocks with low market
`capitalization and stocks with low institutional ownership. The literature also
`finds that high shorting demand predicts abnormally low future returns both at
`the weekly and monthly frequency.
`The second problem is that the monthly reporting frequency does not permit
`researchers to study short-term trading strategies. Recent evidence suggests
`that many short sellers cover their positions very rapidly. For example, Diether
`(2008) finds that almost half the securities lending contracts they study are
`closed out in two weeks (the median contract length is 11 trading days). Also
`note that if a trader sells a stock short in the morning, he can cover the position
`with a purchase before the end of the day without ever having actually to
`
`2 For the earlier literature, see, e.g., Figlewski (1981); Brent, Morse, and Stice (1990); and Senchack and Starks
`(1993).
`
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`Short-Sale Strategies and Return Predictability
`
`borrow the stock. This suggests that even securities lending data truncate the
`holding period of short sellers.3 The notion that short sellers focus on short-
`term trading strategies is consistent with our finding that short sales represent
`on average 23.9% of NYSE and 31.3% of Nasdaq (National Market) reported
`share volume. By comparison, average monthly short interest for the same
`period is about 5.4 days to cover for NYSE stocks and 4.4 days to cover for
`Nasdaq stocks. Hence, it is important to study short-selling activity at a higher
`frequency. This is our main contribution to the literature.
`Previous studies of short selling have sought to test whether short sellers
`time their trades well relative to future returns. However, as far as we know,
`no one has previously examined how short sales relate to past returns. This
`is puzzling, since the main argument for stricter short-sale regulation is that
`short sellers exacerbate downward momentum. Without evidence on how short
`sellers trade relative to past returns, it is impossible to determine whether short
`sellers actually have any impact on momentum. Our second contribution to the
`literature is to examine how short sellers react to past returns.
`We use the regulatory tick-by-tick short-sale data for a cross-section of more
`than 3,800 individual stocks. While our data permit an intraday analysis of
`short selling, we aggregate short sales for each stock to the daily level for the
`purpose of this study. Our paper is the first study of daily short selling to cover
`both Nasdaq and NYSE stocks. This is our third contribution to the literature.
`Our final contribution is that we rely on a very comprehensive data set. It
`includes all short sales executed in the United States, regardless of where the
`trade is printed (the AMEX, the Boston Stock Exchange, the Chicago Stock
`Exchange, the NASD, Nasdaq, the National Stock Exchange, the Philadelphia
`Stock Exchange, or NYSE) for all NYSE- and Nasdaq-listed stocks. The com-
`plete coverage is clearly important as we find that more than 50% (23%) of
`Nasdaq (NYSE) short sales are reported away from the primary listing venue
`during our sample period. By contrast, other authors who study daily short
`sales rely on samples that do not cover all short sales for a particular stock.
`Christophe, Ferri, and Angel (2004) focus their analysis on customer short sales
`that are subject to Nasdaq’s short-sale rules and are reported to Nasdaq’s Au-
`tomated Confirmation Transaction Service (ACT). Boehmer, Jones, and Zhang
`(2008); and Daske, Richardson, and Tuna (2005) focus their analysis on orders
`entered through NYSE’s SuperDOT system that are subject to NYSE’s Uptick
`Rule. According to Boehmer, Jones, and Zhang (2008), NYSE SuperDOT cap-
`tures about 70.5% of all NYSE reported volume. However, they acknowledge
`that it is uncertain whether this trading system captures an equally large pro-
`portion of short-sale volume. Moreover, as mentioned, we find that 23% of the
`total short-sale volume for NYSE-listed stocks is printed away from the NYSE,
`which suggests that the coverage in these two studies is incomplete.
`
`3 Jones (2004) finds that such “in-and-out shorting” represented about 5% of daily volume in the early 1930s.
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`Our results are generally consistent with the return predictability found in
`NYSE SuperDOT short sales for the 2000–2004 period by Boehmer, Jones,
`and Zhang (2008). They find that stocks with relatively heavy shorting un-
`derperform lightly shorted stocks by a risk-adjusted average of 1.16% in the
`following 20 days of trading and conclude that short sellers as a group are
`extremely well informed. The same conclusion is drawn by Christophe, Ferri,
`and Angel (2004) based on short-selling activity in Nasdaq stocks. They find
`that short-selling activity is concentrated in periods preceding disappointing
`earnings announcements, suggesting that short sellers have access to nonpublic
`material information. However, not all studies find that short sellers are pre-
`scient with regard to earnings announcements. Daske, Richardson, and Tuna
`(2005) find that short sales are not concentrated prior to bad news dissemi-
`nated by scheduled earnings announcements and other informational events.4
`It is possible that the differing sample periods explains the difference because
`the data used by Daske, Richardson, and Tuna (2005) are post-RegFD. Thus,
`during their sample period there is much stricter regulation of the release of
`material nonpublic information.
`Our findings are consistent with a recent paper by Avramov, Chordia, and
`Goyal (2006), who study the impact of trades on daily volatility. They find
`that increased activity by contrarian traders (identified as sales following price
`increases) is associated with lower future volatility, while increased activity
`by herding investors (identified as buyers after price increases) is associated
`with higher future volatility. Avramov, Chordia, and Goyal (2006) argue that
`contrarian traders are rational traders who trade to benefit from the deviation
`of prices from fundamentals. As these trades make prices more informative,
`they tend to reduce future volatility. We provide more direct evidence of the
`information content of contrarian short sellers in that they predict future returns.
`Our results are also reminiscent of a recent study of net individual trade
`imbalances on the NYSE during the 2000–2003 period by Kaniel, Saar, and
`Titman (2008). They find that individuals are contrarians, and that their trades
`predict returns up to 20 days out. However, the authors discard the fundamental
`information hypothesis and instead interpret their evidence as consistent with
`the liquidity provision hypothesis. The reason is largely that they find it hard
`to believe that individual traders are more sophisticated than institutions. As
`discussed above, we have good reason to believe that short sellers are more
`sophisticated than the average investor.
`Our study proceeds as follows. We summarize our hypotheses in Section 1,
`and describe the data in Section 2. We examine how short selling relates to
`past returns, spreads, order imbalances, and volatility in Section 3. Cross-
`sectional differences in the relationship between short selling and past returns
`are examined in Section 4. We address whether short-selling activity predicts
`
`4 An earlier draft of this paper finds that Nasdaq short sellers are unable to predict negative earnings announcements
`during our sample period.
`
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`
`future returns in Section 5. Cross-sectional differences in predictability are
`examined in Section 6. We contrast our hypotheses in section 7. A further
`robustness check is provided in Section 8. Section 9 concludes.
`
`1. Hypotheses
`
`Our hypotheses can be summarized as follows:
`
`(1) Short sellers are trading on short-term overreaction if they sell short
`following positive returns and their trades are followed by negative
`returns.
`(2) Short sellers are acting as voluntary liquidity providers if they sell short
`on days with significant buying pressure, and their trades are followed
`by declining buying pressure and negative returns.
`(3) Short sellers are acting as opportunistic risk-bearers during periods of
`elevated asymmetric information if they sell short on days with high
`intraday volatility and wide spreads, and their trades are followed by
`days with lower volatility, narrower spreads, and negative returns.
`(4) Short sellers are acting as opportunistic risk-bearers during periods of
`differences of opinion if they sell short on days with high intraday
`volatility and narrow spreads, and their trades are followed by days
`with lower volatility, wider spreads, and negative returns.
`
`We test these hypotheses in the rest of the paper.
`
`2. Characteristics of Short Selling
`
`A short sale is generally a sale of a security by an investor who does not own
`the security. To deliver the security to the buyer, the short seller borrows the
`security and is charged interest for the loan of the security (the loan fee). The
`rate charged can vary dramatically across stocks depending on loan supply
`and demand. For example, easy-to-borrow stocks may have loan fees as low
`as 0.05% per annum, but some hard-to-borrow stocks have loan fees greater
`than 10% per annum (Cohen, Diether, and Malloy, 2007). If the security price
`falls (rises), the short seller will make a profit (loss) when covering the short
`position by buying the security in the market.
`The SEC requires an investor to follow specific rules when executing a
`short sale. The rules are aimed at reducing the chances that short selling will
`put downward pressure on stock prices. Until 2 May 2005, these rules were
`different for Exchange-Listed Securities (the Uptick Rule, Rule 10a-1 and
`10a-2, NYSE Rule 440B) and Nasdaq National Market (NM) Securities (the
`best-bid test, NASD Rule 3350). Moreover, Nasdaq NM stocks that were traded
`on electronic communication networks (ECNs) had no bid-test restriction.
`On 23 June 2004, the SEC adopted Regulation SHO to establish uniform
`locate-and-delivery requirements, create uniform marking requirements for
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`sales of all equity securities, and to establish a procedure to temporarily suspend
`the price tests for a set of pilot securities during the period 2 May 2005 to
`28 April 2006, in order to examine the effectiveness and necessity of short-
`sale price tests.5 At the same time, the SEC mandated that all self-regulatory
`organizations (SROs) make tick data on short sales publicly available starting
`2 January 2005. The SHO-mandated data include the ticker, price, volume, time,
`listing market, and trader type (exempt or nonexempt from short-sale rules) for
`all short sales. In this study, we do not examine the effects of Regulation SHO
`per se, but our study is made possible by the SEC-mandated short-sale data.
`In related work, we study the effects of suspending the price tests on market
`quality (Diether, Lee, and Werner, 2007).
`The data have a few drawbacks. The main drawback is that the sample period
`is short: 2 January to 30 December 2005. The reason is that the regulatory data
`only became available starting 2 January 2005 (which limits us on the front
`end), and that we need CRSP and Compustat data for the analysis (which
`limits us on the back end). However, the 2005 sample is important because
`we have several reasons to believe that short-selling strategies have changed
`dramatically in recent years: e.g., increased investor pessimism following the
`2000 bubble, increased use of algorithmic trading, and a tremendous growth of
`the hedge-fund industry, which systematically employs long–short strategies.
`Nevertheless, our results should be interpreted with caution given the short
`sample period.
`We also do not know anything about the short sellers in our sample other
`than the time, price, and size of their trades. In an earlier draft of this paper
`we conducted the analysis by trade size. However, given that institutions order-
`split heavily, it is doubtful whether it is possible to use trade size to separate
`retail from institutional trades.6 The data also include a flag for whether or
`not a short sale is exempt from the exchanges’ short-sale rules. This seems
`to be a convenient way to separate out market-maker short sales (which are
`largely exempt) from customer short sales as done by Christophe, Ferri, and
`Angel (2004); and Boehmer, Jones, and Zhang (2008). However, due to a
`no-action letter from the SEC, market participants have been relieved from
`systematically using the “short-exempt” marking rendering the flag useless
`during the Reg SHO sample period.
`Another potential drawback with the regulatory short-sale data is that while
`we see each individual short sale, the data do not flag the associated covering
`transactions. Hence, we cannot determine whether short sellers’ trades are prof-
`itable. Such data are not contained in the audit trail from which the regulatory
`data are drawn and could be obtained only at the clearing level. Instead, we
`have to rely on indirect measures, such as whether or not it is possible to create
`a profitable trading strategy based on daily short-selling activity.
`
`5 On April 20 2006, the SEC announced that the short-sale Pilot has been extended to August 6 2007.
`
`6 For an analysis of short sales by account type, see Boehmer, Jones, and Zhang (2008).
`
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`This study focuses on NYSE- and Nasdaq-listed stocks. We define our uni-
`verse as all NYSE and Nasdaq National Market (NM) stocks that appear in
`CRSP with share code 10 or 11 (common stock) at the end of 2004. We draw
`daily data on returns, prices, shares outstanding, and trading volume for these
`securities for the 2 January 2005 to 30 December 2005 time period from CRSP.
`We also download intraday data from all SROs that report short sales and calcu-
`late daily short-selling measures. Specifically, we compute the number of short
`sales and shares sold short. Finally, we compute daily buy-order imbalances
`using the Lee and Ready (1991) algorithm, and daily effective spreads from
`TAQ. We merge the daily short-sale data with return and volume data from
`CRSP. We then filter the sample by including only common stocks with an
`end-of-year 2004 price greater than or equal to $1. We also exclude stock days
`where there is zero volume reported by CRSP.7
`In addition, we obtain monthly short interest data directly from Nasdaq
`and the NYSE, and data on market capitalization, book-to-market, and average
`daily trading volume (share turnover) from CRSP and COMPUSTAT. We obtain
`institutional ownership data as of the fourth quarter of 2004 from Thompson
`Financial (13-F filings), and option trading volume data from The Options
`Clearing Corporation (www.optionsclearing.com). Our final sample covers
`trading in 1,481 stocks for the NYSE and 2,372 for Nasdaq. For most of
`the analysis, we also exclude stocks designated by Reg SHO as pilot stocks
`as the short-sale rules changed during the sample period for these securities.
`The subsample of non-Reg SHO pilot stocks includes 1,079 NYSE and 2,001
`Nasdaq stocks. Finally, to conform with the previous literature, we perform
`most of our portfolio analysis on the stocks with a lagged price of at least $5.
`Table 1 illustrates the distribution of shorted shares in the top of Panel A,
`and the number of short-sale trades in bottom half of Panel A by market
`venue: American Stock Exchange (AMEX), Archipelago (ARCA), Boston
`Stock Exchange (BSE), Chicago Stock Exchange (CHX), National Association
`of Securities Dealers (NASD),8 NASDAQ, National Stock Exchange (NSX),9
`Philadelphia Stock Exchange (PHLX), and New York Stock Exchange (NYSE).
`The NYSE accounts for almost 77% of shares sold short in NYSE-listed stocks,
`while NASDAQ accounts for 16% and ARCAEX accounts for 4%. NAS-
`DAQ accounts for just over half the shares sold short in Nasdaq-listed stocks,
`while ARCA and NSX each account for roughly one-quarter. The table clearly
`highlights that it is important to consider trading outside the market of primary
`listing. The distribution of shorted shares roughly mirrors the distribution of
`overall trading volume in NYSE- and Nasdaq-listed stocks across market
`
`7 We also set short sales equal to volume in the few instances where short sales exceed reported volume. Our
`results are robust to excluding these stock days from our analysis. We do not exclude stocks with very high
`prices (>$1,000) from our sample. However, we have redone the analysis, dropping them from the sample, and
`the results are virtually identical.
`
`8 NASD operates the alternative display facility (ADF), where trades may be printed.
`
`9 Formerly known as the Cincinnati Stock Exchange.
`
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`Table 1
`Summary statistics: shorting activity
`
`Panel A: Short-sale trading activity across exchanges
`
`AMEX ARCHAX
`
`BSE
`
`CHX
`
`NASD NASDAQ NSX PHLX NYSE
`
`NYSE stocks
`Nasdaq stocks
`
`NYSE stocks
`Nasdaq stocks
`
`0.00
`0.03
`
`0.00
`0.01
`
`4.36
`22.72
`
`7.99
`29.47
`
`Mean shares sold short (in %)
`0.37
`0.00
`16.31
`0.04
`0.65
`49.55
`Mean short-sale trades (in %)
`0.19
`0.00
`11.67
`0.03
`0.22
`34.51
`
`0.97
`0.00
`
`1.02
`0.00
`
`0.82
`27.01
`
`0.49
`35.75
`
`0.55
`0.00
`
`0.11
`0.00
`
`76.62
`0.00
`
`78.54
`0.00
`
`Panel B: Short-selling summary statistics
`NYSE stocks
`Nasdaq stocks
`
`Short sales
`Short trades
`relss (%)
`
`Mean
`
`253.40
`445.01
`23.89
`
`Median
`
`Std. dev Mean Median
`
`Std. dev
`
`109.45
`296.24
`23.96
`
`471.10
`492.79
`5.64
`
`229.63
`616.89
`31.33
`
`41.98
`149.10
`31.72
`
`1075.48
`1905.97
`7.92
`
`Panel C: Mean of relss (in %) across stock characteristics
`ME
`B/M
`Instown
`Price
`
`Put
`
`NYSE stocks
`21.02
`Small
`23.39
`Large
`Nasdaq stocks
`Small
`28.12
`Large
`37.82
`
`Low
`High
`
`Low
`High
`
`NYSE stocks
`24.25
`24.01
`22.77
`24.21
`Nasdaq stocks
`33.85
`27.94
`38.05
`36.32
`
`16.63
`24.12
`
`24.05
`32.45
`
`NYSE stocks
`No
`22.94
`Yes
`24.33
`Nasdaq stocks
`No
`28.12
`Yes
`36.38
`
`Panel A shows short-sale trading activity of NYSE and Nasdaq stocks across exchanges. It reports total number
`of shorted shares in a given exchange for our sample period divided by the total number of shorted shares in all
`exchanges for our sample period. It also reports the total number of short-sale trades in a given exchange for our
`sample period divided by the total number of short-sale trades in all exchanges for our sample period. Panel B
`shows summary statistics for different short-selling measures. Short sales (Short trades) is the number of shorted
`shares (trades) for a stock average over the sample period. relss is the number of shorted shares for a stock
`divided by traded shares per day averaged over the sample period. Panel C shows average relss across different
`stock characteristics. Low (high) ME and B/M refers to market cap and book-to-market (defined as in Fama and
`French, 1993) at the end of 2004 ≤33rd (>67th) NYSE percentile. Low (high) instown refers to institutional
`ownership at the end of 2004 ≤33% (>67%). Low (high) put refers to whether put options can be traded. The
`sample includes only NYSE and Nasdaq stocks with CRSP share code 10 or 11 and with a price greater than
`or equal to $1 at the end of year 2004. Stocks are dropped from the sample if the number of traded shares is
`less than or equal to zero or such information is missing from CRSP. The time period is 3 January 2005 to
`30 December 2005. The sample size is 1,481 stocks for NYSE and 2,372 for Nasdaq.
`
`venues.10 By comparing the two parts of Panel A, we infer that short-sale
`trades are generally larger in the market of primary listing.
`Panels B and C of Table 1 provide descriptive statistics for our daily short-
`selling data. Note that the dispersion across stock days is significant, particularly
`for the Nasdaq sample. To normalize across stocks, we define the relative
`amount of short selling (relss) as the daily number of shares sold short for a
`stock day divided by the total number of