Informational content of short selling disclosure: a tale of two reports

Informational content of short selling disclosure: a tale of two reports Binh Do Monash University and Philip Gray** Monash University This draft: December 2011 Abstract: Prompted by the Global Financial Crisis in 2008, market regulators in many countries are pushing for a more comprehensive and timely disclosure regime for short selling activity. Some jurisdictions, including the U.S., are considering positional (i.e. short interest) reporting at a high frequency to supplement with their existing transactional reporting (i.e. short volume). Others are doing just the opposite, proposing transactional reporting in addition to positional reporting. Whilst correlated, these two short selling measures pertain to different aspects of short selling, thus potentially yielding different information content. Since June 2010, short sellers in the Australian equities market have been required to disclose both their short positions and short transactions on a daily basis. This presents a unique opportunity to empirically examine the information content of the alternate measures of short-selling activity and therefore make a timely contribution to this global policy debate. In brief, we find that short interest dominates short volume in predicting negative returns over a range of future periods from 5 days to 60 days. Specifically, sorting based on short interest earns significant risk-adjusted returns of 1.0% to 1.3% per month whereas volume based sorting generates alphas close to zero. Cross sectional regressions further show that short interest is negatively related to future returns where short volume is not, once short interest is taken into account. The results are robust to firm characteristics, prior returns and different measurements of short volume. Finally, we are able to impute short covering behaviours from our unique dataset. We find that short covering is not related to prior returns, but is negatively related to contemporaneous returns. This evidence suggests short sellers are not vulnerable to short squeeze, instead they take advantage of falling markets to close out their position. JEL Classification: G11, G12, G14 Keywords: Short selling; information content of short selling; market efficiency; short covering. * +613 9903 1399. Email: binh.do@monash.edu ** +613 9903 1472. Email: philip.gray@monash.edu

1. Introduction Over time interval [t 1, t 2 ], short sellers effect a certain amount of short sales, with the total volume measuring the flow, of short selling. At t 2, the outstanding short position reflects the cumulative short selling up to that point, inclusive of the gross volume over interval [t 1, t 2 ] and those over preceding periods, and net of any short covering up to t 2. Such positional, or stock indicator, truly measures the aggregate bearish sentiment the market has over that particular stock at that particular time. On the other hand, the flow measure only provides the transactional information (and a crude one when it is not netted against short covering), thus should have weaker informational content. Using both daily short volume and short position data on the Australia Securities Exchange from 16 June 2010 to 30 September 2011, we find evidence in support of the above conjecture. In particular, using short ratios (daily short volume divided by total volume) as the trading signal, returns to a hedge portfolio that is long the most lightly shorted decile and short the most heavily shorted decile generates a value weighted return of 1.28% (t-stat=1.86) over the next 20 days. The risk-adjusted excess return is just 0.25% and statistically insignificant. In contrast, a similarly formed hedge portfolio based on daily short interest returns 2.06% (t-stat=3.85) with alpha of 1.20% (t-stat=2.36) over the next 20 days. The most heavily shorted decile based on short interest underperforms the most heavily shorted decile based on short volume by a risk-adjusted amount of 0.82% per month, again with strong statistical significance. This stronger prediction of underperformance by short interest holds over shorter intervals (5 days and 10 days), and more so over longer holding periods (40 days and 60 days). Furthermore, cross sectional regressions that control for size, book-to-market, volatility, turnover and prior returns show that future returns are strongly negatively related to prior short interest and not related to shorting volume once short interest is included. These results are obtained in a setting where the observation of short interest is delayed by 4 days 1

compared to just 1 day for short volume, a feature of Australia s short sale reporting regime. The results are robust to different choices of measuring short flow. Our finding contributes to existing studies on the informational content of short selling. The majority of the literature finds that short interest predicts future underperformance (Figlewski, 1981, Desai, Ramesh, Thiagarajan and Balachandran, 2002, Asquith, Pathak and Ritter, 2005, Boehmer, Huszar and Jordan, 2011). Recent studies by Boehmer, Jones and Zhang (2008) and Diether, Lee and Werner (2009b) find that the short ratio is also predictive of negative returns. This latter result is remarkable since the gross volume should be netted against short covering for it to be of any economic value. Boehmer et al (2008) further show that when they control for changes in short interest levels, the short ratio remains strongly predictive of future returns. However, when they control for short ratios, changes in short interest do not predict negative returns. They conclude that the effect of short volume dominates that of short interest. We find the opposite: the informational content of short interest dominates that of short volume. However, our comparison makes use of the short interest level, not changes in short interest which is another (and cleaner) measure of flow. Therefore, our result shows that the stock information dominates the flow information. Our analysis is made possible by the availability of both short interest and short volume data in the same frequency. Since the 2008 financial crisis, the Australian government and the market regulator ASIC have mandated rigorous reporting of short selling, and at the point of write up of this paper, Australia is the only market that requires daily positional reporting. Their rationale for positional reporting is to provide an indication of the level of risk involved in short selling a particular security as well as the market sentiments in that security (ASIC, 2010). In other words, they deem such reporting is beneficial to both short sellers themselves who may be vulnerable to short squeeze, and other investors. Elsewhere 2

around the world, many regulators are debating whether to introduce positional reporting. This study will inform those regulators in their policy setting. Although comparing the informational content of short volume versus short interest is the main purpose, we also make use of our unique data to learn about the short covering behaviour, a second contribution of our study. Using both daily volume and short interest data, we are able to impute the amount of purchases to cover short positions on a daily basis. With this valuable information that has never before been available, we investigate if short covering is related to prior, contemporaneous and future returns. This is an interesting question because selling by short sellers may predict underperformance, but for these investors to make profit, they need to cover at the right time. In addition, short sellers face the risk of having to exit in a crowded trade, or short squeeze. If they tend to exit following price increases, short interest could potentially be a bullish signal. We find that short covering is not related to prior returns over 1, 3, 5 and 10 day periods, not predictive of future returns, but negatively related to contemporaneous returns with statistical significance. This is consistent with short sellers taking advantage of declining markets to close out their position. The next section reviews the literature. Section 3 details the status of short selling disclosure in Australia compared to other major jurisdictions, hence motivating the uniqueness of our dataset. Section 4 contains results on the informational content of short interest versus short volume. Section 5 reports on short covering. Section 6 concludes. 2. Literature review Figlewski (1981) is perhaps the first to provide empirical evidence on short selling effects. Similar to Miller (1977), Figlewski also shows analytically that short sale restrictions lead to overpricing, particularly for stocks with more negative information. Using monthly short interest as a proxy for the extent of negative information, he finds that the most heavily 3

shorted decile portfolio underperforms the most lightly shorted decile. However, the former continues to earn positive returns: high short interest does not predict negative returns. In contrast, using data on stocks in the NASDAQ over period 1988 to 1994, Desai, Ramesh, Thiagarajan and Balachandran (2002) document negative and significant returns by portfolios that have short interest in excess of 2.5%, 5% and 10%. Figlewski s (1981) top decile portfolio has the average short interest of just 0.89%. Asquith, Pathak and Ritter (2005) examine stocks that experience both high short interest (high demand to short) and low institutional ownership (low supply for shorting. They find heavily shorted portfolios that also have low institutional ownership have most negative returns, more so than heavily shorted portfolios across the institutional ownership spectrum. Boehmer, Huszar and Jordan (2011) re-examine the return predictability of short interest by including stocks with no short interest in the lightly shorted portfolio and compare returns over the subsequent month, over period July 1988 to December 2005. They continue to document underperformance of the heavily shorted portfolio, and unlike prior studies, also find that the no short portfolios generate positive and significant abnormal returns. The finding suggests that short sellers can identify not only overvalued stocks to short but also undervalued stocks to avoid. Recently, the literature on the return predictability of short selling has extended to short volumes, or the gross amount of short sales over a defined period. Boehmer, Jones and Zhang (2008) employ daily data from short sale orders submitted electronically through the NYSE SuperDOT system from January 2000 to April 2004. They find that when sorted by the ratio between short volume and total trading volume (referred to in their paper as shorting flow and in our paper as the short ratio), the top quintile portfolio underperforms the bottom quintile by a risk adjusted return of 1.16% per month with t-stat=3.67. They are also able to identify short selling by different categories of traders and find that institutional non-program 4

short sales are the most informed, compared to short sales by individuals, by proprietary trading desks and others such as market makers. Furthermore, they compare the information content between shorting flow and changes in monthly short interest. Using double sorting, they find that even when they control for short interest changes, shorting flow remains predictive of future returns, but not vice versa. Diether, Lee and Werner (2009b) use a more comprehensive short volume dataset that includes all short sales executed in the U.S. The data has been made available since the adoption of Regulation SHO in 2004 as a new regulatory framework governing short selling in the U.S. Using the data for 2005, the authors find that a long-short strategy based on the short ratio observed on day t generates a risk-adjusted return of 0.062%-0.063% on day t+2 (or 1.40% per month), and 0.042% to 0.055% on day t+2 to t+5 (or 0.92% to 1.21% per month). These returns are highly statistically significant. They also find that short sellers increase their trading following positive returns, implying that part of short selling follows contrarian trading. Brent, Morse and Stice (1990) are amongst very few studies that suggest short selling does not predict negative future returns. Using monthly short interest on NYSE stocks from during 1974-1986, they find that short selling activities are related to tax and arbitrage motivations, and stocks that experienced increases in short interest outperform those that have reported declines in short interest. Several studies, including Boehmer et al (2008), cite this paper as evidence that short interest is not informative. Note however that, unlike many studies that find short interest predicts negative returns, Brent et al s work looks at changes in monthly short interest, which in effect represents the net shorting volume over monthly intervals. Like Brent et al, Boehmer et al (2008) also find such measure of short selling is uninformative. 5

Although the literature, especially the more recent ones, have overwhelmingly pointed to a story that short selling data predicts negative returns, it should not be an unsurprising one. Short sellers are heterogeneous traders who short with different motivations, not necessarily just for directional bets. As discussed above, Brent et al (1990) find that part of short selling is motivated by tax and arbitrage considerations. Shorting against the box is a common practice where investors holding the security may short sell to hedge their holding, or to lock in a profit whilst deferring the recognition of a capital gain. Similarly, option market makers routinely short stocks to hedge their sold put options. Do, Do and Chai (2011) find that there is ample short selling in the Australian market during the 2008 short sale ban as option market makers were exempt from the ban. Short sellers can be fundamental analysts who target stocks with weak fundamentals (Dechow, Hutton, Meulbroek and Sloan, 2001), and they can also be technical traders trading against short term overreaction (Diether et al, 2009b). A recent study by Comerton-Forde, Jones and Putnin (2011) finds that short sellers can be a liquidity supplier, stepping in to short sell when prices overshoot in the upside, helping to narrow spreads and stabilize the market in important times. Given short sellers short sell for various reasons, it is puzzling that in overall, short selling appears to be informative on future returns. Theoretical studies to date do not provide adequate reasons why trading by short sellers predict future returns. Miller (1977) argues when divergence of opinion is strong, the number of investors with extremely pessimistic views about the stock is likely to increase. Thus, in the absence of short sale constraints, their short selling will tend to temper the bidding up of the stock price. Conversely, if short sale constraints exist, for example, short sellers are unable to access the sale proceeds, prices may be bid up causing overvaluation. Therefore, in the presence of divergence of opinion, short sale constraints can upwardly bias security prices. Diamond and Verrechia (1987) allow for the possibility of market agents learning 6

from trading by informed investors. When many informed traders face short sale prohibitions, the speed of adjustment to both bad and good news is reduced, although adjustment to bad news is slower. When a higher proportion of traders face short sale restrictions (as opposed to outright prohibition), the opposite effect occurs: prices react more quickly to both good and bad news, especially the latter. The intuition is that short sale restrictions in effect help to remove some noise of uninformed trading from the signal of informed trading. Although both models suggest that short sale prohibitions reduce market efficiency, the latter finds that short sale restrictions increase efficiency. Studies such as Boehmer et al (2008) and Diether et al (2009b) view Diamond and Verrechia s (1987) analysis as supportive of informative short selling. They point out that short sale restrictions necessarily mean that the surviving short sellers must have the strongest conviction, thus strengthening the content of their trading as a group. However, if the argument holds, one would expect the price to adjust quickly, according to Diamond and Verrechia s results. Therefore, the empirical findings that profit can still be made over considerable horizons by following shorting activities, do not reconcile well with the cited models. Perhaps there are more complex learning mechanisms by which various market participants react differentially to short selling information, than in Diamond and Verrechia s (1987) model where learning is simply done by market makers who are the only one that observes informed traders action. Our study provides evidence to the former. 3. Short selling disclosure in Australia and data description In Australia, the market regulator ASIC (Australian Securities and Investments Commission) is responsible for administering legislation governing the financial markets, including setting out guidance for implementation by the securities market operator ASX (Australian Securities Exchange), the national stock market. Although some form of short selling 7

disclosure by the ASX has existed since 2001, a formal disclosure regime only came in place as recently as 2008 as authorities around the world were battling the escalating financial crisis. In Australia, an amendment to Corporations Act 2001 (known as Corporations (Amendment) Short Selling Act 2008) was passed in December 2008 that prohibits naked short sales and establishes the framework for a permanent short selling disclosure regime. 1 Following public consultation by the Treasury of the Australian government, in April 2010 the ASIC issued a guideline (Regulatory Guide 196) summarising short selling provisions and stipulating two reporting requirements: gross short sale transaction reporting and short position reporting. Transactional reporting involves brokers reporting to the ASX, on a daily basis, the number of shares sold short, who in turn aggregates data for each security and discloses publicly on their website by 9.am the following trading date. Positional reporting requires each short seller to report to the ASIC the size of their short position (or the number of shares they have sold short and have not covered) within three business days after the date of the short position, and thereafter until the position is covered. 2 This position data is the short interest that is the subject of numerous U.S. studies. Short sellers with a short position that is less than AUD$100,000 ($100,000) and 0.01% of the total number of shares on issue, are exempt from this requirement. This also means that when a short position has subsequently fallen below these thresholds, it no longer needs to be reported. Once individual short positions are lodged to the ASIC, the regulator will aggregate and publish on their 1 Under section 1020B in the original Corporations Act 2001, subsection 2 permits short selling that relies on existing securities lending arrangements to obtain presently exercisable and unconditional rights to vest the securities in the buyer, generally known as covered short sales. In addition, subsection 4 permits naked short sales if, amongst other exceptions, arrangement has been made to borrow the securities for delivery within 3 days after the short sale, or the securities are on the ASX s approved list. Under Corporations (Amendment) Short Selling Act 2008, naked short sales are prohibited with subsection 4 almost completely repealed with exceptions now limited to cases where, for example, the sale is to facilitate hedging by market makers, or implicit by ways of an exchange traded options, or where there is prior purchase agreement (see Appendix for more details on exceptions). Also under this amendment, a new provision (section 5B of Part 7.9) sets out reporting requirements for short sellers, brokers and the market operator ASX. The ensuing regulation, Corporations Amendment Regulations 2009 (No. 8), came out in November 2009, providing further details to the amendment, in particular the disclosure requirements. 2 Both domestic and foreign short sellers are required to report their short positions. 8

website the following trading day. That is, the total short position held in a product on day T will be announced on day T+4. In short, on each trading day, market participants observe, at the stock level, the daily gross short sale volume up to the previous day, as well as the short position (above a threshold) held 4 trading days ago. The former is equivalent to the short volume used in Boehmer, Jones and Zhang (2008) and Diether, Lee and Werner (2009b) whereas the latter, when divided by the number of shares outstanding, is the short interest data used in numerous US studies (for example, Figlewski, 1981, Asquith, et al., 2005 and Boehmer, et al., 2011). Whilst the daily reporting of short interest in Australia commenced on 16 June 2010, daily volume reports have been available since 19 November 2008 as what was intended to be an interim arrangement following the 2008 short sale ban has now been made permanent. This study is based on 347 daily observations of short volume and short interest on nearly 2000 stocks over a period from 16 June 2010 to 30 September 2011. What is remarkable about Australia s short selling disclosure, hence the uniqueness of our dataset, is that no other jurisdictions in advanced economies have adopted such comprehensive and high-frequency disclosure regime. In the US, the short volume data has been available daily since the Securities and Exchange Commission (SEC) s adoption of Regulation SHO in June 2004 (Diether, Lee and Werner, 2009a). On the other hand, short interest has been reported much less frequently: once a month and since September 2007, twice monthly, with about two weeks delay. 3 The Dodd-Frank Wall Street Reform and Consumer Protection Act certainly recognized the importance of more timely disclosure of short selling when in mid 2011, it requested the SEC to conduct studies on the feasibility, benefits and costs of requiring reporting in real time of short sale positions of publicly 3 For example, according to the NYSE website, http://www.nyxdata.com/data-products/nyse-group-short- Interest, short interests as at January 14, 2011 were released on January 26, 2011 and short interests as at January 31, 2011 were released on February 9, 2011. 9

listed securities, and considering a voluntary pilot program in which public companies would agree to have all trades of their shares marked long, short, market maker short, buy or buy-to-cover and reported as such in real time through the Consolidated Tape (SEC, 2011). 4 Similarly, the disclosure regime in the U.K. is less complete than that in Australia, despite recent changes in the regulation. Since the 2008 short sale ban, the UK Financial Services Authority (FSA) has required short positions above 0.25% of the shares outstanding to be disclosed publicly. Further disclosure is required if the position subsequently exceeds 0.35%, 0.45%, 0.55% and so on. A subsequent decline below the 0.25% threshold also needs to be reported. This regime only applies on short selling during a rights issue period or for financial stocks. 5 Individual short sellers disclose their positions on their website whilst the aggregate data is only available through data vendors. To our knowledge, short volume is not reported in the UK. 6 Other significant jurisdictions also have short sale reporting regimes that are incomplete (where either transaction reporting or position reporting is required, not both), and/or less timely. In Hong Kong, short volume data is available twice daily, however short interest reporting is not yet instituted. Recently, the Hong Kong Securities and Futures Commission (SFC) has proposed a regime where weekly reporting is required for short positions that exceed 0.02% of the number of shares on issues or HK$30 million ($4 million), whichever is 4 Note also that, unlike the Australian market where both types of short sale data are available for free download, the so-called self-regulating organizations (SROs) in the U.S. such as the NYSE charge a fee for accessing their short data. This can be a nontrivial issue since such costs may potentially limit access by a wider market, thus affecting the informational content of the data. In fact, one of the questions in the SEC s survey (SEC, 2011) concerns whether costs or other factors limit to currently available data. 5 See FINMAR 2 Short Selling, available at http://fsahandbook.info/fsa/html/handbook/finmar/2 6 The Committee of European Securities Regulators (CESR) also proposed a pan-european disclosure regime that is based on a two tiered system (http://www.cesr-eu.org/data/document/10_088.pdf). Positions above 0.2% of the shares outstanding would be disclosed to the relevant regulators whereas those exceeding 0.5% would also be disclosed to the market. Changes of positions at increments of 0.1% would also be reported. 10

lower. 7 In Japan, since November 2010, the Financial Services Agency has required reporting of short positions greater than 0.25% of the total issues, with no requirement for transactional reporting. In Canada, short positions have been reported on a bi-monthly basis. Recently, the Investment Industry Regulatory Organization of Canada (IIROC) is considering introducing short sale trade summaries which is comparable to Australia s short sales reports. This proposed reporting is also at a bi-monthly frequency to correspond to the positional reporting. 8 Our data, whilst more comprehensive and frequent than that available elsewhere, have a couple of drawbacks. As noted above, short positions of less than 0.01% and $100,000 in value are not reported. However, if subsequent movements take those positions beyond the thresholds, they will then be included in the aggregate short position data. That means a jump in short interest for a particular stock may be due to several small positions now exceeding the thresholds and hence get reported in this period, and not so much a result of an overall increase in short selling for that stock. A second issue is that certain forms of exempted short sales are subject to differential disclosure requirements where positional reporting is required but not transactional reporting: namely, exercise of exchange traded options, hedging risk from market making activities and client facilitation services (see the Appendix for full details). 9 This differential treatment is consistent with the ASIC s stated objectives in which the transaction reporting seeks to explain share price movements and assist regulators in carrying out market surveillance and investigating alleged cases of market misconduct whereas the position reporting is to provide indication of the level of risk involved in short 7 See the SFC s Consultation Conclusions on Increasing Short Position Transparency, 2010, at http://www.sfc.hk/sfc/doc/en/speeches/consult/consultationconclusion2march2010english.pdf 8 See the proposal at http://docs.iiroc.ca/displaydocument.aspx?documentid=14604580516b48f88a0bcfa629781242&language =en 9 For example, if a put holder exercises the option without having already owned or borrowed the underlying shares, they are effecting a naked short sale. The ASIC stipulates that such short sale is permitted and the short seller does not need to report the sale but they must include it in their position reporting. 11

selling a particular security as well as the market sentiments in that security (ASIC, 2010, p. 5-6). Table 1 reports descriptive statistics on two short sale datasets: daily gross short sale reports and daily short position reports over period June 16, 2010-September 2011. Return and accounting data are sourced from Datastream. [Table 1 about here] Out of 1,866 stocks that trade on the ASX over the sample period as recorded in Datastream, 1,003 stocks report short volume and/or short positions (Panel A). These stocks understandably have larger market capitalization distributions than typical stocks in the market, but by no means are restricted to large caps only. 25% of the stocks in the reporting sample have market caps of less than $19 million, which is very tiny by it and lower than the market median of $24 million. Stocks that appear in both short volume and position reports (not necessarily on all days) are 685, and are larger caps within the reporting sample. Obviously, stocks that report either short volume or short position, not both (the last row of Panel A), are small caps within the reporting sample. Panel B illustrates the distribution on the short volume sample. On average, 320 stocks report gross short sales each day, with short selling, defined throughout this paper as the short ratio, representing 13.29% of total volume on average (median=10.66%). This level of short selling, albeit sizeable, is markedly less than what, for instance, Diether et al (2009b), report for the U.S. market in recent years: 24% for the NYSE stocks and 31% for the NASDAQ stocks in 2005. 10 The short interest distribution (Panel C) similarly points to a relatively subdued level of short selling. On average, daily outstanding short positions account for just 0.65% of the total number of shares on issue (median= 0.20%), compared to 4-5% in the US 10 The SEC (2011) estimates that in 2010, orders marked shorts account for nearly 50% of listed equity volume. 12

market (Diether et al, 2009b). 11 The greater skewness in short interests compared to short ratios is largely due to the inherent difference in transactional reporting and positional reporting, with the former excluding observations of no short sales whereas the latter does include short interests that do not change over adjacent days. Panel D (E) reports the time series mean of average short ratios (short interest) computed on 25 size and book-to-market portfolios. Consistent with the previous panels, Panels D and E are constructed using stocks that appear in our short volume and short position datasets respectively. On both measures of short selling, there is a strong positive relation between size and short sale activity across all BM groups. This is as expected for at least 2 reasons (1) large stocks tend to be optionable, which translates to high short selling and (2) such stocks should have lower impediments to short sales, hence short selling is more widespread. Controlling for size, stocks with higher book-to-market ratios (i.e. value stocks) generally have higher short interest, however the pattern is much weaker when short ratios are used as a short selling measure. Boehmer et al (2008) also document weak association between short ratios and BM after controlling for size. However, the positive relation between BM and short interest contradicts prior studies. Dechow, Hutton, Meulbroeuk and Sloan (2001) find that short sellers target firms of low fundamentals (such as earnings and book values) to market prices. Diether et al (2009b) also document that growth stocks have greater short selling on average than value stocks. We however note that, these findings are based on single sort without controlling for the size effect which is widely known to interact with and confound the BM effect. Since small (large) stocks tend to have high (low) BM, thus the greater short 11 Note that the US market has experienced continual increases in short selling activity. Asquith et al (2005) document an upward trend in short selling on NYSE, Amex and NASDAQ over period 1980-2002, however, note that even at its peak in 2002, the median firm had only 1% of its shares shorted. The 4-5% short interest reported of 2005 in Diether et al (2009b) therefore represents further escalation in short selling in that market. In that context, the low level of short selling in Australia may be the result of institutional differences such as a less developed security lending market, a less active hedge fund industry, or may be because the Australian economy has fared relatively better than most other developed countries after the financial crisis, hence less vulnerable to short selling. 13

selling observed on growth stocks in the cited studies may be due to the fact that they are dominated by large caps which attract greater short selling as argued above. Panels D and E 12 13 control for this interaction. Finally, Panel F provides the cross-sectional correlation between short interest and three short ratios, measured over 1 day, 5 day and 10 day periods. For example, SR5 is total short sales over the prior 5 days divided by total volume over the same period. SR5 and SR10 seek to smooth out daily fluctuation, and are used in Boehmer et al (2008) s analysis of short volume and future returns. As seen from Panel F, the correlations range from 0.3 to 0.4, implying that although short ratios and short interest both measure the magnitude of short selling, they each contain unique information. 4. Short selling and subsequent stock returns This section examines the first research question: whether short volume or short position is more predictive of future stock returns. By predictive, we ask which of the two measures predict more negative subsequent returns. In other words, we implicitly assume that the overpricing story prevails: high short selling indicates current overpricing and predicts negative future returns. Theoretically, Miller (1977) and Figlewski (1981) show that short sale constraints, for example in the form of inability to invest the short proceeds, lead to negative information not embedded in the stock price. This causes the current price to be inflated, leading to negative future returns as the market eventually adjusts. Alternatively, the negative relation between short selling and future return may simply suggest that short sellers 12 Jones and Lamont (2002) find that growth stocks are accompanied by higher short selling costs, after controlling for size. Whilst their result appears to contradict ours, they are based on loan fees as a proxy for short selling demand whereas ours are based on short interest. 13 The positive pattern between short interest and BM holds when we recompute using all stocks in the market instead of just those that appear in the short position data. Alternatively, when we do not control for the size effect, that is, compute the short measures across 5 BM portfolios, we find no apparent relation between short selling and BM. Perhaps the size effect that we suspect confounding the results in the cited studies on short interest is stronger than that in our sample which, by design, filters out tiny stocks. 14

are able to identify overpriced stocks using their private signals and/or superior analytical skills. Regardless of the underlying explanation, numerous empirical studies, as outlined above, indeed support the overpricing hypothesis. We take this as our starting point and proceed to investigate which of the two widely used short selling measures better predict subsequent negative returns. One can think of our analysis as comparing the strength of trading signals given by gross short sales and short positions. 4.1. Single sorts Following the standard methodology in the literature, we sort stocks into portfolios based on each of these short sale measures, compute subsequent portfolio returns, and subject them to statistical tests and risk adjustments. Specifically, on each day t, we form decile portfolios based on short ratios measured over the previous 5 day intervals (day t-5 to t-1). We then skip 1 day and hold these decile portfolios for several periods: 5 days, 10 days, 20 days, 40 days, 60 days and 120 days (that is day t+1 to t+5, day t+1 to t+10, day t+1 to t+20 and so on). The 1-day skip is to avoid concerns about bid-ask bounce, and is a standard treatment in the literature. We do the same for short interest, except that the short interest based portfolios formed on day t use short interest reported for day t-4 (due to ASIC s 4-day delay in publishing the position reports). Figure 1 illustrates the timeline of these implementations. Note that such delayed formation of the short interest based portfolios biases against short interest in favour of short ratios in terms of the informational content. As it turns out, such bias does not translate to the dominance of the latter, thus strengthening our results. [Figure 1 about here] We repeat our portfolio formation on a daily basis over our sample through 30 September 2011. Due to the mismatch in the reference periods between the short ratio based strategy and the short interest based strategy, we take care to ensure we end up with comparable return 15

time series. Taking the first reporting date of the short position reports (16 June 2010) as the first reference point, the first holding period for the short interest strategy commences 5 trading days later, that is 23 June 2010. Therefore, for the same first holding period for the short ratio strategy, the first formation period is the five days that ends on 21 June 2010. The next holding period for both strategies is simply the first one shifted by 1 day. The result is 325 daily portfolio returns over the period from 23 June 2010 to 30 September 2011 that are daily averages of returns across overlapping portfolios. To correct for the resulting autocorrelation, we use Newey-West standard errors with T, T being the length of the holding period. Table 2 and 3 report single sort results on short ratios and short interest, respectively. In these tables, returns are reported on a per month basis (by scaling daily returns by 20) and based on value weighting. The last two rows represent the hedge portfolio that is long the most lightly shorted decile and short the most heavily shorted decile. For this long-short portfolio, we also reports the risk adjusted excess return using Fama-French s (1993) three-factor model augmented with a 6-month momentum factor and a 1-month contrarian factor. For this regression, we construct daily factor returns using Datastream. Average short measures for each portfolio as well as portfolio characteristics are also reported in Tables 2 and 3. [Table 2 about here] With regards to portfolio characteristics, there is a vast cross-sectional difference amongst the decile portfolios in terms of trading volume that is classified of short sales. As seen from Table 2, the most heavily shorted portfolio (portfolio 10) has the mean short ratio of 33.68% compared to just 0.17% for the most lightly portfolio (portfolio 1). Consistent with the descriptive statistics, there is a strong positive relation between size and short ratios and unclear relation between BM and short ratios (when not controlling for size). Interestingly, 16

highly shorted stocks have lower volatility than lightly shorted counterparts. Contradicting Diether, et al., 2009b who find that short sellers trade on short-term overreaction of stock prices, Table 2 shows a negative and monotonic relation between short ratios and past 1 month returns: over our sample period, short sellers on the ASX target stocks that have underperformed in the prior month, thus exhibiting a momentum trading behaviour over short term periods. 14 Turning to the return predictability of short selling, three observations stand out. First, returns almost monotonically decline as short ratios increase, confirming the overpricing hypothesis. Second, the return differential between the most lightly shorted and most heavily shorted decile, which is often used as an indicator of the magnitude of an anomaly, is quite material, in the order of 1% per month, but not statistically significant. The risk adjusted excess return, or alpha, is much smaller and again statistically insignificant. Third, the longshort return drops visibly beyond 20-day holding horizons. Taking the last two points together, short volumes appear to have weak predictive power and this power particularly diminishes outside 1 month since observing the data. Also using short ratios as the measure of short selling, Boehmer et al (2008) document a long-short excess return of 0.54% per month (t-stat=1.56) but statistically significant alpha of 1.16 (t-stat=3.67) (see Table II). Similarly, Diether et al (2009) document profitable results using short ratios as the trading signal, with alphas in the order of 0.9% to 1.4% per month (see their Table 5). These U.S. studies use Fama-French s (1993) three factor model as their risk model. Although we use a five-factor model due to the previously documented finding that short selling on the ASX appears to be related to past returns, using the 3-factor model does not alter the result. When doing so, the value weighted alpha for the long-short portfolio 14 Albeit not reported in Table 2, there is a similarly negative relation between short ratios and 6-month prior returns: short sellers appear to trade the momentum effect over both short and medium terms. 17

that is based on short ratios remains close to zero (not in Table 2). Also not tabulated is the equal weighted return, which is significant for holding periods not more than 20 days. For example, for a 20-day holding period, the equal weight alpha is 1.06% (t-stat=2.14) for the 5- factor model and 1.21% (t-stat=2.42). The SMB loadings are positive with t-stats in the order of 10 across all holding lengths. Clearly, the size effect is playing an important role in our results as short sellers on the ASX appear to shun smaller stocks. In the same manner, Table 3 tabulates results based on short interest as the measure of short selling. Portfolio characteristics qualitatively remain the same. The negative pattern between short selling and future returns continue to prevail. Compared to Table 2, the magnitude and statistical significance are much stronger across all reported holding horizons. For example, the 20-day holding excess return is 2.06% per month (t-stat=3.85) with alpha of 1.20% per month (t-stat=2.36). This is compared to a raw return of 1.28% (t-stat=1.86) and alpha of 0.25% (t-stat=0.71) if using short volumes as the trading signal. Remarkably, alphas continue to be significant for 40-day and 60-day holding periods (t-stats are 1.91 and 2.26 respectively). This evidence suggests short interest carries information content that is both richer and longer lasting than that by short volume. The above analysis of the extreme portfolios may simply suggest that short interest is better at ranking stocks in the relative sense instead of identifying losers, or overpriced stocks in the absolute sense. For the last two rows in Table 3, we directly compare the performance of the most heavily shorted portfolio by short ratios against the most heavily shorted portfolio by short interest. We do so by subtracting the latter s return from the former, so that short interest s superiority would be evidenced by positive and significance return differentials. And that is what we find. With the exception of 5-day holding intervals, the return differential is positive and significant at both raw and risk-adjusted levels. The weaker finding for the 5-day holding confirms that gross short volumes may only have a pricing 18

effect in the very short term. Even so, that may possibly be due to the delay in the short interest publication. We conduct two additional exercises to alleviate concerns regarding our choice of short measurements. First, we note that the gross short sale, or volume, reports, do not report stocks that have zero short sales on the day. Such zero short sales could potentially have some information effect, in the same way that zero short interest can have positive pricing impact as documented in Boehmer, Huszar and Jordan (2011). Table 2 is based on the short volume reports as they are received by the market. In the first additional test, we deem that a stock has zero short volume on a given day if it has short interest reported in the position report on that day, and has gross short sales reported on other days. Panel A of Table 4 reports results for this exercise, which repeats Table 2 using the expanded short volume sample. Unlike Table 2, portfolio 1 in this panel consists of stocks that zero short volumes over the prior 5 days, a setup that potentially strengthens the informational content of short transactions. [Table 4 about here] The results show that including the unreported zero short sale stocks does not alter our prior finding that short volumes carry little information about future returns. The hedge return in Panel A of Table 4 is negative and insignificant as the zero short volume stocks generate lower returns than the most lightly shorted portfolio in Table 2. This is surprising since the complete absence of short selling activity should be better news than little short selling. We suspect that the market reacts to the short sale reports to the extent of what are in the report, and not what are not in the reports. The second exercise alleviates concerns that the 5-day period used to construct the short ratio may be too short to capture the magnitude and momentum of short transactions (note the 5- day measure follows Boehmer et al, 2008). Panel B of Table 4 reports results where short 19

ratios are defined over 10 trading days. Once again, the finding does not change and the hedge return is negative and insignificant. The deterioration when 10-day short selling is used instead of 5-day short selling further confirms the short term nature of any impact that short selling volume may have on future returns. 4.2. Cross-sectional regressions The above single sorts can be problematic when short selling behaviour differs crosssectionally. Short volume and short interest may carry distinct information since short turnovers can differ amongst stocks. For example, two stocks may have similar levels of short transactions over a certain time period but the one whose average short seller takes longer to cover has higher short interest. Single sorts do not control for such dimension which may vary cross sectionally. In addition, the short ratio and short interest may be not comparable as measures of short selling. The former is defined with regards to total volume, whereas the latter to total shares outstanding. Too many alternative constructions exist to be considered. Cross-sectional regressions enable us to control for such variations whilst assessing the explanatory power of each short sale data relative to the other. We perform Fama-MacBeth s (1973) regressions by regressing stock returns against their previously observed short ratios (Model 1), or short interest (Model 2), or both (Model 3), and characteristic variables. To allow for possible autocorrelation in returns, we also include the past 1 month and 6 month returns. Specifically, Model 1 takes the following form:,, log, B,,, 1, 6,, (1) 20

Model 2 would have short interest SI i,t-5 in place of SR i,t-2 whereas Model 3 would have both SI i,t-5 and SR i,t-2. Across all three models, volatility i,t-2 is realized volatility based on daily returns for a six-month period that ends on day t-2, turnover i,t-2 is the ratio of total volumes for a six-month period that ends on t-2, divided by the number of shares outstanding. 1monthret i,t-2 and 6monthret i,t-2 are the return over 1 and 6 months respectively, both ending on t-2. The dependent variable, Ret i,t is the buy-and-hold return for a T-day period that commences on day t. We estimate the regressions daily over the sample period, collect the estimated coefficients across all days, and report their mean and t-statistics (based on Newey- West standard errors) in Table 5. Results for T=5 days, 10 days, 20 days and 40 days are reported, together with adjusted R-squared. Table 5 shows that both short ratio and short interest negatively predict future returns after controlling for a whole host of characteristics and past returns. However, when both are used to explain future returns, only short interest has a statistically significant coefficient. For example, for results on a 20-day holding period, the coefficient on the short ratio variable is - 0.03 with t-stat=-2.46. When short interest is included, the coefficient on the short ratio is - 0.01 with t-stat=-0.65 whilst that on short interest remains highly statistically significant with t-stat=-3.29. The same applies for the other holding periods. Clearly, for a given future return, short interest commands greater explanatory power than short volumes, although the latter captures more recent short selling than the former (recall that short interest announcement is delayed by 4 days and our empirical setup adheres to this reality). 15 5. Short covering behaviour and its determinant We now turn attention to short covering, which is the second aspect of this paper. Short covering is interesting for at least 2 reasons. Whilst numerous studies, including ours, suggest 15 Adjusted-R squared do not change much across the regression models. One noteworthy observation is that the adjusted R squared is around 5-6% which is slightly higher to the 4% reported in Boehmer et al (2008). 21

short selling predicts negative returns, it does not necessarily imply that short sellers make money. For short sellers to be able to capitalize on their insight, they need to close the position at the right time. Information on when short sellers cover their position helps to answer this question. Second, it is interesting to see if short sellers are vulnerable to short squeeze, as often suggested by popular press. Answer to this question not only adds insight into short sellers as sophisticated traders but also informs regulators in their policy setting that does not unfairly disadvantage short sellers. As pointed out in the introduction, existing literature on short selling is silent on short covering due to lack of direct data. Using data on securities lending, Diether (2008) finds that almost half of the securities lending contracts are closed out in two weeks. No studies directly look at covering of actual short positions. Since we observe both daily gross short sales and daily short positions, we can impute daily short covering using the following equality: (2) As pointed out in the data description, some transactions require positional reporting but not transactional reporting. In addition, there is a threshold level above which short positions are to be reported whereas all short transactions are to be included in the short sale reports. Finally, failure to report means that both position and transaction data may not be complete. Therefore, our short covering estimate is not exact. However, given that few transactions are subject to such differential reporting requirements and the threshold level is quite low (0.01% of total shares outstanding and $100,000 in value), the measurement error is unlikely to be significant. To further reduce this error, we define short covering over N days using the following equation: 22

(3) Short covering ratios are then computed as the short covering volume divided by total volume over the same period, thus representing the fraction of purchases that are initiated by short sellers to close out their short positions. Table 6 provides some statistics on our short covering ratios computed over 1 day, 5 days and 10 days. The 5 day and 10 day short covering estimates are over overlapping periods that are staggered by 1 day. [Table 6 about here] From Panel A, of about 600 stocks that have records in both short volume and short position reports, 227 stocks have a full set of daily estimates of short covering throughout the sample. That is, they have short volume and short interest data in each of the reporting days. There are 320-329 daily sets of estimates, or about 140,000 stock-day estimates. Daily estimates suggest that each day, on average 14.75% of the volume transacted represent short covering. There is significant variation in these daily estimates, 15% of which are negative. Since short covering ratios are nonnegative by definition, these negative estimates are due to the data issues discussed above. When we define short covering over 5 and 10 day periods, not only the percentage of negative estimates drops by half, the standard deviation also drops significantly. Panel B reports correlations of short covering against prior and future returns, so as to give us a simple preview on whether short covering is related to returns. There appears to be very weak correlation between short covering and past returns over several intervals and negative correlation between short covering and future returns. 23

We now proceed to test what may be driving the short covering behaviour. If short sellers increase short covering following rallies in the stock price, that is they experience short squeeze, we would see a positive relation between prior returns and the short covering ratios, after controlling for other factors. Once again, we apply the Fama-MacBeth framework, regressing, on a daily basis, short covering ratios against past returns, short interest, log size, the contemporaneous return. We alternate across 4 past returns: 1 day returns, 3 day returns, 5 day returns, and 10 day returns. We also estimate a model that has the subsequent 10-day return as an explanatory variable. The average coefficients and Newey-West t-statistics are reported in Table 7. The results are for 5-day short covering estimates, although those for 10 day periods are very similar. [Table 7 about here] We find that short covering activities are not related to any of the past returns considered or to future returns. The coefficients on past returns are negative and insignificant. The negative sign suggests that short sellers accumulate stocks to cover their position as the price declines, or at least do not seem to rush to buy back as prices rise. We therefore can rejection the short squeeze possibility, at least for our sample period. Interestingly, short covering and contemporaneous returns are negative and significant: short sellers repurchase stocks as prices fall. Taken together, there is no evidence that short sellers decapitate to stock rallies. 6. Conclusion Using daily data on short volume and short interest in the Australian Securities Exchange, we find that short interest dominates short volume in predicting future underperformance. The result is obtained using both sorting and regression methods, and is robust to different measurements of shorting volume, firm characteristics and past returns. As a second dimension of our study, we also find that short covering is not related to past returns but 24

negatively related to contemporaneous returns. The finding suggests that short sellers do not appear to rush to buy back as the stock rallies; instead they take advantage of fall prices to close their position. In short, short squeeze does not occur in our data. 25

Reference Asquith, Paul, Parag Pathak, and Jay Ritter, 2005, Short Interest, institutional ownership, and stock returns, Journal of Financial Economics 78, 243 276. Australian Securities and Investments Commission, 2010, Regulatory Guide 196 Short Selling. Boehmer, Ekkehart, Zsuzsa R. Huszar, and Brad Jordan, 2011, The Good News in Short Interest, Journal of Financial Economics Forthcoming. Boehmer, Ekkehart, Charles M. Jones, and Xiaoyan Zhang, 2008, Which Shorts Are Informed?, Journal of Finance 63, 491 527. Brent, Averil, Dale Morse, and E. Kay Stice, 1990, Short interest: Explanations and Tests, Journal of Quantitative and Financial Analysis 25, 273 289. Comerton Forde, Carole, Charles M. Jones, and Talis J. Putnins, 2011, Shorting at Close Range: A Tale of Two Types, SSRN elibrary. Dechow, Patricia M., Amy P. Hutton, Lisa Meulbroek, and Richard G. Sloan, 2001, Short sellers, fundamental analysis, and stock returns, Journal of Financial Economics 61, 77 106. Desai, Hemang, K. Ramesh, S. Ramu Thiagarajan, and Bala V. Balachandran, 2002, An Investigation of the Informational Role of Short Interest in the Nasdaq Market, Journal of Finance 57, 2263 2287. Diether, Karl B., 2008, Short selling, timing and profitability, Working Paper. Diether, Karl B., Kuan Hui Lee, and Ingrid M. Werner, 2009a, It's SHO Time! Short Sale Price Tests and Market Quality, Journal of Finance 64, 37 73. Diether, Karl B., Kuan Hui Lee, and Ingrid M. Werner, 2009b, Short Sale Strategies and Return Predictability, Review of Financial Studies 22, 575 607. Do, Binh, Viet Do, and Daniel Chai, 2011, Does the 2008 short sale ban affect the enforcement of the Law of One Price: Australian evidence, Accounting and Finance Forthcoming. Fama, Eugene, and Kenneth French, 1993, Common Risk Factors in Returns on Stocks and Bonds, Journal of Financial Economics 33, 3 56. Fama, Eugene, and James MacBeth, 1973, Risk, Return and Equilibrium: Empirical Tests, Journal of Political Economy 81, 607 636. Figlewski, Stephen, 1981, The Informational Effect of Restrictions on Short Sales: Some Empirical Evidence, Journal of Quantitative and Financial Analysis 16, 463 476. Jones, Charles M., and Owen A. Lamont, 2002, Short sale Constraints and Stock Returns, Journal of Financial Economics 66, 207 239. Miller, Edward M., 1977, Risk, Uncertainty, and Divergence of Opinion, Journal of Finance 32, 1151 1168. Securities and Exchange Commission, 2011, Release No. 34 64383: Short Sale Reporting Study Required by Dodd Frank Act Section 417(a)(2). 26

Table 1 Summary statistics This study is based on two sets of reports that cover daily short selling activities for stocks in the Australian Securities Exchange (ASX) from 16 June 2010 to 30 September 2011. Gross short sale reports provide the daily volumes transacted as a short sale for each security, and are released daily to the public by the ASX (market operator). Short position reports provide the quantity of shares that have been sold short and remain uncovered, by security, and are published daily by the ASIC (market regulator). The volume (position) data is published 1 (4) days following the reporting day. SR in Panel B is daily short ratio, defined as daily short volume divided by total volume. SI in Panel B is short interest, defined as short position divided by the number of shares outstanding. The BM and size portfolios are based on quintile sorting. Panel A: The shorting sample Market Cap ($m) Percentiles Stocks 25th Median 75th ASX population 1,866 8 24 122 Stocks in short volume sample 841 27 120 436 Stocks in short position sample 847 27 113 434 Stocks in either sample 1,003 19 75 326 Stocks in both samples 685 59 182 690 Stocks not in both samples 318 8 14 33 Panel B: Daily short volume statistics Mean StdDev Min Max 25th Median 75th No. of stocks reported 320 25 258 385 298 322 338 SR 13.29% 11.93% 0.01% 74.10% 3.83% 10.66% 19.48% Panel C: Daily short position statistics Mean StdDev Min Max 25th Median 75th No. of stocks reported 457 26 338 500 433 462 480 SI 0.65% 1.21% 0.00% 13.89% 0.03% 0.20% 0.74% Panel D: Short ratio for 25 book to market and size portfolios Small 2 3 4 Big Low 6% 7% 12% 17% 19% 2 6% 7% 13% 17% 20% 3 7% 9% 15% 17% 19% 4 7% 11% 14% 17% 20% High 9% 12% 15% 16% 19% Panel E: Short interest for 25 book to market and size portfolios Small 2 3 4 Big Low 0.13% 0.22% 0.26% 1.21% 1.10% 2 0.14% 0.32% 0.27% 1.09% 1.20% 3 0.13% 0.23% 0.44% 1.10% 1.03% 4 0.11% 0.19% 0.51% 0.98% 1.20% High 0.25% 0.63% 0.93% 0.83% 3.12% Panel F: Cross sectional correlation SR1 SR5 SR10 SI 0.30 0.40 0.42 27

Table 2 Short volume and future returns This table reports returns and characteristics of decile portfolios that are formed daily based on previous 5-day short ratios (short volume/total volume), and held over various horizons. 1-10 is a portfolio that is long portfolio 1 (i.e. the most lightly shorted decile) and short portfolio 10 (i.e. the most heavily shorted decile). The sample consists of gross short sale reports released daily by the ASX over period 16 June 2010 to 30 September 2011. Raw are monthly returns, scaled from daily returns which in turn are computed as the average of returns across overlapping portfolios. Alpha is the intercept term of the regression of the raw return against the five factor model (Fama-French 3 factors plus momentum and contrarian factors). t-statistics are based on Newey-West standard errors where the number of lags is the length of the holding period. * indicates statistical significance at 10% level. 1 6 5 day 10 day 20 day 40 day 60 day Number Short month month Portfolio of stocks ratio Size B/M return return Raw Alpha Raw Alpha Raw Alpha Raw Alpha Raw Alpha 1 38 0.17% 256 0.52 6.38% 28.54% 0.79% 0.84% 1.03% 0.71% 0.58% 0.69 0.68 0.79 0.52 0.37 2 38 0.81% 346 0.54 2.86% 22.27% 1.08% 1.36% 1.32% 0.80% 0.83% 0.98 1.18 1.10 0.62 0.58 3 38 2.01% 467 0.63 2.26% 16.29% 0.71% 0.85% 0.66% 0.52% 0.20% 0.67 0.73 0.54 0.42 0.13 4 38 3.98% 695 0.66 1.67% 17.31% 0.65% 0.35% 0.71% 0.69% 0.63% 0.69 0.34 0.70 0.64 0.55 5 37 6.83% 1,175 0.70 1.09% 12.92% 0.35% 0.44% 0.43% 0.39% 0.42% 0.36 0.41 0.43 0.41 0.41 6 38 10.08% 2,155 0.67 0.09% 7.68% 0.38% 0.38% 0.23% 0.23% 0.05% 0.41 0.42 0.26 0.27 0.05 7 38 13.39% 3,456 0.67 0.24% 4.62% 0.15% 0.21% 0.30% 0.48% 0.05% 0.17 0.24 0.37 0.70 0.05 8 38 16.91% 5,158 0.66 0.23% 0.72% 0.16% 0.05% 0.20% 0.13% 0.13% 0.19 0.06 0.24 0.18 0.17 9 38 21.50% 7,620 0.63 0.27% 0.38% 0.48% 0.47% 0.39% 0.51% 0.38% 0.49 0.48 0.44 0.59 0.44 10 38 33.68% 10,220 0.63 0.66% 1.08% 0.39% 0.30% 0.26% 0.33% 0.17% 0.39 0.29 0.25 0.35 0.18 1 10 1.18% 0.20% 1.14% 0.10% 1.28% 0.25% 1.04% 0.07% 0.75% 0.35% 1.67 0.47 1.67 0.24 1.86* 0.71 1.57 0.17 1.00 0.85 28

Table 3 - Short interest and future returns This table reports returns and characteristics of decile portfolios that are formed daily based on previous 5-day short interest (short position/total shares outstanding), and held over various horizons. 1-10 is a portfolio that is long portfolio 1 (i.e. the most lightly shorted decile) and short portfolio 10 (i.e. the most heavily shorted decile). SR-SI is a portfolio that is long the most heavily shorted decile based on short ratios, and long the most heavily shorted decile based on short interest. The sample consists of short position reports released daily by the ASIC with 4 day delays over period 16 June 2010 to 30 September 2011 Raw are monthly returns, scaled from daily returns which in turn are computed as the average of returns across overlapping portfolios. Alpha is the intercept term of the regression of the raw return against the five factor model (Fama- French 3 factors plus momentum and contrarian factors). t-statistics are based on Newey-West standard errors where the number of lags is the length of the holding period. *,**,*** indicate statistical significance at 10%, 5% and 1% levels respectively. 6 5 day 10 day 20 day 40 day 60 day Number Short month Portfolio of stocks interest Size B/M 1 month return return Raw Alpha Raw Alpha Raw Alpha Raw Alpha Raw Alpha 1 44 0.00% 261 0.33 1.72% 9.05% 1.14% 0.96% 1.08% 0.86% 1.10% 1.29 0.98 1.08 0.80 0.98 2 44 0.01% 454 0.61 1.83% 12.97% 0.45% 0.51% 0.96% 1.10% 1.14% 0.51 0.53 0.96 1.04 1.05 3 44 0.03% 423 0.27 1.72% 15.69% 0.37% 0.85% 0.59% 0.65% 0.36% 0.34 0.74 0.52 0.61 0.30 4 44 0.08% 742 0.59 1.35% 14.32% 0.34% 0.45% 0.55% 0.50% 0.59% 0.32 0.41 0.53 0.53 0.58 5 44 0.15% 1,324 0.77 0.99% 14.26% 0.81% 0.84% 0.94% 0.76% 0.78% 0.93 0.91 1.03 0.79 0.78 6 44 0.27% 3,280 0.64 1.06% 9.00% 0.13% 0.03% 0.07% 0.24% 0.15% 0.14 0.03 0.09 0.35 0.19 7 44 0.45% 4,401 0.51 0.91% 7.08% 0.02% 0.09% 0.04% 0.08% 0.17% 0.02 0.11 0.06 0.13 0.26 8 44 0.75% 6,323 0.64 0.88% 0.63% 0.27% 0.42% 0.52% 0.38% 0.16% 0.27 0.41 0.51 0.45 0.20 9 44 1.30% 6,296 0.66 0.20% 0.12% 0.24% 0.27% 0.05% 0.19% 0.41% 0.24 0.25 0.05 0.19 0.34 10 44 3.41% 3,719 0.83 0.92% 0.95% 0.76% 0.94% 0.97% 0.96% 0.76% 0.72 0.85 0.86 0.81 0.62 1 10 1.90% 1.30% 1.90% 1.17% 2.06% 1.20% 1.82% 1.00% 1.86% 1.17% 3.24*** 2.66** 3.47*** 2.39** 3.85*** 2.36** 3.46*** 1.91* 4.01*** 2.26** SR SI 0.37% 0.51% 0.64% 0.78% 0.72% 0.82% 0.63% 0.84% 0.58% 0.61% 1.10 1.76* 1.71* 2.55** 1.83* 2.57** 1.80* 2.94** 1.80* 2.21** 29

Table 4 Other single sort results Panel A reports single sort results where the data includes zero short volume for stocks that report short interest but not gross short sales. Panel B reports single sort results where short ratio is defined as total short volume over the prior 10 trading day divided by total volume over the same period. 1-10 is a portfolio that is long portfolio 1 (i.e. the most lightly shorted decile in terms of short ratios) and short portfolio 10 (i.e. the most heavily shorted decile in terms of short ratios). SR-SI is a portfolio that is long the most heavily shorted decile based on short ratios, and long the most heavily shorted decile based on short interest. Alpha is the intercept term of the regression of the raw return against the five factor model (Fama-French 3 factors plus momentum and contrarian factors). t-statistics are based on Newey-West standard errors where the number of lags is the length of the holding period. t-statistics are based on Newey-West standard errors where the number of lags is the length of the holding period. *,** indicate statistical significance at 10% and 5% levels respectively. Panel A: Using short position reports to augment short volume data Number of Short 1 month 6 month 5 day 10 day 20 day 40 day 60 day Portfolio stocks ratio Size B/M return return Raw Alpha Raw Alpha Raw Alpha Raw Alpha Raw Alpha 1 65 0.00% 130 0.28 0.14% 3.11% 0.20% 0.18% 0.21% 0.28% 0.45% 0.21 0.18 0.20 0.26 0.36 10 44 32.18% 10,277 0.61 0.63% 1.29% 0.44% 0.35% 0.34% 0.43% 0.28% 0.44 0.35 0.34 0.46 0.29 1 10 0.64% 0.30% 0.53% 0.43% 0.55% 0.50% 0.71% 0.41% 0.72% 0.43% 0.97 0.64 0.81 0.89 1.00 0.99 1.56 0.85 1.56 0.89 SR SI 0.33% 0.45% 0.59% 0.72% 0.63% 0.73% 0.52% 0.69% 0.48% 0.74% 1.06 1.67* 1.61 2.40** 1.71* 2.35** 1.62 2.40** 1.48 2.61** Panel B: Using short volume over prior 10 days Number of Short 1 month 6 month 5 day 10 day 20 day 40 day 60 day Portfolio stocks ratio Size B/M return return Raw Alpha Raw Alpha Raw Alpha Raw Alpha Raw Alpha 1 41 0.11% 199 0.55 6.86% 31.68% 0.72% 0.97% 0.83% 0.67% 0.67% 0.60 0.78 0.62 0.46 0.42 10 41 30.73% 11,338 0.61 0.55% 1.81% 0.05% 0.19% 0.14% 0.17% 0.04% 0.06 0.19 0.14 0.18 0.04 1 10 0.78% 0.26% 1.17% 0.07% 0.97% 0.20% 0.84% 0.34% 0.71% 0.49% 1.00 0.58 1.57 0.14 1.27 0.49 1.18 0.83 0.93 1.02 SR SI 0.50% 0.61% 0.54% 0.66% 0.64% 0.76% 0.59% 0.78% 0.52% 0.78% 1.66* 2.39** 1.51 2.28** 1.69* 2.47** 1.83* 2.60** 1.73* 2.69** 30

Table 5 Regression results Stock returns are regressed against (1) short ratios, (2) short interest and (3) both short ratios and short interest, plus characteristics and past returns, in the spirit of Fama-MacBeth (1973). For each holding period, six rows are reported, with the first two pertaining to the average estimate of the coefficient and its t-statistic for regression (1), the next two rows for regression (2) and the last two rows for regression (3). t- statistics are based on Newey-West standard errors where the number of lags is the length of the holding period. *,**,*** indicate statistical significance at 10%, 5% and 1% levels respectively. intercept SR SI size BM volatility turnover 1 month ret 6 month ret adjusted R2 5 day 0.00 0.01 0.00 0.00 0.01 0.00 0.02 0.01 0.060 0.24 1.71* 0.88 2.24** 0.27 1.49 3.69*** 2.77** 0.00 0.09 0.00 0.00 0.01 0.00 0.02 0.01 0.060 0.35 3.46*** 0.25 2.25 0.22 0.92 3.69*** 2.58** 0.00 0.00 0.08 0.00 0.00 0.01 0.00 0.02 0.01 0.060 0.22 0.52 3.20*** 0.42 2.26** 0.15 0.85 3.68*** 2.58** 10 day 0.00 0.01 0.00 0.00 0.02 0.01 0.04 0.01 0.059 0.30 1.93* 0.92 2.70** 0.16 1.76* 3.75*** 2.80** 0.00 0.18 0.00 0.00 0.01 0.00 0.04 0.01 0.059 0.16 3.50*** 0.03 2.74** 0.12 1.11 3.74*** 2.58** 0.00 0.01 0.17 0.00 0.00 0.00 0.00 0.04 0.01 0.059 0.30 0.75 3.14** 0.41 2.73** 0.02 1.00 3.73*** 2.59** 20 day 0.01 0.03 0.00 0.01 0.01 0.01 0.05 0.02 0.062 0.75 2.46** 0.39 2.36** 0.03 2.73** 2.63** 3.11*** 0.00 0.39 0.00 0.01 0.00 0.01 0.05 0.02 0.062 0.63 4.28*** 0.50 2.41** 0.02 1.51 2.65** 2.91** 0.01 0.01 0.37 0.00 0.01 0.02 0.01 0.05 0.02 0.063 0.72 0.65 3.29*** 0.20 2.42** 0.09 1.49 2.62** 2.91** 40 day 0.01 0.04 0.00 0.01 0.17 0.03 0.04 0.05 0.00 0.052 0.58 2.17** 0.33 2.90** 0.39 4.65*** 1.62 4.75*** 0.00 0.01 0.68 0.00 0.01 0.12 0.02 0.04 0.05 0.054 0.65 3.94*** 0.27 2.95** 0.26 2.48** 1.58 4.37*** 0.01 0.00 0.67 0.00 0.01 0.12 0.02 0.04 0.05 0.054 0.55 0.16 2.82** 0.28 2.99** 0.28 2.82** 1.63 4.45*** 31

Table 6 Descriptive statistics on short covering estimates Short covering is imputed from daily short interest and short volume using the stock-flow equality in Equation (3).. *,**,*** indicate statistical significance at 10%, 5% and 1% levels respectively. Newey-West standard errors are used in computing the test statistics. Panel A: Short covering ratios Num of stocks Num of periods Num of estimates Full record stocks Mean StdDev 25th Median 75th Estimates <0 1 day 657 329 143,223 227 14.75% 304.88% 0.83% 4.26% 18.78% 15% 5 days 616 325 139,648 227 10.51% 41.31% 0.26% 5.97% 16.19% 8% 10 days 602 320 136,187 227 10.19% 28.04% 0.61% 6.60% 15.88% 6% Panel B: Cross sectional correlation 5 day 10 day short interest short ratio short covering short interest short ratio short covering ret_t 0.03*** 0.02* 0.03*** 0.04** 0.02 0.03** ret_t 3 0.02** 0.03*** 0.00 0.02** 0.02 0.01 ret_t 5 0.02** 0.03** 0.00 0.03** 0.02 0.02 ret_t 10 0.03** 0.03* 0.01 0.04** 0.02 0.03** ret_t 15 0.04*** 0.02 0.02* 0.05*** 0.01 0.03* ret_t 20 0.05*** 0.01 0.01 0.06*** 0.00 0.02 ret_t+20 0.06*** 0.05*** 0.04*** 0.07*** 0.05** 0.03** short interest 0.40*** 0.23*** 0.42*** 0.28*** short ratio 0.53*** 0.69*** 32

Table 7 Determinants of short covering Short covering ratios over 5 day periods are regressed against short interest, size, contemporaneous and (1) prior 1-day returns, (2) prior 3-day returns (3) prior 5-day returns (4) prior 10 day returns and (5) prior 10-day returns and future 10-day returns, in the spirit of Fama-MacBeth (1973). The first two rows are the average estimate of the coefficients and their t-statistics for regression (1). The next two rows are for regression (2) and so on. t-statistics are based on Newey-West standard errors with 5 lags. *,**,*** indicate statistical significance at 10%, 5% and 1% levels respectively. constant short interest log(size) contemporaneous ret prior 1 day ret prior 3 day ret prior 5 day ret prior 10 day ret subsequent 10 day ret 0.11 4.97 0.00 0.05 0.08 7.10 16.68*** 1.11 2.37** 1.21 0.11 4.97 0.00 0.05 0.03 7.02 16.74*** 1.09 2.35** 0.96 0.11 4.97 0.00 0.05 0.03 6.86 16.70*** 1.07 2.26** 0.83 0.11 4.97 0.00 0.06 0.02 6.76 16.63*** 0.95 2.35** 0.94 0.11 5.03 0.00 0.05 0.03 0.00 6.63 16.54*** 0.97 2.30** 1.02 0.05 33

Figure 1 Trading implementation timeline for a 5-day holding period Short volume trading strategy t 5 t 4 t 3 t 2 t 1 t t+1 t+2 t+3 t+4 t+5 reference period formation day holding period Short interest trading strategy t 4 t 3 t 2 t 1 t t+1 t+2 t+3 t+4 t+5 reference point formation day holding period 34

Appendix: Exemptions to the naked short sales prohibitions and reporting requirements This table is constructed with reference to the ASIC Regulatory Guide 196 dated April 2010. The subsequent version of the guide, dated April 2011, is largely the same in this aspect. Only exemptions concerning common stocks are included. Situation Description Transactional reporting Prior purchase The short seller, before the time of the Not required agreements sale, has contracted to buy and is Writing of exchange traded options Unobtained financial products Exercise of exchange traded options Selling before completing a recall of loaned securities Hedging risk from market making activities Client facilitation services Deferred purchase agreements (DPA) waiting for delivery A short position established via writing a call option or buying a put option without holding the underlying The short seller, at the time of the sale, is able to obtain the securities by exercising exchange traded options Short sales resulting from exercise of a put option or sale of a call option that is later exercised Short sales by owner of securities placed in an established securities lending program Short sales effected by a market maker to hedge their long position, provided that by the end of the day, the market maker must acquire, enter into a contract to acquire, or entered into a securities lending arrangement Short sales made to a client in response to client s buy order, provided the short seller has an existing business of providing facilitation services, and that they must, at the end of the day, acquire, enter into a contract to acquire, or entered into a securities lending arrangement Short sales effected by a DPA issuer who has received the purchase price and undertakes to deliver the securities at maturity (at least 12 months later) Not required Not required Not required Not required Not required Not required Not required Positional reporting Not required Not required Not required Required Not required Required Required Not required 35