Does Option Trading Affect the Return Predictability of Short Selling Activity?

Transcription

1 Does Option Trading Affect the Return Predictability of Short Selling Activity? Kalok Chan* Department of Finance Hong Kong University of Science & Technology Clear Water Bay, Hong Kong Hung Wan Kot Department of Finance and Decision Sciences Hong Kong Baptist University Kowloon Tong, Hong Kong Sophie X. Ni Department of Finance Hong Kong University of Science & Technology Clear Water Bay, Hong Kong This draft: April 2012 * Corresponding author. We thank Victor Fong, Wenlan Qian, and seminar participants at the 2011 Asia Finance Association Meetings (Macau), the 2011 China International Conference in Finance (Wuhan), the Cheung Kong Graduate School of Business, Fudan University, the University of Macau and the Shanghai Advanced Institute of Finance for their useful comments. We also thank Harry Leung for his capable research assistance. An earlier version of this paper was circulated under the title All are smart: Do short sellers convey more information than option investors? 1

2 Does Option Trading Affect the Return Predictability of Short Selling Activity? Abstract We analyze the effect of option trading on the return predictability of short interest. There is no difference in the return predictability of short-interest ratios between stocks with and without traded options. The predictability of the put-call open interest ratio (PCOIR) is weaker than that of the short-interest ratio (SIR), which does not change after controlling for the PCOIR. While the predictability of the SIR lasts twelve months, the predictability of the PCOIR disappears after the first month. Both the expected and unexpected components of the SIR can predict stock returns, which suggests that the return predictability of short interest is related to both short-sale constraints and information discovery. In contrast, only the unexpected component of PCOIR, not the expected component, can predict stock returns. Keywords: Short-interest ratio, put-call ratio, return predictability, short-sale constraints Classification: G12, G15, G18 2

3 1. Introduction The information content of short selling has been the focus of much research over the years. During the 2008 financial crisis, short sales were banned for financial stocks and even non-financial stocks around the world, triggering many debates on the price discovery role of short selling in financial markets (Beber and Pagano (2012)). Extensive research argues that short sellers are informed traders that possess negative information about company performance, and there is evidence that short-selling activity can predict negative returns. 1 In other words, the negative relationship between short interest and future returns is a reflection of the information possessed by short sellers. However, as a number of studies show, we not only observe negative future abnormal returns for highly shorted stocks, but also the underperformance lasts for twelve months. 2 Many studies attribute the long-run underperformance of highly shorted stocks to short-sale constraints, i.e. the ability of short sellers to borrow stocks and profit from expected declines in stock prices. Short sellers must incur significant costs in the form of equity loan fees to trade highly shorted stocks. It is more appropriate to subtract the loan fees when calculating net returns to the short sellers. Therefore, investors might not be able to profit from the underperformance of highly shorted stocks. This also implies that the short-interest level does not convey any new information. Most previous studies examine the return predictability of short-selling activity in the stock market alone. Very few of these include the options market activity to examine how it affects the return predictability of short-selling activity. This represents a gap in the literature because, in addition to the short selling, informed traders can also trade put options to take advantage of the negative information they might obtain. Indeed, some studies provide empirical evidence that the options market contains a great deal of informed trading activity, suggesting that informed traders do trade in the options market. If so, the existence of options market activity might affect the return predictability of short-selling activity. 1 For example, Aitken, Frino, McCorry, and Swan (1998), Boehmer, Jones, and Zhang (2008), Dechow, Hutton, Meulbroek, and Sloan (2001), Desai, Ramesh, Thiagarajan, and Balachandran (2002), Diamond and Verrechia (1987), and Diether, Lee and Werner (2007). 2 For example, Desai, Ramesh, Thiagarajan, and Balachandran (2002), Asquith, Pathak, and Ritter (2005). 1

4 As Grundy, Lim, and Verwijmeren (2012) suggest, short-selling and options market activities are complements rather than substitutes. They argue that if options market makers are unable to hedge short positions in put options through the short sale of underlying stocks, they provide less liquidity in the options market, such that informed traders might not trade in the options market. If informed investors are free to short sell in equity markets and trade in the options market, one possibility is that the information content of short-selling activity might decrease when the options market is introduced. Another possibility is that when stocks with traded options are available, the information content of short-selling activity could be sub-subsumed by options market activity. We analyze the effect of options market activity on the return predictability of short-selling activity. The key variables of this analysis are SIRs in the stock market and PCOIRs in the options market. The short-interest ratio (SIR) is the shares sold short as a percentage of shares outstanding and the put-call open interest ratio (PCOIR) is the amount of put-option open interest relative to the amount of call and put option open interest. This paper contributes to the literature by examining the following issues. First, we investigate whether the SIR s predictive ability for future returns is lower for stocks with traded options than it is for stocks without. Second, we examine the return predictability of the SIR after controlling for the information content of the PCOIR. If informed investors also trade in the options market, they buy put options when they expect the stock price to decline. Therefore, when the PCOIR increases, this may signal negative information that might mitigate some of the information content of the SIR. Our results confirm the return predictability of the SIR, with the most lightly shorted decile portfolio outperforming the most heavily shorted decile portfolio by 1.40% in the first month. There is, however, no difference in terms of the return predictability between stocks with options and stocks without. Regression analysis shows that the return predictability of the SIR is not weaker for stocks with traded options. In fact, the return predictability of the SIR is even slightly stronger following the introduction of stock options. Therefore, the availability of stock options does not reduce the return predictability of the SIR. In comparing the information content of the SIR and the PCOIR, we find that 2

5 the predictive ability of the PCOIR is weaker than that of the SIR and the predictive ability of the SIR remains stable after controlling for the PCOIR. Although the predictive ability of the SIR lasts twelve months, the predictive ability of the PCOIR is weaker after the first month. Therefore, the information content of the SIR and the PCOIR are not the same. Previous studies (Asquith et al. (2005)) find that the predictive ability of the SIR declines with the amount of institutional ownership (IO). One interpretation is that a low level of IO implies a limited supply of lendable shares, which makes it more costly to borrow shares and prompts larger negative returns for high SIR stocks. We confirm that the predictive ability of the SIR declines when the stocks have a smaller amount of IO. Interestingly, the amount of IO also affects the predictive ability of the PCOIR, which suggests that the information content of option activity is also affected by short-sale constraints. Another contribution of this paper is that we decompose the SIR and the PCOIR into their expected and unexpected components and examine the return predictability of each component. In the case of short interest, while it is likely that the unexpected component will be associated with new information, the expected component is more likely to reflect firm attributes, such as short-sale costs. For example, according to Huszar and Qian (2011), highly shorted stocks may incur higher stock borrowing costs, such that the abnormal returns to these stocks disappear once the loan fees are taken into account. They argue that the return predictability of the SIR, due to the expected component, is not informationbased. Our empirical evidence finds that both the expected and unexpected components of the SIR contain predictive ability for future returns. This suggests that the return predictability of short-selling activity in the stock market is related to both the cost of shorting and new information. However, as the performance of a similar decomposition of the PCOIR shows, only the unexpected component, and not the expected component of the PCOIR contains predictive ability for future returns. In other words, the return predictability of the PCOIR is primarily the result of new information. Our analysis also shows that while an unexpected increase (but not an unexpected decrease) in the SIR can predict subsequent stock returns, an unexpected decrease (but not an unexpected increase) in the PCOIR can predict future returns. Therefore, negative information is discovered through an increase in the short-selling activity in the stock 3

6 market rather than through an increase in the put options market activity. The remainder of this paper is organized as follows. Section 2 reviews three strands of literature: return predictability from short-interest, return predictability from the options market and the choice between buying options and short selling. Section 3 presents data and preliminary statistics. Section 4 discusses the empirical results on the effect of options market trading on the return predictability of the SIR, based on either a sorted-portfolio approach or cross-sectional regression analysis. We also compare the return predictability of the SIR and the PCOIR. Section 5 concludes the paper. 2. Literature Review and Hypotheses A. Literature Review A.1 Predictive ability of short interest There is extensive evidence of the return predictability of short interest. Using data on the monthly short-interest positions of all NYSE and AMEX stocks from 1976 to 1993, Asquith and Meulbroek (1996) document a strong and negative relation between short interest and subsequent abnormal stock returns. According to monthly short-interest data for NASDAQ stocks from 1988 to 1994, Desai, Ramesh, Thiagarajan, and Balachandran (2002) find that heavily shorted firms experience significant negative abnormal returns ranging from to -1.13% per month after controlling for the market, size, book-to-market and momentum factors. These negative abnormal returns increase with the level of short interest, which indicates that a higher level of short interest is a bearish signal. The predictive ability of short interest extends to daily intervals, even at the intraday level. For example, Aitken, Frino, McCorry and Swan (1998) find that stock prices decrease -0.2% following short sales within fifteen minutes or twenty trades, based on intraday short-sales data on the Australian Stock Exchange from 1994 to There is also evidence that stocks with high short-interest levels followed by negative abnormal returns are consistent with the short-sale constraint hypothesis (Miller (1977)), which states that stock prices tend to be upward-biased due to short-sale constraints because pessimistic investors are not able to 4

7 participate in the equity market due to short-sale constraints and the sole participation of optimistic investors bid prices above a level at which all investors are able to participate. Chen, Hong and Stein (2002) obtain similar results by developing a model that allows for risk aversion and show that stocks under short constraints reflect optimistic beliefs, and thus have lower future returns. Diamond and Verrecchia (1987) examine the effects of short-sale constraints in a rational-expectation framework. They show that the price of a short-sale-constrained stock adjusts more slowly to unfavorable private information than it does to favorable private information. However, they argue that rational investors recognize the existence of short-sale constraints and adjust their beliefs, such that no overpricing of securities exists, on average. The short-sale constraint is generally unobservable. A few studies attempt to infer the short-sale cost through the rebate rate, which is the interest rate that institutional short sellers receive on the proceeds of their sales. If there are more shares available for borrowing than the demand for short selling, short sellers earn a rebate that is approximately equal to the Fed funds overnight rate. If the demand for shorting a stock exceeds the amount available to borrow at the Fed funds rate, the rebate rate falls below the Fed funds rate. Therefore, a lower rebate rate is an indication of a higher short-sale cost. Some studies employ proprietary databases on rebates data (Jones and Lamont (2002); Geczy, Musto, and Reed (2002); D Avolio (2002)). In particular, Avolio (2002) finds that stocks with lower rebate rates have higher SIRs and lower subsequent returns. One problem associated with previous studies is that rebate rates are available for only a small fraction of companies. Asquith, Pathak and Ritter (2005) identify short-sale constraints based on IO, whereby they posit that the SIR is a proxy for short-sale demand while IO is a proxy for lendable supply. They find that stocks with high short interest generally underperform in the market. Furthermore, when the high short-interest portfolios are further ranked by IO, they find a positive and monotoinc relation between returns and IO. In other words, the lower the amount of institutional ownership, the more negative the portfolio s abnormal returns. 3 3 Cohen, Diether and Malloy (2007) obtain proprietary data on stock loan fees and quantities from a large 5

8 A.2 Stock return predictability from the options market Previous studies document evidence of informed trading in the options market. There are a number of reasons that informed traders prefer to trade stock options instead of the underlying stocks. First, lower transaction costs and greater financial leverage may induce informed traders to trade in the options market instead of the stock market (Black (1975); Mayhew, Sarin, and Shastri (1995)). Second, investors with private information about the volatility of the underlying stock prices can only make their bets on volatility in the options market (Back (1993)). For these reasons, the options market may not be redundant, but it can play an important role in discovering information. However, the lower liquidity of the options market may also discourage informed traders from trading options. Easley, O Hara and Srinivas (hereafter EOS (1998)) characterize this scenario as a separating equilibrium in which only uninformed traders transact in the options market while all informed traders transact in the liquid stock market. In this case, option trading conveys little information. Although numerous empirical studies investigate the informational linkage between the stock and options markets, there is no conclusive evidence as to which market plays a greater role in discovering information. According to intraday transaction data, Stephan and Whaley (1990) find that stock price movements lead option price movements. However, Chan, Chung, and Johnson (1993) caution that the stock lead documented in Stephan and Whaley (1990) might be due to price discreteness in the options market. They show that the stock lead disappears when the bid and ask quotes are used instead of transaction prices. Vijh (1990) finds that the price effects of large option trades are generally small, which suggests that option trades are not information-related. However, Chan, Chung and Fong (2002) find that both stock and option quote revisions have predictive ability for each other, which suggests that valuable information is also contained in the options market. In a recent study, Pan and Poteshman (2006) examine a unique dataset and provide strong institutional investors, and are able to differentiate between supply and demand shifts in the shorting market. They find that stock returns are predicted only by changes in short demand, not shorting supply. 6

9 evidence that option trading volume contains information future stock prices. They construct put-call ratios from option volume initiated by buyers to enter new positions and find that stocks with low put-call ratios outperform stocks with high put-call ratios by 0.4% on the next day and more than 1% over a week. Their evidence confirms that informed traders do trade in the options market. A.3 Short sales vs. put options While informed investors with positive information about a stock can trade in both the equity and options markets, informed investors with negative information find it especially attractive to trade in the options market. Short sellers face borrowing costs that can fluctuate depending on the supply and demand for equity loans. Consequently, when investors possess negative information about future stock prices and equity borrowing costs are high, they may be better off trading in the options market and buying put options instead of short selling in the equity market. Some studies investigate the relationship between short-sale constraints and options. Senchack and Starks (1993) examine the stock price reactions to unexpected increases in short interest. They find evidence of small, negative abnormal returns for a short period around the announcement date, mostly for stocks with listed options and not for stocks without. Danielsen and Sorescu (2001) analyze stock price performance following option listing. As the introduction of options effectively provides a lower cost for establishing a short position, the listing of options mitigates the short-sale constraints. Consistent with the overvaluation effect, Danielsen and Sorescu (2001) find that the option introductions are followed by negative abnormal returns in underlying stocks. Finally, Ofek, Richardson, and Whitelaw (2004) show that violations of put-call parity are asymmetric in the direction of short-sale constraints and their magnitudes are strongly related to the cost and difficulty of short selling. Recent studies examine the relationship between short selling and put option activity. Blau and Wade (2009) show that while short selling and put option activity predict negative returns at the daily level, put-call ratios contain less information about future stock price movements than short selling 7

10 ratios. 4 Their results suggest that a greater portion of pessimistic, informed trading occurs in the stock market than in the options market. Furthermore, the short selling of stocks without tradable options is influenced more by the proxy for equity borrowing costs than by the short selling of stocks with options. Grundy, Lim and Verwijmeren (2012) examine the effects of the September 2008 short-sale ban on the derivatives market and find that option volume decreased during the period of the ban with the effect strongest for put options. They also find that adjusted relative bid-ask spreads tended to increase for banned stocks during the ban period. Their results suggest that buying put options and short sales are complementary rather than substitutive actions. B. Hypotheses Based on the literature, we construct a number of hypotheses relating to the effect of options market activity on the return predictability of short interest. The first hypothesis relates to the return predictability of short interest for stocks with and without stock options. According to previous studies (Black (1975); Mayhew, Sarin, and Shastri (1995)), informed traders prefer to trade stock options instead of the underlying stocks. Therefore, if stock options are not available, informed investors have no choice but to trade in the stock market, such that the shortselling activity conveys negative information. However, if stock options are available, informed investors might be better off trading in the options market to mitigate short-sale constraints, in which case the shortselling activity conveys less information. This leads to the first hypothesis: H1: The stock return predictability of short interest is weaker for stocks with traded options than it is for those without such options. The second hypothesis relates to the information content of short-selling activity after controlling for options market activity. According to the literature, both the stock and options markets contain valuable private information (Chan, Chung, and Fong (2002); Pan Poteshman (2006)), which suggests 4 Blau and Wade s (2009) put-call ratio is constructed based on trading volume rather than the open interest of what we investigate in this paper. 8

11 that informed investors will trade in both markets. Therefore, even if short-selling activity contains private information, the information content may overlap with the options market activity. This leads to the second hypothesis: H2: The return predictability of short -interest is weaker once we control for the information content of options market activity. The third hypothesis pertains to the source for the return predictability of short-selling activity compared to that of options market activity. A number of studies argue that the negative abnormal returns associated with highly-shorted stocks is a reflection of short-sale constraints (Jones and Lamont (2002); Asquith, Pathak, and Ritter (2005)). When the short-interest level is high and the cost of shorting is large, informed investors short sell only if they anticipate a net positive return from trading in futures. Consequently, apart from reflecting private information, the return predictability of short interest is also related to short-sale costs. Because there are no short-sale constraints in the options market, the return predictability of options market activity comes primarily from the discovery of private information. This leads to the third hypothesis: H3: The return predictability of short interest is related to both short-sale constraints and the discovery of private information while the return predictability of options market activity is primarily due to the discovery of private information. 3. Data and Preliminary Statistics The data on monthly stock returns and the number of shares outstanding for domestic common stocks listed on NYSE, Amex and NASDAQ (share codes 10 and 11) for the period from 1996 to 2008 are from the Center for Research on Security Prices (CRSP) at the University of Chicago. Monthly shortinterest data are from the exchanges, with the exception of Amex from 2005 to 2008, which are from Compustat. We calculate the SIR by dividing the number of shares shorted by the number of shares outstanding. Data on institutional ownership are from 13-F filings, available from Thomson Financial. We define the IO percentage as the sum of the holdings of all of the institutions for each stock in each 9

12 quarter divided by the number of shares outstanding obtained from the CRSP. We obtain options open interest data from OptionMetrics for the period. Because the monthly short-interest data reflect the short position as of the settlement on the fifteenth of each month, we retrieve the options open interest data mid-month to match the short-interest data. For each stock we aggregate the open interest across all put and call contracts mid-month, respectively, and calculate the PCOIR as the total amount of put open interest relative to the total amount of call and put open interest. We also compute the total open interest ratio, which is the ratio of total open interest (in equivalent number of shares) aggregated across all put and call options, relative to the number of shares outstanding. Table 1 presents the number of firms with data retrieved from different sources. In 2008, out of 4731 firms retrieved from the CRSP, 4575 have short-interest data and 2309 have options data. NYSE firms have a higher fraction of stocks with options and the corresponding numbers are 81% (1145 out of 1416), 8% (41 out of 495) and 40% (1123 out of 2820) for NYSE, Amex and Nasdaq, respectively. Table 2 describes the time series of several variables. For each variable we retrieve the monthly figure for each firm and then take the average within a year. We then compute the cross-sectional average across all of the firms on an equal-weighted basis. We observe an upward trend in most variables from 1996 to In the sample of stocks with options, IO increases from 51% to 72%, SIR increases from 2.59% to 9.86% and the total open interest ratio increases from 0.97% to 4.45%. The PCOIR increases from 26% in 1996 to 39% in 2008, which suggests that over time, put open interest increases faster than call open interest. In the sample of stocks without listed options, there is also an upward trend for the IO and SIRs. Compared to firms with listed options, firms without such options are smaller in firm size, have lower IO percentages and lower SIRs. This suggests that stocks without options are more difficult to short than those with options. This is consistent with D Avolio (2002), who finds that stocks with low IO and no listed options are more likely to be on special in the equity lending market (i.e. negative rebate rate). For stocks without options, low short-interest might indicate a biding shorting constraint rather than an absence of interest in shorting the stock. It is therefore interesting to examine the effect of options 10

13 availability on the SIR s information content. Because high short-interest stocks are larger in firm size and have higher IO, it is possible that differences in firm size and IO, rather than short interest, might explain the cross-sectional variation of returns across firms. In principle, the firm size difference should not be an issue because the four-factor model used for risk adjustment explicitly controls for size. However, it is necessary to control for IO. Table 3 reports simple correlations among several key variables. The options dummy takes a value of 1 for stocks with listed options and a value of 0 for stocks without such options. The correlation analysis confirms that stocks with options are larger and have higher IO. When a stock has a higher amount of IO, there are more shares available for borrowing, which explains why the SIR is positively correlated with IO. Given that options availability, firm size, SIR and IO are positively correlated, we must control for their effects when investigating the predictive power of the SIR. The correlation between the PCOIR and the SIR is 0.15, which suggests that the two variables are driven by different factors. As a preliminary analysis, Figure 1 presents the average monthly future raw returns of ten decile portfolios, sorted by the SIR) and the PCOIR, being held for 1-, 2-, 3-, 6-, 9- and 12-month periods. These portfolio returns are calculated on an equal-weighted basis. Calculating portfolio returns based on valueweighting produces a similar, but slightly weaker, pattern. Panel A shows a clear monotonic pattern in which future raw returns decline with the SIR decile, with the evidence persisting for different holding periods. In contrast, Panel B shows that the stock return predictability of the PCOIR is much weaker, despite the fact that the lowest PCOIR portfolio outperforms the highest PCOIR portfolio consistently across different holding periods. It should be noted that for either the highest SIR decile or the highest PCOIR decile, the average portfolio returns remain positive. Therefore, a strategy that takes only a short position in the stocks with the highest SIR or the highest PCOIR will lose money. Only an arbitrage strategy that takes a long position in the high SIR or high PCOIR stocks and a short position in the low SIR or low PCOIR stocks produces positive returns. 4. Empirical Analysis 11

14 A. Calendar-Time Portfolios This section provides an analysis of the return predictability of SIRs and put-call ratios. We sort stocks into ten deciles based on these predictors and compute monthly returns for each decile portfolio in future months. To adjust for the risk, we adopt a calendar-time portfolio approach to measure the abnormal performance of portfolios sorted based on the predictors. According to Desai, Ramesh, Thiagarajan and Balachandran (2002) there are a few advantages to this approach. First, the variance of the portfolio automatically takes into account the cross-sectional correlation among the individual securities that comprise the portfolio. Second, the calendar-time event portfolio approach represents an implementable investment strategy. At the beginning of each month in the sample period, we form portfolios of firms based on the SIR or the PCOIR. These portfolios are rebalanced monthly so that we have monthly portfolio returns for the whole sample period. We then estimate abnormal returns from the four-factor risk adjustment model for each portfolio: R port,t R f,t = α + b RMFT t + s SMB t + h HML t + m MOM t + ε (1) where R port,t R f,t is the realized portfolio return in excess of risk-free on month t, RMFT t is the realized market risk premium on month t, SMB t is the return on a portfolio of small stocks minus the return on a portfolio of large stocks on month t, HML t is the return on a portfolio of high book-to-market stocks minus a portfolio of low book-to-market stocks on month t and MOM t is the return on a portfolio of prior winners minus the return on a portfolio of prior losers. The intercept α is our measure of monthly abnormal return performance. Data for the factor portfolio returns are from Kenneth French s web site at Dartmouth. We construct portfolios using both equal- and value-weighted approaches. Consistent with previous studies, we find that the predictive ability of the SIR or the PCOIR is more pronounced for equal-weighted portfolios. Some studies argue that value-weighted portfolios are more appropriate for performance evaluation because value-weights reflect the holdings of average investors. However, 12

15 informed investors or arbitrageurs are not the average investors and do not have to hold value-weighted portfolios. Instead, they construct portfolios that give them the highest abnormal returns, which in this case are the equal-weighted ones. For this reason and to conserve space, we only present the abnormal returns analyses for equal-weighted portfolios. Table 4 presents the future abnormal returns for portfolios based on the SIR in the 1 st, 2 nd, 3 rd, 4 th - 6 th, 7 th -9 th and 10 th -12 th months subsequent to portfolio formation. Panel A reports results for equalweighted portfolios using all firms. Consistent with previous studies, we find that abnormal returns decrease monotonically with the SIR. The monthly abnormal returns for the 1 st month are 0.92% for the lowest SIR portfolio and -0.48% for the highest SIR portfolio, which implies an SIR premium of 1.4%. Consistent with Boehmer, Huszar, and Jordan (2010), the predictive power of the SIR comes more from the lightly shorted stocks than from their heavily shorted counterparts, because a larger part of the SIR premium stems from the abnormal returns of low SIR stocks. The SIR premiums persist for longer holding periods and are 1.35%, 1.30%, 1.15%, 0.88% and 0.68% for the 2 nd, 3 rd, 4 th -6 th, 7 th -9 th and 10 th - 12 th months, respectively, which is inconsistent with market efficiency because investors can learn from the SIR to predict future returns, even up to twelve months. However, because the arbitrage strategy requires that investors buy low SIR stocks and short sell high SIR stocks, the cost of borrowing shares of high SIR stocks might render the strategy unprofitable. Nevertheless, long-only investors can improve the portfolio performance if they tilt the portfolio holdings toward low SIR stocks and avoid high SIR stocks. Panels B and C report results partitioned for two sub-samples: stocks with listed options and stocks without. To save space, only abnormal returns in the lowest and highest SIR deciles are presented. We continue to observe that abnormal returns decrease monotonically from the low SIR to the high SIR portfolios in both sub-samples. For example, in the 1 st month, the lowest SIR portfolio outperforms the highest SIR portfolio by 1.29% for stocks without options and by 1.17% for stocks with options. Therefore, even for optionable stocks, whereby investors can buy put options when the underlying stocks are overpriced, this does not appear to diminish the stock return predictability of the SIR. The evidence is inconsistent with H1, which specifies that the return predictability of the SIR is weaker for stocks with 13

16 options than for those without. It should be noted that for both subsamples, the predictive ability of the SIR continues for the longer holding period. For example, during the 10 th -12 th months, the lowest SIR portfolio outperforms the highest SIR portfolio by a monthly return of 0.81% for stocks without options and by 0.72% for stocks with options. The evidence that monthly short-interest data also contain positive information is not entirely consistent with the short-sale constraint hypothesis. Although the short-sale constraint can explain why the prices of heavily shorted stocks cannot be immediately corrected for overvaluation, it cannot explain why the prices of lightly shorted stocks adjust slowly to reflect positive information from public shortinterest data. What our evidence shows is that a low level of the SIR can predict higher abnormal returns, at which point investors simply take a long position in the low SIR stocks for up to twelve months to capture the higher returns. The evidence contradicts market efficiency. Table 5 reports the average monthly returns for stocks ranked by PCOIR. Because this analysis is limited to stocks with options, the firms used in Table 5 are a subset of those in Table 4. We find that the PCOIR also contains predictive ability for future returns. Monthly abnormal returns for the 1 st month are 0.62% for the lowest PCOIR portfolio and -0.27% for the highest, which implies a PCOIR premium of 0.89%. The PCOIR premiums persist for longer holding periods and are 0.67%, 0.72%, 0.72%, 0.68% and 0.45% for the 2 nd, 3 rd, 4 th -6 th, 7 th -9 th and 10 th -12 th months, respectively. Compared to Table 4, the PCOIR premiums are smaller than the SIR premiums, and the cross-sectional regression analysis in the next section investigates whether the SIR premiums are affected after controlling for the PCOIR premiums. B. Regression Analysis of the Predictive Ability of SIRs and Put-Call Ratios Our analyses demonstrate that both SIRs and put-call ratios can predict future returns. To explore their incremental predictive abilities we employ a cross-sectional regression analysis using the different predictors (i.e. the SIR and the PCOIR) as explanatory variables to compare their predictive abilities. Table 6 reports the cross-sectional regression results for individual stocks. The dependent variable is the future abnormal returns for an individual stock estimated from the four-factor model and 14

17 the explanatory variables are the SIR and PCOIR of the stock. We also relate these two variables with institutional ownership (IO) to control for its effect while investigating the information content of SIRs and put-call ratios. A number of studies (e.g. Chen, Hong, and Stein (2002); Asquith, Pathak, and Ritter (2005)) postulate that IO is a proxy for short-sale constraints. They posit that short-sale constraints are strongly linked to the availability of shares to borrow and argue that these constraints are relaxed when there is an increase in the number of institutional investors owning the stock. D Avolio (2002) documents that institutional investors are the main suppliers of stock loans and show that the amount of IO explains much of the variation in loan supply across stocks. Using IO as a proxy for short-sale constraints, Nagel (2005) finds that some stock return anomalies (e.g. the under-performance of stocks with high market-tomarket stocks, analyst forecast dispersions, turnovers or volatility) are most pronounced among stocks with low IO. We estimate the regressions using a panel specification with time-fixed effects and standard errors estimated to allow for firm clustering. 5 Table 6 reports the results using abnormal returns in different subsequent months. We begin by discussing the results based on the 1 st month s abnormal returns. In the univariate regression analysis, the SIR is significantly negative while the PCOIR is negative and marginally significant, which confirms that abnormal returns decline when the SIR or PCOIR is higher. In the multivariate regression analysis, we find that both variables are still considerably significant, which suggests that the SIR and the PCOIR have incremental predictive ability for future returns. In fact, in comparing the coefficients of the SIR in the univariate regression to those in the multivariate regression, we do not find that the return predictability of the SIR is much reduced, even after controlling for the information content of the PCOIR. Therefore, the results are not consistent with H2, which posits that the information content of short interest and options market activities are similar. We find that the interaction term SIR*IO is positive and statistically significant, which suggests that a lower level of IO increases the return predictability from the SIR. This result appears to be 5 We also adopt the Fama-MacBeth regression approach, whereby we estimate the regression for each month and then compute the time-series averages of the coefficients from the monthly regression. The results are qualitatively similar. 15

18 consistent with Asquith, Pathak and Ritter (2005), who explain that a limited supply of shares to borrow (low IO) indicates a tighter short-sale constraint, such that there is a stronger negative relationship between the SIR and future returns. However, we also find that the interaction term PCOIR*IO is positive and statistically significant, which indicates that a lower level of IO also increases the predictive ability of the PCOIR. Because the options market is not subject to short-sale constraint, the amount of IO should not affect the return predictability of the PCOIR via the effect of short-sale constraint. An alternative interpretation is that a lower level of IO results in a larger mispricing of the stocks, such that both the SIR and the PCOIR have larger stock return predictability. For the analysis based on abnormal returns in other subsequent months, the results are generally similar. The SIR remains significant in predicting future abnormal returns while the predictive ability of the PCOIR disappears once we control for the information content of the SIR. In fact, for some future months, the PCOIR does not have any predictive ability for subsequent returns even without controlling for the SIR. One interpretation is that options trading is based on short-term information, such that the PCOIR does not have much information content after the 1 st month. In contrast, short-sellers in the stock market have information over the long-term, such that the SIR can predict future returns even in the longterm. The interaction term SIR*IO is no longer positive and statistically significant in future months, which suggests that the predictive ability of the SIR in the long-term is not weakened by a higher amount of IO. As a robustness test, we also investigate the incremental information content of SIR by relating it to another put-call ratio, namely the put-call buy volume ratio (PCBVR). The ratios are constructed using the CBOE dataset that consists of daily records of non-market maker trading volume activity for all CBOE listed options from 1996 to 2008, from which we extract the open-buys that are initiated by a buyer to open a new option position. According to Pan and Poteshman (2006), open-buys contain more information about future returns than simply the option volume, at least over short time intervals. We therefore focus on open-buys and aggregate daily open-buy contracts for call options and buy options each month, then calculate the PCBVR as the total number of open-buy put contracts to the total number 16

19 of open-buy call and put contracts. We then perform a cross-sectional regression analysis of abnormal returns on individual stocks with the monthly SIR and PCBVR of the stocks as explanatory variables. The results are not reported in this paper, but will be made available upon request. Briefly, while the PCBVR has a larger return predictability than the PCOIR, even after controlling for the PCBVR, the SIR remains statistically significant in predicting future returns. Similar to the PCOIR, the predictive ability of the PCBVR is for the relatively short-term and it does not affect the return predictability of the SIR in the long-term. C. Predictive Ability of the SIR with and without Stock Options The analysis so far suggests that the return predictability of the SIR appears to be unaffected by options market activity. First, using abnormal returns in the first month subsequent to sorting stocks based on the SIR, the lowest SIR portfolio outperforms the highest SIR portfolio by more than 1% in both samples of stocks with and without options. This result is inconsistent with H1, which specifies that the return predictability of short interest is weaker for stocks with options than it is for those without. Second, the return predictability of the SIR is not reduced even after controlling for the options market activity (either based on open interest or buy volume), and the return predictability of the SIR is greater than that of options market activity, especially for the long-term. This evidence is inconsistent with H2, which states that the information content of short-interest and options market activity are similar. In Table 7, we conduct additional tests of H1 relating to the effect of options market activity on the return predictability of the SIR, based on regression analysis. We introduce dummy variables for options market activity and relate them to the SIR in predicting future abnormal returns. Three dummy variables are introduced. The first (optiond) equals 1 for stocks with listed options and 0 for stocks without. If the existence of listed options can alleviate the short-sale constraints and divert informed trading from the stock market to the options market, we should see lower return predictability for the SIR, such that the interaction term (SIR*optionD) is expected to be positive. The results in Table 7 show that this is not the case. Regardless of which future month of abnormal returns we use as the dependent 17

20 variable, the interaction term (SIR*optionD) is not significantly positive. One possible explanation for the insignificance of this interaction term is that the options market must be relatively liquid, otherwise informed investors do not move from the stock market to the options market. We therefore introduce the second dummy variable (optionactived), which equals 1 for stocks with option trading volume above the median and 0 for stocks below the median, relating it to the SIR in predicting future abnormal returns. However, Table 8 indicates that the interaction term (SIR*optionActiveD) remains insignificant in predicting future abnormal returns. Another explanation for the insignificance of the interaction term (SIR *optiond) in explaining future abnormal returns is that firms with listed options might have larger market capitalization and more liquid stock market activity than firms without listed options, such that one cannot strictly compare the predictive ability of the SIR for these two samples of stocks. To address this issue, we introduce the third dummy variable (optionintrodd), which equals 1 for the stocks in the year after the introduction of listed options and 0 in the year before the introduction. Because we focus exclusively on the stocks around the years before and after the introduction of listed options, there are much fewer observations for that regression specification. The advantage of this regression specification, however, is that we can remove the effect of stock characteristics and investigate how the predictive ability of the SIR will be affected around the year that the listed stocks are introduced. The results in Table 7 show that the interaction term (SIR*optionIntrodD) is significantly negative, except in predicting abnormal returns in the10 th -12 th months. This evidence is interesting, but contrary to our expectations because it suggests that there is more information conveyed in the SIR after the options are introduced. Overall, regardless of which regression specification we employ, the evidence is not consistent with H1, which specifies that option trading has an effect on the return predictability of the SIR. Rather, this result shows that informed investors do not necessarily trade stock options even if they are available, because the lower liquidity of the options market might discourage informed traders from trading options (Easley, O Hara, and Srinivas (1998)). As Grundy, Lim and Verwijmeren (2012) argue, short selling and options market activities are complements rather than substitutes. With the 18

21 introduction of listed options, this also increases the liquidity of underlying stocks, such that informed traders can short-sell the stocks more easily. Therefore, while some informed trading occurs in the options market, investors will also short-sell stocks to take advantage of the negative information to ensure that the return predictability of the SIR does not decline. D. Analysis Based on Expected and Unexpected SIRs The evidence so far shows that the SIRs and put-call interest (or buy volume) ratios contain predictive ability for future returns. Although the predictive ability for put-call ratios is most prevalent for the 1 st month, the predictive ability for the SIR continues even in the long-term. Furthermore, the information content of short interest and put-call interest does not seem to overlap. In this section, we conduct tests on H3, which posits that the return predictability of short interest is related to both short-sale constraints and the discovery of private information while the return predictability of options market activity is driven by the discovery of private information. To determine whether the return predictability of the SIR is related to new information, we decompose the realized SIR into expected and unexpected components and see which component predicts subsequent stock returns. According to Huszar and Qian (2011), the highly shorted stocks might incur higher stock borrowing costs, such that the abnormal returns to these stocks will disappear once the loan fees are taken into account. Consequently, the predictive ability of the SIR, which is more likely to be captured by the expected component, is related to short-sale constraint and has nothing to do with new information. If there is any new information for the stock, it should be reflected in the unexpected component rather than in the expected component. Unlike Huszer and Qian (2011), who estimate the expected SIR using a number of firm characteristics, we estimate the expected SIR (E_SIR) in the current month based on the moving average of the SIR in the last twelve months: 19

22 12 SIR = 1 = t i, t E _ SIRi,t (2) 12 and the unexpected SIR (U_SIR) based on the difference between the realized SIR and the expected SIR: U _ SIRi,t = (3) SIRi,t E _ SIRi,t We then examine whether the expected SIR or the unexpected SIR contains predictive ability for future returns. We perform a similar decomposition for the PCOIR, decomposing it into an expected component (E_PCOIR) and an unexpected component (U_PCOIR) with the expected component calculated based on the twelve-month moving average of the PCOIR. We also employ a regression analysis to examine the explanatory power of the E_SIR, U_SIR, E_PCOIR and U_PCOIR in predicting abnormal returns (Table 8). For the SIR, both the E_SIR and U_SIR possess predictive ability for future abnormal returns, both short- and long-term. These two variables do not subsume the other in predicting future abnormal returns, which suggests that the predictive ability of the SIR is related to both short-sale costs (from the expected SIR) and information discovery (from the unexpected SIR). However, for the PCOIR, only the unexpected component (U_PCOIR) and not the expected component (E_PCOIR) contains predictive ability for future abnormal returns. Therefore, the return predictability of the PCOIR is related to information discovery, but not related to short-sale constraints. To summarize, our results are consistent with H3, which states that the return predictability of short interest is related to both short-sale constraints and the discovery of private information while the return predictability of options market activity is primarily driven by the discovery of private information. We also conduct an additional analysis that examines whether the positive and negative components of unexpected SIR and PCOIR have a different degree of return predictability to determine whether the positive and negative information is incorporated symmetrically in the stock and options 20