Price discovery in the corporate bond market: The informational role of short interest

Transcription

1 Price discovery in the corporate bond market: The informational role of short interest By Paul A. Griffin,* Graduate School of Management, University of California, Davis, and Hyun A Hong, Fogelman College of Business & Economics, University of Memphis Version of: October 11, 2011 Comments welcome * Corresponding author: Graduate School of Management, University of California, Davis, (tel.), (fax), pagriffin@ucdavis.edu. All errors and omissions are ours.

2 Price discovery in the corporate bond market: The informational role of short interest Abstract This paper identifies a precursory role of short sellers in conveying adverse information to the corporate bond market. We study this in two ways, by examining subsequent calendar month excess (risk-adjusted) bond returns for portfolios formed on the basis of high short interest in a prior month, and by analyzing daily excess bond returns around earnings announcements. Our findings are consistent with the view that short interest plays an informational role in setting bond prices. In the context of earnings announcements, this occurs because short traders benefit from useful information prior to a news announcement, possibly from leakage or private access, and perform a more rigorous analysis of the announcement itself, where such analysis is reflected in prices with delay. A zero-investment hedge portfolio of bonds based on the most and least extreme high short interest positions generates a statistically significant annualized excess return of 3.84 percent. Taken together, these findings support the theoretical prediction of Diamond and Verrecchia (1987) that higher-levels of short interest convey adverse, pre-public information, thereby contributing to price discovery in securities markets. JEL Classification: G12, G14, G24, M41. Keywords: Short interest, corporate bonds, excess bond returns, Regulation SHO, price discovery, earnings announcements. ii

3 Price discovery in the corporate bond market: The informational role of short interest 1 Introduction In the four weeks prior to Standard & Poor s after-hours August 5, 2011 announcement that it would downgrade U.S. debt, NYSE short interest on all stocks rose almost 150 percent, from an average of 84 million shares per day in July 2011 to a peak of 209 million shares on August 8, the first day of post-announcement trading. A week later, total short interest had reverted to a more normal level of 108 million total shares. Over the same interval, Treasury bonds, as per Barclays Capital 20+ year Treasury Bond index (TBT), plummeted by 19.4 percent from a July 2011 average of $33.01 to close on August 8 at $26.32 on peak volume of 44 million shares. Treasury bondholders lost substantially as yields rose during this period. Did the NYSE short interest telegraph information to Treasury bond investors during this period? Clearly, short positions in stocks increased and bond prices decreased. But those short positions also declined rapidly after the Treasury bond downgrade as short traders changed their views on prices. A natural question to ask is and the one we ask in this paper did those high short positions in stocks convey additional insights to bond investors not reflected in other information available to the market, and if so when? Diamond and Verrecchia (1987) provide strong theoretical guidance that if short selling reflects unpublicized adverse information, it should promote accurate pricing of securities. In this study, we test the Diamond-Verrecchia prediction in the context of the debt market. Rather than focusing on a single event such as the S&P U.S. debt downgrade and a single security such as an exchange-traded bond index, we examine the relation between short interest and bond returns for a large sample of corporate bonds. To understand how short traders information relates to bond price discovery, we use of corporate instead of government bonds, allowing us to condition our 1

4 results on different levels of default risk and related financial statement factors. In addition, the high frequency nature of the short sales trading data from the Securities and Exchange Commission s Regulation SHO (hereafter Reg. SHO) allows us to study daily short interest and bond returns around earnings announcements. This helps us understand why short sellers might have an advantage in the bond market. For instance, short sellers may be privy to information from leakage prior to firms earnings announcement or they may be more capable of interpreting those announcements more accurately. Specifically, we study the relation between short interest in stocks for NYSE-listed firms covered under Reg. SHO and those same firms bond returns during the sample period from January 2005 to July We use data for Reg. SHO companies as short trading was unrestricted in this period for these companies. This topic is important for several reasons. First, our review of the accounting and finance literature uncovers a conspicuous absence of work on this topic. This is surprising given that numerous studies examine relations between short interest and stock returns (reviewed in section 2). We fill this literature imbalance by investigating whether high short interest stocks experience negative and significant future excess bond returns. Given the size of the corporate bond market, studying the role of short interest in bond price discovery and bond market efficiency should be important in its own right. Bonds represent a significant source of external financing for public companies, which substantially exceeds equity financing for many. During the ten-year period from 2001 to 2010, for example, U.S. corporate bond issuances amounted to $8.58 trillion, compared to $583 billion for equity issuances. 2 Second, evidence of whether high short interest in stocks might aid in price discovery for bonds of the same company should be important for 1 The Securities and Exchange Commission s Regulation SHO required that certain exchanges make short sale data available publicly for research to study the effects of relatively unrestricted short selling on market volatility, price efficiency, and liquidity. (SEC 2004)

5 regulatory bodies like the SEC, that are obliged to understand how both stock and bond investors process information. That said, studies by regulators about the costs and consequences of short selling have typically ignored possible links between short interest in stocks and the bond market despite regulators views on how short selling might affect market efficiency, competition, and capital formation (e.g., SEC 2007). Third, the asymmetric risk of bonds versus stocks means that empirical findings about short interest and stock returns may not map directly into corporate bond market behavior. This occurs because bondholders are fixed claimants in the firm and have an asymmetric interest in the downside risk of their securities relative to the upside potential, compared to stockholders who are residual claimants. Knowledge of high short interest firms, for example, could provide additional information to bondholders about default risk and the possibility of a downgrade. Our investigation first finds that bond investors experience significantly negative excess (risk-adjusted) returns over several months following high short interest positions, and that those excess bond returns vary negatively in the level of short interest and in the increase in abnormal short interest over the prior month. We establish these results based on a calendar-time portfolio approach that estimates abnormal bond return for the high short interest firms after controlling for the five risk factors in Fama and French (1993). A matched sample of firms without abnormal short interest, on the other hand, experiences future bond returns that have no association with the level of short interest. This matched sample result increases the likelihood that the precursor bond signal derives from short interest per se rather than a missing risk factor. To advance our understanding of how adverse information conveyed by short sellers influences bond investors who have an asymmetric payoff function, we partition the bond issuances into investment grade or speculative grade. As already noted, knowledge of high short interest could provide additional information about a firm s default risk and the possibility of a 3

6 bond downgrade due to bondholders asymmetric payoff function. However, the information from short positions should be more germane to holders of speculative-grade bonds, in that they face greater downside risk or uncertainty relative to those of investment grade bonds. Our empirical evidence supports the preceding logic. When we partition our observations on investment grade or speculative grade bonds, we find that significantly negative excess returns follow high short interest positions for speculative grade bonds, whereas insignificantly negative excess returns follow high short interest positions for investment grade bonds. Finally, we examine whether the information advantage of high or increasing short interest is consistent with bond investors recognition of short interest information before and after an earnings announcement. We choose earnings announcement as the event of interest as bond returns have been shown to have a contemporaneous relation with unexpected earnings (Datta and Dhillon 1993, DeFond and Zhang 2011). We find a significant negative relation between pre-earnings announcement abnormal short interest and announcement period excess bond returns. What is more, there is a significant negative relation between earnings announcement period abnormal short interest and post-announcement excess bond returns. These results indicate that short traders pre-earnings announcement positions associate with significantly negative excess bond returns in the announcement interval. They therefore support the view that short traders have an information advantage in the bond market. We also find that bond price discovery extends beyond the earnings announcement interval, as abnormal high short interest upon earnings announcement associates with significantly negative post-earnings announcement excess bond returns, arguably because short sellers have a superior ability to interpret the public signal to their advantage (Engelberg et al. 2010). Overall, our results document that bond prices impound high or increased short interest in a company s stock with delay. Strategies based on high short interest not only generate 4

7 significantly negative excess bond returns over the several future calendar months, but around the earnings announcement interval as well. While these results parallel findings regarding short interest and stock returns (e.g., Desai et al. 2002), they are also important in their own right because they extend our knowledge about price discovery and market efficiency in the bond market by showing that the informed actions of short traders have a direct and material pricing consequence for corporate bonds. The economy-wide ramifications of the news telegraphed by high short interest increase substantially when we realize that corporate bonds are no less immune than stocks to the analysis and insights of specialist short traders whose actions flag important information for bond price discovery. Many firms use bonds far more extensively than stocks as a form of external financing. Our paper proceeds as follows. Section 2 reviews the prior literature and states the hypotheses to be tested. Section 3 outlines the sample and data. Section 4 presents the results and section 5 concludes. 2 Literature and hypotheses 2.1 Short interest and stock returns Our study builds upon two literatures, where the first involves short interest and stock returns (this subsection) and the second relates to bond investors response to news events (subsection 2.2). 3 The first literature covers three strands of interest. First, several studies examine relations between short interest and stock returns, in calendar time and around specific events (Senchack and Starks 1993, Desai et al. 2002, Christophe et al. 2004, Pownall and Simko 2005, Akbas et al. 2008, Boehmer et al. 2008, Diether et al. 2009, Boehmer and Wu 2010, Lui 3 Since Jones and Larsen (2004) review the early literature, this section concentrates on the more recent studies of short interest and information content, although we do not provide a comprehensive coverage. 5

8 and Swanson 2011). With some exceptions (e.g., Woolridge and Dickinson 1994), these studies support the Diamond and Verrechia (1987) proposition that short sellers trade mostly to exploit an information advantage rather than to hedge or speculate. A second strand delves into the information advantage short sellers might telegraph through trading with other investors. Some studies contend that short sellers derive an information advantage through better analysis of accounting information like accruals (Desai et al. 2006, Cao et al. 2007, Bhojraj and Swaminathan 2009), financial ratios (Dechow et al. 2001), earnings surprises (Christophe et al. 2004, Lasser et al. 2010), and news items more generally (Engelberg et al. 2010). Others posit that short traders information advantage stems from their superior use of private or costly predictive information to anticipate price-sensitive adverse events such as earnings restatements (Efendi et al. 2006), analysts recommendations (Drake et al. 2011), and securities litigation (Griffin and Grundfest 2002, Griffin 2003, Karpoff and Lou 2010). The evidence is mixed on which of these two views dominates, that is, whether short sellers have superior capability of interpreting public information or gain from using private or costly predictive information not easily exploitable by others. This conundrum may stem from definitional issues, however, such as whether short traders use private or costly public information to make better predictions of news events. 4 In this regard, Griffin and Grundfest (2002) suggest that insiders and short traders use similar costly public information around securities litigation events, where short sellers information most likely derives from superior analysis of company insiders Form 4 (insider transaction) filings and public class action 4 We use costly in the term costly public information to reflect those additional search, information acquisition, and analysis costs that may enhance the investment value of a public disclosure or news event. 6

9 documents rather than the illegal use of non-public information. 5 More recently, however, Kahn and Lu (2011) document significant increases in short sales immediately prior to large insider sales, implying that some leakage to short traders might occur prior to a Form 4 filing. A third strand examines the effects of regulations on short interest. These include the uptick rule (Aitken et al. 1998, Ali and Trombley 2005), the costs and difficulties of short selling as trade-limiting factors (Jones and Lamont 2002, Chen et al. 2002), and other constraints on short selling. These studies generally conclude that such constraints and costs can lead to overpricing in the equity markets. This third strand does not examine responses by bond investors as we do here. In sum, our review of this literature identifies several unexplored issues. Our review also spotlights a central hypothesis for our tests (stated formally below) that short selling reflects an information-based activity, telegraphed to investors through trading, whose implications are ultimately reflected in market prices, though not necessarily instantaneously. Stock market prices have been used extensively to test and support information-based hypotheses about short trading, whereas corporate bond prices have not. Formally, we test the following hypothesis, stated in the alternative form, that: H1: Higher or increasing short interest associates with future negative excess bond returns. 5 Jin et al. (2011) analyze a variant of these hypotheses in the context of options markets and find that options traders reflect two kinds of informed trading: trading based on (1) access to private or costly public information (superior information discovery) or (2) better analysis of public news and events (superior information processing). 7

10 2.2 Corporate bond investors reaction to news events Compared to forty-plus years of studies of stock price reactions to news items, the literature on bond or debt market response is remarkably limited (Armstrong et al. 2010, p. 212). Data considerations aside, and despite encouragement by others, 6 we find this surprising, as there are valid reasons why corporate bond prices might respond differently to stock prices for the same news event. For example, bond investors have a fixed claim on the firm, and thus they have an asymmetrical interest in the downside risk of their securities relative to their upside potential. Two recent studies support this view. Easton et al. (2009) find that losses but not profits mostly explain the positive association between bond returns and earnings; and DeFond and Zhang (2011) report that bond prices anticipate much of the information in bad news, but none of the information in good news. On the other hand, if viewed within an options framework, bond prices should adjust oppositely to stock prices, especially when the new information signals increased risk or higher future dividends (Handjinicolaou and Kalay 1984). Other studies show that bond markets respond to news events similarly to stocks. Bhojraj and Swaminathan (2009) show that bond markets reflect the accrual anomaly, and Datta and Dhillon (1993) find that bond returns vary symmetrically with unexpected earnings. 7 On balance, these studies suggest that bond investors response to an earnings surprise likely reflects at least two general factors: a response to the amount and quality of information about future cash flows, in particular, negative cash flows (a positive relation for stocks and bonds) and 6 For example, Beyer et al. (2010, p. 336) state it is an economic fact that the debt markets are a significant part of any developed economy. As such, research should consider the role of debt markets in the firm s information environment... 7 Bonds and stocks might also respond differently to the same information because of differences in investors, for example, bonds may attract more longer-term and institutional investors, whereas stocks may cater more to transient, higher risk investors. 8

11 an offsetting response to higher uncertainty about those cash flows (a negative relation for stocks and a positive relation for bonds, assuming an options framework). We anticipate that our analysis will help understand which of these two announcement effects dominates regarding bond prices. Lastly, Hotchkiss and Ronen (2002) find that bond prices incorporate financial news quickly and contemporaneously, similar to a stock market efficient with respect to public information. None of these bond price response studies tests the precursory role short interest has in delivering adverse information to corporate bond investors or in conditioning their response to earnings announcements. In this study, we posit two possible reasons that explain the precursor role of short interest to convey adverse information to bond investors, namely, short sellers : (a) pre-earnings announcement use of private or costly information not easily exploited by others (superior information access) or (b) superior post-earnings announcement ability to interpret the public earnings signal, including estimating the downside risk or uncertainty of firms future cash flows (superior public information analysis). Understanding why high short interest relates to subsequent negative excess bond returns expands our understanding of price discovery and efficiency in the corporate bond market. We examine this issue by focusing on the predictive ability of short sellers around the earnings announcement date. If short sellers obtain negative, pre-public information, we predict that short selling immediately prior to the announcements will associate with negative excess bond returns around the earnings announcement date. On the other hand, if short sellers have a superior ability to process publicly available accounting information subsequent to earnings announcement, high short selling immediately after the earnings announcement date should be correlated with negative future excess bond returns. Formally, we test the following hypothesis, stated in the alternative form, that: 9

12 H2: Pre-public information prior to the earnings announcement (H2a) or superior ability of short sellers to process the publicly available earnings announcement (H2b) explain the relation between short interest and subsequent negative excess bond returns. 3 Sample and data We base our tests on a merged sample of firms with short interest and outstanding corporate bonds. Our short interest sample comprises all NYSE firms covered by the pilot program of the SEC s Regulation SHO (SEC 2004), which the NYSE included in its release of Reg. SHO short interest data for the period January 3, 2005 through August 6, As Reg. SHO exempted approximately one-third of the Russell 3000, including the NYSE firms in our sample, from short sale restrictions such as the uptick test under Rule 10a-1 of the 1934 Securities and Exchange Act, we study short interest positions essentially free of those restrictions. With minimal restrictions, short selling should increase its contribution to price discovery through informed trading (Alexander and Peterson 2008), making it more difficult to find a lead relation between short interest and market prices should one exist. The Reg. SHO data also help correct a selection-bias problem (Chen et al. 2002) in that non-reg. SHO short interest data, as used in many other studies, may not proxy well for the amount of short interest but for the restrictions, which can lead to inconsistent estimators in a regression analysis (Heckman 1979). We aggregate the highfrequency (tick-by-tick) short selling data at the monthly level for the calendar-time portfolio analysis in subsections , and at the daily level for the earnings announcement study in subsection 4.5. We then merge this dataset with Mergent s Fixed Income Securities Database (FISD) for Academia, with the constraint that we have at least one calendar month buy-and-hold bond return 10

13 for each company, where we base such return on the sale price data in the FISD Time Sales Data file for our sample period January 2005 to July This merge procedure produces a sample of 3,092 company observations. We also collect firms month-end bond ratings from the FISD Bond Ratings file. Following Bhojraj and Swaminathan (2009) and others, we impose several sample selection criteria. First, we exclude bonds with conversion features, as the prices of these bonds could reflect the underlying stock into which those bonds might convert. This means that our results could reflect stock price effects. Second, we exclude bonds without coupon or with maturity less than a year because these bonds may suffer from illiquidity, and therefore be subject to bond pricing errors. 9 Third, in a further attempt to eliminate pricing errors, we eliminate large return reversals (95 percent followed by -45 percent or vice versa). 10 The monthly corporate bond return as of time t, r t, is defined as: r! =!!!!"!!!"#$"%!!!!!!!!"!!!!!!!!!"!!! (1) 8 The Mergent FISD database contains bond purchases and sales made by property and life insurers and reported to state insurance departments. As such, the generality of our results may be reduced, since insurance companies are subject to different regulatory and institutional constraints. Mergent FISD is a comprehensive source of bond trading data in at least two ways, however. First, the data are the only source of bond-exchange transaction data and have been used in a large body of studies (e.g., Campbell and Taksler 2003, Cai et al. 2005, Davydenko and Strebulaev 2007, Easton et al. 2009). Second, insurance companies own percent of the value of all outstanding corporate bonds (Hong and Warga 2000, Schultz 2001, Campbell and Taksler, 2003). Another data source for bond trading is the Trade Reporting and Compliance Engine (TRACE), provided by the Financial Industry Regulatory Authority's over-thecounter corporate bond market real-time price dissemination service. While TRACE includes almost all over-the-counter trades, a major disadvantage of TRACE for our study is that TRACE has only limited coverage before Nonetheless, when we predict bond returns following earnings announcement using TRACE, in unreported analysis we show qualitatively identical results to those in table 3. 9 Elton et al. (2001) note that eliminating bonds not included in the indices eliminates all bonds with maturity less than a year. 10 The preceding filters eliminate the largest and most suspicious outliers in our sample. We note, however, that our results do not change appreciably without these filters. 11

14 where P t is the bond transaction price at time t, AI t is accrued interest which is the coupon payment divided by the ratio of days since the first payment date to the days between last payment and next payment, and C t is the semi-annual coupon payment at time t. Note that the sum of quoted price and the accrued interest corresponds to the bond s cash price. Also, where a company has several outstanding bond issues, we calculate the calendar month bond return for the company as a value-weighted mean of the individual bonds returns in a particular month and assign the median bond rating to that average. We calculate short interest as the ratio of shares shorted (from Reg. SHO) to the total number of common shares outstanding (hereafter, shares outstanding) at the end of each calendar month (from CRSP). Table 1 summarizes the full sample of NYSE firms covered by Reg. SHO that have at least one monthly buy-and-hold bond return from FISD during the sample period. Panel A reports statistics pertaining to the level and the distribution of short interest, where we winsorize the distribution of short interest at the top and bottom one percentile to prevent undue influence of extreme observations. After winsorizing, panel A continues to show a right-skewed distribution for short interest. At the 75 th sample percentile, shares shorted represent 4.12 percent of outstanding shares versus 0.73 percent for the 25 th percentile. Mean shares shorted, on the other hand, represent percent of outstanding shares, which is higher than the 75 th percentile. Panel B reports the distribution of firms based on two-digit SIC industry codes as defined in Campbell (1996). Over one-half of the 3,092 firms in the full sample comprise the combination of finance and real estate (38.16%), basic industry (10.28%), and consumer durables (8.15%). We check for industry effects in a later section. Table 2 reports statistics for the subsamples of firms categorized by level of short interest from at least 2.5 percent of outstanding shares. Panel A shows that as the level of short interest rises, the number of company-month bond return observations falls, where each number of 12

15 observations in the panel represents the number of return observations in a particular subsample. For example, the 3,457 observations for the 2.5 percent subsample in 2005 represent the number of company-months that had short interest of at least 2.5 percent of the shares outstanding at the end of a calendar month within the sample period of January 2005 to July Panel B reports the distribution of firms/bonds in the 2.5 percent short interest subsample by industry and bond rating. The number of firms with at least 2.5 percent short interest is 660, compared to the full sample of 3,092 firms. Firms with short positions of less than 2.5 percent clearly represent a large proportion of the full sample. The removal of short positions of less than 2.5 percent also impacts the distribution of firms by industry, which now has fewer finance and real estate firms but is otherwise generally similar to the full sample (panel B, table1). Panel C shows the distribution of firms/bonds in each of the five subsamples (2.5, 5, 7.5, 10, and 12.5 percent) by bond rating. In general, for each level of short interest, the rating worsens as the number of firms/bonds increases. If high short interest and lower bond rating both reflect adversely on the company, possibly because of common adverse information, we would expect such relation. However, we do not test this as a formal hypothesis, as a bond rating tends to be a lagging signal about bond quality (Weinstein 1977, Pinches and Singleton 1978), whereas short interest is a position taken whose success depends on future price performance in stocks or bonds. Finally, in panel D, we compare some key characteristics of two subsamples of the high short interest firms. This panel shows that the higher short interest sample associates with the lower decile ranks of the fundamental ratios, namely, median book-to-market (B/M) ratio, median earnings-to-price (E/P) ratio, and median cash-to-price (C/P) ratio, measured one month before portfolio entry at EN-1. These results agree with findings that short sellers gain when stock investors inflate firms market values compared to their intrinsic value (Desai 2002, Dechow et al. 2001). 13

16 4 Results We present the results in five parts. We first estimate excess bond return by regressing raw return for each portfolio based on the level of short interest on the five risk factors suggested by Fama and French (1993) (table 3) and repeat this analysis for portfolios formed on the basis of at least one percentage change in short interest over the prior month (table 4). Mitchell and Stafford (2000) show that time-series regressions of portfolios formed in calendar time minimize crosssectional dependence and, thus, they provide conservative significance tests of the regression coefficients. Second, to check the robustness of our results to alternative proxies for default and term risk, we estimate a second model where we calculate excess bond return adjusted for bond rating and duration for different portfolios of high short interest (table 5) (Bhojraj et al. 2009). Third, we calculate excess bond return by subtracting from raw return the bond return for a matched sample. Barber and Lyon (1997) illustrate that the matching-firm approach generates well-specified test statistics. We select a matching firm that is classified into the same size and book-to-market ranking as the sample firm in the month before attaining the threshold level of short interest, and to be closest in prior average six-month bond return (bond return momentum) to the sample firm (table 6). For each of the three excess return calculations, we test for a relation between short interest in month t and excess bond return over k > 0 months after t. We define k in subsection Fourth, we test if short interest leads excess bond return around earnings announcements to check if our results for future calendar-time excess bond returns also hold for future event-time excess bond returns (table 8). By conditioning our tests on a well-publicized event such as an earnings announcement, we assess whether short sellers information advantage might derive from better analysis of post-announcement information or from superior access to pre-announcement information. Fifth, as part of a series of robustness tests, we show how the results vary across additional subsamples, for example, based on proxies for earnings surprise and corporate governance. 14

17 4.1 Calendar time portfolio returns Analysis of short interest level. Table 3 reports the coefficients from a time-series regression of calendar-month excess portfolio return on the five factors suggested by the Fama and French (1993) bond return model. We estimate the following time-series regression model: R t rf t = α + β 1 RMRF t + β 2 SMB t + β 3 HML t + β 4 Term t + β 5 Default t + ε t, (1) where t is a month index from January 2005 to July 2007, R t is the portfolio bond return for the sample firms in month t, rf t is the risk-free rate (measured as the one month Treasury bill rate); RMRF t (market factor) is the return of the value-weighted stock market portfolio minus the return on the one month Treasury Bill; SMB t and HML t are returns on zero-investment, factormimicking portfolios for size and book-to-market equity in stock returns, respectively; Term t is the return on the 30-year Treasury Bond minus the return on the one month Treasury Bill; Default t is the value-weighted return of all corporate bonds in FISD Mergent with a maturity greater than 10 years minus the return on the 30-year Treasury Bond, and ε is residual error. As in Asquith and Meulbroek (1995) and Desai et al. (2002), we define a high short interest (or heavily-shorted) firm as one where the number of shares sold short is at least 2.5 percent of common shares outstanding at the end of the month. Since the 2.5 percent cutoff is arbitrary, we test our primary hypothesis (e.g., whether high short-interest firms experience negative bond returns) by employing cutoffs at the 5 percent, 7.5 percent, 10 percent, and 12.5 percent levels. For every month from January 2005 to July 2007, we construct a portfolio of all firms that had at least 2.5 percent short interest (5 percent, 7.5 percent, 10 percent, and 12.5 percent as the case may be) every month. At the end of each month, we add new firms when they attain the threshold short interest level in the previous month, and we drop firms when their short interest 15

18 falls below the threshold level in the previous month. Thus, a firm enters the sample in month EN+1 and remains in the sample for k months through month EX+1, where k = EX+1 - EN+1 > 0. We show p-values in parentheses, and ***, **, and * indicate significance levels of 0.01, 0.05, and 0.10 respectively, versus a null coefficient of zero. 11 Table 3 shows the results of estimating equation (1) for an equally-weighted hedge portfolio (panel A) and a value-weighted hedge portfolio (panel B). Results based on a value-weighted hedge portfolio give more weight to large firms, whose returns may be better specified by the Fama-French model. As the results are similar, we concentrate on panel A. The model coefficients are what we would expect, that is, the cross-sectional and time-series variability of monthly bond returns are explained primarily by the bond market factors such as default risk, and term risk factors. As such, these results are consistent with the Fama and French (1993) asset pricing model and they control appropriately for the key risk factors documented to explain bond returns, where the remaining excess return resides in the intercept or alpha. Second, the alpha coefficients decrease monotonically in the level of short interest, and the difference in alpha between the highest (12.5%) and lowest short interest position (2.5%) is significantly negative at a p-value of based on a two-tailed t test. This result supports our hypothesis (H1), that high short interest (short interest above 2.5%) in month t varies negatively with mean excess bond return over months t + k, where k equals the number of months the firm remains in the high short interest portfolio; and that the negative mean monthly excess bond return increases in the percentage of outstanding shares subject to short trades. The significant difference in mean monthly return in panel A translates to an annualized excess return of 3.84 percent on a zero 11 Mitchell and Stafford (2000) show that time series regressions of portfolios formed in calendar time minimize cross-sectional dependence and, thus, they provide conservative significance tests of the regression coefficients relative to, say, a matching approach. Nonetheless, we show matching approach results as an alternative test in subsection

19 investment hedge portfolio. The results in panel B are similar. The coefficients are as expected and the monthly alpha difference between the 12.5 percent and 2.5 percent portfolio converts to an annualized excess hedge return of 3.48 percent Analysis of change in short interest While table 3 suggests that high short interest at month t implies a negative bond return for several months after t, those high short interest positions could have been established prior to month t, and so for some firms they may not signal timely news about future bond returns. We therefore investigate whether qualitatively equivalent results hold for increases in short position, as such increases should represent newer information about a future price change at month t. As a check on our analysis, we also estimate excess bond return for decreases in short position. If a short position decreases, some bond investors may have unwound their positions, not expecting a further decline in prices. Therefore, we would not expect to observe the same monotonic relation between the level of short interest or short interest increase and bond return as in table 3 (and also panel A of table 4 below). Is it reasonable, however, to expect negative alphas for each level of short interest change. While short interest may have increased or decreased the overall short position could still be high, that is, at least 2.5 percent of shares outstanding per our minimum threshold, within the k-month period. Table 4 follows the same methodology as table 3, except that we now form the portfolios on the basis of change in short interest. For example, a plus or minus 1.0 percent change in short interest portfolio comprises firms whose short interest changes at least 1.0 percent at the end of a given month. We define the 1.5 percent, 2.0 percent, 2.5 percent, and 3.0 percent change portfolios similarly. For every month from January 2005 to July 2007, we form each portfolio using all the firms that had at least a 1.0 percent short interest change (1.5 percent, 2.0 percent, 2.5 percent, and 3.0 percent) over the previous month. At the end of each month we add new 17

20 firms when their short interest change is larger than or equal to the threshold in the previous month, and we drop firms when their short interest change becomes smaller than the threshold in the previous month (EN-1). As before, a firm enters the portfolio in month EN+1 and remains in the portfolio for k months through month EX+1. Table 4 documents two key results pertaining to our information hypothesis (H1). First, panel A shows for increases in short interest that the same monotonic relation holds as before, that is, the greater the short interest increase in month t, the more negative the excess return in months t + k. Moreover, the hedge portfolio return (equal weighting) is negative and significant at p <.0001, with an annualized excess return of 2.28 percent. On the other hand, for increases in short interest, as shown in panel B, we observe no monotonic relation and the hedge return is much lower, with an annualized excess return of 0.36 percent. As expected, panel B also shows no significant excess return for a portfolio for short interest changes of less than one percent (-1 percent to +1 percent). Overall, these results support our first hypothesis (H1) that high short interest and previous month increases in short interest signal precursory adverse news for bond investors, whereas smaller short interest changes and/or larger decreases in short interest do not. 4.2 Rating- and duration-adjusted excess bond returns An alternative approach calculates excess bond return adjusted for rating and duration, where investors require less return for a higher rated bond and more return for bonds of longer duration. We do this in two ways. First, we assign the bond rating to one of three categories, namely, AAA/AA, A and BBB-, and below BBB-, and measure duration in years as per the Macaulay calculation (weighted average time until receipt of interest and principal cash flows, in years). The portfolio formation procedure remains the same as before. Panel A shows the calculations computed as follows. Step one sorts all available bonds (including those that belong to firms for which short interest data are not available) into the three ratings categories. Next, we 18

21 sort the firms into high, medium, and low duration categories. To compute excess bond return we then subtract from each bond s return the return of the rating/duration portfolio to which the bond belongs. Lastly, we equally-weight these excess returns to compute a benchmark excess bond return for each short-interest portfolio. Panel A shows results similar to the previous table, namely, that bond investors experience negative excess returns over months t + k for each level of high short interest in month t, and that such excess return decreases negatively as the short interest level increases. A second approach calculates rating- and duration-adjusted excess bond returns from a cross-sectional regression of raw return on Rating, Duration, and Issue, where Rating equals a numerical score of AAA = 1 through DD = 25, following the approach in Jiang (2008); Duration uses the Macaulay calculation, in years; Issue equals one if the firm issues debt or equity in the previous 12 months, 0 otherwise; and ε is residual error. We use these variables to estimate the following model: R t = γ 0 + γ 1 Rating t + γ 2 Duration t + γ 3 Issue t + ε t. (2) Panel B reports the results from the regressions and shows results similar to panel A of table 4, and table 3. Mean excess bond return over months t + k (the intercept term in the regression) becomes progressively negative the higher the level of short interest at month t. Our finding of a negative lead relation between short interest and excess bond return is, therefore, replicated when we use a different financial model of the risk factors that drive corporate bond return. 4.3 Matched sample excess bond returns To enhance the reliability of our results, we construct a matched sample of companies as a third approach to estimating excess bond returns. Assuming appropriate matching, this approach can exclude additional risk factors not reflected in the five-factor or the rating- and duration- 19

22 adjusted excess returns models. Barber and Lyon (1997), for example, provide empirical evidence that the use of matching firms as a benchmark improves the power and specification of the test statistics. We require a matching firm to have the same size and book-to-market quintile ranking as the sample firm in the month before attaining the threshold level of short interest (in percent as 2.5, 5, 7.5, 10, and 12.5). 12 We also require a matching firm to be closest in six-month corporate bond return momentum to the sample firm (based on average bond return over months - 7 to -1). 13 For each sample firm, we identify four matching firms that are in the same size quintile and the book-to-market quintile as the sample firm, and have a prior six-month bond return (momentum) that is closest to the sample firm. We select four matching firms such that two of the matching firms have a prior six-month return (momentum) larger than the sample firm and the other two have a prior six-month return smaller than the sample firm. We then select a matching firm in month -1 (e.g., EN 1) relative to the firm s entry into a high short interest sample in month 1 (EN+1). EN+1 refers to the month in event time when the sample firm first enters the short interest sample. To avoid any overlap between the treatment and the control samples, we exclude a sample firm from the control group from six months prior to its entry in the sample (EN 6) until 36 months after it exits the sample (EX + 36). From the two matching firms that are closest in six-month return momentum to the sample firm (one with six-month return momentum larger than the sample firm and the other with six- 12 To avoid a look-ahead bias, we use the book value of common equity in a given month only if that month is at least four months after the company s fiscal year-end, otherwise we use the book value of equity from the previous fiscal year. 13 Desai et al. (2002) match high short interest firms on momentum because the sample firms experience a large run-up in price prior to becoming heavily shorted. Lyon et al. (1999) show that controlling for the prior performance is especially critical when the sample firms experience extreme prior performance. 20

23 month return momentum smaller than the sample firm), one of the two is randomly assigned as the first matching firm. The other firm is designated as the second matching firm. The remaining two matching firms are randomly assigned as the third and the fourth matching firms. We use the first matching firm s returns as the benchmark against which we compare the performance of the sample firm. If the first matching firm disappears, we use that firm s returns until its last available return date. After that, we use the second matching firm s returns and so on. This procedure guarantees that the matching firms are picked at the same time as the sample firms and avoids the potential hindsight bias in the selection of matching firms. If all four matching firms disappear, we use the value-weighted return of all corporate bonds in the FISD Mergent database from that point on. We then select a matching firm in month 0 relative to the firm s entry into a high short interest sample in month 1. We define cumulative excess bond return over months t = B to T as follows:! EX_CAR! =!!!.!!!!!"#!,!!!"#$!!"_!"#!,!! (3) In other words, we estimate EX_CAR T from the time-series of firm-average monthly excess returns where ret i,t and matched_ret i,t are monthly raw returns for the shorted firms and returns for the matched firms (N is the number of firms in month t), respectively. For example, EX_CAR T, where t = 1 to T cumulates the bond return difference from month EN+1 to EN+T. 14 This procedure, therefore, differs from the calculations in the earlier tables in that a firm enters a high 14 While many studies use buy-and-hold returns to analyze long-run stock return performance, Fama (1998) raises several concerns about the validity of this measure: the distribution of buy-and-hold returns is skewed, small initial differences can bias the calculation of buy-and-hold returns, and time-period overlaps in calculating buy-and-hold returns causing cross-correlation problems (Teoh et al. 1998, p. 1949). We thus report results based on cumulative (abnormal) returns in addition to the buy-and-hold (abnormal) returns to assess the robustness of our empirical results. 21

24 short interest portfolio when the threshold is met and stays in that portfolio for the entire holding period regardless of whether short interest in a later month falls below that threshold. We also define buy-and-hold excess bond return over months t = B to T as follows: EX_BH! =!!!!!!!!!!!!"#!,!!!!!!!!"#$!!"_!"#!,!! (4) EX_BH T is thus a measure of excess portfolio buy-and-hold return over months B to T calculated from the cross-section of multi-month bond returns for the shorted firms (1+ret i,t ) offset by an equivalent multi-month bond return for the matched firm (1+matched_ret i,t ). Similar to equation (3), a firm enters a high short interest portfolio and remains there for the entire holding period. Table 6, panel A and panel B, present the results for the 2.5 percent sample and for the 12.5 percent sample, respectively. We report the mean cumulative raw return (CAR) and the mean buy-and-hold raw return (BH) for the sample firms (RAWS), the corresponding mean return for the matched sample (RAWM), and the mean excess return (EX_CAR and EX_BH) for the sample firms. We also report the p-values associated with a two-sided t test that the mean excess return equals zero. Panel A shows two key results for mean cumulative raw return (CAR) for the 2.5 percent short interest subsample. First, EX_CAR for t = EN-6 to EN-1 and EX_CAR for EN 0 are no different from zero, indicating that bond investors earn zero excess returns prior to the high short interest strategy starting in month 1. Second, for holding periods starting after month 0, mean RAWM exceeds mean RAWS, and mean excess bond returns are all uniformly negative for the different holding periods. For example, cumulative excess return for the short interest sample relative to the matched sample differs by percent over the next 12 months (t = EN+1, 22

25 EN+12) and by percent for the next 24 months (t = EN+1, EN+24). 15 The results of the mean buy-and-hold raw return (BH) are qualitatively identical with those of the mean cumulative raw return (CAR), except that the buy-and-hold returns are now more negative, reflecting the effects of compounding. The empirical findings in the matched-firm approach are thus robust to different ways of cumulating bond returns. Panel B of table 6 reports similar results for the 12.5 percent short interest subsample. Unreported analysis also shows equivalent results for the cumulative (EX_CAR) and buy-andhold (EX_BH) excess returns for the other high short interest samples. In sum, using an alternative approach, we obtain similar results consistent with our hypothesis that high short interest in stocks signals future negative excess bond returns. 4.4 Investment versus speculative grade bond rating In this section, we investigate the informational role of short selling in the corporate bond market, conditional on whether the bond is rated investment grade (AAA to BBB-) or speculative grade. As already noted, due to bondholders asymmetric payoff function, knowledge of high short interest firms could provide additional information to bondholders about default risk and the possibility of a bond downgrade. Since investors price investment-grade bonds with more room for upside potential, which implies that holders of investment-grade bonds are reasonably certain about future payoffs, information in short selling about future cash flows may less likely affect their capital allocation decision. In contrast, holders of speculative-grade bonds have greater downside risk or uncertainty and thus the information in short selling affect their capital 15 These 12-month cumulative or buy-and-hold returns are less that the equivalent 12-month returns based on tables 3 and 4 because in those tables a firm is assumed to exit the portfolio when its short interest drops below the threshold level, which could signal to bond investors that prices are more likely to rise than fall in the future. However, some caution should be exercised as the results compare different holding periods and samples. 23