Short Interest, Stock Returns and Credit Ratings1

Xu Guo

Hunan University2

and

Chunchi Wu

State University of New York at Buffalo3

Current version: September 20, 2018

Abstract

This paper investigates the role of credit risk in the relationship between short-selling activity

and future stock returns. We find that the predictive power of short interest for future returns is

concentrated in the worst-rated stocks and the short interest trading strategy generates abnormal

profits primarily for these stocks. Trading profits are derived mainly from taking the short

position in stocks with the largest increase in short interest. These firms experience worse

performance subsequently, which are predicted by short interest changes. The profitability of

short interest strategy is robust to various controls for cross-sectional effects and firm

characteristics.

JEL classification: G12; G13

Keywords: Short interest; return predictability; financial distress; credit ratings; anomaly

1 We are grateful for helpful comments and suggestions from Chen Gu and participants at the 2018 Eastern

Financial Association Conference and 2018 European Financial Management Association (FMA) Conference. 2 Center for Economics, Finance and Management Studies (CEFMS), Hunan University, Changsha, China. Phone:

+86(18813900652) and email: xuguo@buffalo.edu. 3 School of Management, State University of New York at Buffalo, Buffalo, NY. Phone: (716)645-0448 and email:

chunchiw@buffalo.edu.

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1. Introduction

Short-selling activities have witnessed a rapid growth in the recent decades, and the role of

equity short interest in asset pricing has attracted considerable attention from academics. The

negative cross-sectional relationship between short interest level and future stock returns has

been well established. However, none of the existing studies has investigated short-selling

activities for firms of different credit ratings to assess the implications of financial distress for

the predictive power of short interest for stock returns. Moreover, most studies have focused on

the ability of the level of short interest, rather than changes, to predict stock returns. Recently,

Rapach, Ringgenberg and Zhou (2016) show that the level of short interest exhibits a trend and

as a consequence, it is a poor proxy for changes in short-sellers’ beliefs that drive stock returns.

They suggest that changes in short interest or a detrended series contain better information for

future stock price movements.

This paper contributes to the current literature by investigating the predictive power of short

interest for future returns of stocks with different credit ratings. We are particularly interested in

assessing the role of the distress factor in affecting asset returns and the ability of short interest to

predict future stock price changes. Following Rapach et al. (2016), we employ the measure of

changes in short interest to better capture variations in short-sellers’ beliefs. We document strong

evidence that the negative return predictability of short interest is concentrated in financially

distressed firms. In addition, we find that changes in short interest have stronger predictive

power for future stock returns than the level of short interest.

Our motivation for exploring the implications of financial distress for return predictability of

short interest follows the literature that suggests financial distress as an important risk factor in

the cross-section of expected stock returns (see, for example, Vassalou and Xing, 2004;

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Avramov, Chordia, Jostova and Philipov, 2007, 2009, 2013; Jostova, Nikolova, Philipov and

Stahel, 2013). Fama and French (1993) first suggest that the size and value factors proxy for a

priced financial distressed factor and Vassalou and Xing (2004) provide evidence that supports

this argument. Avramov et al. (2007) find that stock momentum concentrates in speculative-

grade firms and Avramov et al. (2009) show that the distress factor explains the anomaly of

trading strategies based on analyst forecast dispersion. These papers suggest the existence of a

priced distress factor that could potentially also related to the anomaly of short-interest based

trading strategies.

In a comprehensive study, Avramov, Chordia, Jostova and Philipov (2013) show

commonalities across asset pricing anomalies in the stock market. They find that a distress factor

explains most of the abnormal returns of anomaly-based trading strategies in the stock market

such as price momentum, earnings momentum, credit risk, analyst earnings forecast dispersion,

idiosyncratic volatility, capital investments, accruals, and book-to-market anomalies. However,

their study does not cover the short interest anomaly. This omission raises an important question

whether the distress factor also plays a critical role in the anomaly of short-interest based trading

strategies in the stock market.

Distressed firms behave differently from other firms in many ways. When firms are in

financial distress, the uncertainty about their fundamentals rises dramatically. Financial distress

affects firms’ future performance through complicated channels such as bond covenant

violations, regulatory restrictions, and deterioration in customer, creditor, supplier and employee

relations. The deteriorating firm conditions often lead to a rating downgrade and severe

depression of stock prices. These adverse effects are difficult to overcome, but if the

management is able to steer a turnaround, stock prices can bounce back sharply. The drastic

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stock price movement associated with either a downfall or turnaround of a financially distressed

firm presents a unique opportunity for the informed investors to profit from their private

information. Agents who have superior information, or advanced knowledge about the

conditions of distressed firms, have a high likelihood of making more abnormal profits by

trading these stocks than others. This rationale provides one explanation for why the profitability

of anomaly-based trading strategies is greater for stocks of high credit risk firms (see Avramov et

al., 2013).

Using credit ratings as an ex-ante indicator for the future performance, we explore the

predictive power of short interest changes for stock returns of firms with different default risk.

Consistent with previous findings, our results show that a high (low) short interest level is

associated with a low (high) future stock return. Importantly, we uncover the evidence that the

profitability of short-selling strategies based on the level of short interest is concentrated in the

firms of high default risk. Moreover, we document that the change in short interest has higher

predictive power for future stock returns of financially distressed firms than the level of short

interest. This finding confirms that the short interest change is a better proxy for the change in

short-sellers’ beliefs and therefore provide a more reliable signal for future price changes.

The evidence based on portfolio sorts and cross-sectional regressions shows that the

profitability of short-interest based trading strategies is concentrated in the firms with a rating of

BB+ and below. A long-short trading strategy of buying stocks with the most negative change in

the short interest ratio (P1) and selling stocks with the most positive change in the short interest

ratio (P10) generates a 14.6% annualized risk-adjusted return for financially distressed firms,

which is of economic significance. The profit from the short side accounts for about 70% of this

trading profit. The stocks with a large increase in the short interest experience very low returns

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subsequently, resulting in a sizable profit from the short side. The profitability of the short-

interest based trading strategy is robust to controlling for various cross-sectional effects and

firm/stock characteristics. Results suggest that changes in short interest contain rich information

for firms’ future stock performance.

Empirical evidence shows that an aggravation in short interest is a credible signal for the

poor future performance while an extenuation in short interest predicts the positive future

performance of the firm. The strategy of shorting the distressed stocks with an aggravation in

short interest and buying those with an extenuation in short interest generates anomalous profits.

This finding is consistent with the contention that short sellers have superior value-relevant

information for firms’ future stock returns. Furthermore, this information advantage appears to

be most acute for the financially distressed firms. Hence, while investors may earn profits by

exploiting the short interest information, the short-selling strategy appears to be most profitable

for trading the stocks of distressed firms.

Further analysis of the economic source of short-selling strategy profits reveals that changes

in short interest contain important information about firms’ future profitability. We find that

short interest changes predict future operating performance of distressed firms. At the same time,

stock returns of distressed firms are more sensitive to changes in corporate earnings. The high

sensitivity of returns to firms’ profitability renders the private information of short sellers more

valuable for trading distressed stocks, thus generating greater profits of short-selling these stocks.

The predictive power of short interest for future stock returns varies across firms of different

characteristics. The trading gains from the short-selling strategy are higher for stocks of firms

with small capitalization, high leverage, low trading volume, high analyst forecast dispersion,

and low institutional ownership. While the predictive power of short interest for future returns

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varies with firm characteristics, the short-selling effect is not subsumed by the effects of these

characteristics. The effect of short interest changes persists even after controlling for these

characteristics. The results strongly suggest that short interest has independent predictive power

for future returns over and beyond the usual predictors in the stock market.

This paper proceeds as follows. Section 2 reviews the short sale literature and Section 3

discusses the data used in our tests. Section 4 presents empirical results for the predictive power

of short interest for firms with different ratings. We show that a long-short trading strategy

produces abnormal returns mainly for the stocks of financially distressed firms. Section 5

provides robustness tests, and Section 6 examines the source of return profitability. Finally,

Section 7 summarizes the major findings and concludes the paper.

2. Literature review

Short-selling activities are prevalent in stock markets. In liquid markets, there are typically

many short sale orders executed in margin accounts on any given day. Asquith et al. (2010)

document that short sales account for 29.7% of all stock trades and 27.9% of total trading

volume, based on a randomly selected sample of common stocks from the CRSP. Diether et al.

(2009)4 and Boehmer, Jones, and Zhang (2008) report similar findings for active short-selling

activity. These findings indicate that short sales are quite common in stock markets.

The literature has suggested that short-selling can be motivated by factors such as deferring

capital gains tax, hedging needs, or exploiting the overpriced stocks (Brent et al. (1990)). A

strand of research has focused on the information content of short interest and its ability to

predict returns in equity markets (see Desai et al., 2002; Diether et al., 2009; Boehmer et al.,

4 Diether et al. (2009) report that short sales represent 24% of NYSE and 31% of NASDAQ volume.

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2010; Rapach et al., 2016).5 A common finding is that short-selling activity predicts future stock

returns. Desai et al. (2002) document evidence that trading stocks with a high short interest ratio

generates significant negative abnormal returns. Using SHO-mandated data in 2005, Diether et al.

(2009) find that portfolios which long lightly shorted stocks and short heavily shorted ones

deliver abnormal profits. In addition, they find evidence that short-sellers trade against short-

term overpricing of stocks with overreactions to good news.

Substantial research has also supported the hypothesis that short sellers possess private

information about stock valuation. Boehmer, Huszar, and Jordan (2010) find that short interest

contains information for future stock returns: relatively heavily traded stocks with low short

interest experience both statistically and economically significant positive abnormal returns.

They attribute this return predictability to the private information possessed by short sellers

about firms’ future performance. Akbas, Boehmer, Erturk, and Sorescu (2017) provide evidence

that short sellers’ positions convey information for firms’ future profitability. There is a

significant relationship between short interest level and future earnings surprises, and revisions

in analyst earnings forecasts. Their finding suggests that asymmetric information for firms’

future performance is an important channel through which short interest draws its predictive

power for future stock returns.

Another strand of research investigates the relationship between market-level short sale

activities and market returns. An early study by Seneca (1967) finds that a large amount of

aggregate short interest is associated with a low S&P 500 index level. Rapach et al. (2016)

employ a short interest index, which is essentially a detrended aggregate short interest series, to

predict aggregate stock returns and find that it is a powerful predictor for the future market

5 Short interest also appears to have predictive power in other markets. For the bond market, see Kecskés et al.

(2013) and Henry et al. (2014); for the CDS market, see Griffin et al. (2016).

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performance. Similarly, using a data set of daily short interest, Lynch et al. (2014) find that an

increase in aggregate shorting is associated with a decrease in market excess returns over the

following five to twenty days. Thus, aggregate short interest amount contains information for

future stock market returns.

An issue of considerable interest is the link of short sale constraints to asset pricing. Miller

(1977) first shows that the presence of short sale constraints impedes adjustment of stock prices

to private information, especially negative information, which results in higher stock prices in

the presence of heterogeneous investor beliefs. In contrast, Diamond and Verrecchia (1987)

demonstrate that under the assumption that investors are rational and fully aware of the effects of

short sale constraints, short sale constraints eliminate informative trades but do not bias stock

prices upwards. Empirically, Engelberg et al. (2018) find evidence that short-selling risk affects

cross-sectional stock returns.

An interesting question is why short interest remains high for stocks with short sale

constraints. Analyzing short-selling activities using a broad data set of stocks from NYSE,

AMEX and NASDAQ, Boehmer, Huszar, and Jordan (2010) find that short sale constraints and

costs of short-selling are not really binding. Several studies have examined the effects of short

interest for stocks with short sale constraints. Asquith et al. (2005) study a group of stocks with

high short interest level and low institutional ownership, in which the demand exceeds the supply

most and therefore, short sale constraints are higher for these stocks. They find that the

constrained group of stocks, which are more difficult to short, underperforms the unconstrained

group.6 They interpret this result as suggesting that overvalued stocks are sold short and thus

experience low subsequent returns. Daniel et al. (2016) find that momentum anomaly is violated

6 They argue that short interest is a proxy for demand to sell short and institutional ownership is a proxy for supply

(shares to borrow), so the constrained group is short-sale constrained.

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for the constrained winners or stocks with high short interest, as these stocks experience lower

future returns.

A number of short-sale studies focus on borrowing costs or rebate rates, which are typically

based on proprietary data. The rebate rate is the interest rate returned by the lender of the equity

for the use of the collateral—high rebate rates imply low borrowing costs, ceteris paribus.

D’avolio (2002) finds that less than 10% of stocks are expensive to borrow and the value-

weighted mean loan fees are only 17 basis points for the remaining 90% of stocks. This suggests

that stocks are generally inexpensive to borrow. Geczy et al. (2002) report similar findings for

the low cost of short selling.

Despite voluminous research on short selling, to our knowledge, none of the previous studies

has examined the profitability of shorting stocks for distressed firms. Our paper is motivated by

the literature that suggests a distress factor is priced in expected stock returns. Vassalou and Xing

(2004) show that size and book-to-market effects, two well-known market anomalies, are

concentrated in the firms with high default risk. Avramov, Chordia, Jostova and Philipov (2007)

find that stock momentum, which is by far perceived as the most persistent and robust anomaly

in asset pricing (see Schwert, 2003; Fama and French, 1996), exists primarily in speculative-

grade firms. Avramov et al. (2009) find the profitability of forecast dispersion strategies is

concentrated in firms with high credit risk. The well-established negative cross-sectional relation

between the dispersion in analysts’ earnings forecasts and future stock returns only holds during

the periods that firms experience deteriorating credit ratings.7 Avramov et al. (2013) further

show that most anomalies in the stock market exist in the group of the worst-rated firms.

Distressed firms are opaque and hence, insiders or informed traders have more advantages over

uninformed traders in trading the stocks of these firms. Given that financial distress seems to

7 See Diether et al. (2002).

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explain most anomalies in the stock market, a natural question is whether the predictability of

short interest for future stock returns is also driven by a distress factor.

In this paper, we assess the implications of financial distress for the profitability of short

interest trading strategies. To our knowledge, our work represents the first effort to examine the

role of credit ratings in return predictability using short interest data at the firm level. We first

examine short interest levels for different rating groups and find that the well-established

negative relation between short interest level and future stock returns exists primarily in stocks

with a speculative rating. We then employ the measure of changes in short interest to assess its

ability to predict future stock returns. Rapach et al. (2016) find that short interest exhibits a

significant upward trend that reflects the continued development of the equity lending market

and a rise in the number hedge funds that devoted an increasing amount of capital to short

arbitrage. This trend can obscure the true information content of short interest.8 In light of this

finding, we use changes in short interest to remove the secular trend and better capture the

change in informed traders’ beliefs. Finally, we examine the economic source of the predictive

power of short interest changes. We now turn to empirical tests.

3. Data

Our main data source is the Compustat database, which provides short interest data beginning

from 1973. The data set contains the monthly level of short interest for each stock. Exchanges

typically disclose the number of shares that are held short for a given firm around the 15th of

each month. We merge the short interest data with the data from the Center for Research in

8 For short interest ratio of individual stock, we also compute the percentage of stocks that with ending short interest

ratio higher than the short interest ratio when it enters our sample, which is 68% (1905 out of 2792 stocks). For these

stocks, the mean and median of ending short interest ratio over beginning short interest ratio is 112 and 4.44,

respectively.

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Security Prices (CRSP) by the cusip number. 9 Following Rapach et al. (2016), we drop

observations with stock prices below $5 per share as well as firms with market value below the

fifth percentile of NYSE market capitalization using the breakpoints from Kenneth French’s

website.10 This filtering ensures that our empirical results are not driven by small and illiquid

stocks.

We restrict our samples to stocks listed in NYSE, NASDAQ and AMEX (shrcd = 10 or 11,

exchcd = 1, 2 or 3). We divide each stock’s short interest level by its total shares outstanding

from the CRSP to obtain the short interest ratio (SIR). Figure 1 displays the aggregate short

interest ratio in our sample. The short interest ratio has a strong upward trend and reaches a peak

during the financial crisis period, consistent with the finding of Rapach et al. (2016).

When the change in short interest ratio, △ SIRit=SIRit-SIRi,t-1, is positive, the amount of stock

i being shorted should be higher than that being covered in month t. If the main reason for short-

selling is to exploit the overpricing of stocks, the magnitude of △ SIRit reflects the extent of stock

overvaluation. In the baseline analysis, we use changes in the short interest ratio as a predictor

for future stock returns. For robustness, we also report results based on the short interest level.

[Figure 1 here]

We use the S&P ratings, which are available monthly from 1986. According to the S&P, the

credit rating reflects the agent’s opinion for the issuer’s overall creditworthiness, apart from its

ability to repay individual debt obligations. We merge the ratings with the short interest ratio

data by gvkey. The merged sample includes 2,792 stocks and 301,868 stock-month observations

over the period from January 1986 to February 2017. In subsample analysis, we divide stocks

9 From year 2007, short interest data are reported twice a month, one in the middle and the other in the end of the

month. For consistency, we use the mid-month number throughout our analysis. 10 Following Boehmer et al. (2010), we don’t apply these two filters to the analysis using the short interest level.

12

into investment grade (IG) and non-investment grade (NIG) groups.11

Table 1 presents the summary statistics of our sample. The NIG group contains 36.30% of all

the stock-month observations. For the IG group, BBB stocks have the highest proportion, which

is 32.38%, followed by A (23.78%), AA (6.18%) and AAA (1.37%). We also break down our

sample period into two periods using December 2000 as the cut-off. The second period has more

observations, which could be attributed to the easier access to the equity lending market and the

increase in the number of hedge funds. It is worth noting that the increase in the sample size is

mainly due to more firms with a rating of BBB+ and lower. As expected, the stocks of lower-

rated firms have a higher mean and standard deviation of returns.

[Insert Table 1 here]

4. Empirical results

4.1 Credit ratings and short interest level/change

To establish the link between short selling activities and credit ratings, we first calculate the

average numerical credit rating for each of the ten portfolios sorted by short interest level or

change. In each month t, we sort stocks into decile portfolios by SIRit or △ SIRit. For portfolio

sorts by short interest level, P1 consists of the most lightly shorted stocks and P10 contains the

most heavily shorted stocks. For the analysis by short interest change, P1 includes the stocks

with the largest decrease (or most negative change) in the short interest ratio, that is, short-selling

activities extenuate most among all stocks in month t, and P10 includes the stocks with the

largest increase (or most positive change) in the short interest ratio. We assign a numerical

indicator to each rating (see column 2 of Table 1) and report the average rating score for each

decile portfolio in Table 2.

Panel A of Table 2 shows the distribution of SIR and ∆SIR. The mean SIR is 3.56% while the

11 See Table 1 for the details.

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mean ∆SIR is only 0.02%, suggesting that SIR is sticky and there is little variation month by

month. Panel B reports the rating information for decile portfolios sorted by short interest level.

The results show that lightly shorted stocks have higher ratings than heavily shorted stocks. The

rating score of P1 is 8.47 (between BBB and BBB+) and it increases to 11.86 (BB) for P10,

which consists of the most heavily shorted stocks. The high-low difference in ratings is more

than three notches.

Panel C of Table 2 shows the results for the decile portfolios sorted by short interest changes.

The rating distribution exhibits a U-shaped with both P1 and P10 tilted towards low rated stocks.

The average numerical rating score of P1 is 10.39 (BBB-), the rating score decreases to 8.51

(between BBB and BBB+) for P5, and then bounces back to 10.52 (BBB-) for P10. The results

show that both high and low portfolios of short interest changes contain lower-grade stocks.

[Insert Table 2 here]

4.2 Short interest level/change and return predictability

In this section, we examine the relation between short interest variables and future stock

returns. Excess returns are adjusted by stock characteristics (see Daniel, Grinblatt, Titman and

Wermers, 1997) and systematic risks. Diether (2008) documents that the majority of securities

lending contracts are closed out in two weeks. In light of the short-lived nature of short sales, we

focus on the one-month horizon in testing the cross-sectional return predictability. To obtain the

return adjusted for systematic risk (α), we use the four-factor model that includes the Carhart

(1997) momentum factor:

, +1 1 , +1 2 +1 3 +1 4 +1 5 +1 , +1ip t ip ip M t ip t ip t ip t ip t ip tr r SMB HML UMD LIQ e

The dependent variable is the value-weighted return in excess of the risk-free rate on portfolio p

in rating group i and month t+1, rM,t+1 is the market excess return, SMBt+1 is the size factor,

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HMLt+1 is the value factor, and UMDt+1 is the momentum factor. For robustness, we also report

results with the five-factor model that includes the Pastor-Stambaugh liquidity factor to control

for the effect of liquidity risk.12

[Insert Table 3 here]

4.2.1 Short interest level and stock return predictability

Conventional studies on the relation between short-selling activity and future stock returns

focus on the information in the level of short interest. We thus begin our analysis by examining

the relationship between the short interest level and future abnormal stock returns in different

rating categories in the spirit of Boehmer et al. (2010).

Stocks are sorted into decile portfolios by short interest level where P1 includes the lightly

shorted stocks and P10 the heavily shorted stocks. 13 The left panel of Table 3 reports the

long/short portfolio test for differences in returns/alphas based on short-interest level sorts.

Column 1 shows that for the lightly shorted portfolio P1, the monthly raw return is 0.89%, and it

decreases to 0.62% for P10. Column 2 shows the results of portfolio sorts by characteristic-

adjusted returns. Following Daniel, Grinblatt, Titman and Wermers (DGTW, 1997), in each

month t, we first sort stocks into quintiles by firm size and for each size quintile, we further sort

stocks into book-to-market quintiles. In constructing the benchmark portfolios, we use all stocks

listed in NYSE, NASDAQ and AMEX. We then compute the value-weighted returns for each of

the 25 portfolios sorted by size and book-to-market ratio. Finally, we adjust the return of a stock

in our sample by the return of a benchmark portfolio that matches the size and book-to-market

ratio of that stock. Results show that the DGTW adjusted return spread between P1 and P10 is

0.47%, which is significant at the one percent level.

12 Data for the Pastor-Stambaugh liquidity factor come from WRDS. 13 These portfolios represent stocks with SIR 10% and SIR 90% similar to Boehmer et al. (2010).

15

In next two columns, we present the results based on the abnormal returns (alphas) of the

Carhart four-factor model and the five-factor model with Pastor-Stambaugh liquidity. Results

continue to show a significant spread between P1 and P10, and these two alpha spreads are even

higher than the DGTW spread. In general, our results are consistent with the finding of Boehmer

et al. (2010). Our alpha spreads are somewhat larger, which could be due to the difference in our

data sample.14

We next divide our sample into investment-grade (IG) and non-investment grade (NIG)

subsamples, and report the results separately. The results show that differences in returns/alphas

are highly significant for NIG but insignificant for the investment-grade stocks. An interesting

finding is that alphas are significant for the heavily shorted portfolio, whereas they are

insignificant for the lightly shorted portfolio for both NIG and IG groups.

Our results are overall consistent with previous findings that the level of short interest has

predictive power for future stock returns. Importantly, we uncover the evidence that return

predictability by short interest level is much higher for the speculative-grade stocks. A long-short

trading strategy that longs the lightly shorted portfolio (P1) and shorts the heavily shorted

portfolio (P10) delivers a monthly abnormal return slightly above 1% or an annualized return of

about 13% for speculative-grade stocks, which is of economic significance.

4.2.2 Short interest changes and stock return predictability

Rapach et al (2016) suggests that changes in short interest (detrended) is a better proxy for

the short-sellers’ beliefs. Their study suggests that changes in short interest △ SIRit contain more

predictive information for future stock returns. To assess the informational role of short interest

changes, we next investigate the relationship between △ SIRit and future stock returns.

14 Our results are somewhat different from Boehmer et al. (2010) due to different data sample. Our sample period is

from January 1986 to February 2017 whereas their sample period is from June 1988 to December 2005. Also, we

impose a criterion that excludes small and illiquid stocks as in Rapach et al. (2016).

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The right panel of Table 3 reports monthly value-weighted average returns for the high (P1)

and low (P10) portfolios sorted by △ SIRit. P1 consists of stocks with the largest decrease in the

short interest ratio and P10 consists of stocks with the largest increase in the short interest ratio.

Unreported results show the future returns (t+1) exhibit a decreasing trend from P1 to P10,

indicating a negative relationship between changes in short interest and subsequent stock returns

for the full sample and NIG. For the full sample, the return spread is 0.33% per month and

significant at the five percent level. The return spread increases to 1.09% per month for NIG,

which is significant at the one percent level. In contrast, the return spread is only 0.21% per

month for investment-grade stocks and it is insignificant. Result again suggests that return

predictability is primarily driven by distressed stocks.

Figure 2 plots the cumulative returns to a long-short portfolio that buys stocks in P1 and

shorts stocks in P10 for NIG over the entire sample period. These value-weighted portfolios are

then held for one calendar month. We assume an initial investment of $1 at the beginning of

January 1986. To compare with market return, we include the cumulative return of the S&P 500

index. Results show that the initial investment increases to more than $30 at the end of the

sample period, which is about three times higher than the S&P 500 index return. The high excess

returns of trading strategies based on short interest changes thus present a significant anomaly.

[Figure 2 here]

Column 2 in the right panel reports the results of characteristic-adjusted returns. After

adjusting for the characteristics of size and book-to-market ratio, the results continue to show

significant differences in DGTW adjusted returns for the whole sample (0.37%) and NIG (1.28%)

whereas the high-low return spread is not significant at the five percent level for IG. Results

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show that the profitability of the long-short trading strategy is robust to control for the effects of

size and book-to-market characteristics.

Column 3 reports the abnormal returns (alphas) from the four-factor model. The high-low

risk-adjusted portfolio returns (P1 – P10) are significant at the five percent level for the full

sample that includes all stocks. However, when dividing the sample into NIG and IG, we again

find that results are significant only for the firms with a speculative-grade rating. A long-short

trading strategy that longs the portfolio with the most negative change in the short interest ratio

(P1) and shorts the portfolio with the most positive change in the short interest ratio (P10)

delivers a monthly abnormal return of 1.14% or an annualized return of about 15% for

speculative-grade stocks. This return is highly statistically significant and economically

meaningful. Compared to the result based on the level of short interest, the risk-adjusted return

from ∆SIR sorts is larger than that from SIR level sorts.

Short sellers profit from a decrease in stock prices of distressed stocks. The most negative

monthly return is from P10 of NIG, which is -78 basis points whereas the most positive monthly

return is from P1, which is 35 basis points. Results show that stocks with a large increase in short

interest experience a significant drop in stock prices subsequently. This produces the anomalous

profits from the short side of the short-interest trading strategy. As shown, the short position

(P10) accounts for 70% of the profit from the long-short trading strategy for distressed stocks.

Financial distress thus provides a link between changes in short-interest and the subsequent

profitability of the short-interest based trading strategy.

To further control for the effect of liquidity risk, we run the regressions for a five-factor

model with the Pástor-Stambaugh liquidity factor. The results in the last column of the right

panel in Table 3 show a similar pattern after controlling for the effect of liquidity risk. For the

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firms with a speculative-grade rating, the monthly alpha spread is 1.13% and significant at the

one percent level. Thus, the results are robust to controlling for the effect of the liquidity risk

factor. Results again show that short interest changes predict the future returns of stocks in the

cross-section and this return predictability concentrates in distressed stocks.

In summary, we find that the long-short trading strategy produces the largest profits for

speculative-grade stocks, regardless of whether we use the level or change of short interest as a

predictor for future returns. More importantly, the results are stronger when we use short interest

changes as a predictor, consistent with the argument of Rapach et al. (2016) that the change in

short interest is a more robust proxy for the change in investors’ beliefs. Hence, we focus on the

results based on the signal of ∆SIR in the remaining analysis.

4.3 Bivariate portfolio sorts for NIG

In this section, we control for a battery of cross-sectional effects by using bivariate portfolio

sorts to examine the robustness of our results for speculative-grade stocks to various firm/stock

characteristics.

4.3.1 Control for firm size

The cost of borrowing stocks is related to firm size. It is usually cheaper to borrow stocks of

large firms as more stocks are available for lending. This raises a concern of whether the short

interest effect for speculative-grade stocks may depend on firm size. To address this concern, we

conduct the bivariate sorts by controlling for the effect of firm size. Following Ang et al. (2006),

we first sort the stocks by firm size into terciles and for each size portfolio, we further sort stocks

into quintile portfolios based on changes in short interest (△SIRit).15 Finally, we average the

alphas of each △SIR quintile across the size terciles. The resulting quintile portfolio alphas have

effective control for the size effect as each quintile portfolio of stocks contains the same

15 We label these quintile portfolios as P1,…, P5.

19

distribution of firm size. In this analysis, we focus on the four-factor model in obtaining alphas

for the 3 x 5 portfolios from bivariate sorts. Our results are robust to the use of the five-factor

model.

The top-left panel of Table 4 presents the results of bivariate portfolio sorts by size and short

interest changes. The high-low return spreads between P1 and P5 in month t+1 are significant at

the 1% level across all size terciles, where small firms have the largest spread. The return spread

decreases as firm size becomes larger, which suggests the predictive power of △ SIR is stronger

for smaller firms. When averaging the returns of each △ SIR portfolios across firm size, we

obtain a spread of 88 basis points per month, which is significant at the 1% level.16 Results show

that the effect of short interest is robust to controlling for the effect of firm size. Thus, short

interest changes have an independent effect over and beyond that of firm size.

[Insert Table 4 here]

4.3.2 Control for leverage

As the risk of stocks depends on firms’ leverage, expected returns are positively related to

leverage. This raises a concern that the returns of the portfolios formed by short interest changes

could reflect the effect of leverage. To address this concern, we control for the effect of leverage.

For each month, we sort stocks into terciles by firm leverage and for each leverage portfolio, we

further sort stocks into quintiles by the change in short interest. We then average the returns of

each △ SIRit portfolio across the leverage terciles and report the difference in the low and high

portfolio returns (P1 - P5).

The middle-left panel of Table 4 shows that the long-short portfolio return increases

monotonically with leverage. All return spreads are significant at least at the 5% level. Results

show that the short interest trading strategy generates greater profits for firms with higher

16 We use value-weighted returns but results are qualitatively similar for equal-weighted returns.

20

leverage. The last row shows the results average across leverage terciles. The long-short trading

strategy delivers a monthly abnormal return of 77 basis points after controlling for the leverage

effect, which is significant at the 1% level. Thus, leverage cannot explain away the profit of the

long-short strategy of short interest.

4.3.3 Control for trading volume

Liquidity can affect short-selling activity as liquid stocks are easier to short.17 The short

interest effect could therefore reflect the effect of liquidity. To investigate this possibility, we

examine whether the profitability of the short interest trading strategy is driven by the differences

in the trading volume of stocks.

We perform bivariate portfolio sorts by volume and short interest changes using the same

sorting procedure as above. The bottom-left panel in Table 4 reports the results. As shown, the

short interest trading strategy tends to generate higher profits for stocks with lower trading

volume. Thus, lower liquidity leads to higher return spreads. Controlling for the effect of volume

(see the last row), we find that the long-short strategy continues to deliver a significant monthly

abnormal return of 67 basis points with a t-statistic of 2.76. Results show that volume cannot

explain the short interest effect.

4.3.4 Control for book-to-market ratio

The book-to-market ratio is positively related to expected returns. Value firms, which have a

high book-to-market (B/M) ratio, usually outperform. To see if the return predictability of short

interest is due to the value effect, we examine the robustness of our results to controlling for the

effect of book-to-market ratio.

The top-right panel of Table 4 reports the results of bivariate sorts. Return spreads (P1 - P5)

are all significant at least at the 5% level. The last row reports the results of portfolio returns

17 See Gervais et al. (2001), and Kaniel et al. (2012)

21

averaged across B/M terciles. Results show that the short interest strategy remains profitable

even after controlling for the effect of book-to-market ratio. The long-short portfolio return is 94

basis points per month, which is significant at the 1% level. Thus, the book-to-market factor

cannot explain away the short interest effect.

4.3.5 Control for dispersion in analysts’ forecasts

Short interest can be linked to the divergence of opinion. Boehme, Danielsen and Sorescu

(2006) find that dispersion of investors’ opinion in the presence of short-sale constraints explains

stock price overvaluation. The divergence of opinion is related to information quality or

uncertainty. The short interest profitability could be due to higher information quality of stocks

in P1 and lower information quality of stocks in P5. To see if the profit of the short interest

trading strategy is derived from information uncertainty, we control for the divergence of opinion.

We use dispersion in analysts’ earnings forecasts as a proxy for heterogeneity of opinion, which

is defined as the mean of forecasted EPS divided by the standard deviation of analyst forecasts.18

The data of analyst forecast dispersion are from IBES (Institutional Brokers' Estimate System).

Lower dispersion in analysts’ earnings forecasts implies less information uncertainty. We use a

similar double-sort procedure to control for the effect of dispersion in analysts’ forecasts.

The middle-right panel in Table 4 shows that the short interest trading strategy generates

higher returns for stocks with higher dispersion. This finding implies that the informed short-

sellers’ profit increases when analyst earnings forecasts are more disperse. The last row reports

the portfolio returns averaged cross the dispersion terciles. Controlling for the effect of analyst

forecast dispersion, the long-short strategy delivers a monthly abnormal return of 90 basis points,

which is significant at the 1% level. Results show that dispersion in analysts’ earnings forecasts

cannot account for the profitability of the short interest trading strategy.

18 This definition is used in Barron and Stuerke (1998).

22

4.3.6 Control for institutional ownership

In the short interest literature, institutional ownership has been used as a proxy for short-sale

constraints (Asquith, Pathak and Ritter, 2005; Nagel, 2005). When institutional ownership is low,

fewer stocks are available for lending. Asquith, Pathak and Ritter (2005) study the performance

of a constrained portfolio with high short-selling activity and low institutional ownership and

find that it underperforms. Daniel, Klos and Rottke (2016) extend this line of research to explain

the momentum anomaly and find that the constrained portfolio exhibits no momentum.19

To see whether the short interest effect is robust to the institutional factor, we control for the

effect of institutional ownership. We download the institutional ownership data from Thomson-

Reuters Institutional 13-F filings, a quarterly database that discloses shareholdings by institutions.

To obtain institutional ownership, for each quarter, we aggregate all institutional holdings for a

stock and divide total institutional holdings by the number of shares outstanding taken from the

CRSP. This ratio represents the institutional ownership in percentage for each stock in a given

quarter. We then perform bivariate sorts using this institutional holding ratio.

The last panel of Table 4 shows that the short interest trading profit increases as institutional

ownership decreases. The long-short portfolio returns are 75 basis points for the stocks with high

institutional ownership and 87 basis points for the stocks with low institutional ownership. This

result seems intuitive. The short-sales constraint is higher for stocks with lower institutional

ownership. For these stocks, changes in the short interest ratio provide a stronger signal for

future returns. Consistent with this view, the predictive power of △ SIR is higher for stocks with

lower institutional ownership.

The last row reports the portfolio returns averaged across the terciles of institutional

19 Of the winners, they perform a 5*5 sorts by △ SIR and IO, and the overpriced winner portfolio is identified by

high △ SIR and low IO.

23

ownership. The return difference between P1 and P5 is 86 basis points per month, which is

significant at the 1% level (t = 3.52). Results show that the short interest effect is robust to

controlling for the effect of institutional ownership.

In summary, the profitability of the long-short trading strategy based on short interest

changes is robust to controlling for the effects of firm size, leverage, volume, book-to-market

ratio, dispersion in analysts’ forecasts, and institutional ownership. While there are variations in

the profits of the short interest trading strategy across firm/stock characteristics, changes in short

interest appear to have a significant independent predictive effect on future returns over and

beyond the effects of these characteristics.

4.4 Cross-sectional regressions

We further assess the robustness of return predictability by △ SIR using cross-sectional

regressions. The cross-sectional regression has the advantage of controlling for the effects of

multiple factors simultaneously. We run the Fama-MacBeth (1973) regression of future returns

on △ SIR and other control variables each month and average the parameter estimates over time

to infer the significance of these parameters.

Table 5 reports the results of Fama-MacBeth regressions with different specifications. In

column 1, we run the regression using the full sample and control for the effects of conventional

characteristics such as size, book-to-market ratio and lagged six-month returns, which have been

shown to have cross-sectional predictive power for stock returns in the literature. The coefficient

of △ SIR is -6.90 and significant at the 5% level, suggesting that future stock returns decrease

with △ SIR, consistent with the findings in portfolio analysis. The coefficients of size and book-

to-market factors are also significant at the conventional level.

In column 2, we add leverage, institutional ownership, volume and dispersion in analysts’

24

forecasts as control variables. Leverage enters with a negative sign, which is consistent with the

finding of Fama and French (1992).20 The positive and significant coefficient of volume implies

a volume premium, which is in line with the finding of Gervais et al. (2001). Dispersion has a

negative sign, which implies that stocks with high dispersion are associated with low expected

returns, consistent the findings of Diether et al. (2002) and Johnson (2004). Controlling for all

variables, the coefficient on △ SIR remains negative and significant at the 5% level. Results

show that the effect of short interest is robust to these cross-sectional effects.

We next run the regressions for NIG and IG separately. As shown, when including all control

variables, the size of △ SIR coefficient is much larger for speculative-grade stocks and

significant at the 1% level. In contrast, △ SIR has weak power in predicting cross-sectional

expected returns for investment-grade stocks. Although the coefficient of △ SIR is still negative

for these stocks, it is statistically insignificant.

Collectively, regression analysis continues to suggest that the predictive power of short

interest changes for future returns is concentrated in the worst-rated stocks. The predictability of

stock returns with short interest disappears when the firms rated BB+ or below are excluded

from the sample. Results suggest that the distress factor plays an important role in determining

the profitability of short interest trading strategy. This finding is robust to controlling for a wide

spectrum of cross-sectional effects.

[Insert Table 5 here]

5. Robustness

In this section, we perform additional tests for robustness. We first examine the predictive

power of short interest for different subperiods and the subsample that includes only NYSE

20 Leverage ratio is defined as A/BE (total assets/book value of common equity). Leverage ratio used in this paper is

defined as total debts/total assets. It is easy to show these two measurements move in the same direction.

25

stocks to see if short-interest return predictability is sample- or time-dependent. We also conduct

tests based on finer rating categories to reveal more detailed rating effects. Lastly, we explore the

overreaction hypothesis by exploiting a mispricing score proposed by Stambaugh et al. (2015) to

further understand the short-selling behavior.

5.1 Subperiod analysis

A potential concern is that abnormal returns are dependent on the sample period. To address

this concern, we perform subperiod analysis. We divide the whole sample period into two

subperiods: January 1986 ̶ December 2000 and January 2001 ̶ February 2017. We regress value-

weighted portfolio returns on risk factors to obtain the risk-adjusted returns using the four-factor

model for both subperiods.

The left panel of Table 6 presents the future abnormal returns for subperiods. Again, we find

that changes in short interest have a predictive power primarily for speculative-grade stocks. For

both subperiods, the differences in abnormal returns between P1 and P10 are only statistically

significant for firms with a speculative grade. A long-short portfolio that longs P1 and shorts P10

delivers a monthly abnormal return (alpha) of 1.11% in period 1, and 1.12% in period 2 for NIG,

suggesting that results are not dependent on the sample period.

The literature has documented that investors’ sentiment affects return predictability. Baker

and Wurgler (2006, 2007) find that there is a negative relationship between investors’ sentiment

and the cross-section of returns for stocks whose value is more subjective and harder to arbitrage.

To explore whether return predictability of △ SIR is robust to time variations in investors’

sentiment, we divide the whole sample period into two subperiods using the sentiment index

proposed by Baker and Wurgler (2006, 2007). The middle panel of Table 6 reports the results for

the periods with high and low sentiments. The risk-adjusted return (alpha) spread is 1.18% in the

26

low sentiment period and 1.19% in the high sentiment period for speculative-grade stocks, both

significant at least at the five percent level. The predictive power of △ SIR for future returns is

somewhat higher in the period with high investment sentiment.

The literature has also suggested that return predictability depends on macroeconomic

conditions (see Rapach et al., 2010). To see whether the predictive power of short interest

depends on the economy, we divide the whole sample period into two subperiods using the real

GDP growth rate provided by the Federal Reserve Bank in St. Louis to explore the link of cross-

sectional return predictability of △ SIR to macroeconomic conditions. The right panel of Table 6

reports the results. The alpha spread for speculative-grade firms is 1.46% in the bad economy

and 1.04% in the good economy, suggesting that returns are more predictable in the bad

economy. This result is consistent with the finding of time-series forecasts in Rapach et al. (2010)

that stock returns are more predictable in the bad economy.

[Insert Table 6 here]

5.2 Using only NYSE stocks

To see if our results are sensitive to sample selection, we reconstruct a sample that includes

only the stocks listed in the NYSE. We form the portfolios using the same procedure as in the

baseline analysis and regress value-weighted returns on risk factors to obtain alphas using the

four-factor model. Panel A of Table 7 shows that the long-short portfolio returns are significant

at the one percent level for the speculative-grade firms and at the five percent level for the full

sample. These findings are in line with the results based on the broad sample in Table 3 and

show the robustness of results to the exclusion of AMEX and NASDAQ stocks.

[Insert Table 7 here]

5.3 Results by rating category

27

To further reveal the segment of firms driving the return predictability, we show the

profitability of the short interest trading strategy for more detailed credit rating subsamples.

Since the number of observations are much smaller for ratings AAA and below B, we focus on

the data for the ratings from AA to B. For each S&P rating group in month t, we sort stocks into

deciles based on △ SIRit.

Panel B of Table 7 presents the abnormal returns of the short interest trading strategy for

individual rating categories. Results show that the spreads between the high and low portfolio

risk-adjusted returns are significant at the one percent level for rating groups BB and B. For the

lowest investment-grade category, BBB, the risk-adjusted return spread is significant only at the

10% level and with a much smaller magnitude. Finally, for ratings higher than BBB, return

spreads are not significant. Results again show that the short interest trading profits are

concentrated in lower-grade stocks.

5.4 Overreaction and stock mispricing

Diether et al. (2009) document that short-sellers trade against short-term overpricing of

stocks. Rapach, et al. (2016) show that changes in short interest are a more reliable measure for

variations in short-sellers’ beliefs, which better explains short sellers’ behavior. An increase in

short interest reflects the pressure of shorting a stock. If the overreaction hypothesis holds, we

should observe a tendency to short sell a stock more when its price increases; that is, short

interest should rise as stock return increases. A positive relation of short interest changes with

stock returns will therefore lend support to the overreaction hypothesis.

To examine this possible relation, we sort stocks into deciles by contemporaneous returns for

the full sample, and NIG and IG subsamples.21 The right side of Panel A in Table 8 reports the

contemporaneous mean △SIR for high and low return portfolios. The result for the full sample in

21 We label these decile portfolios as Low, 2, …, 9, High.

28

the first line shows average changes in short interest increase from Low to High portfolios,

suggesting that stocks with the highest current returns are associated with the largest increase in

short-selling activities. The difference in the change of short interest between the high and low

return portfolios is 0.10, which is significant at the one percent level. The results strongly support

the overreaction hypothesis. When dividing the whole sample into NIG and IG, we find the

difference in △SIR between high and low return portfolios is significant for both rating groups.

More importantly, the speculative-grade stocks have a much larger spread of short interest

changes. Thus, while short interest rises when stock return increases for all stocks, this positive

relation is much stronger for the stocks of distressed firms. The left side of Panel A from Table 8

reports the contemporaneous mean SIR for high and low return portfolios. However, the

difference in short interest level between high and low return portfolios is insignificant. Results

show that the overpricing effect is more apparent when we measure the short-selling pressure by

changes in short interest.

[Insert Table 8 here]

Short-sellers tend to increase their trading activity following positive stock returns. However,

stocks with positive returns are not necessarily overvalued. It is possible that these stocks may

have been undervalued and their prices are adjusting back to their fair values. To address this

concern, we construct the mispricing score proposed by Stambaugh et al. (2015) to direct

measure each stock’s propensity to be overvalued or undervalued, and re-examine the

overreaction hypothesis. The mispricing score is a percentile number averaged across the ranking

percentiles for each of the 11 anomalies studied by Stambaugh et al. (2015). The higher the score,

the greater the degree of overpricing.22

22 The 11 anomalies are Failure Probability, O-score Bankruptcy Probability, Net Stock Issues, Composite Equity

Issues, Total Accruals, Net Operating Assets, Momentum, Gross Profitability, Asset Growth, Return on Assets and

29

We sort our sample into deciles by the mispricing score (MS) where MS1 consists of the

most underpriced stocks and MS10 is the portfolio with the most overpriced stocks. The average

score for MS1 (MS10) is 29 (72). Panel B of Table 6 reports the average short interest level as

well as changes in short interest for these mispricing portfolios. The short interest level increases

from MS1 to MS10, and the difference between MS1 and MS10 is 2.78%, which is significant at

the 1% level. The right side reports the distribution of ∆SIR. The difference in ∆SIRs between

MS1 and MS10 is 5.0 bps, which is significant at the 1% level. ∆SIR of MS1 portfolio is

negative, suggesting that short-selling activity attenuates for underpriced stocks. In the next two

rows, we find a similar pattern across ratings. Notably, firms with a speculative-grade rating

have higher mispricing score. The spreads of high-low mispricing scores, short interest level and

changes are all highest for distressed firms. The results lend support to the overreaction

hypothesis that the stocks heavily shorted have most serious mispricing (or overpriced most).

Moreover, this relation is most pronounced for distressed firms.

6. Change in short interest and firms’ future profitability

The results above show that the short interest anomaly is primarily driven by credit risk. An

issue of interest is what the source is for the predictive power of short interest. In this section, we

examine the possible source of the predictive power of short interest changes for stock returns of

distressed firms.

Active short-sellers generally trade more following a stock price increase (see Table 8), but

the evidence shows that they only earn significant profits by trading speculative-grade stocks.

The long-short trading strategy based on short interest changes delivers a monthly abnormal

return of 1.14% for these stocks, which is robust to controlling for various cross-sectional effects.

Investment-to-Assets, respectively. Stambaugh et al. (2015) rank firms each month by anomaly type and average the

ranks across all anomalies. For the detail of the procedure for constructing the mispricing score, see the discussion in

Stambaugh et al. (2015).

30

A question that naturally arises is why the short-interest trading strategy is profitable only for

speculative-grade firms.

Speculative-grade or distressed firms differ from other firms in several ways, which could

explain the differential effect of short interest. First of all, if stock returns of distressed firms are

more sensitive to earnings surprise, it will be more profitable for the informed traders to trade the

worst-rated stocks to maximize the benefit of their private information for firm profitability. To

investigate this possibility, we examine the sensitivity of stock returns to earnings shocks. We

use profit margin (PM) as the measure for firms’ performance, which is the net income (NIQ)

over sales (SALEQ) adjusted for the industry norm. We use the first two digits of NAICS (North

American Industry Classification System) to identify the industry that the firm belongs to and

then subtract the industry median from each firm’s profit margin.

To investigate the sensitivity of stock returns to changes in income, we regress stock excess

returns against the firm’s profitability measure and other control variables. We run the

regressions using the full sample as well as the subsamples of investment-grade and speculative-

grade stocks separately. Results in Table 10 consistently show that returns are more sensitive to

changes in firms’ profitability for speculative-trade stocks than for investment-grade stocks.

[Insert Table 9 here]

An issue is whether changes in short interest can predict firms’ future profitability. To

address this issue, we run the cross-sectional regression of future profit margin on current △ SIR.

Table 10 reports the results of predictive regressions of PM with different control variables.

Results show that △ SIR has significant predictive power for future profitability of distressed

firms. Lower value of short interest changes, △ SIR, is associated with higher future firm profits.

31

Conversely, there is no significant relation between short interest changes and future profitability

of investment-grade firms.

Taken together, these results suggest that the profitability of the short interest strategy is

linked to the ability of short interest changes to predict future profitability of distressed firms. A

possible reason that the short interest trading strategy generates more positive abnormal returns

of for distressed stocks is that returns of these stocks are more sensitive to firms’ profitability.

The results are consistent with the contention that trading the distressed firms’ stocks allows

informed traders to maximize their information advantage and therefore generate higher returns.

[Insert Table 10 here]

7. Conclusion

In this paper, we explore the role of the distress factor in the predictive relation between short

interest and future stock returns. We first examine short interest level and credit ratings, and find

that the well-established pattern of return predictability is most evident in the group of

speculative-grade stocks. We then focus on the role of changes in short interest, which is a

detrended variable and thus serves as a better measure for variations in short-sellers’ beliefs, in

predicting firm-level stock returns. We find that the predictive power of short interest changes

for future returns is the strongest for high credit risk firms with a rating of BB+ and below.

The trading strategies based on short interest changes generate significant profits for

distressed stocks. For the speculative-grade firms, a portfolio that goes long in stocks with the

largest decrease in the short interest ratio (P1) and short in stocks with the largest increase in the

short interest ratio (P10) delivers an abnormal return (alpha) of 14.6% per annum. The

profitability of this trading strategy is robust to controlling for various cross-sectional effects. In

32

contrast, the short interest trading strategy generates no significant profits for investment-grade

stocks.

We investigate the economic source of short interest predictive power for stock returns of

distressed firms. We find that firms in the speculative-grade group are more sensitive to changes

in earnings, which increases the profitability of trading on the information in short interest. In

addition, we find that short interest changes predict the future performance of distressed firms.

These findings explain why the short interest based trading strategies generate abnormal profits

for distressed firms. Results are consistent with the view that short interest’s predictive power

derives from the ability of informed short sellers to anticipate firms’ future performance.

Our study contributes to the current literature by uncovering the fact that the short interest

anomaly is linked to financial distress. We show that financial distress leads to sharp responses

in stock prices to changes in earnings and drives the abnormal returns of short interest trading

strategies. Our results confirm previous findings for the importance of a distress factor in asset

pricing, and suggest that there exist commonalities across asset pricing anomalies.

33

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36

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868.

37

Figure 1

Aggregate short interest ratio

0

.02

.04

.06

.08

.1

Eq

ua

l-w

eig

hte

d s

ho

rt i

nte

res

t ra

tio

1986 1991 1995 2001 2006 2011 2016Year

38

Figure 2

Portfolio cumulative return

This graph plots the cumulative returns to a long-short portfolio that buys stocks in P1 and shorts

stocks in P10 for NIG. These value-weighted portfolios are then held for one calendar month.

01

02

03

04

0

Cu

mu

lati

ve

Re

turn

1986 1991 1996 2001 2006 2011 2016t

long-short portfolio cumulative returns

cumulative returns of S&P 500 index

39

Table 1

Summary statistics

The table presents the composition of the sample of firms that are rated by S&P and have short interest data in

Compustat. We exclude observations with stock price below $5 or firms with market capitalization below the fifth

percentile breakpoint of NYSE market capitalization using the breakpoints from Kenneth French’s website. We

assign a numerical indicator to each rating in column 2 and group the sample firms into 5 rating categories in

column 3. The cut-off for the two periods is December 2000. The sample period is from January 1986 to February

2017.

Rating Numerical

ID

Rating

group

Period 1 Period 2 Number of

observations

Percent of

total obs.

Mean return S.D of returns

AAA 1

IG

2,764 1,365 4,129 1.37% 1.11 6.70

AA+ 2 1,540 380 1,920 0.64% 1.46 7.54

AA 3 5,868 2,078 7,946 2.63% 1.07 7.40

AA- 4 5,534 3,244 8,778 2.91% 0.99 7.64

A+ 5 8,932 8,147 17,079 5.66% 1.09 7.77

A 6 14,887 15,234 30,121 9.98% 1.17 8.35

A- 7 10,940 13,638 24,578 8.14% 1.17 8.60

BBB+ 8 10,859 18,681 29,540 9.79% 1.06 9.02

BBB 9 11,889 26,716 38,605 12.79% 1.17 9.23

BBB- 10 8,505 21,076 29,581 9.80% 1.19 10.14

BB+ 11

NIG

6,251 14,046 20,297 6.72% 1.16 11.23

BB 12 6,815 17,824 24,639 8.16% 1.36 12.25

BB- 13 7,082 20,272 27,354 9.06% 1.50 13.37

B+ 14 7,065 14,752 21,817 7.23% 1.76 14.36

B 15 1,389 8,805 10,194 3.38% 2.72 16.33

B- 16 550 3,317 3,867 1.28% 4.21 18.43

CCC+ 17 234 759 993 0.33% 5.34 22.26

CCC 18 171 157 328 0.11% 8.30 28.06

CCC- 19 34 13 47 0.02% 10.46 19.83

CC 20 4 51 55 0.02% 14.30 57.96

C 21 0 0 0 0.00%

Total 111,313 190,555 301,868 100.00% 1.35 10.87

40

Table 2

Portfolio sorts by short-selling activities

Panel A provides summary statistics of the level and changes of short interest, SIRit and ∆SIRit. Panel

B presents the mean SIRit and portfolio mean numerical rating for decile portfolios sorted by SIRit.

Panel C presents the mean ∆SIRit and portfolio mean numerical rating for decile portfolios sorted by

∆SIRit. ∆SIRit is the change in short interest ratio for stock i from month t-1 to month t. We exclude

observations with stock price below $5 or firms with market capitalization below the fifth percentile

of NYSE market capitalization using the breakpoints from Kenneth French’s website. The sample

period is from January 1986 to February 2017.

Panel A: Distribution of SIR and ∆SIR

Mean Std Median Min Max Skewness Kurtosis

SIRit (%) 3.56 4.71 1.92 0.00 100.00 3.55 25.84

∆SIRit (%) 0.02 1.18 0.00 -88.94 89.31 1.91 526.90

Panel B: Decile portfolios sorted by SIR

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10

Mean SIRit (%) 0.39 0.73 1.03 1.35 1.74 2.26 2.96 3.98 5.84 12.41

Mean Rating 8.47 7.94 7.93 8.10 8.42 8.81 9.29 9.82 10.53 11.86

Panel C: Decile portfolios sorted by ∆SIR

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10

Mean ∆SIRit (%) -1.52 -0.44 -0.23 -0.11 -0.03 0.04 0.13 0.25 0.49 1.64

Mean Rating 10.39 9.30 8.74 8.50 8.51 8.51 8.49 8.80 9.38 10.52

41

Table 3

Short interest level/changes and subsequent stock returns

The table reports value-weighted monthly returns (Raw), characteristic-matched adjusted returns (DGTW) and four (five)-factor Fama-French-Carhart

(Pastor-Stambaugh) alphas in month t+1 for portfolios formed by ranking SIRit (∆SIRit) and rating. SIRit is short interest level for stock i in month t.

∆SIRit is the change in short interest ratio for stock i from month t-1 to month t. (∆)SIR 10% and (∆)SIR 90% include stocks with (∆)SIRit from the 10th

and 90th percentiles. We exclude observations with stock price below $5 or firms with market capitalization below the fifth percentile of NYSE market

capitalization using the breakpoints from Kenneth French’s website. We subtract stock raw returns by benchmark returns from the size and book-to-

market portfolio to which the stock belongs to calculate the DGTW returns. To estimate the portfolio alpha, we use the following factor model:

, +1 1 , +1 2 +1 3 +1 4 +1 5 +1 , +1ip t ip ip M t ip t ip t ip t ip t ip tr r SMB HML UMD LIQ e

where rip,t+1 is the value-weighted return in excess of the risk-free rate on portfolio p in rating group i and month t+1, rM,t+1 is the market excess return,

SMBt+1 is the size factor, HMLt+1 is the value factor, UMDt+1 is the momentum factor and LIQt+1 is the Pástor-Stambaugh liquidity factor. We estimate

alphas using the four- and five-factor models, respectively. The sample period is from January 1986 to February 2017. The signs ***, ** and * indicate

significance at the 1%, 5% and 10% levels, respectively.

SIR

(level)

Raw ret. DGTW ret. 4-factor

alpha

5-factor

alpha ∆SIR

(change)

Raw ret. DGTW ret. 4-factor

alpha

5-factor

alpha

All P1 0.89 -0.02 0.08 0.10 P1 1.21 0.17 0.15 0.14

(0.79) (0.97) (1.37) (1.25)

P10 0.62 -0.49 -0.55*** -0.54*** P10 0.88 -0.20 -0.17 -0.18

(-4.42) (-4.33) (-1.58) (-1.64)

P1 – P10 0.27 0.47*** 0.63*** 0.64*** P1 – P10 0.33** 0.37*** 0.32** 0.31**

(1.28) (2.76) (3.73) (3.77) (2.43) (2.82) (2.30) (2.26)

NIG P1 1.22 0.02 0.27 0.25 P1 1.48 0.49 0.35 0.34

(1.46) (1.37) (1.43) (1.37)

P10 0.36 -1.01 -0.81*** -0.80*** P10 0.39 -0.80 -0.78*** -0.80***

(-3.69) (-3.62) (-3.51) (-2.72)

P1 – P10 0.86*** 1.03*** 1.08*** 1.05*** P1 – P10 1.09*** 1.28*** 1.14*** 1.13***

(3.01) (3.68) (3.93) (3.82) (3.66) (4.15) (3.70) (3.67)

IG P1 0.79 -0.09 -0.03 -0.02 P1 1.17 0.16 0.17* 0.16

(-0.29) (-0.17) (1.66) (1.56)

P10 0.78 -0.26 -0.30** -0.28** P10 0.96 -0.09 -0.04 -0.03

(-2.49) (-2.29) (-0.35) (-0.28)

P1 – P10 0.01 0.17 0.27 0.26 P1 – P10 0.21 0.25* 0.21 0.19

(0.05) (1.00) (1.61) (1.52) (1.52) (1.85) (1.50) (1.38)

42

Table 4

Control for various cross-sectional effects

The table reports future four-factor Fama-French-Carhart alphas for portfolios formed by first sorting stocks with speculative-grade on a candidate variable into

terciles and then on ∆SIRit into quintiles. Candidate variables include firm size, leverage ratio, trading volume, book-to-market ratio, dispersion in analysts’

forecasts and institutional ownership. ∆SIRit is the change in short interest ratio for stock i from month t-1 to month t. We exclude observations with stock price

below $5 or firms with market capitalization below the fifth percentile breakpoint of NYSE market capitalization using the breakpoints from Kenneth

French’s website. To estimate the portfolio alpha, we use the following factor model:

, +1 1 , +1 2 +1 3 +1 4 +1 , +1ip t ip ip M t ip t ip t ip t ip tr r SMB HML UMD e

where rip,t+1 is the value-weighted return in excess of the risk-free rate on portfolio p in rating group i and month t+1, rM,t+1 is the market excess return, SMBt+1 is

the size factor, HMLt+1 is the value factor and UMDt+1 is the momentum factor. The sample period is from January 1986 to February 2017. The signs ***, ** and *

indicate significance at the 1%, 5% and 10% levels, respectively.

P1 P5 P1 – P5 P1 P5 P1 – P5

Control for

size

Small 0.46**

(2.23)

-0.57**

(-2.46)

1.04***

(3.70)

Control for

B/M

Low 0.30

(1.12)

-0.67**

(-2.56)

0.97***

(2.93)

Medium 0.24

(1.26)

-0.71***

(-3.43)

0.95***

(3.97)

Medium 0.35

(1.33)

-0.48*

(-1.85)

0.83**

(2.54)

Large 0.19

(0.81)

-0.67***

(-3.05)

0.86***

(2.98)

High 0.51**

(2.03)

-0.38

(-1.37)

0.89***

(2.79)

Mean 0.21

(1.12)

-0.67***

(-3.69)

0.88***

(3.88)

Mean 0.34*

(1.71)

-0.60***

(-3.21)

0.94***

(4.20)

Control for

leverage

Low 0.32

(1.19)

-0.44

(-1.58)

0.76**

(2.21)

Control for

dispersion

Low 0.80***

(2.94)

-0.03

(-0.11)

0.83**

(2.18)

Medium 0.45*

(1.82)

-0.42

(-1.63)

0.87***

(2.96)

Medium 0.35

(1.08)

-0.57*

(-1.88)

0.87**

(2.14)

High 0.50**

(2.06)

-0.47**

(-2.11)

0.98***

(3.34)

High -0.02

(-0.06)

-1.20***

(-3.01)

1.18**

(2.42)

Mean 0.31*

(1.70)

-0.46**

(-2.48)

0.77***

(3.61)

Mean 0.36*

(1.74)

-0.53**

(-2.34)

0.90***

(3.35)

Control for

volume

Low 0.37*

(1.94)

-0.48***

(-2.59)

0.85***

(3.65)

Control for

institutional

ownership

Low 0.26

(0.88)

-0.61**

(-2.44)

0.87**

(2.36)

Medium 0.38*

(1.86)

-0.32

(-1.53)

0.70***

(2.79)

Medium 0.28

(1.03)

-0.55**

(-2.11)

0.83**

(2.46)

High 0.46*

(1.78)

-0.22

(-0.81)

0.68**

(1.98)

High 0.25

(1.02)

-0.50*

(-1.95)

0.75**

(2.27)

Mean

0.42**

(2.18)

-0.26

(-1.28)

0.67***

(2.76)

Mean 0.28

(1.35)

-0.58***

(-3.12)

0.86***

(3.52)

43

Table 5

Cross-sectional regressions of future excess returns on changes in short interest ratio

Each month t, we run cross-sectional regressions of stock excess returns of next month (%) on the

change in short interest ratio and control variables: '

, 1i t t t it t t tr SIR X e

ri,t+1 is stock i’s excess return in month t+1. ∆SIRit is the change in short interest ratio for stock i from

month t-1 to month t. X is a vector of control variables including size, book to market ratio, past six-

month returns, leverage, institutional ownership, volume and analyst forecast dispersion. We report the

time-series average of these cross-sectional regression coefficients with their associated sample t-

statistics. The sample period is from January 1986 to February 2017. The signs ***, **, and * indicate

significance at the 1%, 5% and 10% levels, respectively.

Variables Full sample Full sample NIG NIG IG IG

∆SIR -6.90** -10.77** -12.23** -29.06*** -3.25 -1.75

(-2.21) (-2.39) (-2.21) (-2.98) (-0.98) (-0.37)

Ln(size) -0.17*** -0.34*** -0.52*** -1.00*** -0.03 -0.14*

(-4.70) (-4.14) (-7.39) (-6.64) (-1.01) (-1.93)

B/M 0.56*** 0.64*** 0.84*** 0.67*** 0.35*** 0.47***

(7.05) (5.16) (6.69) (3.11) (3.68) (3.67)

r[-6,-1] -0.38 -0.12 0.13 0.40 -0.84** -0.53

(-1.11) (-0.35) (0.38) (0.96) (-2.04) (-1.35)

Leverage -0.25 -0.93* -0.27

(-1.16) (-1.83) (-1.18)

Institutional Ownership -0.68*** -1.53*** 0.02

(-3.45) (-3.19) (0.09)

Volume 0.29*** 0.57*** 0.16**

(3.80) (5.04) (2.29)

Dispersion -0.30*** -0.14 -0.61***

(-3.30) (-0.86) (-4.99)

Constant 3.35*** 2.99*** 7.91*** 9.71*** 1.30** 1.09*

(5.16) (4.14) (8.25) (7.04) (2.30) (1.67)

Observations 285,081 201,482 98,251 64,441 186,830 137,041

Avg R-squared 0.04 0.08 0.05 0.13 0.05 0.09

44

Table 6

Subperiod Analysis

The table reports future four-factor Fama-French-Carhart alphas for decile portfolios sorted by ∆SIRit and rating for different subperiods. ∆SIRit is the change in

short interest ratio for stock i from month t-1 to month t. We exclude observations with stock price below $5 or firms with market capitalization below the fifth

percentile breakpoint of NYSE market capitalization using the breakpoints from Kenneth French’s website. We report alphas at month t+1 estimated by the

following factor model:

, +1 1 , +1 2 +1 3 +1 4 +1 , +1ip t ip ip M t ip t ip t ip t ip tr r SMB HML UMD e

where rip,t+1 is the value-weighted return in excess of the risk-free rate on portfolio p in rating group i and month t+1, rM,t+1 is the market excess return, SMBt+1 is

the size factor, HMLt+1 is the value factor and UMDt+1 is the momentum factor. The last column reports the difference between P1 and P10. The sample period

is from January 1986 to February 2017. The signs ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

P1 P10 P1 – P10 P1 P10 P1 – P10 P1 P10 P1 – P10

Jan 1986 – Dec 2000 Low sentiment (BW) Low GDP growth rate

All 0.13

(0.79)

-0.24

(-1.43)

0.37*

(1.76)

0.14

(0.93)

-0.23*

(-1.71)

0.37**

(1.98)

0.21

(1.31)

-0.16

(-0.97)

0.37*

(1.72)

NIG 0.41

(0.97)

-0.70*

(-1.94)

1.11**

(2.10)

0.31

(1.09)

-0.87***

(-3.15)

1.18***

(3.07)

0.32

(1.03)

-1.13***

(-3.56)

1.46***

(3.42)

IG 0.07

(0.45)

-0.14

(-0.89)

0.21

(0.96)

0.17

(1.18)

0.00

(0.01)

0.17

(0.89)

0.17

(1.07)

-0.09

(-0.55)

0.26

(1.20)

Jan 2001 – February 2017 High sentiment (BW) High GDP growth rate

All 0.12

(0.86)

-0.22

(-1.50)

0.34*

(1.74)

0.17

(0.99)

-0.25

(-1.34)

0.42*

(1.83)

0.13

(0.89)

-0.21

(-1.38)

0.34*

(1.77)

NIG 0.27

(1.18)

-0.85***

(-3.32)

1.12***

(3.59)

0.38

(0.95)

-0.81**

(-2.22)

1.19**

(2.36)

0.30

(0.78)

-0.74***

(-2.74)

1.04**

(2.45)

IG 0.19

(1.31)

0.01

(0.05)

0.18

(0.92)

0.14

(0.81)

-0.14

(-0.79)

0.28

(1.18)

0.13

(0.95)

-0.13

(-0.87)

0.26

(1.39)

45

Table 7

Results based on NYSE stocks and finer S&P ratings

The table reports future four-factor Fama-French-Carhart alphas for quintile portfolios sorted

by ∆SIRit and finer S&P rating (modifier). ∆SIRit is the change in short interest ratio for stock

i from month t-1 to month t. We report alphas at month t+1 estimated by the following factor

model:

, +1 1 , +1 2 +1 3 +1 4 +1 , +1ip t ip ip M t ip t ip t ip t ip tr r SMB HML UMD e

where rip,t+1 is the value-weighted return in excess of the risk-free rate on portfolio p in rating

group i and month t+1, rM,t+1 is the market excess return, SMBt+1 is the size factor, HMLt+1 is

the value factor and UMDt+1 is the momentum factor. The sample period is from January

1986 to February 2017. The signs ***, ** and * indicate significance at the 1%, 5% and 10%

levels, respectively.

Panel A: NYSE stocks only

Rating # of obs. P1 P10 P1 – P10

All 260,963 0.12

(1.06)

-0.22*

(-1.94)

0.33**

(2.32)

NIG 83,729 0.12

(0.48)

-0.79***

(-3.25)

0.91***

(2.78)

IG 177,234 0.12

(1.08)

-0.07

(-0.58)

0.18

(1.27)

Panel B: Finer S&P ratings

S&P rating # of obs. P1 P10 P1 – P10

AA 18,608 0.11

(0.54)

-0.16

(-0.73)

0.27

(0.93)

A 71,612 0.15

(1.10)

-0.07

(-0.49)

0.22

(1.11)

BBB 97,418 0.21

(1.50)

-0.11

(-0.76)

0.32*

(1.74)

BB 71,960 0.44*

(1.67)

-0.60**

(-2.17)

1.04***

(3.08)

B 35,588 0.69*

(1.76)

-0.84**

(-2.17)

1.53***

(2.90)

46

Table 8

Overreaction hypothesis and stock mispricing

The table presents the mean ∆SIRit for decile portfolios sorted by contemporaneous stock returns.

∆SIRit is the change in short interest ratio for stock i from month t-1 to month t. We exclude

observations with stock price below $5 or firms with market capitalization below the fifth percentile

of NYSE market capitalization using the breakpoints from Kenneth French’s website. The last

column reports the difference between Low and High (Diff). The sample period is from January 1986

to February 2017. The signs *** and ** indicate significance at the 1% and 5% levels, respectively.

Panel A: SIR/∆SIR of portfolios sorted by contemporaneous returns

SIRt (%) ∆SIRt (%)

Low High Diff t-sta Low High Diff t-sta

All sample 4.38 4.30 -0.08 -0.42 -0.01 0.09 0.10*** 5.23

NIG 5.77 5.48 -0.37* -1.35 -0.00 0.14 0.14*** 4.40

IG 2.71 2.73 0.02 0.16 -0.02 0.07 0.09*** 5.83

Panel B: SIR/∆SIR of portfolios sorted by the mispricing score of Stambaugh et al. (2015)

SIRt (%) ∆SIRt (%)

MS1 MS10 Diff t-sta MS1 MS10 Diff t-sta

All sample 2.35 5.13 2.78*** 17.07 -0.01 0.04 0.05*** 2.83

NIG 4.49 6.62 2.13*** 10.27 -0.01 0.08 0.09*** 3.12

IG 1.97 2.91 0.94*** 8.58 -0.01 0.03 0.04*** 2.65

47

Table 9

Cross-sectional regressions of excess returns on profit margin

Each month t, we run cross-sectional regressions of stock excess returns (%) on profit margin

and other controls: '

1 1 1it t t it t t tr PM X e

rit is stock i’s excess return in month t. PMit is stock i’s profit margin in month t. PM is net

income (NIQ) over sales (SALEQ). For quarterly accounting ratio ending in quarter q, all the

three months in that quarter are evenly assigned with the corresponding ratio. X is a vector of

control variables including size, book to market ratio, past six-month returns. We report the time-

series average of these cross-sectional regression coefficients with their associated sample t-

statistics. The sample period is from January 1986 to February 2017. The signs ***, **, and *

indicate significance at the 1%, 5% and 10% levels, respectively.

Variables Full sample NIG IG

PM 9.509*** 10.713*** 9.290***

(9.25) (6.26) (9.19)

Ln(size) -0.210*** -0.533*** -0.063*

(-5.65) (-7.53) (-1.93)

B/M 0.671*** 0.942*** 0.442***

(8.18) (7.34) (4.71)

r[-6,-1] -0.537 -0.067 -0.975**

(-1.55) (-0.19) (-2.36)

Constant 3.790*** 8.080*** 1.660***

(5.78) (8.36) (2.87)

Observations 265,042 95,390 169,652

Avg R-squared 0.046 0.056 0.056

48

Table 10

Changes in short interest ratio as a predictor of firms’ future profits

Each month t, we run cross-sectional regressions of firm profit margin (PM) of next month

on the change in short interest ratio and other controls: '

, 1i t t t it t t tPM SIR X e

PMi,t+1 is stock i’s profit margin in month t+1, measured by net income (NIQ) over sales

(SALEQ). For quarterly profit margin ending in quarter q, all the three months in that quarter

are evenly assigned with the quarterly profit margin. ∆SIRit is the change in short interest

ratio for stock i from month t-1 to month t. X is a vector of control variables including size,

book to market ratio, past six-month returns. We report the time-series average of these

cross-sectional regression coefficients with their associated sample t-statistics. The sample

period is from January 1986 to February 2017. The signs ***, **, and * indicate

significance at the 1%, 5% and 10% levels, respectively.

Variables Full sample NIG IG

∆SIR -0.038*** -0.054*** -0.013

(-2.81) (-3.03) (-0.70)

Ln(size) 0.005*** 0.003*** 0.003***

(47.61) (11.40) (31.69)

B/M -0.006*** -0.005*** -0.010***

(-13.99) (-6.93) (-21.04)

r[-6,-1] 0.017*** 0.019*** 0.015***

(16.21) (15.44) (14.82)

Constant -0.072*** -0.046*** -0.043***

(-41.30) (-13.06) (-24.92)

Observations 265,392 95,677 169,715

Avg R-squared 0.087 0.063 0.082