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Accounting Accruals and Short Selling

Bilal Erturk Assistant Professor of Finance

Spears School of Business Oklahoma State University Email: [email protected]

Giorgio Gotti

Assistant Professor of Accounting College of Business

University of Texas at El Paso Email: [email protected]

Tony Kang

Associate Professor of Accounting Spears School of Business Oklahoma State University

Email: [email protected]

Ramesh Rao Professor of Finance

Spears School of Business Oklahoma State University

Email: [email protected]

The paper has benefited from comments received at the 2013 AAA conference and presentations at the following workshops: Virginia Tech, University of North Texas, King Fahd University of Petroleum and Minerals, American University of Sharjah, IBS-Hyderabad, Chulalongkorn University, and University of Hong Kong.


Abstract

We find that the positive association between short interest and total accruals is attributable to discretionary accruals. We do not find that short interest is positively related to non-discretionary accruals. Further, our results are stronger for firms that meet or narrowly beat earnings threshold using discretionary accruals. For highly shorted stocks, we document a significant abnormal return differential between high and low accrual stocks when ranked by total and discretionary accruals but not non-discretionary accruals. We provide large scale cross-sectional evidence that short sellers play a useful role in uncovering opportunistic earnings management. Key words: Accounting accruals, Discretionary Accruals, Short Selling JEL classification: M40, M41, O16

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1. Introduction

The main objective of this study is to evaluate whether short sellers effectively take

trading positions based on different components of accruals that are known to have different

implications for future earnings and stock returns. Prior studies that examine the association

between financial reporting attributes and short sellers’ behaviors provide mixed evidence (e.g.,

Richardson 2003; Desai, Krishnamurthy, and Venkataraman 2006; Karpoff and Lou 2010;

Hirshleifer et al. 2011). In a recent study, Hirshleifer, Teoh and Yu (2011) find a positive

association between short selling and total accruals in a cross-section of firms. They also

document that the accrual anomaly (Sloan 1996) is exacerbated when constraints on short

arbitrage are more severe.1 However, these studies do not examine whether short sellers

differentiate between different components of accounting accruals, even though they are shown

to have different implications for future earnings and stock returns (e.g., DeFond and Park 2001;

Xie 2001). Since not all portions of accruals are equally opportunistic, this is an important

distinction that enables us to tell whether short sellers really play a role in detecting financial

misreporting, i.e., opportunistic earnings management. To the extent that short sellers are

sophisticated investors that understand the implications of different components of earnings for

future firm performance, they are most likely to take short positions for the component of

earnings that will experience the greatest reversal. Our study fills this void in this literature.

A key innovation of this study is that we examine the associations between short interest

and discretionary and nondiscretionary components of accruals separately. The discretionary

component of accruals is likely to reflect opportunistically managed portion of earnings, whereas

1 Similar to Hirshleifer et al. (2011), Karpoff and Lou (2010) suggest that short sellers anticipate the eventual discovery and severity of financial misconduct, helping to uncover misconduct and to keep share prices closer to fundamental values.

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the non-discretionary component is expected to capture manager’s assessment of future cash

flows from current operations. Nevertheless, prior studies that examine the relation between

accounting accruals and short selling solely focus on total accruals. To the extent short sellers are

a sophisticated group of investors who can process and benefit from financial statement

information, one would expect them to take more short positions in stocks with greater

discretionary accruals that are shown to be less persistent and reverse to a greater extent in the

future periods than non-discretionary accruals (e.g., DeFond and Park 2001; Xie 2001).

Accrual accounting is at the heart of earnings measurements and financial reporting

(Barth, Beaver, Hand and Landsman 2005). As stated in SFAC No.1 (FASB, 1978), accounting

accruals are intended to convey useful signals about future cash flows that are not captured in

current period cash flows. In estimating accruals, managers use their judgment and exercise

discretion. To the extent that managers exercise best accounting judgment and use the reporting

discretion, accounting accruals will signal future cash flows (e.g., Dechow et al. 1998; Barth,

Beaver, Lang and Landsman 1999; Barth, Cram and Nelson 2001). In this case, current accruals

will relate positively to future stock returns.

However, prior research suggests that managers sometimes use their reporting discretion

in an opportunistic manner (e.g., Dechow and Sloan 1991; Sweeney 1994; Becker et al. 1998;

Rangan 1998; Teoh et al. 1998a, 1998; Guidry et al. 1999), leading to a negative association

between current accruals and future cash flows and stock returns (e.g., Sloan 1996; DeFond and

Park 2001; Xie 2001; Cheng and Thomas 2006). Those studies show that managers often use

their discretion in reporting accruals to either opportunistically inflate to meet certain earnings

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thresholds or deflate to smooth out earnings over time. 2 In line with this idea, Sloan (1996) finds

that accruals reverse in the future and that current period accruals are negatively associated with

future stock returns. Xie (2001) shows that this association is primarily driven by the

discretionary component of accruals. His results show that the market overprices discretionary

accruals because investors overestimate the persistence of these accruals. DeFond and Park

(2001) also suggest that the market overprices discretionary accruals because investors under-

anticipate the future reversal of those accruals.

While critics argue that short sellers undermine investors’ confidence in financial

markets3, advocates claim that short selling facilitates market efficiency and the price discovery

process (Boehmer and Wu 2012), as investors who identify overpriced firms can sell short,

thereby incorporating their unfavorable information into market prices (Karpoff and Lou 2010).

If short sellers effectively trade on information contained in current period accruals, short

interest, i.e., the amount of short position taken in a stock, will be positively associated with the

discretionary component of accruals, which has shown to be negatively associated with future

stock returns. In contrast, short interest is likely to be higher in firms with low non-discretionary

accruals, to the extent that non-discretionary accruals signal future cash flows (e.g., DeFond and

Park 2001; Xie 2001).

In order to measure the incremental effect of accruals on short interest, we calibrate short

interest by controlling for other known determinants of short interest such as firm size, book-to-

market, momentum, and institutional ownership. Our evidence shows a positive association

2 For example, while the extant literature suggests that most of discretionary accruals are reversed in the future, Tucker and Zarowin (2006) suggest that not all earnings discretions are necessarily opportunistic. Their evidence suggests that income smoothing can improve earnings informativeness. 3 For instance, some argue that short sellers can spread false rumors about a firm in which (s)he has a short position and profit from the resulting decline in the stock price (Karpoff and Lou 2010). The former SEC Chairman Christopher Cox called such behavior “distort and short” (The WSJ, July 24, 2008).

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between short interest and discretionary accruals but no such relationship is observed for

nondiscretionary accruals. In many cases, we observe a negative relation between short interest

and nondiscretionary accruals. We further observe that short interest ratio is greater for firms

with the highest discretionary accruals relative to those with the lowest discretionary accruals.

We also find that short interest ratio is actually lower for firms with the highest non-discretionary

accruals compared to those with the lowest non-discretionary accruals. This is consistent with the

idea that “ungarbled” accruals signal future profitability (e.g., Barth et al. 2001).

Moreover, examining changes in short interest reveals that short sellers increase their

positions after observing high discretionary accruals are reported. However, we don’t find such a

relationship for non-discretionary accruals. These results suggest that short sellers discern the

implications of discretionary and nondiscretionary components of accruals for future returns and

take trading positions accordingly. This result suggests that Hirshleifer et al.’s (2011) finding is

largely driven by the discretionary component of accruals. To bolster this claim, further analysis

shows that the positive association between short interest and total accruals (Hirshleifer et al.

2011) is observed only when the proportion of discretionary accruals in total accruals is high.

This also suggests that our finding is not simply driven by the mechanical relation between total

and discretionary accruals. We further find that the positive relationship between short interest

and accruals is stronger for firms using accruals (both total and discretionary) to meet or

narrowly beat the earnings threshold. This provides corroborating evidence to our earlier cross-

sectional test results, as these firms are more likely to have engaged in opportunistic earnings

management to avoid reporting negative earnings surprises (Matsumoto 2002). These results are

robust to controlling for institutional monitoring and short selling constraints proxied by

institutional ownership.

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Our final set of results examines the effect of accruals and their components on short

seller profitability. For purposes of this test, we focus on stocks with the greatest short interest

following quarterly earnings announcements. Our portfolio analysis shows a significant

differential in the buy-and-hold abnormal returns between extreme portfolios classified by total

and discretionary accruals but not by non-discretionary accruals. Short sellers experience

abnormal return spreads of 4.5 (3.8) percent between high and low total (discretionary) accrual

portfolios, while the spread is insignificant in the case of non-discretionary accruals. This

suggests that trading by short sellers on information contained in discretionary accruals yields

significant incremental returns but that short seller trading profitability is likely unrelated to non-

discretionary accruals.

Our study makes several important contributions to the literature. First, this study

contributes to the long-standing debate over whether short sellers improve market efficiency or

encourage harmful manipulations of corporate performance (Jones and Lamont 2002). Our

finding extends prior studies that document short sellers profit from financial misconducts (e.g.,

Karpoff and Lou 2010, Desai, Krishnamurthy, and Venkataraman 2006) by providing more

direct, large-scale evidence that short sellers effectively take trading positions based on

opportunistic accrual-based earnings management actions that may not ultimately be subject to

legal enforcement actions.4 In contrast to the prior studies that examine a set of firms that were

clearly overpriced “ex-post”, i.e., firms that were subject to SEC enforcement for financial

misrepresentation (Karpoff and Lou 2010) and restated earnings (Desai et al. 2006), we focus on

4 Desai et al. (2006) use the GAO restatement data, but the reliability of these data is often questioned. For example, Hennes, Leone, and Miller (2008) report that 76% of the restatement data in the GAO database are simple errors rather than misrepresentation or fraud.

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firms that are overpriced “ex-ante.”5 Our approach enables us to provide large scale evidence on

short sellers’ role in market price discovery – their ability to identify overpriced stocks in a

setting that is less obvious and likely more difficult to characterize as financial misconduct. In

this regard, we provide more powerful evidence over short-sellers’ ability to capitalize on

overpriced stocks through financial statement analysis.

This study also contributes to prior literature on investors’ capital allocation decisions

based on accounting accruals (e.g., Sloan 1996; DeFond and Park 2001; Xie 2001; Desai et al.

2006; Hirshleifer et al. 2011). Our results show that the positive relation between total accruals

and short interest (e.g., Hirshleifer et al. 2011) is primarily driven by the discretionary

component of accruals, i.e., short sellers target firms with large discretionary accruals, which are

known to experience greater reversals in the future periods, but not the ones with high non-

discretionary accruals. There is also weak evidence that non-discretionary accruals are associated

negatively with short interest, consistent with the signaling role of accruals for future cash flows.

Supporting the idea that short sellers represent a sophisticated group of investors, our evidence

suggests that they are able to see through the different implications of the two components of

accounting accruals, and correct capital misallocations from opportunistic financial reporting.

The remainder of this article is organized as follows. In the next section, we review

relevant literature and develop empirically testable hypotheses. We describe the sample and

research design in Section 3. We discuss the results in Section 4. We conclude in Section 5.

2. Related Literature and Hypotheses

5 We examine a large sample of panel data than the ones based on samples of firms that were subject to SEC enforcement action or have available GAO restatement data. For example, Karpoff and Lou’s (2010) sample contains 454 firms during 1988 – 2005, only a small fraction of publicly traded firms.

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Prior literature finds that short sellers are able to predict short-horizon abnormal returns

(Diether et al., 2009). There are a few possible reasons for how they are able to achieve this

result. The first explanation suggests that market frictions (among many, Diamond and

Verrecchia, 1987) and investor behaviors (Daniel et al., 1998; Hong and Stein, 1999) allow the

market price in the short horizon to deviate from the underlying fundamental value, and that

short sellers – as more sophisticated investors – trade in these situations. Indeed there is evidence

(Boehmer et al. 2008) that 75% of short sales are executed by institutional investors, despite the

fact that many institutions (for instance many mutual funds) are not allowed to short stocks.

An alternative explanation on why short sellers are able to predict short-horizon abnormal

returns is that they provide liquidity to the market, when there is a significant short term

imbalance between buy and sell orders. When the buying pressure reverts, prices also revert to

the fundamental value and the short sellers cover their positions at a profit. In this case short

sellers are compensated for providing immediacy (Grossman and Miller, 1988; Campbell et al.

1993). Finally, another possible explanation considers short sellers to step in and absorb part of

the market risk in periods of high volatility, and for that they are compensated with a trading

profit.

Short selling requires an investor to borrow shares from another invesor who is willing to

lend. The lender generally requires cash collateral, equal to 102% of the market value of the

borrowed shares, from the borrower. As Hirshleifer et al. (2011) note, Federal Reserve

Regulation T requires short sellers to post an additional 50% in margin when the lender is a U.S.

broker-dealer. The lender pays the short seller interest, i.e., the rebate rate, on the collateral.6

6 As Hirshleifer et al. (2011) point out, the spread between the rebate rate and the market interest rate on cash funds (the loan fee) constitutes a direct cost to the short seller. In addition, the lenders have the right to call a loan at their disposal.

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While there is a rather extensive literature that shows how short sellers are informed

traders (e.g., Diether et al., 2009), some studies suggest otherwise (Henry and Koski 2010). For

instance there is evidence that short sellers target their trading on companies characterized by

market over-pricing compared with their fundamental ratios (Dechow et al., 2001), earnings

restatements and high accruals (Efendi et al., 2005; Desai et al. 2006), analysts forecast revisions

and earnings surprises (Francis et al. 2006, Christophe et al. 2004), analyst recommendation

changes (Christophe et al., 2010), financial misconduct as represented in SEC enforcement

actions (Karpoff and Lou, 2010), and that they are trading to exploit the earnings announcement

drift and the accrual anomaly (Cao et al., 2006). In contrast, Henry and Koski (2010) find no

evidence of informed short selling around SEO announcements.

The Financial Accounting Standards Board (FASB) states in Objectives of Financial

Reporting by Business Enterprises (1978) that the primary objective of accounting data is to

provide information to help present and potential investors, creditors, and others assess the

amounts, timing, and uncertainty of prospective net cash inflows to the related enterprise (para

37). Prior research generally shows that future cash flow prediction is enhanced by considering

accounting accruals data in addition to current period cash flow information. Finger (1994) finds

that used alone or together with current period cash flows, contemporaneous earnings are a

significant predictor of future cash flow. Lorek and Willinger (1996) examine the time-series

properties and predictive ability of cash-flow data, and conclude that accounting accruals have

predictive ability for future cash flows incremental to current cash flows.

Dechow, Kothari and Watts (1998) show that accrual earnings better predict future

operating cash flows than current operating cash flows, confirming the idea that earnings convey

information about future cash flows not contained in current period cash flows. Barth et al.

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(1999) find that accounting accruals are incrementally informative for future cash flows over and

beyond current accruals. Barth et al. (2001) find that accruals and its components predict future

cash flows after controlling for current cash flows, consistent with the idea that accounting

accruals convey useful signals for future cash flows. Specifically, they find that accrual

components such as change in accounts payable/receivable, change in inventory, and

depreciation and amortization, are useful in predicting future cash flows, incremental to current

cash flow.

While a primary role of accounting accruals is enhancing the prediction of future cash

flows, research evidence shows that managers at times use their discretion to report earnings in

an opportunistic manner (e.g., Dechow and Sloan 1991; Becker et al. 1998; Rangan 1998; Teoh

et al. 1998a, 1998b; Healy and Wahlen 1999). The evidence suggests that managers use accruals

to opportunistically increase or decrease earnings in various contexts, e.g., prior to initial public

offerings and seasoned equity offerings (Rangan 1998; Teoh et al. 1998a, 1998b), when the

firm’s compensation contract is tied to the earnings (Dechow and Sloan 1991; Guidry et al. 1999;

Healy 1985; Holthausen et al. 1995), and when the firm is likely to violate debt covenants

(Sweeney 1994). Thus, a more realistic view of accounting accruals is that it reflects both

managers’ candid assessment of future cash flows as well as opportunistically biased accrual

judgments driven by various conflicts of interest they face.

If accounting accruals predict future cash flows, they will also relate to future stock

returns which reflects expectation of future cash flows. Interestingly, however, Sloan (1996)

finds that total accruals are negatively associated with future stock returns because investors tend

to over-estimate the persistence of accruals and over-price them as a result. Xie (2001) shows

that Sloan’s (1996) findings are primarily driven by the “discretionary” component of accruals,

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which is likely to capture the opportunistically managed portion of accruals. He does not find

that non-discretionary portion of accruals are negatively associated with future stock returns.

These findings suggest that different components of accruals have different implications for

future stock returns.

To the extent that short sellers are a group of sophisticated investors who are able to

accurately process financial statement information, they are likely to distinguish different

components of accounting accruals that have different implications for future cash flows.

Specifically, they are more likely to sell short shares of a firm with inflated earnings due to

reporting opportunism. Accordingly, we formulate our first hypothesis as follows:

H1: Short interest are greater for firms with greater discretionary accruals.

To rule out alternative explanations, we also examine the sensitivity of discretionary

accrual -short selling relationship to the underlying incentives for short artbitrage. It is possible

that the profitability of short selling arbitrage is greater in cases of stronger external monitoring

and where short selling constraints are lower. Using institutional ownership as a proxy for

external monitoring and short selling constraints, we predict that the positive relationship

between short selling and discretionary accruals will be increasing in insititutional ownership.

Next, we examine whether short sellers are more cautious about accruals when the firm

narrowly meets or beats the analyst forecasts. Matsumoto (2002) reports that managers tend to

manage earnings to avoid negative earnings surprises. Her results suggest that managers have

greater incentives to opportunistically manage earnings when they are likely to miss the earnings

threshold. This implies that when discretionary accruals are used to meet or narrowly beat the

analyst expectation, the discretionary accruals are more likely to be opportunistic in nature. In

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this regard, we examine whether the positive relationship between accruals and short interest is

stronger for “suspect firms”, i.e., firms that just meet or narrowly beat the analyst expectation

using discretionary accruals, than “non-suspect firms”. To the extent that short sellers are able to

see through the manager’s intention, short interest is likely to be higher for suspect firms. This

leads us to predict the following.

H2: The relationship between accruals and short interest is stronger for “suspect

firms” (firms that would not have met the analyst expectation without

discretionary accruals).

For our final hypothesis we examine the question of what role accruals and their

components play in short seller profitability. Specifically, focusing on stocks with the highest

short interest following earnings announcements, we evaluate differential abnormal returns

available to short sellers from trading the two extreme accrual portfolios. If our primary

hypothesis is valid, we should find significant return spreads from trading on the extreme

discretionary accrual stocks and less significant or insignificant spread returns from trading on

non-discretionary accruals. A higher return from shorting high discretionary accrual stocks

relative to low discretionary accrual stocks would imply that the information uncovered from

high discretionary accruals is economically meaningful and enhances short sellers’ profits. On

the other hand, a finding that short seller returns are not different between low and high non-

discretionary accrual portfolios would imply short seller trades vis-à-vis these stocks are

unrelated to any information that may be contained in non-discretionary accruals. More formally

this hypothesis may be stated as:

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H3: Short sellers will experience significantly higher (less significant or insignificant)

returns by trading on high discretionary (non-discretionary) accrual stocks

relative to low discretionary (non-discretionary) accrual stocks.

3. Data and Research Design

The sample consists of stocks listed on NYSE, Amex, and NASDAQ with data between

January 1988 and December 2010. We gather data from several different sources. Quarterly

accounting information is obtained from Compustat XFeed US. Monthly short interest is

obtained from the individual exchanges. Short interest is defined as the open short position as of

settlement on the 15th of each month (or the preceding day if it is not a business day) as a

percentage of total shares outstanding. This information represents short sale trades that

occurred three or five business days prior as the settlement period changed from five to three

days on June 7, 1995. After establishing the short trade date, we relate each firm-quarter accrual

observation to the first available short interest information that comes at least one week after the

earnings announcement date. We impose this minimum one week lag between earnings

announcements and short sale trade dates so that short sellers have at least one week to execute

their trades. Market information including stock prices, shares outstanding, and returns are taken

from CRSP. Our measurement interval is quarterly, so all variables are expressed in this

frequency. Appendix 1 lists the variables used in the study and their definitions. Each firm-

quarter observation is required to have sufficient data to calculate the models in the paper.

To estimate discretionary accruals we adopt a performance matched Jones model, as

presented first by Kothari et al. (2005) and used, among many, by Tucker and Zarowin (2006):

1 & (1)

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Total accruals, change in sales, and gross property plant and equipment are deflated by the

previous quarter total assets. We also control for return on assets as Kothari et al. (2005) found

that the traditional Jones model is misspecified for firms with extremely good/bad performances.

We estimate the regression in model (1) and measure non-discretionary accruals as the fitted

values from the regression, and the discretionary accruals as the residuals from the regression.

[Insert Table I about here]

Table I presents sample descriptive statistics. The average short interest in our sample is

2.4 percent while the 90th percentile is about 5.8 percent demonstrating a significant difference

between highly and lightly shorted stocks. The difference between the 10th and 90th percentile of

firms based on total accruals is almost 10 percent of total assets while the average total accruals

in our sample is only 0.214 percent. We observe that total accruals account for about 5.1 percent

of total assets for firms that are aggressive in using accruals (the 90th percentile). On the other

hand, non-discretionary accruals are not as much dispersed with only 0.825 percent difference

between the 10th and 90th percentile of firms.

We use two measures of short interest: raw short interest (SI) and abnormal short interest

(ABSI). We define ABSIit for firm i in quarter t as the difference between SI and and expected SI

for the quarter:

ABSIit = SIit –E(SIit) (2)

We calculate expected short interest, E(SI) in a manner similar to the one employed in Karpoff

and Lou (2010). Using prior evidence as a basis, Karpoff and Lou suggest that E(SI) is a

function of firm size, book-to-market (BM), momentum, and institutional ownership. Following

their approach, each quarter, we classify all stocks into tertiles independently by size, book-to-

market, momentum, and institutional ownership yielding a total of 81 portfolios. Each of the 81

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portfolios is further classified into industry groups based on two-digit SIC codes. Size is defined

as the natural logarithm of the number of shares outstanding multiplied by the price per share.

Book-to-market ratio is calculated by dividing book value of equity by market value of equity.

Momentum is the mean stock return performance over the previous 4 quarters. Institutional

ownership (IO) is the number of shares held by institutions as reported by the 13-F filings

divided by total number of shares outstanding. We then estimate the following cross-sectional

regression on a quarterly basis:

∑ 1 (3)

Where SIit is the short interest for firm i in quarter t and the independent variables are dummy

variables for Size, BM, Momentum, and IO. The dummy variables equal one if the variable is in

the medium or high tertiles, and zero otherwise. Industry dummy variable, Indit,is equal to 1 if

firm i belongs to industry k in quarter t. The fitted value for SIit in the above equation serves as

our estimate for E(SIit) for any given stock and quarter depending upon where the stock falls with

respect to the 81 portfolios classified by size, book-to-market, momentum, institutional

ownership, and industry group.

[Insert Table II about here]

Table II presents the time-series averages of the estimates of quarterly cross-sectional

regressions of equation (3) using the Fama-MacBeth procedure. The t-statistics are corrected for

serial correlation using three lags per Newey-West (1987). We note that all of the dummy

variable coefficients are statistically significant. We observe that short interest for small size

firms is signficantly less than for the medium and large size firms. In the case of book-to-market

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and momentum, short interest for the lowest tertile stocks is greater than for stocks in the

medium and high tertiles though the pattern is not monotonic between the medium and high

tertiles. Finally, short interest is monotinically increasing in institutional ownership. Similar

patterns are observed by Karpoff and Lou (2010).

4. Results

We first present univariate results followed by the multivariate results. The univariate

results are shown in Table III. For the univariate results we examine short interest levels and its

changes for sample firms classified into tertiles by accruals in each quarter. The table presents

time-series averages of short interest and its changes for each tertile and consists of three panels,

one for each measure of accruals: total, discretionary, and non-discretionary accruals. We

examine short interest (SI), abnormal short interest (ABSI), and their changes (SI, ABSI). The

table also shows the difference in the short interest variables between the low and high accruals

portfolios and their significance. For total accruals, we observe that, with the exception of ABSI,

all other measures of short interest increase as we go from the low to medium to high accrual

portfolios. In the case of ABSI, short interest for the high accrual portfolio exceeds the short

interest for both the low and medium accrual portfolios with the medium portfolio showing

values below that of the low accrual portfolio. Across all measures of short interest, we find that

short interest for the high acrrual firm portfolio is always significantly greater than short interest

for the low accrual firm portfolio.

[Insert Table III about here]

The next two panels disaggregate the results into accrual components. We generally

observe a monotonic increase in short selling as we go from the low to medium to high

discretionary accrual portfolios. Similar to the case of total accruals, in every instance the

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difference between the high and low accrual portfolios is positive and statistically significant at

conventional levels. In the case of non-discretionary accruals it is clear that short interest is not

increasing in accruals. If anything, there may be an inverse relationship.

Overall, the univariate results indicate that the previously documented positive

relationship between short selling and accruals is attributable to discretionary accruals and not to

non-discretionary accruals. As noted earlier, accounting accruals can convey useful signals about

future cash flows but managers may also use their discretion to opportunistically manage

earnings using accruals. Such opportunistic behavior manifests itself in the discretionary portion

of accruals. Consistent with this view, our univarirate results indicate that short sellers focus on

firms with high discretionary accruals, which are subject to opportunistic earnings management,

rather than simply firms with high total accruals.

[Insert Table IV about here]

Next, we discuss the multivariate results. Table IV presents multivariate results using

ABSI as the dependent variable. The independent variables consist of total accruals or its

components.

. . .

(4)

We present results for the full sample as well as subsamples classified by institutional

ownership. We examine differential sensitivity to institutional ownership because prior evidence

suggests that the intensity of short selling is a function of short selling constraints. Hirshleifer et

al. (2011) and others find that short selling is greater where the availability of loanable shares is

greater. These studies find that institutional ownership is a good instrument to proxy for

availability and liquidity of shares. In the context of our study, we expect that the short selling –

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accrual relationship is going to be stronger for the subset of firms with greater institutional

ownership.

Table IV Panel A presents pooled regression results using actual values of firm level

variables as the independent variables and using fractile ranks of short interest, i.e., decile

rankings independently constructed each quarter for the independent variables, and then

standardized to take values ranging between zero and one. We use the decile rankings to ensure

that our results are not affected by a few extreme observations and to be able to compare

incremental contribution of each variable to short selling. The results for the two approaches are

qualitatively similar therefore our discussion will mainly focus on using actual values for the

independent variables. The first three columns present regression estimates separately for each of

the three accrual measures (Total, Discretionary, and Non-Discretionary accruals). Column four

presents estimates with the discretionary and non-discretionary accruals considered

simultaneously. The last two columns present regression estimates for firms classified into low

and high subsets by IO. The low and high subsets are identified relative to the median value.

Across all columns, consistent with the univariate results, we observe that ABSI is positively

related to total and discretionary accruals and negatively related to non-discretionary accruals.

The D.Accruals coefficient tends to be larger in the high IO group, consistent with the idea of the

availability of loanable shares constraining short sellers arbitrage behaviors. The difference in

the D.Accruals coefficient between the high and low IO groups has a t-statistic of 2.36 (two-

tailed).

Table IV Panel B replicates Table IV Panel A analysis but using Fama-MacBeth

regressions. The results are qualitatively similar. In particular, we find that stocks in the top

decile based on discretionary accruals have about 0.20% more short interest ratio relative to

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stocks in the bottom decile depending on different regression specifications. Considering the

unconditional average short interest ratio of 1.2% in our sample, this result is economically

significant since the mean short interest in the highest discretionary accrual decile would be

about 18% higher than the mean short interest of the lowest discretionary accrual decile. On the

other hand, short interest is about 0.60% lower for stocks in the top decile non-discretionary

accrual portfolio compared to those in the bottom decile.7

[Insert Table V and VI about here]

Table IV provides convincing evidence that short sellers target the dicretionary portion of

total accruals to identify potential opportunistic earnings management effects. To further ensure

the relevance of discretionary accruals as the driving element in the accruals-short seller

relationship, we conduct additional tests using subsamples of firms with certain discretionary

accrual characteristics that are likely to be of interest to short sellers. Tables V and VI present

results replicating the analysis included in Table IV limiting the sample to firms with positive

(income increasing) accruals (Table V) and to firms in the highest accrual decile (Table VI).

Since positive accruals firms are on average more likely to experience negative returns in the

future (e.g., Sloan 1996; Xie 2001), short sellers are likely to target these firms. We also

separately examine the highest decile accrual firms as short sellers may particularly target these

firms because of their greater susceptabiliy to opporuntistic behavior (Hirshleifer, Teoh and Yu

(2011)). Overall across all columns in both tables the results are, again, consistent with the

7 In untabulated results we test for the sensitivity of our findings to NASDAQ vs. NYSE and AMEX listed firms. Hirshleifer et al. (2011) find that short-selling of accounting accruals is more pronounced in NASDAQ firms than in NYSE firms. They argue that NASDAQ firms are generally harder to value as they tend to be smaller, informationally more opaque, and more growth oriented. When actual values of accruals are used, we observe similar results in both samples, indicating that the documented association between short interest and discretionary accruals are not limited to the NASDAQ sample. However, consistent with Hirshleifer et al. (2011), NASDAQ firms demonstrate a stronger association between short interest and accruals when decile rankings are used. This result stems from the fact that NASDAQ firms display twice as large cross-sectional variation in accruals relative to NYSE firms. Therefore, our study shows that short sellers do not seem to discriminate between accrual values of NASDAQ and NYSE firms.

19

univariate results and regression results shown in table IV: we observe that ABSI is positively

related to total and discretionary accruals and negatively related to non-discretionary accruals.

The results reported in Table V and VI are consistent with this idea, and the coefficient values on

T.Accruals and D.Accruals are greater than the ones reported in the previous tables and the t-

statistics are more significant. For example, in Table V we find that stocks in the top decile based

on discretionary accruals have about 0.50% more short interest ratio relative to stocks in the

bottom decile, which is more than the twice the differential observed in Table IV.

Next, we classify our sample by proportion of discretionary accruals to total accruals. If

short sellers are uncovering opportunistic earnings management evident from discretionary

accruals, we would expect stronger results for firms with greater proportions of discretionary

accruals to total accruals. Table VII presents the results. We document that short selling is

significantly positively related to discretionary accruals only for the subset of firms with the

highest third in proportion of discretionary accruals to total accruals. The relationship is

insignificant in the other two or of opposite sign.

[Insert Table VII about here]

The regression results presented to this point are based on level variables. It is generally

acknowledged that the more powerful results are obtained when variables are measured in their

change form. Table VIII is structured similarly to Table IV with the exception that all the

variables are in their change form. The dependent variable is the change in ABSI over two

quarters (ABSI) and the independent variables are measured as changes over two quarters

preceding the measurement period for short interest.

[Insert Table VIII about here]

20

The results validate the conclusions from the level regressions and show that abnormal

short interest increases following an increase in total accruals and discretionary accruals. One

departure between the levels and change regressions is that non-discretionary accruals are no

longer significant; earlier the coefficient was significantly negative. Table VIII also presents

results using changes as standardized decile ranked variables for the independent variables. The

results are similar to what we observed using pooled regressions. Our results here suggest that

the relationship between short selling and accruals manifests itself more generally across the

broad sample of firms. Hirshleifer et al. (2011) also use decile rankings but focus only firms

whose total accrual changes from any given decile in the base year to the tenth decile in the

following year. For this subset of firms they find a positive relationship between total accrual

changes and short interest. Their study does not provide a clear explanation as to why they

examine this subset of firms. It is not clear why short sellers would not be interested in a firm

that, for example, goes from decile 1 to decile 8 compared to one going from decile 3 to decile

10. Indeed our results indicate that the positive short selling - accruals relationship likely holds

across the spectrum of changes in accruals regardless of the particular decile. Our results in

Table VIII indicate that the positive short seller – accrual relationship is evidenced across the

broad sample of inter-decile changes in accruals.

In the spirit of Hirshleifer et al. (2011) in Table IX we present results for the association

between abnormal short interest and accruals for firms that changed to and from the highest

accrual deciles. In this specification of the basic model, the independent variables take the value

of -1 if a firm ranks among the highest decile in quarter q-1 but not in quarter t, +1 if a firm does

not rank among the highest decile in quarter q-1 but becomes so in quarter q, and 0 otherwise.

The qualitative results are similar to those discussed earlier in Table VIII where we consider

21

changes across all deciles. This provides additional support that the findings reported initially by

Hirshleifer et al. (2011) hold more generally across the cross section firms and not just confined

to changes to and from the highest accrual decile.

[Insert Table IX about here]

In Table X we present results for our second hypothesis on whether firms that just meet

or beat analysts’ EPS forecast exhibit a stronger association between abnormal short interest and

accruals. We compute JustMBF following extant literature (Koh et al. 2008) as a dummy

variable equal to one if the firm’s actual EPS just meets or exceeds the last analysts’ forecast at

least 3 days before the quarterly earnings announcement by a cent per share or less. We exclude

firms in regulated industries because they likely have different incentives than those in non-

regulated industries: financial institutions (SIC codes 6000–6999), utilities (SIC codes 4800–

4999), and other quasi-regulated industries (SIC codes 4000–4499) (Matsumoto 2002). We

winsorized at +/-1% the difference between the earnings per share forecast error, computed as

the difference between actual EPS minus last analysts’ EPS forecast at least 3 days before the

quarterly earnings announcement.

The model we adopt for the analysis follow Fan et al. (2010):

∗ ∗

(5)

We test for the hypothesis that Accruals coefficient is higher for JustMBF firms than for

NonJustMBF testing whether 3 4. For each quarter t, short interest is regressed on firm

characteristics obtained at the end of quarter t-1. JustMBF (NonJustMBF) is an indicator variable

that equals 1 (0) if a firm-quarter observation has actual EPS meets or exceeds the last analysts’

22

forecast at least 3 days before the quarterly earnings announcement by a cent per share or less,

and 0 (1) otherwise.

[Insert Table X about here]

Results from Table X show that firms that just meet or beat the EPS forecasts have a

more strong association between abnormal short interest and accruals. However the results are

different across columns: while for both total and discretionary accruals (column 1 and 2) the

coefficients for both sub-samples (firms that just meet-beat EPS forecast vs. firms that don’t) are

positive and significant, with a statistically higher coefficient for the JustMBF sub-sample, for

non-discretionary accruals (column 3) we have a non significant coefficient for the JustMBF

sub-sample, and a negative and significant coefficient for the sub-sample of firms that do not just

meet-beat the EPS forecast. Overall the results from Table X show a strong positive association

between abnormal short selling and discretionary accruals, stronger for firms that just meet or

beat by 1 cent or less the last analysts’ forecast at least 3 days before quarterly earnings

announcement.

Our third hypothesis focuses on the role accruals and their components play in impacting

short seller profitability. For this test we first rank all the stocks independently based on their

accruals and short interest. For the subset of stocks with the greatest short interest, we examine

the one-year abnormal return differential between the highest and lowest accrual portfolios.8 If

short interest is driven by the potential for uncovering information related to discretionary

accruals (and not non-discretionary accruals) and that such information yields differential

returns, abnormal return spreads for high vs.low accrual portfolios should be signficant for

8 The two subsets were generated by independently ranking the firms on short interest and on accruals. In order to make sure short sellers had sufficient time to access and process information, we use the first available short interest information that comes at least one week after the earnings announcement date. The abnormal returns are annual buy-and-hold returns starting one month after the short date following the quarterly earnings announcement relative to the benchmark portfolio of firms in the same industry/quarter.

23

stocks ranked by discretionary accruals and less significant or insignificant for stocks ranked on

non-discretionary accruals. We report these results in Table XI.

[Insert Table XI about here]

We start by examining the abnormal return spread between the two extreme accrual

portfolios without regard to short seller holdings. We do this to confirm previous findings that

accruals contain information about future returns and that the strength of this relationship varies

for the two components of accruals. This is reported in Panel A. We then examine the return

spreads for the subset of firms that have the highest short interest. These are presented in Panel

B. From Panel A we note that the return differental between low and high total accrual firms in

the cross-section of firms is approximately 5.5%. This result is in line with what prior studies

report, suggesting that low total accrual firms earn substantially higher returns than high total

accrual firms. For discretionary accruals the 5% return spread between the two extreme

portfolios is in line with what we observed for total accruals. In contrast, consistent with what

prior studies report, the non-discretionary sorting results in a much smaller differential of 1.4%.

Overall, the return patterns in Panel A confirm evidence from prior research that current period

accruals are negatively associated with future stock returns and that this is mostly due to

discretionary accruals (Xie 2001).

In Panel B we report the returns for the sub-sample of high short-selling firms (top

quintile short selling firms). As can be seen, the spread in abnormal returns between the high

and low accrual portfolios is approximately 4.5% for total accruals and 3.8% for discretionary

accruals, both significant at conventional levels. Interestingly, the return differential is not

significantly different from zero in the case of non-discretionary accruals. Note, however, that

the returns for the low and high non-discretionary acccruals are sizeable at approximately 4%.

24

The insignificant difference suggests that for these stocks short seller motivation is unrelated to

any information that may be contained in the non-discretionary component of accruals. Our

results imply that the signficant differential in returns between high and low accrual portfolios

with high short interest is related to information contained in high discretionary accruals and not

non-discretionary accruals and that short sellers can reap signficant abnormal returns by trading

on this information. These results are consistent with the notion that short sellers engage in

informed trading in anticipation of future reversal of accruals, especially discretionary accruals.

5. Conclusions

The role of short sellers in uncovering meaningful information about firms is a

controversial one. We focus on the relation between short selling behavior and the quality of

accruals inherent in firms’ reported earnings. Prior literature is ambiguous in this regard.

Richardson (2003) does not find that short sellers trade on the basis of information contained in

accounting accruals. Hirshleifer et al. (2011) on the other hand find that short selling and

accruals are positively related though this relation primarily exists only in the top decile of

accrual firms. The focus of these studies was total accruals. However, the literature on accruals

suggests that accruals have both informative and opportunistic elements. If short sellers indeed

are sophisticated investors that serve to improve the price discovery process, we should expect

them to trade actively in stocks associated with greater opportunistic earnings management. By

disaggregating accruals into discretionary and non discretionary components we are able to

priovide a more complete picture about the role short sellers play in uncovering opportunistic

earnings management.

25

Using a broad sample of firms from the NYSE, Amex, and NASDAQ stock exchanges,

we document a significant positive relationship between short interest and discretionary accruals.

However, the relation between short interest and non discretionary accruals is either insignificant

or negative depending on the particular model. The results are robust to alternate measures of

short interest. In additional analysis, our results are not sensitive to short-selling constraints.

Furthermore, we find that the positive relationship between short interest and accruals is stronger

for firms using total accruals or discretionary accruals to meet or narrowly beat the analyst

earnings forecasts. This is consistent with our earlier assertion since these firms are more likely

to have engaged in opportunistic earnings management to avoid reporting negative earnings

surprises. Finally, our stock return analysis suggests that trading by short sellers on information

contained in discretionary accruals yields significant incremental returns but that short seller

trading profitability is likely unrelated to non-discretionary accruals.

26

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Appendix 1: Variable Definitions (in parentheses the Compustat XpressFeed US quarterly data items)

Variable Name Description Total Accruals Total Accruals calculated as [Change in current assets (actq) – Change in cash and

short term investment (cheq) – Change in current liabilities (lctq) + Change of debt in current liabilities (dlcq) – Change in depreciation and amortization (dpq)] deflated by total asset at the beginning of the period (atq)

Disc. Accruals Residuals of the regression 1 &

Non Disc. Accruals Fitted values of the regression 1 &

Short interest is the number of shares sold short divided by the total number of shares outstanding. Abnormal short interest is the defined as the residual from cross-sectional regression estimates of short interest on size, book-to-market, momentum, institutional ownership, and industry dummies

Short interest Short interest is the number of shares sold short divided by the total number of shares outstanding

Abnormal short int. Abnormal short interest is the defined as the residual from cross-sectional regression estimates of short interest on size, book-to-market, momentum, institutional ownership, and industry dummies

A Total Assets (atq) Sales Sales revenues (saleq) in millions ΔSales Percentage of change in Sales, calculated as / PP&E Gross property, plant and equipment deflated by total assets at the beginning of the

period (atq) ROA Returns on assets, calculated as Net Income + Interest expenses over total assets at the

beginning of the period (atq) Size Natural log of market value of equity BM Ratio of book value to market value Momentum Mean monthly stock returns over the previous four quarters IO The number of shares held by institutions as reported on the 13-F filings divided by

total number of shares outstanding JustMBF We compute JustMBF following extant literature (Koh et al. 2008) as a dummy variable

equal to one if the firm’s actual EPS just meets or exceeds the last analysts’ forecast at least 3 days before the quarterly earnings announcement by a cent per share or less

30

Table I Descriptive Sample Statistics

This table presents descriptive statistics for the sample of stocks listed on NYSE, Amex, and NASDAQ between January 1988 and December 2010. Short interest is the number of shares sold short divided by the total number of shares outstanding. Institutional ownership is the number of shares owned by institutions provided by 13-F filings as a percentage of total shares outstanding. Discretionary and Non-discretionary accruals are calculated using Kothari et al. (2005). We require a firm-quarter observation to have information on all of the above variables to be included in the sample. Each quarter we calculate mean, median, and various percentile values of each variable and then we report their time-series averages.

Total

Accruals Discretionary

AccrualsNon-discretionary

AccrualsShort

Interest Institutional Ownership

Mean 0.214% 0.050% 0.164% 2.357% 47.455%10th Percentile -4.789% -4.957% -0.153% 0.032% 7.367%25th Percentile -1.741% -1.989% 0.028% 0.311% 23.127%Median 0.139% -0.084% 0.132% 1.218% 49.330%75th Percentile 2.054% 1.887% 0.303% 2.871% 70.195%90th Percentile 5.086% 5.067% 0.672% 5.752% 83.004%

31

Table II Modeling Abnormal Short Interest

The sample consists of stocks listed on NYSE, Amex, and NASDAQ between January 1988 and December 2010. For each quarter t, short interest is regressed on firm characteristics obtained at the end of quarter t-1. Abnormal short interest is defined as the residual from such quarterly cross-sectional regressions. Short interest is the number of shares sold short divided by the total number of shares outstanding, presented in percentage form. Size is the natural logarithm of the number of shares outstanding multiplied by the price per share. Book-to-market ratio is calculated by dividing book value of equity by market value of equity. Momentum is the mean stock return performance over the previous 4 quarters from q-4 to q. Institutional ownership is the number of shares owned by institutions provided by 13-F filings as a percentage of total shares outstanding. Each quarter stocks are grouped into 81 portfolios constructed independently based on size, book-to-market, momentum, and institutional ownership. The explanatory variables are dummy variables that jointly define the 81 portfolios based on size, book-to-market, momentum, and institutional ownership. Regressions also include industry dummies, which are defined based on two-digit SIC codes from CRSP. T-statistics in parentheses are based on the time series of coefficient estimates from the quarterly cross-sectional regressions using Newey-West correction with 4 lags.

Intercept 0.821(4.30)

Size (medium) 1.498(5.12)

Size (high) 1.058(8.30)

B/M (medium) -0.613(-5.39)

B/M (high) -0.532(-2.50)

Momentum (medium) -0.926(-9.11)

Momentum (high) -0.641(-5.33)

Institutional ownership (medium) 1.021 (4.31)Institutional ownership (high) 2.226 (4.46)Industry controls Yes Adj R2 19.72%

32

Table III Short Interest and Accruals – Univariate Analysis

The sample consists of stocks listed on NYSE, Amex, and NASDAQ between January 1988 and December 2010. Each quarter firms are sorted into tertiles based on total accruals, non-discretionary accruals, and discretionary accruals as of quarter q. The future firm characteristics are presented for each of the three equally-weighted portfolios. Short interest and abnormal short interest are calculated at least one week after earnings announcement dates. Change in short interest and abnormal short interest are calculated as quarterly changes. Short interest is defined as the number of shares sold short divided by the total number of shares outstanding. Abnormal short interest is calculated, as described in Table II, by the residuals from quarterly cross-sectional regressions of short interest on market capitalization, book-to-market, momentum, institutional ownership, and industry dummies. All of the variables are presented in percentage form. T-statistics are based on quarterly time series using Newey-West correction with 4 lags. Portfolios sorted by total accruals

Total Accruals Short Interest

Abnormal Short

InterestChange in

Short Interest

Change in Abnormal

Short Interest

Low -5.058 2.278 0.008 0.002 -0.033Medium 0.199 2.363 -0.113 0.029 0.014High 6.087 2.423 0.110 0.111 0.045High-Low 11.145 0.145 0.102 0.109 0.078t-statistics 47.53 4.04 3.58 6.35 3.88 Portfolios sorted by discretionary accruals

Discretionary Accruals Short Interest

Abnormal Short

InterestChange in

Short Interest

Change in Abnormal

Short Interest

Low -5.107 2.184 -0.132 -0.011 -0.034Medium -0.116 2.275 -0.156 0.027 0.008High 5.312 2.347 0.054 0.116 0.080High-Low 10.419 0.163 0.186 0.127 0.114t-statistics 48.10 3.41 5.19 5.39 4.68 Portfolios sorted by non-discretionary accruals

Non-discretionary

Accruals Short Interest

Abnormal Short

InterestChange in

Short Interest

Change in Abnormal

Short Interest

Low -0.525 2.412 0.164 0.046 0.029Medium 0.132 2.164 -0.209 0.051 0.029High 0.833 2.275 -0.175 0.041 0.001High-Low 1.358 -0.137 -0.339 -0.005 -0.028t-statistics 23.79 -2.62 -7.70 -0.14 -0.87

33

Table IV – Future Abnormal Short Interest and Accruals The sample consists of quarterly observations for common stocks listed on the NYSE, Amex, and NASDAQ between January 1988 and December 2010. The dependent variable is abnormal short interest as defined in Table II and measured at least one week after earnings announcement dates. Total accruals are the net income before extraordinary items less cash from operations, normalized by sales. Discretionary and Non-discretionary accruals are calculated using Kothari et al. (2005). We present regression results when actual values of independent variables are used and when we transform all independent variables into decile ranks each quarter and standardize them to take values between zero and one. In Panel A cross-sectional and time series pooled regressions are run and t-statistics in parentheses are calculated using standard errors robust to clustering by firm and time. In Panel B Fama-MacBeth calendar time quarterly cross-sectional regressions are run and t-statistics are based on the time series of coefficient estimates from the quarterly cross-sectional regressions using Newey-West correction with 4 lags. Panel A: Pooled regressions

Actual values of independent variables Standardized rank values of independent variables

All All All All Low IO High IO All All All All Low IO High IO

Intercept -0.083 -0.075 -0.082 -0.075 -0.066 -0.073 -0.144 -0.251 -0.201 0.131 0.064 0.253(-1.81) (-1.78) (-1.97) (-1.71) (-1.27) (-1.32) (-2.99) (-4.02) (-4.17) (1.79) (0.62) (3.01)

T. Accruals 0.991 0.128 (4.41) (3.03)

N. Accruals -3.811 -5.304 -3.506 -9.634 -0.664 -0.663 -0.512 -0.877 (-3.97) (-5.61) (-4.08) (-4.07) (-8.44) (-8.14) (-5.67) (-8.14)

D. Accruals 1.501 1.843 1.427 2.635 0.242 0.240 0.201 0.267 (6.83) (7.57) (6.08) (6.11) (5.67) (5.53) (3.97) (3.87)

N. Obs. 123,717 123,717 123,717 123,717 61,835 61,882 123,717 123,717 123,717 123,717 61,835 61,882Adj. R2 0.26% 0.25% 0.49% 0.96% 0.59% 0.18% 0.09% 0.26% 0.33% 0.29% 0.26% 0.52%

Panel B: Fama and MacBeth (1973) regressions Actual values of independent variables Standardized rank values of independent variables


Intercept -0.097 -0.085 -0.098 -0.079 -0.067 -0.066 -0.153 -0.216 -0.218 0.123 0.034 0.270(-5.70) (-5.20) (-5.69) (-4.93) (-3.25) (-3.67) (-5.08) (-6.27) (-6.56) (2.92) (0.57) (3.76)

T. Accruals 0.868 0.109 (3.72) (2.62)

N. Accruals -5.178 -6.707 -4.773 -16.372 -0.631 -0.648 -0.474 -0.878(-4.80) (-5.61) (-3.08) (-4.67) (-9.23) (-8.74) (-5.64) (-7.68)

D. Accruals 1.568 1.556 1.488 1.766 0.239 0.203 0.201 0.186(8.68) (5.36) (6.69) (2.42) (5.96) (4.41) (5.20) (2.17)

N. Obs. 123,717 123,717 123,717 123,717 61,835 61,882 123,717 123,717 123,717 123,717 61,835 61,882Adj. R2 0.41% 0.66% 0.51% 1.29% 1.89% 2.01% 0.77% 0.79% 0.78% 1.54% 1.97% 2.10%

34

Table V – Future Abnormal Short Interest and Accruals – Positive Accrual Subsample The sample consists of quarterly observations for common stocks listed on the NYSE, Amex, and NASDAQ between January 1988 and December 2010. The dependent variable is abnormal short interest as defined in Table II and measured at least one week after earnings announcement dates. Total accruals are the net income before extraordinary items less cash from operations, normalized by sales. Discretionary and Non-discretionary accruals are calculated using Kothari et al. (2005). We present regression results when actual values of independent variables are used and when we transform all independent variables into decile ranks each quarter and standardize them to take values between zero and one. In Panel A cross-sectional and time series pooled regressions are run and t-statistics in parentheses are calculated using standard errors robust to clustering by firm and time. In Panel B Fama-MacBeth calendar time quarterly cross-sectional regressions are run and t-statistics are based on the time series of coefficient estimates from the quarterly cross-sectional regressions using Newey-West correction with 4 lags. Panel A: Pooled regressions



Intercept -0.152 -0.049 -0.177 -0.177 -0.162 -0.203 -0.285 -0.231 -0.344 -0.053 -0.058 -0.031(-3.82) (-1.35) (-4.43) (-4.47) (-3.14) (-3.68) (-5.99) (-3.68) (-7.36) (-0.71) (-0.59) (-0.34)

T. Accruals 2.458 0.454 (7.45) (7.68)

N. Accruals -2.971 -6.61 -5.982 -7.938 -0.579 -0.485 -0.423 -0.578 (-2.43) (-5.81) (-4.72) (-3.09) (-6.74) (-5.57) (-4.14) (-4.64)

D. Accruals 3.406 3.982 3.151 5.742 0.571 0.475 0.382 0.598 (8.99) (9.43) (7.82) (7.59) (9.31) (7.72) (5.99) (5.44)

N. Obs. 64,557 64,557 64,557 64,557 32,256 32,301 64,557 64,557 64,557 64,557 32,256 32,301Adj. R2 0.13% 0.15% 0.21% 0.35% 0.21% 0.42% 0.13% 0.21% 0.21% 0.34% 0.22% 0.53%

Panel B: Fama and MacBeth (1973) regressions Actual values of independent variables Standardized rank values of independent variables


Intercept -0.161 -0.049 -0.188 -0.159 -0.127 -0.220 -0.279 0.211 -0.348 -0.048 -0.039 -0.047(-6.15) (-2.45) (-7.23) (-6.04) (-4.52) (-5.48) (-7.46) (5.36) (-8.56) (-0.91) (-0.67) (-0.69)

T. Accruals 2.591 0.417 (5.92) (7.25)

N. Accruals -6.253 -9.066 -9.153 -11.280 -0.562 -0.476 -0.411 -0.561(-3.55) (-5.69) (-3.87) (-2.66) (-8.05) (-6.70) (-4.41) (-5.28)

D. Accruals 3.783 3.638 2.556 6.496 0.554 0.433 0.322 0.573(8.90) (7.39) (8.59) (5.70) (9.18) (7.74) (6.57) (5.60)

N. Obs. 64,557 64,557 64,557 64,557 32,256 32,301 64,557 64,557 64,557 64,557 32,256 32,301Adj. R2 0.44% 0.29% 0.53% 0.86% 1.57% 1.69% 0.47% 0.54% 0.49% 0.95% 1.45% 1.52%

35

Table VI – Future Abnormal Short Interest and Accruals – Highest Accrual Decile The sample consists of quarterly observations for common stocks listed on the NYSE, Amex, and NASDAQ between January 1988 and December 2010. The dependent variable is abnormal short interest as defined in Table II and measured at least one week after earnings announcement dates. Panel A presents regression results when pooled regression specification is used. In Panel B we employ Fama and MacBeth (1973) regressions. Total accruals are the net income before extraordinary items less cash from operations, normalized by sales. Discretionary and Non-discretionary accruals are calculated using Kothari et al. (2005). The independent variables are dummy variables taking the value of 1 if they rank among the highest decile in a quarter, and 0 otherwise. T-statistics in parentheses are calculated using standard errors robust to clustering by firm and time in Panel A and to Newey-West correction with 4 lags in Panel B. Panel A: Pooled regressions

All All All AllLow

IOHigh

IO

Intercept -0.035 0.009 -0.043 -0.033 -0.062 -0.006 (-0.96) (0.24) (-1.17) (-0.85) (-1.06) (-0.13)

High T. Accruals Dummy 0.353 (7.81)

High N. Accruals Dummy -0.091 -0.106 -0.186 -0.024 (-1.55) (-1.82) (-2.64) (-0.28)

High D. Accruals Dummy 0.431 0.435 0.306 0.701 (9.11) (9.25) (5.76) (7.62)

N. Obs. 123,717 123,717 123,717 123,717 61,835 61,882 Adj. R2 0.09% 0.05% 0.12% 0.12% 0.09% 0.24%

Panel B: Fama and MacBeth (1973) regressions

All All All AllLow

IOHigh

IO

Intercept -0.036 -0.009 -0.044 -0.034 -0.072 0.002 (-9.81) (2.48) (-7.29) (-5.49) (-4.57) (0.14)

High T. Accruals Dummy 0.366 (9.63)

High N. Accruals Dummy -0.097 -0.104 -0.191 -0.030 (-2.51) (-2.72) (-3.16) (-0.45)

High D. Accruals Dummy 0.445 0.444 0.315 0.703 (9.17) (9.27) (6.78) (8.50)

N. Obs. 123,717 123,717 123,717 123,717 61,835 61,882 Adj. R2 0.26% 0.13% 0.31% 0.45% 0.75% 0.73%

36

Table VII – Future Abnormal Short Interest and Accruals – The Role of Discretionary Accruals in Total Accruals

The sample consists of quarterly observations for common stocks listed on the NYSE, Amex, and NASDAQ between January 1988 and December 2010. Abnormal short interest is defined in Table II and measured at least one week after earnings announcement dates. Total accruals are the net income before extraordinary items less cash from operations, normalized by sales. Discretionary and Non-discretionary accruals are calculated using Kothari et al. (2005). We calculate the proportion of discretionary accruals in total accruals and regress abnormal short interest on total accruals separately for firms in the bottom 30%, middle 40%, and top 30% of firms based on the proportion. We require discretionary and non-discretionary accruals to have the same sign to calculate the proportion. In panel A cross-sectional and time-series pooled regression specification is used and t-statistics in parentheses are calculated using standard errors robust to clustering by firm and time. In Panel B we employ Fama and MacBeth (1973) regressions and t-statistics are based on the time series coefficient estimates from the quarterly cross-sectional regressions using Newey-West correction with 4 lags. We present regression results when actual values of independent variables are used and when we transform all independent variables into decile ranks each quarter and standardize them to take values between zero and one. In both panels cross-sectional and time series pooled regressions are run and t-statistics in parentheses are calculated using standard errors robust to clustering by firm and time. Panel A: Abnormal short interest – pooled regressions


Proportion of D. Accruals in T. Accruals Proportion of D. Accruals in T. Accruals

Low Medium High Low Medium High

Intercept -0.142 -0.033 0.062 0.110 0.117 -0.023(-3.45) (-0.66) (0.96) (1.69) (1.24) (-0.37)

T. Accruals 0.377 -0.866 0.924 -0.496 -0.324 0.191(1.38) (-1.47) (2.96) (-5.20) (-2.54) (2.20)

N. Obs. 18,876 25,243 18,883 18,876 25,243 18,883Adj. R2 0.02% 0.01% 0.04% 0.24% 0.06% 0.10% Panel B: Abnormal short interest – Fama and MacBeth (1973) regressions


Proportion of D. Accruals in T. Accruals Proportion of D. Accruals in T. Accruals

Low Medium High Low Medium High

Intercept -0.157 -0.046 0.042 -0.036 0.156 0.026(-5.99) (-1.56) (1.11) (-0.55) (2.38) (0.35)

T. Accruals -1.005 -1.087 1.118 -0.360 -0.313 0.172(-0.85) (-1.54) (2.65) (-2.13) (-3.70) (2.77)

N. Obs. 14,067 19,004 14,176 14,067 19,004 14,176Adj. R2 0.03% 0.02% 0.14% 0.32% 0.02% 0.09%

37

Table VIII – Future Changes in Abnormal Short Interest and Accruals The sample consists of quarterly observations for common stocks listed on the NYSE, Amex, and NASDAQ between January 1988 and December 2010. The dependent variable is change in abnormal short interest as defined in Table II and measured at least one week after earnings announcement dates. Total accruals are the net income before extraordinary items less cash from operations, normalized by sales. Discretionary and Non-discretionary accruals are calculated using Kothari et al. (2005). We present regression results when actual values of independent variables are used and when we transform all independent variables into decile ranks each quarter and standardize them to take values between zero and one. In Panel A cross-sectional and time series pooled regressions are run and t-statistics in parentheses are calculated using standard errors robust to clustering by firm and time. In Panel B Fama-MacBeth calendar time quarterly cross-sectional regressions are run and t-statistics are based on the time series of coefficient estimates from quarterly cross-sectional regressions using Newey-West correction with 4 lags. Panel A: Pooled regressions



Intercept 0.011 0.010 0.011 0.011 0.021 0.003 -0.051 0.070 -0.069 -0.009 -0.013 -0.003(1.62) (1.43) (1.63) (1.61) (1.90) (0.27) (-3.12) (4.73) (-4.44) (-0.47) (-0.49) (-0.09)

Δ T. Accruals 0.583 0.121 (5.94) (3.48)

Δ N. Accruals -0.935 -1.464 -0.879 -2.857 -0.120 -0.118 -0.109 -0.129(-1.91) (-2.50) (-1.26) (-3.39) (-4.99) (-5.01) (-3.64) (-3.41)

Δ D. Accruals 0.709 0.781 0.757 0.798 0.160 0.159 0.174 0.139(6.01) (6.88) (6.11) (4.61) (6.12) (6.05) (6.03) (3.99)

N. Obs. 116,883 116,883 116,883 116,883 57,172 59,711 116,883 116,883 116,883 116,883 57,172 59,711Adj. R2 0.08% 0.06% 0.11% 0.13% 0.14% 0.09% 0.05% 0.04% 0.07% 0.10% 0.12% 0.08%



Intercept 0.017 0.018 0.018 0.019 0.021 0.022 -0.055 0.074 -0.072 -0.009 -0.015 0.033(1.99) (2.67) (2.08) (2.34) (1.39) (0.91) (-1.52) (3.55) (-2.44) (-0.21) (-0.44) (0.62)

Δ T. Accruals 0.593 0.129 (2.10) (2.19)

Δ N. Accruals -2.530 -2.811 -1.110 -3.855 -0.107 -0.111 -0.093 -0.133(-1.88) (-1.96) (-1.75) (-2.08) (-2.54) (-2.76) (-1.82) (-2.99)

Δ D. Accruals 0.725 0.623 0.748 0.453 0.173 0.167 0.176 0.121(2.46) (2.78) (6.63) (1.83) (3.42) (3.19) (5.81) (2.98)

N. Obs. 116,883 116,883 116,883 116,883 57,172 59,711 116,883 116,883 116,883 116,883 57,172 59,711Adj. R2 1.11% 0.81% 1.24% 1.44% 0.91% 2.04% 0.51% 0.41% 0.49% 0.89% 0.39% 0.31%

38

Table IX – Future Changes in Abnormal Short Interest and Accruals – Changes to and from Highest Accrual Decile

The sample consists of quarterly observations for common stocks listed on the NYSE, Amex, and NASDAQ between January 1988 and December 2010. The dependent variable is change in abnormal short interest as defined in Table II and measured at least one week after earnings announcement dates. Panel A presents regression results when pooled regression specification is used. In Panel B we employ Fama and MacBeth (1973) regressions. Total accruals are the net income before extraordinary items less cash from operations, normalized by sales. Discretionary and Non-discretionary accruals are calculated using Kothari et al. (2005). The independent variables take the value of -1 if a firm ranks among the highest decile in quarter q-1, but not so in quarter q, +1 if a firm does not rank among the highest decile in quarter q-1, but becomes so in quarter q, and 0 otherwise. T-statistics in parentheses are calculated using standard errors robust to clustering by firm and time in Panel A and to Newey-West correction with 4 lags in Panel B. Panel A: Pooled regressions

All All All AllLow

IOHigh

IO

Intercept 0.011 0.014 0.009 0.012 0.019 0.004(1.46) (2.78) (1.91) (2.41) (2.51) (0.61)

Δ T. Accruals 0.102 (5.89)

Δ N. Accruals -0.154 -0.157 -0.152 -0.162(-6.49) (-6.64) (-5.76) (-4.61)

Δ D. Accruals 0.122 0.126 0.143 0.096(6.43) (6.62) (8.41) (2.38)

N. Obs. 116,883 116,883 116,883 116,883 57,172 59,711Adj. R2 0.04% 0.08% 0.06% 0.14% 0.18% 0.11%


All All All AllLow

IOHigh

IO

Intercept 0.016 0.022 0.014 0.018 0.027 0.001(2.19) (2.28) (1.90) (2.13) (1.56) (0.11)

Δ T. Accruals 0.075 (1.82)

Δ N. Accruals -0.179 -0.179 -0.190 -0.156(-5.62) (-5.80) (-5.58) (-4.56)

Δ D. Accruals 0.131 0.129 0.156 0.093(6.39) (6.68) (7.48) (1.63)

N. Obs. 116,883 116,883 116,883 116,883 57,172 59,711Adj. R2 0.03% 0.11% 0.08% 0.18% 0.43% 0.45%

39

Table X – Short Interest and Firms that Just Meet or Beat EPS Analysts’ Forecast We compute meet or beat earnings following extant literature (Koh et al. 2008) as a dummy variable equal to one if the firm’s actual EPS meets or exceeds the last analysts’ forecast at least 3 days before the quarterly earnings announcement by a cent per share or less. We exclude firms in regulated industries because they likely have different incentives than those in non-regulated industries: financial institutions (SIC codes 6000–6999), utilities (SIC codes 4800–4999), and other quasi-regulated industries (SIC codes 4000–4499) (Matsumoto 2002). We winsorize at +/-1% the difference between the earnings per share forecast error, computed as the difference between actual EPS minus last analysts’ EPS forecast at least 3 days before the quarterly earnings announcement. The dependent variable is abnormal short interest as defined in Table II and measured at least one week after earnings announcement dates. JustMBF (NonJustMBF) is an indicator variable that equals 1 (0) if a firm-quarter observation has actual EPS meets or exceeds the last analysts’ forecast at least 3 days before the quarterly earnings announcement by a cent per share or less, and 0 (1) otherwise. Accruals are in the 3 different specifications are calculated as explained in Appendix 1. Model from Fan et al. (2010):

∗ ∗ Test of hypothesis that accruals coefficient is higher for JustMBF firms than for NonJustMBF: β β . T-statistics are presented in parentheses below the coefficient estimates. Total Accruals Disc. Accruals Non-Disc. Accruals NonJustMBF 0.139 0.043 0.065 (10.03) (2.81) (4.18) JustMBF -0.156 -0.313 -0.302 (-7.25) (-13.18) (-12.50) Accruals*NonJustMBF (A) 0.828 1.790 -10.01 (3.70) (6.24) (-9.39) Accruals*JustMBF (B) 2.371 2.730 -0.134 (6.86) (6.07) (-0.08) N. Obs. 115,467 81,609 81,609 Adj. R2 0.002 0.003 0.003 Test (A-B) -1.542 -0.939 -9.875 (-3.75) (-1.76) (-5.12)

40

Table XI – Abnormal Returns Based on Accruals and Short Interest

The sample consists of quarterly observations for common stocks listed on the NYSE, Amex, and NASDAQ between January 1988 and December 2010. Following Hirshleifer et al. (2011) we sort firms in our sample into quintiles by the value of their accruals (total, discretionary, non-discretionary) and define the top (bottom) quintile as high (low) accrual firms. We, then, independently rank the full sample by their abnormal short interest and identify firms in the (1) lowest accrual quintile and highest short seller quintile and (2) highest accrual quintile and highest short seller quintile. The return analysis includes the annual buy-and-hold returns starting 1 month after the short date. The abnormal return is calculated by subtracting the equal-weighted mean return of a benchmark portfolio that includes stocks from firms in the same industry/quarter from the raw buy-and-hold return of the stock. Short interest is defined as the number of shares sold short divided by the total number of shares outstanding. The T-statistics reported in parentheses test whether the mean abnormal return for the firms in the portfolio is different from zero. Low - High tests the null hypothesis of average abnormal return for the low accruals portfolio equal to the average abnormal return for the high accruals portfolio. Panel A: All Firms Stock Abnormal Returns Total Accruals Disc. Accruals Non-disc. Accruals

Low Accruals 0.020 (5.34)***

0.025 (5.87)***

-0.005 (-0.89)

High Accruals -0.034 (-10.20)***

-0.025 (-6.19)***

-0.019 (-5.91)***

Hedge (Low - High)

0.055 (10.80)***

0.050 (8.53)***

0.014 (2.31) **

Panel B: High Short Interest Firms Stock Abnormal Returns Total Accruals Disc. Accruals Non-disc. Accruals

Low Accruals -0.016 (-1.79)**

-0.003 (-0.26)

-0.040 (-3.37)***

High Accruals -0.061 (-8.05)***

-0.040 (-4.32)***

-0.045 (-5.47)***

Hedge (Low - High)

0.045 (3.91)***

0.038 (2.72)**

0.005 (0.39)

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