Pricing of Book-Tax Differences: Evidence from Short Arbitrage. Sabrina Chi Sam M. Walton School of Business University of Arkansas

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1 Pricing of Book-Tax Differences: Evidence from Short Arbitrage Sabrina Chi Sam M. Walton School of Business University of Arkansas Morton Pincus Paul Merage School of Business University of California, Irvine Siew Hong Teoh Paul Merage School of Business University of California, Irvine September 21, 2010 We appreciate helpful comments from Richard Sloan, Ashley Wang, an anonymous referee for the 2010 AAA annual meeting, and Michael Donohoe, the AAA annual meeting discussant. 1

2 Pricing of Book-Tax Differences: Evidence from Short Arbitrage Abstract: We extend research investigating whether the market is slow to impound earnings growth information in book-tax differences (BTDs) into stock price, especially in the period since SFAS No. 109 went into force, and find substantial evidence of mispricing. Return predictability of the ratio of taxable-to-book incomes (TI/BI, Lev and Nissim 2004; Weber 2009) is incremental to accruals, and only for temporary but not permanent components of TI/BI, consistent with investor mispricing of the component in BTDs containing more discretionary items. Further, we document a positive association between short selling and TI/BI and the temporary component of TI/BI, consistent with the presence of short arbitrage of investor mispricing of the temporary component of BTDs, and the link is stronger when the supply of loanable shares is larger and so short selling is easier. Consistent with limits to short arbitrage, we also find that BTDs predict returns asymmetrically, with larger returns on the short side of the anomaly relative to the long side, reflective of constraints on short arbitrage. Key Words: Book-tax differences, arbitrage, short sales, market efficiency, anomalies. 2

3 Pricing of Book-Tax Differences: Evidence from Short Arbitrage 1. Motivation In this study, we examine empirically whether the ratio of taxable income-to-book income (TI/BI) predicts abnormal stock returns, especially for the period beginning in 1993 during which the current accounting standard for income taxes was in force and corporate tax rates were stable. Since book income and taxable income differ in part because of accounting accruals, we examine whether TI/BI predictability for stock returns is incremental to accruals. Differences between book income and taxable income are either temporary or permanent, so we also examine whether the temporary or permanent components of TI/BI drive return predictability. Given evidence that TI/BI predicts stock returns, we examine whether there is short arbitrage of the TI/BI anomaly, and further, the extent to which short arbitrage based on TI/BI succeeds in correcting mispricing by studying return asymmetry of the TI/BI anomaly. Lev and Nissim (2004) introduced the TI/BI 1 ratio as a measure of book-tax differences (BTDs). BTD is the difference between book income, which is income reported in financial statements following Generally Accepted Accounting Principles (GAAP), and taxable income, which is income reported to the Internal Revenue Service based on the Internal Revenue Code (IRC) and related regulations. Lev and Nissim (2004) report evidence that TI/BI contains information that is useful in predicting future earnings growth 2 and Weber (2009) also finds that TI/BI explains analysts forecast errors. In addition, both Lev and Nissim and Weber show that TI/BI predicts future stock returns, consistent with a pricing anomaly. Their evidence for the TI/BI anomaly is 1 Lev and Nissim (2004) and Weber (2009) denote TI/BI as TAX. 2 Revsine et al. (2002, 630) state that analysts can use tax footnotes to glean information not provided elsewhere in the financial statements to better understand a firm s performance and future prospects. 1

4 strong in the period prior to when the current accounting standard for income taxes, SFAS No. 109, went into effect. SFAS No. 109 imposes a greater need to predict future outcomes relative to previous income tax accounting standards, and thus it incorporates additional information in accounting numbers that is potentially value-relevant albeit subjectively determined. Lev and Nissim (2004) argue that SFAS No. 109 affords managers greater discretion and raises the possibility of greater earnings management. Lev and Nissim (2004, 1068) detect a weaker TI/BI anomaly for the post-sfas No. 109 period than for the pre-1993 period, and return predictability is obtained in the SFAS No. 109 period examined only if observations in one year (1998 TI/BI associated with returns) are removed, and they infer that during the 1990s investors became increasingly adept at using the forward-looking information in taxable income (or correlated information) in securities valuation. 3 Weber (2009) reports return predictability in each year he examined ( ) but his results are also weaker in the post-sfas No. 109 period. Moreover, his return predictability results hold only for low analyst following firms. He also finds that TI/BI predicts analysts forecast errors, and return predictability comes from the predicted analysts forecast errors and not directly from TI/BI. He concludes from this evidence that inefficient use of TI/BI information by analysts cause investors, who rely on them, to misprice the firm, and so the TI/BI anomaly is not the result of mismeasured risk factors. The late 1990s and early 2000s include the boom and bust of the dot-com bubble, the occurrence of accounting scandals, and a recession. Given the mixed or weak 3 Deferred income taxes under SFAS No. 109 incorporate enacted future tax rates and adjustments for deferred tax assets that are unlikely to yield future tax benefits. Also see Ayers (1998). 2

5 evidence of a TI/BI anomaly in prior studies during the time examined when SFAS No. 109 was in effect, it is helpful to researchers to re-examine return predictability of TI/BI now that a longer post-sfas No. 109 time period has occurred before we address whether smart investors arbitrage the anomaly. Our sample covers 1988 to 2006, a period for which we have short sale data. We find that TI/BI predicts future earnings growth and that portfolios of firms with high TI/BI earn positive abnormal returns whereas those with low TI/BI earn negative abnormal returns, so that a hedge strategy of going long in high TI/BI and short in low TI/BI earns an abnormal monthly trading profit of 0.87 percent (i.e., in excess of 10 percent annualized) over our entire sample period. The hedge portfolio return is 0.93 percent per month in the pre-sfas No. 109 period and 0.82 percent per month in the post-sfas No. 109 period (both significant at the 1 percent level). This confirms that the TI/BI anomaly remains present and fairly strong in the post-sfas No. 109 period. Also, consistent with Weber (2009), we document that the TI/BI anomaly is stronger when analyst following is below the sample median. Graham et al. (2009) point out that As a whole, the tax (and non-tax) accounting literature has not adequately explained why and how tax information affects future stock returns. In other words, why is the market slow to impound current period tax information? As noted above, BTDs arise from differences between U.S. GAAP for book income and the IRC for taxable income. One source for the difference between book and taxable incomes is operating accruals, and so TI/BI anomaly may simply be the accruals anomaly (Sloan 1996) in another guise. Therefore, we formally investigate whether the TI/BI anomaly is incremental to the accruals anomaly. 3

6 We find the accruals anomaly earns hedge profits of 1.37 percent per month in our sample. But a joint hedge strategy that goes long in the lowest accruals and highest TI/BI quintiles and shorts the highest accruals and lowest TI/BI quintiles earns a hedge profit of 2.41 percent per month. This suggests significant incremental returns attributed to TI/BI of approximately 1 percent per month. In addition, a joint accruals and TI/BI hedge portfolio of firms with no or low analyst following, respectively, earns 3.23 and 2.97 percent monthly returns, compared to a 1.59 percent return for the high analyst following group in our sample. These results are consistent with Weber s (2009). To understand further why tax-related information is mispriced, we consider separately the return predictability of the temporary versus permanent components of BTDs. There is generally more discretion in computing book income relative to taxable income, and accounting accruals reflecting that discretion typically give rise to temporary BTDs (Hanlon and Heitzman 2010). 4 The prior literature suggests that higher deferred tax expense, which captures temporary BTDs, is associated with higher earnings management to avoid missing certain earnings targets (Phillips et al. 2003), and that large extreme (positive and negative) deferred tax expenses are associated with earnings persistence (Hanlon 2005). Thus, we build a temporary TI/BI ratio from deferred tax expense and examine the return predictability of the temporary versus permanent components of TI/BI ratios. The results indicate that the temporary component of TI/BI predicts returns incremental to accruals but the permanent component of TI/BI does not when accruals are 4 Hanlon and Heitzman (2010, p. 11) note that The argument is that accounting accruals reflect more discretion than the tax laws allow, thus, temporary differences between book and tax income reveal something about discretion in non-tax accounting accruals (e.g., bad debt accrual, warranty expense, deferred revenue, etc.). This theory does not generally extend to permanent differences because permanent differences are not driven by the accounting accruals process. 4

7 considered. In hedge portfolios double-sorted by accruals and, respectively, temporary TI/BI and permanent TI/BI, the incremental hedge returns for temporary TI/BI is a statistically significant 0.55 percent per month while the hedge returns for permanent TI/BI are statistically insignificant (-0.32 percent). This evidence is consistent with investors discounting insufficiently for firms earnings management behaviors as reflected in the temporary component of TI/BI. Short selling is the sale of securities that one does not own but has borrowed from institutional investors, brokerages, or broker-dealers with the intention of buying the stock back at a later date (hopefully at a lower price) to return the shares to the lenders. Critics argue that short selling encourages unscrupulous traders to manipulate the market, which leads to price distortions, increased volatility, and a loss of investor confidence. 5 Short selling has also been blamed for driving some of the leading financial institutions to the edge of collapse in the financial crisis of The U.S. Securities and Exchange Commission (SEC) temporarily banned short selling of financial stocks in the midst of the 2008 financial crisis after the bankruptcy of Lehman Brothers and proposed new regulations on short selling. 6 In July 2010, Germany banned naked short selling out of concern that short sales have adverse effects on the fragile financial industry. 7 5 In 2005, Amr I. Elgindy, was convicted of bribing Federal Bureau of Investigation (FBI) agents to obtain confidential information and then establishing short positions in companies that are under investigation by the federal authorities. He was also convicted of leaking the information over the Internet to help ensure that the prices of those companies shares fell after he took short positions in their stocks (see 6 See Lehman Legacy Alters Global Markets, The Wall Street Journal (September 14, 2009, C1) on short selling bans during the 2008 financial crisis. Also see for a description of new short sale regulations. 7 See Germany Passes Diluted 'Naked' Ban: by A. Thomas, The Wall Street Journal (July 2, 2010), Also see EU Draft Rules Target Short Selling by S. Fidler, The Wall Street Journal (September 15, 2010), C2. 5

8 Proponents, on the other hand, argue short selling facilitates market efficiency when short arbitrageurs uncover overpriced stocks and correct the mispricing through shorting the firms stocks. For example, James S. Chanos, a known short seller, was among the first to unearth problems in Enron s financial reports. 8 In addition to anecdotal evidence, several academic studies report that short arbitrageurs use accounting information, such as Forms 10-K and 10-Q, to detect overvaluation or upward earnings management (Dechow et al. 2001; Desai et al. 2006; Karpoff and Lou 2010). Dechow et al. (2001) document that short sellers position themselves in stocks with low fundamental-to-price ratios (cash flow-to-price, earnings-to-price, book-tomarket, and value-to-market) and then cover their positions as the ratios mean-revert. Using a sample of U.S.-traded firms from , Richardson (2003) does not find evidence that short sellers trade on the basis of information contained in accruals. However, subsequent research obtains different results. Desai et al. (2006) find that short sellers accumulate positions in firms that restate earnings several months in advance of the restatement announcement and subsequently unwind these positions after the drop in share price associated with the restatement; they also show there is a larger increase in short interest for firms with higher levels of accruals prior to the restatement. Karpoff and Lou (2010) document that abnormal short interest increases steadily in the 19 months prior to when an SEC enforcement action is publicly revealed. The amount of this increase and the level of short interest immediately before the SEC enforcement action announcement are positively related to the level of 8 See Short Sellers Keep the Market Honest, The Wall Street Journal (September 22, 2008, p. A23). James S. Chanos found the problematic gain on sale accounting method for long-term energy trades (in which firms recognize gains up front, estimated as the present value of the future profits from the energy trades made today) and various disclosures regarding related party transactions in Enron s 1999 Form 10- K and 2000 Form 10-Qs, and profited from the information. 6

9 total accruals. The findings in Desai et al. (2006) and Karpoff and Lou (2010) suggest that short sellers precede the market in identifying firms with poor earnings quality. A study that is most directly related to ours is Hirshleifer et al. (2010). They document a positive link between short selling and accruals during They find short arbitrage primarily among firms with the largest positive accruals, and where ease of arbitrage is highest such as in firms with a sufficiently high supply of loanable shares (proxied by institutional holdings). Moreover, consistent with limits to short arbitrage relative to long arbitrage, there is an asymmetry between the up- and down-sides of the accrual anomaly. The abnormal returns on the short side of the accruals anomaly are larger in absolute value than the abnormal returns on the long side. Thus, there is short arbitrage of the accrual anomaly, but short sale constraints limit its effectiveness. We extend the research on short arbitrage exploiting accounting information. By testing whether there is short selling based on TI/BI, we provide further corroborative evidence on whether book-tax differences are indeed mispriced by investors or whether the abnormal returns earned on TI/BI are mismeasured risk premia. Evidence that there is short arbitrage of the TI/BI (and the temporary TI/BI) anomaly would suggest that some sophisticated investors understand the implications of TI/BI and the temporary component of TI/BI for future cash flows and profit from their knowledge. Our findings suggest that short selling increases with TI/BI and with the temporary component of TI/BI but not the permanent component. Our evidence that short sellers understand the implications of TI/BI for future cash flows whereas evidence from Weber (2009) that financial analysts do not is consistent with past evidence for the accruals anomaly. Bradshaw et al. (2001) and Teoh and Wong 7

10 (2002) find that analysts behave as if they are inefficient processors of accrual information either because of ignorance or for agency reasons, and Hirshleifer et al. (2010) find that short sellers do arbitrage the accruals anomaly. Short sellers are sophisticated investors (see Diamond and Verrecchia 1987) and trade either for themselves or directly for clients. Hence, they have strong economic incentives to exploit profitable trading opportunities where they exist and so will use all available public information efficiently. On the other hand, analysts, while being experts at analyzing financial statements, face strategic incentives not to offend management when forecasting earnings so as to maintain access to information, or to preserve future lucrative underwriting business. Finally, we examine the asymmetry of returns for the TI/BI (and temporary TI/BI) anomaly to evaluate the effectiveness of short arbitrage in removing mispricing. If short arbitrage of the anomaly is less effective than long arbitrage, then we expect an asymmetry between the predictability of returns on the up- and down-sides of the anomaly. The results indicate the presence of return asymmetry on the short side. We organize the rest of the paper as follows. The next section discusses BTDs and short interest. Section 3 discusses the sample and research design. Sections 4 and 5 describe, respectively, our hedge returns and short interest analyses. Section 6 concludes. 2. Book-Tax Differences and Short Interest We discuss institutional features of book-tax-differences and short selling. 2.1 Book-Tax Differences (BTDs) Book income is determined by the GAAP accrual system governing revenue recognition and matching principles. On the other hand, taxable income is governed by 8

11 the IRC code and Section 446(a) states, Taxable income shall be computed under the method of accounting on the basis of which the taxpayer regularly computes his income in keeping his books. Accordingly, both book and taxable incomes are computed on an accrual basis. 9 However, book income and taxable income are computed for different purposes and not surprisingly there are differences in their respective reporting principles and rules. Financial accounting as governed by GAAP aims to provide investors and other external parties with information useful for decision-making with regard to firm value and managerial stewardship. Taxable income, on the other hand, is computed primarily to determine firms tax liabilities, but some tax rules also serve the role of encouraging certain types of investments and activities. Differences between book and tax accounting for various transactions give rise to either permanent or temporary differences. 10 Permanent differences occur when an item affects taxable income but never affects book income, or vice versa, such as interest revenue on municipal bonds. Temporary differences arise when the book and tax treatments for a transaction differ in a given year, but have the same cumulative effect over the life of the firm (ignoring the time value of money). For example, book depreciation is based on the service life of a fixed asset, whereas tax depreciation follows statutory depreciation rates. Thus, over the life of the fixed asset total book depreciation and total tax depreciation are equal, but for any year they typically will differ. Temporary BTDs include future taxable and future deductible amounts. Future taxable amounts create (or increase) deferred tax liabilities and require recognition of 9 Small corporations (average annual gross receipts of $5 million or less for their three most recent taxable years) may use the cash method for tax purposes (IRC Section 448). 10 See Graham et al. (2009) for an extensive review of the accounting for income taxes and research thereof; also see, for example, Phillips et al. (2003), Hanlon (2005), and Hanlon and Heitzman (2010). 9

12 deferred tax expense. In contrast, future deductible amounts create (or increase) deferred tax assets and require recognition of deferred tax benefits; they also reduce deferred tax expense. All else equal, an increase in deferred tax liabilities is consistent with a firm currently recognizing revenue and/or deferring expense for book purposes relative to its tax reporting (i.e., book income exceeds taxable income), and an increase in deferred tax assets is consistent with a firm currently recognizing expense and/or deferring revenue for book relative to taxable income (book income is less than taxable income). Temporary BTDs reflect the generally greater discretion to manage book earnings relative to taxable income and yield an indicator of earnings management. Since permanent BTDs generally are less common and less subject to earnings management, they are less likely to be a source of mispricing. Thus, we test whether a trading strategy based on temporary BTDs can yield hedge profits but not one based on permanent BTDs. Hirshleifer and Teoh (2003) argue that limited attention provides an explanation for accounting anomalies. Limited attention can derive either from a finite cost of processing information or from finite cognitive power. Book-tax differences, overall and especially those arising from temporary sources, demand greater cognition and time to process so that investors with limited attention may miss their implications for future cash flows. Therefore, if investors have limited attention, they will not discount sufficiently for earnings management information contained in temporary BTDs. That is, investors with limited attention and focused primarily on book income to value a firm may miss relevant but less salient information from BTDs, and so misprice the firm. This is similar in spirit to the functional fixation explanation for post-earnings announcement drift (Bernard and Thomas 1989) and the accruals anomaly (Sloan 1996). 10

13 2.2 Short Selling Rule 3b-3 of the Securities Exchange Act of 1934 defines a short sale as any sale of a security which the seller does not own or any sale which is consummated by the delivery of a security borrowed by, or for the account of, the seller. Thus, in order to sell short an investor must borrow shares from another investor who owns them and is willing to lend. The short seller typically leaves cash collateral, equal to 102 percent of the market value of the borrowed shares, with the lender. 11 The lender pays the short seller interest, referred to as the rebate rate, on the collateral. The spread between the rebate rate and the market interest rate on cash funds, often referred to as the loan fee, is a direct cost to the short seller. Current regulations allow the lender the right to recall a loan at any time. If recalled, the borrower can either cover the short by buying back the shares and returning them to the lender, or reestablish the short at a higher loan fee. As an additional source of risk for short sellers, a short squeeze occurs when short sellers cover their positions on a stock. 12 Since covering their positions involves buying shares, the short squeeze can cause an ever further rise in the stock s price, which in turn may trigger additional margin calls and short covering. 13 In sum, short arbitrage is risky and costly. While somewhat controversial, short selling is, in principle, a legitimate trading strategy. It is a way to trade on bad news about a company, which means it can help stock prices more fully reflect value-relevant information about a firm. As such, it can 11 Federal Reserve Regulation T requires short sellers to post an additional 50 percent in margin when the lender is a U.S. broker-dealer. 12 A short squeeze is a rapid increase in the price of a stock that occurs when there is excess demand and inadequate supply for the stock. 13 NASDAQ s short sale rule prohibits members from selling a NASDAQ National Market stock at or below the inside best bid when that price is lower than the previous inside best bid in that stock. The inside best bid is the highest bid price among all competing market makers in a NASDAQ security. 11

14 serve to make share prices more efficient. Short sellers assume the risk that they will be able to buy the stock at a more favorable price than the price at which they sold short, just as investors on the long side assume the risk of being able to later sell their shares at a more favorable price than what they paid to purchase the shares. An important difference, however, is that the maximum loss from going long is the amount invested whereas it is unbounded when shorting a stock. Short interest reflects the open short positions of stocks with settlements on the last business day on or before the 15 th of each calendar month for both NYSE- and NASDAQ-listed companies. This date is referred to as the settlement date for each monthly observation. It normally takes several days to settle a short-sale trade. The last date when a short-sale trade occurs for a monthly record is called the trade date, which varies each month. Prior to June 1995, it was five days before the settlement date; it is currently three days. To calculate short interest in NASDAQ stocks, which comprise more than 80 percent of our sample, member firms (brokerage firms) are instructed to report monthly to the National Association of Securities Dealers Regulation, Inc. s (NASDR) Customer Advocacy and Quality Management Department their short positions for all accounts, in shares, warrants, units, ADRs, and convertible preferred stocks resulting from short sales. Once the short position reports are received by NASDR, the short interest is compiled for each NASDAQ security. The compiled short interest data are published on the eighth business day after the reporting settlement date. To test whether there is short arbitrage of TI/BI, we examine whether short interest is high for firms with low TI/BI. Since arbitrage is easier when the supply of 12

15 loanable shares proxied by institutional ownership is greater, we also test whether the expected negative relation between short interest and TI/BI is stronger in high institutional ownership firms. Finally, as previously discussed, temporary BTDs typically reflect more discretion and also reverse over time, in contrast to permanent BTDs. Thus, we expect greater short arbitrage when the temporary BTD component of TI/BI is large vis-à-vis when the permanent BTD component of TI/BI is large. 3. Data and Research Design 3.1 Sample Selection We select the sample by first merging the monthly CRSP stock returns file with the monthly short interest file from the New York Stock Exchange (NYSE) or the National Association of Securities Dealers Automated Quotations (NASDAQ) according to the stock ticker and calendar month. We exclude: (1) foreign firms since they likely follow different GAAPs and tax laws; (2) financial services and utility industry firms since they have different reporting requirements; (3) mutual funds, trusts, real estate investment trusts, limited partnerships, and other flow through entities since these enterprises do not report income taxes; and (4) loss firms because computing and interpreting TI/BI for loss firms is problematic. 14 If a match is found, the sample is then matched with the annual Compustat file, and firms must have sufficient data to compute regression variables (discussed below) from Compustat and also have necessary returns and volume data from CRSP. The final sample is 71,355 firm/month observations from 1988 through 2006 (3,811 unique firms), with 12,886 observations in the pre-sfas No. 14 We place no constraints on fiscal year-ends. 13

16 109 period ( ) and 58,469 observations in the post-sfas No. 109 period ( ). There are 12,584 NYSE observations and 58,771 NASDAQ observations. 3.2 Measurement of Short Interest We obtain monthly short interest data from NYSE and NASDAQ for our full sample period ( ). We calculate short interest (SI) in a given firm/year as the short position reported by the NYSE or NASDAQ in the fifth month after the fiscal yearend divided by the number of shares the firm has outstanding as reported on CRSP for the same month (Asquith at el. 2005; Hirshleifer et al. 2010). The four-month gap between the fiscal year-end and the short position date ensures that the short sellers have the financial report information available to them prior to taking short positions. 3.3 Measurement of BTD Variables Following Lev and Nissim (2004), we estimate TI/BI, the tax fundamental variable that captures BTDs, as follows: TI TaxableIncome*(1 t). BI NetIncome Net income is measured as book income before extraordinary items. We estimate taxable income by grossing up the current portion of the reported income tax expense using the top statutory corporate tax rate. As in previous research (e.g., Gleason and Mills 2002), the current portion of the income tax expense is computed as the sum of current federal and foreign income tax expense, or, if either of these amounts is missing, as the difference between total income tax expense and the deferred portion of the income tax expense. The top U.S. statutory corporate federal income tax rate, t, is 35 percent from 1993 onward (34 percent for ). Since net income is after tax, we multiply taxable income by (1 - t) to make the comparison between TI and BI meaningful. Low TI/BI 14

17 ratio values imply that current taxable income is less than current book income and are expected to indicate that current book earnings is not likely to be sustainable (i.e., that current book income is of relatively low quality). We also decompose TI/BI into portions that reflect temporary BTDs and permanent BTDs. 15 We follow Hanlon (2005), Frank et al. (2009), and Jackson (2009) to compute the temporary and permanent components, respectively: TEMP = DTE t * (1 - t), and PERM = [(Net Income - Taxable Income) * (1 - t)] - TEMP. Deferred tax expense (DTE) is the sum of deferred federal and foreign tax expenses, or, when either of these amounts is missing, is the deferred portion of total income tax expense. Under SFAS No. 109, deferred tax expense is the change of firms deferred tax assets and liabilities during the current year. Consistent with the calculation of the TI/BI ratio, we multiply by (1 - t) to express TEMP on an after-tax basis. We calculate the permanent component (PERM) as the difference between total BTDs and temporary differences. In the multivariate analyses, we use the ratio of each component to book income (i.e., TEMP/BI and PERM/BI). Note that by construction, increases in TEMP/BI and PERM/BI represent decreases in TI/BI. In some test specifications where the BTD variables may not be linear, we sort the variables into quintile ranks and use the rank variables R_TI/BI, R_TEMP, and R_PERM. 3.4 Measurement of Accruals 15 Lev and Nissim (2004, 1042) state that while most previous studies focus on a single tax-related component temporary differences, permanent differences, or tax accruals our tax fundamental captures all three tax components, creating a potentially powerful earnings quality indicator. Lev and Nissim also note that Compustat does not provide sufficient information to allow for the estimation of tax accruals. Tax accruals include changes in the deferred tax asset valuation allowance account, tax cushion reserves, and foreign income permanently reinvested. Hence, any tax accruals are included in PERM/BI. 15

18 In many of our test specifications, we control for accruals to establish incremental contribution beyond accruals of the BTD variables to explain future performance and short interest. We calculate Accruals as the difference between income before extraordinary items and cash flows from operations as reported on the statement of cash flows, and scale by average total assets, and we rank Accruals into quintiles by year (R_Acc). We also define HighAcc as an indicator variable that equals 1 if a firm-year is in the highest accruals quintile Empirical Models for the Relation Between Earnings Performance and BTDs We first examine whether BTD variables contain information about future earnings performance before evaluating whether investors understand that information and value the firm accordingly. We regress earnings growth on the BTD variables, accruals, and other determinants of earnings growth in the following regression: G t = a 0 + a 1 R_TI/BI t-1 + a 2 R_Acc t-1 + a 3 lnsize t-1 + a 4 BM t-1 + a 5 E/P t-1 + a 6 E t-1 + a 7 Dividend t-1 + a 8 RDCAPEX t-1 + ε (1) The dependent variable G is earnings growth measured as the annual change in earnings scaled by total assets. The key explanatory variables are the ranks of TI/BI and Accruals. A positive a 1 coefficient implies that high TI/BI implies predicts higher future earnings. Following Lev and Nissim (2004), the control variables for other determinants of earnings growth are size, book-to-market ratio, earnings-price ratio, prior earnings level, dividends, and investment. LnSize is computed as the logarithm of the number of shares outstanding multiplied by the fiscal year-end price. BM is defined as book value of common equity divided by market value, which is measured as the number of common shares outstanding multiplied by price per share at the end of fiscal year. E is earnings 16 We obtain similar results if we control for cash flows from operations instead of accruals. 16

19 before extraordinary items scaled by total assets, and E/P is earnings before extraordinary items divided by market value at the end of the fiscal year. Dividend is total asset-scaled dividends. RDCAPEX is the ratio of R&D and capital expenditures to sales. In a second regression, we test for a difference in the relation between earnings performance relation and BTDs in the pre- and post-sfas No. 109 periods: G t = a 0 + a 1 R_TI/BI t-1 + a 2 R_Acc t-1 + a 3 R_TI/BI t-1 *POST109 + a 4 POST109 + a 5 lnsize t-1 + a 6 BM t-1 + a 7 E/P t-1 + a 8 E t-1 + a 9 Dividend t-1 + a 10 RDCAPEX t-1 + ε (2) where POST109 is an indicator variable equal to one starting in 1993 and zero otherwise. Finally, we consider separately the effects of temporary and permanent components of TI/BI for future earnings performance in the following regression: G t = a 0 + a 1 R_TEMP/BI t-1 + a 2 R_PERM/BI t-1 + a 3 R_Acc t-1 + a 4 lnsize t-1 + a 5 BM t-1 + a 6 E/P t-1 + a 7 E t-1 + a 8 Dividend t-1 + a 9 RDCAPEX t-1 + ε (3) For models (1) through (3), we run pooled OLS regressions and estimate standard errors clustered by year and two-digit SIC code to address potential cross-sectional and serial correlation problems (Petersen 2008; Gow et al. 2010). 3.6 Empirical Models for Stock Returns and BTDs We compute quintile abnormal returns to examine whether BTDs variables predict returns. Quintile portfolios are formed monthly based on industry-ranked values of TI/BI of the most recent available fiscal year, allowing for a four-month lag between fiscal year-end and the portfolio formation month. We estimate equal-weighted monthly abnormal returns in each quintile using the portfolio characteristics-adjusted approach in Daniel et al. (1997) to control for size, book-to-market, and 12-month stock return momentum. 17

20 Following this approach, we form benchmark portfolios by sequential sorts, specifically, by first sorting all observations each month into size quintiles, then within each size quintile further sorting into book-to-market quintiles, and finally sorting each of the 25 size and book-to-market portfolios into quintiles based on firms past 12-month returns (skipping the most recent month). Within each of these 125 groups we weigh stocks equally. We subtract the return of the equal-weighted benchmark portfolio to which a stock belongs from the return of the stock to form a size, book-to-market, and momentum-hedged return for any stock. Reported t-statistics are based on the time series of monthly mean portfolio returns. The hedge returns to a TI/BI trading strategy that goes long in the highest TI/BI quintile and shorts the lowest TI/BI quintile are computed by subtracting quintile 1 excess returns from quintile 5 excess returns, R H - R L. We also report hedge profits for a joint Accruals and TI/BI strategy. As in prior literature, the accruals hedge profits are earned by going long in the lowest accrual portfolio and shorting the highest accrual portfolio. Firms are double-sorted independently into Accruals quintiles and TI/BI quintiles. The hedge profits are calculated as the excess returns for the portfolio of firms that belong jointly to the top TI/BI quintile and the bottom Accruals quintile minus the excess returns for the portfolio of firm that belong jointly to the bottom TI/BI quintile and the top Accruals quintile. We calculate the joint Accruals and TI/BI hedge profits separately for no, low, and high analyst following sub-groups. Further, we also consider separately the hedge profits to a trading strategy based on temporary and permanent components of BTDs. These are calculated similar to hedge profits for TI/BI except that firms are now ranked by respectively the TEMP/BI and 18

21 PERM/BI variables described earlier. Since TEMP/BI and PERM/BI are negatively correlated with TI/BI, the trading strategy is long in the lowest TEMP/BI or PERM/BI quintiles and short in the lowest TEMP/BI or PERM/BI quintiles. We also calculate the trading profits from a joint strategy of TEMP/BI or PERM/BI with Accruals. As previously discussed, short arbitrage is risky and costly and limits to short arbitrage relative to long arbitrage create an asymmetry in returns between the up- and down-sides of an anomaly. We therefore measure the abnormal return asymmetry as the - (H TI/BI + L TI/BI ), which is the negative of the sum of the returns to the top and bottom TI/BI quintiles to measure the effectiveness of short arbitrage of the TI/BI anomaly. Similarly, we calculate the asymmetry of abnormal returns for the joint Accruals and TI/BI anomalies, as well for TEMP/BI and PERM/BI. A larger asymmetry reflects larger constraints on short selling and therefore less effective short arbitrage. 3.7 Empirical Models for Short Interest and BTDs We estimate regression (4) to test the relation between short interest and TI/BI to test whether investors arbitrage the book-tax anomalies: SI t = a 0 + a 1 TI/BI t-1 + a 2 HighAcc t-1 + a 3 lnsize t + a 4 BM t + a 5 Turnover t + a 6 lnio t + a 7 Mom t + a 8 STD t + a 9 Exchg t + a 10 lnaf t + a 11 Leverage t + ε (4) If short sellers understand and use TI/BI as an indicator of the earnings quality and future earnings, then short interest in a firm s shares should decrease with TI/BI (and thus increase with BTDs). Hence, we expect that a 1 < 0. To test for short arbitrage of the BTD components, we replace TI/BI with TEMP/BI and PERM/BI in regression (4). We control for other determinants of short selling based on previous research (e.g., Dechow et al. 2001; Jones and Lamont 2002; D Avolio 2002; Asquith et al. 2005; 19

22 Nagel 2005; Ali and Trombley 2006; Desai et al. 2006; Cohen et al. 2007; Karpoff and Lou 2010; Hirshleifer et al. 2010). As before, we control for accruals, since short sellers accumulate positions in firms with high levels of accruals. A key control variable in the short interest regression is institutional ownership, since institutions are the main source of supply of loanable shares to short arbitrageurs. That is, the level of institutional holdings (IO) is an important proxy for ease of short selling, and empirically short selling is positively associated with institutional ownership. We obtain the institutional ownership data from the CDA/Spectrum database, and compute IO as the total number of a firm s shares held by institutions divided by the total number of shares outstanding at the end of each quarter, multiplied by 100 to express as a percentage. We match monthly SI with IO of the latest available quarter. We use log of IO in the multivariate analyses. We also consider two other proxies to control for ease of short selling: the number of analysts following a firm (AF) and a firm s financial leverage (LEVERAGE). We compute AF and LEVERAGE, respectively, as the log of (1 + AF) and total long-term debt divided by total assets. In addition, short arbitrage is more active among liquid and volatile stocks because of a lower risk of short squeezes. We use firm size and share turnover to proxy for liquidity. Size is previously explained, and TURNOVER is monthly stock trading volume in millions of dollars divided by firm size. We measure volatility as the standard deviation of residuals for daily market-adjusted returns over a one-year window ending one month prior to the month of the reported short position (STD). Short interest is also expected to be high in low book-to-market (BM) ratio firms and in stocks with low past 20

23 returns (i.e., momentum, or MOM). MOM is calculated as the compounded monthly return for the window (-12, -2) from the short position report month. Finally, we include a 0/1 indicator variable to control for stock exchange venue (EXCHG = 1 for NYSE) to further control for any other factors that are associated with ease or constraints on short arbitrage that is not picked up by the included control variables. See Table 1 for a summary of all variable definitions and data sources. [Insert Table 1] We estimate regression (5) to test whether high institutional ownership facilitates short selling. The key variable of interest is the interaction term of TI/BI*lnIO. We predict that a more ample supply of loanable shares facilitates short arbitrage of TI/BI and so the coefficient b 1a is expected to be negative: SI t = b 0 + b 1 TI/BI t-1 + b 2 HighAcc t-1 + b 1a TI/BI t-1 *lnio t + b 2a HighAcc t-1 *lnio t + b 3 lnsize t + b 4 BM t + b 5 Turnover t + b 6 lnio t + b 7 Mom t + b 8 STD t + b 9 Exchg t + b 10 lnaf t + b 11 Leverage t + ε (5) We also interact HighAccruals and lnio in regression (5) following Hirshleifer et al. (2010) to control for short selling associated with the accruals anomaly. For models (4) and (5), we run pooled OLS regressions and estimate standard errors clustered by year and two-digit SIC code to address potential cross-sectional and serial correlation problems (Petersen 2008; Gow et al. 2010). 4. Empirical Results: TI/BI Anomaly 4.1 Descriptive Statistics Table 2 presents summary statistics for the sample. All financial statement variables are winzorized at the 1 st and 99 th percentiles. Mean monthly short interest (SI) 21

24 is 2.18 (median = 0.49) percent of shares outstanding, similar in magnitude to evidence in prior literature (e.g., Hirshleifer et al. 2010). Mean (median) SI is 1.18 (0.16) percent in the pre-sfas No. 109 period and grows to 2.39 (0.64) percent in the post-sfas No. 109 period. The full sample average TI/BI is and the median is The pre- SFAS No. 109 period mean (median) TI/BI is (0.8813) and the post-sfas No. 109 mean (median) is (0.7860). 17 With regard to TEMP/BI, its mean is and its median is 0. The mean (median) PERM/BI is 0.19 (0.0792). 18 Because SFAS No. 109 imposed major changes in the accounting for income taxes, we consider temporary and permanent BTD components of TI/BI only for the period in which SFAS No. 109 was in force, consistent with prior research (e.g., Phillips et al. 2003; Hanlon 2005). Mean and median Accruals are negative, consistent with previous findings (Dechow 1994), primarily reflecting the depreciation accrual. Mean Accruals is percent of average total assets (median = -3.5 percent). The mean firm size is $571 million (median = $154 million); mean BM is (median = 0.467). Sample firms have, on average, 44 percent of shares owned by institutions and are followed by an average of 5.79 analysts (median = 4). [Insert Table 2] 4.2 TI/BI and Earnings Growth The results for the regression of the change in earnings (G) on the rank of TI/BI (R_TI/BI), the tax fundamental, and determinants of earnings growth appear in Model 1 of Table 3. The coefficient on R_TI/BI is positive and significant at the 1 percent level. This is consistent with the results in Lev and Nissim (2004) and indicates that TI/BI 17 Weber (2009) reports a mean TI/BI of for Lev and Nissim (2004) do not report means or medians, but we can estimate a mean TI/BI of from data in their Table 1, Panel B for Using unwinzorized data, [1 - the mean of TI/BI] equals [mean of TEMP/BI + mean of PERM/BI]. 22

25 predicts future earnings growth; i.e., on average, higher current taxable income relative to current book income predicts higher future book income. In Model 2 of Table 3, we test to determine if this result holds in both the pre- and post-sfas No. 109 periods. We find the coefficient on R_TI/BI is again significant while the coefficient on the interaction of R_TI/BI and an indicator variable for the post- SFAS No. 109 period is insignificant. This suggests there is no difference in the significance of R_TI/BI between the pre- and post-sfas No. 109 periods. Finally, Model 3 s results reveal that when we substitute the ranks of both the temporary and permanent components of TI/BI in place of R_TI/BI, the coefficient on each component is negative and significant. Recall, decreases in TEMP/BI and PERM/BI represent increases in TI/BI; thus both BTD components predict future earnings growth. [Insert Table 3] 4.3. TI/BI Anomaly in Various Sample Periods Evidence of TI/BI mispricing is shown in Table 4, and Table 5 presents evidence of any TEMP/BI and PERM/BI mispricing. [Insert Table 4] The second column of Panel A of Table 4 indicates that the hedge returns from taking long positions in the highest TI/BI quintile (H) and a short position in lowest TI/BI quintile (L) are substantial and significant. Over the full sample period ( ) the return spread between H and L quintiles based on TI/BI is 87 basis points per month (t = 4.22). Panels B and C, respectively, show hedge returns of 0.93 percent per month in the pre-sfas No. 109 period and 0.82 percent per month in the post-sfas No. 109 period, with results in both periods significant at the 1 percent level. Thus, we find evidence of 23

26 the presence of the TI/BI anomaly overall and in both sub-periods. This suggests that extending the post-sfas No. 109 sample period to cover 14-years relative to the shorter periods examined in prior studies, and thus including years following the dot-com bubble, accounting scandals, and recession of the late 1990s and early 2000s, likely explains why we find strong evidence of the TI/BI anomaly in the post-sfas No. 109 period. 4.4 TI/BI Anomaly Incremental to Accruals Anomaly Evidence for whether the TI/BI anomaly is incremental to the accruals anomaly is also reported in Table 4. We report hedge profits for the accrual anomaly in the first column of Panel A and for a joint Accruals and TI/BI strategy in the right-hand set of columns. As in prior literature, the Accruals hedge profits are earned by going long in the lowest accrual portfolio and shorting the highest accrual portfolio. Firms are doublesorted independently into Accruals quintiles and TI/BI quintiles. The hedge profits are calculated as the excess returns for the portfolio of firms that belong jointly to the top TI/BI quintile and the bottom Accruals quintile minus the excess returns for the portfolio of firm that belong jointly to the bottom TI/BI quintile and the top Accruals quintile. The results in Panel A, Table 4 also indicate the presence of the accrual anomaly in our sample for the overall period 1988 to 2006 (and for the pre- and post-sfas No. 109 subperiods in Panels B and C, respectively). A strategy of going long in the lowest Accruals quintile and short in the highest Accruals quintile yields positive average monthly hedge portfolio excess returns of 1.37 percent (t = 7.56). For the same sample period, as noted above, the TI/BI hedge strategy earns an economically and statistically significant 0.87 percent per month (t = 4.22), though smaller than the accruals anomaly. A joint strategy based on both Accruals and TI/BI earns more than the sum of the 24

27 individual strategy hedge profits at a substantial 2.41 percent per month (t = 5.77). These results, which are also qualitatively similar in the pre- and post-sfas No. 109 periods, suggest the TI/BI anomaly is distinct and incremental to the accruals anomaly TI/BI Anomaly Conditional on Analyst Following Table 4 also considers the extent of the TI/BI anomaly conditional on analysts following. Given the findings in Weber (2009), we group observations after double sorting by Accruals and TI/BI into three groups: no analyst following, low analyst following, and high analyst following. The latter two groups reflect the number of unique analysts following relative to the median number of analysts following firms in our sample of firms with nonzero analyst following. In Panel D of Table 4, the joint Accruals and TI/BI hedge returns are 3.23 and 2.97 percent, respectively, for the no analyst and low analyst following groups, compared to 1.59 percent for the high analyst following sample. Weber (2009) uses a different methodology but our results are generally consistent with his findings and show a strong presence of the TI/BI anomaly when there is no analyst following and when there is low analyst following i.e., in relatively weak information environments. The results are especially apparent in the post-sfas No. 109 period. 4.6 Temporary and Permanent TI/BI Anomalies? In Table 5, we report hedge profits to a trading strategy based on the components of TI/BI to explore whether temporary and/or permanent BTDs drive the TI/BI anomaly. 19 In untabulated results, we find that moving from the lowest to the highest quintile, (i) hedge returns for the accrual anomaly increase monotonically, overall and in each subperiod, and (ii) hedge returns for the TI/BI anomaly generally increase monotonically, overall and in each subperiod. We also find there are positive monthly hedge returns (i) 63 percent of the time for Accruals, 60 percent for TI/BI, and 61 percent for the joint Accruals-TI/BI hedge portfolios in the overall sample; (ii) 80 percent for Accruals, 71 percent for TI/BI, and 75 percent for Accruals-TI/BI hedge portfolios in the pre-sfas No. 109 period; and (iii) 55 percent for Accruals, 60 percent for TI/BI, and 57 percent for Accruals-TI/BI hedge portfolios in the post- SFAS No. 109 period. 25