Post Loss/Profit Announcement Drift

We document a market failure to fully respond to loss/profit quarterly announcements. The annualized post portfolio formation return spread between two portfolios formed on extreme losses and extreme profits is approximately 21 percent. This loss/profit anomaly is incremental to previously documented accounting-related anomalies, and is robust to alternative risk adjustments, distress risk, firm size, short sales constraints, transaction costs, and sample periods. In an effort to explain this finding, we show that this mispricing is related to differences between conditional and unconditional probabilities of losses/profits, as if stock prices do not fully reflect conditional probabilities in a timely fashion.


Introduction
Market observers, academics, and regulators seem to agree that investors consider earnings releases important corporate events. Notwithstanding the attention investors seem to pay to earnings releases, academic studies have found that investors fail to fully incorporate the implications of earnings news into stock prices in a timely fashion. One strand of this literature (e.g., Foster et al. 1984;Bernard and Thomas 1990;Ball and Bartov 1996) documents predictable stock price changes around future earnings announcements (up to four quarters ahead), and attributed this finding to investors' misperception of the time-series process underlying standardized unexpected earnings (SUE). Another strand of this literature (e.g., Sloan 1996) has documented accrual mispricing due to investors' apparent misperception of the time-series process underlying the cash flows and accruals components of earnings. Sloan (1996, p. 305), for example, concludes, "The earnings expectations embedded in stock prices consistently deviate from rational expectations in the direction predicted by naïve fixation on earnings." Although these two earnings-related anomalies are distinct from each other (Collins and Hribar 2000), the explanation underlying them is quite similar. The common storyline is that investors appear to use simplified time-series models to forecast earnings. The idea that humans, who are endowed with limited processing capacity, rely on simplified models, or imperfect decision making procedures (i.e., heuristics), to solve complex problems is rooted in the field of social cognition (e.g., Simon 1957;Kahneman andTversky 1973a, 1973b). Because individuals trade off correct inference and efficiency, they make decisions based on only a subset of the information available to them. The partial use of information may lead, in turn, to a cognitive 2 bias (e.g., Daniel et al. 1998; Barberis et al. 1998;Hirshleifer and Teoh 2003). According to this literature, the behavior of stock market indexes, the cross-section of average returns, and individual investors is inconsistent with the assumption that agents apply Bayes' law in their decision-making; rather, predictions underweight or even overlook distributional information. 1 The premise that investors make decisions based on normatively inappropriate simplifications, as well as findings in prior research showing mispricing of earnings information, motivates us to further investigate investors' assessment of quarterly earnings releases. However, unlike prior research that has focused on the pricing of earnings surprises (SUE) or earnings components (accruals and cash flows) we focus on the pricing of earnings signs, a loss versus a profit, and their magnitudes.
Our motivation to examine the market valuation of earnings signs and particularly losses follows from four strands of the literature. One strand of the literature shows that it is difficult to correctly characterize and value even simple time-series processes underlying earnings (see, e.g., Maines and Hand 1996;Brown and Han 2000;Bloomfield and Hales 2002). A second strand of the literature demonstrates that losses are harder to predict. Specifically, prior studies (e.g., Basu et al. 1996;Brown 2001) document that annual and quarterly earnings surprises (measured as reported earnings minus the most recent individual analyst forecast thereof) of loss firms are substantially larger than those of profit firms. Further, Hayn (1995) and Collins et al. (1999) find that the inclusion of losses dampens the earnings response coefficient and the R 2 of the returnearnings regression. Based on this evidence both Hayn (1995) and Collins et al. (1999) conclude that losses are less informative than profits about firms' future prospects. Third, the accounting literature and the financial press assert that when firms report losses, traditional valuation models, such as the discounted residual earnings model, do not yield reliable estimates of firm 3 value, and widely-used heuristics (e.g., price-earnings ratios) are not useful. Finally, evidence from the psychological literature suggests that behavioral biases are larger when uncertainty is greater (see, e.g., Daniel et al. 1998Daniel et al. , 2001Hirshleifer 2001). Thus, the difficulty market participants face in predicting and valuing quarterly earnings in general, and losses in particular, may create considerable price uncertainty particularly around loss announcements. This uncertainty, in turn, may create more opportunities for potential mispricing.
Employing a broad sample of 458,693 firm-quarters (15,143 distinct firms) that spans three decades, 1976-2005, we find that over the 120-trading-day window following the earnings announcement day, firms in an extreme loss portfolio (lowest earnings decile) exhibit a significantly negative drift (buy-and-hold size-adjusted return) of nearly six percent, whereas firms in an extreme profit decile portfolio (highest earnings decile) exhibit a significantly positive drift of over four percent. Further, a hedge portfolio that takes a long position in the extreme profit firms and a short position in the extreme loss firms generates approximately 10 percent abnormal return, which translates into an annualized return of approximately 21 percent.
Further tests indicate that this abnormal return is more substantial than, and incremental to the returns generated by previously documented accounting-based trading strategies, most notably the post-earnings announcement drift, the book-to-market anomaly, and the accruals anomaly.
Sensitivity tests show that this loss/profit effect is robust to alternative risk adjustments (sizeadjusted returns and Carhart's 1997 four factor model returns), up and down markets, distress risk, short sales constraints, and transaction costs. Finally, the results hold for the entire 30-year sample period, 1976-2005, as well as for three 10-year subperiods: 1976-1985, 1986-1995, and 1996-2005.
What may explain this mispricing? If investors rely on simplified models to assess a 4 firm's future prospects, as findings in behavioral finance literature suggest, they may be assessing the probability of a loss/profit in quarter q based on its unconditional probability rather than the more complex and hard to calibrate conditional probability. This type of behavior would result in an underestimation of the probability of a loss/profit in quarter q for firms with a previous loss/profit if, as we assert, conditional probabilities are higher than unconditional probabilities. Consequently, a post loss/profit announcement drift in stock returns would be observed as investors revise upward their priors of a loss/profit in the period leading up to the earnings release of the subsequent quarter. Further, if the drift (partially) represents a market failure to fully reflect conditional probabilities in a timely fashion in stock prices, there should be a positive relation between the magnitude of the drift (the stock-price valuation error) and the difference between conditional and unconditional probabilities (our proxy for investor misperception of the probability of a future loss/profit). In support of this behavioral explanation for the stock price underreaction to loss/profit announcements, we find that conditional probabilities indeed exceed unconditional probabilities. Moreover, differences between conditional and unconditional probabilities are significantly correlated with future abnormal portfolio returns. That is, the higher the difference between conditional and unconditional probabilities, the higher the future abnormal returns. Finally, we document a negative relation between the number of analysts following a stock, a proxy for earnings forecast information available to investors, and the return from the loss/profit strategy. In other words, the less information available to investors, the greater the mispricing as our behavioral explanation would predict.
Our findings contribute to two literatures: the literature on the mispricing of earnings and the literature on the time-series properties of earnings in general, and losses in particular. Our 5 contribution to the literature on the mispricing of earnings concerns showing that losses underlie the earnings-levels anomaly and that this anomaly is incremental to, and more pronounced than previously documented earnings-related anomalies. Our findings also offer a behavioral explanation for this anomaly-which is consistent with an assertion in behavioral finance theories-that due to their limited processing ability investors rely on partial information when pricing stocks, and consequently make systematic valuation errors. This explanation puts in perspective the interpretation for the muted market response to losses asserted by prior studies that "the market regards losses as being transitory" (Collins et al. 1999, p. 57).
Our second contribution, the one related to the literature on the time-series properties of earnings, concerns studying the predictability of losses/profits based on their conditional probabilities. This new focus on conditional probabilities, earnings signs, and the tails of the earnings distribution to predict a future loss/profit, rather than on estimating earnings' timeseries models using random samples, provides new insights. For example, the conditional probability of a loss is higher than its unconditional probability, and is increasing in the magnitude of the previous quarterly loss. Consequently, considering the conditional probability of a loss leads to the conclusion that losses are unlikely to reverse quickly, particularly when they are large. Conversely, assessing the likelihood of losses based on the time-series models commonly used in the accounting literature to characterize the process underlying earnings (e.g., Brown and Rozeff 1979;Foster 1977) may lead to the opposite conclusion that losses are transitory (i.e., are likely to reverse to a profit quickly).
The next section describes the data. Section 3 outlines the tests and the results of our primary empirical findings. Section 4 delineates the tests and results from supplementary tests assessing the relation between the returns from the loss/profit strategy and those of previously 6 documented accounting-based trading strategies. Section 5 offers a behavioral explanation for the post loss/profit announcement drift and assesses its validity. Section 6 considers the effect of distress risk, short sales constraints, transaction costs, and firm size, on our primary findings.
The final section, Section 7, offers concluding remarks.

Sample selection
The data are obtained from the Compustat quarterly database and the CRSP daily returns database. Our analyses include a set of primary tests followed by a set of three supplementary tests. The sample selection procedures for both sets of tests are summarized in Table 1

Variable definitions
We consider three alternative definitions for our earnings variable. The first definition is 8 earnings before extraordinary items and discontinued operations (Compustat Quarterly data8).
The second definition is earnings before extraordinary items, discontinued operations, and special items (Compustat Quarterly data8 -Compustat Quarterly data32), and the third definition is net income (Compustat Quarterly data69). All three measures are scaled by beginning-ofquarter total assets (Compustat Quarterly data44) to alleviate a potential heteroscedasticity problem that may arise when earnings data are pooled across firms and over time. 4 We measure buy-and-hold abnormal returns, for firm i over n trading days, as follows: where, R it is the daily return for firm i on day t, inclusive of dividends and other distributions, and ER it is the expected return on day t for that firm. If a firm delists during the return accumulation window, we compute the remaining return by using the CRSP daily delisting return, reinvesting any remaining proceeds in the appropriate benchmark portfolio, and adjusting the corresponding market return to reflect the effect of the delisting return on our measures of expected returns (see Shumway 1997; Beaver et al. 2007). 5 We use two alternative measures to estimate expected returns. The first measure is based on firm size (market capitalization) and the second measure is based on Carhart's (1997) four factor model. Our first measure of daily expected return for firm i on day t, the one based on firm size, is defined as the value-weighted return for all firms in firm i's size-matched decile on 4 The results (not tabulated for parsimony) remain very similar when we use beginning-of-quarter market value of equity as a scalar. Also, since the results from the tests that follow were robust to the earnings definition, we tabulate in the paper the results based on earnings before extraordinary items and discontinued operations (the first definition). This choice is standard in the earnings-related anomalies literature (e.g., Bernard and Thomas 1990), and thus allows comparisons with previous research. 5 Poor performance-related delistings (delisting codes 500 and 520-584) often have missing delisting returns in the CRSP database (Shumway 1997). To correct for this bias, we set missing performance-related delisting returns to -100 percent as recommended by Shumway (1997). Overall, the percentage of delisting sample firms is small (approximately 0.8 percent and 2 percent for the 60-day and 120-day return windows, respectively), which is not surprising given our relatively short return windows. Still, we replicate our tests excluding delisting returns. The results, not tabulated for parsimony, are indistinguishable from the tabulated results.
day t, where size is measured using market capitalization at the beginning of the most recent calendar year. Using size-adjusted returns is common in prior research on earnings-related anomalies (e.g., Bernard and Thomas 1990;Ball and Bartov 1996;Sloan 1996;Dechow et al. 2008), and thus allows comparisons of our results with the findings of this research.
To assess the sensitivity of our findings to alternative risk adjustments, we also compute daily expected returns based on Carhart's (1997) four factor model. Along the lines of prior research (e.g., Ogneva and Subramanyam 2007), we first estimate the following model using a 40-trading-day hold-out period, starting 55 trading days prior to the earnings announcement date: where R it is defined as before, RF t is the one-month T-bill daily return, RMRF t is the daily excess return on a value-weighted aggregate equity market proxy, SMB t is the return on a zeroinvestment factor mimicking portfolio for size, HML t is the return on a zero-investment factor mimicking portfolio for book-to-market value of equity; and UMD t is the return on a zeroinvestment factor mimicking portfolio for momentum factor. 6 We then use the estimated slope coefficients from Equation (2), b i , s i , h i , and p i , to compute the expected return for firm i on day t as follows: As in previous research (e.g., Thomas 1989, 1990), standardized unexpected earnings (SUE) are generated via a seasonal random walk with a drift model. More specifically, for firm i in quarter q, we first estimate the model by using the most recent 12 quarters of data (i.e., quarters q-12 through q-1). We compute SUE i,q by taking the difference between the 10 reported quarterly earnings per share and expected quarterly earnings per share generated by the model, scaled by the standard deviation of forecast errors over the estimation period.
A firm's book-to-market ratio is defined as book value of equity divided by market capitalization, where market capitalization is the product of the number of shares outstanding and the closing stock price as reported in Compustat. Along the lines of Hribar and Collins (2002), accruals are defined as the difference between earnings before extraordinary items and discontinued operations and net operating cash flows from continuing operations (measured as total cash from operations less the cash portion of discontinued operations and extraordinary items), scaled by average total assets. We compute accruals by starting with earnings before extraordinary items and discontinued operations, not net income, so as to remain consistent throughout the paper with our definition of earnings. Our results are not sensitive to this definition of accruals.
Finally, in testing the sensitivity of our findings to distress risk, and along the lines of prior research (e.g., Dichev 1998; Khan 2008), we use Altman's (1968) Z score as a proxy for firm financial distress. We calculate Altman's (1968) Z score following two alternative specifications. First, we use Altman's (1968) model and original coefficients, as follows: 7 Z = 1.2 (working capital / total assets) + 1.4 (retained earnings / total assets) + 3.3 (earnings before extraordinary items and discontinued operations / total assets) (4) + 0.6 (market value of equity / total liabilities) + 1 (sales / total assets).
In both specifications, a Z score below 1.81 indicates that bankruptcy is likely, a Z score above 2.99 indicates that bankruptcy is unlikely, and a Z score between 1.81 and 2.99 is in the "zone of ignorance" or "gray area" (see Altman 1968;Begley et al. 1996).

3.
Primary tests: Do stock prices fully react to loss/profit announcements?

Methodology
Our primary tests concern whether stock prices fully react in a timely fashion to loss/profit announcements. To that end, we partition all firm-quarter observations into ten earnings deciles. The lowest decile (decile 1) contains firms with the highest losses and the highest decile (decile 10) contains firms with the highest profits. Prior research on earningsbased anomalies (e.g., Bernard and Thomas 1990;Ball and Bartov 1996) sort firms into earnings deciles every fiscal quarter based on the distribution of reported earnings in that quarter. This choice involves a potential look-ahead bias, as for firms that announce quarterly earnings early the distribution of reported earnings is not known at the time the portfolio is formed. To address this problem, we compute cut-off points based on the previous fiscal quarter's earnings distribution. 9 For each of the ten portfolios, we compute buy-and-hold abnormal returns over two 8 As mentioned in Shumway (2001), the published version of Begley et al. (1996) contains two typographical errors. The coefficients reported above are the corrected ones. In terms of Compustat's quarterly data items, Altman's (1968) Z score using Altman's (1968) model and new coefficients re-estimated by Begley et al. (1996) is computed as: Z' = 10.4 (data40-data49)/data44 + 1.0 (data58/data44) + 10.6 (data8/data44) + 0.3 [(data61*data14)/data54] -0.17 (data2/data44).
windows, [1,60] and [1,120], where day zero is the quarterly earnings announcement date. 10 If investors underreact to loss/profit announcements, we expect the post announcement returns to vary systematically across the earnings deciles, being most negative for the High Loss portfolio and most positive for the High Profit portfolio, and the spread between the High Profit portfolio and the High Loss portfolio to be significantly positive. Table 2 reports the results on buy-and-hold abnormal stock returns for the ten portfolios formed on earnings levels. 11 Interestingly, the earnings announcement returns, [-2, 0] window, are significantly negative for the High Loss portfolio, -1.02 percent (t-statistic = -22.15), and significantly positive for the High Profit portfolio, 1.87 percent (t-statistic = 47.84) suggesting that losses/profits per se are bad/good news. 12 More important, the stock price responses to the loss/profit announcements are incomplete as a substantial drift in the post loss/profit announcement periods is observed for all ten portfolios. Furthermore, consistent with an underreaction to loss/profit announcements, the drift increases monotonically across the ten 10 As a sensitivity analysis, we replicate our tests using the return windows [2, 60] and [2, 120], as well as [3,60] and [3,120], where day zero is the quarterly earnings announcement date. The results, not tabulated for parsimony, are indistinguishable from the tabulated results. 11 The number of observations varies across deciles from 44,993 (decile 2) to 47,078 (decile 10). The reason for this variation is that, as discussed above, we compute cut-off points based on the previous fiscal quarter's earnings distribution to avoid a potential look-ahead bias. Also, the [-2, 0] window is standard in the literature (see, e.g., Bernard and Thomas 1990;Ball and Bartov 1996). Still, we checked the sensitivity of the results to this choice by replicating the tests using [-2, 1], [-2, 2] and [-1, 1] windows and the results were robust. 12 While we present in the tables the results for both size-adjusted returns and Carhart's (1997) four factor model returns, consistent with prior literature and for brevity we discuss only the former. We note that the use of the Carhart's (1997) four factor model results in a shift to the left of the return distribution for our strategy, as well as for the previously documented earnings-based anomalies. This shift leads to a difference between the two alternative return measures in terms of the relative contribution of the long and short portfolios to the hedge portfolio returns, that is, the long portfolio generates approximately 40 (10) percent of the hedge returns using sizeadjusted (Carhart's 1997 four factor model) returns. Still, this difference should not be overemphasized, as it is similar across all anomalies and has little effect on the hedge portfolio returns, and thus on our inferences. For example, for the window [1, 120], using size-adjusted returns and Carhart's (1997) four factor model returns, the return spreads between the High Profit and High Loss portfolios are quite similar: 10.21 percent and 11.78 percent, respectively.

Results
13 earnings deciles. It is most negative for the High Loss portfolio: -3.12 percent (t-statistic = -16.10) and -5.79 percent (t-statistic = -21.53) and most positive for the High Profit portfolio:  percent using Carhart's (1997) four factor model, both highly statistically significant. 13 Perhaps even more important, our strategy is robust to up and down markets, as well as to "hot" and "cold" initial public offering (IPO) years. 14 Specifically, the loss/profit strategy is successful in generating superior returns in both the 23 up market years and the seven down market years. In up (down) markets our strategy yields, on average, 8.5 (15.4) percent using size-adjusted returns, and 10.1 (15.1) percent using Carhart's (1997) four factor model. This alleviates concerns that 13 In Table 2, the mean return of the hedge portfolio is slightly higher, 10.21 percent using size-adjusted returns and 11.78 percent using Carhart's (1997) four factor model. The (minor) discrepancy between the results presented in Figure 1 and those in Table 2 follows because in Table 2 we average the 120-trading-day returns across all sample fiscal quarters, whereas in Figure 1 we average these returns across all calendar years.
14 inappropriate risk adjustment underlies our findings. The strategy also generates size-adjusted returns of 8.9 percent and 11.4 percent, and Carhart's (1997) four factor model returns of 11.8 percent and 10.8 percent, in hot and cold IPO years, respectively. This alleviates concerns that the returns of our loss/profit strategy are driven by a changing mix of publicly-traded firms towards younger and more loss-prone firms after 1970 (see Fama andFrench 2001, 2004). 15 Collectively, the results presented in Table 2 and Figure 1 indicate a substantial stock mispricing related to loss/profit announcements that is robust to alternative risk adjustments and time periods. 16 A natural question arises at this point: is this loss/profit effect distinct from and incremental to previously documented accounting-based anomalies?
The first anomaly that might come to mind concerns stock mispricing based on E/P multiples studied by Basu (1977Basu ( , 1983 and Lakonishok et al. (1994). However, the objective of these studies is to test whether stock prices are biased due to inflated investor expectations regarding growth in earnings and dividends (e.g., Basu 1977, p. 663;Lakonishok et al. 1994Lakonishok et al. , p. 1547). This objective, which is distinctly different from our objective of testing the market valuation of earnings signs, leads to fundamental differences in motivation, hypotheses, research design, and findings. In particular, the E/P multiples studies' predictions are silent about loss firms because E/P multiples and earnings growth of loss firms are difficult to interpret, and as a result investors are unlikely to rely on E/P multiples when valuing loss firms. As a result, these 15 studies typically exclude loss firms from their samples (see Basu 1983, p. 133;Lakonishok et al. 1994Lakonishok et al. , p. 1546. 17 Conversely, our research design requires the inclusion of loss firms in our analysis, as we conjecture that loss firms may be associated with a substantial mispricing. In addition, the hedge portfolio return results are considerably different between our study and the E/P multiples studies in two ways. First, Fama and French (1996)  Other accounting-based strategies shown by prior research to be related to future stockprice performance in broad samples, however, may be related to our findings. In the next section we thus explore whether the loss/profit strategy is different from and incremental to the postearnings announcement drift (SUE) strategy, the value-glamour (book-to-market) strategy, and the accruals strategy. 17 Interestingly, Basu (1977), who examines the effect of loss firms on the E/P strategy, finds that the inclusion of loss firms in his sample makes little difference for his findings. The reason for the contradictory findings follows because Basu's (1977) carefully selected sample of only 753 NYSE industrial firms with December fiscal year end in the period 1957-1971 contains a negligible number of loss firms. Our findings, which indicate that the inclusion of loss firms has a substantial effect on the results, may thus be viewed as another contribution of our study relative to Basu's (1977).

Methodology
To test whether the loss/profit strategy is incremental to the post-earnings announcement drift, we examine the loss/profit strategy after controlling for the post-earnings announcement drift (SUE) effect. This examination involves forming portfolios based on the intersection of the two independent rankings of earnings and SUE for each quarter. More specifically, we rank all firm-quarter observations into ten earnings deciles from lowest earnings, High Loss to highest earnings, High Profit. We also independently classify all firm-quarter observations into ten SUE deciles from lowest SUE ("Low SUE") to highest SUE ("High SUE"). We then compute the difference in buy-and-hold abnormal returns between the two most extreme earnings portfolios, the High Loss (decile 1) and the High Profit (decile 10), for each SUE decile separately. In other words, we test for the loss/profit effect after controlling for the SUE effect. Next, we employ a similar methodology to examine the relation between the loss/profit strategy and the valueglamour and accrual anomalies, using book-to-market value of equity and accruals as classification variables, respectively, instead of SUE.
Finally, to assess the incremental effect of the loss/profit strategy simultaneously over the post-earnings announcement drift (SUE) strategy, the value-glamour strategy, and the accruals strategy, we use a regression setting. More formally, we estimate the following model: where BHSAR i,q, [1,120] is the buy-and-hold size-adjusted returns for the window [1, 120], where day zero is the earnings announcement date of quarter q, Earnings i,q is the decile ranking of firm i based on earnings before extraordinary items and discontinued operations in quarter q scaled by total assets in quarter q-1, SUE i,q is the decile ranking of firm i based on standardized unexpected earnings in quarter q (generated using a seasonal random walk with drift model), BM i,q is the decile ranking of firm i based on the ratio of book-to-market value of equity at the end of quarter q, and Accruals i,q is the decile ranking of firm i based on accruals scaled by average total assets in quarter q. The decile rankings for all rank variables are determined every quarter q based on the distribution of the underlying variables in quarter q-1. Each rank variable is scaled to range between zero and one. We estimate Equation (6)

Do losses/profits, SUE, book-to-market, and accruals strategies overlap?
As a first step in assessing the relation between our loss/profit strategy and the three potentially related anomalies, we examine the overlap between our strategy and three variables: SUE, book-to-market (BM), and accruals. Panel A of Table 3 displays the number of observations in the High Loss portfolio (i.e., the short portfolio) and in the High Profit portfolio (i.e., the long portfolio) by deciles of each of the three variables SUE, BM, and accruals. Overall, the findings in Table 3 may be viewed as prima facie evidence that the post loss/profit announcement drift is not another manifestation of the previously documented postearnings-announcement drift (SUE), value-glamour (BM), or accruals effects, and that it is not driven by an omitted variable. Still, in the sections below we further assess these possibilities by directly testing the relation between the loss/profit strategy and other accounting-based anomalies, as well as by performing a battery of sensitivity tests.

Losses/profits, SUE, and future stock returns
Panel A of Table 4  Second, the loss/profit strategy is incremental to the SUE strategy, as the superior return of the loss/profit strategy is observed even after controlling for the SUE strategy. For example, the return on the hedge portfolio, High Profit minus High Loss, is significantly positive for all SUE deciles, yielding 5.33 percent, 6.69 percent, 5.08 percent, 10.35 percent, 7.41 percent, 5.89 percent, 5.68 percent, 10.06 percent, 9.29 percent, and 9.38 percent for portfolios consisting of firms in SUE deciles 1 through 10, respectively. Thus, the return of the loss/profit strategy remains substantial for all hedge portfolios even after controlling for the SUE effect. Further, combining the two strategies does not improve much the hedge portfolio performance relative to its performance based on loss/profit alone. The 120-trading-day hedge portfolio return of the loss/profit strategy is 10.21 percent (see Table 2), and the return of the hedge portfolio based on the joint classification (High Profit and High SUE minus High Loss and Low SUE) is 12.47 percent, whereas the 120-trading-day return of a SUE-based hedge portfolio is 7.67 percent (not tabulated). Overall, the findings in Panel A provide evidence that the loss/profit effect is incremental to, and more pronounced than the SUE effect.
Comparing our results with those of Narayanamoorthy (2006) highlights new insights produced by our approach. Narayanamoorthy (2006) uses regression analysis to study differential post-earnings-announcement drift between loss firms and profit firms. His regression results imply SUE-based hedge portfolio returns of 6.79 percent for profit firms and 5.07 percent (= 6.79 minus 1.72) for loss firms (p. 779, Table 5). 19 He attributes these findings to the lower serial correlation coefficient of SUE for loss firms than that for profit firms. Using portfolio analysis rather than regression analysis and focusing on the tails of the distributions of losses and profits rather than on the total sample of SUE, our results displayed in Panel A of Table 4  taking a short position in the subset of firms in the lowest SUE decile that report a loss and a long position in the subset of firms in the highest SUE decile that report a profit.

Losses/profits, book-to-market, and future stock returns
Panel B of Table 4 Table 2 and the one based on book-to-market alone, 2.43 percent (not tabulated). In fact, the return from the two-way classification is approximately 25 percent higher than the sum of the returns from the two strategies alone, which indicates that combining the loss/profit strategy and the book-to-market strategy improves substantially the performance of the hedge portfolio.
Second, the loss/profit strategy dominates the book-to-market strategy. For example, for all book-to-market deciles the returns are negative for the High Loss portfolio and positive for the High Profit portfolio. In other words, when the book-to-market variable and the loss/profit variable conflict on the sign of a future return, the latter is correct. To see that, consider the portfolio returns of the highest book-to-market decile (High BM) and highest loss decile (High Loss), where the book-to-market (loss/profit) strategy predicts positive (negative) future returns, respectively. Inconsistent with the prediction of the book-to-market strategy, and consistent with the prediction of the loss/profit strategy, the return of this portfolio is significantly negative, -3.20 percent. Likewise, the return of the High Profit and Low BM portfolio is significantly positive, 2.86 percent, which is consistent with the prediction of the loss/profit strategy and contradictory to the prediction of the book-to-market strategy.
The bottom line of Panel B displays the returns of ten hedge portfolios formed based on the loss/profit strategy after controlling for the book-to-market effect. The hedge portfolio return is significantly positive for all book-to-market deciles, ranging from 8.91 percent to 13.38 percent. That is, the return to the loss/profit strategy is largely unchanged even after controlling for the book-to-market effect. Collectively, the results in Panel B demonstrate that the loss/profit effect is incremental to, and more pronounced than the book-to-market effect. 23

Losses/profits, accruals, and future stock returns
Panel C of Table 4 Table 2) and the return of the accrual-based hedge portfolio of 3.28 (not tabulated). Therefore, combining the two strategies improve, albeit slightly, the hedge portfolio performance relative to its performance based on the loss/profit strategy alone.
Next, the bottom line of Panel C displays the returns of ten hedge portfolios formed based on the loss/profit strategy after controlling for the accruals anomaly. These results indicate that the loss/profit strategy yields substantial returns even after controlling for the accruals anomaly, ranging from 4.50 percent to 12.34 percent. Overall, the results in Panel C provide evidence that the loss/profit effect is independent from the accruals anomaly. In summary, the findings in Panels A, B, and C of Table 4 show that the post loss/profit announcement drift is more pronounced than, and incremental to the SUE, book-to-market, and accruals effects.

Losses/profits, SUE, book-to-market, accruals, and future stock returns
So far, we examine the ability of the loss/profit effect to predict future returns after controlling for each of the three alternative explanations separately. In this section, we assess the 24 ability of the loss/profit effect to predict future returns after controlling for all three alternative explanations simultaneously by using a regression analysis as specified by Equation (6). Note that since Equation (6) includes an intercept, a 0 , and since all variables are scaled to range from zero to one, the least square values of a 1 , a 2 , a 3 , and a 4 represent abnormal returns on zeroinvestment (hedge) portfolios, that is, portfolios where the sum of the weights assigned to individual securities is zero. 21 Table 5 displays coefficient estimates for Equation (6) (presented as Models IV) and for three models nested within this equation (presented as Models I, II, and III). Table 5 also displays coefficient estimates for Equation (6) using a least trimmed squares regression approach in which one percent of the observations with the largest squared residuals are excluded before re-estimating the model (presented as Model V). The findings in Table 5 are robust to alternative estimation methods and model specifications, as the results across the five specifications are similar. 22 Consider, for example, the results for Model IV. As predicted, a 1 , a 2 , and a 3 are significantly positive, 0.0907, 0.0625, and 0.0484, respectively, and a 4 is significantly negative, -0.0572. Since each estimated coefficient indicates the hedge portfolio return for one of the four strategies, and since the estimated coefficient on the earnings variable is almost twice as high (in absolute value) as any of the other three estimated coefficients, these findings further demonstrate that the loss/profit effect is incremental to, and more pronounced than the three previously documented accounting-related anomalies.

Methodology
In this section, we test a behavioral explanation for the post loss/profit announcement drift documented above. The behavioral finance literature (e.g., Mullainathan 2002;Daniel et al. 2002) argues that investors appear to rely on simplified models to assess a firm's future prospects. If so, investors may be relying on unconditional probabilities for a loss/profit rather than their conditional probabilities when predicting a future loss/profit. This type of behavior would result in an underestimation of the probability of a loss (profit) in quarter q for firms with a loss (profit) in quarter q-1 if, as we assert, conditional probabilities are higher than unconditional probabilities. Consequently, a drift would be observed after the earnings announcement date, as news arrives to the market and investors correct their errors.
To test this explanation, we perform two tests. First, we examine whether the conditional probability of a current quarterly loss/profit is increasing in the magnitude of the previous quarterly loss/profit. This test employs a chi-square statistic. We design a ten-by-two contingency table with earnings deciles in the previous quarter as rows and the frequency of a loss/profit in the current quarter as columns. We calculate a chi-square statistic to test independence (see Conover 1980, pp. 153-156). Our second test examines the relation between future abnormal returns, our measure of the valuation errors, and the difference between conditional and unconditional probabilities of a loss/profit, our measure of investor misperception of the likelihood of a future loss/profit. To test for this relation, we compute for each earnings decile the difference between conditional and unconditional probabilities of a loss (profit). Then, we compute the correlation between the two measures: the portfolio returns and the differences in probabilities. If stock prices fail to fully reflect the implications of 26 losses/profits for future losses/profits because investors do not fully rely on conditional probabilities, these two measures should be statistically significantly correlated. 23

Tests of the relation between conditional probabilities, unconditional probabilities, and post loss/profit announcement drift
Panel A of Table 6 reports the results for the first test, the one related to whether the conditional probability of a loss/profit in quarter q is increasing in the magnitude of the loss/profit in quarter q-1. Specifically, the ten-by-two contingency table reported in Table 6  Profit portfolio in quarter q-1, only 3,327 (7 percent) report a loss in quarter q. A  2 (9) test of independence rejects the null hypothesis that a loss in quarter q is independent of the magnitude of a loss/profit in quarter q-1; the  2 (9) statistic is statistically significant at a 0.01 level.  Table 2). The differences between conditional and unconditional probabilities of a loss demonstrate a similar pattern: 0.54, 0.34, 0.11, -0.08, -.012, -0.13, -0.15, -0.16, -0.17, and -0.16 (untabulated results).

Panel B of
That is, the larger the valuation error (the absolute value of the abnormal return), the larger the misperception about the probability of a future loss (the absolute value of the difference between conditional and unconditional probabilities). These results support our behavioral explanation for the post loss/profit announcement drift by showing that the loss/profit mispricing is related to differences between conditional and unconditional probabilities of losses/profits, as if stock prices do not fully reflect conditional probabilities in a timely fashion.

Analyst following and post loss/profit announcement drift
Finally, to further assess the validity of our explanation for the loss/profit announcement drift, we examine stock returns for the High Loss portfolio, the High Profit portfolio, and the hedge portfolio (High Profit minus High Loss) after classifying our sample firms into three groups by the number of analysts following a firm. The first group consists of firms with no analyst following, the second of firms followed by one to five analysts (low analyst following), and the third of firms followed by more than five analysts (high analyst following). 24 The idea underlying these tests is that if our explanation for the loss/profit announcement drift is valid, the hedge portfolio return should be greater for less-followed firms with little earnings forecast information available to investors (Bhushan 1994; Brown and Han 2000; Bartov et al. 2000).
The results, displayed in Table 7, clearly show that the hedge return portfolio is decreasing 24 We replicate these tests using alternative classifications of analyst following (for instance, one to ten analysts for the low analyst following group, and more than ten analysts for the high analyst following group). The results from these tests are qualitatively similar and lead to the same inferences.
28 monotonically as the number of analysts following a firm increases. For example, the hedge portfolio returns for the window [1, 120] for the subsample with no analysts following (Panel A), low analyst following (Panel B) and high analyst following (Panel C) are, respectively, 14.03 percent, 8.70 percent, and 3.14 percent (all statistically significant).

Distress risk, short sales constraints, transaction costs, firm size, and post loss/profit announcement drift
In this section we assess the sensitivity of our findings to four alternative explanations: distress risk, short sales constraints, transaction costs, and firm size. We begin by examining the daily buy-and-hold abnormal returns for three portfolios, the High Profit, High Loss, and the hedge (i.e., High Profit minus High Loss) over the window [1,240], as portrayed in Figure 2.  Begley et al. (1996) for years after 1980. For both measures, the cut-off points are Z scores equal to or less than 1.81, which indicate that bankruptcy is likely, Z scores greater than 1.81 yet equal to or less than 2.99, which correspond to the "gray area", and Z scores greater than 2.99, which indicate that bankruptcy is unlikely (Altman 1968;Begley et al. 1996).  Table 2 is 10.21 percent vis-à-vis 9.97 percent for the subsample. Thus, the inclusion of small firms in our sample is unable to explain the post loss/profit announcement drift. To the extent that small firms serve as a proxy for distress risk, short sales constraints, high transaction costs, or an unidentified risk factor, these explanations are unlikely to explain our findings.

Conclusion
Over the last three decades, a large volume of empirical work has documented a variety of ways in which stock returns can be predicted based on publicly available information, in particular earnings information. In this study, we examine whether investors fully price the implications of current losses/profits for future losses/profits. Employing a broad sample spanning 30 years, from 1976 through 2005, we find evidence of a loss/profit mispricing.
Briefly, over the 120-trading-day window following the earnings announcement, firms in an extreme loss portfolio exhibit a negative drift of nearly six percent, whereas firms in an extreme profit portfolio exhibit a positive drift of over four percent. A hedge portfolio that takes a long position in the extreme profit portfolio and a short position in the extreme loss portfolio generates approximately 10 percent abnormal return, which translates into an annualized return of approximately 21 percent. Further, using both univariate and multivariate tests we show that the mispricing associated with our loss/profit strategy is distinct from, and incremental to three previously documented accounting-based anomalies: the post-earnings-announcement drift, the value-glamour anomaly, and the accruals anomaly. Finally, a variety of sensitivity tests shows that this loss/profit anomaly is robust to alternative risk adjustments, distress risk, short sales constraints, transaction costs, and time periods.
What may explain this mispricing? If investors rely on simplified models to assess a firm's future prospects, as behavioral finance theories suggest, this mispricing may follow because investors fail to fully assess the probability of a loss/profit based on its conditional, rather than unconditional, probability. Since the unconditional probability of a loss/profit is lower than the corresponding conditional probability, this type of investor behavior would result in systematic underestimation of the probability of a loss/profit. Consequently, a negative (positive) post loss (profit) announcement drift in stock returns would be observed, and more so for extreme earnings realizations. Consistent with this explanation for the observed loss/profit mispricing, we find that the differences between conditional and unconditional probabilities, our measure of the misperception of the probability of a future loss/profit, are correlated with the levels of loss/profit mispricing. In other words, the higher is the difference between conditional and unconditional probabilities, the larger is the loss/profit mispricing. Still, we note that a test of market efficiency is a joint test of market efficiency and the efficacy of the model used for expected returns. Thus, notwithstanding our serious effort to mitigate the "bad model" problem by employing alternative measures for abnormal returns and by performing a battery of sensitivity tests, it is impossible to rule out entirely the possibility that the "bad model" problem (partially) explains our findings.
The primary contribution of our study is that the earnings signs, a loss versus a profit, are mispriced. This finding is statistically significant and economically important. Further, our study shows that this mispricing is related to differences between conditional and unconditional probabilities of losses/profits, as if stock prices do not fully reflect conditional probabilities in a timely fashion. 27 Finally, we demonstrate that considering conditional rather than unconditional probability of losses/profits, and in particular focusing on the tails of the earnings distribution (i.e., extreme losses/profits), lead to new insights about the likelihood of losses/profits. Our findings thus have implications to our understanding of the time-series properties of earnings and on investors' valuation of loss/profit firms. 27 A natural question that often arises in the context of earnings-related anomalies is whether it is plausible for a mispricing to persist for so long (i.e., decades). One answer to this intriguing question is provided by behavioral research. For example, Libby et al. (2002, p. 778) observe, "Learning to overcome biases is difficult because of the uncertainty and poor feedback inherent in complex environments." Ogneva, M., Subramanyam, K.R., 2007. Does the stock market underreact to going concern opinions? Evidence from the U.S. and Australia. Tetlock, P., Saar-Tsechansky, M., Macskassy, S., 2008. More than words: Quantifying language to measure firms' fundamentals. Journal of Finance 63, 1437-1467.  [1,120], where day zero is the quarterly earnings announcement date. Abnormal returns are measured using sizeadjusted returns (SAR) and Carhart's (1997) four-factor model (FF). For firms that delist during the return window, the remaining return is calculated by using the delisting return from the CRSP database, and then reinvesting any remaining proceeds in the appropriate benchmark portfolio. Earnings are earnings before extraordinary items and discontinued operations (Compustat Quarterly data8) scaled by beginning-of-quarter total assets (Compustat Quarterly data44). The Loss/Profit strategy consists of a long position in the highest Earnings decile (High Profit) and a short position in the lowest Earnings decile (High Loss). The cut-off points are determined every quarter based on the distribution of Earnings in the previous quarter. MKTRET represents the value-weighted annual market return. The annual number of initial public offerings (IPOs) corresponds to the number of non-financial IPOs by U.S. companies completed every year between 1976 and 2005, as reported by Thomson's SDC (Securities Data Company) database. Hot (cold), i.e., high (low), IPO years are defined as years during which the annual number of IPOs is above (below) median over the period 1976-2005.  , where day zero is the quarterly earnings announcement date. Abnormal returns are measured using size-adjusted returns. For firms that delist during the return window, the remaining return is calculated by using the delisting return from the CRSP database, and then reinvesting any remaining proceeds in the appropriate size-matched portfolio. Earnings are earnings before extraordinary items and discontinued operations (Compustat Quarterly data8) scaled by beginning-of-quarter total assets (Compustat Quarterly data44). High Loss (High Profit) corresponds to firmquarter observations classified into the lowest (highest) Earnings decile. The Loss/Profit strategy consists of a long position in the highest Earnings decile (High Profit) and a short position in the lowest Earnings decile (High Loss). The cut-off points are determined every quarter q based on the distribution of Earnings in quarter q-1.

First Set of Supplementary Tests: Loss/Profit Effect vs. PEAD Effect
Primary tests sample with additional data constraints to compute SUE, i.e. quarterly earnings data on Compustat for at least 13 consecutive quarters. b 359,909 12,824

Second Set of Supplementary Tests: Loss/Profit Effect vs. Value/Glamour Effect
Primary tests sample with additional data constraints to compute book-to-market value of equity ratio. c 448,500 15,101

Third Set of Supplementary Tests: Loss/Profit Effect vs. Accruals Effect
Primary tests sample with additional data constraints to compute accruals. d 267,416 10,695 Table notes: a Required data on Compustat is earnings before extraordinary items and discontinued operations (Compustat Quarterly data8) in quarter q, and total assets (Compustat Quarterly data44) in quarter q-1. Required data on CRSP is a daily return on quarter q's earnings announcement date. b SUE is the standardized unexpected earnings (generated using a seasonal random walk with drift model).
Required data on Compustat to compute SUE in quarter q is earnings per share excluding extraordinary items and discontinued operations (Compustat Quarterly data9) from quarters q-12 to q (an estimation period spanning the most recent 12 quarters is required). c Required data on Compustat to compute the ratio of book-to-market value of equity is: Compustat Quarterly data59 / (data61 * data14) in quarter q. d Required data on Compustat to compute accruals is earnings before extraordinary items and discontinued operations (Compustat Quarterly data76), net cash flow from operating activities (Compustat Quarterly data108), and extraordinary income and discontinued operations (Compustat Quarterly data78) in quarter q, as well as total assets (Compustat Quarterly data44) in quarters q and q-1. Due to the unavailability of cash flow data prior to 1988, this sample spans 1988-2005.   [1,120], where day zero is the earnings announcement date of quarter q. t-statistics are in parenthesis. Alternate t-statistics are calculated using the Fama-MacBeth (1973) procedure on the returns to the strategy every quarter. Abnormal returns are measured using size-adjusted returns (SAR) and Carhart's (1997) four factor model (FF). For firms that delist during the return window, the remaining return is calculated by using the delisting return from the CRSP database, and then reinvesting any remaining proceeds in the appropriate benchmark portfolio. Earnings are earnings before extraordinary items and discontinued operations (Compustat Quarterly data8) in quarter q scaled by total assets (Compustat Quarterly data44) in quarter q-1. The full sample (458,693 firm-quarter observations) is classified into deciles of Earnings from lowest Earnings, High Loss, to highest Earnings, High Profit. The cut-off points are determined every quarter q based on the distribution of Earnings in quarter q-1.  Earnings are earnings before extraordinary items and discontinued operations (Compustat Quarterly data8) scaled by total assets (Compustat Quarterly data44) in quarter q-1. SUE is the standardized unexpected earnings (generated using a seasonal random walk with drift model), based on diluted earnings per share excluding extraordinary items (Compustat Quarterly data9). BM is the ratio of book-to-market value of equity (Compustat Quarterly data59 / (data61 * data14)). Accruals are defined as (Compustat Quarterly data76 -(data108 -data78)) scaled by average assets (Compustat Quarterly data44). MVE is the market value of equity (Compustat Quarterly data61 * data14). Assets is total assets (Compustat Quarterly data44). Sales is total sales (Compustat Quarterly data2). Return volatility is the annualized stock return volatility over a 120-trading-day window prior to the quarterly earnings announcement date. Z score is computed using Altman's (1968) model and original coefficients, as follows: 1.2*(working capital / total assets) + 1.4*(retained earnings / total assets) + 3.3*(earnings before extraordinary items and discontinued operations / total assets) + 0.6*(market value of equity / total liabilities) + 1*(total sales / total assets), which in terms of Compustat Quarterly data items corresponds to:      Carhart's (1997) four factor model (FF) return. For firms that delist during the return window, the remaining return is calculated by using the delisting return from the CRSP database, and then reinvesting any remaining proceeds in the appropriate benchmark portfolio. Earnings are earnings before extraordinary items and discontinued operations (Compustat Quarterly data8) in quarter q scaled by total assets (Compustat Quarterly data44) in quarter q-1. SUE is the standardized unexpected earnings in quarter q (generated using a seasonal random walk with drift model), based on diluted earnings per share excluding extraordinary items (Compustat Quarterly data9). BM is the ratio of book-to-market value of equity (Compustat Quarterly data59 / (data61 * data14)) at the end of quarter q. Accruals is the accruals (Compustat Quarterly data76 -(data108 -data78)) in quarter q by average assets (Compustat Quarterly data44) in quarter q. In each panel, the full sample is classified into deciles of Earnings from lowest Earnings, High Loss, to highest Earnings, High Profit. The full sample is also independently classified into deciles of SUE (Panel A), BM (Panel B), and Accruals (Panel C), respectively, from lowest (Low) to highest values (High). The cut-off points for Earnings, SUE, BM, and Accruals are determined every quarter q based on the distribution of the underlying variables in quarter q-1.   [1,120] is the buy-and-hold size-adjusted returns of firm i for the window [1, 120], where day zero is the earnings announcement date of quarter q. For firms that delist during the return window, the remaining return is calculated by using the delisting return from the CRSP database, and then reinvesting any remaining proceeds in the appropriate size-matched portfolio. Earnings i,q is the decile ranking of firm i based on earnings before extraordinary items and discontinued operations (Compustat Quarterly data8) in quarter q scaled by total assets (Compustat Quarterly data44) in quarter q-1. SUE i,q is the decile ranking of firm i based on standardized unexpected earnings in quarter q (generated using a seasonal random walk with drift model), calculated using diluted earnings per share excluding extraordinary items (Compustat Quarterly data9). BM i,q is the decile ranking of firm i based on the ratio of book-to-market value of equity (Compustat Quarterly data59 / (data61 * data14)) at the end of quarter q. Accruals i,q is the decile ranking of firm i based on accruals (Compustat Quarterly data76 -(data108 -data78)) in quarter q scaled by average total assets (Compustat Quarterly data44) in quarter q. The decile rankings for all rank variables are determined every quarter q based on the distribution of the underlying variables in quarter q-1. Each rank variable is scaled to range between zero and one.  Panel A presents the frequency and probability of a loss/profit in quarter q given the magnitude of the Earnings in quarter q-1. The numbers represent frequencies and those in parentheses represent percentages. Earnings are earnings before extraordinary items and discontinued operations (Compustat Quarterly data8) in quarter q scaled by total assets (Compustat Quarterly data44) in quarter q-1. The full sample is classified into deciles of Earnings from lowest Earnings, High Loss, to highest Earnings, High Profit. The cut-off points are determined every quarter based on the distribution of Earnings in the previous quarter. We employ a chi-square test to examine whether the conditional probability of a loss/profit in quarter q is increasing in the magnitude of the loss/profit in quarter q-1.
 2 (9) is the chi-square statistic with nine degrees of freedom (see Conover 1980, pp. 158-159). Critical values for 0.05 and 0.01 significance levels are, respectively, 16.92 and 21.67. Panel B presents the correlation coefficients between differences in conditional and unconditional probabilities of a loss/profit and buy-and-hold abnormal stock returns for the following windows: [-2, 0], [1,60] and [1,120], where day zero is the earnings announcement date of quarter q, as reported in Table 2. p-values are in parenthesis. Abnormal returns are measured using size-adjusted returns (SAR) and Carhart's (1997) four factor model (FF). For firms that delist during the return window, the remaining return is calculated by using the delisting return from the CRSP database, and then reinvesting any remaining proceeds in the appropriate benchmark portfolio.   [1,120], where day zero is the earnings announcement date of quarter q. t-statistics are in parenthesis. Alternate t-statistics are calculated using the Fama-MacBeth (1973) procedure on the returns to the strategy every quarter. Abnormal returns are measured using size-adjusted returns (SAR) and Carhart's (1997) four factor model (FF). For firms that delist during the return window, the remaining return is calculated by using the delisting return from the CRSP database, and then reinvesting any remaining proceeds in the appropriate benchmark portfolio. Earnings are earnings before extraordinary items and discontinued operations (Compustat Quarterly data8) in quarter q scaled by total assets (Compustat Quarterly data44) in quarter q-1. High Loss (High Profit) corresponds to firm-quarter   , 120], where day zero is the earnings announcement date of quarter q. t-statistics are in parenthesis and below them are the numbers of observations. Abnormal returns are measured using size-adjusted returns (SAR). For firms that delist during the return window, the remaining return is calculated by using the delisting return from the CRSP database, and then reinvesting any remaining proceeds in the appropriate size-matched portfolio. Earnings are earnings before extraordinary items and discontinued operations (Compustat Quarterly data8) in quarter q scaled by total assets (Compustat Quarterly data44) in quarter q-1. In Panel A, Z scores in quarter q are computed using Altman's (1968) model and original coefficients, as follows: 1.2*(working capital / total assets) + 1.4*(retained earnings / total assets) + 3.3*(earnings before extraordinary items and discontinued operations / total assets) + 0.6*(market value of equity / total liabilities)   [1,120], where day zero is the earnings announcement date of quarter q. t-statistics are in parenthesis. Alternate t-statistics are calculated using the Fama-MacBeth (1973) procedure on the returns to the strategy every quarter. Abnormal returns are measured using size-adjusted returns (SAR) and Carhart's (1997) four factor model (FF). For firms that delist during the return window, the remaining return is calculated by using the delisting return from the CRSP database, and then reinvesting any remaining proceeds in the appropriate benchmark portfolio. Earnings are earnings before extraordinary items and discontinued operations (Compustat Quarterly data8) in quarter q scaled by total assets (Compustat Quarterly data44) in quarter q-1. The full sample is classified into deciles of Earnings from lowest Earnings, High Loss, to highest Earnings, High Profit. The cut-off points are determined every quarter q based on the distribution of Earnings in quarter q-1. The subsample (79,040 firm-quarter observations) in this table consists of firm-quarter observations with traded options, as reported in the database Optionmetrics, in the window [0,5], where day zero is the earnings announcement date of quarter q. This subsample covers the period 1996-2005.