A New Measure of Earnings Surprises and Post-Earnings-Announcement Drift

A New Measure of Earnings surprises and Post-Earnings-Announcement Drift

By Zhipeng Yan * & Yan Zhao ♦ First draft Sept 2006 This draft Aug 2008 Abstract In this article, we develop a new measure of earnings surprises – the earnings surprise elasticity (ESE), which is defined as the absolute value of earnings announcement abnormal returns (EARs) scaled by earnings surprises (in percentage). The numerator of the ESE captures all the information released around earnings announcement dates and market reactions to the information; the denominator of the ESE gives special emphasis to earnings surprises. We explore the ESE under four different categories in terms of the signs of earnings surprises (+/-) and the signs of EARs (+/-). We find that: a). Across all four sub-samples, larger firms have smaller earnings surprises and higher EARs (both in absolute values), thus have higher ESE quintiles. b). Firms in the highest ESE quintile usually have much smaller post-earnings-announcement cumulative abnormal returns (CARs) in absolute value than firms in the lowest ESE quintile; c). Against conventional wisdom, for around 36% of total observations, earnings surprises and EARs move in opposite directions. d). More than 11% firms have no surprises; those are larger firms with larger institutional shareholdings and followed by more analysts. It is not wise to invest in this group of firms as evidenced in the negative post-earnings-announcement CARs. e). A trading strategy of taking a long position in firms in the lowest ESE quintile when both earnings surprises and EARs are positive and a short position in firms in the lowest ESE quintile when both are negative can generate 5.19% quarterly abnormal return.

* School of Management, New Jersey Institute of Technology, [email protected] ♦International Business School, Brandeis University, [email protected] We thank Blake LeBaron, Carol Osler, Laarni Bulan, Wayne Ferson, Ming Huang and George Hall; seminar participants at Brandeis University for helpful comments and suggestions. As is customary, however, we accept full responsibility for any remaining errors.

1 1. Introduction Post-earnings-announcement drift (PEAD) is the tendency for stocks to earn abnormally high (low) returns in the weeks or even months following a surprisingly positive (negative) earnings announcement. That is, if a firm’s announced earnings exceed (fall short of) the market expectation, the subsequent abnormal returns to its stocks are usually above (below) normal for weeks or even months. It is one of the best documented anomalies above suspicion (Fama (1998)) and poses “the most severe challenge to financial theorists” (Brennan (1991)). In this paper, we aim to add a new dimension to this anomaly by exploring a new measure of earnings surprises. Most prior PEAD studies use Standardized Unexpected Earnings (SUE) to measure the earnings surprises. SUE is defined as the difference between actual and expected earnings, scaled by the standard deviation of the forecast errors during the estimation period, where expected earnings are estimated either from analysts’ forecasts or from a time series model of earnings. A major downside of SUE is that, although it captures the earnings surprises, it neither captures other new information that might also be unveiled around earnings announcement dates; nor does it capture the market reactions to the information. To capture both earnings surprises and stock market reactions to all the information disclosed around earnings announcement dates, we develop a new measure - the earnings surprise elasticity (ESE). We define it as the absolute value of earnings announcement abnormal returns (EARs) scaled by earnings surprises (in percentage). Since stock return is, in fact, the percentage change in stock price if there is no dividend paid out, this ratio is actually an elasticity measure - the percentage change in stock prices that occurs in response to a percent change in earnings surprises. We further explore the ESE under four different categories in terms of the signs of earnings surprises (+/-) and the signs of EARs (+/-). Within each category, we sort firms into quintiles based on the ESE ranks. The ESE measure, together with the four-category classification, has two advantages over the commonly used SUE measure. First, actual earnings are the most important information disclosed by a firm on earnings announcement dates (by definition). The denominator of the ESE measures new information contained in actual earnings: the difference between actual and expected earnings. Of course,

2 firms also release other information, which may not be as important as earnings, but can still give investors a better understanding of the future of the firms and its competitive environment, such as sales, inventories and other forward-looking information. All the information, earnings related or non-earnings related, is impounded into the numerator of the ESE – the EARs – while the SUE measure is totally silent on stock market reactions around earnings announcement dates. Second, we live in a world where ‘profit is king’. It is conventional wisdom that positive (negative) earnings surprises shall lead to positive (negative) stock responses around earnings announcement dates. Surprisingly, we find that around 36% of total observations are against this “conventional wisdom”. That is, in more than one third cases, positive (negative) earnings surprises lead to negative (positive) stock responses around earnings announcement dates. By grouping the stocks into four different categories in terms of signs of earnings surprises (+/-) and EARs (+/-), we can differentiate those “anomalies” from the “conventional wisdom” and tell how significant the other news is. SUE is helpless in this regard. We have a number of new findings in this paper: 1) Across all four sub-samples, larger firms have smaller earnings surprises and higher EARs (in absolute values), thus have higher ESE quintiles and are followed by smaller drifts subsequently. This finding is different from prior works (Chan, Jegadeesh and Lakonishok (1996) and Brandt et al (2006)), they find larger EARs lead to larger drifts in the subsequent periods. 2) When earnings surprises and EARs move in the same direction (53% of the total observations), post-earnings-announcement cumulative abnormal returns (CARs) have the same signs as those of earnings surprises in each ESE quintile. Firms in the higher ESE quintiles usually have much smaller post-earnings-announcement CARs (in absolute value) than firms in the lower ESE quintiles. 3) When earnings surprises and EARs move in opposite directions (36% of the total observations), there must be some extraordinary good news for a stock to have a positive response to the negative earnings surprise; and extraordinary bad news for a stock to have a negative reaction to the positive earnings surprise. When this is the case, post-earnings-

3 announcement CARs usually still have the same signs as those of earnings surprises. 4) When Earnings surprise equal to zero (11% of the total observations), the ESE is not defined. Firms in this group are larger firms with larger institutional shareholdings and followed by more analysts. It is not wise to invest in this group of firms as evidenced in the negative post-earnings-announcement CARs. Also, it may suggest that faced with intense pressure to meet earnings estimates from analysts and investors, the executives in these firms may smooth earnings over accounting periods to achieve the forecasted result. However, the subsequent negative CARs reflect that the firms’ operations might not be as good as the earnings information shows. 5) A strategy of a long position in firms in the lowest ESE quintile when both earnings surprises and EARs are positive and a short position in firms in the lowest ESE quintile when both are negative can generate 5.19% quarterly abnormal return (before transaction costs). 6) The ESE has a significant impact on post-earnings-announcement CARs after controlling for market-related variables – book-to-market ratio, transaction costs, investors’ sophistication, and arbitrage risk – and financial statement variables – inventory, accounts receivable, gross margin and selling and administrative expenses. This paper offers four contributions to the existing literature. First, we add to the literature on drifts by documenting the market reactions to both earnings news and information beyond earnings news. Second, by grouping firms into 4 categories, we discover a different phenomenon from prior work: larger EARs lead to smaller drifts in the subsequent periods; prior works (Chan, Jegadeesh and Lakonishok (1996) and Brandt et al (2006)) find larger EARs lead to larger drifts in the subsequent periods. Third, we unearth a new anomaly that the ESE can also lead to predictable returns in the future. Fourth, prior studies only examine the effect of market-related variables on drifts; we not only control the market-related variables, but also study the effect of additional financial statement variables on drifts. The rest of the paper is organized as follows. Section 2 reviews the prior literature. Section 3 explains the sample selection and methodology. Section 4 presents the empirical findings. Section 5 performs robustness checks and Section 6 concludes the paper.

4 2. Prior research and motivation The post-earnings-announcement drift was first identified by Ball and Brown in the late 1960s. This predictability of stock returns after earnings announcements had attracted substantial research and has been documented consistently in numerous papers over the decades. Rendleman, Jones, and Latane (1982), Foster, Olsen, and Shevlin (1984), Bernard and Thomas (1989) are among the many who replicate the phenomenon with large scale sample sets. They show that a long position in stocks with unexpected earnings in the highest decile, combined with a short position in stocks in the lowest decile, yields an estimated high abnormal return. Even recent research, such as Collins and Hribar (2000), Liang (2003), Livnat (2003), Jegadeesh and Livnat (2006), Narayanamoorthy (2003), Francis et al. (2004), Mendenhall (2004), Livnat and Mendenhall (2006), Brandt et al (2006), continue to document the abnormal return after the earnings announcement. Studies also demonstrate that the magnitude of the drift is different for different subsets of firms. For example, Bartov, Radhakrishnan and Krinsky (2000) find the drift is smaller for firms with greater proportions of institutional investors. Bhushan (1994), Mikhail, Walther and Willis (2003) show firms followed by more experienced analysts have a smaller drift. Mendenhall (2004) shows the drift is larger for firms subject to higher arbitrage risks. Bhushan (1994) and Stoll (2000) show recent stock prices and recent dollar trading volumes are significantly associated with the transaction costs; the drift is larger for firms subject to higher trading costs. Bernard and Thomas (1989) and Bartov, Radhakrishnan and Krinsky (2000) find the drift is smaller for larger firms and larger for smaller firms. The above research efforts identify the factors that are associated with different drift levels. All these drift studies predominantly focus on earnings surprises and very little attention is paid to other unveiled information around earnings announcement dates or market reaction to this information. The commonly used earnings surprises measure, SUE, is totally silent when other important news is released with the earnings announcement 1,2 .

1 For example, Apple Computer Inc. released quarterly earnings on Jan 17, 2001. Although the earnings were below expectations, analysts were cheered by news that the company had sharply cut inventories of computers on retailers' shelves. Apple's shares, jumped 11% the following day. The Wall Street Journal, “ More Questions About Options for Apple ”, August 7, 2006. 2 For another example, on May 4, 2006, Procter & Gamble Co. reported net sales rose 21% to $17.25 billion, and earnings rose to 63 cents a share for the quarter ended March 31, which was higher than expected earnings of 61 cents

5 Recent efforts to examine whether share prices incorporate other information find some interesting results. For example, Jegadeesh and Livnat (2006) show the magnitude of the drift after earnings announcements is dependent on the sales surprises disclosed simultaneously with earnings. When the two signals confirm each other, the magnitude of the drift is larger; a trading strategy based on both earnings and sales surprises yields a higher abnormal return than a trading strategy which is based only on earnings surprises. Rajgopal, Shevlin and Venkatachalam (2003) find investors over-estimate the valuation of order backlogs that are disclosed in financial statements, a long(short) position in the lowest(highest) decile of order backlog generates significant abnormal returns. Gu (2005) finds patent citation impact is positively associated with future earnings, but investors do not fully incorporate patent impact into stock prices. All of these papers focus on single non-earnings information, such as sales surprises, order backlog, patent citation and, hence, limit the generalization of their findings to a broader set of information. Chan, Jegadeesh and Lakonishok (1996) and Brandt et al (2006) shed some light on how to capture a broader set of information and market reactions around earnings announcement dates by sorting firms on EARs. Both papers find that the portfolios with higher EARs generate substantially higher post-earnings-announcement CARs than the portfolio with lower EARs. Although the EARs measure incorporates all information and market reactions around earnings announcement dates, it doesn’t separate the earnings information from other information. Since other investigators have repeatedly found that earnings forecasts have an important influence on stock prices (see, Brown et al., (1985)), our paper employs a new measure of surprise – ESE – which captures all information EARs captures while giving special emphasis to earnings surprises.

3. Sample selection, methodology and summary statistics The mean analyst forecast of quarterly earnings per share (EPS), standard deviation of the forecasts, earnings announcement dates and actual realized EPS are taken from the

a share. However, analysts surveyed by Thomson Financial had expected higher sales of $17.6 billion. At the end of the day, investors sent P&G shares tumbling, disappointed that sales and the company's outlook fell short of analysts' expectations. www.wsj.com , “ the Evening Wrap ”, May 4, 2006.

6 Institutional-Brokers-Estimate-System summary statistics files (I/B/E/S). To avoid using stale forecasts, we select variable values in the last I/B/E/S Statistical Period prior to the earnings announcement date. Our sample period runs from the third quarter of 1985 through the second quarter of 2005 and we include all the firms from I/B/E/S during this period. We match the earnings forecasts for each company with stock daily returns. The returns are provided by the Center for Research on Security Prices (CRSP) at the University of Chicago. Care is taken to adjust for dividends, stock splits and stock dividends so that all current and past returns, earnings figures, and forecasts are expressed on a comparable basis. When we need book-to-market ratio and other accounting variables, we further merge I/B/E/S with Compustat file tape. Prior studies (Foster, Olsen and Shevlin (1984), Bernard and Thomas (1898, 1990)) show the magnitude of the earnings related anomalies vary according to firm size, to control the firms-size effect; we use value-weighted returns on ten Fama-French portfolios formed on size as benchmark returns to compute the abnormal returns. All the benchmark returns and breakpoints of each decile are taken from Kenneth French’s on-line data library.

3.1 Estimation of SUE, ESE and post-earnings-announcement CARs Following Mendenhall (2004), SUE is defined as actual quarterly EPS minus the latest mean analyst forecast of quarterly EPS from I/B/E/S divided by the standard deviation of the forecasts:

Eiq,− mean( E iq , ) SUE i, q = ^ ------(1) std( E i, q )

Where Ei, q is the actual EPS for firms i in quarter q, mean( E i, q ) is the mean analyst

^ forecast of EPS for firms i in quarter q, and std( E i, q ) is the standard deviation of the analysts’ forecasts for firms i in quarter q. Our alternative measure of the earnings surprises, the ESE is the absolute value of the ratio of EARs over the earnings surprises:

7 EARi,q ESEi,q = ------(2) EarningsSurprisei,q

Ei,q − mean(Ei,q ) EarningsSurprisei,q = mean(Ei,q )

t= +1 t = 1 EARiq,=∏(1 +− R it , ) ∏ (1 + R sizet , ) t=−1 t =− 1

Where, EarningsSurprisei,q is measured as the difference between actual and expected

EPS scaled by expected EPS.

EAR i,q is the abnormal return for firms i in quarter q recorded over a three-day window centered on the announcement date. We cumulate returns until one day after the announcement date to account for two reasons. One is for the possibility of firms announcing earnings after the closing bell. The other is for the possibility of delayed stock price reactions to earnings news, particularly since our sample includes NASDAQ issues, which may be less frequently traded (Chan, Jegadeesh and Lakonishok (1996)). Ri,t is the daily return for firms i in day t. Rsize,t is the daily value-weighted return on Fama-French size portfolio to which stock i belongs. The ten Fama-French size portfolios are constructed at the end of each June using the June market equity and NYSE breakpoints. Size adjusted post-earnings-announcement CARs are calculated in a similar manner to EARs:

tn= tn = CARin,=∏(1 +− R it , ) ∏ (1 + R sizet , ) t=2 t = 2

3 Where CAR i, n is the sized adjusted cumulative abnormal return for firms i from the second day after the earnings announcement to n th day after the announcement.

3.2. Portfolio assignment

3 Many firms in a few trading days and a few firms in many trading days have missing return values, mainly due to missing prices or not trading on the current exchange. We replace the missing values with concurrent benchmark portfolio returns. However, to make CAR calculation meaningful, we require that the total number of missing days be less than ten percent of the number of total trading days. For instance, to calculate 6-month (126 trading days) CARs, if a firm during this 6-month post announcement period have more than 13 missing return values, then the 6-month CAR of this firm for this quarter is excluded from our sample.

8 For every quarter between July 1985 and June 2005, we form portfolios as follows: We first group firms into 4 sub-samples in terms of the signs of earnings surprises and EARs: both earnings surprises and EARs are positive; both earnings surprises and EARs are negative; earnings surprises are positive while EARs are negative; earnings surprises are negative while EARs are positive. Within each sub-sample, we compute the ESE values for every firm. We then calculate quintile breakpoints by ranking the ESE values in the current quarter, (rank 1 is the lowest ESE quintile). By using the current quarter’s data, instead of the previous quarter’s data, we may introduce potential look-ahead bias, which assumes that the entire cross-sectional distribution of the ESE values is known when a firm announces its earnings for quarter q. There are two reasons for us to use the current quarter’s data. First of all, because of restrictions on sample selection, in many cases, more than one quarter elapse before the next earnings announcement data become available for a firm. Therefore, we cannot get the ‘previous’ quarter’s data. Second, most researchers report that post-earnings-announcement CARs are insensitive to the assignment of firms into a SUE decile using the current quarter’s SUE ranks, instead of using SUE cutoffs from the previous quarter (Bernard and Thomas(1990), Jegadeesh and Livnat (2006)). We also replicate all the results by using the breakpoints of the distribution of the ESE in the previous quarter. The results remain almost the same. We then examine the pattern of post-earnings-announcement CARs in each ESE quintile (ESE1 to ESE5) within each sub-sample in subsequent periods, starting from the second day after the earnings announcement up to 5 trading days, 10 trading days, 1 month (22 trading days), 2 months (43 trading days), 3 months (63 trading days), 6 months (126 trading days), 9 months (189 trading days) and 1 year (252 trading days) after the earnings announcement. Finally, we aim to find the most profitable trading strategy based upon our ESE quintiles and sub-sample classification. The trading strategy is to long the portfolio with the most positive post-earnings-announcement CARs and short the portfolio with the most negative post-earnings-announcement CARs. These two portfolios may not be in the same sub sample. For comparison, we also study no-earnings-surprise firms. Since ESE does not exist when realized earnings equal expected earnings, we sort no-surprise firms into 5 quintiles in

9 terms of their 3-day earnings announcement abnormal returns.

3.3. Explanatory Variables As stated in section 2, the magnitude of drift is different for different subsets of firms. To control the effects of the arbitrage risk, transaction costs, investors’ sophistication, and book- to-market ratio, six market-related variables are used as control variables to test ESE’s impact on post-earnings-announcement drifts: arbitrage risk; recent trading price and volume; institutional holdings, number of analysts following and book-to-market ratio. Furthermore, recently development 4 in both finance and accounting shows that key financial statement variables and ratios can indicate the quality of a firm and therefore help separate winners from losers. Lev and Thiagarajan (1993) identify 12 accounting-related fundamental signals referred to repeatedly in analyst’ reports and financial statement analysis texts. We focus on four variables that are available in Compustat Quarterly file: inventory, accounts receivable, gross margin, selling and administrative expenses. These signals 5 are calculated so that the association between each signal and expected abnormal returns is negative . For instance, inventory increases that outrun cost of sales are normally considered a negative signal because such increases suggest difficulties in generating sales and future earnings are expected to decline as management attempts to lower inventory levels. A. Market-related variables Arbitrage risk (ARBRISK) Consistent with Jegadeesh and Livnat (2006), we estimate the arbitrage risk ( ARBRISK ) as one minus the squared correlation between the monthly return on firm i and monthly return on the S&P 500 index, both obtained from CRSP. The correlation is estimated over the 60 months ending one month prior to the earnings announcement. We require firms have at least 24 monthly returns during the 60 month period for calculating the correlation. The arbitrage risk is the percentage of return variance that cannot be hedged. Mendenhall (2004) shows that the drift is larger when the arbitrage risk is higher, so a positive relation between the arbitrage risk and post-earnings-announcement CARs is expected.

4 For instances, Piotroski (2000), Mohanram (2005) and Jegadeesh and Livnat (2006). 5 For a detailed explanation of each variable, please refer to Lev and Thiagarajan (1993)

10 Recent price (PRICE) Consistent with Mendenhall (2004), the explanatory variable PRICE is defined as the CRSP closing stock price 20 days prior to the earnings announcement. Since the stock price is negatively related to commissions, we expect a negative relation between PRICE and post- earnings-announcement CARs. Recent trading volume (VOLUME) We follow Mendenhall (2004) to estimate the trading volume ( VOLUME ), which is the CRSP daily closing price times the CRSP daily shares traded averaged over day -270 to -21 relative to the announcement. Bhushan (1994) argues that the recent dollar trading volume reduces the trading costs, and therefore we expect the trading volume is negatively related with post-earnings-announcement CARs. Institution holdings (INST) Consistent with Bartov, Radhakrishnan and Krinsky (2000) and Jegadeesh and Livnat(2006), we first aggregate the number of shares held by all institutional shareholders at the end of quarter q-1, as reported on all 13-f fillings in the Thomas Financial database maintained by WRDS. This number of shares is divided by the number of shares outstanding at the end of quarter q-1 for firm i to obtain the proportion of outstanding shares held by institutions (INST ). It is expected that the drift should be smaller for firms with greater proportions of institutional investors, so a negative relation between post-earnings- announcement CARs (in absolute values) and INST is expected. Number of analysts (ANUM) The number of analysts who follow the stock ( ANUM ) is the number of analysts reporting quarterly forecasts to I/B/E/S in the last IBES Statistical Period prior to the earnings announcement date. It is also a proxy for transaction costs and is expected to be negatively related to post-earnings-announcement CARs (in absolute values). Book to market ratio (BM) Following Fama and French (1992), we define BM as the ratio of book equity (BE) to market equity (ME). We define BE as total assets (Compustat annual item 6) less total liabilities (Item 181) and preferred stock (Item 10) plus deferred taxes (Item 35) and convertible debt (Item 97). When preferred stock is missing, it is replaced with the

11 redemption value of preferred stock (Item 56). When Item 56 is missing, it is replaced with the carrying value of preferred stock (Item 130). ME is defined as common shares outstanding (Item 25) times price (Item 199). The BM for the period between July in year j and June in year j+1 is calculated at the end of June of year j. BE is the book equity for the last fiscal year end in j-1. ME is price times shares outstanding at the end of December of j-1. B. Financial statement variables Inventory (INV) The inventory signal is computed as the percentage change in inventory (Compustat Quarterly file item 78) minus the percentage change in sales (item 12). The percentage change is the change of the current quarter value relative to the value of the same quarter of previous year. Inventory increases that outrun cost of sales are normally considered a negative signal because such increases suggest difficulties in generating sales and future earnings are expected to decline as management attempts to lower inventory levels. Accounts receivable (AR) The accounts receivable signal is measured as the percentage change in accounts receivables (item 2) minus the percentage change in sales. Disproportionate (to sales) increases in accounts receivable are considered as a negative signal almost as often as inventory increases, which may suggest difficulties in selling the firm’s products (generally triggering credit extensions) and earnings manipulation, as yet unrealized revenues are recorded as sales. Gross margin (GM) The gross margin signal is measured as the percentage change in sales minus percentage change in gross margin (item 12 – item 41). A disproportionate (to sales) decrease in the gross margin obviously affects the long-term performance of the firms and is therefore viewed negatively by analysts (Graham et al. (1962) and Hawkins (1986)). Selling and administrative expenses (SA) The S&A (item 189) signal is computed as the percentage change in S&A minus the percentage change in sales. The administrative costs are approximately fixed; therefore, a disproportionate (to sales) increase is considered a negative signal suggesting, a loss of managerial cost control or an unusual sales effort (Bernstein (1988)).

12 C. Difference between market-related variables and financial statement variables There is an important and subtle difference between market-related variables and financial statement variables. Financial statement variables indicate the ‘quality’ of a firm, while market-related variables signal the magnitude of a drift (in absolute value). For example, ceteris paribus, a firm having a bad inventory signal supposedly has a less positive drift (smaller in absolute value) or a more negative drift (larger in absolute value). However, on the other hand, a firm with larger institutional shareholdings may either have a smaller positive drift or a smaller negative drift (both in absolute values); in other words, drifts of firms with larger institutional shareholdings are closer to zero. Prior studies of PEADs mostly only control for market-related variables. We believe it is more appropriate to include financial statement variables.

3.4. Sub-sample observations Table 1 contains the number and frequency of total firms-quarter observations in each sub-sample over our sample period. Six sub-samples are formed according to different signs of earnings surprises (+/-/0) and EARs (+/-). Panel A shows the total number of observations in each sub-sample. Panel B shows the frequency of total observations in each category. At least two interesting results warrant detailed discussion. Firstly, about 20% of firms have positive EARs when earnings surprises are negative and almost 16% firms have negative EARs when earnings surpass expectations. In total, around 36% of total firms-quarter observations have EARs and earnings surprises that move in different directions. Three possible explanations can be provided for these two types of “anomalies”. First, we (or analysts) cannot correctly estimate the expected earnings, which are used to compute earnings surprises. Second, we cannot correctly measure earnings announcement returns, which largely hinges on the benchmark and window period we choose. We will discuss these two issues in the section of robustness check. The third reason, which we believe is most probable, is that there exists some other significant news disclosed around earnings announcement dates. This suggests there must be some extraordinary good news for a stock to have a positive response to a negative earnings surprise; and extraordinary bad news for a stock to have a negative reaction to a positive earnings surprise.

13 For the observations that earnings surprises and EARs move in the same direction (about 53% of the total observations), there are three possibilities under this situation. Firstly, no news but earnings information is announced. Secondly, some other positive (negative) information, together with positive (negative) earnings surprises, is revealed. It reinforces the impact of earnings surprises. Lastly, some other positive (negative) information is released, along with negative (positive) earnings information. But it is not strong enough to overturn the impact of earnings surprises. When earnings surprises equal to zero (11% of total observations), the ESE is not defined, we are going to examine this special case in the later section. The second interesting result from Table 1 is that the number of firms with positive EARs roughly equals the number of firms with negative EARs (51% vs. 49%). On the other hand, the number of firms with positive or no earnings surprises is significantly larger than the number of firms with negative earnings surprises (60% vs. 40%). One possible explanation to these asymmetrical earnings surprises is that faced with intense pressure to meet earnings estimates from analysts and investors, executives at many companies tend to smooth earnings over accounting periods to achieve or beat the forecasted result.

3.5. Summary statistics Table 2 reports summary statistics of key variables. The most striking finding is that ‘earnings surprise is the real king’. The signs of earnings surprises (positive, negative, or no surprise) can effectively separate different groups of firms apart. Almost all market-related variables and financial statement variables have similar values within the same earnings surprise category, no matter what the sign of EARs is. Do earnings surprises’ signs convey information of the basic nature of a firm? The answer to this question is beyond the scope of this paper. Here, we only aim to illustrate the differences among firms in different categories. Under this big picture, we have a number of interesting findings. Firstly, right-on-target firms are on average relatively larger with smaller book-to-market ratios, larger institutional shareholdings, larger average trading volumes and followed by more analysts. On the other hand, firms that miss earnings expectations are much smaller with much higher book-to-market ratios, smaller institutional shareholdings, smaller trading

14 volumes and followed by fewer analysts. It seems to indicate that larger and/or glamour firms have more capability of meeting analysts’ forecasts. More surprisingly, this happens quite often. As we have shown in the previous section, no-surprise firms account for 11% of total observations. However, simply living up to expectations isn’t necessarily a sign of quality or reliability. The 3-month post announcement CARs are negative for right-on-target firms whether they have positive or negative 3-day EARs. Secondly, positive earnings surprises, either in real value or in percentage, are, on average, much smaller than negative earnings surprises. On average, actual earnings of positive-surprise firms only surpass expected earnings by 5.4 cents (or 5.12 cents when EARs are negative), while actual earnings of negative-surprise firms are less than average forecasts by 10.7 cents (or 12.3 cents when EARs are negative). One possible explanation is that many firms employ the “big bath” strategy of manipulating their income statements to make poor results look even worse. For example, if a CEO concludes that the minimum earnings target can't be made in a given period, she will have an incentive to move earnings from the present to the future since the CEO's compensation doesn't change regardless if she misses the targets by a little or a lot. By shifting profits forward - by prepaying expenses, taking write-offs and/or delaying the realization of revenues - the CEO increases the chances of getting a large bonus in the future. Since earnings surprises are the denominators of the ESE, positive-surprise firms have higher ESE ratios relative to negative-surprise firms because they have smaller earnings surprises (in absolute values) and relative similar EARs (numerator of the ESE). Thirdly, positive-surprise firms have better financial performance than no-surprise firms, which have better performance than negative-surprise firms. Positive-surprise firms have highest sales growth rates and lowest values 6 in five accounting-related fundamental signals, expect for CAPEX signal. They have slightly higher CAPEX signal than no-surprise firms. Finally, the key focus of this paper – PEAD (here, we use 3-month CARs), differs across all six groups. On average, positive-surprise firms have positive drifts; and negative- and no-surprise firms have negative drifts. The following section shows how to exploit this

6 Fundamental signals are designed in such a way that the association between each signal and expected future abnormal returns is negative, the lower the values of signals.

15 pattern in a systematic way by using our new earnings surprise measure – ESE.

4. Empirical results 4.1 Replication of basic SUE strategy To provide a benchmark and comparison for our subsequent ESE strategy, we first examine the SUE hedge portfolio and replicate the PEAD phenomenon. For every quarter between July 1985 and June 2005, 10 portfolios are formed based on the SUE deciles. SUE deciles are numbered 1-10 with SUE1 representing firms with the most negative unexpected earnings (scaled by standard deviation of forecasted earnings) and SUE10 representing firms with the most positive unexpected earnings (scaled). Figure 1 shows there is a PEAD effect subsequent to earnings announcement dates. The PEAD is increasing monotonically with scaled earnings surprises. The largest positive drift is associated with the highest SUE decile (SUE10), and the largest negative drift follows the lowest SUE decile (SUE1). They demonstrate that the information in the earnings is useful in that if actual earnings differ from expected earnings, the market typically reacts in the same direction. This replication is consistent with prior work (e.g. Bernard and Thomas (1989), Collins and Hribar(2000)). Table 3 includes the numbers on which Figure 1 is based. It shows that the sign and magnitude of the SUE values (column 3) are significantly 7 associated with the sign and magnitude of EARs in the 3-day window centered the earnings announcements. The PEAD in a longer time frame subsequent to earnings announcements is also associated with the sign and magnitude of the SUE values. Thus, a long position in stocks in the highest SUE decile (SUE10) and a short position in stocks in the lowest SUE decile (SUE1) yield an average abnormal return of about 0.48%, 4.41% and 7.34% over the 5 days, 3 months, and 9 months subsequent to the earnings announcement respectively. Table 1 also shows how the drift varies by firm size – the larger the market equity (SUE4 - SUE8), the smaller (in absolute values) the drift subsequent to earnings announcements. It is consistent with Foster, Olsen and Shevlin (1984), who report that the PEAD is larger for smaller firms (SUE1 and SUE10).

7 The correlation between SUE and EARs is 0.16, significant at the 1% level.

4.2 Examination of the ESE strategy Table 4 and Figure 2 report results on post-earnings-announcement CARs for ESE portfolios in four sub-samples (in terms of the signs of the earnings surprises and EARs). To reduce influence of extreme values, all the values, except for average observations in a quarter, are winsorized at 1 and 99 percent. One caveat about winsorization: returns are not symmetric around zero. In theory, the smallest daily return is -1 and since the benchmark portfolios are much less volatile than a single stock, the smallest daily abnormal return cannot be far below -1. In fact, during our sample period, the smallest daily return for any size portfolio is -19.7%. On the other hand, the largest daily return can be very large. Actually, the largest one day increase in stock price is 1290% during the sample period. Therefore, winsorization makes mean returns smaller and in fact makes our trading strategy look less profitable (we will discuss it shortly). To be conservative, we stick with winsorization. Generally, across all four sub-samples, larger firms in terms of market equity usually are associated with much smaller earnings surprises and higher EARs (in absolute values), and thus, have higher ESE quintiles. Since large firms are usually followed by more analysts and are more mature, it is understandable that larger firms’ earnings are more predictable and, therefore, have relatively small earnings surprises. Also, since investors expect that large firms are more mature and stable than small firms, any deviation from the ‘expected’ may lead to higher market responses. Furthermore, small stocks may react to earnings news more slowly and may be less frequently traded than large stocks (Chan, Jegadeesh and Lakonishok (1996)). Thus large firms have relatively high EARs. Panel A of Table 4 shows the drifts when both earnings surprises and EARs are positive. In this sub-sample, the drifts are all positive, however, firms in the highest ESE quintile (ESE5) have significantly smaller positive post-earnings-announcement CARs than firms in the lowest ESE quintile (ESE1). The equally weighted portfolio post-earnings-announcement CARs for portfolio ESE5 are about -0.06%, 1.33% and 2.49% over the 5 days, 3 months, and 1 year periods subsequent to earnings announcements respectively, while the post- earnings-announcement CARs for portfolio ESE1 are about 0.16%, 2.44% and 5.42% during

17 the corresponding periods respectively. Although we don’t sort firms in terms of EARs, our result is still different from those of Chan, Jegadeesh and Lakonishok (1996) and Brandt et al. (2006), who rank all stocks by their EARs. They find that as EARs get larger, the post-earnings-announcement CARs get larger. We, however, find that firms with larger EARs in absolute values have smaller post- earnings-announcement CARs subsequent to earnings announcements. One possible reason for the sharp difference between our results and others is that we are using a different sample. Chan, Jegadeesh and Lakonishok (1996) and Brandt et al (2006) rank all the stocks according to their EARs, while Panel A only studies stocks with positive EARs and positive earnings surprises. All other firms having positive EARs but negative earnings surprises are studied separately in Panel C, but may be included in prior research together with firms in Panel A. As we shall discuss later on in this section, firms in Panel A differ greatly from firms in Panel C. Therefore, by studying firms at a finer level, we are able to discover a new phenomenon that may not be found when different categories are combined together. Figure 2.A clearly shows the dramatic difference between the ESE1 and ESE5 quintiles. Starting from day-1, the 3-day EARs for the ESE1 quintile are only 2.47%, compared with 8.55% in the ESE5 quintile; also the PEAD curve in the ESE1 quintile is much steeper than that of the ESE5 quintile. When both earnings surprises and EARs are negative (Panel B of Table 4), the drifts are all negative, as indicated in Figure 2.B, which is like a mirror of Figure 2.A: firms in the higher ESE quintiles have relatively smaller drifts. When earnings surprises are negative and EARs are positive (Panel C of Table 4), there must be other significant good news released with bad earnings surprises so that EARs can be positive around announcement dates. For this group of firms, the negative drifts still exist, but in a smaller magnitude due to the two opposite forces. It seems that even other significant good news can push stock prices higher around earnings announcement dates in the face of negative earnings surprises, this good news is not strong enough. The drifts of firms with positive earnings surprises and negative EARs are showed in Panel D of Table 4. This time, earnings surprises are positive (column 6), while some other significant bad news is so ‘bad’ that it leads to negative EARs (column 5). For portfolios

18 with the lower ESE quintiles (ESE1-ESE4), post-earnings-announcement CARs are normally positive, in the same direction of earnings surprises. Like firms in Panel C, there are two opposite forces underlying post-earnings-announcement CARs. Investors still under- estimate the good earnings news and thus post-earnings-announcement CARs are positive. The only exception is portfolios ESE5, post-earnings-announcement CARs are mostly negative. Finally, for the special group of the firms with no earnings surprises (Panel E of Table 4), the ESE measure is not meaningfully defined. For comparison, we sort all no-surprise firms in each quarter into 5 quintiles according to EARs. Surprisingly, the post-earnings- announcement CARs are normally negative across quintiles, which might be an indication that faced with intense pressure to meet earnings estimates from analysts and investors, the executives in these firms may smooth earnings over accounting periods to achieve the forecasted result. However, the subsequent negative CARs reflect the firms’ true statuses that the firms’ operation is not as good as the earnings information shows. Based on our findings in Table 4, we can easily design several profitable trading strategies. Figure 3 shows the most profitable trading strategy – three-month (63 trading days) abnormal returns to a trading strategy that takes a long position in firms in the lowest ESE quintile when both earnings surprises and EARs are positive (portfolio ESE1 in Panel A of Table 4) and a short position in firms in the lowest ESE quintile when both are negative (portfolio ESE1 in Panel B of Table 4). We employ quarterly earnings announcement data in our analysis, that is, we receive new information every quarter and construct our portfolios quarterly. The mean return over 80 quarters (from July 1985 to June 2005) in Figure 3 is 5.19% before transaction costs. The incidence of losses is 7.5% (6 out of 80 quarters); and the most extreme loss is only -3.16%. The sum of all negative quarters is only -7.94% while the sum of all positive quarters is approximately 422.86%. Furthermore, this long-short portfolio shows relatively low correlation with the whole equity market. The beta coefficient between this portfolio and equal-weighted CRSP NYSE/AMEX/NASDAQ (quarterly) portfolio is only -0.01 and is not significantly different from zero. Since firms in ESE1 quintile – both in Panel A or and Panel B – are relatively small, transaction costs must be relatively high. However, even when we assume that the average round-trip transaction cost is 2% quarterly,

19 the annual return for this strategy is 12.76%.

4.3 Examination of ESE strategy after controlling for book-to-market effect Another effect that is closely related to PEAD but has been largely neglected in PEAD studies is book-to-market (BM) effect. La Porta, Lakonishok, Shleifer, and Vishny (LLSV, 1997) find post-earnings-announcement returns are substantially higher for value stocks than for glamour stocks, thus it is necessary to examine ESE strategy after controlling for book- to-market ratio. Table 5 reports three months (63 trading days) PEAD of portfolios formed by 2-way classifications (BM and ESE). In all 4 panels, ESE still has a significant impact on post- earnings-announcement CARs after controlling for book-to-market equity ratio. In each panel, firms in ESE5 quintile all have significantly smaller (in absolute values) post- earnings-announcement CARs than those in ESE1 quintile, which is consistent with what we find in Table 4. Also, as BM ratio gets higher, the post-earnings-announcement CARs get higher. This is consistent with the findings of LLSV (1997). They report that firms with higher BM ratios have higher post-earnings-announcement CARs.

4.4 Control for explanatory variables by regression Following Bhushan (1994), Bartov, Radhakrishnan and Krinsky (2000), and Mendenhall (2004), we use interactive variables to test whether the ESE-drifts are different for stocks with different characteristics. The model specification is Model 1:

Abnormalreturn=α + β ESE + ε

Model 2:

Abnormalreturn=++αββ12 ESE ESE* PRICE + β 3 ESE * ANUM + β 4 ESE * VOLUM E

+β5ESE* INST + β 6 ESE * ARBRISK +++ βββ 789 ESE *** BM ESE INV ESE AR

+β10ESE* GM + β 11 ESE * SA + ε Here, we use three-month post-earnings-announcement CARs as the dependent variable for the test. To allow for time trends in variables, such as analyst following, and to account for possible nonlinearities, we follow Mendenhall (2004) and code each explanatory variable

20 the same way we code the ESE. We first classify each independent variable into quintiles based on the sample distribution in each calendar quarter, with 1 representing the smallest quintile and 5 representing the largest, and then scale them to range between 0 and 1 (0, 0.25, 0.5(median), 0.75, 1). Following Fama and MacBeth (1973), we run OLS regression for every quarter between July 1985 and June 2005, and then calculate the statistics as time-series averages. The regression results are presented in Table 6. Panel A of Table 6 reports the results for Model 1, the three-month post-earnings- announcement CARs are regressed on the ESE. The signs of the coefficients on the ESE are all as expected. When earnings surprises are positive, post-earnings-announcement CARs are normally positive, as also indicated in Table 4, and the higher the ESE quintile the lower the post-earnings-announcement CARs, thus, negative signs of coefficients of the ESE are expected in these two regressions. However, in regression B, post-earnings-announcement CARs are normally negative, as also indicated in Table 4, which means the higher the ESE quintile the higher the post-earnings-announcement CARs, thus, positive signs of coefficients of the ESE are expected in these two regressions. The coefficients on the ESE in regression A is -1.14 with t-statistic of -4.17, which suggests that the abnormal returns are 1.14% higher for firms in the ESE1 quintile than that of ESE5 quintile, thus, the drift is larger in the ESE1 quintile than that of ESE5 quintile, since the abnormal returns are normally positive in this regression. The coefficient on the ESE in regression B is 0.84 with t-statistic of 2.09, which suggests that the post-earnings- announcement CARs are 0.84% higher for firms in the ESE5 quintile than that of ESE1 quintile, thus, the drift (the absolute value of the abnormal returns) is larger in the ESE1 quintile than that of ESE5 quintile, since the abnormal returns are normally negative in thise regression. Panel B of Table 6 reports the results for Model 2, which augments Model 1 by including interactive variables to control for the potential effects of market-related and financial statement variables. In regression A, the marginal effect of the ESE for median firm characteristics (all 10 control variables = 0.5) is -0.64% (5.28% -5.92% (which is computed as the summation of coefficients on the 10 control variables multiplied by 0.5)), which

21 suggest the higher the ESE quintile the lower the abnormal returns and smaller the drift. The coefficient on ESE*INST is -1.70, with a t-statistic of -2.06, indicating that, after attempting to control for other potential explanatory variables, the spread between the abnormal returns of the highest and lowest ESE quintiles is about 1.70% smaller for firms in the highest institutional holdings quintile than for firms in the lowest institutional holdings quintile. This is the same as expected, since institutional holdings proxy for investors’ sophistication, the higher the institutional holdings, the smaller the drifts. This finding is consistent with Bartov, Radhakrishnan and Krinsky (2000). The coefficients on ESE*INV and ESE*AR are -3.75 and -1.14, respectively and both are significant. This means that all else equal, the spread between the abnormal returns of the highest and lowest ESE quintiles is about 3.75% (1.14%) smaller for firms in the highest inventory (account receivable) quintile than for firms in the lowest inventory (account receivable) quintile. This is also expected, since inventory increases and account receivable increases suggest difficulties in sales. This finding is consistent with Lev and Thiagarajan (1993). The results for other control variables are not significant. In regression B, the marginal effect of the ESE for median firm characteristics is 0.56%, which suggest the lower the ESE quintile the smaller the abnormal returns (in real values), since in this regression, the abnormal returns are normally negative, thus, the drift (in absolute values) is larger in lower ESE quintiles than in higher ESE quintiles. The coefficient on ESE*BM is -1.70 with a t-statistic of -1.81. This finding is consistent with LSV (1994) and LLSV (1997), who find firms with higher book-to-market ratio (glamour stocks) have lower abnormal returns than firms with lower book-to-market ratio (value stocks). In summary, the ESE effect is still significant with the presence of control variable. Trading strategies based on ESE quintiles are profitable.

5. Robustness checks 5.1 Different measures of earnings surprises The key issue in studying any ‘earnings surprises effect’ is how should we measure

22 expected earnings as correctly as possible, since the earnings surprise by definition is the difference between actual earnings announced and the expected earnings. There are two competing measures of expected earnings. Some researchers (Mendenhall(2004)) define expected earnings as the mean analyst forecast from I/B/E/S, while others (Bernard and Thomas (1990)) use a time series model based on Compustat restated earnings data to estimate expected earnings. Affleck-Graves and Mendenhall (1992) and Doyle, Lundholm, and Soliman (2003) use both time series and analysts’ forecasts in their studies. Livnat and Mendenhall (2006) show that the PEAD is significantly larger when defining the earnings surprises using analysts’ forecasts and actual earnings from I/B/E/S. Their results suggest that this disparity is attributable to differences between analysts’ forecasts and those of time-series models. All our major results are based on analysts’ forecasts in I/B/E/S summary statistics file. The main reason is that Compustat alters the original earnings value whenever earnings are restated after an announcement, while I/B/E/S includes the originally reported earnings in its actual earnings figures. This means that for many observations, a Compustat earnings figure was not the one actually seen by investors 8. Furthermore, tracking changes in analysts’ forecasts is also a popular technique used by investment managers. Conroy and Harris(1987) and Kross, Ro, Schroeder(1990) find that analysts’ forecasts often outperform time-series models. On the other hand, analysts’ forecasts may be colored by other incentives, such as the desire to encourage investors to trade and hence generate brokerage commissions. As a result, analysts’ forecasts may not be a clean measure of expected earnings (Chan, Jegadeesh and Lakonishok (1996)). For comparison, we use Compustat Quarterly file to measure expected earnings. Foster, Olsen, and Shevlin(1984) examine different times series models for expected earnings and how the resulting measures of unanticipated earnings are associated with future returns. They find that a seasonal random walk model performs as well as more complex models. Following Livnat and Mendenhall (2006), we simply define actual earnings per share four quarters ago as expected earnings. Therefore, the ESE is defined as follows:

8 see Livnat and Mendenhall (2006) for detailed discussion.

23 EARi,q ESEi,q = Ei,q − Ei,q−4

Ei,q−4

Where EARi,q is the abnormal return for firms i in quarter q recorded over a three-day

window centered on the announcement date. Both Ei,q and Ei,q−4 are Compustat restated earnings at quarter q in the current year and the previous year respectively. Table 7 reports the results. The major conclusions remain. Generally, across all four sub- samples, larger firms in terms of market equity usually are associated with much smaller earnings surprises and higher EARs (in absolute values), and thus have higher ESE quintiles. The magnitude of the post-earnings-announcement CARs is slightly smaller than those of the portfolio formed on the analysts’ expected earnings. Figure 4 shows the three-month (63 trading days) abnormal returns to a trading strategy which takes a long position in firms in the lowest ESE quintile when both earnings surprises and EARs are positive and a short position in firms in the lowest ESE quintile when both are negative. The mean return over 80 quarters (from July 1985 to June 2005) in Figure 5 is 4.58% before transaction costs. The incidence of losses is 23.75% (19 out of 80 quarters), and the most extreme loss is -20.31%. The sum of all negative quarters is only -64.28% while the sum of all positive quarters is approximately 430.8%. The major results remain the same, but due to different measures of expected earnings, the strategy is slightly less profitable than the strategy before. Another potential source of measurement error of expected earnings stems from the fact that the forecasts on the I/B/E/S tape may be stale. We further use those forecasts that are less than 30 days old by using I/B/E/S detailed file directly. All main results remain the same.

5.2 Timing of the portfolio formation Another key issue in studying the PEAD is the timing of the portfolio formation. How sensitive the post-earnings-announcement CARs are to the different timing of the portfolio formation. All our major results are based on the portfolio formed on the second day after earnings announcements. We calculate ESE quintiles on the second day after the announcement and then immediately form portfolios based on ESE quintiles. For

24 comparison, we now wait for five trading days and form our portfolio on the sixth trading day after earnings announcements. Table 8 shows the results. The major conclusions remain. Generally, across all four sub- samples, larger firms in terms of market equity usually are associated with much smaller earnings surprises and higher EARs (in absolute values), and thus have higher ESE quintiles. However, due to the delay of portfolio formation, the magnitude of the post-earnings- announcement CARs is slightly smaller than those of the portfolio formed on the second day after earnings announcements. Based on our findings in table 8, Figure 5 shows the three- month (63 trading days) abnormal returns to a trading strategy which takes a long position in firms in the lowest ESE quintile when both earnings surprises and EARs are positive and a short position in firms in the lowest ESE quintile when both are negative. The mean return over 80 quarters (from July 1985 to June 2005) in Figure 5 is 5.15% before transaction costs. The incidence of losses is 10.00% (7 out of 80 quarters), and the most extreme loss is -3.52%. The sum of all negative quarters is only -16.58% while the sum of all positive quarters is approximately 428.70%. The major results remain the same, but due to the time delay, the strategy is slightly less profitable than the original strategy. Finally, we also use 5-day earnings announcement returns (from day-2 to day+2) instead of 3-day EARs and employ different benchmarks, including S&P 500 index and Fama- French size & book-to-market portfolios while computing cumulative abnormal returns. All the main results remain.

6. Conclusion We are motivated by two stylized facts in writing this paper. First, positive (negative) earnings surprises sometimes can lead to negative (positive) stock market responses. Second, for the same one percent ‘earnings surprise’, some stocks react much more dramatically than other stocks. To capture this two stylized facts, we first develop a new measure of earnings surprises – the earnings surprise elasticity (ESE) – which is defined as the absolute value of EARs scaled by earnings surprises (in percentage). We further explore the ESE under four different categories in terms of the signs of the earnings surprises (+/-) and the signs of EARs (+/-). The ESE measure, together with the four-category classification, has several

25 advantages over the commonly used SUE measure. The ESE can capture new information contained in the actual earnings and other news released around earnings announcement dates. By sorting all stocks into ESE quintiles, we can explore both earnings surprises and stock market reactions to all the information disclosed around earnings announcement dates. Therefore, the ESE is a good candidate to test whether stock prices over-(under-) react to both earnings information and all other released information. We have several findings. Across all four sub-samples, larger firms have smaller earnings surprises and higher EARs (in absolute values), thus have higher ESE quintiles. Firms in the highest ESE quintile on average have much smaller post-earnings-announcement CARs (in absolute value) than firms in the lowest ESE quintile. For around 36% of total observations, earnings surprises and EARs move in the opposite direction, which suggests other released information offsets the effect of announced earnings. More than 11% observations have zero earnings surprises. This group of firms are usually larger firms with larger institutional shareholdings, larger average trading volumes and followed by more analysts. However, it seems not wise to buy these right-on-target firms, as evidenced in the negative post announcement drifts. A strategy of a long position in firms in the lowest ESE quintile when both earnings surprises and EARs are positive and a short position in firms in the lowest ESE quintile when both are negative can generate 5.19% quarterly abnormal returns (before transaction costs). The ESE still has a significant impact on post-earnings-announcement CARs after controlling for market-related variables and financial statement variables. This paper offers four contributions to the existing literature. First, we add to the literature on drifts by documenting the market reaction to both earnings news and information beyond earnings news. Second, by grouping firms into 4 categories, we discover a different phenomenon from prior work, in our study, larger EARs lead to smaller drifts (in absolute values) in the subsequent periods; while in prior work (Chan, Jegadeesh and Lakonishok (1996) and Brandt et al (2006)) larger EARs lead to larger drift in the subsequent periods. Third, we unearth a new anomaly, which is that the ESE can also lead to predictable returns in the future. Fourth, prior studies only examine the effect of market- related variables on drifts; we not only control the market-related variables, but also study the effect of additional financial statement variables on drifts.

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29 Table 1 The number and frequency of firms in each sub-sample based on the signs of earnings surprises and EARs For every quarter between July 1985 and June 2005, 4 sub-samples are formed according to different signs of earnings surprises and EARs. The numbers presented in the table are the total firms-quarter observations over 40 quarters. Earnings surprises are defined as the difference between actual announced earnings and expected earnings, which is the latest mean analyst forecast from I/B/E/S. EARs are calculated as the difference between cumulative normal return and the cumulative return of benchmark portfolios. The benchmark portfolios are 10 Fama-French size portfolios. The sample includes all domestic primary firms with coverage on Center for Research in Security Prices (CRSP), and I/B/E/S summary statistics file.

Panel A: the number of firms in each sub-sample Earnings Earnings Earnings surprises<0 surprises=0 surprises>0 EARs<0 55,959 14,149 46,680 116,788 EARs>0 37,643 12,968 69,293 119,904 93,602 27,117 115,973 236,692 Panel B: the frequency of firms in each sub-sample EARs<0 23.64% 5.98% 19.72% 49.34% EARs>0 15.90% 5.48% 29.28% 50.66% 39.55% 11.46% 49.00% 100.00%

30 Table 2 Descriptive statistics – mean and median (in parenthesis) values, 1985-2005

ME: market equity. EARs: earnings announcement cumulative abnormal returns. BM: book-to-market equity ratio. ESE: absolute value of the ratio of EARs over the percentage change in actual earnings relative to expected earnings. 3-month CARs: cumulative abnormal return over 3-month (63 trading days) period starting from the second day after earnings announcement. SURPRISE (in cents) : actual earnings per share minus expected earnings. SURPRISE (%): the difference between actual earnings and expected earnings scaled by the absolute value of expected earnings, where expected earnings are the latest mean analyst forecast from I/B/E/S. SALE : %∆sales. INV : %∆inventory - %∆sales. AR: %∆receivables - %∆sales. GM : %∆sales - %∆gross margin. SA: %∆Sales and Administrative expenses - %∆sales. ARBRISK is one minus the squared correlation between a firm’s monthly return and monthly return on the S&P 500 index for 60 months ending 1 month prior to the announcement. PRICE : the average price of closing prices between day -20 and day -1. VOLUME : the daily trading volume averaged over days -270 through -21 relative to the announcement. INST: the fraction of the firm’s shares held by institutions that file Form 13f with the SEC in the calendar quarter prior to the announcement. ANUM is the number of analysts in I/B/E/S summary file. Note : %∆ variable means the percentage change in the variable compared to the same quarter of the previous year (DUQLQJV6XUSULVHV! (DUQLQJV6XUSULVHV (DUQLQJV6XUSULVHV ($5V! ($5V ($5V! ($5V ($5V! ($5V

0(LQPLOOLRQV 2128.47 2082.10 1372.22 1352.86 2513.63 2570.88 (425.64) (423.58) (248.72) (241.82) (417.53) (421.16) ($5V 5.61 -4.20 4.65 -5.55 4.83 -5.10 (3.79) (-2.69) (2.85) (-3.63) (3.19) (-3.40) %0 68.27 68.04 74.19 73.05 57.12 55.82 (55.18) (54.72) (60.42) (59.30) (44.84) (43.57) (6( 63.30 62.41 49.90 47.84 (26.97) (22.70) (13.67) (14.54) 0217+&$5 2.05 0.42 -0.99 -1.91 -0.20 -1.48 (0.83) (-0.25) (-1.93) (-2.68) (-0.76) (-2.03) 68535,6(LQFHQWV 5.41 5.12 -10.71 -12.31 0.00 0.00 (2.63) (2.00) (-3.00) (-4.00) (0.00) (0.00) 68535,6( 32.06 28.31 -72.35 -83.54 0.00 0.00 (12.90) (10.87) (-18.46) (-22.73) (0.00) (0.00) 6$/( 24.51 23.65 15.33 12.57 21.44 20.08 (13.69) (12.24) (7.10) (5.66) (12.32) (11.09) ,19 -0.46 3.06 8.54 11.35 5.32 8.65 (-5.02) (-3.14) (1.58) (3.25) (-0.79) (0.49) $5 1.50 3.41 5.36 7.44 4.52 5.70 (-0.63) (0.00) (1.55) (2.59) (1.24) (1.47) *0 1.22 4.03 12.23 14.26 4.44 5.00 (-1.87) (-1.29) (2.36) (3.35) (0.01) (0.14) -4.85 -3.46 2.71 5.00 -1.71 -1.33 6$ (-2.18) (-1.41) (2.03) (3.23) (-0.45) (-0.37) $5%5,6. 84.83 84.76 84.02 84.45 85.65 85.85 (88.50) (88.42) (87.88) (88.20) (89.37) (89.41) 35,&(LQ'ROODUV 25.85 25.75 19.69 19.33 21.86 22.08 (21.99) (21.71) (15.45) (14.98) (17.98) (18.38) 92/80(VKDUHV 346201.89 370424.21 228870.06 238646.01 403278.27 457570.40 (94005.63) (103691.00) (59717.92) (64837.65) (105772.62) (121578.71) ,167 46.93 45.50 38.63 38.92 46.76 47.33 (47.08) (44.99) (36.38) (36.74) (47.29) (47.83) 180(67 5.69 5.64 4.42 4.40 6.18 6.38 (4.00) (4.00) (3.00) (3.00) (4.00) (4.00)

31 Table 3 Post-earnings-announcement CARs – the SUE strategy For every quarter between July 1985 and June 2005, 10 decile portfolios are formed in ascending order based on the SUE deciles. The values presented in the table are averages over all formation periods. Obs : the average number of firms in a quarter. SUE : actual earnings per share minus the latest mean analyst forecast from I/B/E/S divided by the standard deviation of the forecasts; ME : market equity at the earnings announcement date, in million dollars; EARs : earnings announcement abnormal returns; Earnings surprises : the difference between actual announced earnings and expected earnings scaled by expected earnings, where expected earnings is the latest mean analyst forecast from I/B/E/S; 5 day ( 10 day, 1mth, 2mth, 3mth, 6mth, 9mth, 1year ): cumulative abnormal returns within 5 (10, 22, 43, 63, 126, 189, 252) trading days starting from the second day after earnings announcement. The bench market portfolios are 10 Fama-French size portfolio. The sample includes all domestic primary firms with coverage on Center for Research in Security Prices (CRSP), and I/B/E/S summary statistics file.

Earnings rank obs ME SUE Surprises EARs 5day 10day 1mth 2mth 3mth 6mth 9mth 1year SUE1 140 1587.13 -940.53 -181.79 -2.21 -0.18 -0.28 -0.31 -1.44 -1.77 -2.72 -3.29 -2.25 SUE2 139 2333.64 -277.34 -91.45 -1.48 -0.07 0.02 0.00 -1.11 -1.50 -1.95 -2.73 -2.75 SUE3 139 2805.77 -153.04 -58.01 -0.79 0.02 0.06 0.05 -1.03 -1.30 -2.21 -3.00 -3.43 SUE4 140 3271.03 -86.50 -34.67 -0.50 0.07 0.13 0.06 -0.80 -0.86 -1.23 -2.03 -2.18 SUE5 139 3640.24 -34.41 -15.81 0.11 0.11 0.07 0.01 -0.78 -0.91 -1.28 -1.70 -1.57 SUE6 138 3816.07 14.44 2.20 0.23 0.01 0.00 0.09 -0.40 -0.23 -0.23 -0.16 -0.03 SUE7 139 3984.75 66.28 16.53 0.70 0.04 0.15 0.24 0.14 0.32 0.15 0.42 1.01 SUE8 139 3993.10 130.84 25.38 1.20 0.09 0.28 0.59 0.38 0.85 1.40 1.81 2.57 SUE9 139 3583.08 239.11 34.11 1.79 0.06 0.24 0.36 0.45 1.17 1.29 1.66 2.11 SUE10 138 2565.57 645.16 56.89 2.63 0.30 0.63 1.02 1.75 2.64 3.34 4.05 4.36 Spread between SUE10 and SUE1 0.48 0.91 1.33 3.19 4.41 6.06 7.34 6.60 t-statistics 0.46 5.52 4.93 8.95 8.90 8.86 9.13 6.08

Note: a. The correlation between SUE and ME is 0.03, significant at the 1% level. b. All values are in percentage, except for obs and me, which is in million dollars. c. Observations with the absolute value of earnings surprises or EARs less than 0.1% are dropped for calculation. d. All the values except for obs are winsorized at 1 and 99 percentiles. e. For each sub-sample, we run t test of post-earnings-announcement CARs between portfolio SUE1 and portfolio SUE10. *, **, *** represents the difference between the two portfolios is significant at the 10%, 5% and 1% level respectively.

32 Table 4 Post-earnings-announcement CARs – the ESE strategy For every quarter between July 1985 and June 2005, 4 sub-samples (Panel A, B, C and D) are formed according to different signs of earnings surprises and EARs. Within each sub-sample, 5 quintile portfolios are formed in ascending order based on the absolute value of the ratio of EARs over earnings surprises (in percentage) – earnings surprise elasticity (ESE). The values presented in the table are averages over all formation periods. Obs : the average number of firms in a quarter. ESE : the average ESE; ME : market equity at the earnings announcement date, in million dollars; EARs : earnings announcement abnormal returns; Earnings Surprises : the difference between actual announced earnings and expected earnings scaled by expected earnings, where expected earnings is the latest mean analyst forecast from I/B/E/S; 5 day ( 10 day, 1mth, 2mth, 3mth, 6mth, 9mth, 1year ): cumulative abnormal returns within 5 (10, 22, 43, 63, 126, 189, 252) trading days starting from the second day after earnings announcement. The bench market portfolios are 10 Fama-French size portfolios. The sample includes all domestic primary firms with coverage on Center for Research in Security Prices (CRSP), and I/B/E/S summary statistics file.

Earnings rank obs ME ESE Surprises EARs 5day 10day 1mth 2mth 3mth 6mth 9mth 1year Panel A: Earnings surprises>0 & EARs>0 ESE1 171 1312.29 3.21 89.03 2.47 0.16 0.49 0.84 1.55 2.44 3.74 4.34 5.42 ESE2 170 1646.92 12.02 37.60 4.17 0.18 0.51 1.07 1.49 2.28 3.26 4.48 6.19 ESE3 171 1762.02 27.29 21.35 5.40 0.20 0.58 1.15 1.84 2.90 3.90 5.21 6.47 ESE4 170 2056.38 57.99 12.48 6.60 0.14 0.35 0.80 1.27 2.01 2.48 3.37 4.13 ESE5 170 2436.25 197.06 5.75 8.55 -0.06*** 0.13*** 0.54** 0.59*** 1.13*** 1.33*** 2.05*** 2.49*** Panel B: Earnings surprises <0 & EARs<0 ESE1 138 753.00 1.41 -278.02 -3.24 -0.33 -0.50 -1.02 -2.12 -2.75 -4.62 -5.73 -5.00 ESE2 138 984.62 6.01 -83.08 -4.42 -0.13 -0.22 -0.50 -1.45 -2.06 -3.51 -4.42 -3.73 ESE3 138 1194.79 15.68 -37.89 -5.40 -0.15 -0.13 -0.45 -1.41 -1.96 -3.00 -3.38 -3.38 ESE4 138 1488.35 38.99 -19.03 -6.55 0.03 -0.01 -0.17 -1.09 -1.70 -2.68 -3.50 -3.06 ESE5 137 2120.76 172.02 -7.05 -8.26 -0.01*** -0.07*** -0.37*** -1.41** -1.87*** -2.43*** -3.04*** -2.93*** Spread between ESE1 in Panel A and ESE1 in Panel B 0.49 0.99 1.87 3.67 5.19 8.36 10.07 10.42 t-statistics 4.61 7.25 8.66 12.17 13.01 13.61 14.20 13.81 Panel C: Earnings surprises <0 & EARs >0 ESE1 92 804.03 1.21 -246.90 2.34 -0.36 -0.54 -0.87 -1.97 -2.38 -3.94 -4.67 -3.54 ESE2 92 1109.37 5.55 -71.60 3.49 -0.32 -0.44 -0.64 -1.43 -1.57 -2.15 -2.51 -2.00 ESE3 92 1351.85 15.03 -31.47 4.36 -0.40 -0.47 -0.51 -1.03 -1.28 -1.78 -2.31 -1.26 ESE4 92 1528.33 39.83 -16.04 5.62 -0.28 -0.28 -0.22 -0.55 -0.40 -0.18 0.01 0.84 ESE5 92 1945.70 185.43 -5.67 7.43 -0.27 -0.18*** -0.01*** -0.31*** -0.12*** -0.07*** 0.64*** 1.69*** Panel D: Earnings surprises >0 & EARs <0 ESE1 114 1266.42 2.46 87.83 -1.72 0.02 0.26 0.57 0.98 1.53 2.01 2.77 3.48 ESE2 114 1557.34 9.58 32.56 -2.78 -0.05 0.14 0.39 0.55 1.22 1.89 2.14 3.03 ESE3 114 1739.60 23.19 17.33 -3.69 0.11 0.26 0.40 0.43 0.91 1.39 2.05 2.24 ESE4 114 1982.36 53.40 9.68 -4.82 0.11 0.19 0.28 0.12 0.25 0.22 0.19 0.29 ESE5 114 2419.10 203.45 4.50 -7.23 0.09 -0.02*** -0.08*** -0.60*** -0.68*** -0.59*** -0.82*** -0.83*** Panel E: Earnings surprises =0 ESE1 79 1210.70 0.00 -9.08 -0.05 -0.12 0.01 -1.60 -1.53 -2.29 -3.76 -3.60 ESE2 79 1977.71 0.00 -2.63 -0.19 -0.24 -0.16 -0.60 -0.57 -0.39 -0.33 0.33 ESE3 79 2271.19 0.00 -0.17 -0.08 0.08 0.61 0.22 -0.17 -0.50 -0.92 -0.20 ESE4 79 2097.03 0.00 2.35 -0.07 -0.04 0.16 -0.21 -0.17 -0.45 -0.27 -0.34 ESE5 78 1281.19 0.00 9.09 -0.27 -0.28 0.03 -0.38*** -0.20*** -0.80** -0.14*** 0.92*** Note: a. All values are in percentage, except for obs and me, which is in million dollars. b. Observations with the absolute value of earnings surprises or EARs less than 0.1% are dropped for calculation. c. All the values except for obs are winsorized at 1 and 99 percentiles. d. For each sub-sample, we run t test of post-earnings-announcement CARs between portfolio ESE1 and portfolio ESE5. *, **, *** represents the difference between the two portfolios is significant at the 10%, 5% and 1% level respectively.

33 Table 5 Three-month Post-earnings-announcement CARs – ranked by 2-way classifications (ESE and Book-to-market equity ratio) For every quarter between July 1985 and June 2005, 4 sub-samples (Panel A, B, C and D) are formed according to different signs of earnings surprises and EARs. Within each sub-sample, 5 quintile portfolios are formed in ascending order based on the absolute value of the ratio of EARs over earnings surprises (in percentage) – earnings surprise elasticity (ESE). Stocks in each sub-sample are also independently ranked by book-to-market equity ratio (BM). The BM for the period between July, year t and June, year t+1 is calculated at the end of June of year t. BE is the book equity for the last fiscal year end in t-1. ME is price times shares outstanding at the end of December of t-1. BM equals BE/ME. The values presented in the table are averages over all formation periods. The sample includes all domestic primary firms with coverage on Center for Research in Security Prices (CRSP), Compustat industry annual file, and I/B/E/S summary statistics file.

Panel A: Earnings surprises >0 & EARs Panel B: Earnings surprises <0 & EARs >0 <0 ESE1 ESE2 ESE3 ESE4 ESE5 ESE1 ESE2 ESE3 ESE4 ESE5 BM1(Glamour) 0.82 1.84 1.61 1.95 0.82 -4.37 -1.84 -1.89 -2.36 -1.70 BM2 2.38 2.10 3.32 1.45 1.36 -2.87 -2.15 -1.82 -1.62 -1.75 BM3 2.87 3.27 2.94 3.06 1.77 -1.54 -1.36 -1.57 -1.27 -0.70 BM4 2.38 1.62 3.07 3.22 1.27 -2.73 -2.00 -1.03 -0.82 -1.35 BM5(Value) 3.55 3.55 3.53 2.09 1.04 -2.23 -1.55 -1.25 -0.71 -1.08 Panel C: Earnings surprises <0 & EARs Panel D: Earnings surprises >0 & EARs >0 <0 BM1(Glamour) -3.86 -1.88 -2.37 -1.20 -0.61 1.36 0.25 0.82 -0.34 -1.39 BM2 -2.01 -1.64 -0.85 -0.39 -1.43 0.60 1.18 0.72 0.15 -0.41 BM3 -1.98 -1.50 -0.79 0.24 0.30 1.30 1.58 1.49 1.04 -0.63 BM4 -1.64 -2.73 -0.94 -0.48 0.82 2.65 3.02 2.07 1.34 0.49 BM5(Value) -1.64 -0.55 -0.42 1.24 1.47 2.33 2.03 0.35 1.77 -0.52

34 Table 6 Determinants of the PEAD based on the ESE

For every quarter between July 1985 and June 2005, 4 sub-samples (Panel A, B, C and D) are formed according to different signs of earnings surprises and EARs. Three-month post-earnings-announcement CARs : cumulative abnormal returns over 3-month (63 trading days) period starting from the second day after earnings announcement.. ESE: absolute value of the ratio of EARs over the percentage change in actual earnings relative to expected earnings. ARBRISK: one minus the squared correlation between the monthly return on firm i and monthly return on the S&P 500 index for 60 months ending 1 month prior to the announcement. PRICE: the average price of closing prices between day -20 and day -1. VOLUME: recent daily dollar trading volume averaged over days -270 through -21 relative to the announcement. ANUM: the number of analysts providing quarterly earnings forecasts to I/B/E/S in the 90 days prior to the announcement. INST: the fraction of the firm’s shares held by institutions that file Form 13f with the SEC in the calendar quarter prior to the announcement. BM : book-market-equity ratio. INV : %∆inventory - %∆sales. AR : %∆receivables - %∆sales. GM : %∆sales - %∆gross margin. SA : %∆Sales and Administrative expenses - %∆sales. ESE and all firms’ characteristics have been converted to coded scores ranging from 0 to 1. Coefficients have been multiplied by 100. All the statistics are calculated using the method of Fama-MecBeth (1973)

Dependent variable: three-month post-earnings-announcement CARs

Regression A Regression B

Earnings surprises>0 Earnings surprises<0

Independent Variable & EARs >0 & EARs <0 Panel A Intercept 2.78 -2.32 -13.52 (-6.75) ESE -1.14 0.84 (-4.17) -2.09 adj-R2 0.1 0.2 Panel B Intercept 2.91 -2.09 -10.63 (-4.86) ESE 5.28 4.09 -4.45 -2.85 ESE*PRICE -1.76 0.34 (-1.65) -0.24 ESE*ANUM 0 1.12 0 -1.2 ESE*VOLUME -1.39 -0.63 (-0.97) (-0.43) ESE*INST -1.7 -0.65 (-2.06) (-0.74) ESE*ARBRISK 0.37 -1.23 -0.51 (-1.28) ESE*BM -0.52 1.7 (-0.61) -1.81 ESE*INV -3.75 -3.29 (-5.93) (-5.31) ESE*AR -1.14 -1.85 (-1.82) (-2.48) ESE*GM -1.37 -1.62 (-1.71) (-2.22) ESE*SA -0.58 -0.97 (-0.82) (-1.22) adj-R2 2.6 3.1

35 Table 7 Robustness Check – Post-earnings-announcement CARs, using earnings of the same quarter in the previous year as the measure of expected earnings For every quarter between July 1985 and June 2005, 4 sub-samples (Panel A, B, C and D) are formed according to different signs of earnings surprises and EARs. Within each sub-sample, 5 quintile portfolios are formed in ascending order based on the absolute value of the ratio of EARs over earnings surprises (in percentage) – earnings surprise elasticity (ESE). The values presented in the table are averages over all formation periods. Obs : the average number of firms in a quarter. ESE : the average ESE, equals the absolute value of the ratio of EARs over earnings surprises; ME : market equity at the earnings announcement date, in million dollars; EARs: earnings announcement abnormal returns; Earnings Surprises : the difference between actual announced earnings and expected earnings scaled by expected earnings, where expected earnings is earnings of the same quarter in the previous year; 5 day ( 10 day, 1mth, 2mth, 3mth, 6mth, 9mth, 1year ): cumulative abnormal returns within 5 (10, 22, 43, 63, 126, 189, 252) trading days starting from the second day after earnings announcement. The bench market portfolios are 10 Fama-French size portfolios.

Earnings rank obs ME ESE Surprises EARs 5day 10day 1mth 2mth 3mth 6mth 9mth 1year Panel A: Earnings surprises>0 & EARs>0 ESE1 133 1810.14 1.19 375.54 2.47 0.04 0.60 0.97 1.16 1.98 2.07 1.85 2.05 ESE2 136 1816.59 3.53 120.01 3.91 -0.09 0.17 0.42 1.05 1.93 2.48 3.06 3.67 ESE3 136 2025.97 8.20 67.30 5.16 0.01 0.23 0.59 0.83 1.45 2.69 3.96 4.98 ESE4 136 2163.19 17.47 41.83 6.79 0.25 0.53 1.08 1.62 2.28 2.90 4.67 5.89 ESE5 138 2182.08 92.89 18.10 8.54 -0.11 0.13 0.46 0.38** 1.01** 1.41 2.21 3.06 Panel B: Earnings surprises <0 & EARs<0 ESE1 85 1177.06 1.93 -596.45 -3.06 -0.34 -0.48 -0.98 -2.02 -2.60 -4.90 -6.50 -5.70 ESE2 88 1330.63 2.72 -180.73 -4.34 -0.09 -0.14 -0.54 -1.94 -1.75 -2.01 -2.20 -3.65 ESE3 87 1442.26 7.11 -86.80 -5.48 0.15 0.01 -0.18 -0.90 -1.47 -2.97 -4.17 -4.18 ESE4 88 1436.81 16.27 -47.16 -6.87 0.12 0.05 0.05 -0.93 -1.10 -2.17 -2.79 -2.12 ESE5 88 1582.40 107.46 -17.20 -8.67 0.10*** 0.07*** -0.24*** -1.42* -1.91* -3.19* -3.85** -3.37 Spread between ESE1 in Panel A and ESE1 in Panel B 0.38 1.07 1.94 3.18 4.58 6.96 8.35 7.75 t-statistics 1.55 2.11 3.81 4.80 5.38 4.19 4.67 3.54 Panel C: Earnings surprises <0 & EARs >0 ESE1 67 1313.16 0.85 -556.08 2.58 -0.27 -0.17 -0.04 -0.36 -0.40 -2.50 -3.18 -1.70 ESE2 67 1459.54 2.63 -149.75 3.49 -0.21 -0.05 0.15 -0.37 -0.33 -1.86 -2.40 -2.08 ESE3 67 1652.49 6.79 -77.50 4.81 -0.36 -0.40 -0.50 -1.40 -1.18 -1.36 -1.44 -1.11 ESE4 67 1782.52 17.23 -41.14 6.38 -0.20 -0.17 -0.48 -0.99 -1.08 -1.07 -1.72 -0.42 ESE5 67 1678.14 116.53 -15.07 8.47 -0.43 -0.37 -0.02 -0.29 -0.38 -0.21** 0.45*** 1.56* Panel D: Earnings surprises >0 & EARs <0 ESE1 107 1739.26 0.81 350.40 -1.90 -0.24 -0.25 0.10 0.35 0.64 1.38 1.21 1.65 ESE2 108 1847.86 3.40 103.71 -3.21 -0.22 -0.15 -0.07 -0.05 0.17 0.59 0.23 0.77 ESE3 108 2057.64 7.79 58.98 -4.30 -0.02 0.10 0.21 0.67 0.64 1.26 1.47 1.71 ESE4 108 2232.74 18.93 33.11 -5.64 -0.02 0.04 0.14 -0.36 -0.32 -0.16 -0.49 -0.04 ESE5 109 2214.04 116.65 13.05 -7.68 -0.10 -0.06 -0.07 -1.00*** -1.32*** -1.42*** -1.04*** -0.63*** Note : a. All values are in percentage, except for obs and me, which is in million dollars. b. Observations with the absolute value of earnings surprises or EARs less than 0.1% are dropped for calculation. c. All the values except for obs are winsorized at 1 and 99 percentiles. d. For each sub-sample, we run t test of post-earnings-announcement CAR between portfolio ESE1 and portfolio ESE5. *, **, *** represents the difference between the two portfolios is significant at the 10%, 5% and 1% level respectively.

36 Table 8 Robustness Check – Portfolio formed on the sixth day after earnings announcement. For every quarter between July 1985 and June 2005, 4 sub-samples (Panel A, B, C and D) are formed according to different signs of earnings surprises and EARs. Within each sub-sample, 5 quintile portfolios are formed in ascending order based on (ESE) on the sixth day after the earnings announcement. The values presented in the table are averages over all formation periods. Obs : the average number of firms in a quarter. ESE : the average ESE, equals the absolute value of the ratio of EARs over earnings surprises; ME : market equity at the earnings announcement date, in million dollars; EARs : earnings announcement abnormal returns; Earnings Surprises : the difference between actual announced earnings and expected earnings scaled by expected earnings, where expected earnings is the latest mean analyst forecast from I/B/E/S; 5 day ( 10 day, 1mth, 2mth, 3mth, 6mth, 9mth ): cumulative abnormal returns within 5 (10, 22, 43, 63, 126, 189) trading days starting from the sixth day after earnings announcement.. The bench market portfolios are 10 Fama-French size portfolio. The sample includes all domestic primary firms with coverage on Center for Research in Security Prices (CRSP), and I/B/E/S summary statistics file.

Earnings rank obs ME ESE Surprises EARs 5day 10day 1mth 2mth 3mth 6mth 9mth 1year Panel A: Earnings surprises>0 & EARs>0 ESE1 171 1312.36 3.21 89.04 2.47 0.31 0.47 0.79 1.58 2.38 3.50 4.11 5.12 ESE2 170 1644.33 12.03 37.61 4.17 0.34 0.61 0.98 1.52 2.16 3.08 4.38 5.78 ESE3 170 1766.43 27.30 21.34 5.40 0.37 0.71 1.05 1.78 2.86 3.79 5.03 6.19 ESE4 170 2055.49 57.99 12.48 6.60 0.22 0.52 0.75 1.34 1.95 2.27 3.12 3.78 ESE5 170 2436.11 197.10 5.75 8.55 0.20* 0.45 0.66 0.76*** 1.28*** 1.60*** 2.13*** 2.34*** Panel B: Earnings surprises <0 & EARs<0 ESE1 138 752.65 1.40 -278.15 -3.24 -0.19 -0.46 -0.99 -2.04 -2.77 -4.54 -5.61 -4.74 ESE2 138 987.16 6.00 -83.14 -4.42 -0.08 -0.18 -0.36 -1.50 -2.11 -3.56 -4.23 -3.63 ESE3 138 1193.63 15.68 -37.89 -5.39 0.02 -0.08 -0.56 -1.46 -2.05 -2.94 -3.63 -3.41 ESE4 138 1487.58 39.00 -19.05 -6.56 -0.01 0.04 -0.37 -1.37 -1.90 -2.77 -3.65 -3.24 ESE5 137 2122.25 172.06 -7.04 -8.26 -0.03** -0.08*** -0.45** -1.55* -1.81*** -2.43*** -3.05*** -2.95** Spread between ESE1 in Panel A and ESE1 in Panel B 0.50 0.93 1.78 3.63 5.15 8.04 9.72 9.86 t-statistics 6.24 7.42 8.01 11.22 11.59 13.10 14.20 12.28 Panel C: Earnings surprises <0 & EARs >0 ESE1 92 798.00 1.21 -247.23 2.35 -0.20 -0.32 -0.76 -1.73 -2.22 -3.81 -4.28 -3.42 ESE2 92 1116.12 5.55 -71.66 3.49 -0.15 -0.23 -0.51 -1.25 -1.46 -1.81 -1.94 -1.70 ESE3 92 1352.51 15.03 -31.38 4.35 -0.09 -0.03 -0.25 -0.71 -0.93 -1.34 -1.90 -0.89 ESE4 92 1530.99 39.83 -16.06 5.63 0.00 0.02 -0.11 -0.21 -0.03 0.15 0.27 0.88 ESE5 92 1944.67 185.33 -5.67 7.42 0.06*** 0.10*** 0.21*** 0.08*** 0.25*** 0.29*** 1.01*** 2.03*** Panel D: Earnings surprises >0 & EARs <0 ESE1 114 1266.26 2.45 87.88 -1.72 0.23 0.42 0.58 1.01 1.46 2.11 2.54 3.25 ESE2 114 1556.84 9.57 32.59 -2.78 0.19 0.22 0.45 0.74 1.38 1.94 2.25 2.95 ESE3 114 1738.36 23.17 17.34 -3.69 0.15 0.26 0.26 0.43 0.86 1.35 1.74 1.95 ESE4 114 1981.07 53.38 9.68 -4.82 0.13 0.17 0.08 -0.04 0.08 0.16 -0.03 -0.16 ESE5 114 2421.76 203.38 4.50 -7.24 -0.10*** -0.05*** -0.24*** -0.76*** -0.70*** -0.69*** -1.05*** -0.91*** Panel E: Earnings surprises =0 ESE1 79 1214.46 0.00 -9.08 -0.10 0.10 -0.22 -1.75 -1.72 -2.54 -4.07 -3.82 ESE2 79 1974.61 0.00 -2.63 -0.04 0.02 -0.01 -0.26 -0.29 -0.18 -0.06 0.46 ESE3 79 2265.62 0.00 -0.17 0.19 0.44 0.51 0.14 0.17 -0.16 -0.83 0.13 ESE4 79 2102.04 0.00 2.35 0.05 0.04 0.22 -0.11 -0.15 -0.39 -0.37 -0.53 ESE5 78 1283.40 0.00 9.09 0.01 0.12 0.14 * -0.12*** -0.07*** -0.39*** 0.04*** 1.11*** Note : a. All values are in percentage, except for obs and me, which is in million dollars. b. Observations with the absolute value of earnings surprises or EARs less than 0.1% are dropped for calculation. c. All the values except for obs are winsorized at 1 and 99 percentiles. d. For each sub-sample, we run t test of post-earnings-announcement CAR between portfolio ESE1 and portfolio ESE5. *, **, *** represents the difference between the two portfolios is significant at the 10%, 5% and 1% level respectively.

37 Figure 1 Post-earnings-announcement CARs for SUE portfolios up to 1 year subsequent to quarterly earnings announcements. SUE1, SUE10 represent the deciles of stocks with the lowest, highest SUE, respectively.

68( 68( 68( 68( 68( 68(

#8 GD\ GD\ GD\ GD\ PWK PWK PWK PWK PWK \HDU 68( 68( 68( 68(

7LPH

38 Figure 2 Post-earnings-announcement drift based on the ESE strategy for 9-month window. For every quarter between July 1985 and June 2005, 4 sub-samples are formed according to different signs of earnings surprises and EARs. ESE1 and ESE5 represent the quintiles of stocks with the lowest and highest ESE.

(6( (6( 3DQHO$(DUQLQJVVXUSULVHV!($5V! 3DQHO%(DUQLQJVVXUSULVHV ($5V (6( (6(

GD\ GD\ PWK PWK PWK PWK PWK

GD\ GD\ \HDU &$5V &$5V

GD\ GD\ PWK PWK PWK PWK PWK GD\ GD\ 7LPH \HDU 7LPH

(6( (6( 3DQHO'(DUQLQJVVXUSULVHV!($5V 3DQHO&(DUQLQJVVXUSULVHV ($5V! (6( (6( GD\ GD\ PWK PWK PWK PWK PWK GD\ GD\ \HDU &$5V &$5V GD\ GD\ PWK PWK PWK PWK PWK GD\ GD\ \HDU 7LPH 7LPH

(6( 3DQHO((DUQLQJVVXUSULVHV (6(

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39 Figure 3 Three-month (63 trading days) abnormal returns to a strategy taking a long position in firms in the lowest ESE quintile when both earnings surprises and EARs are positive and taking a short position in firms in the lowest ESE quintile when both earnings surprises and EARs are negative.

PRQWK&$5V 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 7LPH

Mean return: 5.19% Sum of positive returns = 422.86% Sum of negative returns= -7.94% Minimum return = -3.16% Incidence of loss = 7.5%

Beta = -0.01 (between the long-short portfolio and CRSP NYSE/AMEX/NASDAQ (quarterly) portfolio)

40 Figure 4 Robustness Check – measuring expected earnings as the earnings of the same quarter in the previous year: Three-month (63 trading days) abnormal returns to a strategy taking a long position in firms in the lowest ESE quintile when both earnings surprises and EARs are positive and taking a short position in firms in the lowest ESE quintile when both earnings surprises and EARs are negative.

PRQWK&$5V

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

7LPH

Mean return: 4.58% Sum of positive returns = 430.8% Sum of negative returns= - 64.28% Minimum return = -20.31% Incidence of loss = 23.75%

41 Figure 5 Robustness Check – Portfolio formed on the sixth day after the earnings announcement. Three-month (63 trading days) abnormal returns to a strategy taking a long position in firms in the lowest ESE quintile when both earnings surprises and EARs are positive and taking a short position in firms in the lowest ESE quintile when both earnings surprises and EARs are negative.

PRQWK&$5V

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 7LPH

Mean return: 5.15% Sum of positive returns = 426.70% Sum of negative returns= - 16.58% Minimum return = -3.52% Incidence of loss = 10.00%