Analyst Performance and Post-Analyst Revision Drift

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1 Analyst Performance and Post-Analyst Revision Drift Chattrin Laksanabunsong University of Chicago This is very preliminary. Abstract This paper tests whether changes in analyst performance can lead to short-run stock price continuation following analyst forecast revisions. Using simple measure for change in analyst performance, I show that post-analyst revision drift is more severe among the firms whose analysts get better at their forecasts. This suggests a rational explanation of post-event drift as investors update the accuracy of the analyst forecast revisions. This effect is particularly strong for stocks that are mostly held by institutional investors and for stocks that are covered by bad analysts. An event-driven strategy based on this effect yield a weekly alphas of over 30 basis points. I would like to thank my thesis committee: Toby Moskowitz (chair), Lars Peter Hansen, George Constantinides, and Kelly Shue. University of Chicago, 1

2 1 Introduction In recent years, a body of evidence on asset pricing anomalies has challenged a traditional view that asset prices fully reflect all available public information. Specifically, an extensive literature has documented a pervasive anomaly of short-run stock price continuation. Notable examples are the positive serial correlation of returns at three- to 12-month horizons or price momentum first documented by Jegadeesh and Titman (1993), post-earnings announcement drift in the direction indicated by earnings surprises or earnings momentum (Ball and Brown (1968)), and post-analyst revision drift in the direction of analyst forecast revision (Stickel (1991), Chan, Jegadeesh, and Lakonishok (1996), and Gleason and Lee (2003)). While positive news is usually resulted in stock price appreciation, however, prices following the news announcements generally exhibit a positive abnormal drift. Similarly, negative news is followed by a negative drift. In this way, investment strategies that take advantages of these anomalies can be easily implemented by buying stocks with recent good news and selling stocks with recent bad news, and earn significant returns in the subsequent weeks or months. The interpretation of these phenomena, however, is controversial as there is not any unified rational or irrational explanations that won general acceptance. Prior literature often attributes these price-continuation anomalies to behavioral theories that involve either investors underreaction or overreaction to news. For examples, several papers including Chan, Jegadeesh, and Lakonishok (1996) attribute price continuation to a gradual market response to news. Hong and Stein (1999) use a model where private information diffuses only gradually with two types of investors, newswatchers and momentum traders, looking at different information set to induce initial underreaction to information and subsequent overreaction. Hirshleifer (2001) and Daniel, Hirshleifer, and Subrahmanyam (1998, 2001) use a model motivated by psychological theories, and assume that investors are overconfident in their private signal and have self-attribution bias. As a result, when subsequent (public) information arrives, if their private signals are conformed with the public news, investors tend to attribute their successes to their own intelligence and skill and thereby overweight or overreact to their initial private information. In this paper, I propose an alternative rational 2

3 explanation based on how investors update the accuracy of the signals or news they received. In this paper, I study post-analyst revision drift or PARD. I focus on how price drift after analyst forecast revisions for two reasons. First, new information is public, easily categorized as good or bad, and occurs very frequently. In this case, new information is the recent earnings forecast revision or percentage change in earnings consensus estimates of next fiscal year earnings over the last quarter (skip one week), and I classify upward forecast revisions as good news and downward forecast revisions as bad news. Second, I can easily measure the precision of forecast revisions of the analysts, and track how the precision of analyst revisions varies over time through analysts forecasting performance. Theoretically, an observed signal s can be decomposed into a true value of firm s fundamental, v, plus a forecast error term e; that is, s = v + e. The precision of the signal s would depend on the variance of the forecast error produced by the analysts. Thus, as analyst forecasting performance improves, they become more skilled and credible, and the variance of their forecast error would drop. To better illustrate how investors update their belief of signal precision, consider a following example. Suppose there are two stocks, A and B, a single analyst that covers both stock A and stock B, and one risk-averse rational investor. Assume for simplicity that both stock A and stock B have a current earnings per share consensus of 5 dollars. Now, suppose that the analyst has made an upward revision of stock A s earnings from 5 dollars to 8 dollars and a downward revision of stock B s earnings from 5 dollars to 2 dollars. As a result of the analyst s revisions, earnings consensus change and investors react to the new forecast made by the analyst and while incorporating analyst s expected forecast error that the investor thinks the analyst is expected to make, he adjusts his allocation over these two stocks, pushing the price of stock A up and the price of stock B down. In a period later, without loss of generality, assume that firm B announces its earnings before firm A, and has announced its earnings per share to be exactly 2 dollars like what the analyst has predicted. Since investors are rational, he now learns the analyst forecasting skill to be better than he expects and updates his belief of the analyst s forecast accuracy as he realizes the analyst performance over stock B. Consequently, the investor reallocates and puts more weight onto 3

4 stock A, and the price of stock A continues to go up even after the analyst s revision as investor continues to learn and update his belief of the analyst skill through the analyst s performance on other stocks. On the contrary, if firm B instead announces its earnings per share to be 8 dollars, completely missing the actual earnings, investors may now think of the analyst s skill to be worse than he expects initially. The investor then put less weight in stock A, and the price of stock A may go down. To test for this event-driven stock price drift, I construct the following test portfolios. I first sort stocks into different groups according to the degree to which a change in analyst forecasting performance would induce stock price drift following the analyst revisions. Then, I construct a long-short portfolio strategy for different groups of analyst performance following analyst forecast revisions. The main prediction is that exposure to an analyst performance proxy should forecast a cross-sectional difference in subsequent returns of the test portfolios. Indeed, the result is that once one sorts upward revision and downward revision stocks using an analyst performance measure, post-analyst revision price drift is more severe among the stocks whose analysts get better at their forecasts. To the extent my main prediction above is true, it leads to the following testable implications. First, the prediction that price continues to drift in the direction of analyst forecast revision after good analyst forecasting performance and go in the opposite direction after bad analyst performance rests on the assumption that investors being able to observe analyst performance and update the accuracy of forecast revisions they have observed on a stock. Because institutional investors are active investors and generally more sophisticated, they are more likely to keep a close watch on analyst performance, and be able to update the precision of the signals or forecast revisions they observed. It must be the case that for stocks with high percentage of institutional ownership, their stock prices should continue to move in the direction of forecast revisions after investors observe improved analyst performance and should reverse otherwise. In other words, if investors are rational, whether analyst performance would improve should not be predictable, and therefore past analyst revision should not forecast future returns for stocks with high percentage of institutional ownership. In contrast, for stocks with low percentage of institutional ownership, analyst 4

5 performance should not affect the direction of the drift. In addition, to the extent that these investors do not keep a close watch on analyst revisions, we should observe their delayed reaction to the analyst forecast revisions. Second, the prediction that there is price continuation after investors learn about analyst performance relies on the assumption that investors initially underweight analysts signals. Thus, if signals about the firms have been made by relatively good analysts, their improved performance should not add much information to the initial signals. On the other hand, if signals about the firms have been made initially by bad analysts, investors would first underweight the signals and later update its accuracy as they learn and observe the analyst performance. In this way, it should be the case that for stocks that are covered by relatively bad analysts, stock prices should continue to drift in a direction of forecast revisions only after investors have observed improved forecasting performance of the analysts. On the other hand, if stocks are currently covered by good analysts, their improved performance should not matter much. I find that my empirical results are in line with the above implications. I also test for other explanations that may drive the predictability of post-analyst revision price drift. It could be the case that unrelated to how investors learn and update the accuracy of the signals they received, the effect could be driven by analysts underreaction to forecasts issued by their peers. Motivated by Barberis, Shleifer, and Vishny (1998), Chen, Narayanamoorthy, Sougiannis, and Zhou (2014) suggests that financial analysts underreact to information provided by their peers due to conservatism bias, a failure to update prior belief by underweighing new information, which results in positive serial correlation in consensus analyst forecast revisions or forecast revision momentum. In this way, post-analyst revision drift maybe the outcome of forecast revision momentum as investors react to the earnings consensus estimates provided by the analysts. I find that controlling for the concurrent change in earnings consensus does not affect the magnitude and significance of analyst performance. In addition, it maybe the case that my result is driven by some observations in particular time periods. In October 2000, Securities and Exchange Commission (SEC) implemented the Regulation Fair Disclosure, which full came into effect by the end of Regulation Fair 5

6 Disclosure mandates that all publicly traded companies must disclose material information to all investors at the same time. It could be the case that prior to this regulation, analysts had access to certain material information from managing executives of the firms. In this way, stock prices continue to drift after analyst forecast revisions as such material information becomes publicly available later on. I find that splitting the sample into before and after 2001 does not change my main result. The rest of this paper is organized as follows. Section 2 briefly provides background and literature review. Section 3 describes the data construction while Section 4 details hypothesis development and the methodology of the paper. Section 5 establishes the results. Section 6 examine robustness tests. Section 7 concludes. 2 Related Literature There is a mounting evidence of short-term price continuation. Stickel (1991), Chan, Jegadeesh, and Lakonishok (1996), and Gleason and Lee (2003) document that stocks prices continue to drift up abnormally after analyst forecast revisions. An extensive literature, dating back to Ball and Brown (1968) and other several papers including Bernard and Thomas (1990), suggests that investors underreact to the information contents of earnings, and demonstrate that stock prices continue to drift in the direction of earnings surprises or the returns around earnings announcement dates for a few months following the earnings announcement. Jegadeesh and Titman (1993) form portfolios based on past intermediatehorizon stock returns, and show that past winning stocks continue to outperform past losing stocks on average over the next 3 to 12 months. In addition, momentum also exists outside the US as shown by Rouwenhorst (1998), Griffin, Ji, and Martin (2003), and Chui, Titman, and Wei (2010), and in other asset classes as studied in Asness, Moskowitz, and Pedersen (2013). While prior literature attributes these evidence to investor behavioral bias as investors underreact or overreact to the information they receive. Chan, Jegadeesh, and Lakonishok (1996) show that post-analyst revision drift is part of a general class of momentum strate- 6

7 gies, in which the market response to recent public information is gradual such that stock prices continue to drift in the direction of public news. Motivated by behavioral evidence, Barberis, Shleifer, and Vishny (1998), Daniel, Hirshleifer, and Subrahmanyam (1998), and Hong and Stein (1999) develop a theoretical framework in which investors either underreact or overreact to publicly released information, providing a potential explanation for the underlying cause of short-run price continuation. Motivated by Daniel, Hirshleifer, and Subrahmanyam (1998), Cooper, Gutierrez, and Hameed (2005) use past three-year market return to proxy for investors confidence. They argue that if past three-year market return is positive, investors on average will be more confident in their trading skill, and thus price continues to go up due to investors overreaction. Frazzini (2006) demonstrates that investors are subjected to disposition effect, or a tendency of investors to ride losses and realize gains, which induces investors underreaction to news, and show that stock prices tend to drift more severely in the direction of earnings surprises among stocks that investors have experience large capital gains/losses. In this paper, I contribute to a literature that attempts to rationalize short-run stock price continuation anomaly, or in this case, post-analyst revision price drift by arguing that stock prices continue to drift in the direction of forecast revisions when the signal precision improves as investors learn about analyst forecasting performance. This paper is also related to a large body of literature in accounting and finance that explores the role of analyst forecast, and its impact on stock price formation and dynamics. The literature suggests that forecast accuracy matters. Bailey et al. (2013) and Jackson (2005) study the effect of analyst reputation and forecast bias on the volume of trade generated by analyst forecasts, and find that more accurate analysts have a greater ability to move stock prices while Hong and Kubik (2003) examine the effect of optimism in analyst forecast on analyst career outcomes. Diether, Malloy, and Scherbina (2002) analyze the role of analyst forecast dispersion in predicting the cross section of returns. They find that stocks with higher dispersion in analyst forecasts earn significantly lower future returns than otherwise similar stocks. My paper contributes to this literature by examining how analyst recent forecasting performance affect investors belief of analyst forecast error. 7

8 3 Data Construction The main dataset in this study is the weekly stock return data constructed by cumulating daily return from Monday to Friday in a given week using the daily stock return data file from the Center for Research in Security Prices (CRSP). Only stocks listed on NYSE, NASDAQ, and AMEX with share code 10 or 11 are included in my study. I also merge the stock return data with institutional ownership data in individual stock provided by Thompson Financial. I next obtain analysts forecasts and earnings data from Thomson Reuters I/B/E/S database available since I first compute a forecast revision signal of firm j, Revision, based on changes in next-year fiscal earnings consensus over the past quarter or 13 weeks (skip one week) Revision j t = consensusj t 1 consensus j t 13 consensus j To calculate analyst performance, for each week t (defined as Monday to Friday like before) and for each analyst i, I calculate the analyst i s performance by taking the absolute value of the difference between the analyst i s final earnings forecast and actual realized earnings scaled by last year stock price, and then take the average amongst firms that analyst i s covers and also have earnings announcement in week t: j A t 13 Analyst P erformance i t = 1 forecast i jt actual jt N A price j,t 52 where A is a set of firms that the analyst i covers and also have earnings announcement in week t. Then, I compute an overall analyst performance of a firm j at time t by averaging over individual analyst performances that currently cover the stock at time t. I define investors expectation of analyst performance or ExpectedAnalystError to be an average of overall analyst performance of a firm over the past 52 weeks or 1 year except for last week, and the concurrent or updated analyst performance, UpdatedAnalystError, to be the firm s overall 8

9 analyst performance last week: ExpectedAnalystError j t = UpdatedAnalystError j t = 1 N Bt 52 k=2 ( 1 N Bt k i B t k Analyst P erformance i t k i B t Analyst P erformance i t 1 where B t is a set of analysts that cover stock j at time t. Table 1 Panel A describes basic summary statistics. Since analyst forecasts and earnings data from the I/B/E/S Thompson Reuters are available starting in 1984, my final sample spans the period of 1985 to UpdatedAnalystError has the average of 1.13%. This implies that on average analyst makes a forecast error of 1.13% of the stock price. example, if the current stock price is 100 dollars and the actual fiscal earnings is 10 dollars, the analyst had made a forecast of dollars. ExpectedAnalystError has the average of 1.29%. Perf is a dummy variable that equals to 1 if UpdatedAnalystError is lower than ExpectedAnalystError. Perf has an average of This implies that there is 57% chance that analyst short term performance is better there their past long-term performance. Figure 2 plots out the time series of average forecasting performance of all the analysts at a monthly frequency. Lastly, Revision has an average of 1.06%. This implies that on average analyst upgrade their forecast of the firms earnings by 1.06% over the past quarter. Table 1 Panel B reports simple correlation between analyst forecast revisions and past returns. I find that analyst revision is positively correlated with last four weeks return and past year return at 0.05 and 0.22, respectively. ) For 4 Hypothesis Development and Methodology In this section, I describe the main hypothesis, and design a related investment rule to construct the test portfolios for the main hypothesis. To the extent that the main hypothesis is true, I derive its implications and test its predictions at a stock level. I conjecture stock prices continue to drift in the direction of analyst forecast revisions 9

10 when the forecasting performance of the covering analysts improves, provided that investors can uncover and update the change in analyst forecasting performance. An interaction between analyst forecast revisions and analyst performance implies short-run price stock price drift, and suggests that a long-short strategy, in which a long position in upward revisions stocks is offset by a short position in downward revisions stocks, should generate higher returns, the better the forecasting performance of the covering analyst improves. I refer to maximum profits strategy as the positive performance: a portfolio that longs good news stocks and shorts bad news stocks whose covering analysts forecasting performance has been improved the most. Similarly, I call the other extreme portfolio as negative performance: a portfolio that longs good news stocks and short bad news stocks whose covering analysts forecasting performance has been deteriorated the most. Following Jegadeesh and Titman (1993), Fama (1998) and Frazzini (2006), I use a rolling portfolio approach. The resulting overlapping returns can be interpreted as the returns of a trading strategy that in any given week t holds a series of portfolios selected in the current week as well as in the previous k weeks, where k is the holding period. At the beginning of each week t, an independent sort is used to rank stocks on the basis of their analysts forecast revisions and the change in analysts forecasting performance. The ranked stocks are assigned to one of 5-by-5 portfolios. All stocks are equally weighted within a given portfolio, and the overlapping portfolios are rebalanced every calendar week to maintain equal weights. Analysts forecast revisions are measured using the changes in the earnings forecast consensus over the past quarter or 13 weeks (skip one week). In other words, stocks that have positive change in their earnings consensus estimates are the stocks that have upward forecast revisions, and similarly, stocks that have negative change in their earnings consensus estimates are the stocks that have downward forecast revisions. Changes in analysts forecasting performance are measured by the difference between ExpectedAnalystError and UpdatedAnalystError. That is, stocks whose covering analysts have lower UpdatedAnalystError than ExpectedAnalystError are the stocks that their covering analysts have gotten better at their 10

11 forecasts than they used to be. Hypothesis I In a cross section of stocks, post-analyst revision drift should be stronger among firms whose analysts get better at their forecasts than what investors have expected. Note that the time series of returns based on the above rolling strategies track the calendar week performance of the post-analyst revisions strategy that is based on entirely on observables. I compute abnormal returns from a time-series regression of portfolio excess returns on contemporaneous Fama and French (1993) factors in calendar time. 1 The above prediction that price continues to drift in the direction of analyst forecast revision after improved analyst forecasting performance and go in the opposite direction after deteriorated analyst performance relies on the assumption that investors being able to observe analyst performance and update the accuracy of the signals they have received on a stock. Because institutional investors are active investors and generally more sophisticated, they are more likely to keep a close watch on analyst performance, and update the precision of the signals or news they received. It must be the case that for stocks with high level of institutional ownership, their stock prices should continue to drift in the direction of forecast revisions after good analyst performance and should reverse otherwise. In other words, if investors are rational, whether analyst performance would improve should not be predictable as investors should already incorporate analyst past performance into account. Therefore, past analyst revision alone should not forecast future returns for stocks with high percentage of institutional ownership. In contrast, for stocks with low level of institutional ownership, analyst performance should not affect the direction of the drift. Hypothesis II If investor is rational, past analyst revisions should not forecast future returns. Moreover, stock prices should move in the direction of analyst revisions only when analysts get better at their forecasts, and should move in the opposite direction otherwise. Lastly, the hypothesis relies on the assumption that investors initially underweight ana- 1 The weekly factors and the risk-free rate are from Ken French s website: tuck.dartmouth.edu/pages/faculty/ken.french. 11

12 lysts forecast revisions. Thus, if signals about the firms have been made by relatively good analyst to begin with, their improved performance should not add much information to the initial signals. On the other hand, if forecast revisions have been made by relatively bad analysts, investors first underweight the signals they received, and later update its accuracy as they learn and observe the analysts forecasting performance. In this way, it should be the case that for stocks that are covered by relatively bad analysts, stock prices should continue to drift in a direction of forecast revisions only after investors have observed improved forecasting performance of the analysts. On the other hand, if stocks are currently covered by good analysts, their improved performance should not matter. Hypothesis III As investors initially give signals from bad analysts a lower weighting than those from good analysts, following good performance, a signal from a bad analyst will be re-weighted relatively more than a signal from a good analyst, and as a result, the relevant firms will exhibit greater returns. I test the above predictions at a stock level. Like before, for each firm, I identify the firm s good and bad news based on changes in the earnings forecast consensus over the past quarter or 13 weeks (skip one week). Basically, stocks that have been receiving upward (downward) revisions from the analyst will have positive (negative) change in their earnings consensus estimate. However, for simplicity, I use a dummy variable to identify whether the forecasting performance of the covering analysts have gotten better at their forecasts. That is, a dummy is equal to 1 if the analyst performance last week UpdatedAnalystError is better or lower than their long-term performance ExpectedAnalystError. All of my results are robust to different specifications of analyst performance as I only use a dummy variable to measure whether an overall analyst performance for a particular firm has been improved recently compared to their long-term performance. It would also be interesting to see how change in analyst performance affect stock price dynamics. 12

13 5 Results In this section, I present the result of my paper. Since the results in the stock-level analysis is done at a weekly frequency, I use a 1-week strategy as the baseline when presenting my results. I start by reporting returns to post-analyst revision drift strategy. As shown in Table 2, the last column confirms there is a significant abnormal return from post-analyst revision drift in the sample. The baseline rolling strategy which goes long in top 20% revisions news stocks and goes short in bottom 20% revisions news stocks yield a riskadjusted return of 0.29% per week (t-statistics is 6.76). Good (bad) revisions news stocks show positive (negative) return continuation, and the risk-adjusted return is monotonic as one moves from the bottom to the top quintile of forecast revisions news. Table 3 displays the result of the main hypothesis. Separating stocks according to their respective covering analysts performance generates huge difference in the subsequent returns. The positive performance portfolio or column #5, a strategy that holds a portfolio of top 20% upward revisions stocks with improved analyst performance (top 20% change in analyst forecasting performance) and sells short a portfolio of bottom 20% downward revisions stocks with improve analyst performance (top 20% change in analyst forecasting performance) for 1 week, yields a risk-adjusted return of 0.36% per week (t-statistics is 4.40). On the other hand, the negative performance portfolio or column #1, a strategy that holds a portfolio of top 20% upward revisions stocks with deteriorated analyst performance (bottom 20% change in analyst forecasting performance) and sells short a portfolio of bottom 20% downward revisions stocks with deteriorated analyst performance (bottom 20% change in analyst forecasting performance) for 1 week generates an positive but insignificant abnormal return of 0.04% per week. In addition, we can also see that the risk-adjusted return or alpha increase monotonically as we move across the column or across the quintile-based portfolios as the changes in analyst performance goes from minimum (negative) in portfolio #1 to maximum (positive) in portfolio #5. As shown in the last column of Table 3, the subsequent returns generated by the positive performance portfolio are statistically different from the negative performance portfolio (t-statistics is 2.14). The difference is relatively substantial, being over 32 basis 13

14 points per week. In addition, the alpha also declines monotonically as analyst performance deteriorates for all rolling strategy portfolios. The results show that the majority of the profitability of the post-analyst revision drift is concentrated in positive performance stocks: stocks whose analysts have gotten better at their forecasts continue to drift in the direction of the revisions news. This induces large differences between the returns of the positive and negative performance portfolios. These results are consistent with the main hypothesis in that post-analyst revision drift is related to how analyst performance and the credibility of their forecast revisions. As investors learn about the accuracy of the signal given by the analysts, they put more weight on the signals as the analysts show signs of accurate performance and become more credible. Table 4 reports factors loadings for the 1-week rolling strategy. Both positive and negative performance portfolios have similar market, size and value exposure. However, the two portfolios have starkly different intercepts or alphas (0.357% and 0.035%). The positive alpha stem from the fact that good news portfolio has persistently high returns while bad news portfolios has low returns, even if it is tilted towards small stocks and value stocks, which tend to raise expected returns. Interestingly, a long-short strategy has negative loadings on market, size and value, but they are relatively small. Separating improved analyst performance stocks from deteriorated analyst performance stocks has the effect of exacerbating the post-analyst revision drift anomaly, since it allows for a substantial increase in subsequent returns in comparison to standard post-analyst revision drift while maintaining a relatively market-neutral portfolio. In Table 5, I explore the main hypothesis at the stock level. For simplicity, I define an improvement in analyst performance, Perf, to be a dummy variable. That is, for each stock the dummy variable Perf equals to one if UpdatedAnalystError is lower than ExpectedAnalystError, and is zero otherwise. The analysis also controls for past stock returns, book-to-market ratio, and firm size. Column (2) and (3) in Table 5 displays the main result. Column (2) confirms the standard results of post-event drift that stock prices on average continue to drift in the direction of the analyst forecast revisions, generating return predictability. However, as shown in column (3), stock prices drift following analyst revisions only when their covering 14

15 analysts have gotten better at their forecasts. In other words, as investors learn and update how credible the analysts have become, they put more weight on the forecast revisions from the analysts, and thereby push stock prices in the direction of the analyst revisions. Nevertheless, in a rational world, we would expect the coefficient in front of analyst revision to be significantly negative as investors realize bad analyst performance. Interestingly, only the coefficient of analyst revision (Revision) interacted with analyst performance (Perf ) is significant while analyst revision by itself is no longer statistically significant. This suggests that investors are not always rational as they adjust rationally following good analyst performance, but not so after bad analyst performance. The column (4) to (7) in Table 5 confirm that analyst performance matters for both upward and downward revisions. Column (4) and (6) confirms standard post-analyst revision drift for upward and downward forecast revision. Column (5) and (7) report the effect of analyst performance. As shown in the table, for both upward and downward revision, stock prices move in the direction of analyst revisions following improved analyst performance. However, stock prices go in the opposite direction of analyst revisions after deteriorated analyst performance only for downward revision, but not upward revision. This suggests that investors have different attitude and behave differently towards positive and negative news. The prediction of main hypothesis above rests on the assumption that investors initially underweight analysts forecast revisions. Thus, if signals about the firms have been made by relatively good analyst to begin with, their improved performance over the short term should not add much information to the initial signals. In contrast, if the revisions are forecast by bad analysts, investors first underweight the signals, and later update its accuracy as they learn and observe the analysts forecasting performance. Table 6 explores the heterogeneity across analysts skills by separating stocks by ExpectedAnalystError. As shown in column (6) in Table 6, the coefficient in front of the interaction term is much larger for the stocks covered by bad analysts than good analysts. This implies that improved performance of the analysts does not add much information for stocks that are covered by relatively good analysts while such positive performance add a lot of information for the investors. Inter- 15

16 estingly, the coefficient in front of the analyst revision is significantly negative for stocks with bad analysts but is slightly positive for stocks with good analysts. This suggests that while investors discredit forecast revisions made by bad analysts as they observe deteriorated forecast performance, investors do not degrade the signals given by good analysts when they perform worse in the short run. Table 7 explores the relation between institutional ownership and analyst performance. If stocks prices continue to drift in the direction of analyst revisions after the analysts covering the stocks get better at the their forecasts, it must be the case that investors are able to observe change in analyst performance and update the accuracy of the signals accordingly. As institutional investors are generally active investors and are more sophisticated, institutional investors are more likely to keep a close watch on analyst performance, and update the precision of the signals given by the analysts. Separating stocks by percent of institutional ownership, Table 7 confirms the result. Column (5) confirms my hypothesis that among stocks with high percentage of institutional ownership, past analyst revisions do not forecast future returns (t-statistics is 1.86). This is because in a rational world, investors have already incorporated the probability that analyst performance would improve or deteriorate into account, and therefore change in analyst performance should not be predictable. Column (6) shows that for stocks with high level of institutional ownership, analyst performance matters. When analyst performance has been improved, investors put more weight on the forecast revisions, and thereby push stock prices into the direction of analyst revisions. On the other hand, when analyst performance is deteriorated, investors react by putting less weight on the revisions provided by the analysts, and stock prices go into the opposite direction. In contrast, as shown in column (4), for stocks with low level of institutional ownership, investors do not react to the change in analyst performance. Interestingly, as shown in column (3), investors still react to the past analyst signal as the coefficient in front of Revision is still positive. Column (3) and (4) together suggests that on average non-institutional investors do not keep a close watch on analyst revisions and change in their forecasting performance, and consequently, we observe delayed reaction of these investors to past revisions and do not observe any difference in investors response to changes in analyst 16

17 performance. 6 Robustness Test Although the results are consistent with the hypothesis that stock prices continue to drift after investors observe improved analyst performance, there is a number of other plausible explanations of the data. Chen, Narayanamoorthy, Sougiannis, and Zhou (2014) find that there is a positive serial correlation in consensus analyst forecast revisions or forecast revision momentum, and suggests that stock prices continue to drift in the direction of analyst revisions due to analysts underreaction to information provided by their peers as analysts continue to revise their earnings consensus estimates sluggishly. Thus in the world where investors react directly to the revised earnings consensus estimates, analysts underreaction to other analysts forecast revisions could generate post-analyst revision stock price drift. In column (3) in Table 8, I confirm their findings by providing a direct link between revision momentum and post-revision drift. To ensure that this lead-lag effect is not driving the predictability of my result, in column (4) in Table 8, I find that controlling for a concurrent change in earnings consensus does not affect the magnitude and significance of analyst performance. Interestingly, when controlling for concurrent change in consensus, investors now underweighs the analysts revisions if their forecasting performance deteriorates. The result suggests that while new concurrent consensus does have price impact on the stock, analysts performance still matters, and generate post-event drift. In addition, it maybe the case that my result is driven by some observations in particular time periods. In particular, Securities and Exchange Commission (SEC) implemented Regulation Fair Disclosure in October 2000, which full came into effect by the end of Regulation Fair Disclosure mandates that all publicly traded companies disclose material information to all investors at the same time. It could be the case that before this regulation, analysts had access to certain material information from managing executives for the firms. In this way, stock price continue to drift after analyst forecast revisions as such information 17

18 becomes publicly available. Splitting the data before and after 2001, column (3) to (6) in Table 9 show that my result does not affect my main result. In both subsamples, stock prices continue to move in the direction of analyst forecast revisions only when analysts that cover the stocks get better at their forecasts. 7 Conclusion This paper suggests that change in analyst performance can induce short-run stock price drift following the forecast revisions, or post-analyst revision drift. The price pattern following the forecast revisions news depends on the change in analyst performance. In particular, stocks price continue in the direction of the analyst revisions when the forecasting performance of the covering analysts improves. This paper provides a test of this hypothesis. Using simple measure for analyst performance at a weekly frequency, the calendar-time rolling strategy method used in the portfolio approach allows for a straightforward test and controls for cross-correlation among stocks that the analysts have revised their forecast estimates, which tend to invalidate inference in the event studies performed in event time. The focus here, however, is on short-term stock price continuation as a result of changes in analyst performance. The methodology also allows for an interpretation of the testing procedure as an executable investment strategy whose risk-return profile can be assessed using simple time-series regressions. The results show that there is stock price continuation after the analyst forecast revisions when performance of the covering analyst improves. Post-event drift is larger when the change in analyst performance is strongly positive. Moreover, the magnitude of analyst performance on stock price continuation is stronger for stocks that are covered by relatively bad analysts and for stocks that have a lot of institutional ownership. These findings are consistent with a world in which investors are required to observe the analyst performance and update the accuracy of their forecast revisions. 18

19 References Asness, Clifford S., Toby J. Moskowitz, and Lasse Heje Pedersen, 2013, Value and momentum everywhere, Journal of Finance 58, Bailey, Warren B., Haitao Li, Connie X. Mao, and Rui Zhong, 2003, Regulation Fair Disclosure and earnings information: market, analyst, and corporate responses, Journal of Finance 58, Ball, Ray, and Philip Brown, 1968, An empirical evaluation of accounting income numbers, Journal of Accounting Research 6, Barberis, Nicholas, Andrei Shleifer, and Robert Vishny, 1998, A model of investor sentiment, Journal of Financial Economics 49, Bernard, Victor, and Jacob Thomas, 1989, Post-earnings announcement drift: Delayed price response or risk premium? Journal of Accounting Research 27, Bernard, Victor, and Jacob Thomas, 1990, Evidence that stock prices do not fully reflect the implications of current earnings for future earnings, Journal of Accounting and Economics 13, Chan, Louis K.C., Narasimhan Jegadeesh, and Josef Lakonishok, 1996, Momentum strategies, Journal of Finance 51, Chui, Andy C.W., Sheridan Titman, and K.C. John Wei, 2010, Individualism and Momentum around the World, Journal of Finance 65, Cooper, Michael J., Roberto C. Gutierrez, and Allaudeen Hameed, 2004, Market states and momentum, Journal of Finance 59, Daniel, Kent D., David Hirshleifer, and Avanidhar Subrahmanyam, 1998, Investor psychology and security market under- and over-reactions, Journal of Finance 53, Daniel, Kent D., David Hirshleifer, and Avanidhar Subrahmanyam, 2001, Overconfidence, arbitrage, and equilibrium asset pricing, Journal of Finance 56, Diether, Karl B., Christopher J. Malloy, and Anna Scherbina, 2002, Difference of opinion and the cross section of stock returns, Journal of Finance 57, Fama, Eugene, 1998, Market efficiency, long term returns and behavioral finance, Journal of Financial Economics 49, Fama, Eugene, and Kenneth French, 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33, Frazzini, Andrea, 2006, The disposition effect and underreaction to news, Journal of Finance 61, Gleason, Cristi A., and Charles M. C. Lee, 2003, Analyst forecast revisions and market price discovery, The Accounting Review 78,

20 Griffin, John M., Susan Ji, and J. Spencer Martin, 2003, Momentum investing and business cycle risk: Evidence from pole to pole, Journal of Finance 58, Hong, Harrison, and Jeffrey D. Kubik, 2003, Analyzing the analysts: Career concerns and biased earnings forecasts, Journal of Finance 58, Hong, Harrison, and Jeremy Stein, 1999, A unified theory of underreaction, momentum trading, and overreaction in asset markets, Journal of Finance 54, Jackson, Andrew R., 2005, Trade generation, reputation, and sell-side analysts, Journal of Finance 55, Jegadeesh, Narasimhan, and Sheridan Titman, 1993, Returns to buying winners and selling losers: Implications for stock market efficiency, Journal of Finance 48, Rouwenhorst, K. Geert, 1998, International momentum strategies, Journal of Finance 53, Stickel, Scott, 1991, Common stock returns surrounding earnings forecast revisions: More puzzling evidence, The Accounting Review 66,

21 Figure 1 This figure describe a timeline of the events for stock A and stock B. 21

22 Figure 2 The figure plots the time series of average forecasting performance across different analysts at a monthly frequency. For each analyst i and month t, analyst performance is defined as the absolute value of the difference between the analyst i s final earnings forecast and actual realized earnings scaled by last year stock price, and then take the average amongst firms that analyst i s covers and also have earnings announcement in month t. The time series below plots, for each month t, the average forecasting performance of all the analysts. The data sample starts start from July 1984 to the end of

23 Table 1: Descriptive Statistics This table shows summary statistics and correlations for the data sample. UpdatedAnalystError is an average last week forecast error amongst the analysts that cover the stock and ExpectedAnalystError is an average forecast error over the past year amongst the analysts that cover the stocks. Forecast error is measured by the square difference between analysts earnings consensus and firm s actual earnings normalized by last fiscal year stock prices. Perf is a dummy variable that equals 1 if UpdatedAnalystError is lower than ExpectedAnalystError, and is zero otherwise. Revision is a growth rate of earnings consensus of a firm over the past 3 months (skip one week). ret4wto1w is the last month raw return, and ret52wto5w is the cumulative raw returns from last year to 1 month ago. Panel A: Summary Statistics N Mean Std. Dev Min Max ShortTerm AnalystError (%) LongTerm AnalystError (%) Perf Revisions (%) ret4wto1w tempwgret12to LnBEME LnSize Panel B: Correlation Revisions ret4wto1w ret52wto5w Revisions ret4wto1w ret52wto5w

24 Table 2: Post-Analyst Revisions Drift, Weekly Alphas 1984 to 2013 At the start of each calendar week, stocks are ranked in ascending order on the basis of the percentage change in next fiscal year earnings consensus over the past quarter or 13 weeks (skip one week). Stocks are assigned into one of five equally weighted quintile portfolios. This table includes all available stocks that have analyst forecast data in I/B/E/S Thompson Reuters and stock return data from CRSP and reports Fama and French (1993) three-factor alphas. The dependent variable is the weekly excess return of the Treasury bill rate from the rolling strategy. The explanatory variables are the weekly returns from Fama and French (1993) mimicking portfolios. L-S is the alpha of a zero-cost portfolio that holds the top 20% upward revisions news stocks and sells short the bottom 20% downward revisions news stocks. Alphas are reported in weekly percentage, and t-statistics are shown in the brackets below the coefficient estimates. Rolling Period is the holding period of the rolling strategy, in weeks. Revisions News Quintiles Rolling Period 1 (Bad) (Good) L-S * * 0.292*** [ 2.21] [ 0.37] [0.50] [0.52] [2.52] [6.76] * * 0.265*** [ 2.30] [ 0.21] [0.46] [0.51] [2.29] [5.99] * * 0.250*** [ 2.03] [ 0.08] [0.61] [0.43] [2.14] [5.66] * 0.234*** [ 1.81] [ 0.01] [0.51] [0.40] [2.08] [5.18] * p < 0.05, ** p < 0.01, *** p < T-statistics in brackets. 24

25 Table 3: Weekly Alphas by Performance Quintiles This table reports Fama and French (1993) three-factor alphas for a long-short revisions news strategy in different analyst performance quintiles. At the beginning of every calendar week, stocks are ranked in ascending order on the basis of the percentage change in next fiscal year earnings consensus over the past quarter or 13 weeks (skip one week) and the change in forecasting performance of the analysts that cover the stocks. For k 1,..., 5, portfolio k is defined as a zero-cost portfolio that holds the top 20% upward revisions news stocks in k performance quintile and sells short the bottom 20% downward revisions news stock in the k performance quintile. The last column reports the difference between the positive performance (k = 5) and negative performance (k = 1) portfolios. Alphas are reported in weekly percentage, and t-statistics are shown in the brackets below the coefficient estimates. Rolling Period is the holding period of the rolling strategy, in weeks. Analyst Performance Quintiles Negative Positive Rolling Period ** 0.171*** 0.195*** 0.357*** 0.322* [0.77] [2.63] [3.45] [3.92] [4.40] [2.14] * 0.152*** 0.153*** 0.289*** 0.259* [0.28] [2.58] [3.34] [3.33] [3.80] [2.19] ** 0.126** 0.126** 0.265** 0.234* [0.37] [2.59] [2.97] [3.04] [3.12] [2.13] * 0.102* 0.109** 0.252** 0.214* [0.90] [2.36] [2.52] [2.77] [3.15] [2.27] * p < 0.05, ** p < 0.01, *** p < T-statistics in brackets. 25

26 Table 4: Fama and French (1993) Times Series Regressions: Alphas and Factor Loadings This table reports Fama and French (1993) three-factor alphas and loadings for the positive performance and negative performance portfolios. The dependent variable is the weekly excess return of the Treasury bill from the rolling strategy. The explanatory variables are the weekly returns from Fama and French (1993) mimicking portfolios. The holding period for the rolling strategy is 1 week. Alphas are reported in weekly percentage, and t-statistics are shown in the brackets below the coefficient estimates. Rolling Period is the holding period of the rolling strategy, in weeks. Positive Performance Negative Performance Good News Bad News L-S Good News Bad News L-S α (%) 0.184* 0.173* 0.357*** [ 2.50] [2.47] [4.41] [ 0.09] [0.17] [0.77] MktRf 1.055*** 1.014*** *** 1.023*** 0.065** [24.43] [23.37] [ 1.95] [23.26] [22.56] [ 2.83] SMB 0.820*** 0.678** 0.143*** 0.835*** 0.682** 0.153*** [5.65] [3.17] [ 3.62] [5.40] [3.08] [ 3.64] HML ** 0.182*** *** 0.227*** [0.39] [ 2.82] [ 4.57] [ 0.04] [ 3.84] [ 5.34] * p < 0.05, ** p < 0.01, *** p < T-statistics in brackets. 26

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