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Table IV Postevent Cumulative Abnormal Returns Size and book-to-market adjusted cumulative abnormal returns (CARs) are calculated for each event in our sample as follows. First, we obtain the market capitalization and book-to-market ratio for each irm prior to the event. Then, each irm is matched with a benchmark portfolio return from the Fama and French 25 size and book-to-market sorted portfolios. Third, a t-month CAR (t = 1, y ,6) is calculated by cumulating the event firm return beginning in the first month after the event through event-month + t minus the cumulative matching portfolio return over the same time period. We present CARs for subsamples of events classified by recommendation upgrades, downgrades, and reiterations.Within each recommendation upgrade and downgrade groups, we present CARs for tercile portfolios sorted based on the magnitude of the analysts target price revision at the time of the event. For recommendation reiterations, we present CARs for decile portfolios sorted based on the magnitude of the analysts target price revision at the time of the event. We winsorize monthly return observations at the 2nd and 98th percen-tiles to mitigate the possible efect ofextreme observations.To avoid a possible cross-correlation problem caused by identical return observations, we delete all but one of identical return observations within each portfolio. Standard errors for the CAR estimates are obtained using the sample standard deviation of the abnormal returns. For example, inferences regarding the six-month CAR are based on the cross-sectional standard deviation of the events-firmssix-month CARs. The resulting t-statistics are presented below the CAR estimates. Within each possible recommendation/target price classiication we also report the average of the target price revisions (scaled by preannouncement stock price). For example, for all events in which recommendations were upgraded, the average target price revision was 10 percent. Postevent Month Average Target Price
we ind large and signiicant postevent drifts when the information content in the target price is used. Indeed, the CAR of recommendation reiterations associated with target price revisions in the highest (lowest) tercile is 6.22 ( - 1.88) percent by event month + 6. Finally, when we examine events associated with recommendation downgrades, we find little evidence of drift. The evidence reported in Table IV that prices drift in the direction of the target price revision for both recommendation upgrades and reiterations suggests that target prices contain information regarding future abnormal returns. Since these indings are subject to some methodological concerns (see Fama (1998) and Barber and Lyon (1997)), we now turn to a calendar-time portfolio regression approach, which has been advocated by Fama (1998) and applied by Jaffe (1974), Mandelker (1974), and Brav and Gompers (1997). This approach is conducted by forming a portfolio that includes all events that are announced within the previous t periods (in this paper we set t equal to six months). In our setting, we form the month t portfolio return by either equal weighting or value weighting irm returns for events that occur within the previous six months. Once a irm has been added to the portfolio, if analysts issue additional reports on the firm, we refrain from adding it again to the portfolio. The equal-weight portfolio returns in excess of the risk-free rate are then benchmarked relative to the maintained asset pricing model, and evidence for abnormal performance is based on the magnitude and signiicance of the regression intercept. It is well known that the portfolio approach eliminates the problem of cross-sectional dependence among the sample events and is not susceptible to misleading rejections owing to compounding of single-period returns (Mitchell and Stafford (2000)).17 We address the choice of a benchmark model by relying on Carharts (1997) four-factor model, which is an extension of the three-factor model of Fama and French (1993).18 Thus, the regression framework is given by rp,t - rf t = a + bi RMRFt + b2 SMBt + b3 HMLt + b4 PR12t + et (2) and we focus our inferences on the magnitude and statistical significance of the intercept, a. Table V presents the regression results for portfolios in which monthly returns are weighted equally. Consider first the regression results in which portfolios are formed alternatively based on the three recommendation revision categories without conditioning on target price revisions (denoted All). In contrast to the 17 Mitchell and Staford (2000) point out that the portfolio approach has several potential problems that arise from the changing composition of the portfolio through time, which can potentially lead to heteroskedasticity. We have veriied that heteroskedasticity alters none of the conclusions drawn below. 18 The irst factor, RMRF, is the excess return on the value-weighted market portfolio. The second factor, SMB, is the return on a zero-investment portfolio formed by subtracting the return on a large irm portfolio from the return on a small irm portfolio. The third factor is the return of another mimicking portfolio, HML, defined as the return on a portfolio of high book-to-market stocks less the return on a portfolio of low book-to-market stocks. The fourth factor, PR12, is formed by taking the return on high return stocks minus the return on low return stocks over the preceding year. TableV Calendar Time Regressions The sample is all target price announcements between January 1997 and December 1999. Portfolios are formed by including all events that were announced within the previous six months. The portfolios equally weighted monthly returns, in excess of the risk-free rate, are regressed on the following four factors: RMRF, the excess return on the value-weighted market portfolio; SMB, the return on a zero investment portfolio formed by subtracting the return on a large firm portfolio from the return on a small firm portfolio; HML, the return on a portfolio of high book-to-market stocks less the return on a portfolio of low book-to-market stocks; and PR12, formed by taking the return on high momentum stocks minus the return on low momentum stocks. We report regression results for portfolios classiied by recommendation upgrades, reiterations, and downgrades. We form tercile (decile) portfolios for recommendation upgrades/downgrades (reiterations) based on the magnitude of the analyst target price revision, which we then regress on the four factors. For example, conditional on a recommendation upgrade, we construct a portfolio that includes irms whose target price revision occurred within the pervious six months and was in the top or bottom tercile at the time it was announced. We winsorize monthly return observations at the 2nd and 98th percentiles to mitigate the possible efect of extreme observations. Adjusted R2 and t-statistics are presented for each regression.
results reported inTable IV, we ind no evidence ofabnormal postevent return for all three recommendation revision categories. Next, within each recommendation category, we present regression results for portfolios in which we condition on the magnitude of the target price revision at the time of the portfolio formation. Consider irst recommendation upgrades. It can be seen that irms with the highest target price revision tend to comove with 1 2 3 4 5 6 [ 7 ] 8 9 10 11 12 |