Specifically the authors estimate the following

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for differences of opinion is analyst forecast dispersion. Specifically, the authors estimate the following regression equation: EXRET i ; q ¼ a þ b 1 ² Ln MV ð Þ i ; q þ b 2 ² Ln MB ð Þ i ; q þ b 3 ² DIFOPN i ; q þ e i ; q ð 3 Þ where i represents the firm and q identifies the quarterly earnings announcement. EXRET is the 3-day excess return surrounding the earnings announcement, Ln(MV) is the log of market value of equity, Ln(MB) is the log of the market-to-book ratio, and DIFOPN is the level of forecast dispersion (as opposed to our focus on the change in forecast dispersion). The authors find a significantly negative coefficient for b 3 (see their Table 4 ), which they interpret as consistent with the hypothesis in Miller ( 1977 ) that higher dispersion leads to more over-pricing, which is later corrected when earnings are announced. 24 Note, however, that the results for our variable of interest (the change in forecast dispersion) are not consistent with the hypothesis in Miller ( 1977 ) (the market friction hypothesis ). Moreover, we include the level of forecast dispersion in our regression as an additional control variable, and its coefficient is significantly positive, opposite the sign reported by Dimitrov et al. ( 2007 ). 23 The number of observations for these tests is 56,913, which is less than the number of observations for the full sample (62,706). The reduction in sample size occurs partially due to non-availability of 2-year- ahead earnings forecasts needed to calculate cost of capital using the Ohlson and Juettner-Nauroth ( 2005 ) model. Also, because cost of capital estimates are noisy, we truncate observations with changes in cost of capital estimates greater than 10%. To ensure that results are not sensitive to changes in sample composition, we re-estimate our primary tests using the reduced sample. Results remain significant at the 0.01 level in the predicted direction for the reduced sample, and none of our conclusions change. 24 Using short window returns but in a different setting (i.e., overnight returns instead of earnings announcement returns), Berkman et al. ( 2007 ) provide some evidence consistent with the Miller ( 1977 ) hypothesis. The stock price effects of changes in dispersion of investor beliefs 25 123
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We attempt to reconcile these seemingly conflicting results by first estimating the regression equation in Dimitrov et al. ( 2007 ) for our sample. When we estimate equation (3) using our returns metric, we document qualitatively identical results to those reported in Dimitrov et al. That is, we find a significantly negative coefficient on the level of forecast dispersion. However, we note that Eq. 3 does not account for the amounts and timing of cash flows (the numerator effects in Eq. 1). Prior research has shown that forecast dispersion is negatively associated with forecast errors (Kinney et al. 2002 ). Indeed, we find this same relation in our correlation matrix in Table 3 . Furthermore, we find a similar relation between level of dispersion and analysts’ forecast revisions of future earnings. Thus, omitting forecast error and
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