Available forecast of each individual analyst during

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available forecast of each individual analyst during the 50–90 days before quarter t earnings announcement. For these same analysts, we then calculate dispersion using the latest available forecasts during the 10–49 days before quarter t earnings announcement. This change in dispersion provides a measure of the firm-specific change in dispersion unrelated to quarter t earnings announcement. We include each of these as additional control variables in Eq. (2). For both tests, the inferences regarding our main variable ( D Dispersion ) are unaffected. Furthermore, neither of these control variables is significantly associated with announcement period returns. 5 Summary and conclusions A large body of literature examines the impact of announced earnings on stock prices. This research primarily considers the information contained within earnings about the amounts and timing of future cash flows. Controlling for these effects, we examine the extent to which equity returns surrounding earnings announcements may also be associated with uncertainty or dispersion of investor beliefs. We measure the change in dispersion of investor beliefs using changes in dispersion of individual analysts’ forecasts around the earnings announcement ( D Dispersion ). If announced earnings affect belief dispersion, then a negative relation between announcement period returns and D Dispersion would be expected ( cost of capital hypothesis ). Alternatively, a positive relation would be expected if D Dispersion increases the option value inherent in equity securities ( equity call option hypothesis ) or it reflects investor overpricing ( market friction hypothesis ). To conduct our investigation, we perform two sets of analyses. First, we examine differences in earnings announcement period returns between high and low categories of D Dispersion across 21 forecast error intervals. We find significantly higher returns for low levels of D Dispersion across 19 of the 21 intervals, consistent with the cost of capital hypothesis . Our second set of analyses is to estimate a regression equation that simultaneously controls for several factors that can influence announcement period returns. The primary drawback of the regression approach is the requirement to specify a relation between announcement returns and unexpected earnings. Prior research shows that an improper specification can lead to incorrect inferences (Cheng et al. 1992 ). To mitigate this concern, we also estimate a regression for a specialized sample that seems particularly well-suited for our research objective—a sample with zero earnings surprise. The zero-surprise sample consists of firms whose current forecast error equals zero and whose revisions in future earnings around the current quarter’s earnings announcement are negligible. This specialized sample benefits from the advantages of both the regression methodology of simultaneously controlling for several value-relevant variables and the portfolio
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