The forecast error increases the relative market

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the forecast error increases, the relative market response is expected to decrease and therefore the coefficient on FE * FELin is expected to be negative. Loss firms . Hayn ( 1995 ) documents a lower association between returns and earnings for loss firms relative to profit firms. Thus, we define an indicator variable ( Loss ) that equals one when the firm reports a loss and zero otherwise. We expect the coefficient on FE * Loss to be negative. Market value . Investors’ response to earnings announcements might depend on the level of publicly available information about the firm before the announcement. For example, investors might pay more attention to earnings announcements by smaller firms since other publicly available information is relatively scarce. This argument suggests a negative relation between firm size and the magnitude of the investor response to an earnings surprise. However, an opposing argument is that The stock price effects of changes in dispersion of investor beliefs 9 123
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investors might be more surprised by a forecast error from large firms where analysts have more information that can be used to derive an earnings forecast. We define Market Value as the natural log of price multiplied by shares outstanding at the end of the current quarter. Given the different arguments for including firm size in our model, we do not predict the direction of the coefficient for FE * Market Value . We also include Market Value as a non-interacted control variable. Expected growth . The market response to FE is more sensitive for high-growth firms (Payne and Thomas 2003 ). We define Expected Growth as the most recent forecast of long-term earnings growth rate before the current quarter’s earnings announcement. The coefficient on the interaction between FE and Expected Growth is expected to be positive. We also include Expected Growth as a non-interacted control variable. Dispersion . Prior research shows that the positive returns-earnings relation is dampened by noise in measuring the market’s earnings expectations, as approx- imated by the dispersion in current period’s earnings forecasts (Imhoff and Lobo 1992 ; Kinney et al. 2002 ). 11 This measurement error reduces the predicted relation between announcement returns and forecast error. We measure Dispersion as the standard deviation of the individual analysts’ forecasts used to define FE . We expect the coefficient on the interaction of FE and Dispersion to be negative. We also include Dispersion as a non-interacted control variable. Forecast revisions . We control for the change in the mean of individual analysts’ forecasts of next quarter’s earnings ( ST Revision ) immediately before and after the current quarter’s earnings announcement (scaled by price at the end of the current quarter). The individual analysts’ forecasts used to compute ST Revision are the same as those used to compute D Dispersion . We also control for analysts’ long-term growth revisions. Specifically, we measure the change in the I/B/E/S consensus
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