Market adjusted return from the date of the most

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Market-adjusted return from the date of the most recent I/B/E/S consensus forecast to 2 days before the earnings announcement The stock price effects of changes in dispersion of investor beliefs 13 123
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advantages of both the portfolio approach and the regression approach. 16 Specifically, while simultaneously controlling for several factors that can influence price at the earnings announcement, the zero-surprise sample enhances internal validity by holding constant across the sample the level of analyst forecast error and forecast revision. As a result, we can better isolate the denominator effects of Eq. 1 related to investor uncertainty. Similar to the portfolio approach, the zero-surprise sample avoids problems inherent in the returns-earnings regression equation, which imposes a linear structure on earnings information that is not completely descriptive. In fact, several studies show that the effect of earnings information on equity prices is not perfectly linear. 17 While we make some attempt in our regression analysis with the full sample to control for nonlinearities by including FELin , STRevLin , and LTRevLin , simultaneously controlling for all possible nonlinearities can lead to unwieldy equations and furthermore, prior research has not employed such a model. 18 The zero-surprise sample represents a meaningful portion of the entire population and provides us with a sufficient sample size for reliable tests. 4 Empirical procedures and results 4.1 Sample selection Table 2 , Panel A, outlines our sampling procedures. We initially identify all firm- quarter observations that have data available to calculate the current forecast error ( FE ) during the 1993–2006 period. The sample period corresponds with the increased occurrence of companies meeting analysts’ forecasts (see Lopez and Rees 2002 ; Dechow et al. 2003 ; Payne and Thomas 2003 ; Brown and Caylor 2005 ). Stock-split adjusted forecast data are traditionally provided by I/B/E/S, which can lead to measurement error when determining whether a firm met its earnings forecasts. The measurement error grows in severity over time as stock splits compound (Payne and Thomas 2003 ). To avoid this measurement error, we use unadjusted data provided by I/B/E/S. This results in 104,592 firm-quarter observations. 16 Schipper ( 2005 ) advocates the use of ‘‘purposely biased samples’’ as a way of reducing the effects of confounding factors and allowing for more reliable empirical results. Supplemental tests employing the specialized subsample in this study fall within this type of research strategy. 17 For example, Freeman and Tse ( 1992 ) show that the marginal price response to unexpected earnings decreases as the absolute magnitude of unexpected earnings increases. Kinney et al. ( 2002 ) find a similar nonlinear relation using analysts’ forecast errors as unexpected earnings. Hayn ( 1995 ) finds that the association between returns and earnings is significantly attenuated for loss firms. Many researchers have
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