By Sebastian Gell
Earnings forecasts are ubiquitous in today’s monetary markets. they're crucial signs of destiny company functionality and a place to begin for company valuation. super faulty and overoptimistic forecasts in the course of the most up-to-date monetary hindrance have raised critical doubts concerning the reliability of such forecasts. This thesis for this reason investigates new determinants of forecast error and accuracy. additionally, new determinants of forecast revisions are tested. extra particularly, the thesis solutions the subsequent questions: 1) How do analyst incentives result in forecast mistakes? 2) How do alterations in analyst incentives result in forecast revisions?, and three) What elements force variations in forecast accuracy?
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Additional resources for Determinants of Earnings Forecast Error, Earnings Forecast Revision and Earnings Forecast Accuracy
In order to link forecast revision to forecast errors, I use the following relation: REV. ,. Consequently, the revision between Period I aod Period 2 equals the change in analyst earnings forecast errors. This relationship forms the basis of my forecast revision model. 2 Regression model and hypotheses development Given the close link between forecast revision and errors, I use the extensive forecasterror literatore to derive a comprehensive model for explaining forecast revisions. I build my forecast revision model on previous studies that docoment - for the initial forecast error - a positive relationship between error and earnings skewness (GulWu 2003), fmn size (Lin> 2001), previous earnings chaoges (AbarbaoelllBemard 1992), futore prospects (McNichols/O'Brien 1997), news (Louis/Lys/Sun 2009) aod interim earnings surprises (Stickel 1998), aod a negative relationship between the initial error aod the number of aoalysts issuing earnings forecasts (Lin> 2001), earnings variability aod forecast dispersion (Gu/Wu 2003), aod trading volume (Hayes 1998).
Block REV. 1 illustrates the relation between forecast error, revision and their determinants. 1: The relation between forecast error FE. revision REV. 1: Forecast revisions are driven by the same determinants as forecast errors. a) Revisions are driven by news. b) Revisions are also driven by a change in systematic forecast bias. These revisions result either from an initial optimism bias correction or a new bias induction. I consequently expect that all variables that have a significant impact either on the initial or final forecast error will also significantly determine revisions.
I also include variables that bave been identified to positively impact the final forecast error. More specifically, I include the market-to-book ratio as proxy for growth aod profitability (Brown 2001), measures for litigation risk aod reliaoce on in>plicit clsims (Matsumoto 2002) aod new equity issuance (RicbardsonlTheoIWysocki 2004) in my aoaiysis of forecast errors and revisions. MTB, + P12ISSUE, + P15D,RET, + Pi. 3 + EZ,t. Sol AFLLW, is the natural logarithm of the number of analysts who issued forecasts for fiscal year t - 1.