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Determinants of earnings forecast error, earnings forecast revision and earnings forecast accuracy
(2012)
Earnings forecasts are ubiquitous in today’s financial markets. They are essential indicators of future firm performance and a starting point for firm valuation. Extremely inaccurate and overoptimistic forecasts during the most recent financial crisis have raised serious doubts regarding the reliability of such forecasts. This thesis therefore investigates new determinants of forecast errors and accuracy. In addition, new determinants of forecast revisions are examined. More specifically, the thesis answers the following questions: 1) How do analyst incentives lead to forecast errors? 2) How do changes in analyst incentives lead to forecast revisions?, and 3) What factors drive differences in forecast accuracy?
This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time.
The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.