Understanding Analysts' Earnings Expectations: Biases, Nonlinearities and Predictability
AbstractThis paper studies the asymmetric behavior of negative and positive values of analysts' earnings revisions and links it to the conservatism principle of accounting. Using a new three-state mixture of log-normals model that accounts for differences in the magnitude and persistence of positive, negative and zero revisions, we find evidence that revisions to analysts' earnings expectations can be predicted using publicly available information such as lagged interest rates and past revisions. We also find that our forecasts of revisions to analysts' earnings estimates help predict the actual earnings figure beyond the information contained in analysts' earnings expectations.
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Bibliographic InfoPaper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 7656.
Date of creation: Jan 2010
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Other versions of this item:
- Marco Aiolfi & Marius Rodriguez & Allan Timmermann, 2010. "Understanding Analysts' Earnings Expectations: Biases, Nonlinearities, and Predictability," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 8(3), pages 305-334, Summer.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
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