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 lognormal models 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 to predict the actual earnings figure beyond the information contained in analysts' earnings expectations. Copyright The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: email@example.com., Oxford University Press.
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Bibliographic InfoArticle provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.
Volume (Year): 8 (2010)
Issue (Month): 3 (Summer)
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Other versions of this item:
- Aiolfi, Marco & Rodriguez, Marius & Timmermann, Allan G, 2010. "Understanding Analysts' Earnings Expectations: Biases, Nonlinearities and Predictability," CEPR Discussion Papers 7656, C.E.P.R. Discussion Papers.
- 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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