Regression-Based Earnings Forecasts
Joseph J. Gerakos
Tuck School of Business at Dartmouth College
Robert B. Gramacy
University of Chicago - Booth School of Business
July 31, 2013
Chicago Booth Research Paper No. 12-26
We provide a comprehensive examination of regression-based earnings forecasts. Specifically, we evaluate forecasts of scaled and unscaled net income along a number of relevant dimensions including variable selection, estimation methods, estimation windows, and Winsorization. Overall, we find that forecasts generated using ordinary least squares and lagged net income are broadly more accurate for both earnings constructs. Moreover, at a one year horizon, the random walk model performs as well as modern sophisticated methods that use larger predictor sets. This finding echoes an old result that, given recent applications of forecasts in the literature, may have been forgotten.
Number of Pages in PDF File: 33
Keywords: Earnings forecasts, implied cost of capital, regularized linear models, treed models, principal components
Date posted: July 19, 2012 ; Last revised: October 26, 2013
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