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Regression-Based Earnings Forecasts

33 Pages Posted: 19 Jul 2012 Last revised: 26 Oct 2013

Joseph J. Gerakos

Tuck School of Business at Dartmouth College

Robert B. Gramacy

University of Chicago - Booth School of Business

Date Written: July 31, 2013

Abstract

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.

Keywords: Earnings forecasts, implied cost of capital, regularized linear models, treed models, principal components

Suggested Citation

Gerakos, Joseph J. and Gramacy, Robert B., Regression-Based Earnings Forecasts (July 31, 2013). Chicago Booth Research Paper No. 12-26. Available at SSRN: https://ssrn.com/abstract=2112137 or http://dx.doi.org/10.2139/ssrn.2112137

Joseph J. Gerakos (Contact Author)

Tuck School of Business at Dartmouth College ( email )

Hanover, NH 03755
United States

Robert B. Gramacy

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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