Abstract

http://ssrn.com/abstract=2112137
 
 

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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

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.

Number of Pages in PDF File: 33

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


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Date posted: July 19, 2012 ; Last revised: October 26, 2013

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: http://ssrn.com/abstract=2112137 or http://dx.doi.org/10.2139/ssrn.2112137

Contact Information

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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