Regression-Based Earnings Forecasts

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.