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A New Approach to Estimate Earnings Forecasting Models: MM-Estimation

This study focuses on the impact of model estimation methods on earnings forecast accuracy. Compared with ordinary least squares (OLS) regression combined with winsorization, MM-estimation improves the earnings forecast accuracy of all the models examined, especially for the models with more variables and estimated with more observations. My findings indicate that the impact of influential observations on OLS regression increases with the number of variables in the models and sample size, warnings researchers who use OLS regression in forecasting. My findings explain the puzzling negative relationship between earnings forecast accuracy and the number of model variables found in prior research. Moreover, I demonstrate the valuation implications of the earnings forecasted using MM-estimation. This study contributes to earnings forecasting, valuation, and influential observation treatment in forecasting.