Effects of winsorization: The cases of forecasting non-GAAP and GAAP earnings

This study examines how the winsorization procedure affects the performance of regression-based earnings forecasting models. I find that the impact is multifaceted and depends principally on three factors: the level of data errors in the tails, the characteristics of firms affected by the process, and the use of scaling. For a non-GAAP earnings yield specification, where data input errors exist, winsorization changes the information set in a non-systematic way and helps to improve the performance of regression-based forecasts, especially when the least squares estimator is employed. However, for a non-GAAP earnings per share specification, with fewer data input errors found in the tails of the distribution, winsorization has a particularly strong effect on very large companies, lowering the economic value of earnings predictions. I observe similar results for corresponding GAAP earnings specifications. Robust estimators, such as least absolute deviation, high breakdown-point and Theil-Sen, appear to be a more effective solution than winsorization. Their earnings forecasts consistently yield significant positive abnormal returns across non-GAAP and GAAP earnings specifications.