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

Ruby Brownen‐Trinh

Journal of Business Finance & Accounting, 2019, vol. 46, issue 1-2, 105-135

Abstract: 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.

Date: 2019
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https://doi.org/10.1111/jbfa.12365

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