Effects of winsorization: The cases of forecasting non‐GAAP and GAAP earnings
研究了缩尾处理对回归盈利预测模型的影响，发现其效果取决于数据错误程度、受影响公司特征和缩放方式，对非GAAP盈利收益率模型有改进作用，但对每股收益模型会降低大公司的预测价值，稳健估计是更优方案。
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.