Identifying and treating outliers in finance
研究了金融实证研究中常用的异常值处理技术是否适用于实际数据，提出了多变量识别策略和减少偏差的估计方法，并通过复制四篇顶级期刊研究展示了调整异常值后结果的显著差异。
Abstract Outliers represent a fundamental challenge in the empirical finance research. We investigate whether the routine techniques used in finance research to identify and treat outliers are appropriate for the data structures we observe in practice. Specifically, we propose a multivariate identification strategy that can effectively detect outliers. We also introduce an estimator that minimizes the bias outliers caused in both cross‐sectional and panel regressions and provide outlier mitigation guidance. Using replications of four recently published studies in premier finance journals, we show how adjusting for multivariate outliers can lead to significantly different results.