Identifying and Treating Outliers in Finance
Financial Management, Forthcoming
64 Pages Posted: 16 Jun 2017 Last revised: 26 May 2020
Date Written: December 14, 2018
Outliers represent a fundamental challenge in 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 cause 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.
Keywords: Replication, Research Design, Financial Data, Winzorize, Outliers, Robust Regression
JEL Classification: C31, C52, C87, G31, G32, G34, G38
Suggested Citation: Suggested Citation