Identifying and Treating Outliers in Finance

Vincenzo Verardi, John Adams, Darren Hayunga, Sattar Mansi, David Reeb

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)345-384
Number of pages40
JournalFinancial Management
Volume48
Issue number2
DOIs
Publication statusPublished - 1 Jun 2019

Keywords

  • outliers
  • univariate vs multivariate identification
  • robust regressions
  • winsorizing
  • trimming
  • bias

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    Verardi, V., Adams, J., Hayunga, D., Mansi, S., & Reeb, D. (2019). Identifying and Treating Outliers in Finance. Financial Management, 48(2), 345-384. https://doi.org/10.1111/fima.12269