Identifying and Treating Outliers in Finance

Financial Management (2019), 48(2), 345-384.

64 Pages Posted: 16 Jun 2017 Last revised: 21 Sep 2021

See all articles by John C. Adams

John C. Adams

University of Texas at Arlington

Darren K. Hayunga

University of Georgia - Department of Insurance, Legal Studies, Real Estate

Sattar Mansi

Virginia Polytechnic Institute & State University

David M. Reeb

National University of Singapore

Vincenzo Verardi

FUNDP - University of Namur. CRED

Date Written: December 14, 2018

Abstract

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.

NOTE: Updated (04/22/2021) outlier robust estimator packages available via STATA's SSC.

Note that moremata should be updated (to the latest version on SSC) for these packages to work. These packages are:

1) robstat that estimates various classic and robust measures of location, scale, skewness, and kurtosis, and, optionally, performs robust tests for normality.

Jann, B., V. Verardi, C. Vermandele (2018). robstat: Stata module to estimate robust univariate statistics. Available from http://ideas.repec.org/c/boc/bocode/s458524.html.

2) robmv that computes several estimators of multivariate location and scatter (MCD, MVE, M, S, MM, and S-D). Post-estimation command predict can be used after robmv to generate variables identifying multivariate outliers, containing robust distances, etc.

Jann, B., V. Verardi, C. Vermandele (2021). robmv: Stata module for robust multivariate estimation of location and covariance. Available from http://ideas.repec.org/c/boc/bocode/s458895.html

3) robreg that provides a number of robust estimators for linear regression models (LS, QUANTILE, MVE, LTS, M,S and MM) and has been modified with respect to the previous version. It now allows time-series operators, factor variables, clustered standard errors etc.

Jann, B. (2021). robreg: Stata module providing robust regression estimators. Available from http://ideas.repec.org/c/boc/bocode/s458931.html.

4) xtrobreg that provides robust pairwise-differences estimators for panel data. The “convert” subcommand allows to transform the data permanently and when applying robreg manually one gets (using weights) the equivalent of xtrobreg. The advantage of the conversion is that pairwise differences could then be used with any estimator available in Stata.

Jann, B., V. Verardi (2021). xtrobreg: Stata module providing pairwise-differences and first-differences robust regression estimators. Available from http://ideas.repec.org/c/boc/bocode/s458937.html.

As far as point 4 is concerned, xtrobreg supersedes the xtrobust command developed for this paper. It is conceptually similar but is not identical in particular for unbalanced data, for the way in which it deals with missing values and gaps and the way in which dummy variables are treated.

Keywords: Replication, Research Design, Financial Data, Winzorize, Outliers, Robust Regression

JEL Classification: C31, C52, C87, G31, G32, G34, G38

Suggested Citation

Adams, John C. and Hayunga, Darren K. and Mansi, Sattar and Reeb, David M. and Verardi, Vincenzo, Identifying and Treating Outliers in Finance (December 14, 2018). Financial Management (2019), 48(2), 345-384., Available at SSRN: https://ssrn.com/abstract=2986928 or http://dx.doi.org/10.2139/ssrn.2986928

John C. Adams

University of Texas at Arlington ( email )

Box 19449 UTA
Arlington, TX 76019
United States
904-476-2946 (Phone)

Darren K. Hayunga

University of Georgia - Department of Insurance, Legal Studies, Real Estate ( email )

Athens, GA 30602-6254
United States
706-542-1365 (Phone)

Sattar Mansi (Contact Author)

Virginia Polytechnic Institute & State University ( email )

David M. Reeb

National University of Singapore ( email )

Mochtar Riady Building
15 Kent Ridge Drive
Singapore, 119245
Singapore

HOME PAGE: http://www.davidreeb.net

Vincenzo Verardi

FUNDP - University of Namur. CRED ( email )

8 Rempart de la Vierge
Namur, 5000
Belgium

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