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Identifying Outliers in Finance

52 Pages Posted: 16 Jun 2017  

John C. Adams

University of Texas at Arlington

Darren K. Hayunga

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

Sattar Mansi

Virginia Tech

David M. Reeb

National University of Singapore

Date Written: May 31, 2017

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. We then propose a multivariate outlier identification strategy and show this method effectively identifies outliers, tests for their influence, and minimizes the bias they cause in both cross-sectional and panel regressions. We empirically test this method using replications of four recently published studies in premier finance journals to show how adjusting for multivariate outliers can lead to significantly different results.

Keywords: Research Design, Financial Data, Winzorize, Outliers

JEL Classification: G00, G10, G20, G30

Suggested Citation

Adams, John C. and Hayunga, Darren K. and Mansi, Sattar and Reeb, David M., Identifying Outliers in Finance (May 31, 2017). Available at SSRN: https://ssrn.com/abstract=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)

David M. Reeb

National University of Singapore ( email )

Mochtar Riady Building
15 Kent Ridge Drive
Singapore, 119245
Singapore

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

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