Outliers in Finance Research

55 Pages Posted: 21 Oct 2014 Last revised: 11 May 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

Vincenzo Verardi

FUNDP - University of Namur. CRED

Date Written: May 18, 2015

Abstract

Articles in the top finance journals largely ignore the potential of outlier-induced bias in empirical research. When finance researchers do address outliers they use techniques that tend to cause additional problems. We illustrate the problems via simulations as well as replications of studies recently published in the top journals. To address the issues, we present new technology that mitigates outlier influence in models and data structures common in finance.

Keywords: Outliers; Winsorizing, Trimming, Outlier-robust regression, Good leverage, Bad leverage,Vertical outlier, MM-estimators, L-estimators, S-estimators, Bias, Influence function, Breakdown point

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

Suggested Citation

Adams, John C. and Hayunga, Darren K. and Verardi, Vincenzo, Outliers in Finance Research (May 18, 2015). Available at SSRN: https://ssrn.com/abstract=2511456 or http://dx.doi.org/10.2139/ssrn.2511456

John C. Adams (Contact Author)

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)

Vincenzo Verardi

FUNDP - University of Namur. CRED ( email )

8 Rempart de la Vierge
Namur, 5000
Belgium

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