Selecting Directors Using Machine Learning

61 Pages Posted: 21 Mar 2018 Last revised: 24 May 2019

See all articles by Isil Erel

Isil Erel

Ohio State University (OSU) - Department of Finance

Lea Henny Stern

University of Washington - Michael G. Foster School of Business

Chenhao Tan

University of Colorado at Boulder

Michael S. Weisbach

Ohio State University (OSU) - Department of Finance; National Bureau of Economic Research (NBER); European Corporate Governance Institute (ECGI)

Multiple version iconThere are 3 versions of this paper

Date Written: May 12, 2019

Abstract

Can algorithms assist firms in their decisions on nominating corporate directors? We construct algorithms to make out-of-sample predictions of director performance. Tests of the quality of these predictions show that directors predicted to do poorly indeed do poorly compared to a realistic pool of candidates. Predictably poor performing directors are more likely to be male, have more past and current directorships, fewer qualifications, and larger networks than the directors the algorithm would recommend in their place. Machine learning holds promise for understanding the process by which governance structures are chosen, and has potential to help real-world firms improve their governance.

Keywords: Corporate Governance, Boards of Directors, Machine Learning

JEL Classification: C45, G30, M12, M51

Suggested Citation

Erel, Isil and Stern, Lea Henny and Tan, Chenhao and Weisbach, Michael S., Selecting Directors Using Machine Learning (May 12, 2019). Fisher College of Business Working Paper No. 2018-03-005; European Corporate Governance Institute (ECGI) - Finance Working Paper No. 605/2019. Available at SSRN: https://ssrn.com/abstract=3144080 or http://dx.doi.org/10.2139/ssrn.3144080

Isil Erel

Ohio State University (OSU) - Department of Finance ( email )

2100 Neil Avenue
Columbus, OH 43210-1144
United States

Lea Henny Stern

University of Washington - Michael G. Foster School of Business ( email )

Box 353200
Seattle, WA 98195-3200
United States

Chenhao Tan

University of Colorado at Boulder ( email )

1070 Edinboro Drive
Boulder, CO 80309
United States

Michael S. Weisbach (Contact Author)

Ohio State University (OSU) - Department of Finance ( email )

2100 Neil Avenue
Columbus, OH 43210-1144
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

European Corporate Governance Institute (ECGI) ( email )

c/o ECARES ULB CP 114
B-1050 Brussels
Belgium

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