Selecting Directors Using Machine Learning

55 Pages Posted: 26 Mar 2018 Last revised: 19 Nov 2021

See all articles by Isil Erel

Isil Erel

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

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: March 2018

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.

Suggested Citation

Erel, Isil and Stern, Lea Henny and Tan, Chenhao and Weisbach, Michael S., Selecting Directors Using Machine Learning (March 2018). Available at SSRN: https://ssrn.com/abstract=3149260

Isil Erel (Contact Author)

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

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National Bureau of Economic Research (NBER) ( email )

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European Corporate Governance Institute (ECGI) ( email )

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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

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 the Royal Academies of Belgium
Rue Ducale 1 Hertogsstraat
1000 Brussels
Belgium

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