Selecting Directors Using Machine Learning

87 Pages Posted: 21 Mar 2018 Last revised: 15 Sep 2020

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: September 1, 2020

Abstract

Can algorithms assist firms in their decisions on nominating corporate directors? Directors predicted to do poorly by algorithms indeed do poorly compared to a realistic pool of candidates in out-of-sample tests. Predictably bad directors are more likely to be male, accumulate more directorships, and have larger networks than the directors the algorithm would recommend in their place. Companies with weaker governance structures are more likely to nominate them. 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: C10, C45, G30, M12, M51

Suggested Citation

Erel, Isil and Stern, Lea Henny and Tan, Chenhao and Weisbach, Michael S., Selecting Directors Using Machine Learning (September 1, 2020). Fisher College of Business Working Paper No. 2018-03-005, Charles A. Dice Center Working Paper No. 2018-05, 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

National Bureau of Economic Research (NBER) ( email )

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

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

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