Selecting Directors Using Machine Learning

60 Pages Posted: 17 Aug 2018

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: August 17, 2018

Abstract

Can an algorithm assist firms in their hiring decisions of corporate directors? This paper proposes a method of selecting boards of directors that relies on machine learning. We develop algorithms with the goal of selecting directors that would be preferred by the shareholders of a particular firm. Using shareholder support for individual directors in subsequent elections and firm profitability as performance measures, we construct algorithms to make out-of-sample predictions of these measures of director performance. We then run tests of the quality of these predictions and show that, when compared with a realistic pool of potential candidates, directors predicted to do poorly by our algorithms indeed rank much lower in performance than directors who were predicted to do well. Deviations from the benchmark provided by the algorithms suggest that firm-selected directors are more likely to be male, have previously held more directorships, have fewer qualifications and larger networks. Machine learning holds promise for understanding the process by which existing 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: G34, M12, M51

Suggested Citation

Erel, Isil and Stern, Lea Henny and Tan, Chenhao and Weisbach, Michael S., Selecting Directors Using Machine Learning (August 17, 2018). Available at SSRN: https://ssrn.com/abstract=3233309 or http://dx.doi.org/10.2139/ssrn.3233309

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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