Selecting Directors Using Machine Learning

Fisher College of Business Working Paper No. 2018-03-005

Charles A. Dice Center Working Paper No. 2018-05

55 Pages Posted: 21 Mar 2018 Last revised: 6 Oct 2018

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)

Multiple version iconThere are 3 versions of this paper

Date Written: October 5, 2018

Abstract

Can an algorithm assist firms in their nominating decisions of corporate directors? We construct algorithms tasked with making out-of-sample predictions of director performance. We run tests of the quality of these predictions and show that directors predicted to do poorly indeed do poorly compared to a realistic pool of candidates. Predictably unpopular directors are more likely to be male, have held more directorships, have fewer qualifications, and larger networks than the directors the algorithm recommends. Machine learning holds promise for understanding the process by which governance structures are chosen, and has potential to help 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 (October 5, 2018). Fisher College of Business Working Paper No. 2018-03-005. 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

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