Chenhao Tan

Department of Computer Science
University of Colorado
Boulder, CO 80309

E-Mail: EmailAddress: hidden: you can email any NBER-related person as first underscore last at nber dot org
Institutional Affiliation: University of Colorado, Boulder

NBER Working Papers and Publications

March 2018Selecting Directors Using Machine Learning
with Isil Erel, Léa H. Stern, Michael S. Weisbach: w24435
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.

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