Selecting Directors Using Machine Learning

Isil Erel, Léa H. Stern, Chenhao Tan, Michael S. Weisbach

NBER Working Paper No. 24435
Issued in March 2018, Revised in June 2019
NBER Program(s):Corporate Finance, Law and Economics, Labor Studies

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.

Acknowledgments

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Document Object Identifier (DOI): 10.3386/w24435