Selecting Directors Using Machine Learning

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

No 24435, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: G34 M12 M51 (search for similar items in EconPapers)
Date: 2018-03
New Economics Papers: this item is included in nep-bec, nep-big, nep-cmp and nep-lab
Note: CF LE LS
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Citations: View citations in EconPapers (11)

Published as Selecting Directors Using Machine Learning , Isil Erel, Léa H Stern, Chenhao Tan, Michael S Weisbach. in Big Data: Long-Term Implications for Financial Markets and Firms , Goldstein, Spatt, and Ye. 2021
Published as Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach & Itay Goldstein, 2021. "Selecting Directors Using Machine Learning," The Review of Financial Studies, vol 34(7), pages 3226-3264.

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Chapter: Selecting Directors Using Machine Learning (2021)
Journal Article: Selecting Directors Using Machine Learning (2021)
Working Paper: Selecting Directors Using Machine Learning (2018)
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