Selecting Directors Using Machine Learning

The role of boards of directors in corporate governance: A conceptual framework and survey

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

The Review of Financial Studies, 2021, vol. 34, issue 7, 3226-3264

Abstract: Can algorithms assist firms in their decisions on nominating corporate directors? Directors predicted by algorithms to perform poorly indeed do perform poorly compared to a realistic pool of candidates in out-of-sample tests. Predictably bad directors are more likely to be male, accumulate more directorships, and have larger networks than the directors the algorithm would recommend in their place. Companies with weaker governance structures are more likely to nominate them. Our results suggest that 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: C10 C45 G30 M12 M14 M51 (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (21)

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