Selecting Directors Using Machine Learning

Isil Erel, Lea Henny Stern, Chenhao Tan and Michael Weisbach
Additional contact information
Isil Erel: Ohio State University
Lea Henny Stern: University of Washington
Chenhao Tan: University of Colorado

Working Paper Series from Ohio State University, Charles A. Dice Center for Research in Financial Economics

Abstract: Can an algorithm assist firms in their hiring decisions of corporate directors? This paper proposes a method of selecting boards of directors that relies on machine learning. We develop algorithms with the goal of selecting directors that would be preferred by the shareholders of a particular firm. Using shareholder support for individual directors in subsequent elections and firm profitability as performance measures, we construct algorithms to make out-of-sample predictions of these measures of director performance. We then run tests of the quality of these predictions and show that, when compared with a realistic pool of potential candidates, directors predicted to do poorly by our algorithms indeed rank much lower in performance than directors who were predicted to do well. Deviations from the benchmark provided by the algorithms suggest that firm-selected directors are more likely to be male, have previously held more directorships, have fewer qualifications and larger networks. Machine learning holds promise for understanding the process by which existing 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 and nep-cmp
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Citations: View citations in EconPapers (11)

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Related works:
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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Persistent link: https://EconPapers.repec.org/RePEc:ecl:ohidic:2018-05

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