Selecting Directors Using Machine Learning
Charles A. Dice Center Working Paper No. 2018-05
55 Pages Posted: 21 Mar 2018 Last revised: 6 Oct 2018
Date Written: October 5, 2018
Can an algorithm assist firms in their nominating decisions of corporate directors? We construct algorithms tasked with making out-of-sample predictions of director performance. We run tests of the quality of these predictions and show that directors predicted to do poorly indeed do poorly compared to a realistic pool of candidates. Predictably unpopular directors are more likely to be male, have held more directorships, have fewer qualifications, and larger networks than the directors the algorithm recommends. Machine learning holds promise for understanding the process by which governance structures are chosen, and has potential to help firms improve their governance.
Keywords: Corporate Governance, Boards of Directors, Machine Learning
JEL Classification: C10, C45, G30, M12, M51
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