Selecting Directors Using Machine Learning
Charles A. Dice Center Working Paper No. 2018-05
59 Pages Posted: 21 Mar 2018 Last revised: 29 Aug 2018
Date Written: August 28, 2018
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. Relative to the benchmark provided by the algorithms, 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.
Keywords: Corporate Governance, Boards of Directors, Machine Learning
JEL Classification: G34, M12, M51
Suggested Citation: Suggested Citation