Selecting Directors Using Machine Learning
Fisher College of Business Working Paper No. 2018-03-005
Charles A. Dice Center Working Paper No. 2018-05
European Corporate Governance Institute (ECGI) - Finance Working Paper No. 605/2019
70 Pages Posted: 21 Mar 2018 Last revised: 6 Jan 2021
There are 3 versions of this paper
Selecting Directors Using Machine Learning
Selecting Directors Using Machine Learning
Date Written: December 13, 2020
Abstract
Can algorithms assist firms in their decisions on nominating corporate directors? Directors predicted to do poorly by algorithms indeed do 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.
Keywords: Corporate Governance, Boards of Directors, Machine Learning
JEL Classification: C10, C45, G30, M12, M51
Suggested Citation: Suggested Citation