Selecting Directors Using Machine Learning

Selecting Directors Using Machine Learning

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

Series number :

Serial Number: 

Date posted :

May 27 2019

Last revised :

May 27 2019
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  • Corporate governance • 
  • boards of directors • 
  • Machine Learning

Can algorithms assist firms in their decisions on nominating corporate directors? We construct algorithms to make out-of-sample predictions of director performance. Tests of the quality of these predictions show that directors predicted to do poorly indeed do poorly compared to a realistic pool of candidates.

Predictably poor performing directors are more likely to be male, have more past and current directorships, fewer qualifications, and larger networks than the directors the algorithm would recommend in their place. 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.


Real name:
Léa H. Stern
Real name:
Chenhao Tan