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 :

January 07 2021
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  • Corporate governance • 
  • boards of directors • 
  • Machine Learning

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.


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