Selecting Directors Using 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.
-
-
Copy CitationIsil Erel, Léa H. Stern, Chenhao Tan, and Michael S. Weisbach, "Selecting Directors Using Machine Learning," NBER Working Paper 24435 (2018), https://doi.org/10.3386/w24435.Download Citation
-
Published Versions
Selecting Directors Using Machine Learning, Isil Erel, Léa H Stern, Chenhao Tan, Michael S Weisbach. in Big Data: Long-Term Implications for Financial Markets and Firms, Goldstein, Spatt, and Ye. 2021
Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach & Itay Goldstein, 2021. "Selecting Directors Using Machine Learning," The Review of Financial Studies, vol 34(7), pages 3226-3264. citation courtesy of 
Related
Mentioned in the News
More from the NBER
In addition to working papers, the NBER disseminates affiliates’ latest findings through a range of free periodicals—the NBER Reporter, the NBER Digest, the Bulletin on Health, the Bulletin on the Economics of Alzheimer's, and the Bulletin on Entrepreneurship—as well as research spotlights, lectures, and panel videos.
