The Harvard Law School Forum on Corporate Governance https://corpgov.law.harvard.edu The leading online blog in the fields of corporate governance and financial regulation. Mon, 27 Apr 2026 13:20:01 +0000 en-US hourly 1 https://wordpress.org/?v=6.5.8 https://corpgov.law.harvard.edu/wp-content/uploads/2024/02/cropped-photography-4-e1706898544564-1-32x32.png Selecting Directors Using Machine Learning – The Harvard Law School Forum on Corporate Governance https://corpgov.law.harvard.edu 32 32 Selecting Directors Using Machine Learning https://corpgov.law.harvard.edu/2018/04/09/selecting-directors-using-machine-learning/?utm_source=rss&utm_medium=rss&utm_campaign=selecting-directors-using-machine-learning https://corpgov.law.harvard.edu/2018/04/09/selecting-directors-using-machine-learning/#comments Mon, 09 Apr 2018 13:08:32 +0000 https://corpgov.law.harvard.edu/?p=106089?d=20180409090832EDT Posted by Michael S. Weisbach (The Ohio State University), on Monday, April 9, 2018
Editor's Note:

Michael S. Weisbach is Ralph W. Kurtz Chair in Finance at The Ohio State University Fisher College of Business, and Research Associate at the National Bureau of Economic Research. This post is based on a recent paper by Professor Weisbash; Isil Erel, Distinguished Professor of Finance at The Ohio State University Fisher College of Business; Léa Stern, Assistant Professor of Finance and Business Economics at the University of Washington Foster School of Business; and Chenhao Tan, Assistant Professor at the University of Colorado Boulder.

In this paper, we present a machine-learning approach to selecting the directors of publicly traded companies. In developing the machine learning algorithms, we contribute to our understanding of governance, specifically boards of directors, in at least three ways. First, we evaluate whether it is possible to construct an algorithm that accurately forecasts whether a particular individual will be successful as a director in a particular firm. Second, we compare alternative approaches to forecasting director performance; in particular, how traditional econometric approaches compare to newer machine learning techniques. Third, we use the selections from the algorithms as benchmarks to understand the process through which directors are actually chosen and the types of individuals who are more likely to be chosen as directors counter to the interests of shareholders.

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