Selecting Directors Using Machine Learning

Can an algorithm assist firms in their hiring decisions of corporate directors? This paper proposes a method of selecting boards of directors that relies on machine learning. We develop algorithms with the goal of selecting directors that would be preferred by the shareholders of a particular firm. Using shareholder support for individual directors in subsequent elections and firm profitability as performance measures, we construct algorithms to make out-of-sample predictions of these measures of director performance. We then evaluate of the quality of these predictions in two ways. First, we examine whether the model can accurately forecast the quality of directors who are actually chosen by firms. Second, we consider whether the algorithm could suggest plausible alternative choices of directors for the firm who would have done better.

Compared with a realistic pool of potential candidates, directors predicted to do poorly by our algorithms rank much lower in performance than directors who were predicted to do well. In addition, individuals who were predicted by the model to perform well, and did accept directorships at nearby firms, also performed better at those firms than the directors who were chosen by the firm in question. Deviations from the choices of the algorithms suggest that firm-selected directors are more likely to be male, have previously held more directorships, have fewer qualifications and larger networks. These patterns are consistent with the notion that firms’ choices of directors are too often made using the “old boys network” that leads existing directors to choose new board members similar to themselves. Overall, our results suggest that machine learning holds promise for understanding the process by which existing governance structures are chosen, and has potential to help real world firms improve their governance.