Ever since Adam Smith published The Wealth of Nations in 1776, observers have bemoaned boards of directors as being ineffective as both monitors and advisors of management. Because a CEO often effectively controls the director selection process, he will tend to choose directors who are unlikely to oppose him, and who are unlikely to provide the diverse perspectives necessary to maximize firm value. Institutional investors often are critical of CEOs’ influence over boards and have made efforts to help companies improve their governance. Nonetheless, boards remain highly imperfect.
Research: Could Machine Learning Help Companies Select Better Board Directors?
Using an algorithm could broaden the pool of candidates.
April 09, 2018
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Summary.
Ever since Adam Smith published The Wealth of Nations in 1776, observers have bemoaned boards of directors as being ineffective as both monitors and advisors of management. Because a CEO often effectively controls the director selection process, they will tend to choose directors who are unlikely to oppose them, and who are unlikely to provide the diverse perspectives necessary to maximize firm value. Could machine learning help? Researchers trained a machine learning algorithm to predict directors’ performance, using a dataset of large publicly traded U.S. corporations between 2000 and 2011. The algorithm was able to identify which directors were likely to be unpopular with shareholders. They also found that, compared to the machine learning algorithm, firms tend to choose directors who are much more likely to be male, have a large network, have a lot of board experience, currently serve on more boards, and have a finance background.