Could machine learning help firms select better board directors?

In the not-too-distant future, machine learning techniques will fundamentally change the way corporate boards of directors are chosen, with shareholders being the beneficiaries, a recent study has predicted.

The study, Selecting Directors Using Machine Learning, involved the use of a machine learning algorithm to identify which directors were likely to be unpopular with shareholders, according to an article published by four authors in Harvard Business Review. The study’s authors are Isil Erel, Léa H. Stern, Chenhao Tan and Michel S. Weisbach.

“In a sense, the algorithm is telling us exactly what institutional shareholders have been saying for a long time: that directors who are not old friends of management and come from different backgrounds both do a better job in monitoring management and are often overlooked,” the authors wrote.

The differences between the directors suggested by the algorithm and those actually selected by firms allow the researchers to assess the features that are overrated in the director nomination process. “We found that 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,” the authors said.

For the study, the researchers used a dataset of large publicly traded U.S. corporations between 2000 and 2011. Using a machine learning method called gradient boosting, the authors then evaluated the results using a separate test dataset of directors who joined firms between 2012-14 and whom the algorithm did not observe during this “training period.”

The algorithm was able to identify which directors were likely to be unpopular. In fact, directors who were actually hired – but who the algorithm predicted would be unpopular with shareholders – ended up faring much worse that other available candidates. Hired directors that the algorithm predicted would do well did better than other available candidates.

So why do real-world firms appoint directors that machines can predict will be unpopular with shareholders? The authors put forward at least two possible reasons:

1. CEOs do not want effective directors on their boards. For decades, economists have argued that managers are able to maintain control over their firms by influencing the director selection process to ensure management-friendly boards.
2. It could be that because of behavioural biases, management is not able to select effective directors as well as an algorithm.

Still, machine learning algorithms are not without their flaws, the researchers note. They are prone to bias, too, depending on the data they are fed and the outcomes they are optimized for.

“For the purpose of our study, though, it is clear that algorithms are not prone to agency conflicts and the biases that occur when boards and CEOs meet together to select new directors,” the authors wrote in the article. “Institutional investors are likely to find this attribute particularly appealing and to encourage boards to rely on an algorithm for director selections in the future. How well this approach to selecting directors will be received by management is an open question.”