Review of Financial Studies, 25, 3259-3304, 2012, (Ji-Woong Chung, Berk A. Sensoy, and Michael S. Weisbach)
Wharton-WRDS Best Paper Award WFA 2011
Outstanding Paper Award - 6th International Conference on Asia-Pacific Financial Markets
Featured in Finance and Accounting Memos, Issue 2, 2014
Journal of Corporate Finance, 18 (2), 2012, (Martin Boyer)
Best Paper Bank of Canada Award, NFA 2010
Journal of Financial Intermediation, 23 (4), 504-540, 2014, (Martin Boyer)
with Isil Erel, Chenhao Tan and Mike Weisbach
Revise and Resubmit at the Review of Financial Studies
Can an algorithm assist firms in their nominating decisions of corporate directors? We construct algorithms tasked with making out-of-sample predictions of director performance. We run tests of the quality of these predictions and show that directors predicted to do poorly indeed do poorly compared to a realistic pool of candidates. Predictably unpopular directors are more likely to be male, have held more directorships, have fewer qualifications, and larger networks than the directors the algorithm recommends. Machine learning holds promise for understanding the process by which governance structures are chosen, and has potential to help firms improve their governance.
This study investigates the importance of corporate boards by exploiting the predictions from a learning model in which capital markets process information and learn about the quality of incoming directors. The estimates suggest that upon the arrival of a new director, uncertainty about governance quality accounts for 9.5% of return volatility. The learning framework is useful to evaluate the importance of governance attributes and the kinds of directors that are more influential in which circumstances. For instance, women on boards are especially important at times when monitoring is necessary.
with Benjamin Bennett, Isil Erel and Jesse Wang
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