Completed working papers

  • ​"Selecting Directors Using Machine Learning"
    • Featured in:
    • ​​Conference presentations: Showcasing Women in Finance (University of Miami), 2018 Drexel Corporate Governance Conference, 2018 ICWSM BOD (AI workshop), 2018 NBER Economics of AI conference, 15th Annual Conference on Corporate Finance at Washington University in St Louis, 2019 AFA Annual Meeting, NBER Big Data Workshop (scheduled), Conference on Emerging Technologies in Accounting and Financial Economics at USC (scheduled), 2019 FMA Wine Country (NAPA) Finance Conference (scheduled).
    • ​Abstract: 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. ​

  • "A Learning-Based Approach to Evaluating Boards of Directors"​

    Directors matter, contrary to the “rubber-stamp” view of board governance. The gender of a director also matters, as does the individual’s specific function on the board. This paper uses a Bayesian learning model in the spirit of Pan, Wang and Weisbach (PWW, 2015) to study outstanding questions on boards. This framework reveals that a director has a statistically significant impact on governance-related uncertainty that is about one-third the impact that PWW find for new CEOs. The results help delineate what matters in governance by outlining the channels through which directors make a difference. For instance, while women on boards are not as influential on average, they are especially important when the firm faces acute monitoring needs, consistent with findings in Adams and Ferreira (2009).


Work in progress

  • "Going Along With Machine Predictions: Does it Pay?"
  • ​"Bias, Error or Agency Conflict? Machine vs. Human Black Box in Director Nominations"


  1. "Pay for Performance from Future Fund Flows: The Case of Private Equity"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
  2. "Is Corporate Governance Risk Valued? Evidence from Directors’ and Officers’ Insurance"Journal of Corporate Finance, 18 (2), 2012, (Martin Boyer)
    • ​​Best Paper Bank of Canada Award, NFA 2010
  3. "D&O Insurance and IPO Performance: What Can We Learn from Insurers?", Journal of Financial Intermediation, 23 (4), 504-540, 2014, (Martin Boyer)​​