Publications

 

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)​​ 

Workings Papers

 ​"Selecting Directors Using Machine Learning" 


with Isil Erel, Chenhao Tan and Mike Weisbach


Revise and Resubmit at the Review of Financial Studies



Featured in:

Harvard Business Review

Harvard Law School Forum on Governance and Financial Regulation

Die Zeit


 

  • 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, 2019 FMA Wine Country (NAPA) Finance Conference .

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. ​​


"Learning about Directors"


Under review


 Abstract


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. 

Coming Soon

Feminist Firms

with Benjamin Bennett, Isil Erel and Jesse Wang