A Learning-Based Approach to Evaluating Boards of Directors

60 Pages Posted: 7 Jun 2017

See all articles by Léa H. Stern

Léa H. Stern

University of Washington - Michael G. Foster School of Business

Multiple version iconThere are 2 versions of this paper

Date Written: March 14, 2017

Abstract

Using predictions from a learning model, this paper exploits the cross-sectional variation in the learning-induced decline in stock return volatility over director tenure to infer the marginal value of different kinds of directors. This new framework confirms prior empirical findings and documents new results. For example, directors joining better compensated boards have higher marginal value while the marginal value of a director joining an entrenched board is muted. Furthermore, the estimates imply that governance related uncertainty associated with the arrival of a new director accounts for 7% of return volatility, shedding light on the extent to which governance matters.

Suggested Citation

Stern, Lea H., A Learning-Based Approach to Evaluating Boards of Directors (March 14, 2017). Paris December 2017 Finance Meeting EUROFIDAI - AFFI, Available at SSRN: https://ssrn.com/abstract=2981044 or http://dx.doi.org/10.2139/ssrn.2981044

Lea H. Stern (Contact Author)

University of Washington - Michael G. Foster School of Business ( email )

Box 353200
Seattle, WA 98195-3200
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
52
Abstract Views
1,333
rank
114,829
PlumX Metrics