Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuits

Authors

Blakeley M. McShane, Northwestern University, Kellogg School of ManagementFollow
Oliver P. Watson, Juridigm, Inc.
Sean J. Griffith, Fordham University School of LawFollow
Tom Baker, University of Pennsylvania Law SchoolFollow

Document Type

Article

Publication Title

Journal of Empirical Legal Studies

Volume

9

Publication Date

2012

Keywords

settlements, class action, litigation

Abstract

This article develops models that predict the incidence and amount of settlements for federal class action securities fraud litigation in the post-PLSRA period. We build hierarchical Bayesian models using data that come principally from Riskmetrics and identify several important predictors of settlement incidence (e.g., the number of different types of securities associated with a case, the company return during the class period) and settlement amount (e.g., market capitalization, measures of newsworthiness). Our models also allow us to estimate how the circuit court a case is filed in as well as the industry of the plaintiff firm associate with settlement outcomes. Finally, they allow us to accurately assess the variance of individual case outcomes revealing substantial amounts of heterogeneity in variance across cases.

Recommended Citation

Blakeley M. McShane, Oliver P. Watson, Sean J. Griffith, and Tom Baker, Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuits, 9 J. Empirical L.Stud. 482 (2012)
Available at: https://ir.lawnet.fordham.edu/faculty_scholarship/984