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

Abstract

This paper 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 which comes principally from Risk metrics 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 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. They also allow us to accurately assess the variance of individual case outcomes revealing substantial amounts of heterogeneity in variance across cases.

Keywords: class, action, securities, fraud, lawsuit, litigation, bayesian, hierarchical

JEL Classification: C11, K22, K41