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Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuits
Author(s) -
McShane Blakeley B.,
Watson Oliver P.,
Baker Tom,
Griffith Sean J.
Publication year - 2012
Publication title -
journal of empirical legal studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.529
H-Index - 24
eISSN - 1740-1461
pISSN - 1740-1453
DOI - 10.1111/j.1740-1461.2012.01260.x
Subject(s) - class action , settlement (finance) , securities fraud , plaintiff , capitalization , variance (accounting) , bayesian probability , human settlement , actuarial science , econometrics , business , economics , accounting , financial economics , finance , political science , computer science , artificial intelligence , law , geography , supreme court , state (computer science) , linguistics , philosophy , algorithm , archaeology , payment
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.