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A great deal of the analysis of securities class action lawsuit settlements revolves around measures of aggregate, average and median settlement amounts. These data, while useful, are relatively unhelpful in trying to anticipate the outcome of any particular case, particularly at the outset. To try to develop a way to predict likely case outcome at the outset of a securities class action lawsuit, four academics conducted a detailed statistical analysis of securities class action settlements in order to identify factors that affect outcomes.
In their April 30, 2012 paper entitled "Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuits" (here), Northwestern University Business Professor Blakeley McShane, Juridigm Principal and Vice President Oliver Watson, U. Penn Law Professor Tom Baker and Fordham University Law Professor Sean Griffith set out to create a "predictive model to forecast case outcomes based exclusively on information available at the time the lawsuit is filed."
Their model, described in their paper, "estimates (i) the probability of the settlement versus dismissal of a securities class action lawsuit and (ii) the amount for which the class action will settle conditional on the settlement."
A great deal of the authors' paper is devoted to a description of the methodology used to derive the data on which their analysis is based. Another significant part of the paper is devoted to a description of their analytic methodology, which, as their title suggests, employs high level statistical approaches and techniques. A detailed description of the authors' data derivation and statistical methodologies is beyond the scope of this blog (which is another way of saying that I know my limits).
Please click here to read the entire post.
Read other items of interest from the world of directors & officers liability, with occasional commentary, at the D&O Diary, a blog by Kevin LaCroix.
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