Machine Learning and Predicted Returns for Event Studies in Securities Litigation

Abstract

We investigate the use of modern statistical techniques in the application of event studies conducted on single securities for the purpose of securities litigation. Single-firm event studies are widely used in civil litigation, with billions of dollars in settlements hinging on the outcome of the exercise. Prior work has explored modifying the standard single-firm event study design to provide more robust statistical inference. But little work has been done to determine methods that can directly increase the precision of the excess return estimate. We take a prediction approach to the excess return calculation and find substantial performance improvement is possible using modern machine learning methods.