 Machine Learning and Predicted Returns for Event Studies in Securities LitigationAndrew Baker and Jonah B. Gelbach Journal of Law, Finance, and Accounting, 2020, vol. 5, issue 2, 231-272 Abstract: We investigate the use of machine learning (ML) and other robustestimation techniques in 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. We find that regularization (equivalently, penalized estimation) can yield noticeable improvements in both the variance of event-date abnormal returns and significance-test power. Thus we believe that there is a role for ML methods in event studies used in securities litigation. At the same time, we find that ML-induced performance improvements are smaller than those based on other good practices. Most important are (i) the use of a peer index based on returns for firms in similar industries (how this is computed appears to be less important than that some version be included), and (ii) for significance testing, using the SQ test proposed in Gelbach et al. (2013), because it is robust to the considerable non-normality present in abnormal returns. Keywords: Event studies/market efficiency studies; financial econometrics; asset pricing (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:now:jnllfa:108.00000047 Access Statistics for this article More articles in Journal of Law, Finance, and Accounting from now publishers |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.