Machine Learning and Predicted Returns for Event Studies in Securities Litigation Preliminary and Incomplete

56 Pages Posted: 14 May 2020

See all articles by Andrew C. Baker

Andrew C. Baker

Stanford University, Law School, Stanford University, School of Law, Student

Jonah B. Gelbach

University of California, Berkeley - School of Law

Date Written: May 5, 2020

Abstract

We investigate the use of machine learning (ML) and other robust-estimation 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, Helland, and Klick (2013), because it is robust to the considerable non-normality present in abnormal returns.

Keywords: machine learning, securities litigation, abnormal returns, machine learning methods and event studies

JEL Classification: G0, G1, G2, G15, G24, E44

Suggested Citation

Baker, Andrew C. and Gelbach, Jonah B., Machine Learning and Predicted Returns for Event Studies in Securities Litigation Preliminary and Incomplete (May 5, 2020). Rock Center for Corporate Governance at Stanford University Working Paper No. 244. Available at SSRN: https://ssrn.com/abstract=3600432

Andrew C. Baker (Contact Author)

Stanford University, Law School, Stanford University, School of Law, Student ( email )

Stanford, CA
United States

Jonah B. Gelbach

University of California, Berkeley - School of Law ( email )

215 Boalt Hall
Berkeley, CA 94720-7200
United States

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