The Measure of a MAC: A Quasi-Experimental Protocol for Tokenizing Force Majeure Clauses in M&A Agreements

26 Pages Posted: 26 Jun 2011 Last revised: 16 Feb 2016

Eric L. Talley

Columbia University - School of Law

Drew O'Kane

University of California, Berkeley - School of Law

Date Written: June 25, 2011

Abstract

We develop a protocol for using a well known lawyer-coded data set on Material Adverse Change/Effect clauses in acquisitions agreements to tokenize and calibrate a machine learning algorithm of textual analysis. Our protocol, built on both regular expression (RE) and latent semantic analysis (LSA) approaches, is designed to replicate, correct, and extend the reach of the hand-coded data. Our preliminary results indicate that both approaches perform well, though a hybridized approach improves predictive power even more. We employ Monte Carlo simulations show that our results generally carry over to out-of-sample predictions. We conclude that similar approaches could be used much more broadly in empirical legal scholarship, most specifically in the study of transactional documents in business law.

Keywords: Mergers and Acquisitions, Machine Learning, Latent Semantic Analysis

JEL Classification: K00, C15, C45

Suggested Citation

Talley, Eric L. and O'Kane, Drew, The Measure of a MAC: A Quasi-Experimental Protocol for Tokenizing Force Majeure Clauses in M&A Agreements (June 25, 2011). UC Berkeley Public Law Research Paper No. 1872568 . Available at SSRN: https://ssrn.com/abstract=1872568 or http://dx.doi.org/10.2139/ssrn.1872568

Eric L. Talley (Contact Author)

Columbia University - School of Law ( email )

435 West 116th Street
New York, NY 10025
United States

HOME PAGE: http://www.erictalley.com

Drew O'Kane

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

215 Boalt Hall
Berkeley, CA 94720-7200
United States

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