This paper develops a protocol for using a familiar data set on force majeure provisions in corporate acquisitions agreements to tokenize and calibrate a machine-learning algorithm of textual analysis. Our protocol, built on regular expression (RE) and latent semantic analysis (LSA) approaches, serves to replicate, correct, and extend the hand-coded data. Our preliminary results indicate that both approaches perform well, though a hybridized approach improves predictive power further. Monte Carlo simulations suggest that our results are generally robust to out-of-sample predictions. We conclude that similar approaches could be used more broadly in empirical legal scholarship, especially including in business law.
Banking and Finance Law | Business Organizations Law | Contracts | Law
Eric L. Talley & Drew O'Kane,
The Measure of a MAC: A Quasi-Experimental Protocol for Tokenizing Force Majeure Clauses in M&A Agreements,
Available at: https://scholarship.law.columbia.edu/faculty_scholarship/1699