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The Measure of a MAC: A Machine-Learning Protocol for Analyzing Force Majeure Clauses in M&A Agreements

Listed author(s):
  • Eric Talley
  • Drew O'Kane
Registered author(s):

    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.

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    File URL: http://www.ingentaconnect.com/content/mohr/jite/2012/00000168/00000001/art00019
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    Article provided by Mohr Siebeck, Tübingen in its journal Journal of Institutional and Theoretical Economics.

    Volume (Year): 168 (2012)
    Issue (Month): 1 (March)
    Pages: 181-201

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    Handle: RePEc:mhr:jinste:urn:sici:0932-4569(201203)168:1_181:tmoama_2.0.tx_2-0
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    1. Ronald J. Gilson & Alan Schwartz, 2005. "Understanding MACs: Moral Hazard in Acquisitions," Journal of Law, Economics and Organization, Oxford University Press, vol. 21(2), pages 330-358, October.
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