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

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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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Document Type: Research Article

Publication date: 2012-03-01

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  • Founded as Zeitschrift für die gesamte Staatswissenschaft in 1844.

    As one of the oldest journals in the field of political economy, the Journal of Institutional and Theoretical Economics (JITE) deals traditionally with the problems of economics, social policy, and their legal framework. JITE is listed in the Journal of Economic Literature, the Social Science Citation Index, the International Bibliography of the Social Sciences, and COREJ.

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    From 2013 on all accepted articles are published in an Online First version (in their final layout) to make them searchable and citable by their DOI immediately after peer review and acceptance. Once the article is published in an issue of the journal, the Online First version will be removed.

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