10 Pages Posted: 9 Oct 2016 Last revised: 5 Dec 2016
Date Written: October 7, 2016
Rauterberg & Talley (2017) develop a data set of “corporate opportunity waivers” (COWs) — significant contractual modifications of fiduciary duties — sampled from SEC filings. Part of their analysis utilizes a machine learning (ML) classifier to extend their data set beyond the hand-coded sample. Because the ML approach is likely unfamiliar to some readers, and in the light of its great potential across other areas of law and finance research, this note explains the basic components using a simple example, and it demonstrates strategies for calibrating and evaluating the classifier.
Keywords: Machine Learning, Big Data, Natural Language Processing, Corporate Opportunity Waivers, Fiduciary Duties, Corporate Finance, Corporate Governance, Corporate Law
JEL Classification: C80, K00, O16, G3, G34
Suggested Citation: Suggested Citation
Rauterberg, Gabriel V. and Talley, Eric L., A Machine Learning Classifier for Corporate Opportunity Waivers (October 7, 2016). Columbia Law and Economics Working Paper No. 553. Available at SSRN: https://ssrn.com/abstract=2849491 or http://dx.doi.org/10.2139/ssrn.2849491