Turning Standards into Rules Part 2: How Do Financial Risk Factors Affect Debt vs. Equity Determinations?
(2018) 233 DTR 10 (Bloomberg BNA)
3 Pages Posted: 6 Jun 2019
Date Written: December 4, 2018
Like many legal questions, the question of whether a financial instrument is more debt-like or more equity-like can be thought of as a binary classification problem. If researchers can harness enough information from the body of legal decisions, these tools can provide an accurate prediction for how the courts might rule in a new debt vs. equity scenario. This is not merely a theoretical possibility: my colleagues at the University of Toronto and I have been able to produce accurate predictions of how U.S. courts characterize new financial instruments based on how similar cases have been resolved in the past. We extract information from hundreds of legal opinions and use various machine-learning algorithms to analyze patterns in the data. These patterns highlight the combinations of facts that are most strongly associated with the eventual outcomes of the cases. Following extensive testing and calibration, we can predict the likelihood of outcomes in cases that the system has not previously seen. We report the confidence of our predictions as a percentage based on the probabilistic likelihood of the outcome. As a result, our system is able to produce accurate predictions on a range of tax questions, including whether courts would characterize new financial instruments as debt or equity. The system also allows us to observe how the probability of a given outcome changes when we alter the fact pattern.
Keywords: machine-learning, financial instruments, debt versus equity, characterization
JEL Classification: H2, H20, H29
Suggested Citation: Suggested Citation