Validating Valuation: How Statistical Learning Can Cabin Expert Discretion in Valuation Disputes
Financial valuation is a cornerstone of modern commercial litigation, influencing outcomes across substantive areas of legal dispute, from bankruptcy to tax and corporate law. However, its ubiquity comes with substantial challenges for the judiciary. Conventional approaches to valuation, including discounted cash flow, comparable company, and comparable transactions analyses, leave open substantial areas of discretion to be exploited by economic experts. In our article, we argue that these “expert degrees of freedom” generate inconsistent and overly subjective valuations, with expert reports regularly diverging by orders of magnitude, to the frequent frustration of generalist judges. Using Monte Carlo simulations and a case example, our paper demonstrates the benefits of using contemporary statistical learning techniques to increase the precision of financial valuation while reducing this variability and expert bias.