To a greater extent than other mathematical disciplines, statistics is a product of its time. If Francis Galton, Karl Pearson, Ronald Fisher, and Jerzy Neyman had had access to computers, they may have created an entirely different field. Classical statistics relies on simplistic assumptions (linearity, independence), in-sample analysis, analytical solutions, and asymptotic properties partly because its founders had access to limited computing power. Today, many of these legacy methods continue to be taught at university courses and in professional certification programs, even though computational methods, such as cross-validation, ensemble estimators, regularization, bootstrapping, and Monte Carlo, deliver demonstrably better solutions.

Financial problems pose a particular challenge to those legacy methods, because economic systems exhibit a degree of complexity that is beyond the grasp of classical statistical tools. As a consequence, machine learning (ML) plays an increasingly important role in finance. Only a few years ago, it was rare to find ML applications outside short-term price prediction, trade execution, and setting of credit ratings. Today, it is hard to find a use case where ML is not being deployed in some form. This trend is unlikely to change, as larger data sets, greater computing power, and more efficient algorithms all conspire to unleash a golden age of financial ML. The ML revolution creates opportunities for dynamic firms and challenges for antiquated asset managers.

Firms that resist this revolution will likely share Kodak’s fate.