Machine Learning Methods in Finance: Recent Applications and Prospects

We study how researchers can apply machine learning (ML) methods in finance. We first establish that the three distinct categories of ML (supervised learning, unsupervised learning, reinforcement learning and others) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research of ML in finance and identify three archetypes of applications: i) the construction of superior and novel measures, ii) the reduction of prediction error, and iii) the extension of the standard econometric toolset. With this taxonomy, we provide an outlook on potential future directions for both researchers and practitioners. We finally apply ML to typical problems in finance. Our results suggest large benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance. This draft is preliminary. Comments welcome. Please do not circulate without permission of the authors. JEL classification: C45, G00