Frontiers of Machine Learning and Finance

Matthew F. Dixon, Igor Halperin and Paul Bilokon
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Matthew F. Dixon: Illinois Institute of Technology, Department of Applied Mathematics
Igor Halperin: New York University, Tandon School of Engineering
Paul Bilokon: Imperial College London, Department of Mathematics

Chapter Chapter 12 in Machine Learning in Finance, 2020, pp 519-541 from Springer

Abstract: Abstract This final chapter takes us forward to emerging research topics in quantitative finance and machine learning. Among many interesting emerging topics, we focus here on two broad themes. The first one deals with unification of supervised learning and reinforcement learning as two tasks of perception-action cycles of agents. We outline some recent research ideas in the literature including, in particular, information theory-based versions of reinforcement learning, and discuss their relevance for financial applications. We explain why these ideas have interesting practical implications for RL financial models, where features are selected within the general task of optimization of a long-term objective, rather than outside of it, as is usually performed in “alpha-research.” The second topic presented in this chapter deals with using methods of reinforcement learning to construct models of market dynamics. We also introduce some advanced physics-based approaches for computations for such RL-inspired market models.

Date: 2020
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DOI: 10.1007/978-3-030-41068-1_12

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