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Frontiers of Machine Learning and Finance

In: Machine Learning in Finance

Author

Listed:
  • 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)

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.

Suggested Citation

  • Matthew F. Dixon & Igor Halperin & Paul Bilokon, 2020. "Frontiers of Machine Learning and Finance," Springer Books, in: Machine Learning in Finance, chapter 0, pages 519-541, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41068-1_12
    DOI: 10.1007/978-3-030-41068-1_12
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    Citations

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    Cited by:

    1. Paul Glasserman & Siddharth Hemant Karmarkar, 2025. "Differential ML with a Difference," Papers 2512.05301, arXiv.org, revised Apr 2026.
    2. Michael Scholz, 2025. "Forecast combinations for benchmarks of long-term stock returns using machine learning methods," Annals of Operations Research, Springer, vol. 352(3), pages 583-612, September.
    3. Fermat Leukam & Rock Stephane Koffi & Prudence Djagba, 2025. "Reinforcement Learning for Portfolio Optimization with a Financial Goal and Defined Time Horizons," Papers 2511.18076, arXiv.org.
    4. Xu, Hailun & Yuan, Xianghui & Jin, Liwei & Long, Jun & Xu, Gen, 2026. "Ascertaining price formation in financial markets with machine learning: Evidence from Chinese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 96(C).

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