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Portfolio Allocation and Reinforcement Learning

In: Artificial Intelligence and Beyond for Finance

Author

Listed:
  • René Garcia
  • Alissa Marinenko
Abstract
In this chapter, we briefly review the methodology of reinforcement learning and describe its application to the financial problem of portfolio allocation. In this context, we define the environment as a set of states, captured by such financial variables as stock returns or technical indicators, and of actions, mainly the determination of wealth shares to invest in each asset. Optimal value functions are obtained through the Bellman optimality equation, a well-established principle in both reinforcement learning and portfolio optimization. Deep reinforcement learning algorithms have the advantage of providing approximate solutions since most portfolio problems lack analytical solutions. We describe several algorithms and apply them to classical portfolio allocation problems, where risk minimization and return maximization are combined with or without accounting for trading costs.

Suggested Citation

  • René Garcia & Alissa Marinenko, 2024. "Portfolio Allocation and Reinforcement Learning," World Scientific Book Chapters, in: Marco Corazza & René Garcia & Faisal Shah Khan & Davide La Torre & Hatem Masri (ed.), Artificial Intelligence and Beyond for Finance, chapter 3, pages 103-148, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9781800615212_0003
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    More about this item

    Keywords

    Artificial Intelligence; Machine Learning; Deep Learning; Reinforcement Learning; Sentiment Analysis; Portfolio Management; Financial Forecasting;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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