Portfolio Allocation and Reinforcement Learning

René Garcia and Alissa Marinenko
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René Garcia: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Alissa Marinenko: Unknown

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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.

Date: 2024-08
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Published in Artificial Intelligence and Beyond for Finance., World Scientific Publishing, pp.103-148, 2024, 9781800615205. ⟨10.1142/9781800615212_0003⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04933269

DOI: 10.1142/9781800615212_0003

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