 High-dimensional multi-period portfolio allocation using deep reinforcement learningYifu Jiang, Jose Olmo and Majed Atwi International Review of Economics & Finance, 2025, vol. 98, issue C Abstract: This paper proposes a novel investment strategy based on deep reinforcement learning (DRL) for long-term portfolio allocation in the presence of transaction costs and risk aversion. We design an advanced portfolio policy framework to model the price dynamic patterns using convolutional neural networks (CNN), capture group-wise asset dependence using WaveNet, and solve the optimal asset allocation problem using DRL. These methods are embedded within a multi-period Bellman equation framework. An additional appealing feature of our investment strategy is its ability to optimize dynamically over a large set of potentially correlated risky assets. The performance of this portfolio is tested empirically over different holding periods, risk aversion levels, transaction cost rates, and financial indices. The results demonstrate the effectiveness and superiority of the proposed long-term portfolio allocation strategy compared to several competitors based on machine learning methods and traditional optimization techniques. Keywords: Multi-period portfolio selection; High-dimensional portfolios; Risk aversion; Portfolio constraints; Deep reinforcement learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:98:y:2025:i:c:s1059056025001595 DOI: 10.1016/j.iref.2025.103996 Access Statistics for this article International Review of Economics & Finance is currently edited by H. Beladi and C. Chen More articles in International Review of Economics & Finance from Elsevier |
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