 Deep reinforcement learning for portfolio selectionYifu Jiang, Jose Olmo and Majed Atwi Global Finance Journal, 2024, vol. 62, issue C Abstract: This study proposes an advanced model-free deep reinforcement learning (DRL) framework to construct optimal portfolio strategies in dynamic, complex, and large-dimensional financial markets. Investors' risk aversion and transaction cost constraints are embedded in an extended Markowitz's mean-variance reward function by employing a twin-delayed deep deterministic policy gradient (TD3) algorithm. This study designs a DRL-TD3-based risk and transaction cost-sensitive portfolio that combines advanced exploration strategies and dynamic policy updates. The proposed portfolio method effectively addresses the challenges posed by high-dimensional state and action spaces in complex financial markets. This methodology provides two optimal portfolios by flexibly controlling transaction and risk costs with (i) the constituents of the Dow Jones Industrial Average and (ii) the constituents of the S&P100 index. Results demonstrate a strong portfolio performance of the proposed DRL portfolio compared to those of several competitors from the traditional and DRL literatures. Keywords: Portfolio trading; Portfolio risk awareness; Transaction cost; Deep reinforcement learning; Portfolio constraint (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:glofin:v:62:y:2024:i:c:s1044028324000887 DOI: 10.1016/j.gfj.2024.101016 Access Statistics for this article Global Finance Journal is currently edited by Manuchehr Shahrokhi More articles in Global Finance Journal from Elsevier |
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