Can Artificial Intelligence Trade the Stock Market?

Jędrzej Maskiewicz () and Paweł Sakowski
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Jędrzej Maskiewicz: Quantitative Finance Research Group, Department of Quantitative Finance, Faculty of Economic Sciences, University of Warsaw
Paweł Sakowski: Quantitative Finance Research Group, Department of Quantitative Finance, Faculty of Economic Sciences, University of Warsaw

No 2025-14, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S&P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorable conditions, providing a substantial edge over classical approaches, based on supervised learning in terms of risk-adjusted returns.

Keywords: Reinforcement Learning; Deep Learning; stock market; algorithmic trading; Double Deep Q-Network; Proximal Policy Optimization (search for similar items in EconPapers)
JEL-codes: C14 C4 C45 C53 C58 G13 (search for similar items in EconPapers)
Pages: 63 pages
Date: 2025
New Economics Papers: this item is included in nep-fmk and nep-mst
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2025-14

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