Can Artificial Intelligence Trade the Stock Market?
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909639013040128 |
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| author | Maskiewicz, Jędrzej Sakowski, Paweł |
| author_facet | Maskiewicz, Jędrzej Sakowski, Paweł |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04658 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can Artificial Intelligence Trade the Stock Market? Maskiewicz, Jędrzej Sakowski, Paweł Trading and Market Microstructure Machine Learning Computational Finance 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. |
| title | Can Artificial Intelligence Trade the Stock Market? |
| topic | Trading and Market Microstructure Machine Learning Computational Finance |
| url | https://arxiv.org/abs/2506.04658 |