Can Artificial Intelligence Trade the Stock Market?

Fuente: arXiv
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Hauptverfasser: Maskiewicz, Jędrzej, Sakowski, Paweł
Format: Preprint
Veröffentlicht: 2025
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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