Can Artificial Intelligence Trade the Stock Market?

ArXiv ID: 2506.04658 “View on arXiv”

Authors: Jędrzej Maskiewicz, Paweł Sakowski

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: Deep Reinforcement Learning, Double Deep Q-Network (DDQN), Proximal Policy Optimization (PPO), risk-adjusted returns, trading strategy, Multi-asset (Stocks, Forex, Crypto)

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics and deep learning architectures (Transformers, PPO), while being grounded in practical trading evaluations with real data (2019-2023) across multiple assets, demonstrating high empirical rigor.

  flowchart TD
    A["Research Question: Can AI Trade the Stock Market?"] --> B["Methodology: DDQN & PPO vs Buy & Hold"]
    B --> C["Data: Daily Price Data (2019-2023)"]
    C --> D["Assets: S&P 500, Forex, Bitcoin"]
    D --> E["Simulation & Training"]
    E --> F["Risk Management: Avoiding Unfavorable Conditions"]
    F --> G["Findings: DRL Outperforms Buy & Hold on Risk-Adjusted Returns"]