Paper: arXiv 2506.04658

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.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]