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When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments

Author

Listed:
  • Chong Zhang
  • Xinyi Liu
  • Zhongmou Zhang
  • Mingyu Jin
  • Lingyao Li
  • Zhenting Wang
  • Wenyue Hua
  • Dong Shu
  • Suiyuan Zhu
  • Xiaobo Jin
  • Sujian Li
  • Mengnan Du
  • Yongfeng Zhang

Abstract

Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fundamentals, and global events)? These factors, which frequently influence trading behaviors, are critical elements in the quest for maximizing investors' profits. Our work attempts to solve this problem through large language model based agents. We have developed a multi-agent AI system called StockAgent, driven by LLMs, designed to simulate investors' trading behaviors in response to the real stock market. The StockAgent allows users to evaluate the impact of different external factors on investor trading and to analyze trading behavior and profitability effects. Additionally, StockAgent avoids the test set leakage issue present in existing trading simulation systems based on AI Agents. Specifically, it prevents the model from leveraging prior knowledge it may have acquired related to the test data. We evaluate different LLMs under the framework of StockAgent in a stock trading environment that closely resembles real-world conditions. The experimental results demonstrate the impact of key external factors on stock market trading, including trading behavior and stock price fluctuation rules. This research explores the study of agents' free trading gaps in the context of no prior knowledge related to market data. The patterns identified through StockAgent simulations provide valuable insights for LLM-based investment advice and stock recommendation. The code is available at https://github.com/MingyuJ666/Stockagent.

Suggested Citation

  • Chong Zhang & Xinyi Liu & Zhongmou Zhang & Mingyu Jin & Lingyao Li & Zhenting Wang & Wenyue Hua & Dong Shu & Suiyuan Zhu & Xiaobo Jin & Sujian Li & Mengnan Du & Yongfeng Zhang, 2024. "When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments," Papers 2407.18957, arXiv.org, revised Jun 2026.
    • Handle: RePEc:arx:papers:2407.18957
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    References listed on IDEAS

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    Cited by:

    1. Ryuji Hashimoto & Takehiro Takayanagi & Masahiro Suzuki & Kiyoshi Izumi, 2026. "LLM agents reveal how human bias shapes path-dependent market dynamics," Journal of Computational Social Science, Springer, vol. 9(2), pages 1-26, May.
    2. Antonino Castelli & Paolo Giudici & Alessandro Piergallini, 2025. "Building crypto portfolios with agentic AI," Papers 2507.20468, arXiv.org.
    3. Sedigheh Mahdavi & Jiating & Chen & Pradeep Kumar Joshi & Lina Huertas Guativa & Upmanyu Singh, 2025. "Integrating Large Language Models in Financial Investments and Market Analysis: A Survey," Papers 2507.01990, arXiv.org.
    4. Junyi Yao & Zihao Zheng, 2026. "Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems," Papers 2606.08285, arXiv.org.
    5. Weilong Fu, 2025. "The New Quant: A Survey of Large Language Models in Financial Prediction and Trading," Papers 2510.05533, arXiv.org.
    6. Mostapha Benhenda, 2026. "Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Finance," Papers 2601.13770, arXiv.org.

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