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Large Language Model Agent in Financial Trading: A Survey

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Listed:
  • Han Ding
  • Yinheng Li
  • Junhao Wang
  • Hang Chen

Abstract

Trading is a highly competitive task that requires a combination of strategy, knowledge, and psychological fortitude. With the recent success of large language models(LLMs), it is appealing to apply the emerging intelligence of LLM agents in this competitive arena and understanding if they can outperform professional traders. In this survey, we provide a comprehensive review of the current research on using LLMs as agents in financial trading. We summarize the common architecture used in the agent, the data inputs, and the performance of LLM trading agents in backtesting as well as the challenges presented in these research. This survey aims to provide insights into the current state of LLM-based financial trading agents and outline future research directions in this field.

Suggested Citation

  • Han Ding & Yinheng Li & Junhao Wang & Hang Chen, 2024. "Large Language Model Agent in Financial Trading: A Survey," Papers 2408.06361, arXiv.org.
    • Handle: RePEc:arx:papers:2408.06361
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    References listed on IDEAS

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    1. Yang Li & Yangyang Yu & Haohang Li & Zhi Chen & Khaldoun Khashanah, 2023. "TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance," Papers 2309.03736, arXiv.org.
    2. Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Sep 2024.
    3. Yinheng Li & Shaofei Wang & Han Ding & Hang Chen, 2023. "Large Language Models in Finance: A Survey," Papers 2311.10723, arXiv.org, revised Jul 2024.
    4. Saizhuo Wang & Hang Yuan & Lionel M. Ni & Jian Guo, 2024. "QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model," Papers 2402.03755, arXiv.org.
    5. Kirtac, Kemal & Germano, Guido, 2024. "Sentiment trading with large language models," LSE Research Online Documents on Economics 122592, London School of Economics and Political Science, LSE Library.
    6. Feng Zhang & Ruite Guo & Honggao Cao, 2020. "Information Coefficient as a Performance Measure of Stock Selection Models," Papers 2010.08601, arXiv.org.
    7. Clifford S. Asness & Tobias J. Moskowitz & Lasse Heje Pedersen, 2013. "Value and Momentum Everywhere," Journal of Finance, American Finance Association, vol. 68(3), pages 929-985, June.
    8. Georgios Fatouros & Konstantinos Metaxas & John Soldatos & Dimosthenis Kyriazis, 2024. "Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection," Papers 2401.03737, arXiv.org, revised Apr 2024.
    9. Thanos Konstantinidis & Giorgos Iacovides & Mingxue Xu & Tony G. Constantinides & Danilo Mandic, 2024. "FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications," Papers 2403.12285, arXiv.org.
    10. Dat Mai, 2024. "StockGPT: A GenAI Model for Stock Prediction and Trading," Papers 2404.05101, arXiv.org, revised Oct 2024.
    11. Yujie Ding & Shuai Jia & Tianyi Ma & Bingcheng Mao & Xiuze Zhou & Liuliu Li & Dongming Han, 2023. "Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction," Papers 2310.05627, arXiv.org.
    12. Frank Xing, 2024. "Designing Heterogeneous LLM Agents for Financial Sentiment Analysis," Papers 2401.05799, arXiv.org.
    13. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    14. Yangyang Yu & Haohang Li & Zhi Chen & Yuechen Jiang & Yang Li & Denghui Zhang & Rong Liu & Jordan W. Suchow & Khaldoun Khashanah, 2023. "FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design," Papers 2311.13743, arXiv.org, revised Dec 2023.
    15. Jean Lee & Nicholas Stevens & Soyeon Caren Han & Minseok Song, 2024. "A Survey of Large Language Models in Finance (FinLLMs)," Papers 2402.02315, arXiv.org.
    16. Kirtac, Kemal & Germano, Guido, 2024. "Sentiment trading with large language models," Finance Research Letters, Elsevier, vol. 62(PB).
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