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ChatGPT Informed Graph Neural Network for Stock Movement Prediction

Author

Listed:
  • Zihan Chen
  • Lei Nico Zheng
  • Cheng Lu
  • Jialu Yuan
  • Di Zhu

Abstract

ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier. In this research, we introduce a novel framework that leverages ChatGPT's graph inference capabilities to enhance Graph Neural Networks (GNN). Our framework adeptly extracts evolving network structures from textual data, and incorporates these networks into graph neural networks for subsequent predictive tasks. The experimental results from stock movement forecasting indicate our model has consistently outperformed the state-of-the-art Deep Learning-based benchmarks. Furthermore, the portfolios constructed based on our model's outputs demonstrate higher annualized cumulative returns, alongside reduced volatility and maximum drawdown. This superior performance highlights the potential of ChatGPT for text-based network inferences and underscores its promising implications for the financial sector.

Suggested Citation

  • Zihan Chen & Lei Nico Zheng & Cheng Lu & Jialu Yuan & Di Zhu, 2023. "ChatGPT Informed Graph Neural Network for Stock Movement Prediction," Papers 2306.03763, arXiv.org, revised Sep 2023.
    • Handle: RePEc:arx:papers:2306.03763
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    References listed on IDEAS

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    1. Gerard Hoberg & Gordon Phillips, 2016. "Text-Based Network Industries and Endogenous Product Differentiation," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1423-1465.
    2. Yousaf, Imran & Goodell, John W., 2023. "Responses of US equity market sectors to the Silicon Valley Bank implosion," Finance Research Letters, Elsevier, vol. 55(PB).
    3. 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 2023.
    4. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    5. Chan, Wesley S., 2003. "Stock price reaction to news and no-news: drift and reversal after headlines," Journal of Financial Economics, Elsevier, vol. 70(2), pages 223-260, November.
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    Cited by:

    1. Hanshuang Tong & Jun Li & Ning Wu & Ming Gong & Dongmei Zhang & Qi Zhang, 2024. "Ploutos: Towards interpretable stock movement prediction with financial large language model," Papers 2403.00782, arXiv.org.
    2. Kelvin J. L. Koa & Yunshan Ma & Ritchie Ng & Tat-Seng Chua, 2024. "Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models," Papers 2402.03659, arXiv.org, revised Feb 2024.
    3. Junwei Su & Shan Wu & Jinhui Li, 2024. "MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning," Papers 2401.14199, arXiv.org, revised Feb 2024.
    4. Liping Wang & Jiawei Li & Lifan Zhao & Zhizhuo Kou & Xiaohan Wang & Xinyi Zhu & Hao Wang & Yanyan Shen & Lei Chen, 2023. "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey," Papers 2308.04947, arXiv.org.

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