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The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges

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Listed:
  • Qianqian Xie
  • Weiguang Han
  • Yanzhao Lai
  • Min Peng
  • Jimin Huang

Abstract

Recently, large language models (LLMs) like ChatGPT have demonstrated remarkable performance across a variety of natural language processing tasks. However, their effectiveness in the financial domain, specifically in predicting stock market movements, remains to be explored. In this paper, we conduct an extensive zero-shot analysis of ChatGPT's capabilities in multimodal stock movement prediction, on three tweets and historical stock price datasets. Our findings indicate that ChatGPT is a "Wall Street Neophyte" with limited success in predicting stock movements, as it underperforms not only state-of-the-art methods but also traditional methods like linear regression using price features. Despite the potential of Chain-of-Thought prompting strategies and the inclusion of tweets, ChatGPT's performance remains subpar. Furthermore, we observe limitations in its explainability and stability, suggesting the need for more specialized training or fine-tuning. This research provides insights into ChatGPT's capabilities and serves as a foundation for future work aimed at improving financial market analysis and prediction by leveraging social media sentiment and historical stock data.

Suggested Citation

  • Qianqian Xie & Weiguang Han & Yanzhao Lai & Min Peng & Jimin Huang, 2023. "The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges," Papers 2304.05351, arXiv.org, revised Apr 2023.
    • Handle: RePEc:arx:papers:2304.05351
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    References listed on IDEAS

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    1. Fuli Feng & Huimin Chen & Xiangnan He & Ji Ding & Maosong Sun & Tat-Seng Chua, 2018. "Enhancing Stock Movement Prediction with Adversarial Training," Papers 1810.09936, arXiv.org, revised Jun 2019.
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    Cited by:

    1. Alberto Menéndez Medina & José Antonio Heredia Álvaro, 2024. "Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market," Energies, MDPI, vol. 17(10), pages 1-15, May.
    2. Hoyoung Lee & Youngsoo Choi & Yuhee Kwon, 2024. "Quantifying Qualitative Insights: Leveraging LLMs to Market Predict," Papers 2411.08404, arXiv.org.
    3. Ummara Mumtaz & Summaya Mumtaz, 2023. "Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis," Papers 2311.06273, arXiv.org.
    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.
    5. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.
    6. Chang Zong & Hang Zhou, 2024. "Stock Movement Prediction with Multimodal Stable Fusion via Gated Cross-Attention Mechanism," Papers 2406.06594, arXiv.org, revised Dec 2024.
    7. Qianqian Xie & Dong Li & Mengxi Xiao & Zihao Jiang & Ruoyu Xiang & Xiao Zhang & Zhengyu Chen & Yueru He & Weiguang Han & Yuzhe Yang & Shunian Chen & Yifei Zhang & Lihang Shen & Daniel Kim & Zhiwei Liu, 2024. "Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications," Papers 2408.11878, arXiv.org.

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