华尔街新手：ChatGPT在多模态股价走势预测挑战下的零样本分析
The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges
AI总结：
该研究通过在三个多模态数据集上的零样本实验，分析ChatGPT的股价走势预测能力，发现其表现逊于传统及前沿方法，且存在可解释性与稳定性局限，为金融领域大模型优化提供参考。
摘要：
近年来，ChatGPT等大语言模型（LLM）已在各类自然语言处理任务中展现出卓越性能。然而，它们在金融领域的有效性，尤其是在股市走势预测方面的表现仍有待探索。本文针对ChatGPT的多模态股价走势预测能力，在3个推文及历史股价数据集上开展了广泛的零样本分析。研究结果表明，ChatGPT是“华尔街新手”，在股价走势预测方面成效有限，其表现不仅逊于SOTA方法，甚至不及使用价格特征的线性回归等传统方法。尽管思维链（Chain-of-Thought）提示策略与加入推文数据具备一定潜力，ChatGPT的性能仍未达理想水平。此外，我们还观察到其在可解释性与稳定性方面存在局限，表明需要开展更具针对性的训练或微调。本研究揭示了ChatGPT的能力边界，为未来借助社交媒体情绪与历史股价数据改进金融市场分析与预测的相关工作奠定了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
