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.

翻译：近期，大型语言模型（如ChatGPT）在各类自然语言处理任务中展现出卓越性能。然而，其在金融领域（尤其是股市走势预测）的有效性仍有待探究。本文基于三个推文与历史股价数据集，对ChatGPT在多模态股票走势预测中的零样本能力进行了全面分析。研究结果表明，ChatGPT作为"华尔街新手"，在股票走势预测中表现有限——不仅落后于现有最优方法，甚至不及使用价格特征的传统线性回归方法。尽管思维链提示策略与推文信息整合具有潜力，但ChatGPT的性能依然欠佳。此外，其可解释性与稳定性存在局限性，表明需要更专业的训练或微调。本研究揭示了ChatGPT的能力边界，并为未来借助社交媒体情绪与历史股票数据改进金融市场分析预测的工作奠定了基础。