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，以ChatGPT为代表的大型语言模型（LLMs）在多种自然语言处理任务中展现出卓越性能。然而，其在金融领域，特别是股票市场走势预测方面的有效性仍有待探索。本文针对三项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展开全面分析。研究结果表明，ChatGPT实为"华尔街新秀"，其在股票走势预测方面成效有限，不仅逊于当前最优方法，甚至低于使用价格特征的线性回归等传统方法。尽管链式思考提示策略与推文信息的引入具有一定潜力，但ChatGPT的整体表现仍不尽如人意。此外，其可解释性与稳定性方面存在局限，表明需要更具针对性的训练或微调。本研究揭示了ChatGPT的能力边界，并为未来通过融合社交媒体情绪与历史股票数据改进金融市场分析预测的研究奠定了基础。