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

TL;DR

This paper evaluates ChatGPT's zero-shot ability to predict stock movements using multimodal data, revealing its limited performance compared to traditional and state-of-the-art methods, and highlighting areas for improvement.

Contribution

It provides the first comprehensive zero-shot analysis of ChatGPT in multimodal stock prediction, demonstrating its current limitations and guiding future research directions.

Findings

ChatGPT underperforms compared to traditional methods.

Chain-of-Thought prompting does not significantly improve results.

Limitations include poor explainability and stability.

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.