Can ChatGPT forecast stock price movements? Return predictability and large language models

• 成果类型: Article
• 署名作者: Lopez-Lira, Alejandro; Tang, Yuehua
• 署名单位: State University System of Florida; University of Florida
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2026.104335
• 发表日期: 2026-10
• 页码: 104335
• 关键词: large language models; ChatGPT; Machine Learning; Return predictability; Textual analysis; market efficiency; information acquisition; Sentiment; RISK; news; underreaction; LIMITS

摘要

We document the capability of large language models (LLMs) like ChatGPT to predict stock market reactions from news headlines without direct financial training. Using post-knowledge-cutoff headlines, GPT-4 captures initial market responses, achieving approximately 90% portfolio-day hit rates for the non-tradable initial reaction. GPT-4 scores also significantly predict the subsequent drift, especially for small stocks and negative news. Forecasting ability generally increases with model size, suggesting that financial reasoning is an emerging capacity of complex LLMs. Strategy returns decline as LLM adoption rises, consistent with improved price efficiency. To rationalize these findings, we develop a theoretical model that incorporates LLM technology, information-processing capacity constraints, underreaction, and limits to arbitrage.