Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models

96 Pages Posted: 10 Apr 2023 Last revised: 14 Apr 2024

See all articles by Alejandro Lopez-Lira

Alejandro Lopez-Lira

University of Florida - Department of Finance, Insurance and Real Estate

Yuehua Tang

University of Florida - Department of Finance

Date Written: April 6, 2023

Abstract

We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines, even without direct financial training. ChatGPT scores significantly predict out-of-sample daily stock returns, subsuming traditional methods, and predictability is stronger among smaller stocks and following negative news. To explain these findings, we develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs. The model generates several key predictions, which we empirically test: (i) it establishes a critical threshold in AI capabilities necessary for profitable predictions, (ii) it predicts that only advanced LLMs can effectively interpret complex information, and (iii) it forecasts that widespread LLM adoption can enhance market efficiency. Our results suggest that sophisticated return forecasting is an emerging capability of AI systems and that these technologies can alter information diffusion and decision-making processes in financial markets. Finally, we introduce an interpretability framework to evaluate LLMs' reasoning and accuracy, contributing to AI transparency and economic decision-making.

Keywords: Natural Language Processing (NLP), Generative Pre-training Transformer (GPT), Return Predictability, Large Language Models, ChatGPT

JEL Classification: C53, G10, G11, G12, G14, G17

Suggested Citation

Lopez-Lira, Alejandro and Tang, Yuehua, Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models (April 6, 2023). Available at SSRN: https://ssrn.com/abstract=4412788 or http://dx.doi.org/10.2139/ssrn.4412788

Alejandro Lopez-Lira (Contact Author)

University of Florida - Department of Finance, Insurance and Real Estate ( email )

P.O. Box 117168
Gainesville, FL 32611
United States

HOME PAGE: http://alejandrolopezlira.site/

Yuehua Tang

University of Florida - Department of Finance ( email )

P.O. Box 117168
Gainesville, FL 32611
United States

HOME PAGE: http://sites.google.com/site/yuehuatang

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