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

65 Pages Posted: 10 Apr 2023 Last revised: 27 Nov 2023

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 examine the potential of ChatGPT and other large language models (LLMs) to predict stock market returns using news. Categorizing headlines with ChatGPT as positive, negative, or neutral for companies' stock prices, we document a significant correlation between ChatGPT scores and subsequent daily stock returns, outperforming traditional methods. Basic models like GPT-1 and BERT cannot accurately forecast returns, indicating return forecasting is an emerging capacity of more complex LLMs, which deliver higher Sharpe ratios. We explain these puzzling return predictability patterns by testing implications from economic theories involving information diffusion frictions, limits to arbitrage, and investor sophistication. Predictability strengthens among smaller stocks and following negative news, consistent with these theories. Only advanced LLMs maintain accuracy when interpreting complex news and press releases. Finally, we present an interpretability technique to evaluate LLMs' reasoning. Overall, incorporating advanced language models into investment decisions can improve prediction accuracy and trading performance.

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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