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

69 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 examine the potential of ChatGPT and other large language models (LLMs) to predict stock market returns using news headlines. ChatGPT scores significantly predict subsequent daily stock returns, outperforming traditional methods. A model involving information capacity constraints, limits to arbitrage, and LLMs rationalizes this predictability, which strengthens among smaller stocks and following negative news. The model predicts a distinct impact of LLMs on market efficiency relative to other machine-learning developments due to their simultaneous wide availability. Only advanced LLMs maintain accuracy when interpreting hard-to-read news, while basic models cannot accurately forecast returns, suggesting return forecasting is an emerging capacity of bigger LLMs, which deliver higher Sharpe ratios. We develop an interpretability technique to evaluate LLMs' reasoning.

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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