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

29 Pages Posted: 10 Apr 2023 Last revised: 12 May 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, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and document a positive correlation between these ``ChatGPT scores'' and subsequent daily stock market returns. Further, ChatGPT outperforms traditional sentiment analysis methods. We find that more basic models such as GPT-1, GPT-2, and BERT cannot accurately forecast returns, indicating return predictability is an emerging capacity of complex models. ChatGPT-4's implied Sharpe ratios are larger than ChatGPT-3's; however, the latter model has larger total returns. Our results suggest that incorporating advanced language models into the investment decision-making process can yield more accurate predictions and enhance the performance of quantitative trading strategies. Predictability is concentrated on smaller stocks and more prominent on firms with bad news, consistent with limits-to-arbitrage arguments rather than market inefficiencies.

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

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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