Using ChatGPT to Forecast Stock Price Movements
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Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models
Alejandro Lopez-Lira; Yuehua Tang
- University of Florida
- ?University of Florida - Department of Finance, Insurance and Real Estate
- ?University of Florida - Department of Finance
Strategy in a nutshell
The study uses CRSP daily returns, news headlines, and RavenPack data from October 2021 to December 2022 to analyze ChatGPT’s sentiment scores in predicting stock returns. Headlines are processed through customized prompts where ChatGPT evaluates potential impacts on stock prices, generating “ChatGPT scores” mapped numerically (YES = 1, UNKNOWN = 0, NO = -1). Scores are averaged per company per day and matched to next-day returns, with linear regressions analyzing predictive power. The results show that ChatGPT’s sentiment scores significantly forecast daily stock movements, outperforming traditional methods and models like BERT, GPT-1, and GPT-2, especially for small-cap stocks.
Economic rationale
The strategy leverages ChatGPT’s advanced natural language understanding to extract meaningful signals from financial news, addressing a largely unexplored application of large language models in financial economics. Empirical evidence confirms that ChatGPT’s sentiment analysis provides superior predictive insights compared to conventional sentiment methods, highlighting its potential value in investment decision-making.