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

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