Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models
This addresses the problem of stock market forecasting for investors and researchers, showing incremental improvements in using LLMs for financial analysis.
The study investigated whether large language models like ChatGPT can predict stock price movements from news headlines without financial training, finding that GPT-4 achieved about 90% hit rates for initial market reactions and predicted subsequent drifts, especially for small stocks and negative news.
We document the capability of large language models (LLMs) like ChatGPT to predict stock market reactions from news headlines without direct financial training. Using post-knowledge-cutoff headlines, GPT-4 captures initial market responses, achieving approximately 90% portfolio-day hit rates for the non-tradable initial reaction. GPT-4 scores also significantly predict the subsequent drift, especially for small stocks and negative news. Forecasting ability generally increases with model size, suggesting that financial reasoning is an emerging capacity of complex LLMs. Strategy returns decline as LLM adoption rises, consistent with improved price efficiency. To rationalize these findings, we develop a theoretical model that incorporates LLM technology, information-processing capacity constraints, underreaction, and limits to arbitrage.