  We examine the potential of ChatGPT and other large language models in
predicting stock market returns using news headlines. We use ChatGPT to assess
whether each headline is good, bad, or neutral for firms' stock prices. We
document a significantly positive correlation between ChatGPT scores and
subsequent daily stock returns. We find that ChatGPT outperforms traditional
sentiment analysis methods. More basic models such as GPT-1, GPT-2, and BERT
cannot accurately forecast returns, indicating return predictability is an
emerging capacity of complex language models. Long-short strategies based on
ChatGPT-4 deliver the highest Sharpe ratio. Furthermore, we find predictability
in both small and large stocks, suggesting market underreaction to company
news. Predictability is stronger among smaller stocks and stocks with bad news,
consistent with limits-to-arbitrage also playing an important role. Finally, we
propose a new method to evaluate and understand the models' reasoning
capabilities. Overall, 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.
