We investigate whether an LLM can successfully perform financial statement analysis in a way similar to a professional human analyst. We provide standardized and anonymous financial statements to GPT4 and instruct the model to analyze them to determine the direction of future earnings. Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes. The LLM exhibits a relative advantage over human analysts in situations when the analysts tend to struggle. Furthermore, we find that the prediction accuracy of the LLM is on par with the performance of a narrowly trained state-of-the-art ML model. LLM prediction does not stem from its training memory. Instead, we find that the LLM generates useful narrative insights about a company’s future performance. Lastly, our trading strategies based on GPT’s predictions yield a higher Sharpe ratio and alphas than strategies based on other models. Taken together, our results suggest that LLMs may take a central role in decision-making.

More on this topic

BFI Working Paper·Feb 10, 2025

Policy Interventions and China’s Stock Market in the Early Stages of the COVID-19 Pandemic

Steven Davis, Dingqian Liu, Xuguang Simon Sheng, and Yan Wang
Topics: COVID-19, Financial Markets
BFI Working Paper·Feb 3, 2025

The Long and Short of Financial Development

Douglas W. Diamond, Yunzhi Hu, and Raghuram Rajan
Topics: Financial Markets
BFI Working Paper·Jan 28, 2025

An Informationally-Robust Market Model of Perfect Competition

Benjamin Brooks, Songzi Du, and Linchen Zhang
Topics: Financial Markets