Financial Statement Analysis with Large Language Models

Alex Kim, Accounting PhD Student
Maximilian Muhn, Assistant Professor of Accounting
Valeri Nikolaev, James H. Lorie Professor of Accounting and FMC Faculty Scholar

We (Kim, Muhn and Nikolaev) examine to what extent general-purpose large language models (LLM) such as GPT4 can make informed financial decisions based on mostly numerical financial data. We provide standardized and anonymous financial statements to a pre-trained LLM and design sophisticated chain-of-thought prompts that resemble how human analysts make earnings predictions. Our current results show that – even without additional narrative contexts or industry-specific information – the LLM outperforms the median financial analyst in its ability to forecast annual earnings. The overall prediction accuracy of the LLM is on par with the performance of a specifically trained artificial neural network model. We then show that LLM models’ prediction does not stem from its training memory. Lastly, our trading strategies based on GPT’s prediction yield a higher Sharpe ratio and alphas than strategies based on machine-learning-based models. Our preliminary results provide evidence on the potential of a general-purpose LLM in generating accurate, explainable financial forecasts. Our further analyses will more directly examine (i ) the value of incorporating textual data and (ii ) the source of GPT’s performance by testing different prompts in a factorial design.