Financial Statement Analysis with Large Language Models

54 Pages Posted: 21 May 2024

See all articles by Alex Kim

Alex Kim

University of Chicago Booth School of Business

Maximilian Muhn

University of Chicago - Booth School of Business

Valeri V. Nikolaev

University of Chicago Booth School of Business

Date Written: May 20, 2024

Abstract

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.

Keywords: GPT4, neural network, asset pricing, earnings, direction of earnings changes, analysts, chain-of-thought, financial statement analysis, large language models

JEL Classification: G12, G14, G41, M41

Suggested Citation

Kim, Alex G. and Muhn, Maximilian and Nikolaev, Valeri V., Financial Statement Analysis with Large Language Models (May 20, 2024). Chicago Booth Research Paper, Fama-Miller Working Paper, Available at SSRN: https://ssrn.com/abstract=4835311 or http://dx.doi.org/10.2139/ssrn.4835311

Alex G. Kim (Contact Author)

University of Chicago Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

Maximilian Muhn

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

Valeri V. Nikolaev

University of Chicago Booth School of Business ( email )

5807 South Woodlawn Avenue
Chicago, IL 60637
United States

HOME PAGE: http://faculty.chicagobooth.edu/valeri.nikolaev/index.html

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