{"title":"Financial Statement Analysis with Large Language Models","authors":"Alex Kim, Maximilian Muhn, Valeri Nikolaev","doi":"arxiv-2407.17866","DOIUrl":null,"url":null,"abstract":"We investigate whether an LLM can successfully perform financial statement\nanalysis in a way similar to a professional human analyst. We provide\nstandardized and anonymous financial statements to GPT4 and instruct the model\nto analyze them to determine the direction of future earnings. Even without any\nnarrative or industry-specific information, the LLM outperforms financial\nanalysts in its ability to predict earnings changes. The LLM exhibits a\nrelative advantage over human analysts in situations when the analysts tend to\nstruggle. Furthermore, we find that the prediction accuracy of the LLM is on\npar with the performance of a narrowly trained state-of-the-art ML model. LLM\nprediction does not stem from its training memory. Instead, we find that the\nLLM generates useful narrative insights about a company's future performance.\nLastly, our trading strategies based on GPT's predictions yield a higher Sharpe\nratio and alphas than strategies based on other models. Taken together, our\nresults suggest that LLMs may take a central role in decision-making.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"70 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Statistical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.17866","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.