Join us in returning to NYC on June 5th to collaborate with executive leaders in exploring comprehensive methods for auditing AI models regarding bias, performance, and ethical compliance across diverse organizations. Find out how you can attend here.
Researchers from the University of Chicago have demonstrated that large language models (LLMs) can conduct financial statement analysis with accuracy rivaling and even surpassing that of professional analysts. The findings, published in a working paper titled “Financial Statement Analysis with Large Language Models,” could have major implications for the future of financial analysis and decision-making.
The researchers tested the performance of GPT-4, a state-of-the-art LLM developed by OpenAI, on the task of analyzing corporate financial statements to predict future earnings growth. Remarkably, even when provided only with standardized, anonymized balance sheets, and income statements devoid of any textual context, GPT-4 was able to outperform human analysts.
“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,” the authors write. “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.”
A study by researchers at the University of Chicago found that OpenAI’s GPT-4 model outperformed human analysts in predicting corporate earnings, achieving an accuracy score of 0.604 and an F1 score of 0.609. The researchers used a novel approach of providing structured financial data and “chain-of-thought” prompts to guide the AI’s reasoning. (Source: University of Chicago)
Chain-of-thought prompts emulate human analyst reasoning
A key innovation was the use of “chain-of-thought” prompts that guided GPT-4 to emulate the analytical process of a financial analyst, identifying trends, computing ratios, and synthesizing the …
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Join us in returning to NYC on June 5th to collaborate with executive leaders in exploring comprehensive methods for auditing AI models regarding bias, performance, and ethical compliance across diverse organizations. Find out how you can attend here.
Researchers from the University of Chicago have demonstrated that large language models (LLMs) can conduct financial statement analysis with accuracy rivaling and even surpassing that of professional analysts. The findings, published in a working paper titled “Financial Statement Analysis with Large Language Models,” could have major implications for the future of financial analysis and decision-making.
The researchers tested the performance of GPT-4, a state-of-the-art LLM developed by OpenAI, on the task of analyzing corporate financial statements to predict future earnings growth. Remarkably, even when provided only with standardized, anonymized balance sheets, and income statements devoid of any textual context, GPT-4 was able to outperform human analysts.
“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,” the authors write. “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.”
A study by researchers at the University of Chicago found that OpenAI’s GPT-4 model outperformed human analysts in predicting corporate earnings, achieving an accuracy score of 0.604 and an F1 score of 0.609. The researchers used a novel approach of providing structured financial data and “chain-of-thought” prompts to guide the AI’s reasoning. (Source: University of Chicago)
Chain-of-thought prompts emulate human analyst reasoning
A key innovation was the use of “chain-of-thought” prompts that guided GPT-4 to emulate the analytical process of a financial analyst, identifying trends, computing ratios, and synthesizing the …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]