Can large language models autonomously generate unique and profound insights in fundamental analysis?

Fundamental analysis plays a critical role in equity investing, but its complexity has long limited the involvement of artificial intelligence (AI). Recent advances in large language models (LLMs), however, have opened new possibilities for AI to handle fundamental analysis. Despite this potential, leveraging LLMs to generate practically useful outputs remains a non-trivial challenge, and existing research is still in its early stages. This paper aims to enhance the performance of LLMs in fundamental analysis in a novel way, drawing inspiration from the practices of human analysts. We first propose a novel Autonomous Fundamental Analysis System (AutoFAS), which enables LLM agents to perform analyses on various topics of target companies. Next, we allow LLM agents to autonomously conduct research on specified companies with AutoFAS by exploring various topics they deem important, mimicking the experience accumulation of human analysts. Then, when presented with new research topics, the agents generate reports by referring to their accumulated analyses. Experiments show that, with AutoFAS, LLM agents can autonomously and logically explore various facets of target companies. The evaluation of their analysis on new research topics demonstrates that by drawing on accumulated analyses, they can naturally produce more unique and profound insights. This resembles the human process of generating novel ideas. Our work highlights a promising direction for applying LLMs in complex fundamental analysis, bridging the gap between human expertise and LLMs’ analysis. • We explore the application of LLMs in fundamental analysis. • We built a novel system enabling LLM agents to autonomously perform diverse analyses. • We proposed an innovative direction to enhance LLMs’ analytical performance. • Our system enables LLM agents to build prior knowledge to generate unique insights.