Augmenting Fundamental Analysis with Large Language Models: A RAG-Based System for Generating Investor Briefs

Researchers developed a Retrieval-Augmented Generation (RAG) system using GPT-4o to automate fundamental analysis of companies by processing SEC filings, macroeconomic data, and Kitchin cycle documents. The system generated investor briefs for nine companies over four weeks, which were evaluated by nine individual investors for usefulness.

arXiv:2607.09121v1 Announce Type: new Abstract: In this study, we examine the opportunities brought by Large Language Models LLMs to various aspects of fundamental analysis of companies based on their reports as well as data and documents describing macroeconomic situation like GDP and inflation changes as well as documents filled to the U.S. Securities and Exchange Commission SEC which can be found in EDGAR. We were preprocessing those data and than sending via API to gpt-4o model in a Retrieval-Augmented Generation RAG like regime. We prepared as well a document describing an exemplar investor knowledge based on Kitchin cycles. We were scanning data important for analysis of 9 companies for 4 weeks. Using LLM we were producing automatic briefs about them. They were sent to nine participants who are individual investors to evaluate usefulness of such approach to data analysis.