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

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[Submitted on 10 Jul 2026]

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

View a PDF of the paper titled Augmenting Fundamental Analysis with Large Language Models: A RAG-Based System for Generating Investor Briefs, by Bartosz Zi\'o{\l}ko and Kacper Dobrzeniewski

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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.

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Portfolio Management (q-fin.PM); Trading and Market Microstructure (q-fin.TR)

Cite as: arXiv:2607.09121 [cs.CL]

(or arXiv:2607.09121v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.09121

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Bartosz Ziółko [view email] [v1] Fri, 10 Jul 2026 06:20:40 UTC (557 KB)

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