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LLMs and RAG system generate investor briefs from financial data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a system that uses Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to create investor briefs from financial and macroeconomic data. The system processes reports, SEC filings from EDGAR, and macroeconomic indicators, then uses GPT-4o to generate summaries. These automated briefs were evaluated by nine individual investors over a four-week period to assess their usefulness in data analysis. AI

IMPACT This research demonstrates a practical application of LLMs in financial analysis, potentially streamlining data processing for investors.

RANK_REASON The cluster contains an academic paper detailing a novel application of LLMs and RAG for financial analysis.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs and RAG system generate investor briefs from financial data

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Bartosz Zi\'o{\l}ko, Kacper Dobrzeniewski ·

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

    arXiv:2607.09121v1 Announce Type: cross 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 li…

  2. arXiv cs.AI TIER_1 English(EN) · Kacper Dobrzeniewski ·

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

    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 …

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