Utilizing Modern Large Language Models (LLM) for Financial Trend Analysis and Digest Creation
Abstract
The exponential growth of information presents a significant challenge for researchers and professionals seeking to remain at the forefront of their fields and this paper introduces an innovative framework for automatically generating insightful financial digests using the power of Large Language Models (LLMs), specifically Google's Gemini Pro. By leveraging a combination of data extraction from OpenAlex, strategic prompt engineering, and LLM-driven analysis, we demonstrate the automated example of creating a comprehensive digests that generalize key findings, identify emerging trends. This approach addresses the limitations of traditional analysis methods, enabling the efficient processing of vast amounts of unstructured data and the delivery of actionable insights in an easily digestible format. This paper describes how LLMs work in simple words and how we can use their power to help researchers and scholars save their time and stay informed about current trends. Our study includes step-by-step process, from data acquisition and JSON construction to interaction with Gemini and the automated generation of PDF reports, including a link to the project's GitHub repository for broader accessibility and further development.
Cite
@article{arxiv.2510.01225,
title = {Utilizing Modern Large Language Models (LLM) for Financial Trend Analysis and Digest Creation},
author = {Andrei Lazarev and Dmitrii Sedov},
journal= {arXiv preprint arXiv:2510.01225},
year = {2025}
}
Comments
This is the version of the article accepted for publication in SUMMA 2024 after peer review. The final, published version is available at IEEE Xplore: 10.1109/SUMMA64428.2024.10803746