Deriving Insights and Financial Summaries from Public Data Using Large Language Models

Authors: Naveen Edapurath Vijayan

DOI: https://doi.org/10.5281/zenodo.14593256

Short DOI: https://doi.org/g8xmvm

Country: USA

Full-text Research PDF File:   View   |   Download

Abstract: This paper investigates how large language models (LLMs) can be applied to publicly available financial data to generate automated financial summaries and provide actionable recommendations for investors. We demonstrate how LLMs can process both structured financial data (balance sheets, income statements, stock prices) and unstructured text (earnings calls, management commentary) to derive insights, predict trends, and automate financial reporting. By focusing on a specific publicly traded company, this research outlines the methodology for leveraging LLMs to analyze company performance and generate investor-focused summaries and recommendations.

Keywords: Large Language Models (LLMs), Financial Data Analysis, Natural Language Processing (NLP), Automated Financial Summaries, Investment Recommendations, Structured and Unstructured Data, Sentiment Analysis, Artificial Intelligence (AI), Financial Reporting Automation, Machine Learning in Finance

Paper Id: 231954

Published On: 2024-11-05

Published In: Volume 12, Issue 6, November-December 2024

Cite This: Deriving Insights and Financial Summaries from Public Data Using Large Language Models - Naveen Edapurath Vijayan - IJIRMPS Volume 12, Issue 6, November-December 2024. DOI 10.5281/zenodo.14593256

Share this