Automated Financial News Analyzer using Large Language Models

Ms. C. Revathi 1
Asst.Professor, Department of AIDSAnnamacharya Institute ofTechnology and Sciences, Tirupati –
517520, A.P.revathiais@gmail.com
K.Mahammad sadak 4
Student, Department of AIDSAnnamacharya Institute ofTechnology and Sciences, Tirupati –
517520, A.P.Sadakayyan3036@gmail.com

Shaik Shajid Basha 3
student, Department of AIDSAnnamacharya Institute ofTechnology and Sciences, Tirupati –
517520, A.P.Sajidbasha143@gmail.com
P.Bhuvaneswari 2
Student, Department of AIDSAnnamacharya Institute ofTechnology and Sciences, Tirupati –517520,

A.P.Bhuvaneswari.p0204@gmail.com
A.Hemanth Kumar Reddy 5
Student, Department of AIDSAnnamacharya Institute ofTechnology and Sciences, Tirupati –
517520, A.P.annemhemanthreddy@gmail.com

Abstract:A Market News Analyzer based on Large Language Models (LLMs) is designed to transform unstructured financial news into meaningful insights that supportintelligent decision-making. With the rapid growth of digital news sources, investors and analysts face challenges in processing vast amounts of information in real time. This system leverages advanced language models such as GPT-4 and LLaMA to automatically analyze news articles, social media posts, and financial reports. It performs tasks including sentiment detection,entity recognition, and contextual understanding to identify market trends and potential impacts on stocks and sectors. By integrating natural language processing with machine learning techniques, the system provides a scalable and efficient solution for financial datainterpretation.Index Keywords: Market News Analysis, Large Language Models (LLMs), GPT-4, LLaMA, Natural Language Processing (NLP), Sentiment Analysis, Financial Text Mining, Stock Market Prediction, Trend Analysis, Risk Assessment, Machine Learning, Artificial
Intelligence, Data Analytics, Information Extraction, Real-Time Monitoring