Fundamental stock analysis with LLMs and qualitative data: Impact of government policies and economic trends

Tang, Jia Hui (2025) Fundamental stock analysis with LLMs and qualitative data: Impact of government policies and economic trends. Final Year Project, UTAR.

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Abstract

This report presents the development of a Virtual Analyst system for fundamental stock investment, powered by GPT-4o mini and other advanced technologies. The system leverages Large Language Models (LLMs) for processing and analysing qualitative data to provide comprehensive stock analysis and investment recommendations. The system integrates web scraping techniques to extract valuable information from diverse sources such as government policies, economic reports, news articles, and financial statements. The research process involved designing a modular architecture with five core components: financial report extraction, real-time news collection, inter-company relationship mapping, qualitative analysis of government policies and economic trends, and investment insight generation. Emphasis was placed on the qualitative analysis module, which leverages Retrieval-Augmented Generation (RAG) techniques to deliver contextually relevant insights. Preliminary testing validated the system's ability to generate accurate investment recommendations in JSON format. The conclusion highlights the system’s potential to democratize sophisticated financial tools and to empower retail investors with actionable insights into stock growth prospects. Planning for future work includes real-time data integration and scalability enhancements, ensuring alignment with the project’s objectives of transforming financial decision-making. The proposed methods and technologies have been justified as suitable for achieving the system’s objectives of delivering actionable and contextually relevant insights into stock growth prospects, thus demonstrating the potential to transform decision-making in fundamental stock analysis.

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