Fundamental stock analysis with LLMs and qualitative data: Development of ontology-grounded, graph-based RAG with text-to-Cypher retrieval for Malaysian listed companies

Kam, Chee Qin (2025) Fundamental stock analysis with LLMs and qualitative data: Development of ontology-grounded, graph-based RAG with text-to-Cypher retrieval for Malaysian listed companies. Final Year Project, UTAR.

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Abstract

Fundamental analysis is essential for retail investors pursuing long-term investment, as a company’s profitability ultimately drives its intrinsic value. At its core, fundamental analysis relies on deriving implicit insights—such as operational resilience, governance quality, or future growth potential—from explicit data, including financial disclosures and corporate announcements. Retail investors, however, often lack the expertise, resources, and analytical experience required to perform such analysis effectively. To address this challenge, this study proposes a corporate insight derivation module powered by Large Language Models (LLMs) that systematically transforms explicit corporate disclosures into actionable implicit insights. The module employs a novel ontology-grounded, graph-based Retrieval-Augmented Generation (RAG) pipeline with text-to-Cypher retrieval. It comprises three sub-modules: (i) an Automated Ontology Construction Module, which formalises domain-specific entities and their relationships; (ii) a Graph Construction Module, which integrates heterogeneous corporate data into a coherent knowledge graph capable of multi-hop reasoning; and (iii) a Text-to-Cypher Retrieval Module, enabling natural language queries to access the knowledge graph efficiently. The system leverages disclosures from five ACE Market listed technology companies in Bursa Malaysia as a proof-of-concept. Evaluation results demonstrate that the proposed pipeline successfully derives implicit insights, with the Entity Deduplication process achieving a maximum deduplication rate of 73.0% and an overall rate of 66.5%, producing a compact and coherent knowledge graph. Despite limitations in ontology scalability, dynamic adaptability, and prompt robustness, the pipeline establishes a strong foundation for further refinement. The proposed module holds potential as a practical tool for retail investors, supporting more informed and rational decision-making by bridging the gap between explicit corporate data and implicit investment insights.

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