FinKario: Event-Enhanced Automated Construction of Financial Knowledge Graph

Individual investors are disadvantaged in financial markets, overwhelmed by abundant information and lacking professional analysis.Equity research reports are crucial resources, offering valuable insights.By leveraging these reports, large language models (LLMs) can enhance investors' decision-making and strengthen financial analysis.However, two key challenges limit their effectiveness: (1) the rapid evolution of market events outpaces the slow update cycles of existing knowledge bases, and (2) the long-form, unstructured nature of financial reports hinders timely, context-aware integration by LLMs.To address these challenges, we tackle both data and methodology aspects.We introduce the Event-Enhanced Automated Construction of Financial Knowledge Graph (FinKario), a dataset with over 305,360 entities, 210,328 relational triples, and 19 relation types.FinKario integrates real-time company fundamentals and events through promptdriven extraction guided by institutional templates, providing structured, accessible financial insights for LLMs.We further propose a Two-stage, Graph-based retrieval strategy (FinKario-RAG) to optimize retrieval over evolving, large-scale financial knowledge.Experiments show that FinKario with FinKario-RAG achieves superior trend prediction accuracy, outperforming financial LLMs by 18.81% and institutional strategies by 17.85% on average in backtesting.