用大模型构建可解释的股票知识图谱，挖掘公司间深层关系
Knowledge Graph Construction for Stock Markets with LLM-Based Explainable Reasoning
- 设计股票领域专用知识图谱，融合公司、行业与财务数据
- 结合大模型实现多跳推理，回答复杂投资问题
- 适合金融分析师和量化研究者用于深度决策支持
股票市场具有高度复杂性，企业、行业与财务指标之间存在相互依赖关系。传统研究多聚焦于时间序列预测与单公司分析，依赖数值数据进行股价预测，虽能提供短期洞察，却难以捕捉关联模式、竞争动态及可解释的投资逻辑。为此，我们提出一种专为股票市场设计的知识图谱架构，建模公司、行业、股票指标、财务报表及企业间关系。通过将该架构与大语言模型（LLMs）结合，实现多跳推理与关系查询，生成对复杂金融问题的可解释、深入解答。图1展示了系统流程，包括数据收集、图谱构建、基于大模型的查询处理与答案生成。我们在韩国上市企业上开展实证案例研究，验证了该框架提取传统数据库无法获得的洞察的能力。结果表明，知识图谱与大模型结合在高级投资分析与决策支持中具有巨大潜力。
原文摘要 · Abstract (English)
The stock market is inherently complex, with interdependent relationships among companies, sectors, and financial indicators. Traditional research has largely focused on time-series forecasting and single-company analysis, relying on numerical data for stock price prediction. While such approaches can provide short-term insights, they are limited in capturing relational patterns, competitive dynamics, and explainable investment reasoning. To address these limitations, we propose a knowledge graph schema specifically designed for the stock market, modeling companies, sectors, stock indicators, financial statements, and inter-company relationships. By integrating this schema with large language models (LLMs), our approach enables multi-hop reasoning and relational queries, producing explainable and in-depth answers to complex financial questions. Figure1 illustrates the system pipeline, detailing the flow from data collection and graph construction to LLM-based query processing and answer generation. We validate the proposed framework through practical case studies on Korean listed companies, demonstrating its capability to extract insights that are difficult or impossible to obtain from traditional database queries alone. The results highlight the potential of combining knowledge graphs with LLMs for advanced investment analysis and decision support.
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