用大模型动态挖掘财报电话会中的新兴话题和关联
Agentic Retrieval of Topics and Insights from Earnings Calls
- 构建大模型代理自动提取话题并建立层级关系图谱
- 能准确追踪话题演变，识别出新兴金融趋势
- 适合金融分析师、投资机构做战略研判
通过财报电话会中的话题追踪企业战略重点是金融分析的关键任务。然而，随着行业演进，传统主题建模方法难以动态捕捉新出现的话题及其关联。本文提出一种基于大模型代理的方法，从季度财报电话会中发现并检索新兴话题。该方法利用大模型代理从文档中提取话题，将其结构化为层次化本体，并通过话题本体建立新旧话题之间的关系。我们展示了所提取话题在推断公司层面洞察和长期趋势方面的应用。通过本体一致性、话题演变准确性以及揭示新兴金融趋势的能力对方法进行了评估。
原文摘要 · Abstract (English)
Tracking the strategic focus of companies through topics in their earnings calls is a key task in financial analysis. However, as industries evolve, traditional topic modeling techniques struggle to dynamically capture emerging topics and their relationships. In this work, we propose an LLM-agent driven approach to discover and retrieve emerging topics from quarterly earnings calls. We propose an LLM-agent to extract topics from documents, structure them into a hierarchical ontology, and establish relationships between new and existing topics through a topic ontology. We demonstrate the use of extracted topics to infer company-level insights and emerging trends over time. We evaluate our approach by measuring ontology coherence, topic evolution accuracy, and its ability to surface emerging financial trends.
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