Agentic Retrieval of Topics and Insights from Earnings Calls

• URL: http://arxiv.org/abs/2507.07906v1
• Date: Thu, 10 Jul 2025 16:38:59 GMT
• Title: Agentic Retrieval of Topics and Insights from Earnings Calls
• Authors: Anant Gupta, Rajarshi Bhowmik, Geoffrey Gunow,
• Abstract summary: We propose an LLM-agent driven approach to discover and retrieve emerging topics from quarterly earnings calls.<n>We demonstrate the use of extracted topics to infer company-level insights and emerging trends over time.
• Score: 4.137418441736385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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