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.
@article{arxiv.2507.07906,
title = {Agentic Retrieval of Topics and Insights from Earnings Calls},
author = {Anant Gupta and Rajarshi Bhowmik and Geoffrey Gunow},
journal= {arXiv preprint arXiv:2507.07906},
year = {2025}
}
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The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI, The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval July 13-17, 2025 | Padua, Italy