mlsanityData to 7 Oct 2026

Agentic Retrieval of Topics and Insights from Earnings Calls

arXiv 2507.07906 ↗First posted 10 Jul 2025cs.LG cs.AIcited by 0

On the map: LLM agents and tool use → Financial trading and forecasting agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 7 of its 13 references that are in the map. First posted on arXiv 10 Jul 2025.

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7 of 13 references.

References · 7

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 378
  3. 2021Topic Modelling Meets Deep Neural Networks: A Surveycited by 51
  4. 2024BERTrend: Neural Topic Modeling for Emerging Trends Detectioncited by 13
  5. 2024Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Fieldcited by 10
  6. 2024Evaluating Large Language Models on Financial Report Summarization: An Empirical Studycited by 3
  7. 2024Distilled ChatGPT Topic & Sentiment Modeling with Applications in Financecited by 1

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