NLP pipeline for fixed-income market intelligence: From unstructured data to actionable insights

Chataraju, Tarun (2025) NLP pipeline for fixed-income market intelligence: From unstructured data to actionable insights. World Journal of Advanced Research and Reviews, 26 (2). pp. 1801-1809. ISSN 2581-9615

This article explores the transformative impact of Natural Language Processing (NLP) on fixed-income market analysis and index management. It examines how NLP technologies enable the systematic processing of vast amounts of unstructured textual data - including regulatory filings, earnings calls, central bank communications, and financial news - to extract actionable investment insights. The article presents a comprehensive framework for implementing NLP in fixed-income markets, covering sentiment analysis methodologies, automated data extraction techniques, and integration approaches with traditional quantitative models. Through evidence-based analysis, the article demonstrates how NLP-enhanced strategies consistently outperform conventional approaches across various market conditions, particularly during periods of stress. While acknowledging current limitations in linguistic complexity, temporal stability, interpretability, and data coverage, the article highlights promising future directions including specialized language models for fixed-income analysis, multi-modal approaches, improved interpretability, and applications to niche market segments. The findings underscore the growing importance of NLP as an essential component of modern fixed-income investment processes.