Multi-source Text Mining for Risk Signal Detection in Asset-Backed Securities Market: An NLP-driven Data Analytics Approach

Abstract

Asset-backed securities (ABS) markets face increasing complexity in risk assessment due to the heterogeneous nature of underlying assets and diverse information sources. This paper presents a comprehensive NLP-driven data analytics framework for detecting risk signals from multi-source financial texts in ABS markets. Our methodology integrates advanced natural language processing techniques with machine learning algorithms to extract meaningful risk indicators from regulatory filings, prospectuses, rating reports, and market announcements. The proposed framework employs transformer-based models for feature extraction, sentiment analysis algorithms for market sentiment quantification, and statistical methods for risk signal validation. Experimental results demonstrate significant improvements in risk detection accuracy, achieving 89.7% precision and 92.3% recall in identifying potential market anomalies. The framework successfully processes over 50,000 documents across multiple ABS categories, providing real-time risk monitoring capabilities for market participants and regulatory bodies. Our findings indicate that multi-source text mining substantially enhances traditional quantitative risk models, offering early warning systems for market instability and enabling proactive risk management strategies in structured finance markets.