Semantic Network Analysis of Financial Regulatory Documents: Extracting Early Risk Warning Signals
Yibang Liu, Wenyu Bi, Jiayan Fan · Academic Journal of Sociology and Management · 2025
This paper presents a semantic network analysis framework for extracting early risk warning signals from financial regulatory documents. Financial regulations contain critical information about emerging risks, but their increasing volume and complexity challenge traditional analysis methods. We propose a novel approach that constructs semantic networks from regulatory texts, representing concepts as nodes and their relationships as edges. Our methodology integrates techniques from natural language processing and network science to identify structural patterns indicative of emerging risks. The framework was implemented and tested on a corpus of 2,874 financial regulatory documents published between 2010-2023. Results demonstrate that the semantic network approach outperforms traditional keyword-based monitoring in both risk coverage (79.4% vs 68.7%) and false alarm reduction (11.6% vs 22.5%). The multi-metric ensemble method achieved an F1-score of 0.81 with an average lead time of 82.6 days before explicit regulatory announcements. Validation with 24 regulatory compliance professionals confirmed the practical utility of the approach, showing comparable quality to expert analysis while reducing analysis time from 24.7 to 4.8 hours. This research contributes to both theoretical understanding of regulatory text structures and practical applications for financial compliance and risk management.