Does FOMC Tone Really Matter? Statistical Evidence from Spectral Graph Network Analysis

2025-10-02

AI Summary

This study investigates whether and how the tone of Federal Open Market Committee (FOMC) announcements affects the structural topology of financial market networks. Methodologically, it quantifies policy tone—hawkish versus neutral—using natural language processing, constructs asset-return-based hypergraphs, and innovatively employs the Fiedler value of the hypergraph Laplacian as a spectral measure of network connectivity and systemic stability. An event-study framework identifies dynamic, multi-horizon effects of tone shifts. Results show that FOMC tone significantly reshapes market network architecture: hawkish statements induce short-term fragmentation followed by medium-term reconfiguration, whereas neutral statements trigger delayed fragmentation. This work pioneers the application of hypergraph spectral theory to monetary policy communication analysis, offering a novel theoretical paradigm and empirically testable quantitative tools for understanding the network-mediated transmission of macroeconomic policy.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Economics, Online Markets and Human Computation: Social networks and social learning
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

This study examines the relationship between Federal Open Market Committee (FOMC) announcements and financial market network structure through spectral graph theory. Using hypergraph networks constructed from S&P 100 stocks around FOMC announcement dates (2011--2024), we employ the Fiedler value -- the second eigenvalue of the hypergraph Laplacian -- to measure changes in market connectivity and systemic stability. Our event study methodology reveals that FOMC announcements significantly alter network structure across multiple time horizons. Analysis of policy tone, classified using natural language processing, reveals heterogeneous effects: hawkish announcements induce network fragmentation at short horizons ($k=6$) followed by reconsolidation at medium horizons ($k=14$), while neutral statements show limited immediate impact but exhibit delayed fragmentation. These findings suggest that monetary policy communication affects market architecture through a network structural transmission, with effects varying by announcement timing and policy stance.

Problem

• Analyzes FOMC announcements' impact on financial market network structure
• Measures market connectivity changes using spectral graph theory and Fiedler values
• Examines how policy tone affects network fragmentation and consolidation patterns

Innovation

• Using hypergraph networks from stock data
• Employing Fiedler value to measure connectivity
• Analyzing policy tone effects via NLP classification