Network Topology and Machine Learning for Systemic Risk: From Early Warning to Trading Strategies

This work investigates the application of network topology and machine learning for systemic risk prediction in equity markets. Using daily returns from S&P 500 constituents, we construct dynamic correlation networks to extract high-dimensional topological features, including eigenvalue-based metrics (absorption ratio, network entropy) and graph-theoretic centralities. We evaluate a comparative suite of predictive architectures—ranging from Gradient Boosted Decision Trees to Graph Neural Networks (GraphSAGE, GAT) and LSTMs—validated through walk-forward cross-validation with purging. Our findings reveal that network topology exhibits statistically significant anomalies an average of 67 days prior to crisis onset, with lead-lag analysis confirming predictive causality over traditional implied volatility measures (VIX). Furthermore, we examine market microstructure through the lens of herding behavior (CCK framework) and volatility spillovers (Diebold-Yilmaz), ultimately translating these signals into economically significant trading strategies such as Kelly-style probability scaling.