Thesis · 2026

Network Topology Analysis and Machine Learning Techniques for Systemic Risk Prediction in U.S. Equity Markets

Stefano Blando

University of Rome Tor Vergata - School of Economics and Finance

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@unpublished{blando2025gnn,
  title={Network Topology Analysis for Systemic Risk Prediction},
  author={Blando, Stefano},
  year={2025},
  note={Working Paper},
  institution={University of Rome Tor Vergata}
}

Abstract

This paper investigates the predictive power of Graph Neural Networks (GNNs) in forecasting systemic risk events. By modelling financial markets as dynamic complex networks, we extract topological features that serve as early warning signals. The study demonstrates how these signals can be integrated into algorithmic trading strategies to mitigate downside risk during market turmoil, outperforming traditional benchmark models.

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