Network Topology Analysis and Machine Learning Techniques for Systemic Risk Prediction in U.S. Equity Markets
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.
Type
Publication
University of Rome Tor Vergata - School of Economics and Finance
Authors
Stefano Blando
(he/him)
PhD Student in Artificial Intelligence
Stefano Blando is a PhD student in the National PhD Program in Artificial Intelligence at
Scuola Superiore Sant’Anna and the University of Pisa. His research lies at the intersection
of AI, agent-based modeling, and economics. He studies adaptive multi-agent systems,
statistical verification of economic simulations, and robust quantitative methods for
financial and socio-economic data.