Graph Neural Networks for Cross-Market Financial Prediction and Systemic Risk Modeling
Abstract
The global financial ecosystem is characterized by an intricate web of interdependencies where shocks in one asset class or geographic region propagate rapidly across traditional market boundaries. Conventional predictive models, often rooted in time-series analysis of isolated variables, frequently fail to capture the relational dynamics and topological shifts inherent in these interconnected systems. This paper investigates the utility of Graph Neural Networks (GNNs) as a foundational architecture for cross-market financial prediction and systemic risk modeling. Unlike standard deep learning approaches that treat financial data as Euclidean sequences, GNNs operate directly on the non-Euclidean graph structures defined by supply chains, ownership networks, and correlation matrices. We conduct a system-level analysis of GNN deployment, emphasizing the structural trade-offs between graph density and computational scalability. The discussion extends to the socio-technical infrastructures required to sustain high-fidelity relational modeling, addressing critical issues of data governance, algorithmic fairness, and the environmental sustainability of large-scale graph processing. Furthermore, we examine the policy implications of GNN-driven risk assessment, arguing that while these models provide superior detection of contagion pathways, they also introduce novel systemic vulnerabilities related to model convergence and data poisoning. By synthesizing perspectives from graph theory, systems engineering, and financial policy, this research proposes a robust framework for integrating relational intelligence into the global financial stability architecture.
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