Attention-Based Neural Networks for Cross-Asset Risk Prediction

Abstract

Cross-asset risk prediction has become a central challenge for financial institutions, regulators, and market infrastructures because modern portfolios are exposed to interconnected shocks that move across equities, fixed income, currencies, commodities, and digital assets. This paper examines attention-based neural networks as a system-level architecture for cross-asset risk prediction. Rather than treating attention merely as a predictive technique, the discussion positions it within broader questions of data infrastructure, model governance, robustness, fairness, sustainability, and policy. Attention mechanisms offer advantages for cross-asset settings because they can dynamically weight heterogeneous signals, learn time-varying dependencies, and integrate market microstructure, macroeconomic, and network-based features. However, their deployment also raises concerns about interpretability, regime sensitivity, computational cost, and the concentration of model risk in a small number of opaque platforms. The paper argues that effective cross-asset risk prediction requires a socio-technical design in which attention models are coupled with governance routines, stress-testing protocols, human oversight, and regulatory reporting standards. It reviews architectural trade-offs, data and feature engineering issues, systemic risk implications, deployment constraints, and evaluation practices. It also considers cross-domain lessons from high-frequency trading, network risk measurement, and AI assurance frameworks. The conclusion is that attention-based systems can strengthen cross-asset risk intelligence only if they are embedded in resilient infrastructures and accountable institutional processes. Predictive accuracy alone is insufficient; legitimacy, contestability, and operational sustainability are equally important for systems that inform capital allocation, margin setting, and prudential supervision.