Real-Time Empirical Asset Pricing: Deploying Cloud-Native Machine Learning to Quantify Geopolitical and Policy Uncertainty

The integration of geopolitical risk and economic policy uncertainty into empirical asset pricing models has historically been constrained by low-frequency, retroactive datasets. In modern algorithmic trading environments, macroeconomic shocks impact asset valuations in milliseconds, rendering traditional monthly or quarterly uncertainty indices insufficient for active risk management. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless machine learning pipeline for dynamic asset pricing. By deploying distributed Python middleware integrated with eXtreme Gradient Boosting regression, the proposed system programmatically ingests high-frequency macroeconomic data and policy sentiment feeds, instantly quantifying their impact on asset risk premiums. Preliminary architectural evaluations demonstrate that deploying predictive inference engines at the network edge significantly reduces the latency of pricing adjustments, providing financial economists and institutional investors with a deterministic, scalable tool for navigating behavioral finance anomalies and systemic policy shocks.