Complexity-Aware Vector-Valued Machine Learning of State-Level Bond Returns: Evidence on South African Trade Spillovers Under SALT and OBBBA

This study examines the impact of international trade shocks from South Africa and recent U.S. federal tax reforms on state-level municipal bond returns within the United States. Employing a unique transaction-level dataset comprising more than 50 million municipal bond trades from 2020 to 2024, the empirical approach integrates machine learning estimators with econometric volatility models to examine daily nonlinear spillovers and structural complexity across twenty U.S. states. The study introduces and extends the application of a vector radial basis function neural network framework, leveraging its universal approximation capacity to jointly model multiple state-level outcomes and uncover complex response patterns The empirical results reveal substantial cross-state heterogeneity in bond-return resilience, influenced by variation in state tax regimes, economic complexity, and differential exposure to external financial forces. States exhibiting higher economic adaptability demonstrate faster recovery and weaker shock amplification, whereas structurally rigid states experience persistent volatility and slower mean reversion. These findings demonstrate that complexity-aware predictive modeling, when combined with granular fiscal and trade-linkage data, provides valuable insight into the pathways through which global and domestic shocks propagate into U.S. municipal bond markets and shape subnational financial stability.