Tactical Asset Allocation with Macroeconomic Regime Detection: A Data-Driven Approach to Market Regimes
Tactical asset allocation (TAA) aims to optimize portfolio performance by dynamically adjusting allocations based on changing market conditions. A key challenge in TAA is accurately identifying macroeconomic regimes—distinct periods characterized by different risk-return profiles. Traditional approaches to regime modeling often rely on market price data, which can be noisy and reactive. However, a recent study proposes a machine-learning-based approach that integrates macroeconomic data for more robust regime detection and tactical portfolio adjustments.