A Regime-Based Framework for Systematic Asset Allocation

In a macro environment that changes continuously, many regime frameworks still rely on static rules and coarse classifications. This paper presents a practical research framework for modeling the macro cycle in a more adaptive and data-driven way.

A Regime-Based Framework for Systematic Asset Allocation outlines how raw macroeconomic data—across Growth, Labor, Credit, Inflation, Rates, and Fiscal dimensions—can be standardized, combined, and mapped into intuitive macro regimes. Instead of fixed thresholds or binary labels, the approach uses rolling quantiles to reflect how economic conditions evolve over time.

A key contribution of the research is showing how machine learning can be used to refine, rather than replace, economic intuition. A Random Forest layer improves regime classification by capturing nonlinear and conditional relationships between indicators, helping distinguish meaningful shifts from short-term noise. The paper then uses a portfolio case study to illustrate how regime diagnostics can be translated into diversified, market-neutral return streams, offering a blueprint for connecting macro research to practical alpha construction.

What You’ll Learn

• How to construct macro composites that treat the business cycle as a continuous state, not a switch
• Why adaptive quantile-based regimes are more robust than fixed-rule approaches
• How a Random Forest refinement layer improves regime classification while remaining interpretable
• How regime-aware analysis can be used to design diversified, risk-controlled alpha signals across assets