Combining Macro Regime Clustering and Economic Logic for Sector Rotation

Summary

The report describes a sector-rotation framework that pairs quantitative clustering with macroeconomic reasoning. It first groups industries using features such as returns across market conditions, return on equity, and revenue growth, then makes limited logic-based adjustments to form seven broad sectors. It next applies hierarchical clustering to economic, inflation, and monetary data to identify macro regimes and examine sector returns in comparable historical situations. The approach aims to make allocations responsive to changing conditions rather than repeatedly favoring sectors with persistent long-run strength.

The report cross-checks the clustering output against a policy-and-economy model. It favors sectors when the two approaches agree and steps back from macro signals when their views diverge, on the premise that other forces may be driving sector performance. It reports annualized excess return of 8% and an information ratio of 1.23 for a test period from July 2016 through March 2020. These are reported historical results, not a guarantee; the authors also describe the clustering process as relatively opaque and limited by its inability to incorporate investor judgment directly.

Key ideas

• Industry groups are formed from return, profitability, and revenue-growth features, with some logical adjustments.
• Hierarchical clustering uses economic, inflation, and monetary data to classify macro regimes.
• The framework cross-checks statistical regime signals with a policy-and-economy model.
• It favors allocations supported by both approaches and avoids relying on the macro model when they diverge.
• The reported test results cover July 2016 through March 2020, and the method has interpretability limits.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.