Why this research matters
Most quantitative portfolios still rely on static factor weights, even though markets continuously evolve.
This research explores a dynamic framework that adapts signal weights using macroeconomic regime classification and factor-specific crowding indicators—improving risk-adjusted performance while reducing downside exposure.
Inside the research note:
• Using macroeconomic regimes to dynamically adjust factor weights
• Measuring factor crowding using stock-level indicators
• Understanding the relationship between crowding and future returns
• Why longer-horizon forecasts may improve earnings-based alpha models
• A practical framework for dynamic signal aggregation
Results at a glance
Sharpe Ratio: 2.51 → 2.85
Maximum Drawdown: -12.9% → -9.8%
Approach: Dynamic weighting using market regimes and crowding indicators
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