Mitigating Return Drag and Regime Whipsaws in Sector Rotation: An Asymmetric Downside-Aware Nested Clustered Optimization Framework

Hierarchical clustering and Nested Clustered Optimization (NCO) mitigate the mathematical ill-conditioning of classical Markowitz covariance matrices. However, two structural vulnerabilities persist in equity markets: (1) symmetric variance penalizes upside and downside return dispersion identically, inducing acute return drag against soaring momentum sectors; and (2) unconstrained Hidden Markov Models (HMM) exhibit hyper-active regime switching (whipsaw instability), incurring excessive turnover and transaction drag. We propose Asymmetric Downside-Risk Nested Clustered Optimization with Sticky Regime Dynamics (Asym-Sticky-NCO) for tactical sector rotation across all 11 S&P 500 GICS sectors. The framework substitutes symmetric covariance with the Bawa-Lindenberg downside semi-covariance matrix across both hierarchical tree clustering and two-stage allocation. Furthermore, we introduce a Sticky Gaussian HMM incorporating a diagonal self-transition persistence prior (\k{appa}) and confidence-adaptive boundaries (δ) to enforce macroeconomic persistence. Strictly non-anticipative walk-forward backtesting across 65 out-of-sample months (August 2020 to December 2025) demonstrates that the architecture elevates CAGR from 10.65% (Plain NCO) to 13.41% (+276 bps/year), delivers a 97.74% total return versus 72.99%, suppresses maximum drawdown to 14.22% (versus 23.93% for the S&P 500), and achieves a Calmar ratio of 0.94 (+38.2% over S&P 500; +84.3% over Baseline Regime-NCO). Crucially, sticky dynamics cut regime whipsaws by 75.0% (from 16 to 4 switches) and reduce monthly turnover from 60.78% to 30.48%, addressing core limitations of machine learning asset allocation.