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Explainable Regime Aware Investing

Amine Boukardagha

TL;DR

The results demonstrate that regime inference stability, particularly identity preservation and adaptive complexity control, is a first-order determinant of portfolio drawdown and implementation robustness in daily asset allocation.

Abstract

We propose an explainable regime-aware portfolio construction framework based on a strictly causal Wasserstein Hidden Markov Model. The model combines rolling Gaussian HMM inference with predictive model-order selection and template-based identity tracking using the 2-Wasserstein distance between Gaussian components. This allows regime complexity to adapt dynamically while preserving stable economic interpretation. Regime probabilities are embedded into a transaction-cost-aware mean-variance optimization framework and evaluated on a diversified daily cross-asset universe. Relative to equal-weight and SPX buy-and-hold benchmarks, the Wasserstein HMM achieves materially higher risk-adjusted performance with Sharpe ratios of 2.18 versus 1.59 and 1.18 and substantially lower maximum drawdown of negative 5.43 percent versus negative 14.62 percent for SPX. During the early 2025 equity selloff labeled Liberation Day, the strategy dynamically reduced equity exposure and shifted toward defensive assets, mitigating peak-to-trough losses. Compared to a nonparametric KNN conditional-moment estimator using the same features and optimization layer, the parametric regime model produces materially lower turnover and smoother weight evolution. The results demonstrate that regime inference stability, particularly identity preservation and adaptive complexity control, is a first-order determinant of portfolio drawdown and implementation robustness in daily asset allocation.

Explainable Regime Aware Investing

TL;DR

The results demonstrate that regime inference stability, particularly identity preservation and adaptive complexity control, is a first-order determinant of portfolio drawdown and implementation robustness in daily asset allocation.

Abstract

We propose an explainable regime-aware portfolio construction framework based on a strictly causal Wasserstein Hidden Markov Model. The model combines rolling Gaussian HMM inference with predictive model-order selection and template-based identity tracking using the 2-Wasserstein distance between Gaussian components. This allows regime complexity to adapt dynamically while preserving stable economic interpretation. Regime probabilities are embedded into a transaction-cost-aware mean-variance optimization framework and evaluated on a diversified daily cross-asset universe. Relative to equal-weight and SPX buy-and-hold benchmarks, the Wasserstein HMM achieves materially higher risk-adjusted performance with Sharpe ratios of 2.18 versus 1.59 and 1.18 and substantially lower maximum drawdown of negative 5.43 percent versus negative 14.62 percent for SPX. During the early 2025 equity selloff labeled Liberation Day, the strategy dynamically reduced equity exposure and shifted toward defensive assets, mitigating peak-to-trough losses. Compared to a nonparametric KNN conditional-moment estimator using the same features and optimization layer, the parametric regime model produces materially lower turnover and smoother weight evolution. The results demonstrate that regime inference stability, particularly identity preservation and adaptive complexity control, is a first-order determinant of portfolio drawdown and implementation robustness in daily asset allocation.
Paper Structure (37 sections, 14 equations, 11 figures, 7 tables, 2 algorithms)

This paper contains 37 sections, 14 equations, 11 figures, 7 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Data and Market Universe
  4. Feature Representation
  5. Parametric Regime Detection: Wasserstein Hidden Markov Model
  6. Feature Representation and Strict Causality
  7. Predictive Model-Order Selection
  8. Daily Regime Inference via Gaussian HMM
  9. Template-Based Regime Identity via Wasserstein Distance
  10. Template-Conditional Moment Aggregation
  11. Transaction-Cost-Aware Mean--Variance Optimization
  12. Non-Parametric Regime Inference: Rolling K-Nearest Neighbors Model
  13. Pseudocodes
  14. Parametric Regime Investing: Wasserstein HMM + MVO
  15. Diagnostics.
  16. ...and 22 more sections

Figures (11)

  • Figure 1: Cumulative OOS portfolio performance for parametric regime investing.
  • Figure 2: Cumulative OOS portfolio performance for non-parametric regime investing.
  • Figure 3: Daily turnover time series for non-parametric regime investing.
  • Figure 4: Daily turnover time series for parametric regime investing.
  • Figure 5: Daily portfolio weights for non-parametric regime investing.
  • ...and 6 more figures