Explainable Regime Aware Investing

TL;DR

This paper introduces an explainable, regime-aware portfolio construction method using a Wasserstein Hidden Markov Model, which adaptively captures market regimes to improve risk-adjusted returns and robustness.

Contribution

It presents a novel regime inference framework combining Wasserstein HMM with dynamic complexity control, enhancing interpretability and portfolio stability over prior models.

Findings

Achieves higher Sharpe ratios (2.18 vs. 1.59 and 1.18) compared to benchmarks.

Significantly reduces maximum drawdown to -5.43% from -14.62%.

Demonstrates adaptive regime shifts during market stress, improving risk management.

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.