Paper: arXiv 2610.27751

Authors: Supriadi, Bambang Siswoyo

Abstract

We propose a regime-aware tactical asset allocation (TAA) framework integrating market regime detection with Nested Clustered Optimization (NCO) for multi-asset portfolio construction. The central hypothesis is that incorporating market regime information into the NCO pipeline, specifically at the stages of covariance estimation, hierarchical clustering, and weight optimization, yields portfolios with improved risk-adjusted performance and reduced downside risk relative to regime-agnostic baselines. Market regimes (Bullish, Sideways/Neutral, Bearish) are detected using a Gaussian Hidden Markov Model (HMM) fitted exclusively on information available up to each decision point, thereby preventing look-ahead bias. Regime-conditional covariance matrices and clustering structures parameterize NCO at each rebalancing event. An adaptive rebalancing rule, triggered by regime transitions or material changes in optimal weights, replaces fixed-calendar rebalancing. The framework is evaluated on a heterogeneous monthly multi-asset universe comprising Indonesian government bonds (SBN), the LQ45 equity index, gold (IDR-denominated), foreign exchange (CNY/IDR), and Bitcoin/IDR (BTC). Out-of-sample performance is assessed via rolling walk-forward backtesting. Benchmarks include equal-weight (1/N), buy-and-hold, standard NCO without regime awareness, minimum variance, hierarchical risk parity (HRP), and a rule-based regime strategy. Performance metrics include cumulative return, annualized Sharpe ratio, Sortino ratio, maximum drawdown, Calmar ratio, turnover, and net-of-cost returns. Statistical significance is assessed using the Jobson-Korkie test. The empirical analysis indicates that the proposed regime-aware TAA framework successfully limits tail risk, achieving a maximum drawdown of 4.44% and demonstrating the utility of HMM-based regime detection in multi-asset risk management.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a robust quantitative framework combining HMM and NCO with detailed mathematical integration. It features a strong empirical validation using walk-forward backtesting and multiple benchmarks, demonstrating significant rigor. The novelty lies in the specific integration of regime awareness into the NCO pipeline and the application to an understudied market.

Research Flowchart

  flowchart TD
    A[Research Goal: Regime-Aware Multi-Asset TAA for Improved Risk-Adjusted Performance] --> B{Methodology: Integrate Market Regimes with NCO};
    B --> C[Data/Inputs: Multi-Asset Universe (SBN, LQ45, Gold, CNY/IDR, BTC) & HMM for Regime Detection (Bullish, Sideways, Bearish)];
    C --> D[Computational Processes: Regime-Conditional Covariance & Clustering; NCO Weight Optimization; Adaptive Rebalancing; Rolling Walk-Forward Backtesting];
    D --> E[Key Findings: Proposed Regime-Aware NCO Limits Tail Risk (Max Drawdown 4.44%) & Improves Risk-Adjusted Performance];