Dynamic Asset Allocation with Asset-Specific Regime Forecasts

ArXiv ID: 2406.09578 “View on arXiv”

Authors: Unknown

Abstract

This article introduces a novel hybrid regime identification-forecasting framework designed to enhance multi-asset portfolio construction by integrating asset-specific regime forecasts. Unlike traditional approaches that focus on broad economic regimes affecting the entire asset universe, our framework leverages both unsupervised and supervised learning to generate tailored regime forecasts for individual assets. Initially, we use the statistical jump model, a robust unsupervised regime identification model, to derive regime labels for historical periods, classifying them into bullish or bearish states based on features extracted from an asset return series. Following this, a supervised gradient-boosted decision tree classifier is trained to predict these regimes using a combination of asset-specific return features and cross-asset macro-features. We apply this framework individually to each asset in our universe. Subsequently, return and risk forecasts which incorporate these regime predictions are input into Markowitz mean-variance optimization to determine optimal asset allocation weights. We demonstrate the efficacy of our approach through an empirical study on a multi-asset portfolio comprising twelve risky assets, including global equity, bond, real estate, and commodity indexes spanning from 1991 to 2023. The results consistently show outperformance across various portfolio models, including minimum-variance, mean-variance, and naive-diversified portfolios, highlighting the advantages of integrating asset-specific regime forecasts into dynamic asset allocation.

Keywords: Regime Identification, Gradient Boosted Trees, Portfolio Optimization, Unsupervised Learning, Dynamic Asset Allocation

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced statistical models (statistical jump models, gradient-boosted trees) and Markowitz optimization, indicating high mathematical complexity; it also presents a detailed empirical study with a 32-year backtest on 12 asset classes, demonstrating high implementation rigor and out-of-sample validation.

  flowchart TD
    A["Research Goal:<br>Enhance Multi-Asset Portfolio<br>Construction via Asset-Specific<br>Regime Forecasts"] --> B
    
    subgraph B ["Data & Inputs"]
        direction LR
        B1["12 Risky Assets<br>(1991-2023)"]
        B2["Asset Return Series"]
        B3["Cross-Asset Macro-Features"]
    end

    B --> C["Phase 1: Unsupervised Regime Identification<br>Statistical Jump Model"]
    C --> D["Bullish/Bearish<br>Regime Labels"]
    
    D --> E["Phase 2: Supervised Forecasting<br>Gradient Boosted Trees Classifier"]
    B3 --> E
    
    E --> F["Asset-Specific<br>Regime Forecasts"]
    F --> G["Phase 3: Portfolio Optimization<br>Markowitz Mean-Variance<br>with Regime Forecasts"]
    
    G --> H["Key Findings:<br>Outperformance in Min-Var,<br>Mean-Var, & Naive Portfolios"]