Dynamic Factor Allocation Leveraging Regime-Switching Signals

ArXiv ID: 2410.14841 “View on arXiv”

Authors: Unknown

Abstract

This article explores dynamic factor allocation by analyzing the cyclical performance of factors through regime analysis. The authors focus on a U.S. equity investment universe comprising seven long-only indices representing the market and six style factors: value, size, momentum, quality, low volatility, and growth. Their approach integrates factor-specific regime inferences of each factor index’s active performance relative to the market into the Black-Litterman model to construct a fully-invested, long-only multi-factor portfolio. First, the authors apply the sparse jump model (SJM) to identify bull and bear market regimes for individual factors, using a feature set based on risk and return measures from historical factor active returns, as well as variables reflecting the broader market environment. The regimes identified by the SJM exhibit enhanced stability and interpretability compared to traditional methods. A hypothetical single-factor long-short strategy is then used to assess these regime inferences and fine-tune hyperparameters, resulting in a positive Sharpe ratio of this strategy across all factors with low correlation among them. These regime inferences are then incorporated into the Black-Litterman framework to dynamically adjust allocations among the seven indices, with an equally weighted (EW) portfolio serving as the benchmark. Empirical results show that the constructed multi-factor portfolio significantly improves the information ratio (IR) relative to the market, raising it from just 0.05 for the EW benchmark to approximately 0.4. When measured relative to the EW benchmark itself, the dynamic allocation achieves an IR of around 0.4 to 0.5. The strategy also enhances absolute portfolio performance across key metrics such as the Sharpe ratio and maximum drawdown.

Keywords: Black-Litterman Model, Factor Allocation, Regime Analysis, Sparse Jump Model (SJM), Multi-Factor Portfolio, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced statistical methods like the Sparse Jump Model and Black-Litterman with feature weighting, requiring sophisticated mathematical derivations. It is heavily data-driven, with detailed empirical backtesting on real indices, incorporating transaction costs, and reporting specific performance metrics like Sharpe ratios and Information Ratios.

  flowchart TD
    A["<b>Research Goal</b><br/>Dynamic Factor Allocation<br/>via Regime-Switching"] --> B["<b>Data Inputs</b><br/>U.S. Equity Universe<br/>7 Long-Only Indices<br/>(Market + 6 Style Factors)"]
    
    B --> C["<b>Regime Identification</b><br/>Sparse Jump Model (SJM)<br/>Identifies Bull/Bear Regimes<br/>for Each Factor"]
    
    C --> D["<b>Hyperparameter Tuning</b><br/>Hypothetical Single-Factor<br/>Long-Short Strategy<br/>Validates Regimes"]
    
    D --> E["<b>Dynamic Allocation</b><br/>Black-Litterman Framework<br/>Integrates Regime Signals<br/>& Market Views"]
    
    E --> F["<b>Key Outcomes</b><br/>IR: 0.05 → 0.4 (vs Market)<br/>IR: 0.4-0.5 (vs EW Benchmark)<br/>Improved Sharpe & Drawdown<br/>Low Factor Correlation"]