Tactical Asset Allocation with Macroeconomic Regime Detection

ArXiv ID: 2503.11499 “View on arXiv”

Authors: Unknown

Abstract

This paper extends the tactical asset allocation literature by incorporating regime modeling using techniques from machine learning. We propose a novel model that classifies current regimes, forecasts the distribution of future regimes, and integrates these forecasts with the historical performance of individual assets to optimize portfolio allocations. Utilizing a macroeconomic data set from the FRED-MD database, our approach employs a modified k-means algorithm to ensure consistent regime classification over time. We then leverage these regime predictions to estimate expected returns and volatilities, which are subsequently mapped into portfolio allocations using various sizing schemes. Our method outperforms traditional benchmarks such as equal-weight, buy-and-hold, and random regime models. Additionally, we are the first to apply a regime detection model from a large macroeconomic dataset to tactical asset allocation, demonstrating significant improvements in portfolio performance. Our work presents several key contributions, including a novel data-driven regime detection algorithm tailored for uncertainty in forecasted regimes and applying the FRED-MD data set for tactical asset allocation.

Keywords: Tactical Asset Allocation, Regime Modeling, K-means Clustering, FRED-MD Data, Macroeconomic Forecasting, Portfolio Management

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced machine learning (modified k-means, fuzzy clustering) and optimization techniques with formal mathematical descriptions, indicating moderate-to-high complexity. It uses a substantial real-world dataset (FRED-MD), outlines a complete backtesting pipeline, and reports performance vs. benchmarks, showing good empirical rigor.

  flowchart TD
    A["Research Goal:<br>Enhance Tactical Asset Allocation via<br>Macroeconomic Regime Detection"] --> B["Data Source:<br>FRED-MD Macroeconomic Dataset"]
    B --> C["Key Methodology:<br>Modified K-means Algorithm<br>for Consistent Regime Classification"]
    C --> D["Computational Process:<br>Regime Detection & Forecasting<br>Estimating Return/Volatility"]
    D --> E["Key Findings:<br>Outperforms Benchmarks &<br>Novel TAA Model Implementation"]