Dynamic Asset Allocation with Asset-Specific Regime Forecasts

TL;DR

This paper presents a hybrid framework combining unsupervised and supervised learning to generate asset-specific regime forecasts, improving multi-asset portfolio allocation by integrating these forecasts into traditional optimization models.

Contribution

It introduces a novel asset-specific regime forecasting method using jump models and decision trees, enhancing dynamic asset allocation over traditional broad-regime approaches.

Findings

Outperforms traditional portfolio models in empirical tests

Demonstrates consistent gains across diverse asset classes

Enhances portfolio risk-adjusted returns

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.