  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 unsunpervised 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.
