Portfolio Construction with Hierarchical Momentum

This paper presents a portfolio construction approach that combines the hierarchical clustering of a large asset universe with the stock price momentum. On the one hand, investing in high-momentum stocks stabilizes portfolio performance across economic regimes and enhances risk-adjusted returns. On the other hand, hierarchical clustering of a high-dimensional asset universe ensures sparse diversification and mitigates the problems of increased drawdowns and large turnovers typically present in momentum portfolios. Moreover, the proposed portfolio construction approach avoids the covariance matrix inversion. An out-of-sample backtest on a non-survivorship-biased dataset of international stocks shows that hierarchical-momentum portfolios achieve substantially improved cumulative and risk-adjusted portfolio returns as well as decreased portfolio drawdowns compared to the model-free benchmarks net of transaction costs. Furthermore, we demonstrate that the unique characteristics of the hierarchical-momentum portfolios arise due to both dimensionality reduction via clustering and momentum-based stock selection