Portfolio Construction with Hierarchical Momentum
提出一种结合层次聚类与股票价格动量的投资组合构建方法，通过聚类实现稀疏分散化并避免协方差矩阵求逆，回测表明该策略在扣除交易成本后提升了累计收益和风险调整收益，同时降低了回撤。
This article presents a portfolio construction approach that combines the hierarchical clustering of a large asset universe with the stock price momentum. On one hand, investing in high-momentum stocks enhances returns by capturing the momentum premium. On the other hand, hierarchical clustering of a high-dimensional asset universe ensures sparse diversification, stabilizes the portfolio across economic regimes, and mitigates the problem of increased drawdowns 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, compared to the model-based and model-free benchmarks, hierarchical momentum portfolios achieve improved cumulative and risk-adjusted portfolio returns as well as decreased portfolio drawdowns net of transaction costs. The study further suggests that the unique characteristics of the hierarchical momentum portfolios arise because of both dimensionality reduction via clustering and momentum-based stock selection.