Journal of Investment Strategies

Risk.net

Quant investing in cluster portfolios

Ali N. Akansu, Marco Avellaneda and Anqi Xiong

  • A correlation-based set partitioning algorithm that divides the investment universe dynamically into clusters of assets proposed.
  • The principal eigenvector of each cluster from its correlation matrix is calculated and the corresponding eigenportfolio. The cluster portfolios of varying sizes combined into a single N-asset portfolio based on a weighting scheme for the clusters.
  • The proposed portfolio outperforms hierarchical risk parity (HRP) portfolio, eigenportfolio (EP), and a few other portfolio constructions and relevant ETFs based on several tests performed with market data.
  • The performance comparisons give convincing evidence that cluster-based long-only investment portfolio can outperform passive investing.

This paper discusses portfolio construction for investing in N given assets, eg, constituents of the Dow Jones Industrial Average (DJIA) or large cap stocks, based on partitioning the investment universe into clusters. The clusters are determined from the trailing correlation matrix via an information theoretic algorithm that uses thresholding of high-correlation pairs. We calculate the principal eigenvector of each cluster from its correlation matrix and the corresponding eigenportfolio. The cluster portfolios are combined into a single N-asset portfolio based on a weighting scheme for the clusters. Various tests conducted on components of the DJIA and a 30-stock basket of large cap stocks indicate that the new portfolios are superior to the DJIA and other mean–variance portfolios in terms of their risk-adjusted returns from 2009 to 2019. We also tested the cluster portfolios for a larger basket of 373 Standard & Poor’s 500 components from 2001 to 2019. The test results provide convincing evidence that a cluster-based portfolio can outperform passive investing.

To continue reading...

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here: