On Cluster Analysis of Some Portfolio Optimization

Abstract

The classic mean-variance portfolio optimization approach is criticized in large part for its propensity to overstate estimate error. An estimated inaccuracy of a few percent can skew the entire package. The Black-Litterman technique (Bayesian method) and the resampling method are two common approaches to solving this problem. A more recent approach to the issue’s solution is the clustering method. By clustering, we initially combine the stocks that have a strong correlation and handle the group as a single stock. Following the grouping of the stocks, we will have a few stock clusters. For these clusters, we do the standard mean-variance portfolio optimization. By using the clustering approach, the influence of estimating error may be minimized and the portfolio’s stability can be increased. In this project, we’ll examine how it functions and run experiments to see if clustering techniques enhance the portfolio’s performance and stabilities.