What is a Cluster Analysis?

Cluster analysis is a classification method that groups sets of items with similar characteristics into clusters (groups). It is a statistical technique where data, points, and objects with similar characteristics are subdivided into clusters. The items subdivided only share similar characteristics but are not identical in nature. Although cluster analysis or clustering is popularly used in statistics, it is being used in different fields in the present day. Investors sometimes use cluster analysis to group assets or investment instruments into a portfolio. Cluster analysis helps investors achieve diversification of investment portfolio which enables them to mitigate losses and retain capital or profit.

How is a Cluster Analysis Used? 

In investment, diversification is a quality investors seek to achieve and cluster analysis provides them a statistical way to achieve it. Cluster analysis helps an investor group similar investment instruments into clusters, this tool aids the analysis of different items in order to know the specific items or instruments that are compatible in a cluster. Investors select securities with related returns and minimal risks in a portfolio that makes a safe investment. Essentially, clustering assets that thrive in different sectors of the market help an investor achieve diversification. This statistical technique helps investors identify the different categories of assets and securities in the market.

Drawbacks of Cluster Analysis

There are certain drawbacks of cluster analysis, the major drawback is the excess similarity between the clusters causing an overlap. For instance, in an investment portfolio, the securities therein might close relationships in terms of risks and returns in a way that positions the securities in a close distance. It is recommended that if securities with a large distance are selected in a portfolio, higher impacts will be felt.