Introduction to Clustering Methods In Portfolio Management — Part 2

October’s is coming, and we continue our short series of introductory articles about portfolio clustering methods we will soon use in our new Quantpedia Pro report. In the previous blog, we introduced three clustering methods and discussed the pros and cons of each one. Additionally, we showed a few examples of clustering, and we presented various methods for picking an optimal number of clusters.

This section demonstrates the Partitioning Around Medoids (PAM) — a centroid-based clustering method, Hierarchical Clustering, which uses machine learning and Gaussian Mixture Model based on probability distribution and applies all three methods to an investment portfolio that consists of eight liquid ETFs.