A Second-order Hierarchical Clustering of Cryptocurrencies

Abstract

The clustering of cryptocurrencies - as an emerging field in investment management - is the main topic of this research. Applying the information-based distance matrices, we clustered the 30 most valuable cryptocurrencies. Then, we identified the most influential clustering by the concept of Minimum Spanning Tree (MST) and the centrality measures of graph theory. A second-order clustering, which is defined as the clustering of hierarchical clusterings, is applied to cluster 56 dendrograms. Using the most influential clustering, we identified the main clusters of cryptocurrencies and sub-clusters. The results show that the clustering composition of cryptocurrencies changed at the period I (before COVID-19) and II (pandemic time).