Analysis of the cryptocurrency market using different prototype-based clustering techniques

Abstract

Since the emergence of Bitcoin, cryptocurrencies have grown significantly, not only in terms of capitalization but also in number. Consequently, the cryptocurrency market can be a conducive arena for investors, as it offers many opportunities. However, it is difficult to understand. This study aims to describe, summarize, and segment the main trends of the entire cryptocurrency market in 2018, using data analysis tools. Accordingly, we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies, and one that looks for associations between the clustering results, and other factors that are not involved in clustering. Particularly, the methodology involves applying three different partitional clustering algorithms, where each of them use a different representation for cryptocurrencies, namely, yearly mean, and standard deviation of the returns, distribution of returns that have not been applied to financial markets previously, and the time series of returns. Because each representation provides a different outlook of the market, we also examine the integration of the three clustering results, to obtain a fine-grained analysis of the main trends of the market. In conclusion, we analyze the association of the clustering results with other descriptive features of cryptocurrencies, including the age, technological attributes, and financial ratios derived from them. This will help to enhance the profiling of the clusters with additional descriptive insights, and to find associations with other variables. Consequently, this study describes the whole market based on graphical information, and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly, and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period, we found that the market can be typically segmented in few clusters (five or less), and even considering the intersections, the 6 more populations account for 75% of the market. Regarding the associations between the clusters and descriptive features, we find associations between some clusters with volume, market capitalization, and some financial ratios, which could be explored in future research.

Cryptocurrency technology revolution: are Bitcoin prices and terrorist attacks related?

“…Since its creation in 2008, Bitcoin and blockchain have been researched topics of constant interest to academia and international investors (Zhu et al 2017 ; Kumar and Ajaz 2019 ; Shahzad et al 2021 ; Zhao 2021 ; Zhang et al 2021 ; García-Corral et al 2022 ; Lorenzo and Arroyo 2022 ). Xu et al ( 2019 ) review the current academic research on blockchain, especially in business and economics.…”

Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm

“…This representation is used later for the automatic selection of the cluster, according to the investment strategy and is also consistent with the MV portfolio optimization. In addition, it succinctly summarizes the profitability and volatility of each asset and has been successfully used for clustering cryptocurrencies (Lorenzo and Arroyo 2022). While more sophisticated representations are previous reference, the ( σ , µ ) variables make a faster computation possible, which is crucial due to the intensive calculations of the simulations.…”

“…In particular, we propose the use of a prototype-based clustering algorithm, such as K-Means or K-Medois, as the prototypes will be used as representative elements of the partitions. The usefulness of such methods for characterizing the crypto-asset market in a meaningful way has previously been demonstrated in Lorenzo and Arroyo (2022). Following this work, we use the bivariate representation of the average and standard deviation of the returns together with a partitional clustering algorithm as a preliminary step before the portfolio optimization.…”

Blockchain and digital finance

“…Moreover, Cui and Maghyereh ( 2022 ) study the higher-order moment co-movements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic. Lorenzo and Arroyo ( 2022 ) describe, summarize, and segment the main trends of the cryptocurrency market in 2018. Ma and Tanizaki ( 2022 ) investigate the phenomenon of price clustering in the Bitcoin market that is denominated in the Japanese yen.…”