![]() Such a taxonomy is useful, even if it only covers approximately 5% of the existing cryptocurrencies at that time. For example, the Cryptocompare website Footnote 2 analyzed over 200 cryptoassets, according to regulatory aspects, level of decentralization, supply issuance, economic incentives, and others. So far, several categorization efforts have been made. Consequently, it is highly volatile with large upswings, bubbles, and sudden market downturns.īeing a market so novel, big, diverse, and volatile, it needs to be clearly understood. Moreover, the cryptocurrency market is new, based on a still developing technology that is highly speculative and small compared to others. However, cryptocurrencies have no physical assets, or governments to return their value to. Regarding other assets, the value of cryptocurrencies swing based on news events. Cryptocurrencies with different technologies, purposes, and user bases, coexist, and form a highly heterogeneous market that is difficult to understand and manage, for those addressing a good investment allocation. While cryptocurrencies were originally created to enable anonymous wire transfers and online purchases, they have become a powerful investment tool. It has become a huge new market in the very short term, considering that Bitcoin (Nakamoto 2009), the first peer-to-peer and decentralized digital currency was produced in 2008, and the first Bitcoin was mined in 2009. ![]() The cryptocurrency market comprises more than 4000 cryptotocoins, Footnote 1 with over 800 trades per second, and more than 280 exchanges. ![]() 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. 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 20 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. ![]() This will help to enhance the profiling of the clusters with additional descriptive insights, and to find associations with other variables. 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. ![]() 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. 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. 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. This study aims to describe, summarize, and segment the main trends of the entire cryptocurrency market in 2018, using data analysis tools. Consequently, the cryptocurrency market can be a conducive arena for investors, as it offers many opportunities. Since the emergence of Bitcoin, cryptocurrencies have grown significantly, not only in terms of capitalization but also in number. ![]()
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