Cluster analysis on the structure of the cryptocurrency market via Bitcoin–Ethereum filtering

Abstract The purpose of this research is to analyze the structure of the cryptocurrency market based on the correlation-based agglomerative hierarchical clustering and minimum spanning tree. In order to detect a reasonable and distinct collective behavior among the market entities, we propose a filtering mechanism, called Bitcoin–Ethereum filtering, to exclude their linear influences to other cryptocurrencies. In this regard, we carefully examine the market structures for the cases of before and after filtering in terms of the Total, Pre-, and Post-regulation periods. Based on the results, we discover the leadership of the Bitcoin and Ethereum in the market, six homogeneous clusters composed of relatively less-traded cryptocurrencies, and transformation of the market structure after the announcement of regulations from various countries.

[1]  Luciano da Fontoura Costa,et al.  The structure and resilience of financial market networks. , 2012, Chaos.

[2]  M. K. Tiwari,et al.  Clustering Indian stock market data for portfolio management , 2010, Expert Syst. Appl..

[3]  Woojin Chang,et al.  Analyzing systemic risk using non-linear marginal expected shortfall and its minimum spanning tree , 2018 .

[4]  Ladislav Kristoufek,et al.  On Bitcoin markets (in)efficiency and its evolution , 2018, Physica A: Statistical Mechanics and its Applications.

[5]  Carmela Iorio,et al.  A P-spline based clustering approach for portfolio selection , 2018, Expert Syst. Appl..

[6]  Kimmo Kaski,et al.  Spectral and network methods in the analysis of correlation matrices of stock returns , 2007 .

[7]  J. Kruskal.  On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[8]  Wei Zhang,et al.  The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average , 2018, Physica A: Statistical Mechanics and its Applications.

[9]  W. Sharpe.  The Sharpe Ratio , 1994 .

[10]  João Dias,et al.  Spanning trees and the Eurozone crisis , 2013 .

[11]  V. Plerou,et al.  Quantifying and interpreting collective behavior in financial markets. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Woojin Chang,et al.  Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree , 2011 .

[13]  Andrew Urquhart.  The Inefficiency of Bitcoin , 2016 .

[14]  R. Mantegna.  Hierarchical structure in financial markets , 1998, cond-mat/9802256.

[15]  Francisco A. Rodrigues,et al.  Collective behavior in financial markets , 2011 .

[16]  Roberto Bellotti,et al.  Clustering stock market companies via chaotic map synchronization , 2005 .

[17]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[18]  Tetsuya Takaishi,et al.  Statistical properties and multifractality of Bitcoin , 2017, Physica A: Statistical Mechanics and its Applications.

[19]  Sagar Kamarthi,et al.  Phase synchronization based minimum spanning trees for analysis of financial time series with nonlinear correlations , 2016 .

[20]  J. Kwapień,et al.  Physical approach to complex systems , 2012 .

[21]  Panos M. Pardalos,et al.  Dynamics of cluster structures in a financial market network , 2014 .

[22]  P. Ciaian,et al.  Virtual Relationships: Short- and Long-run Evidence from BitCoin and Altcoin Markets , 2017, 1706.07216.

[23]  K. Kaski,et al.  Dynamics of market correlations: taxonomy and portfolio analysis. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  Leonidas Sandoval Junior,et al.  Correlation of financial markets in times of crisis , 2011, 1102.1339.

[25]  Young Min Kim,et al.  Using genetic algorithm to support clustering-based portfolio optimization by investor information , 2017, Appl. Soft Comput..

[26]  P. Phillips.  Testing for a Unit Root in Time Series Regression , 1988 .

[27]  Raphael H. Heiberger,et al.  Stock network stability in times of crisis , 2014 .

[28]  B. Rosenow,et al.  Collective behavior of stock price movements—a random matrix theory approach , 2001 .

[29]  Fabrizio Lillo,et al.  Cluster analysis for portfolio optimization , 2005, physics/0507006.

[30]  Woojin Chang,et al.  Clustering stocks using partial correlation coefficients , 2016 .

[31]  Ed Wilson Alves De Almeida,et al.  Multifractal analysis of Bitcoin market , 2018, Physica A: Statistical Mechanics and its Applications.

[32]  Chris Chatfield,et al.  Introduction to Statistical Time Series. , 1976 .

[33]  K. Kaski,et al.  Dynamic asset trees and Black Monday , 2002, cond-mat/0212037.

[34]  Mohamed Majapa,et al.  Topology of the South African stock market network across the 2008 financial crisis , 2016 .

[35]  Paolo Coletti,et al.  Comparing minimum spanning trees of the Italian stock market using returns and volumes , 2016 .

[36]  Raj Kumar Pan,et al.  Collective behavior of stock price movements in an emerging market. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  G. Chow.  Tests of equality between sets of coefficients in two linear regressions (econometrics voi 28 , 1960 .

[38]  María José Basgall,et al.  Some Stylized Facts of the Bitcoin Market , 2017, 1708.04532.

[39]  Juan Gabriel Brida,et al.  Hierarchical Structure of the German Stock Market , 2007, Expert Syst. Appl..

[40]  Fabrizio Lillo,et al.  Correlation, Hierarchies, and Networks in Financial Markets , 2008, 0809.4615.

[41]  Seong Eun Maeng,et al.  Effects of global financial crisis on network structure in a local stock market , 2014 .

[42]  Aviral Kumar Tiwari,et al.  Informational efficiency of Bitcoin—An extension , 2018 .

[43]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[44]  Woojin Chang,et al.  Time-varying causal network of the Korean financial system based on firm-specific risk premiums , 2016 .

[45]  Xintian Zhuang,et al.  A network analysis of the Chinese stock market , 2009 .

[46]  Teresa B. Ludermir,et al.  Collective behavior of cryptocurrency price changes , 2018, Physica A: Statistical Mechanics and its Applications.

[47]  Burak Saltoǧlu,et al.  Network centrality measures and systemic risk: An application to the Turkish financial crisis , 2014 .