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

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  • Song, Jung Yoon
  • Chang, Woojin
  • Song, Jae Wook

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.

Suggested Citation

  • Song, Jung Yoon & Chang, Woojin & Song, Jae Wook, 2019. "Cluster analysis on the structure of the cryptocurrency market via Bitcoin–Ethereum filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119304893
    DOI: 10.1016/j.physa.2019.121339
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    Citations

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    Cited by:

    1. María de la O González & Francisco Jareño & Frank S. Skinner, 2020. "Nonlinear Autoregressive Distributed Lag Approach: An Application on the Connectedness between Bitcoin Returns and the Other Ten Most Relevant Cryptocurrency Returns," Mathematics, MDPI, vol. 8(5), pages 1-22, May.
    2. Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
    3. Nie, Chun-Xiao, 2022. "Analysis of critical events in the correlation dynamics of cryptocurrency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    4. Luis Lorenzo & Javier Arroyo, 2023. "Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-40, December.
    5. Qiao, Xingzhi & Zhu, Huiming & Hau, Liya, 2020. "Time-frequency co-movement of cryptocurrency return and volatility: Evidence from wavelet coherence analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    6. Kong, Xiaolin & Ma, Chaoqun & Ren, Yi-Shuai & Narayan, Seema & Nguyen, Thong Trung & Baltas, Konstantinos, 2023. "Changes in the market structure and risk management of Bitcoin and its forked coins," Research in International Business and Finance, Elsevier, vol. 65(C).
    7. Umar, Zaghum & Jareño, Francisco & González, María de la O, 2021. "The impact of COVID-19-related media coverage on the return and volatility connectedness of cryptocurrencies and fiat currencies," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    8. Lahmiri, Salim & Bekiros, Stelios, 2020. "Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    9. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    10. Chen, James Ming & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Clustering commodity markets in space and time: Clarifying returns, volatility, and trading regimes through unsupervised machine learning," Resources Policy, Elsevier, vol. 73(C).
    11. Luis Lorenzo & Javier Arroyo, 2022. "Analysis of the cryptocurrency market using different prototype-based clustering techniques," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-46, December.
    12. Jiang, Shangrong & Li, Xuerong & Wang, Shouyang, 2021. "Exploring evolution trends in cryptocurrency study: From underlying technology to economic applications," Finance Research Letters, Elsevier, vol. 38(C).
    13. Nguyen, An Pham Ngoc & Mai, Tai Tan & Bezbradica, Marija & Crane, Martin, 2023. "Volatility and returns connectedness in cryptocurrency markets: Insights from graph-based methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    14. Kate Murray & Andrea Rossi & Diego Carraro & Andrea Visentin, 2023. "On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles," Forecasting, MDPI, vol. 5(1), pages 1-14, January.
    15. Tristan Millington & Mahesan Niranjan, 2020. "Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation," Papers 2005.03963, arXiv.org, revised Nov 2020.
    16. Kim, Hyeonoh & Ha, Chang Yong & Ahn, Kwangwon, 2022. "Preference heterogeneity in Bitcoin and its forks' network," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    17. Millington, Tristan & Niranjan, Mahesan, 2021. "Construction of minimum spanning trees from financial returns using rank correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    18. Cho, Younghwan & Song, Jae Wook, 2023. "Hierarchical risk parity using security selection based on peripheral assets of correlation-based minimum spanning trees," Finance Research Letters, Elsevier, vol. 53(C).

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