IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2307.16874.html
   My bibliography  Save this paper

Shifting Cryptocurrency Influence: A High-Resolution Network Analysis of Market Leaders

Author

Listed:
  • Arnav Hiray
  • Pratvi Shah
  • Vishwa Shah
  • Agam Shah
  • Sudheer Chava
  • Mukesh Tiwari

Abstract

Over the last decade, the cryptocurrency market has experienced unprecedented growth, emerging as a prominent financial market. As this market rapidly evolves, it necessitates re-evaluating which cryptocurrencies command the market and steer the direction of blockchain technology. We implement a network-based cryptocurrency market analysis to investigate this changing landscape. We use novel hourly-resolution data and Kendall's Tau correlation to explore the interconnectedness of the cryptocurrency market. We observed critical differences in the hierarchy of cryptocurrencies determined by our method compared to rankings derived from daily data and Pearson's correlation. This divergence emphasizes the potential information loss stemming from daily data aggregation and highlights the limitations of Pearson's correlation. Our findings show that in the early stages of this growth, Bitcoin held a leading role. However, during the 2021 bull run, the landscape changed drastically. We see that while Ethereum has emerged as the overall leader, it was FTT and its associated exchange, FTX, that greatly led to the increase at the beginning of the bull run. We also find that highly-influential cryptocurrencies are increasingly gaining a commanding influence over the market as time progresses, despite the growing number of cryptocurrencies making up the market.

Suggested Citation

  • Arnav Hiray & Pratvi Shah & Vishwa Shah & Agam Shah & Sudheer Chava & Mukesh Tiwari, 2023. "Shifting Cryptocurrency Influence: A High-Resolution Network Analysis of Market Leaders," Papers 2307.16874, arXiv.org, revised Jan 2024.
    • Handle: RePEc:arx:papers:2307.16874
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2307.16874
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    2. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    3. Paweł Fiedor, 2014. "Information-theoretic approach to lead-lag effect on financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(8), pages 1-9, August.
    4. Sensoy, Ahmet & Tabak, Benjamin M., 2014. "Dynamic spanning trees in stock market networks: The case of Asia-Pacific," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 387-402.
    5. Arthur A. B. Pessa & Matjaz Perc & Haroldo V. Ribeiro, 2023. "Age and market capitalization drive large price variations of cryptocurrencies," Papers 2302.12319, arXiv.org.
    6. Jiaqi Liang & Linjing Li & Daniel Zeng, 2018. "Evolutionary dynamics of cryptocurrency transaction networks: An empirical study," Papers 1808.08585, arXiv.org.
    7. Dion Harmon & Blake Stacey & Yavni Bar-Yam & Yaneer Bar-Yam, 2010. "Networks of Economic Market Interdependence and Systemic Risk," Papers 1011.3707, arXiv.org, revised Nov 2010.
    8. Yong Kheng Goh & Haslifah M Hasim & Chris G Antonopoulos, 2018. "Inference of financial networks using the normalised mutual information rate," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-21, February.
    9. Dror Y Kenett & Michele Tumminello & Asaf Madi & Gitit Gur-Gershgoren & Rosario N Mantegna & Eshel Ben-Jacob, 2010. "Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-14, December.
    10. Brunetti, Celso & Harris, Jeffrey H. & Mankad, Shawn & Michailidis, George, 2019. "Interconnectedness in the interbank market," Journal of Financial Economics, Elsevier, vol. 133(2), pages 520-538.
    11. Christian Borghesi & Matteo Marsili & Salvatore Miccich`e, 2007. "Emergence of time-horizon invariant correlation structure in financial returns by subtraction of the market mode," Papers physics/0702106, arXiv.org.
    12. Edwin van den Heuvel & Zhuozhao Zhan, 2022. "Myths About Linear and Monotonic Associations: Pearson’s r, Spearman’s ρ, and Kendall’s τ," The American Statistician, Taylor & Francis Journals, vol. 76(1), pages 44-52, January.
    13. Gautier Marti & Philippe Very & Philippe Donnat & Frank Nielsen, 2015. "A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series," Papers 1509.05475, arXiv.org.
    14. Jiaqi Liang & Linjing Li & Daniel Zeng, 2018. "Evolutionary dynamics of cryptocurrency transaction networks: An empirical study," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-18, August.
    15. Tomas Scagliarini & Giuseppe Pappalardo & Alessio Emanuele Biondo & Alessandro Pluchino & Andrea Rapisarda & Sebastiano Stramaglia, 2022. "Pairwise and high-order dependencies in the cryptocurrency trading network," Papers 2207.04004, arXiv.org.
    16. Aste, T. & Di Matteo, T. & Hyde, S.T., 2005. "Complex networks on hyperbolic surfaces," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(1), pages 20-26.
    17. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    2. Esmalifalak, Hamidreza, 2022. "Euclidean (dis)similarity in financial network analysis," Global Finance Journal, Elsevier, vol. 53(C).
    3. Nicol'o Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Risk diversification: a study of persistence with a filtered correlation-network approach," Papers 1410.5621, arXiv.org.
    4. Chester Curme & Michele Tumminello & Rosario N. Mantegna & H. Eugene Stanley & Dror Y. Kenett, 2015. "Emergence of statistically validated financial intraday lead-lag relationships," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1375-1386, August.
    5. Xue Guo & Hu Zhang & Tianhai Tian, 2018. "Development of stock correlation networks using mutual information and financial big data," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-16, April.
    6. Chen, Yanhua & Li, Youwei & Pantelous, Athanasios A. & Stanley, H. Eugene, 2022. "Short-run disequilibrium adjustment and long-run equilibrium in the international stock markets: A network-based approach," International Review of Financial Analysis, Elsevier, vol. 79(C).
    7. Musmeci, Nicoló & Nicosia, Vincenzo & Aste, Tomaso & Di Matteo, Tiziana & Latora, Vito, 2017. "The multiplex dependency structure of financial markets," LSE Research Online Documents on Economics 85337, London School of Economics and Political Science, LSE Library.
    8. Deev, Oleg & Lyócsa, Štefan, 2020. "Connectedness of financial institutions in Europe: A network approach across quantiles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    9. Nicolò Musmeci & Vincenzo Nicosia & Tomaso Aste & Tiziana Di Matteo & Vito Latora, 2017. "The Multiplex Dependency Structure of Financial Markets," Complexity, Hindawi, vol. 2017, pages 1-13, September.
    10. Brida, Juan Gabriel & Gómez, David Matesanz & Seijas, Maria Nela, 2017. "Debt and growth: A non-parametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 883-894.
    11. Qian, Biyu & Wang, Gang-Jin & Feng, Yusen & Xie, Chi, 2022. "Partial cross-quantilogram networks: Measuring quantile connectedness of financial institutions," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    12. Brida, Juan Gabriel & Matesanz, David & Seijas, Maria Nela, 2016. "Network analysis of returns and volume trading in stock markets: The Euro Stoxx case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 751-764.
    13. Sebastiano Michele Zema & Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2021. "Mesoscopic Structure of the Stock Market and Portfolio Optimization," Papers 2112.06544, arXiv.org.
    14. Sandoval, Leonidas, 2014. "To lag or not to lag? How to compare indices of stock markets that operate on different times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 227-243.
    15. Nicoló Musmeci & Tomaso Aste & T Di Matteo, 2015. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-24, March.
    16. Mr. Jorge A Chan-Lau, 2017. "Variance Decomposition Networks: Potential Pitfalls and a Simple Solution," IMF Working Papers 2017/107, International Monetary Fund.
    17. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    18. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    19. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    20. Ling, Yu-Xiu & Xie, Chi & Wang, Gang-Jin, 2022. "Interconnectedness between convertible bonds and underlying stocks in the Chinese capital market: A multilayer network perspective," Emerging Markets Review, Elsevier, vol. 52(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2307.16874. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.