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Dynamic Evolution Analysis of Cryptocurrency Market: A Network Science Study

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  • Maziar Mardan
  • Ida Khosravipour

Abstract

In this article, network analysis has been employed to study the dynamic evolution of the cryptocurrency market from 1 January 2020 to 1 January 2024. This approach facilitates an in-depth exploration of the market’s response to several major events during this period, including the coronavirus disease of 2019 (COVID-19) pandemic and the bankruptcy of FTX, one of the largest cryptocurrency exchanges. The study focuses on analysing key network characteristics of the cryptocurrency market, namely: (a) degree centrality, (b) betweenness centrality, (c) clustering coefficient and (d) average path length. Additionally, we explore the co-movements within the market, categorising cryptocurrencies into functional groups for a comparative analysis. This approach enables us to examine shifts in the cryptocurrency network topology, providing insights into how different groups of cryptocurrencies interact with and influence each other. Through this network analysis, we aim to shed light on the intricate interrelationships among cryptocurrencies. The findings of this study are intended to provide investors with valuable insights, potentially guiding the development of more informed and strategic diversification strategies in the dynamic and evolving landscape of the cryptocurrency market. JEL Codes: G11, G12, D85

Suggested Citation

  • Maziar Mardan & Ida Khosravipour, 2026. "Dynamic Evolution Analysis of Cryptocurrency Market: A Network Science Study," Journal of Interdisciplinary Economics, , vol. 38(1), pages 63-80, January.
  • Handle: RePEc:sae:jinter:v:38:y:2026:i:1:p:63-80
    DOI: 10.1177/02601079241265744
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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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