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Behavioral structure of users in cryptocurrency market

Author

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  • Ayana T Aspembitova
  • Ling Feng
  • Lock Yue Chew

Abstract

Human behavior as they engaged in financial activities is intimately connected to the observed market dynamics. Despite many existing theories and studies on the fundamental motivations of the behavior of humans in financial systems, there is still limited empirical deduction of the behavioral compositions of the financial agents from a detailed market analysis. Blockchain technology has provided an avenue for the latter investigation with its voluminous data and its transparency of financial transactions. It has enabled us to perform empirical inference on the behavioral patterns of users in the market, which we explore in the bitcoin and ethereum cryptocurrency markets. In our study, we first determine various properties of the bitcoin and ethereum users by a temporal complex network analysis. After which, we develop methodology by combining k-means clustering and Support Vector Machines to derive behavioral types of users in the two cryptocurrency markets. Interestingly, we found four distinct strategies that are common in both markets: optimists, pessimists, positive traders and negative traders. The composition of user behavior is remarkably different between the bitcoin and ethereum market during periods of local price fluctuations and large systemic events. We observe that bitcoin (ethereum) users tend to take a short-term (long-term) view of the market during the local events. For the large systemic events, ethereum (bitcoin) users are found to consistently display a greater sense of pessimism (optimism) towards the future of the market.

Suggested Citation

  • Ayana T Aspembitova & Ling Feng & Lock Yue Chew, 2021. "Behavioral structure of users in cryptocurrency market," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-19, January.
    • Handle: RePEc:plo:pone00:0242600
    DOI: 10.1371/journal.pone.0242600
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    References listed on IDEAS

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    1. Marco Cipriani & Antonio Guarino, 2009. "Herd Behavior in Financial Markets: An Experiment with Financial Market Professionals," Journal of the European Economic Association, MIT Press, vol. 7(1), pages 206-233, March.
    2. Wilko Bolt & Maarten R.C. Van Oordt, 2020. "On the Value of Virtual Currencies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(4), pages 835-862, June.
    3. Wilko Bolt & Maarten R.C. Van Oordt, 2020. "On the Value of Virtual Currencies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(4), pages 835-862, June.
    4. Marco Cipriani & Antonio Guarino, 2005. "Herd Behavior in a Laboratory Financial Market," American Economic Review, American Economic Association, vol. 95(5), pages 1427-1443, December.
    5. Anderson, Lisa R & Holt, Charles A, 1997. "Information Cascades in the Laboratory," American Economic Review, American Economic Association, vol. 87(5), pages 847-862, December.
    6. D'aniel Kondor & M'arton P'osfai & Istv'an Csabai & G'abor Vattay, 2013. "Do the rich get richer? An empirical analysis of the BitCoin transaction network," Papers 1308.3892, arXiv.org, revised Mar 2014.
    7. Dániel Kondor & Márton Pósfai & István Csabai & Gábor Vattay, 2014. "Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
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    Cited by:

    1. Son, Dong-Hoon, 2023. "On-demand ride-sourcing markets with cryptocurrency-based fare-reward scheme," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    2. Zdenek Smutny & Zdenek Sulc & Jan Lansky, 2021. "Motivations, Barriers and Risk-Taking When Investing in Cryptocurrencies," Mathematics, MDPI, vol. 9(14), pages 1-22, July.
    3. V. Anandhabalaji & Manivannan Babu & J. Gayathri & J. Sathya & G. Indhumathi & R. Brintha & Justin Nelson Michael, 2023. "Examining the Volatility of Conventional Cryptocurrencies and Sustainable Cryptocurrency during Covid-19: Based on Energy Consumption," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 344-352, November.
    4. Petar, Radanliev, 2023. "The Rise and Fall of Cryptocurrencies: Defining the Economic and Social Values of Blockchain Technologies, assessing the Opportunities, and defining the Financial and Cybersecurity Risks of the Metave," MPRA Paper 118249, University Library of Munich, Germany.

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