IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v12y2024i4p107-d1505156.html
   My bibliography  Save this article

Blockchain Tokens, Price Volatility, and Active User Base: An Empirical Analysis Based on Tokenomics

Author

Listed:
  • Roberto Moncada

    (Department of Economics, Social Studies, Applied Mathematics and Statistics, University of Turin, Lungo Dora Siena, 100A, 10153 Turin, Italy
    LINKS Foundation, Via Pier Carlo Boggio, 61, 10138 Turin, Italy
    Collegio Carlo Alberto, Piazza Vincenzo Arbarello, 8, 10122 Turin, Italy)

  • Enrico Ferro

    (LINKS Foundation, Via Pier Carlo Boggio, 61, 10138 Turin, Italy)

  • Maurizio Fiaschetti

    (UCL Institute of Finance and Technology, Chadwick Building, Gower Street, London WC1 E6BT, UK)

  • Francesca Medda

    (UCL Institute of Finance and Technology, Chadwick Building, Gower Street, London WC1 E6BT, UK)

Abstract

Blockchain tokens have accumulated tremendous market value but remain highly controversial, given their price volatility and seemingly speculative nature. Ironically, this very characteristic can foster token retention as users wait for occasions of appreciation. In this paper, we conduct an empirical analysis with 58 tokens in two steps: first, an investigation of the drivers of user activity and token price volatility using a new blockchain token classification framework, searching for possible tokenomics links. Our findings suggest that there is an intrinsic relationship between the way tokens are used as a means of exchange and how token usage dynamics influence user engagement oppositely to market stability. Only some features, such as earning potential and voting rights, foster token-holding strategies, while only Ethereum ecosystem membership has positive effects on price volatility. Second, we analyze the direct relationship between price volatility and active users. Results show that, on average, a 10% increase in volatility is related to a decrease in active addresses ranging between 3.96% and 5.88%. The finding is supportive of the hypothesis that token price volatility may be treated as an opportunity to increase token retention.

Suggested Citation

  • Roberto Moncada & Enrico Ferro & Maurizio Fiaschetti & Francesca Medda, 2024. "Blockchain Tokens, Price Volatility, and Active User Base: An Empirical Analysis Based on Tokenomics," IJFS, MDPI, vol. 12(4), pages 1-30, October.
    • Handle: RePEc:gam:jijfss:v:12:y:2024:i:4:p:107-:d:1505156
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/12/4/107/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7072/12/4/107/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Urquhart, Andrew, 2022. "Under the hood of the Ethereum blockchain," Finance Research Letters, Elsevier, vol. 47(PA).
    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. Ravi Kashyap, 2024. "The Concentration Risk Indicator: Raising the Bar for Financial Stability and Portfolio Performance Measurement," Papers 2408.07271, arXiv.org.
    2. Tom Celig & Tim Alvaro Ockenga & Detlef Schoder, 2025. "Distributional equality in Ethereum? On-chain analysis of Ether supply distribution and supply dynamics," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-15, December.
    3. Mukul Bhatnagar & Sanjay Taneja & Ramona Rupeika-Apoga, 2023. "Demystifying the Effect of the News (Shocks) on Crypto Market Volatility," JRFM, MDPI, vol. 16(2), pages 1-16, February.
    4. Mustafa Tevfik Kartal & Mustafa Kevser & Fatih Ayhan, 2023. "Asymmetric effects of global factors on return of cryptocurrencies by novel nonlinear quantile approaches," Economic Change and Restructuring, Springer, vol. 56(3), pages 1515-1535, June.
    5. Ravi Kashyap, 2023. "Arguably Adequate Aqueduct Algorithm: Crossing A Bridge-Less Block-Chain Chasm," Papers 2311.10717, arXiv.org.
    6. Shang, Dawei & Guo, Ziyu & Wang, Hui, 2024. "Enhancing digital cryptocurrency trading price prediction with an attention-based convolutional and recurrent neural network approach: The case of Ethereum," Finance Research Letters, Elsevier, vol. 67(PB).
    7. Bakhtiar, Tiam & Luo, Xiaojun & Adelopo, Ismail, 2023. "Network effects and store-of-value features in the cryptocurrency market," Technology in Society, Elsevier, vol. 74(C).
    8. Łęt Blanka & Sobański Konrad & Świder Wojciech & Włosik Katarzyna, 2022. "Is the cryptocurrency market efficient? Evidence from an analysis of fundamental factors for Bitcoin and Ethereum," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 58(4), pages 351-370, December.

    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:gam:jijfss:v:12:y:2024:i:4:p:107-:d:1505156. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.