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Predicting cryptocurrency defaults

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  • Klaus Grobys
  • Niranjan Sapkota

Abstract

We examine all available 146 Proof-of-Work-based cryptocurrencies that started trading prior to the end of 2014 and track their performance until December 2018. We find that about 60% of those cryptocurrencies were eventually in default. The substantial sums of money involved mean those bankruptcies will have an enormous societal impact. Employing cryptocurrency-specific data, we estimate a model based on linear discriminant analysis to predict such defaults. Our model is capable of explaining 87% of cryptocurrency bankruptcies after only one month of trading and could serve as a screening tool for investors keen to boost overall portfolio performance and avoid investing in unreliable cryptocurrencies.

Suggested Citation

  • Klaus Grobys & Niranjan Sapkota, 2020. "Predicting cryptocurrency defaults," Applied Economics, Taylor & Francis Journals, vol. 52(46), pages 5060-5076, October.
    • Handle: RePEc:taf:applec:v:52:y:2020:i:46:p:5060-5076
    DOI: 10.1080/00036846.2020.1752903
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    Cited by:

    1. Dean Fantazzini, 2022. "Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death," JRFM, MDPI, vol. 15(7), pages 1-34, July.
    2. Klaus Grobys, 2021. "When the blockchain does not block: on hackings and uncertainty in the cryptocurrency market," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1267-1279, August.
    3. Cynthia Weiyi Cai & Rui Xue & Bi Zhou, 2023. "Cryptocurrency puzzles: a comprehensive review and re-introduction," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(1), pages 26-50, June.
    4. Klaus Grobys & Timothy King & Niranjan Sapkota, 2022. "A Fractal View on Losses Attributable to Scams in the Market for Initial Coin Offerings," JRFM, MDPI, vol. 15(12), pages 1-18, December.
    5. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
    6. Grobys, Klaus, 2024. "On co-dependent power-law behavior across cryptocurrencies," Finance Research Letters, Elsevier, vol. 63(C).
    7. Grobys, Klaus & Dufitinema, Josephine & Sapkota, Niranjan & Kolari, James W., 2022. "What’s the expected loss when Bitcoin is under cyberattack? A fractal process analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    8. Sapkota, Niranjan & Grobys, Klaus, 2023. "Fear sells: On the sentiment deceptions and fundraising success of initial coin offerings," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).

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