Predicting cryptocurrency defaults

Klaus Grobys and Niranjan Sapkota

Applied Economics, 2020, vol. 52, issue 46, 5060-5076

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.

Date: 2020
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DOI: 10.1080/00036846.2020.1752903

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