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RESEARCH PRODUCT

Predicting Cryptocurrency Defaults

Niranjan SapkotaKlaus GrobysKlaus Grobys

subject

CryptocurrencyActuarial scienceBankruptcySocietal impact of nanotechnologyPortfolioScreening toolDefaultBusinessLinear discriminant analysisCredit risk

description

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.

yearjournalcountryeditionlanguage
2019-01-01SSRN Electronic Journal
https://doi.org/10.2139/ssrn.3383535