Predicting Cryptocurrency Defaults

23 Pages Posted: 16 May 2019 Last revised: 24 Nov 2019

See all articles by Niranjan Sapkota

Niranjan Sapkota

University of Vaasa

Klaus Grobys

University of Vaasa; University of Jyväskyla

Date Written: November 20, 2019

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.

Keywords: Cryptocurrency, Bitcoin, Bankruptcy, Default, Credit Risk, Linear Discriminant Analysis

JEL Classification: G12, G14

Suggested Citation

Sapkota, Niranjan and Grobys, Klaus, Predicting Cryptocurrency Defaults (November 20, 2019). Available at SSRN: https://ssrn.com/abstract=3383535 or http://dx.doi.org/10.2139/ssrn.3383535

Niranjan Sapkota (Contact Author)

University of Vaasa ( email )

P.O. Box 700
FIN-65101 Vaasa, FI-65101
Finland

Klaus Grobys

University of Vaasa ( email )

P.O. Box 700
Wolffintie 34
FIN-65101 Vaasa
Finland

University of Jyväskyla ( email )

Jyväskyla
Finland

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