Early Warning Systems for Cryptocurrency Markets: Predicting 'Zombie' Assets Using Machine Learning

20 Pages Posted: 30 Aug 2024

See all articles by Barbara Bedowska-Sojka

Barbara Bedowska-Sojka

Poznań University of Economics and Business

Piotr Wojcik

University of Warsaw - Faculty of Economic Sciences

Daniel Traian Pele

Bucharest University of Economic Studies; Romanian Academy - Institute for Economic Forecasting

Abstract

Investors face the risk of cryptocurrencies disappearing from the market and becoming zombies. Our study aims to predict which cryptocurrencies will become untradable using predictors based on descriptive statistics of yield, volume and market capitalisation. The sample includes crypto assets that have been listed on the markets for at least 210 days in the period from January 2015 to December 2022. We apply various machine learning algorithms and novel XAI tools, namely permutation-based feature importance and PDPs, to identify the main factors explaining the disappearance of cryptos and to understand the shape of the relationships. Our study shows that machine learning models allow us to predict that cryptocurrencies will become zombies within the next 28 days with 84\% out-of-time balanced accuracy. The tree-based models, especially random forests, outperformed traditional econometric approaches. The variables with the greatest explanatory power are related to volumes and returns calculated in previous periods.

Keywords: cryptocurrency, Machine Learning, coin, token, prediction, random forests

Suggested Citation

Bedowska-Sojka, Barbara and Wojcik, Piotr and Pele, Daniel Traian, Early Warning Systems for Cryptocurrency Markets: Predicting 'Zombie' Assets Using Machine Learning. Available at SSRN: https://ssrn.com/abstract=4942031 or http://dx.doi.org/10.2139/ssrn.4942031

Barbara Bedowska-Sojka (Contact Author)

Poznań University of Economics and Business ( email )

Al. Niepodległości 10
Poznań, Great Poland 61-875
Poland

Piotr Wojcik

University of Warsaw - Faculty of Economic Sciences ( email )

Dluga Street 44/50
Warsaw, 00-241
Poland

Daniel Traian Pele

Bucharest University of Economic Studies

Piata Romana nr. 6
Bucharest
Romania

Romanian Academy - Institute for Economic Forecasting ( email )

Calea 13 Septembrie nr. 13
Bucharest, 050711
Romania

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