Early Warning Systems for Cryptocurrency Markets: Predicting 'Zombie' Assets Using Machine Learning
20 Pages Posted: 30 Aug 2024
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
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