Clustering and Modelling of the Top 30 Cryptocurrency Prices Using Dynamic Time Warping and Machine Learning Methods

Tomáš Šťastný (), Jiří Koudelka (), Diana Bílková () and Luboš Marek ()
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Tomáš Šťastný: Faculty of Informatics and Statistics, Prague University of Economics and Business, W. Churchill Sq. 1938/4, 13067 Prague, Czech Republic
Jiří Koudelka: Faculty of Informatics and Statistics, Prague University of Economics and Business, W. Churchill Sq. 1938/4, 13067 Prague, Czech Republic
Diana Bílková: Faculty of Informatics and Statistics, Prague University of Economics and Business, W. Churchill Sq. 1938/4, 13067 Prague, Czech Republic
Luboš Marek: Faculty of Informatics and Statistics, Prague University of Economics and Business, W. Churchill Sq. 1938/4, 13067 Prague, Czech Republic

Mathematics, 2022, vol. 10, issue 19, 1-25

Abstract: Cryptocurrencies are a new field of investment opportunities that has experienced a significant growth in the last decade. The crypto market was capitalized at more than USD 3000 bn, having grown from USD 10 m over the period 2011–2021. Generating high returns, investments in cryptocurrencies have also shown high levels of price volatility. By comparing the performance of cryptocurrencies (measured by the crypto index) and standard equities (included in the S&P 500 index), we found that the former has outperformed the latter 14 times over the last two years. In the present paper, we analyzed the 2012–2022 global crypto market developments and main constituents. With a focus on the top 30 cryptocurrencies and their prices, as of 9 April 2022, covering data of the two major market stress events—outbreaks of the COVID-19 pandemic (February 2020) and the Russian invasion of Ukraine (February 2022). We applied the dynamic time warping method including barycentre averaging and k-Shape clustering of time series. The use of the dynamic time warping has been essential for the preparation of data for subsequent clustering and forecasting. In addition, we compared performance of cryptocurrencies and equities. Cryptocurrency time series are rather short, sometimes involving high levels of volatility and including multiple data gaps, whereas equity time series are much longer and well-established. Identifying similarities between them allows analysts to predict crypto prices by considering the evolution of similar equity instruments and their responses to historical events and stress periods. Moreover, we tested various forecasting methods on the 30 cryptocurrencies to compare traditional econometric methods with machine learning approaches.

Keywords: cryptocurrency; dynamic time warping; machine learning; cluster analysis; ARIMA (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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