Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting

Kerolly Kedma Felix do Nascimento (), Fábio Sandro dos Santos, Jader Silva Jale, Silvio Fernando Alves Xavier Júnior and Tiago A. E. Ferreira ()
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Kerolly Kedma Felix do Nascimento: Federal Rural University of Pernambuco
Fábio Sandro dos Santos: Federal Rural University of Pernambuco
Jader Silva Jale: Federal Rural University of Pernambuco
Silvio Fernando Alves Xavier Júnior: State University of Paraíba
Tiago A. E. Ferreira: Federal Rural University of Pernambuco

Computational Economics, 2023, vol. 61, issue 3, No 9, 1095-1114

Abstract: Abstract With the growing popularity of digital currencies known as cryptocurrencies, there is a need to develop models capable of robustly analyzing and predicting the value of future returns in these markets. In this article, we extract behavior rules to predict the values of future returns in the Bitcoin, Ethereum, Litecoin, and Ripple closing series. We used categorical data in the analyses and Markov chain models from the first to the tenth order to propose a new way of establishing possible future scenarios, in which we analyze the dependence of memory on the dynamics of the process. We used the measurements of accuracy Mean Quadratic Error, Absolute Error Mean Percentage, and Absolute Standard Deviation for the choice of the best models. Our findings reveal that cryptocurrencies have long-range memory. Bitcoin, Ethereum, and Ripple exposed seven steps of memory, while Litecoin displayed nine memory steps. From the transitions between states that happened the most, we defined decision rules that assisted in the definition of future returns in the series. Our results can support the decisions of traders, investors, crypto-traders, and policy-makers.

Keywords: Digital market; Rule support; Granularity; Time series forecasting; Markov chains; Long-range memory (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10614-022-10237-7

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