A Study on Cryptocurrency Log-Return Price Prediction Using Multivariate Time-Series Model

Sang Ha Sung, Jong Min Kim, Byung Kwon Park, Sangjin Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Cryptocurrencies are highly volatile investment assets and are difficult to predict. In this study, various cryptocurrency data are used as features to predict the log-return price of major cryptocurrencies. The original contribution of this study is the selection of the most influential major features for each cryptocurrency using the volatility features of cryptocurrency, derived from the autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) models, along with the closing price of the cryptocurrency. In addition, we sought to predict the log-return price of cryptocurrencies by implementing various types of time-series model. Based on the selected major features, the log-return price of cryptocurrency was predicted through the autoregressive integrated moving average (ARIMA) time-series prediction model and the artificial neural network-based time-series prediction model. As a result of log-return price prediction, the neural-network-based time-series prediction models showed superior predictive power compared to the traditional time-series prediction model.

Original languageEnglish (US)
Article number448
JournalAxioms
Volume11
Issue number9
DOIs
StatePublished - Sep 2022

Bibliographical note

Funding Information:
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A3A2075240).

Publisher Copyright:
© 2022 by the authors.

Keywords

  • cryptocurrency
  • deep learning
  • forecasting
  • time-series

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