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Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies

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  • Dante Miller

    (Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA)

  • Jong-Min Kim

    (Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA)

Abstract

In this study, we predicted the log returns of the top 10 cryptocurrencies based on market cap, using univariate and multivariate machine learning methods such as recurrent neural networks, deep learning neural networks, Holt’s exponential smoothing, autoregressive integrated moving average, ForecastX, and long short-term memory networks. The multivariate long short-term memory networks performed better than the univariate machine learning methods in terms of the prediction error measures.

Suggested Citation

  • Dante Miller & Jong-Min Kim, 2021. "Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies," JRFM, MDPI, vol. 14(10), pages 1-10, October.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:10:p:486-:d:655642
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    References listed on IDEAS

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    1. Erdinc Akyildirim & Ahmet Goncu & Ahmet Sensoy, 2021. "Prediction of cryptocurrency returns using machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 3-36, February.
    2. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2019. "On long memory effects in the volatility measure of Cryptocurrencies," Finance Research Letters, Elsevier, vol. 28(C), pages 95-100.
    3. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
    4. Wang, Pengfei & Zhang, Wei & Li, Xiao & Shen, Dehua, 2019. "Is cryptocurrency a hedge or a safe haven for international indices? A comprehensive and dynamic perspective," Finance Research Letters, Elsevier, vol. 31(C), pages 1-18.
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    Cited by:

    1. Jong-Min Kim & Chanho Cho & Chulhee Jun, 2022. "Forecasting the Price of the Cryptocurrency Using Linear and Nonlinear Error Correction Model," JRFM, MDPI, vol. 15(2), pages 1-10, February.
    2. Queiroz, R.G.S. & Kristoufek, L. & David, S.A., 2024. "A combined framework to explore cryptocurrency volatility and dependence using multivariate GARCH and Copula modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 652(C).
    3. Bhaskar Tripathi & Rakesh Kumar Sharma, 2023. "Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1919-1945, December.
    4. Federico D'Amario & Milos Ciganovic, 2022. "Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach," Papers 2210.00883, arXiv.org.
    5. Cynthia Weiyi Cai & Rui Xue & Bi Zhou, 2023. "Cryptocurrency puzzles: a comprehensive review and re-introduction," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(1), pages 26-50, June.

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