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Cryptocurrency price and volatility predictions with machine learning

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A Correction to this article was published on 30 September 2023

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Abstract

In recent years, the digital currency has gained significant popularity owing to its increasing dependence on computers and the Internet. Among various forms of virtual currency, cryptocurrency has emerged as a prominent contender. The advent of digital currency has opened new avenues in the software industry, particularly in finance, data storage, and data collection. This evolution has given rise to exciting opportunities for businesses to explore the potential of digital currency and leverage its benefits. Cryptocurrency (crypto) is very volatile regarding the market value, which carries a host of unknowns that make it difficult to predict and analyze future prices. This paper discusses the use of six types of machine-learning models (Linear Regression, LSTM, Bi-LSTM, GRU, TARCH, and VAR) to predict the Bitcoin and DogeCoin prices; General Least-Squares Regression and Neural Networks algorithms to predict the volatility of a given cryptocurrency and its prices from 2014 to 2023 with daily cryptocurrency volatility data. The results show that high-performance computing techniques such as GRU (Gated Recurrent Unit) neural networks (0.0468 RMSPE) regression models to predict relatively accurate crypto price volatility and past available cryptocurrency price data are proven to be used to verify the prediction results.

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Author notes

  1. Samir Poudel, Rajendra Paudyal, Burak Cankaya, Naomi Sterlingsdottir, Marissa Murphy, Shital Pandey, Jorge Vargas, and Khem Poudel have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science, Middle Tennessee State University, TN, Murfreesboro, United States

    Samir Poudel & Khem Poudel

  2. Department of Computer Science, George Mason University, University Drive, Fairfax, VA, 22030, USA

    Rajendra Paudyal

  3. Department of Management and Technology, Embry-Riddle Aeronautical University, 1 Aerospace Blvd, Daytona Beach, FL, 32114, USA

    Burak Cankaya

  4. Department of Mathematics and Data Science, Embry-Riddle Aeronautical University, 1 Aerospace Blvd, Daytona Beach, FL, 32114, USA

    Naomi Sterlingsdottir, Marissa Murphy & Shital Pandey

  5. Department of Engineering Technology, Middle Tennessee State University, TN, Murfreesboro, USA

    Jorge Vargas

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  1. Samir Poudel
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Correspondence to Burak Cankaya.

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The original online version of this article was revised: Modifications have been made in the article note and author Jorge Vargas’s affiliation. Full information regarding the corrections made can be found in the correction for this article.

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Poudel, S., Paudyal, R., Cankaya, B. et al. Cryptocurrency price and volatility predictions with machine learning. J Market Anal 11, 642–660 (2023). https://doi.org/10.1057/s41270-023-00239-1

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