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Cryptocurrency price and volatility predictions with machine learning

Author

Listed:
  • Samir Poudel

    (Middle Tennessee State University)

  • Rajendra Paudyal

    (George Mason University)

  • Burak Cankaya

    (Embry-Riddle Aeronautical University)

  • Naomi Sterlingsdottir

    (Embry-Riddle Aeronautical University)

  • Marissa Murphy

    (Embry-Riddle Aeronautical University)

  • Shital Pandey

    (Embry-Riddle Aeronautical University)

  • Jorge Vargas

    (Middle Tennessee State University)

  • Khem Poudel

    (Middle Tennessee State University)

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.

Suggested Citation

  • Samir Poudel & Rajendra Paudyal & Burak Cankaya & Naomi Sterlingsdottir & Marissa Murphy & Shital Pandey & Jorge Vargas & Khem Poudel, 2023. "Cryptocurrency price and volatility predictions with machine learning," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 642-660, December.
    • Handle: RePEc:pal:jmarka:v:11:y:2023:i:4:d:10.1057_s41270-023-00239-1
    DOI: 10.1057/s41270-023-00239-1
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    References listed on IDEAS

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