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Using Genetic Algorithm and NARX Neural Network to Forecast Daily Bitcoin Price

Author

Listed:
  • Jin-Bom Han

    (Kim Il Sung University)

  • Sun-Hak Kim

    (Kim Il Sung University)

  • Myong-Hun Jang

    (Kim Il Sung University)

  • Kum-Sun Ri

    (Kim Il Sung University)

Abstract

The main purpose of this paper is to suggest daily bitcoin return model using a genetic algorithm and NARX neural network. We found that the genetic algorithm is effective to decide the architecture of the NARX neural network than information criteria-Akaike information criterion and the Schwarz information criterion using a Monte Carlo simulation and a hypothesis test. Finally, we forecasted daily bitcoin geometric return using this hybrid model of the genetic algorithm and NARX neural network and compare it with a feed-forward neural network forecasting model through a hypothesis test.

Suggested Citation

  • Jin-Bom Han & Sun-Hak Kim & Myong-Hun Jang & Kum-Sun Ri, 2020. "Using Genetic Algorithm and NARX Neural Network to Forecast Daily Bitcoin Price," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 337-353, August.
    • Handle: RePEc:kap:compec:v:56:y:2020:i:2:d:10.1007_s10614-019-09928-5
    DOI: 10.1007/s10614-019-09928-5
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    References listed on IDEAS

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    Cited by:

    1. Amin Aminimehr & Ali Raoofi & Akbar Aminimehr & Amirhossein Aminimehr, 2022. "A Comprehensive Study of Market Prediction from Efficient Market Hypothesis up to Late Intelligent Market Prediction Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 781-815, August.
    2. Hakan Pabuccu & Adrian Barbu, 2024. "Feature selection with annealing for forecasting financial time series," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-26, December.
    3. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
    4. Surinder Singh Khurana & Parvinder Singh & Naresh Kumar Garg, 2024. "OG-CAT: A Novel Algorithmic Trading Alternative to Investment in Crypto Market," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1735-1756, May.
    5. Satya Prakash Yadav & Krishna Kant Agrawal & Bhoopesh Singh Bhati & Fadi Al-Turjman & Leonardo Mostarda, 2022. "Blockchain-Based Cryptocurrency Regulation: An Overview," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1659-1675, April.
    6. Yang, Boyu & Sun, Yuying & Wang, Shouyang, 2020. "A novel two-stage approach for cryptocurrency analysis," International Review of Financial Analysis, Elsevier, vol. 72(C).
    7. Goodell, John W. & Ben Jabeur, Sami & Saâdaoui, Foued & Nasir, Muhammad Ali, 2023. "Explainable artificial intelligence modeling to forecast bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 88(C).
    8. Gyana Ranjan Patra & Mihir Narayan Mohanty, 2023. "Price Prediction of Cryptocurrency Using a Multi-Layer Gated Recurrent Unit Network with Multi Features," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1525-1544, December.

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