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Bitcoin Price Prediction: A Machine Learning Sample Dimension Approach

Author

Listed:
  • Sumit Ranjan

    (Madras School of Economics)

  • Parthajit Kayal

    (Madras School of Economics)

  • Malvika Saraf

    (Madras School of Economics)

Abstract

The purpose of the paper is to predict Bitcoin prices using various machine learning techniques. Due to its high volatility attribute, accurate price prediction is the need of the hour for sound investment decision-making. At the offset, this study categorizes Bitcoin price by daily and high-frequency price (5-min interval price). For its daily and 5-min interval price prediction, a set of high-dimensional features and fundamental trading features are employed, respectively. Thereafter, we find that statistical methods like Logistic Regression predict daily price with 64.84% accuracy while complex machine learning algorithms like XGBoost predict 5-min interval price with an accuracy level of 59.4%. This work on Bitcoin price prediction recognizes the significance of sample dimensions in machine learning algorithms.

Suggested Citation

  • Sumit Ranjan & Parthajit Kayal & Malvika Saraf, 2023. "Bitcoin Price Prediction: A Machine Learning Sample Dimension Approach," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1617-1636, April.
    • Handle: RePEc:kap:compec:v:61:y:2023:i:4:d:10.1007_s10614-022-10262-6
    DOI: 10.1007/s10614-022-10262-6
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    References listed on IDEAS

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    1. Pavel Ciaian & Miroslava Rajcaniova & d’Artis Kancs, 2016. "The economics of BitCoin price formation," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1799-1815, April.
    2. Adam Hayes, 2014. "What Factors Give Cryptocurrencies Their Value: An Empirical Analysis," Working Papers 1406, New School for Social Research, Department of Economics, revised Mar 2015.
    3. Barro, Robert J, 1979. "Money and the Price Level under the Gold Standard," Economic Journal, Royal Economic Society, vol. 89(353), pages 13-33, March.
    4. repec:men:wpaper:58_2015 is not listed on IDEAS
    5. Jaroslav Bukovina & Matus Marticek, 2016. "Sentiment and Bitcoin Volatility," MENDELU Working Papers in Business and Economics 2016-58, Mendel University in Brno, Faculty of Business and Economics.
    6. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
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    Cited by:

    1. Riaz Ud Din & Salman Ahmed & Saddam Hussain Khan, 2024. "A Novel Decision Ensemble Framework: Customized Attention-BiLSTM and XGBoost for Speculative Stock Price Forecasting," Papers 2401.11621, arXiv.org.
    2. Varshini, Anu & Kayal, Parthajit & Maiti, Moinak, 2024. "How good are different machine and deep learning models in forecasting the future price of metals? Full sample versus sub-sample," Resources Policy, Elsevier, vol. 92(C).

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