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Analysis of Bitcoin Price Prediction Using Machine Learning

Author

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  • Junwei Chen

    (Graduate School of Economics, Kobe University, Kobe 657-8501, Japan)

Abstract

The research purpose of this paper is to obtain an algorithm model with high prediction accuracy for the price of Bitcoin on the next day through random forest regression and LSTM, and to explain which variables have influence on the price of Bitcoin. There is much prior literature on Bitcoin price prediction research, and the research methods mainly revolve around the ARMA model of time series and the LSTM algorithm of deep learning. Although it cannot be proved by the Diebold–Mariano test that the prediction accuracy of random forest regression is significantly better than that of LSTM, the prediction errors RMSE and MAPE of random forest regression are better than those of LSTM. The changes in the variables that determine the price of Bitcoin in each period are also obtained through random forest regression. From 2015 to 2018, three US stock market indexes, NASDAQ, DJI, and S&P500 and oil price, and ETH price have impact on Bitcoin prices. Since 2018, the important variables have become ETH price and Japanese stock market index JP225. The relationship between accuracy and the number of periods of explanatory variables brought into the model shows that for predicting the price of Bitcoin for the next day, the model with only one lag of the explanatory variables has the best prediction accuracy.

Suggested Citation

  • Junwei Chen, 2023. "Analysis of Bitcoin Price Prediction Using Machine Learning," JRFM, MDPI, vol. 16(1), pages 1-25, January.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:1:p:51-:d:1034543
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    References listed on IDEAS

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    Cited by:

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    2. Jing, Ruixue & Rocha, Luis E.C., 2023. "A network-based strategy of price correlations for optimal cryptocurrency portfolios," Finance Research Letters, Elsevier, vol. 58(PC).
    3. Ayush Singh & Anshu K. Jha & Amit N. Kumar, 2024. "Prediction of Cryptocurrency Prices through a Path Dependent Monte Carlo Simulation," Papers 2405.12988, arXiv.org.
    4. Olcay Ozupek & Reyat Yilmaz & Bita Ghasemkhani & Derya Birant & Recep Alp Kut, 2024. "A Novel Hybrid Model (EMD-TI-LSTM) for Enhanced Financial Forecasting with Machine Learning," Mathematics, MDPI, vol. 12(17), pages 1-36, September.
    5. Moiz Qureshi & Hasnain Iftikhar & Paulo Canas Rodrigues & Mohd Ziaur Rehman & S. A. Atif Salar, 2024. "Statistical Modeling to Improve Time Series Forecasting Using Machine Learning, Time Series, and Hybrid Models: A Case Study of Bitcoin Price Forecasting," Mathematics, MDPI, vol. 12(23), pages 1-15, November.

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