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Predicting Bitcoin Prices via Machine Learning and Time Series Models

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  • Yu-Min Lian
  • Jia-Ling Chen
  • Hsueh-Chien Cheng

Abstract

In this study, we predict Bitcoin price trends using the back propagation neural network (BPNN), autoregressive integrated moving average (ARIMA), and generalized autoregressive conditional heteroscedasticity (GARCH) models. Based on principal component analysis (PCA), we extract two new input components for BPNN from Bitcoin’s three-day closing prices, MA5, MA20, daily trading volume, Ether price, and Ripple price. The training set covers the period between September 1, 2015 and March 31, 2020, and the forecasting set covers the period between April 1, 2020 and June 30, 2020. Empirical results reveal (1) the predictive ability of BPNN over that of the ARIMA models; (2) BPNN with two hidden layers is able to predict price trends more precisely than that with only one hidden layer; (3) in terms of time series models, the ARIMA-GARCH family of models demonstrates better predictive performance than ARIMA models; and (4) among the ARIMAGARCH family of models, the ARIMA-EGARCH model is proven to produce the best predictive results on price, and the ARIMA-GARCH model predicts more accurately than the ARIMA-GJR-GARCH model. Specifically, our findings provide a reference on Bitcoin for market participants. JEL classification numbers: C32, C45, C53, G17. Keywords: Bitcoin, Back propagation neural network, Autoregressive integrated moving average, Generalized autoregressive conditional heteroscedasticity, Principal component analysis.

Suggested Citation

  • Yu-Min Lian & Jia-Ling Chen & Hsueh-Chien Cheng, 2022. "Predicting Bitcoin Prices via Machine Learning and Time Series Models," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(5), pages 1-2.
    • Handle: RePEc:spt:apfiba:v:12:y:2022:i:5:f:12_5_2
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    References listed on IDEAS

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    Cited by:

    1. Zhengmeng Xu & Yujie Wang & Xiaotong Feng & Yilin Wang & Yanli Li & Hai Lin, 2023. "Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field and CNNs for Stock Return Predictions," Papers 2310.07427, arXiv.org, revised Dec 2023.
    2. 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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    More about this item

    Keywords

    bitcoin; back propagation neural network; autoregressive integrated moving average; generalized autoregressive conditional heteroscedasticity; principal component analysis.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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