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Forecasting the price of Bitcoin using deep learning

Author

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  • Liu, Mingxi
  • Li, Guowen
  • Li, Jianping
  • Zhu, Xiaoqian
  • Yao, Yinhong

Abstract

After constructing a feature system with 40 determinants that affect the price of Bitcoin considering aspects of the cryptocurrency market, public attention, and the macroeconomic environment, a deep learning method named stacked denoising autoencoders (SDAE) is utilized to predict the price of Bitcoin. The results show that compared with the most popular machine learning methods, such as back propagation neural network (BPNN) and support vector regression (SVR) methods, the SDAE model performs better in both directional and level prediction, measured using commonly used indicators, i.e., mean absolute percentage error (MAPE), root mean squared error (RMSE), and directional accuracy (DA).

Suggested Citation

  • Liu, Mingxi & Li, Guowen & Li, Jianping & Zhu, Xiaoqian & Yao, Yinhong, 2021. "Forecasting the price of Bitcoin using deep learning," Finance Research Letters, Elsevier, vol. 40(C).
    • Handle: RePEc:eee:finlet:v:40:y:2021:i:c:s1544612320304864
    DOI: 10.1016/j.frl.2020.101755
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    4. Jinghua Wang & Geoffrey M. Ngene & Yan Shi & Ann Nduati Mungai, 2023. "An Investigation of the Predictability of Uncertainty Indices on Bitcoin Returns," JRFM, MDPI, vol. 16(10), pages 1-12, October.
    5. Lili Pan & Lin Wang & Qianqian Feng, 2022. "A Bibliometric Analysis of Risk Management in Foreign Direct Investment: Insights and Implications," Sustainability, MDPI, vol. 14(12), pages 1-18, June.
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    7. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "How well do investor sentiment and ensemble learning predict Bitcoin prices?," Research in International Business and Finance, Elsevier, vol. 64(C).
    8. Wei Liu & Yoshihisa Suzuki & Shuyi Du, 2024. "Forecasting the Stock Price of Listed Innovative SMEs Using Machine Learning Methods Based on Bayesian optimization: Evidence from China," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 2035-2068, May.
    9. Samuka Mohanty & Rajashree Dash, 2022. "Neural Network-Based Bitcoin Pricing Using a New Mutated Climb Monkey Algorithm with TOPSIS Analysis for Sustainable Development," Mathematics, MDPI, vol. 10(22), pages 1-23, November.
    10. Cynthia Weiyi Cai & Rui Xue & Bi Zhou, 2023. "Cryptocurrency puzzles: a comprehensive review and re-introduction," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(1), pages 26-50, June.
    11. Naseh Majidi & Mahdi Shamsi & Farokh Marvasti, 2022. "Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning," Papers 2210.03469, arXiv.org.
    12. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    13. Liu, Yujun & Li, Zhongfei & Nekhili, Ramzi & Sultan, Jahangir, 2023. "Forecasting cryptocurrency returns with machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).
    14. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).
    15. Samuka Mohanty & Rajashree Dash, 2023. "A New Dual Normalization for Enhancing the Bitcoin Pricing Capability of an Optimized Low Complexity Neural Net with TOPSIS Evaluation," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    16. Kui Wang & Jie Wan & Gang Li & Hao Sun, 2022. "A Hybrid Algorithm-Level Ensemble Model for Imbalanced Credit Default Prediction in the Energy Industry," Energies, MDPI, vol. 15(14), pages 1-18, July.

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