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A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin

Author

Listed:
  • Zi Ye

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    These authors contributed equally to this work.)

  • Yinxu Wu

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    These authors contributed equally to this work.)

  • Hui Chen

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Yi Pan

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Qingshan Jiang

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

Abstract

Cryptocurrencies can be considered as mathematical money. As the most famous cryptocurrency, the Bitcoin price forecasting model is one of the popular mathematical models in financial technology because of its large price fluctuations and complexity. This paper proposes a novel ensemble deep learning model to predict Bitcoin’s next 30 min prices by using price data, technical indicators and sentiment indexes, which integrates two kinds of neural networks, long short-term memory (LSTM) and gate recurrent unit (GRU), with stacking ensemble technique to improve the accuracy of decision. Because of the real-time updates of comments on social media, this paper uses social media texts instead of news websites as the source data of public opinion. It is processed by linguistic statistical method to form the sentiment indexes. Meanwhile, as a financial market forecasting model, the model selects the technical indicators as input as well. Real data from September 2017 to January 2021 is used to train and evaluate the model. The experimental results show that the near-real time prediction has a better performance, with a mean absolute error (MAE) 88.74% better than the daily prediction. The purpose of this work is to explain our solution and show that the ensemble method has better performance and can better help investors in making the right investment decision than other traditional models.

Suggested Citation

  • Zi Ye & Yinxu Wu & Hui Chen & Yi Pan & Qingshan Jiang, 2022. "A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin," Mathematics, MDPI, vol. 10(8), pages 1-21, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1307-:d:793900
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    References listed on IDEAS

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    Cited by:

    1. Rolando Rubilar-Torrealba & Karime Chahuán-Jiménez & Hanns de la Fuente-Mella, 2023. "A Stochastic Analysis of the Effect of Trading Parameters on the Stability of the Financial Markets Using a Bayesian Approach," Mathematics, MDPI, vol. 11(11), pages 1-14, May.
    2. José Luis Miralles-Quirós & María Mar Miralles-Quirós, 2022. "Mathematics, Cryptocurrencies and Blockchain Technology," Mathematics, MDPI, vol. 10(12), pages 1-2, June.
    3. Yingjie Zhu & Jiageng Ma & Fangqing Gu & Jie Wang & Zhijuan Li & Youyao Zhang & Jiani Xu & Yifan Li & Yiwen Wang & Xiangqun Yang, 2023. "Price Prediction of Bitcoin Based on Adaptive Feature Selection and Model Optimization," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    4. Ali Mohammadjafari, 2024. "Comparative Study of Bitcoin Price Prediction," Papers 2405.08089, arXiv.org.
    5. Farman Ullah Khan & Faridoon Khan & Parvez Ahmed Shaikh, 2023. "Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms," Future Business Journal, Springer, vol. 9(1), pages 1-11, December.
    6. Rasoul Amirzadeh & Dhananjay Thiruvady & Asef Nazari & Mong Shan Ee, 2023. "Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships," Papers 2306.08157, arXiv.org, revised Oct 2024.

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