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Two-Stage Hybrid Machine Learning Model for High-Frequency Intraday Bitcoin Price Prediction Based on Technical Indicators, Variational Mode Decomposition, and Support Vector Regression

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  • Samuel Asante Gyamerah
  • Ning Cai

Abstract

Due to the inherent chaotic and fractal dynamics in the price series of Bitcoin, this paper proposes a two-stage Bitcoin price prediction model by combining the advantage of variational mode decomposition (VMD) and technical analysis. VMD eliminates the noise signals and stochastic volatility in the price data by decomposing the data into variational mode functions, while technical analysis uses statistical trends obtained from past trading activity and price changes to construct technical indicators. The support vector regression (SVR) accepts input from a hybrid of technical indicators (TI) and reconstructed variational mode functions (rVMF). The model is trained, validated, and tested in a period characterized by unprecedented economic turmoil due to the COVID-19 pandemic, allowing the evaluation of the model in the presence of the pandemic. The constructed hybrid model outperforms the single SVR model that uses only TI and rVMF as features. The ability to predict a minute intraday Bitcoin price has a huge propensity to reduce investors’ exposure to risk and provides better assurances of annualized returns.

Suggested Citation

  • Samuel Asante Gyamerah & Ning Cai, 2021. "Two-Stage Hybrid Machine Learning Model for High-Frequency Intraday Bitcoin Price Prediction Based on Technical Indicators, Variational Mode Decomposition, and Support Vector Regression," Complexity, Hindawi, vol. 2021, pages 1-15, December.
    • Handle: RePEc:hin:complx:1767708
    DOI: 10.1155/2021/1767708
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    References listed on IDEAS

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    1. Zargar, Faisal Nazir & Kumar, Dilip, 2019. "Informational inefficiency of Bitcoin: A study based on high-frequency data," Research in International Business and Finance, Elsevier, vol. 47(C), pages 344-353.
    2. Hu, Yang & Valera, Harold Glenn A. & Oxley, Les, 2019. "Market efficiency of the top market-cap cryptocurrencies: Further evidence from a panel framework," Finance Research Letters, Elsevier, vol. 31(C), pages 138-145.
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    Cited by:

    1. Xiao Li & Linda Du, 2023. "Bitcoin daily price prediction through understanding blockchain transaction pattern with machine learning methods," Journal of Combinatorial Optimization, Springer, vol. 45(1), pages 1-24, January.
    2. Samuel Asante Gyamerah & Victor Owusu, 2024. "Short- and long-term weather prediction based on a hybrid of CEEMDAN, LMD, and ANN," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-23, July.
    3. Narongsak Sukma & Chakkrit Snae Namahoot, 2025. "Enhancing Trading Strategies: A Multi-indicator Analysis for Profitable Algorithmic Trading," Computational Economics, Springer;Society for Computational Economics, vol. 65(6), pages 3807-3840, June.
    4. Nagula, Pavan Kumar & Alexakis, Christos, 2022. "A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).

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