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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DOI: 10.1155/2021/1767708
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References listed on IDEAS
- 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.
- 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:
- 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.
- 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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