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A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price

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  • Nagula, Pavan Kumar
  • Alexakis, Christos

Abstract

Several machine learning techniques and hybrid architectures for predicting bitcoin price movement have been presented in the past. Our paper proposes a hybrid model encompassing classification and regression models for predicting bitcoin prices. Our analysis found that the automated feature interactions learner (deep cross networks) error performance using a plethora of technical indicators, including crypto-specific technical indicator difficulty ribbon compression and control variables such as Metcalfe’s value of bitcoin, number of unique active addresses, bitcoin network hash rate, and S&P 500 log returns, in a hybrid architecture is better than the single-stage architecture. The hybrid model predicted a 100% directional hit rate and maintained steady volatility in returns for the out-of-sample period. Our paper concludes that in terms of risk (Sharpe ratio 1.03) and profitability (260% and 82%), the hybrid model’s bitcoin futures strategy performed better than the deep cross network regression and buy-and-hold benchmark strategies.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:beexfi:v:36:y:2022:i:c:s2214635022000673
    DOI: 10.1016/j.jbef.2022.100741
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    References listed on IDEAS

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    Cited by:

    1. José Parra-Moyano & Daniel Partida & Moritz Gessl & Somnath Mazumdar, 2024. "Analyzing swings in Bitcoin returns: a comparative study of the LPPL and sentiment-informed random forest models," Digital Finance, Springer, vol. 6(3), pages 427-439, September.
    2. Pavan Kumar Nagula & Christos Alexakis, 2022. "A Novel Machine Learning Approach for Predicting the NIFTY50 Index in India," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 28(3), pages 155-170, November.
    3. Kaijian He & Qian Yang & Lei Ji & Jingcheng Pan & Yingchao Zou, 2023. "Financial Time Series Forecasting with the Deep Learning Ensemble Model," Mathematics, MDPI, vol. 11(4), pages 1-15, February.

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    More about this item

    Keywords

    Efficient market hypothesis; Hybrid architecture; Machine learning; Technical indicators interactions; Deep cross networks; Bitcoin;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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