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

Author

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  • Pavan Kumar Nagula

    (ESC Rennes School of Business - ESC [Rennes] - ESC Rennes School of Business)

  • Christos Alexakis

    (ESC Rennes School of Business - ESC [Rennes] - ESC Rennes School of Business)

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

  • Pavan Kumar Nagula & Christos Alexakis, 2022. "A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price," Post-Print hal-03877093, HAL.
    • Handle: RePEc:hal:journl:hal-03877093
    DOI: 10.1016/j.jbef.2022.100741
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    Cited by:

    1. 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.

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