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Article Dans Une Revue Journal of Behavioral and Experimental Finance Année : 2022

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

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Résumé

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.
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Dates et versions

hal-03877093 , version 1 (29-11-2022)

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Pavan Kumar Nagula, Christos Alexakis. A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price. Journal of Behavioral and Experimental Finance, 2022, 36, pp.100741. ⟨10.1016/j.jbef.2022.100741⟩. ⟨hal-03877093⟩
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