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

Pavan Kumar Nagula and Christos Alexakis

Journal of Behavioral and Experimental Finance, 2022, vol. 36, issue C

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.

Keywords: Efficient market hypothesis; Hybrid architecture; Machine learning; Technical indicators interactions; Deep cross networks; Bitcoin (search for similar items in EconPapers)
JEL-codes: G13 G14 G17 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:beexfi:v:36:y:2022:i:c:s2214635022000673

DOI: 10.1016/j.jbef.2022.100741

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