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

Pavan Kumar Nagula and Christos Alexakis
Additional contact information
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

Post-Print from HAL

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.

Date: 2022-12
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Published in Journal of Behavioral and Experimental Finance, 2022, 36, pp.100741. ⟨10.1016/j.jbef.2022.100741⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03877093

DOI: 10.1016/j.jbef.2022.100741

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().