Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage

WEI, Mingzhe, SERMPINIS, Georgios and STASINAKIS, Charalampos (2022). Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage. Journal of Forecasting.

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Abstract

This paper explores the use of machine learning algorithms and narrative sentiments when applied to the task of forecasting and trading Bitcoin. The forecasting framework starts from the selection among 295 individual prediction models. Three machine learning approaches, namely, neural networks, support vector machines, and gradient boosting approach, are used to further improve the forecasting performance of individual models. By taking data‐snooping bias into account, three different metrics are applied to examine the forecasting ability of each model. Our results suggest that the machine learning techniques always outperform the best individual model whereas the gradient boosting framework has the best performance among all the models. Finally, a time‐varying leverage trading strategy combined with narrative sentiments and volatility is proposed to enhance trading performance. This suggests that the hybrid leverage strategy provides the highest Bitcoin profits consistently among all trading exercises.

Item Type:	Article
Additional Information:	** Article version: VoR ** From Wiley via Jisc Publications Router ** Licence for VoR version of this article: http://creativecommons.org/licenses/by-nc-nd/4.0/ **Journal IDs: issn 0277-6693; issn 1099-131X **Article IDs: publisher-id: for2922 **History: published 11-11-2022; accepted 09-10-2022; rev-recd 27-04-2022; submitted 13-12-2021
Uncontrolled Keywords:	RESEARCH ARTICLE, RESEARCH ARTICLES, cryptocurrencies, forecast combinations, narratives, trading strategies
Identification Number:	https://doi.org/10.1002/for.2922
SWORD Depositor:	Colin Knott
Depositing User:	Colin Knott
Date Deposited:	16 Nov 2022 11:10
Last Modified:	17 Nov 2022 10:50
URI:	https://shura.shu.ac.uk/id/eprint/31032

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