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

Wei, mingzhe, Sermpinis, Georgios, Stasinakis, Charalampos (2023) Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage. Journal of Forecasting, 42 (4). pp. 852-871. ISSN 0277-6693. (doi:10.1002/for.2922) (KAR id:103539)


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 while 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
DOI/Identification number: 10.1002/for.2922
Subjects: H Social Sciences
H Social Sciences > HG Finance
Divisions: Divisions > Kent Business School - Division > Department of Accounting and Finance
Funders: Sheffield Hallam University (
University of Glasgow (
Depositing User: Mingzhe Wei
Date Deposited: 30 Oct 2023 20:14 UTC
Last Modified: 01 Nov 2023 11:24 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Wei, mingzhe.

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