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

Wei, M., Sermpinis, G. and Stasinakis, C. (2023) Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage. Journal of Forecasting, 42(4), pp. 852-871. (doi: 10.1002/for.2922)

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 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: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: WEI, MINGZHE and Stasinakis, Professor Charalampos and Sermpinis, Professor Georgios
• Authors: Wei, M., Sermpinis, G., and Stasinakis, C.
• College/School: College of Social Sciences > Adam Smith Business School > Accounting and Finance
• Journal Name: Journal of Forecasting
• Publisher: Wiley
• ISSN: 0277-6693
• ISSN (Online): 1099-131X
• Published Online: 17 October 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Journal of Forecasting 42(4):852-871
• Publisher Policy: Reproduced under a Creative Commons License