A survey of deep learning applications in cryptocurrency

Junhuan Zhang; Kewei Cai; Jiaqi Wen

doi:10.1016/j.isci.2023.108509

A survey of deep learning applications in cryptocurrency

iScience. 2023 Nov 22;27(1):108509. doi: 10.1016/j.isci.2023.108509. eCollection 2024 Jan 19.

Authors

Junhuan Zhang^{1

2}, Kewei Cai¹, Jiaqi Wen³

Affiliations

¹ School of Economics and Management, Beihang University, Beijing, China.
² Key Laboratory of Complex System Analysis, Management and Decision (Beihang University), Ministry of Education, Beijing, China.
³ School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.

Abstract

This study aims to comprehensively review a recently emerging multidisciplinary area related to the application of deep learning methods in cryptocurrency research. We first review popular deep learning models employed in multiple financial application scenarios, including convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning. We also give an overview of cryptocurrencies by outlining the cryptocurrency history and discussing primary representative currencies. Based on the reviewed deep learning methods and cryptocurrencies, we conduct a literature review on deep learning methods in cryptocurrency research across various modeling tasks, including price prediction, portfolio construction, bubble analysis, abnormal trading, trading regulations and initial coin offering in cryptocurrency. Moreover, we discuss and evaluate the reviewed studies from perspectives of modeling approaches, empirical data, experiment results and specific innovations. Finally, we conclude this literature review by informing future research directions and foci for deep learning in cryptocurrency.

Keywords: Artificial intelligence; Economics; Machine learning; Social sciences.

Publication types

Review