中文版 | English
Title

Forecasting cryptocurrency returns with machine learning

Author
Corresponding AuthorLi, Zhongfei
Publication Years
2023
DOI
Source Title
ISSN
0275-5319
EISSN
1878-3384
Volume64
Abstract
This article employs machine learning models to predict returns for 3703 cryptocurrencies for the 2013 - 2021 period. Based on daily data, we build an equal (capital)-weighted portfolio that generates 7.1 % (2.4 %) daily return with a 1.95 (0.27) Sharpe ratio. We obtain an out-of-sample R2 of 4.855 %. Our results suggest that cryptocurrencies behave like conventional assets than fiat currencies since variables, including lagged returns, can predict future returns. As assets, cryp-tocurrencies are not weakly efficient, and production costs do not determine their prices. Returns for small cryptocurrencies are more predictable than larger ones. The predictive power of the 1 -day lagged return is stronger than all other features (predictors) combined. The results offer new insights for crypto investors, traders, and financial analysts.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Corresponding
Funding Project
Major Program of the National Natural Science Foundation of China[71991474] ; Innovative Research Group Project of National Natural Science Foundation of China[71721001]
WOS Research Area
Business & Economics
WOS Subject
Business, Finance
WOS Accession No
WOS:000945801300001
Publisher
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/502116
DepartmentSchool of Business
Affiliation
1.Sun Yat Sen Univ, Sch Business, Guangzhou, Peoples R China
2.Southern Univ Sci & Technol, Sch Business, Shenzhen, Peoples R China
3.Appl Sci Univ, Dept Accounting & Finance, Al Eker, Bahrain
4.Bentley Univ, McCallum Grad Sch, Waltham, MA USA
Corresponding Author AffilicationSchool of Business
Recommended Citation
GB/T 7714
Liu, Yujun,Li, Zhongfei,Nekhili, Ramzi,et al. Forecasting cryptocurrency returns with machine learning[J]. Research in International Business and Finance,2023,64.
APA
Liu, Yujun,Li, Zhongfei,Nekhili, Ramzi,&Sultan, Jahangir.(2023).Forecasting cryptocurrency returns with machine learning.Research in International Business and Finance,64.
MLA
Liu, Yujun,et al."Forecasting cryptocurrency returns with machine learning".Research in International Business and Finance 64(2023).
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