Title | Forecasting cryptocurrency returns with machine learning |
Author | |
Corresponding Author | Li, Zhongfei |
Publication Years | 2023
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DOI | |
Source Title | |
ISSN | 0275-5319
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EISSN | 1878-3384
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Volume | 64 |
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
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SUSTech Authorship | Corresponding
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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]
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WOS Research Area | Business & Economics
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WOS Subject | Business, Finance
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WOS Accession No | WOS:000945801300001
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Publisher | |
Data Source | Web of Science
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Citation statistics |
Cited Times [WOS]:1
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Document Type | Journal Article |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/502116 |
Department | School 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 Affilication | School 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.
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APA |
Liu, Yujun,Li, Zhongfei,Nekhili, Ramzi,&Sultan, Jahangir.(2023).Forecasting cryptocurrency returns with machine learning.Research in International Business and Finance,64.
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MLA |
Liu, Yujun,et al."Forecasting cryptocurrency returns with machine learning".Research in International Business and Finance 64(2023).
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