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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Prediction of cryptocurrency returns using machine learning |
| Organization Unit | |
| Authors |
|
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Annals of Operations Research |
| Publisher | Springer |
| Geographical Reach | international |
| ISSN | 0254-5330 |
| Volume | 297 |
| Number | 1-2 |
| Page Range | 3 - 36 |
| Date | 2021 |
| Abstract Text | In this study, the predictability of the most liquid twelve cryptocurrencies are analyzed at the daily and minute level frequencies using the machine learning classification algorithms including the support vector machines, logistic regression, artificial neural networks, and random forests with the past price information and technical indicators as model features. The average classification accuracy of four algorithms are consistently all above the 50% threshold for all cryptocurrencies and for all the timescales showing that there exists predictability of trends in prices to a certain degree in the cryptocurrency markets. Machine learning classification algorithms reach about 55–65% predictive accuracy on average at the daily or minute level frequencies, while the support vector machines demonstrate the best and consistent results in terms of predictive accuracy compared to the logistic regression, artificial neural networks and random forest classification algorithms. |
| Free access at | Official URL |
| Official URL | https://link.springer.com/article/10.1007/s10479-020-03575-y |
| Digital Object Identifier | 10.1007/s10479-020-03575-y |
| Other Identification Number | merlin-id:20611 |
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