Metadata only
Date
2021-02Type
- Journal Article
Citations
Cited 42 times in
Web of Science
Cited 47 times in
Scopus
ETH Bibliography
yes
Altmetrics
Abstract
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. Show more
Publication status
publishedExternal links
Journal / series
Annals of Operations ResearchVolume
Pages / Article No.
Publisher
SpringerSubject
Cryptocurrency; Machine learning; Artificial neural networks; Support vector machine; Random forest; Logistic regressionMore
Show all metadata
Citations
Cited 42 times in
Web of Science
Cited 47 times in
Scopus
ETH Bibliography
yes
Altmetrics