Predicting bitcoin price movements using sentiment analysis: a machine learning approach Gurrib, Ikhlaas Kamalov, Firuz 2021-12-27T13:16:45Z 2021-12-27T13:16:45Z © 2021 2022-04-22
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in Studies in Economics and Finance (2022), available online at: en_US
dc.description.abstract Purpose: Cryptocurrencies such as Bitcoin (BTC) attracted a lot of attention in recent months due to their unprecedented price fluctuations. This paper aims to propose a new method for predicting the direction of BTC price using linear discriminant analysis (LDA) together with sentiment analysis. Design/methodology/approach: Concretely, the authors train an LDA-based classifier that uses the current BTC price information and BTC news announcements headlines to forecast the next-day direction of BTC prices. The authors compare the results with a Support Vector Machine (SVM) model and random guess approach. The use of BTC price information and news announcements related to crypto enables us to value the importance of these different sources and types of information. Findings: Relative to the LDA results, the SVM model was more accurate in predicting BTC next day’s price movement. All models yielded better forecasts of an increase in tomorrow’s BTC price compared to forecasting a decrease in the crypto price. The inclusion of news sentiment resulted in the highest forecast accuracy of 0.585 on the test data, which is superior to a random guess. The LDA (SVM) model with asset specific (news sentiment and asset specific) input features ranked first within their respective model classifiers, suggesting both BTC news sentiment and asset specific are prized factors in predicting tomorrow’s price direction. Originality/value: To the best of the authors’ knowledge, this is the first study to analyze the potential effect of crypto-related sentiment and BTC specific news on BTC’s price using LDA and sentiment analysis. © 2021, Emerald Publishing Limited. en_US
dc.identifier.citation Gurrib, I., & Kamalov, F. (2022). Predicting bitcoin price movements using sentiment analysis: A machine learning approach. Studies in Economics and Finance, 39(3), 347-364, en_US
dc.identifier.issn 10867376
dc.language.iso en en_US
dc.publisher Emerald Group Holdings Ltd. en_US
dc.relation Authors Affiliations : Gurrib, I., School of Graduate Studies, Canadian University of Dubai, Dubai, United Arab Emirates; Kamalov, F., Faculty of Engineering and Architecture, Canadian University of Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseries Studies in Economics and Finance; Volume 39, Issue 3
dc.rights This article is © Emerald Publishing Limited and permission has been granted for this version to appear here ( Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Publishing Limited.
dc.rights.holder Copyright : © 2021, Emerald Publishing Limited.
dc.subject Bitcoin en_US
dc.subject Forecasting en_US
dc.subject Linear discriminant analysis en_US
dc.subject News announcements en_US
dc.subject Sentiment analysis en_US
dc.title Predicting bitcoin price movements using sentiment analysis: a machine learning approach en_US
dc.type Article en_US
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