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Predicting Ethereum prices using machine learning and block chain information

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dc.contributor.authorKim, H.-M.[Kim, H.-M.]-
dc.contributor.authorBock, G.-W.[Bock, G.-W.]-
dc.contributor.authorLee, G.[Lee, G.]-
dc.date.accessioned2021-07-29T01:46:23Z-
dc.date.available2021-07-29T01:46:23Z-
dc.date.created2020-07-13-
dc.date.issued2019-
dc.identifier.issn0000-0000-
dc.identifier.urihttps://scholarworks.bwise.kr/skku/handle/2021.sw.skku/13962-
dc.description.abstractWith the growing interest in cryptocurrency and its algorithm, studies on cryptocurrency price predations have been extensively conducted in various academic disciplines. Since the cryptocurrency is generated and consumed by the Blockchain system, it has been considered that Blockchain-specific information would be the main components in predicting cryptocurrency prices. Specifically, this point of view has been largely employed in the studies of Bitcoin price predictions. However, this study recognizes that Ethereum, a popular and leading cryptocurrency in the market, has distinct Blockchain information as compared to that of Bitcoin. We attempt to investigate the relationships between inherent Ethereum Blockchain information and Ethereum prices. Furthermore, the research examines how Blockchain information of other coins in the market is associated with Ethereum prices. The results of data analysis show that Ethereum Blockchain information and Blockchain information of other coins have strong correlations with the final Ethereum prices. © 2019 Association for Information Systems. All rights reserved.-
dc.language영어-
dc.language.isoen-
dc.publisherAssociation for Information Systems-
dc.subjectBitcoin-
dc.subjectCosts-
dc.subjectEthereum-
dc.subjectForecasting-
dc.subjectInformation systems-
dc.subjectInformation use-
dc.subjectLearning systems-
dc.subjectMachine learning-
dc.subjectPrice prediction-
dc.subjectSpecific information-
dc.subjectStrong correlation-
dc.subjectBlockchain-
dc.titlePredicting Ethereum prices using machine learning and block chain information-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, H.-M.[Kim, H.-M.]-
dc.contributor.affiliatedAuthorBock, G.-W.[Bock, G.-W.]-
dc.contributor.affiliatedAuthorLee, G.[Lee, G.]-
dc.identifier.scopusid2-s2.0-85073508126-
dc.identifier.bibliographicCitation25th Americas Conference on Information Systems, AMCIS 2019-
dc.relation.isPartOf25th Americas Conference on Information Systems, AMCIS 2019-
dc.citation.title25th Americas Conference on Information Systems, AMCIS 2019-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass3-
dc.subject.keywordPlusBitcoin-
dc.subject.keywordPlusCosts-
dc.subject.keywordPlusEthereum-
dc.subject.keywordPlusForecasting-
dc.subject.keywordPlusInformation systems-
dc.subject.keywordPlusInformation use-
dc.subject.keywordPlusLearning systems-
dc.subject.keywordPlusMachine learning-
dc.subject.keywordPlusPrice prediction-
dc.subject.keywordPlusSpecific information-
dc.subject.keywordPlusStrong correlation-
dc.subject.keywordPlusBlockchain-
dc.subject.keywordAuthorBlockchain-
dc.subject.keywordAuthorBlockchain information-
dc.subject.keywordAuthorEthereum-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorPrediction-
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