Predicting Ethereum prices using machine learning and block chain information
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, H.-M.[Kim, H.-M.] | - |
dc.contributor.author | Bock, G.-W.[Bock, G.-W.] | - |
dc.contributor.author | Lee, G.[Lee, G.] | - |
dc.date.accessioned | 2021-07-29T01:46:23Z | - |
dc.date.available | 2021-07-29T01:46:23Z | - |
dc.date.created | 2020-07-13 | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/13962 | - |
dc.description.abstract | With 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.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Information Systems | - |
dc.subject | Bitcoin | - |
dc.subject | Costs | - |
dc.subject | Ethereum | - |
dc.subject | Forecasting | - |
dc.subject | Information systems | - |
dc.subject | Information use | - |
dc.subject | Learning systems | - |
dc.subject | Machine learning | - |
dc.subject | Price prediction | - |
dc.subject | Specific information | - |
dc.subject | Strong correlation | - |
dc.subject | Blockchain | - |
dc.title | Predicting Ethereum prices using machine learning and block chain information | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, H.-M.[Kim, H.-M.] | - |
dc.contributor.affiliatedAuthor | Bock, G.-W.[Bock, G.-W.] | - |
dc.contributor.affiliatedAuthor | Lee, G.[Lee, G.] | - |
dc.identifier.scopusid | 2-s2.0-85073508126 | - |
dc.identifier.bibliographicCitation | 25th Americas Conference on Information Systems, AMCIS 2019 | - |
dc.relation.isPartOf | 25th Americas Conference on Information Systems, AMCIS 2019 | - |
dc.citation.title | 25th Americas Conference on Information Systems, AMCIS 2019 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 3 | - |
dc.subject.keywordPlus | Bitcoin | - |
dc.subject.keywordPlus | Costs | - |
dc.subject.keywordPlus | Ethereum | - |
dc.subject.keywordPlus | Forecasting | - |
dc.subject.keywordPlus | Information systems | - |
dc.subject.keywordPlus | Information use | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Machine learning | - |
dc.subject.keywordPlus | Price prediction | - |
dc.subject.keywordPlus | Specific information | - |
dc.subject.keywordPlus | Strong correlation | - |
dc.subject.keywordPlus | Blockchain | - |
dc.subject.keywordAuthor | Blockchain | - |
dc.subject.keywordAuthor | Blockchain information | - |
dc.subject.keywordAuthor | Ethereum | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Prediction | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(03063) 25-2, SUNGKYUNKWAN-RO, JONGNO-GU, SEOUL, KOREAsamsunglib@skku.edu
COPYRIGHT © 2021 SUNGKYUNKWAN UNIVERSITY ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.