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Stock Market Prediction Using Machine Learning

dc.contributor.authorSubasi, Abdulhamit
dc.contributor.authorSarirete, Akila
dc.contributor.authorBagedo, K.
dc.contributor.authorShams, A.
dc.contributor.authorAmir, F.
dc.date.accessioned2022-12-26T08:43:35Z
dc.date.available2022-12-26T08:43:35Z
dc.date.issued2021
dc.identifier.issn1877-0509
dc.identifier.doi10.1016/j.procs.2021.10.071
dc.identifier.urihttp://hdl.handle.net/20.500.14131/390
dc.description.abstractDue to the complex nature of stock market prediction, it has been a trending area of interest. This paper presents a comparison of the prediction by inputting different classifiers. Furthermore, the results of the comparison are done on an accuracy basis. Each machine learning algorithm is tested against the National Association of Securities Dealers Automated Quotations System (NASDAQ), New York Stock Exchange (NYSE), Nikkei, and Financial Times Stock Exchange (FTSE). Furthermore, several machine learning algorithms are compared with a normal and a leaked data set.
dc.publisherElsevier B.V.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectStock market
dc.subjectMachine learning
dc.subjectNYSE
dc.subjectFTSE
dc.subjectNASDAQ
dc.subjectNikkei
dc.titleStock Market Prediction Using Machine Learning
dc.typeArticle
dc.source.journalProcedia Computer Science
dc.source.volume194
refterms.dateFOA2022-12-26T09:13:26Z
dc.source.pages173-179
dc.contributor.departmentComputer Science


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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/