Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2010
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dc.contributor.authorSezer, Ömer Berat-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorDoğdu, Erdoğan.-
dc.date.accessioned2019-07-10T14:42:45Z
dc.date.available2019-07-10T14:42:45Z
dc.date.issued2017-04-13
dc.identifier.citationSezer, O. B., Ozbayoglu, A. M., & Dogdu, E. (2017, April). An artificial neural network-based stock trading system using technical analysis and big data framework. In Proceedings of the SouthEast Conference (pp. 223-226). ACM.en_US
dc.identifier.isbn978-145035024-2
dc.identifier.urihttps://dl.acm.org/citation.cfm?doid=3077286.3077294-
dc.identifier.urihttps://arxiv.org/pdf/1712.09592.pdf-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/2010-
dc.descriptionACM SouthEast Regional Conference (2017 : Kennesaw; United States)
dc.description.abstractIn this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural net- work model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance. Copyright 2017 ACM.en_US
dc.description.sponsorshipAssoc. for Computing Machinery (ACM)
dc.language.isoenen_US
dc.publisherAssociation for Computing Machinery, Inc.en_US
dc.relation.ispartofProceedings of the SouthEast Conference, ACMSE 2017en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlgorithmic tradingen_US
dc.subjectArtificial neural networken_US
dc.subjectMulti layer perceptronen_US
dc.subjectStock marketen_US
dc.subjectTechnical analysisen_US
dc.titleAn Artificial Neural Network-Based Stock Trading System Using Technical Analysis and Big Data Frameworken_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage223
dc.identifier.endpage226
dc.authorid0000-0001-7998-5735-
dc.identifier.scopus2-s2.0-85021417788en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.identifier.doi10.1145/3077286.3077294-
dc.authorwosidH-2328-2011-
dc.authorscopusid6505999525-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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