Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1995
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dc.contributor.authorSezer, Ömer Berat-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorDoğdu, Erdoğan-
dc.date.accessioned2019-07-10T14:42:44Z
dc.date.available2019-07-10T14:42:44Z
dc.date.issued2017
dc.identifier.citationSezer, O. B., Ozbayoglu, M., & Dogdu, E. (2017). A Deep neural-network based stock trading system based on evolutionary optimized technical analysis parameters. Procedia computer science, 114, 473-480.en_US
dc.identifier.issn1877-0509
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1877050917318252?via%3Dihub-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/1995-
dc.descriptionComplex Adaptive Systems Conference on Engineering Cyber Physical Systems (CAS) (2017 : Chicago, IL)
dc.description.abstractIn this study, we propose a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. The model is developed utilizing Apache Spark big data platform. The optimized parameters are then passed to a deep MLP neural network for buy-sell-hold predictions. Dow 30 stocks are chosen for model validation. Each Dow stock is trained separately using daily close prices between 1996-2016 and tested between 2007-2016. The results indicate that optimizing the technical indicator parameters not only enhances the stock trading performance but also provides a model that might be used as an alternative to Buy and Hold and other standard technical analysis models. (c) 2017 The Authors. Published by Elsevier B.V.en_US
dc.language.isoenen_US
dc.publisherELSEVIER Science BVen_US
dc.relation.ispartofProcedia Computer Scienceen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectStock Tradingen_US
dc.subjectStock Marketen_US
dc.subjectDeep Neural-Networken_US
dc.subjectEvolutionary Algorithmsen_US
dc.subjectTechnical Analysisen_US
dc.titleA Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parametersen_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.volume114
dc.identifier.startpage473
dc.identifier.endpage480
dc.relation.tubitakinfo:eu-repo/grantAgreement/TÜBİTAK/EEEAG/215E248en_US
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000419234000057en_US
dc.identifier.scopus2-s2.0-85039995536en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.identifier.doi10.1016/j.procs.2017.09.031-
dc.authorwosidH-2328-2011-
dc.authorscopusid6505999525-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusquality--
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextopen-
item.fulltextWith 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
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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