Bilgilendirme: Sürüm Güncellemesi ve versiyon yükseltmesi nedeniyle, geçici süreyle zaman zaman kesintiler yaşanabilir ve veri içeriğinde değişkenlikler gözlemlenebilir. Göstereceğiniz anlayış için teşekkür ederiz.
 

A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters

dc.contributor.author Ozbayoglu, Murat
dc.contributor.author Dogdu, Erdogan
dc.contributor.author Sezer, Omer Berat
dc.contributor.authorID 142876 tr_TR
dc.date.accessioned 2019-12-18T12:03:30Z
dc.date.accessioned 2025-09-18T12:09:26Z
dc.date.available 2019-12-18T12:03:30Z
dc.date.available 2025-09-18T12:09:26Z
dc.date.issued 2017
dc.description Ozbayoglu, Murat/0000-0001-7998-5735; Dogdu, Erdogan/0000-0001-5987-0164 en_US
dc.description.abstract In 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.description.sponsorship TUBITAK (The Scientific and Technological Research Council of Turkey) [215E248] en_US
dc.description.sponsorship This paper is funded by TUBITAK (The Scientific and Technological Research Council of Turkey) through project grant no. 215E248. en_US
dc.identifier.citation Sezer, Omer Berat; Ozbayoglu, Murat; Dogdu, Erdogan (2017). A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters, Conference: Complex Adaptive Systems Conference on Engineering Cyber Physical Systems (CAS) Location: Chicago, IL Date: OCT 30-NOV 01, 2017, Complex Adaptive Systems Conference With Theme: Engineering Cyber Physical Systems, Cas, 114, 473-480. en_US
dc.identifier.doi 10.1016/j.procs.2017.09.031
dc.identifier.issn 1877-0509
dc.identifier.scopus 2-s2.0-85039995536
dc.identifier.uri https://doi.org/10.1016/j.procs.2017.09.031
dc.identifier.uri https://hdl.handle.net/123456789/11417
dc.language.iso en en_US
dc.publisher Elsevier Science Bv en_US
dc.relation.ispartof Complex Adaptive Systems Conference on Engineering Cyber Physical Systems (CAS) -- OCT 30-NOV 01, 2017 -- Chicago, IL en_US
dc.relation.ispartofseries Procedia Computer Science
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Stock Trading en_US
dc.subject Stock Market en_US
dc.subject Deep Neural-Network en_US
dc.subject Evolutionary Algorithms en_US
dc.subject Technical Analysis en_US
dc.title A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters en_US
dc.title A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters tr_TR
dc.type Conference Object en_US
dspace.entity.type Publication
gdc.author.id Ozbayoglu, Murat/0000-0001-7998-5735
gdc.author.id Dogdu, Erdogan/0000-0001-5987-0164
gdc.author.institutional Doğdu, Erdoğan
gdc.author.scopusid 57207586168
gdc.author.scopusid 57947593100
gdc.author.scopusid 6603501593
gdc.author.wosid Ozbayoglu, Murat/H-2328-2011
gdc.description.department Çankaya University en_US
gdc.description.departmenttemp [Sezer, Omer Berat; Ozbayoglu, Murat] TOBB Univ Econ & Technol, Dept Comp Engn, TR-06560 Ankara, Turkey; [Dogdu, Erdogan] Cankaya Univ, Dept Comp Engn, TR-06790 Ankara, Turkey; [Sezer, Omer Berat] TUBITAK Space Technol Res Inst, TR-06531 Ankara, Turkey en_US
gdc.description.endpage 480 en_US
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q2
gdc.description.startpage 473 en_US
gdc.description.volume 114 en_US
gdc.description.woscitationindex Conference Proceedings Citation Index - Science
gdc.identifier.openalex W2762958083
gdc.identifier.wos WOS:000419234000057
gdc.openalex.fwci 7.89225857
gdc.openalex.normalizedpercentile 0.97
gdc.openalex.toppercent TOP 10%
gdc.opencitations.count 77
gdc.plumx.crossrefcites 50
gdc.plumx.facebookshareslikecount 2
gdc.plumx.mendeley 197
gdc.plumx.scopuscites 87
gdc.scopus.citedcount 86
gdc.wos.citedcount 56
relation.isAuthorOfPublication 0d453674-7998-4d57-a06c-03e13bb1e314
relation.isAuthorOfPublication.latestForDiscovery 0d453674-7998-4d57-a06c-03e13bb1e314
relation.isOrgUnitOfPublication 12489df3-847d-4936-8339-f3d38607992f
relation.isOrgUnitOfPublication 43797d4e-4177-4b74-bd9b-38623b8aeefa
relation.isOrgUnitOfPublication 0b9123e4-4136-493b-9ffd-be856af2cdb1
relation.isOrgUnitOfPublication.latestForDiscovery 12489df3-847d-4936-8339-f3d38607992f

Files