Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3851
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dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorGüdelek, M. U.-
dc.contributor.authorSezer, O. B.-
dc.date.accessioned2020-10-22T16:40:34Z-
dc.date.available2020-10-22T16:40:34Z-
dc.date.issued2020-08
dc.identifier.citationOzbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 106384.en_US
dc.identifier.issn15684946
dc.identifier.urihttps://hdl.handle.net/20.500.11851/3851-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1568494620303240?via%3Dihub-
dc.description.abstractComputational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field. © 2020 Elsevier B.V.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofApplied Soft Computing Journalen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAlgorithmic tradingen_US
dc.subjectcomputational intelligenceen_US
dc.subjectdeep learningen_US
dc.subjectfinanceen_US
dc.subjectfinancial applicationsen_US
dc.subjectfraud detectionen_US
dc.subjectmachine learningen_US
dc.subjectportfolio managementen_US
dc.subjectrisk assessmenten_US
dc.titleDeep learning for financial applications: A surveyen_US
dc.typeReviewen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Artificial Intelligence Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümütr_TR
dc.identifier.volume93
dc.relation.tubitakinfo:eu-repo/grantAgreement/TÜBİTAK/EEEAG/215E248en_US
dc.authorid0000-0001-7998-5735-
dc.identifier.scopus2-s2.0-85084842457en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.identifier.doi10.1016/j.asoc.2020.106384-
dc.relation.publicationcategoryDiğeren_US
item.openairetypeReview-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.grantfulltextnone-
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
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering
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