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https://hdl.handle.net/20.500.11851/3851
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Özbayoğlu, Ahmet Murat | - |
dc.contributor.author | Güdelek, M. U. | - |
dc.contributor.author | Sezer, O. B. | - |
dc.date.accessioned | 2020-10-22T16:40:34Z | - |
dc.date.available | 2020-10-22T16:40:34Z | - |
dc.date.issued | 2020-08 | |
dc.identifier.citation | Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 106384. | en_US |
dc.identifier.issn | 15684946 | |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/3851 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S1568494620303240?via%3Dihub | - |
dc.description.abstract | Computational 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.iso | en | en_US |
dc.publisher | Elsevier Ltd | en_US |
dc.relation.ispartof | Applied Soft Computing Journal | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Algorithmic trading | en_US |
dc.subject | computational intelligence | en_US |
dc.subject | deep learning | en_US |
dc.subject | finance | en_US |
dc.subject | financial applications | en_US |
dc.subject | fraud detection | en_US |
dc.subject | machine learning | en_US |
dc.subject | portfolio management | en_US |
dc.subject | risk assessment | en_US |
dc.title | Deep Learning for Financial Applications: a Survey | en_US |
dc.type | Review | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Artificial Intelligence Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.department | Fakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 93 | |
dc.relation.tubitak | info:eu-repo/grantAgreement/TÜBİTAK/EEEAG/215E248 | en_US |
dc.authorid | 0000-0001-7998-5735 | - |
dc.identifier.wos | WOS:000554904800016 | en_US |
dc.identifier.scopus | 2-s2.0-85084842457 | en_US |
dc.institutionauthor | Özbayoğlu, Ahmet Murat | - |
dc.identifier.doi | 10.1016/j.asoc.2020.106384 | - |
dc.relation.publicationcategory | Diğer | en_US |
dc.identifier.scopusquality | Q1 | - |
item.openairetype | Review | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.1. 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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