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dc.contributor.authorHasan, Afan
dc.contributor.authorKalıpsız, Oya
dc.contributor.authorAkyokuş, Selim
dc.date.accessioned2020-08-14T12:30:18Z
dc.date.available2020-08-14T12:30:18Z
dc.date.issued2020en_US
dc.identifier.citationHasan, A., Kalıpsız, O. ve Akyokuş, S. (2020). Modeling traders' behavior with deep learning and machine learning methods: Evidence from BIST 100 index. Complexity, 2020. https://dx.doi.org/10.1155/2020/8285149en_US
dc.identifier.issn1076-2787
dc.identifier.issn1099-0526
dc.identifier.urihttps://dx.doi.org/10.1155/2020/8285149
dc.identifier.urihttps://hdl.handle.net/20.500.12511/5743
dc.description.abstractAlthough the vast majority of fundamental analysts believe that technical analysts' estimates and technical indicators used in these analyses are unresponsive, recent research has revealed that both professionals and individual traders are using technical indicators. A correct estimate of the direction of the financial market is a very challenging activity, primarily due to the nonlinear nature of the financial time series. Deep learning and machine learning methods on the other hand have achieved very successful results in many different areas where human beings are challenged. In this study, technical indicators were integrated into the methods of deep learning and machine learning, and the behavior of the traders was modeled in order to increase the accuracy of forecasting of the financial market direction. A set of technical indicators has been examined based on their application in technical analysis as input features to predict the oncoming (one-period-ahead) direction of Istanbul Stock Exchange (BIST100) national index. To predict the direction of the index, Deep Neural Network (DNN), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) classification techniques are used. The performance of these models is evaluated on the basis of various performance metrics such as confusion matrix, compound return, and max drawdown.en_US
dc.language.isoengen_US
dc.publisherWiley-Hindawien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectDeep Learningen_US
dc.subjectBISTen_US
dc.subjectMachine Learningen_US
dc.titleModeling traders' behavior with deep learning and machine learning methods: Evidence from BIST 100 indexen_US
dc.typearticleen_US
dc.relation.ispartofComplexityen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.authorid0000-0003-0793-1601en_US
dc.identifier.volume2020en_US
dc.relation.ecinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/115K179 (1001-Scientific)
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1155/2020/8285149en_US
dc.identifier.wosqualityQ2en_US
dc.identifier.scopusqualityQ1en_US


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