A Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machines

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dc.contributor.authorEmir, Senol
dc.contributor.authorDincer, Hasan
dc.contributor.authorTimor, Mehpare
dc.date.accessioned2014-06-27T09:13:10Z
dc.date.available2014-06-27T09:13:10Z
dc.date.issued2012
dc.departmentÄ°stanbul Beykent Ãœniversitesien_US
dc.descriptionReview of Economics & Finance Submitted on 13/Mar./2012tr_TR
dc.description.abstractThe basic aim of this article is to provide a model to explain stock performance utmost level. To reach this purpose, at the initial step, the model results composed of fundamental and technical analysis variables considered separately; in the second step, building the model composed of fundamental and technical analysis parameters which has best explaining ability was the focal point of this study. Artificial Neural Network (ANN) is an approach that has been widely used for financial classification problems for a long time. In addition, promising results of a novel machine learning method known as the Support Vector Machines (SVM) have been presented in several studies compared to the ANN. The stock performance results relying on fundamental analysis have shown more successful classification rates than the models based on technical analysis. Moreover, it was also experienced that the models constructed by using SVM method in the both type of analyses have shown more prominent results.en_US
dc.identifier.issn1923-7529
dc.language.isoenen_US
dc.publisherAcademic Research Centre of Canadatr_TR
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.subjectStock selectiontr_TR
dc.subjectFundamental analysistr_TR
dc.subjectTechnical analysistr_TR
dc.subjectSupport vector machinestr_TR
dc.subjectArtificial neural networkstr_TR
dc.titleA Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machinesen_US
dc.typeArticleen_US

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