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dc.contributor.authorBeluco, Adrianopt_BR
dc.contributor.authorBandeira, Denise Lindstrompt_BR
dc.contributor.authorBeluco, Alexandrept_BR
dc.date.accessioned2017-10-12T02:50:05Zpt_BR
dc.date.issued2017pt_BR
dc.identifier.issn1911-8074pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/169485pt_BR
dc.description.abstractNeural networks are well suited to predict future results of time series for various data types. This paper proposes a hybrid neural network model to describe the results of the database of the New York Stock Exchange (NYSE). This hybrid model brings together a self organizing map (SOM) with a multilayer perceptron with back propagation algorithm (MLP-BP). The SOM aims to segment the database into different clusters, where the differences between them are highlighted. The MLP-BP is used to construct a descriptive mathematical model that describes the relationship between the indicators and the closing value of each cluster. The model was developed from a database consisting of the NYSE Composite US 100 Index over the period of 2 April 2004 to 31 December 2015. As input variables for neural networks, ten technical financial indicators were used. The model results were fairly accurate, with a mean absolute percentage error varying between 0.16% and 0.38%.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofJournal of Risk and Financial Management. Basel, Switzerland. Vol. 10, n. 1 (jan./mar. 2017), 13 p.pt_BR
dc.rightsOpen Accessen
dc.subjectIndicadores financeirospt_BR
dc.subjectModeling financial indicatorsen
dc.subjectÍndices financeirospt_BR
dc.subjectNYSE indexesen
dc.subjectPerceptronspt_BR
dc.subjectSelf organizing mapsen
dc.subjectAlgoritmospt_BR
dc.subjectMultilayer perceptronen
dc.subjectBack propagation algorithmen
dc.subjectSoftware matlabpt_BR
dc.subjectSoftware Matlaben
dc.titleModeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Modelpt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001050811pt_BR
dc.type.originEstrangeiropt_BR


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