Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model
dc.contributor.author | Beluco, Adriano | pt_BR |
dc.contributor.author | Bandeira, Denise Lindstrom | pt_BR |
dc.contributor.author | Beluco, Alexandre | pt_BR |
dc.date.accessioned | 2017-10-12T02:50:05Z | pt_BR |
dc.date.issued | 2017 | pt_BR |
dc.identifier.issn | 1911-8074 | pt_BR |
dc.identifier.uri | http://hdl.handle.net/10183/169485 | pt_BR |
dc.description.abstract | Neural 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.mimetype | application/pdf | pt_BR |
dc.language.iso | eng | pt_BR |
dc.relation.ispartof | Journal of Risk and Financial Management. Basel, Switzerland. Vol. 10, n. 1 (jan./mar. 2017), 13 p. | pt_BR |
dc.rights | Open Access | en |
dc.subject | Indicadores financeiros | pt_BR |
dc.subject | Modeling financial indicators | en |
dc.subject | Índices financeiros | pt_BR |
dc.subject | NYSE indexes | en |
dc.subject | Perceptrons | pt_BR |
dc.subject | Self organizing maps | en |
dc.subject | Algoritmos | pt_BR |
dc.subject | Multilayer perceptron | en |
dc.subject | Back propagation algorithm | en |
dc.subject | Software matlab | pt_BR |
dc.subject | Software Matlab | en |
dc.title | Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model | pt_BR |
dc.type | Artigo de periódico | pt_BR |
dc.identifier.nrb | 001050811 | pt_BR |
dc.type.origin | Estrangeiro | pt_BR |
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