Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/178586
Authors: 
Beluco, Adriano
Bandeira, Denise L.
Beluco, Alexandre
Year of Publication: 
2017
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 10 [Year:] 2017 [Issue:] 1 [Pages:] 1-13
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%.
Subjects: 
modeling financial indicators
NYSE indexes
self organizing maps
multilayer perceptron
back propagation algorithm
software Matlab
JEL: 
C53
E37
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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