Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model

• Main Authors: Adriano Beluco, Denise L. Bandeira, Alexandre Beluco
• Format: Article
• Language: English
• Published: MDPI AG 2017-02-01
• Series: Journal of Risk and Financial Management
• Subjects: modeling financial indicators; NYSE indexes; self organizing maps; multilayer perceptron; back propagation algorithm; software Matlab
• Online Access: http://www.mdpi.com/1911-8074/10/1/6

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%.