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

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%.

Site Selection of Retail Shops Based on Spatial Accessibility and Hybrid BP Neural Network

“…The simple and stable structure of BP neural network results in its easy understanding and fast training. Different from convolutional neural networks, the output of BP neural network can be a series of categories or continuous values, thereby making it suitable to solve classification and regression problems [53,63]. The accuracy of BP neural network has been proven better than traditional regression and classification methods.…”