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Computational Intelligence and Neuroscience
Volume 2016, Article ID 4742515, 14 pages
http://dx.doi.org/10.1155/2016/4742515
Research Article

Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

1School of Science, Beijing Jiaotong University, Beijing 100044, China
2School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China

Received 9 June 2015; Accepted 30 August 2015

Academic Editor: Sandhya Samarasinghe

Copyright © 2016 Jie Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.