Financial Time Series Prediction Using Elman Recurrent Random Neural Networks.

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ID: 70117
2016
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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.
Reference Key
wang2016financialcomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wang, Jie;Wang, Jun;Fang, Wen;Niu, Hongli;
Journal Computational Intelligence and Neuroscience
Year 2016
DOI
10.1155/2016/4742515
URL
Keywords
dimensionality reduction correlation network dynamic correlation financial asset returns network clustering

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