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- W2372785076 abstract "Since stock market is a nonlinear system with internal structural complexity and external factors variability,proposed forecasting index system which involved Shanghai Composite Index's price,volume,and macroeconomic indicators closely related to stock market.Then analyzed the long-run equilibrium and causal relationship among the variables.Based on Bayesian theory,in order to optimize structure and ensure generalization ability of network,added network complexity function to error function which could delete insensitive hidden layer neurons through dynamic adjusting penalty factor.The empirical results with different forecasting index systems indicate that the pruning structure neural network model based on BP algorithm can be an effective way to improve forecasting accuracy.Comparing with other neural network models,the proposed model can improve forecasting performance with higher forecasting accuracy and more concise structure." @default.
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- W2372785076 date "2011-01-01" @default.
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- W2372785076 title "Pruning structure neural network model for stock index forecasting" @default.
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