A novel model by evolving partially connected neural network for stock price trend forecasting
Expert Systems with Applications: An International Journal
A partially connected neural evolutionary network for stock price index forecasting
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
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Aiming at the complexity of interior and variety of exterior structure of stock price system, this paper analyzes principles of stock prediction based on BP neural network, provides prediction model for stock market by utilizing three-layered feed forward neural networks, presents topology of network, principles of determining the number of hidden layers, selection and pretreatment of sample data and determination of preliminary parameters. In order to avoid local extremum and promote convergence speed, Levenberg-Marquardt BP algorithm has been adopted. Simulation experiment based on representative index from Shanghai stock exchange market, through training on selecting samples and prediction model, indicates that this algorithm can make efficient short-term prediction.