Research On Blue Chip Stock Trend Analysis System Based On Neural Networks

With the economic growth and the conversion of people's investment consciousness, the stock has become an important part of people's life in modem time. The good proceeds are based on the high risk of failure. In order to gain good proceeds with low risk, investors always want to look for effective analytic methods and tools. Therefore the study and prediction of disciplinarian in stock market have great theoretical significance and applicable value.The application of neural networks to forecast the Stock tendency. Provide operational support to the stock this topic conducted in-depth exploration and research is proposed in the paper. Design and implementation of a stock forecasting system based on neural network. Firstly, a brief introduction about widely adopted forecasting methods is proposed and their advantages and disadvantages are analyzed in the paper. Then, a particular description about multi-layer feedforward neural networks and genetic algorithm are provided. The practical application of neural network classification method are pointed out to optimize neural networks in order to improve the system's precision and reliability. Based on it, a based on multi-neural network integration Stock tendency type forecast method for prediction of stock analysis and stock movements are proposed used improve the neural network classification apply in the multi-neural network integration Stock tendency type forecast method. To realized the operation for stock decision support according to the results of stock and the further increased the system forecast precision.A basic model of stock forecasting system is implemented in the paper. The algorithm selecting, decision support model building, structure designing and function achieving are analysed. An excellent stock forecasting platform based on neural network classification with Visual C++ 6.0 and Matlab 6.5 is developed and a higher reliability of the embryonic stock forecasting system is achieved.