With the economic growth and the conversion of people's investment consciousness, stock has become an important part of people's life in modern time. The Stock market is an extremely complex non-linear dynamics system, participating in stock investment; Stock forecast has greatly been one of focuses of public topic. The proceeds of stock investment always equal the risk. So establishing a stock forecasting model, with has higher operation rate and precision, has theoretical significance and applicable value. The stock forecast of information is very big, the traditional forecasting method subjective factors are strong, poor prediction effect, using the principal component analysis method to deal with samples, forming new training samples, reduce artificial neural network modeling the input of the network when number, while eliminating the correlation of input factor and simplify network structure, can greatly improve the network learning rate. Get the artificial neural network model can achieve high precision, influence factors for the more and mechanism is not clear of artificial neural network to predict the stock market provides an effective method.
