Stock Prediction Theory And Application Based On GA-BP Neural Network

With the development of financial markets, the stock market has gradually become an important part of people's economic life. As an investment region of high risk and high profit, stock market always attracts many attentions, so analyzing and forecasting the share price is very important in both academic field and practice.Stock market is a very complex nonlinear dynamic system, the traditional methods and tools have not met its challenge. The thesis presents a method of modeling stock market using neural network that is based on study of stock prediction methods. Because BP algorithm's convergence rate is slow and easily trapped in local minimum, this article tries to seek networks ensemble based on BP algorithm, associating with GA algorithm, under the guidance of data mining,for stock forecasting. The paper uses the GABP model to predict the Shanghai Stock Index and individual stocks. Empirical results show that the BP neural network for forecasting stock is feasible, and GA-BP algorithm is also improved forecasting accuracy.