Bp Neural Network Stock Prediction Application

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. 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, which has higher operation rate and precision, has theoretical significance and applicable value.This dissertation analyses the main problems being existent in the process of stock market prediction and compares various stock forecasting methods. The feasibility of forecasting stock trend by using BP neural network is discussed. BP neural network finds out the disciplinarian of stock market through learning historical datum and store them in the weights and valve values of the neural network for forecasting the trend in the future.Based on studying these existing problems of BP algorithms in stack forecasting, including the slow learning speed, local extremum and the low prediction precision, an improved BP neural network algorithm is presented. This algorithm reduces number of hidden nodes and enhances the convergence speed by re-selecting activation function and adjusting weight value of transformation function, scaling coefficient and displacement parameter in output layer and hidden layer.According to the principle of stock prediction based on BP network, the prediction model of stock has been established. The stock is predicted by adopting improved BP algorithm and the simulation experiments are conducted through MATLAB. In actual simulation, the convergence performance of BP algorithm and improved BP algorithm has been compared. At last taking the stock price of Hunan SANYI Heavy Machinery for example, the established prediction model is trained and then its stock datum are predicted using the trained network and good effect has been gained.