Neural Network Methods Applied Research In The Stock Market Prediction

With the economic growth and the conversion of people's investment consciousness,the stock has become a important part of people's life in modern time.The investment of stock has become one of focuses of public topic.The proceeds of stock investment always equal the risk.That means the good proceeds is based on the high risk of failure.In order to gain good proceeds with low risk, investors always want to find its internal disciplinarian and 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 complexity of inside structure and levity of exterior complication in system of stock market make stock market prediction a complex problem.The traditional methods and tools have not met its needs. The thesis presents a method of modeling stock market using BP neural network that is based on thorough study of the difficult problems facing stock predication and compare of vary stock prediction methods.Stock market is a very complex nonlinear dynamic system.Neural network has the capability of approximating any nonlinear system and the speciality of self-learning and self-adapting.The experiments prove that the method of modeling stock market using neural network has a satisfying result in stock prediction.The trend of stock market looks like disorderly, but its has internal disciplinarian actually, which is the base of stock prediction using neural network.BP neural network find out the disciplinarian of stock market through study of historical datum and store them in the weights and valve values of the neural network for forecasting the trend in the future. The thesis analyses the theory of stock market prediction based on BP neural network and the prediction model of stock market has been established using three-layer feedforward neural network..The problems including the structure of network, the number of hidden nodes, the choose and pretreatment of swatch datum and the determination of preliminary parameters have been discussed . In order to avoid local extremum and promote convergence speed, Levenberg-Marquardt BP algorithm has been adopted.The performance of standard BP algorithm and other ameliorated BP algorithm has been compared in experiments.At last shanghai stock index have been applied to train the established network model, then stock datum have been predicted using the trained network and good effect has been gained. Theoretical analysis and experiment result show that the method of stock prediction using BP neural network is feasible and efficient.It has favorable applicable foreground。...