Research And Application Of Stock Index Forecasting Based On BP Neural Network

Stock market is a highly complicate nonlinear system, its variation not only has own regulation, but also is influenced by many other factors, such as politics, economics and psychology.To be an investment region of high risk and high profit, Stock market attracts many investors’attentions all along. How to obtain profit through setting up the accurate forecasting model of stock price is attracting the people’s attentions.Technique analysis, psychology analysis and traditional prediction based on statistics meet many difficulties in stock market analysis. With the developing of Chaos and Fractal Market Hypothesis (FMH) theory, people have tried neural network to forecast the change of the Stock market. The neural network is suitable for processing information in economic field and ayalying time alignments, because it enjoys the virtue of self-organization and adaptability and can learn the economical knowledge from historical data, so it is very practical to solve problems in stock market prediction. BP (Back Propagation) is a neural network which is adopted widely. The core is the BP arithmetic, a strict and effective method to derivative problem for system based on multi-subsystem, which has simple configuration and mature arithmetic. To compare with the traditional statistical regress method, BP network can not only study the example of training set, but also abstract some general theory and rule. It has strong characteristic of approximation of non-linear functions, which is much fit for stock index analyzed and predicted in a short-term.This article tries to use a neural network on the base of BP arithmetic to forecast the share index of Shanghai stock exchange. Meanwhile it makes some improvement to the original forecast method according to the limitation and disadvantage of the BP network original shape.