Based On Neural Network Stock Prediction Analysis And Research

The stock market, which is the main character of market economy, attracts millions of investors from its birth. Because high-risk and high-payoff are the characters of the stock investment, investors care for stock market, analyze financial data and try to predict the trend of stock. As stock market is a kind of complex non-linear dynamic system, the prediction results of traditional prediction technology are unsatisfactory. Neural Network is an estimator of adaptive resonance function, which is not dependent on models, and could fulfill arbitrarily functional relation without them. At present, Neural Networks are becoming a more powerful tool in predicting and establishing non-linear dynamic system.This paper tries to find the joint point of Data Mining technology and stock prediction by studying the application of data mining algorithm, especially neural network in stock prediction. The paper puts forward stock prediction pattern based on BP Neural Network, depending on Neural Networks' approachability to non-linear function. Compared with the prediction method of Time Order, one of the traditional prediction methods, BP algorithm is verified to be feasible and effective on stock prediction.The theoretical significance lies in that it explores a new risk analysis and assessment technique of stock investment, improves the methodology, introduces Data Mining technology into the stock prediction system, and pushes forward the stock prediction methodology all round. The practical application embodies that it provides a quantitative analysis technique of stock risk analysis and assessment based on multi-factor system, offers a scientifically comprehensive method based on BP Neural Network, and presents individual investors and institution investors a new train of thought when they make certain investment decisions.