Design And Implementation Of The Neural Network Stock Analysis Model System

The stock market reflects the fluctuation of the market economy, and receives ten million investors'attention since its initial development. The stock market is characterized by high-risk, high-yield, so investors are concerned about the analysis of the stock market and trying to forecast the trend of the stock market. However, stock market is impacted by the politics, economy and many other factors, coupled with the complexity of its internal law, such as price (stock index) changes in the non-linear, and shares data with high noise characteristics, therefore the traditional mathematical statistical techniques to forecast the stock market has not yielded satisfactory results. Neural networks can approximate any complex non-linear relations and has robustness and fault-tolerant features. Therefore, it is very suitable for the analysis of stock data.In this paper, we apply data mining technology to Chinese stock market in order to research the trend of price, it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer. After building the data pre-processing set before data mining, lots of widely used stock market technical indicators such as the KD indicators, similarities and differences between exponential smoothing moving average MACD, Relative Strength Index RSI, will be introduced into the model. Finally, we get a better predictive model to improve forecast accuracy.