The Bank Of China Stock Price Forecast Empirical Analysis Based On BP Neural Network And GARCH Model

With China’s financial market in line with international standards, the financial derivatives market set up, financial instruments under the condition of diversified high leverage also poses a huge financial risk. Complex financial market to the requirement of financial investment analysis tools are higher, spawned a variety of ways to for stock price prediction.. For different data and different market environment requires a different analysis methods. Neural network algorithm of distributed data storage, and the characteristics of the learning feedback mechanism makes it have a unique role in prediction, etc. This article select the stock’s closing price of the bank of China, using the BP neural network (feedforward model) and GARCH model to forecast the stock price of, through the comparison and analysis concluded that BP neural network in the number of hidden layer nodes is5for market data fitting is best. And GARCH model is effective in of stock price forecasting, mainly because of the bank of China shares data with rush thick tail and stability characteristics. Finally concluded two forecasting methods are able to predict short-term stock price and the bank of China but the BP neural network prediction method is superior to the GARCH model prediction method.