In the stock market,it is a crucial issue concerned by all market participants to predict stock returns accurately.However,due to the complicated factors affecting stock market,experience shows that it is very difficult to improve the accuracy of forecasting by setting up single forecasting model.In this article,the deficiencies of the present methods for stock returns forecasting are described and a new approachon forecasting stock returns with the improvement of prediction accuracy is proposed by performing error analysis and correction.First,the grey neural network forecasting model is established by using the training sample data,and then it is used to carry out the preliminary prediction of stock returns.Second,the EGARCH is introduced to analyze the prediction error sequence and predict the subsequent error.Third last,the correction of preliminary prediction values is calibrated.The simulation calculation of the Shanghai composite index as an example is performed and the results show that the precision of prediction is increasing by 9.3 percent significantly compared with the prediction accuracy before correction.Researches also suggest that the error correction process is valid,and then thus the feasibility of this method is verified.
NETL
NSTL
维普
万方数据
