Study Of The Stock-forecast Using ARMA(1,1)-Generalized Regression Neural Network Model

The stock market prediction has become one of the core issues of modern financial research.In this paper,in order to reduce the prediction error and improve the accuracy of model forecast,I want to explore a kind of combination forecast model of the stock market on the basis of predecessors' research.In this paper,the data is the daily closing price of S&P 500 Index from January 4th,2010 to March 11 th,2016.We use four years data as the sample data to build models,and forecast the closing price of following six days,which is used to test the forecasting effect of the models based on 50 experiments.In this paper,first of all,we use the method of autoregressive moving average to bulid model and make forecast.The model can fit linear relationship well,but it is weak in nonlinear mapping,and it is difficult to determine the optimal model structure.Thus the forecasting effect is not good.Then we build the generalized regression neural network model which is used to make forecast.The neural network model has strong ability in nonlinear mapping.Its computing speed is fast,and it has only one subjective parameter.But the forecasting effect between ARMA and GRNN has little difference.Finally,by exploring the appropriate combination of ARMA model and GRNN model,we can obtain ARMA(1,1)-GRNN model,which has better forecasting effect when we use it to make short-term forecast.ARMA(1,1)reflects the relationships between the current time series observations and previous observations and its previous random disturbance,that is to say ARMA(1,1)shares the preliminary data statistical characteristic into the GRNN model.We can draw conclusions by comparing the forecast results of the three models: 1).In terms of trend forecast,there are 90% experimental groups,in which forecasting effect of ARMA(1,1)-GRNN model is better than ARMA model and GRNN model,and the trend forecast accuracy of ARMA(1,1)-GRNN model is high.There are 94% experimental groups,in which the trend forecast accuracy of ARMA(1,1)-GRNN model is at 83.33% and above.2).In the aspect of prediction error,from the point of error sum of squares,there are 98% experimental groups,in which forecasting effect of ARMA(1,1)-GRNN model is better than ARMA model and GRNN model;From the point of the relative error,there are 98% experimental groups,in which the forecasting effect of ARMA(1,1)-GRNN model is better than ARMA model and GRNN model,and the forecast precision of ARMA(1,1)-GRNN model is high,ARMA(1,1)-GRNN model forecast relative error is within 1% in the 94% experimental groups;From the point of absolute error,there are 98% experimental groups,in which forecasting effect of ARMA(1,1)-GRNN model is better than ARMA model and GRNN model.So it can be seen that the forecasting effect of ARMA(1,1)-GRNN model is better than ARMA model and GRNN model in the short-term forecast by using S&P 500 Index.The results show that we can improve the forecasting effect by combining single models appropriately.