Using Difference Method to Improve the Prediction Accuracy—An Empirical Study Based on China Shanghai and Shenzhen 300 Stock Index

Abstract

When prediction models such as AR, ARIMA, ARMA, and MA are usually used to forecast the future values of a time series, because the residual values are easily ignored, the prediction accuracy has been influenced a lot. For improving the prediction accuracy, this paper suggests use difference method (DF) to deal with the residual item of autoregressive model (AR). Based on the China Shanghai and Shenzhen 300 Stock Index (Hushen300), the empirical study has found: First, the AR-DF model can result in higher correlation between the real value of HUSHEN return index and its prediction value than pure AR model; second, the average residual value of AR-DF model is quite smaller than the pure AR model; Third, the hit ratio of AR-DF model has increased more than 25.30 percent points than pure AR model, which can increase from 50.99% to 62.65% when the lag order is up to 700.