Comparison of Forecasting Performance of AR, STAR and ANN Models on the Chinese Stock Market Index

Abstract

This paper investigates whether it is possible to exploit the nonlinear behavior of daily returns to improve forecasting on Chinese Shanghai stock market index over short and long horizons. We compare out-of-sample forecasts of daily returns for the Chinese Shanghai Stock Market Index, generated by five competing models, namely a linear AR model, the LSTAR and ESTAR smooth transition autoregressive models and two ANN models: MLP and JCN. The research results show that the nonlinear ANN models may be an appropriate way to improve forecasts. The return on the Chinese Shanghai Stock Market Index could be predicted more accurately by using ANN models, and the neural network technique could be said to represent a slight improvement in prediction of the stock index with respect to AR model and STAR models.