Australian All Ordinaries Index: re-examine the utilities of the explanatory variables using three different measures

The stock markets are generally nonlinear dynamic systems. Therefore, estimating stock market output depends mainly on nonlinear relationships of input variables. Additionally researchers have demonstrated that financial markets display self-similarity. To forecast such systems, a nonlinear modeling tool is required. The paper compares accuracy of prediction using the following techniques: neural network, linear regression and exponential moving average, and measures their performance using mean absolute percentage error (MAPE), Thiel's U Statistics (U-STAT) and R Square (RSQ). Using the All Ordinaries Index and a sliding window through the data, our results show that the explanatory variables can improve the predictive power of two techniques when predicting future changes in the index. Only the MAPE but not the other two measures show the effect in this setting.