A Neural Network Approach For The Directional Prediction Of A Stock Market: An Application To The Australian All Ordinary Index

Abstract

This study investigates the capability of neural network-based approaches for predicting the direction (up or down) of a stock market. The Australian Stock market index (AORD) was selected as the target market. It includes several aspects: univariate feature selection from the historical time series of the target market, intermarket analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. It was found that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the study period of about fourteen years. Multilayer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the AORD. The architecture of this network consists of 33 input features, one hidden layer with three neurons and four output neurons. The best input features set includes the relative returns from one to six days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.