Journal of Applied Mathematics and Decision Sciences
Volume 2009 (2009), Article ID 125308, 22 pages
Research Article

Modified Neural Network Algorithms for Predicting Trading Signals of Stock Market Indices

1Department of Statistics, University of Colombo, P.O. Box 1490, Colombo 3, Sri Lanka
2Graduate School of Information Technology and Mathematical Sciences, University of Ballarat, P.O. Box 663, Ballarat, Victoria 3353, Australia

Received 29 November 2008; Revised 17 February 2009; Accepted 8 April 2009

Academic Editor: Lean Yu

Copyright © 2009 C. D. Tilakaratne et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The aim of this paper is to present modified neural network algorithms to predict whether it is best to buy, hold, or sell shares (trading signals) of stock market indices. Most commonly used classification techniques are not successful in predicting trading signals when the distribution of the actual trading signals, among these three classes, is imbalanced. The modified network algorithms are based on the structure of feedforward neural networks and a modified Ordinary Least Squares (OLSs) error function. An adjustment relating to the contribution from the historical data used for training the networks and penalisation of incorrectly classified trading signals were accounted for, when modifying the OLS function. A global optimization algorithm was employed to train these networks. These algorithms were employed to predict the trading signals of the Australian All Ordinary Index. The algorithms with the modified error functions introduced by this study produced better predictions.