Predicting Trading Signals of Stock Market Indices Using Neural Networks

  • Chandima D. Tilakaratne
  • Musa A. Mammadov
  • Sidney A. Morris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5360)


The aim of this paper is to develop new neural network algorithms to predict trading signals: buy, hold and sell, of stock market indices. Most commonly used classification techniques are not suitable to predict trading signals when the distribution of the actual trading signals, among theses three classes, is imbalanced. In this paper, new algorithms were developed based on the structure of feedforward neural networks and a modified Ordinary Least Squares (OLS) error function. An adjustment relating to the contribution from the historical data used for training the networks, and the penalization of incorrectly classified trading signals were accounted for when modifying the OLS function. A global optimization algorithm was employed to train these networks. The algorithms developed in this study were employed to predict the trading signals of day (t+1) of the Australian All Ordinary Index. The algorithms with the modified error functions introduced by this study produced better predictions.


Neural networks Classification Stock market predictions Global optimization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Chandima D. Tilakaratne
    • 1
    • 2
  • Musa A. Mammadov
    • 2
  • Sidney A. Morris
    • 2
  1. 1.Department of StatisticsUniversity of ColomboColombo 3Sri Lanka
  2. 2.Center for Informatics and Applied Optimization School of Information Technology and Mathematical SciencesUniversity of BallaratBallaratAustralia

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