Predicting trading signals of stock market indices using neural networks

Tilakaratne C. D., MƏMMƏDOV M., Morris S. A.

21st Australasian Joint Conference on Artificial Intelligence, AI 2008, Auckland, New Zealand, 1 - 05 December 2008, vol.5360 LNAI, pp.522-531, (Full Text) identifier

  • Nəşrin Növü: Conference Paper / Full Text
  • Cild: 5360 LNAI
  • Doi nömrəsi: 10.1007/978-3-540-89378-3_53
  • Çap olunduğu şəhər: Auckland
  • Ölkə: New Zealand
  • Səhifə sayı: pp.522-531
  • Açar sözlər: Classification, Neural networks, Stock market predictions
  • Açıq Arxiv Kolleksiyası: Konfrans Materialı
  • Adres: Bəli

Qısa məlumat

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. © 2008 Springer Berlin Heidelberg.