Modified Neural Network Algorithms for Predicting Trading Signals of Stock Market Indices. (17th June 2009)

• Record Type: Journal Article
• Authors: Tilakaratne, C. D.; Mammadov, M. A.; Morris, S. A.
• Other Names: Yu Lean Academic Editor.
• Is Part Of: Journal of applied mathematics & decision sciences. Volume 2009(2009)
• Publication Date: 2009-06-17
• Subjects: Operations research -- Periodicals; Mathematical models -- Periodicals; Statistics -- Periodicals; Quality control -- Periodicals; Mathematical models; Operations research; Quality control; Statistics; Periodicals; 519.05
• DOI: 10.1155/2009/125308
• Languages: English
• ISSNs: 1173-9126

Abstract

Abstract : 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.