A novel approach for predicting trading signals of a stock market index
- Authors: Tilakaratne, Chandima , Mammadov, Musa , Morris, Sidney
- Date: 2010
- Type: Text , Book chapter
- Relation: Forecasting models: Methods and applications p. 145-160
- Full Text: false
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Modified neural network algorithms for predicting trading signals of stock market indices
- Authors: Tilakaratne, Chandima , Mammadov, Musa , Morris, Sidney
- Date: 2009
- Type: Text , Journal article
- Relation: Journal of Applied Mathematics and Decision Sciences Vol. 2009, no. (2009), p.
- Full Text: false
- Reviewed:
- Description: 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 feed forward 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.
Predicting trading signals of stock market indices using neural networks
- Authors: Tilakaratne, Chandima , Mammadov, Musa , Morris, Sidney
- Date: 2008
- Type: Text , Conference paper
- Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Auckland 1 December 2008 through 5 December 2008 Vol. 5360 LNAI, p. 522-531
- Full Text: false
- Description: 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.
Effectiveness of using quantified intermarket influence for predicting trading signals of stock markets
- Authors: Tilakaratne, Chandima , Mammadov, Musa , Morris, Sidney
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Data Mining and Analytics 2007: Sixth Australasian Data Mining Conference, AusDM 2007 Vol. 70, p. 171-179
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Quantification of intermarket influence on the Australian All Ordinary Index based on optimization techniques
- Authors: Tilakaratne, Chandima , Morris, Sidney , Mammadov, Musa , Hurst, Cameron
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at CTAC 2006: The 13th Biennial Computational Techniques and Applications Conference p. 42-49
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