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Modified neural network algorithms for predicting trading signals of stock market indices

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journal contribution
posted on 2009-01-01, 00:00 authored by C D Tilakaratne, Musa MammadovMusa Mammadov, S A Morris
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.

History

Journal

Journal of Applied Mathematics and Decision Sciences

Volume

2009

Article number

125308

Pagination

1 - 22

Publisher

Hindawi Publishing Corporation

Location

London, Eng.

ISSN

1173-9126

eISSN

1532-7612

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

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