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Predicting trading signals of stock market indices using neural networks

conference contribution
posted on 2008-12-01, 00:00 authored by C D Tilakaratne, Musa MammadovMusa Mammadov, S A Morris
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.

History

Volume

5360 LNAI

Pagination

522 - 531

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783540893776

ISBN-10

3540893776

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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