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Application of Genetic Algorithms to the Optimisation of Neural Network Configuration for Stock Market Forecasting

Citation

Hulme, D and Xu, S, Application of Genetic Algorithms to the Optimisation of Neural Network Configuration for Stock Market Forecasting, AI 2001: Advances in Artificial Intelligence, December 10-14, 2001, Adelaide, pp. 285-296. ISBN 3-540-42960-3 (2001) [Refereed Conference Paper]

DOI: doi:10.1007/3-540-45656-2_25

Abstract

© Springer-Verlag Berlin Heidelberg 2001. Neural networks are recognised as an effective tool for predicting stock prices (Shin & Han, 2000), but little is known about which configurations are best and for which indices. The present study uses genetic algorithms to find a near optimal learning rate, momentum, tolerance and network architecture for 47 indices listed on the Australian Stock Exchange (ASX). Some relationships were determined between stock index and neural network attributes, and important observations were made for the further development of a methodology for determining optimal neural network configurations.

Item Details

Item Type:Refereed Conference Paper
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Hulme, D (Mr Daniel Hulme)
UTAS Author:Xu, S (Dr Shuxiang Xu)
ID Code:22843
Year Published:2001
Deposited By:Computing
Deposited On:2001-08-01
Last Modified:2015-02-24
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