Neural network technology for stock market index prediction

D. Komo, Chein I. Chang, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Two neural network models, the radial basis function (RBF) and backpropagation, applied to stock market index predictions are compared. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in the experiments. A notable success has been achieved with the proposed models producing over 80% prediction accuracies observed based on the monthly Dow Jones Industrial Index predictions. These models have also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the RBF neural network is preferred over the multilayer perceptron network and is a promising candidate for stock market index predictions.

Original languageEnglish
Title of host publicationISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages543-546
Number of pages4
ISBN (Electronic)078031865X, 9780780318656
DOIs
Publication statusPublished - 1994
Event1994 International Symposium on Speech, Image Processing and Neural Networks, ISSIPNN 1994 - Hong Kong, Hong Kong
Duration: 1994 Apr 131994 Apr 16

Publication series

NameISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings

Conference

Conference1994 International Symposium on Speech, Image Processing and Neural Networks, ISSIPNN 1994
CountryHong Kong
CityHong Kong
Period94/4/1394/4/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Linguistics and Language

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