Stock market index prediction using neural networks

Darmadi Komo, C. I. Chang, Hanseok Ko

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

A neural network approach to stock market index prediction is presented. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in our experiments where Radial Basis Function based neural networks have been designed to model these indices over the period from January 1988 to Dec 1992. A notable success has been achieved with the proposed model producing over 90% prediction accuracies observed based on monthly Dow Jones Industrial Index predictions. The model has also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the Radial Basis Function neural network represents an excellent candidate to predict stock market index.

Original languageEnglish
Pages (from-to)516-526
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume2243
DOIs
Publication statusPublished - 1994 Mar 2
Externally publishedYes
EventApplications of Artificial Neural Networks V 1994 - Orlando, United States
Duration: 1994 Apr 41994 Apr 8

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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