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Proceedings Paper

Stock market index prediction using neural networks
Author(s): Darmadi Komo; Chein-I Chang; Hanseok Ko
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Paper 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.

Paper Details

Date Published: 2 March 1994
PDF: 11 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.170000
Show Author Affiliations
Darmadi Komo, Univ. of Maryland/Baltimore County (United States)
Chein-I Chang, Univ. of Maryland/Baltimore County (United States)
Hanseok Ko, Univ. of Maryland/Baltimore County (United States)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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