Neural network technology for stock market index prediction

D. Komo; Chein I. Chang; Hanseok Ko

doi:10.1109/SIPNN.1994.344854

Neural network technology for stock market index prediction

D. Komo, Chein I. Chang, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 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 language	English
Title of host publication	ISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	543-546
Number of pages	4
ISBN (Electronic)	078031865X, 9780780318656
DOIs	https://doi.org/10.1109/SIPNN.1994.344854
Publication status	Published - 1994
Externally published	Yes
Event	1994 International Symposium on Speech, Image Processing and Neural Networks, ISSIPNN 1994 - Hong Kong, Hong Kong Duration: 1994 Apr 13 → 1994 Apr 16

Publication series

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

Conference

Conference	1994 International Symposium on Speech, Image Processing and Neural Networks, ISSIPNN 1994
Country/Territory	Hong Kong
City	Hong Kong
Period	94/4/13 → 94/4/16

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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10.1109/SIPNN.1994.344854

Cite this

Komo, D., Chang, C. I., & Ko, H. (1994). Neural network technology for stock market index prediction. In ISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings (pp. 543-546). [344854] (ISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIPNN.1994.344854

Neural network technology for stock market index prediction. / Komo, D.; Chang, Chein I.; Ko, Hanseok.
ISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc., 1994. p. 543-546 344854 (ISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Komo, D, Chang, CI & Ko, H 1994, Neural network technology for stock market index prediction. in ISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings., 344854, ISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 543-546, 1994 International Symposium on Speech, Image Processing and Neural Networks, ISSIPNN 1994, Hong Kong, Hong Kong, 94/4/13. https://doi.org/10.1109/SIPNN.1994.344854

Komo D, Chang CI, Ko H. Neural network technology for stock market index prediction. In ISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc. 1994. p. 543-546. 344854. (ISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings). doi: 10.1109/SIPNN.1994.344854

Komo, D. ; Chang, Chein I. ; Ko, Hanseok. / Neural network technology for stock market index prediction. ISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc., 1994. pp. 543-546 (ISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings).

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title = "Neural network technology for stock market index prediction",

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.",

author = "D. Komo and Chang, {Chein I.} and Hanseok Ko",

note = "Publisher Copyright: {\textcopyright} 1994 IEEE. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 1994 International Symposium on Speech, Image Processing and Neural Networks, ISSIPNN 1994 ; Conference date: 13-04-1994 Through 16-04-1994",

year = "1994",

doi = "10.1109/SIPNN.1994.344854",

language = "English",

series = "ISSIPNN 1994 - 1994 International Symposium on Speech, Image Processing and Neural Networks, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "543--546",

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1994 International Symposium on Speech, Image Processing and Neural Networks, ISSIPNN 1994

Y2 - 13 April 1994 through 16 April 1994

ER -

Neural network technology for stock market index prediction

Abstract

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