Stock market index prediction using neural networks

Darmadi Komo; C. I. Chang; Hanseok Ko

doi:10.1117/12.170000

Stock market index prediction using neural networks

Darmadi Komo, C. I. Chang, Hanseok Ko

Research output: Contribution to journal › Conference article › peer-review

4 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 language	English
Pages (from-to)	516-526
Number of pages	11
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	2243
DOIs	https://doi.org/10.1117/12.170000
Publication status	Published - 1994 Mar 2
Externally published	Yes
Event	Applications of Artificial Neural Networks V 1994 - Orlando, United States Duration: 1994 Apr 4 → 1994 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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10.1117/12.170000

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title = "Stock market index prediction using neural networks",

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.",

author = "Darmadi Komo and Chang, {C. I.} and Hanseok Ko",

note = "Publisher Copyright: {\textcopyright} 1994 SPIE. All rights reserved. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; Applications of Artificial Neural Networks V 1994 ; Conference date: 04-04-1994 Through 08-04-1994",

year = "1994",

month = mar,

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doi = "10.1117/12.170000",

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TY - JOUR

T1 - Stock market index prediction using neural networks

AU - Komo, Darmadi

AU - Chang, C. I.

AU - Ko, Hanseok

PY - 1994/3/2

Y1 - 1994/3/2

N2 - 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.

AB - 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.

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DO - 10.1117/12.170000

M3 - Conference article

AN - SCOPUS:85064606883

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VL - 2243

SP - 516

EP - 526

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Applications of Artificial Neural Networks V 1994

Y2 - 4 April 1994 through 8 April 1994

ER -

Stock market index prediction using neural networks

Abstract

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