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Stock Market Prediction using Feed-forward Artificial Neural Network

by Suraiya Jabin
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 9
Year of Publication: 2014
Authors: Suraiya Jabin
10.5120/17399-7959

Suraiya Jabin . Stock Market Prediction using Feed-forward Artificial Neural Network. International Journal of Computer Applications. 99, 9 ( August 2014), 4-8. DOI=10.5120/17399-7959

@article{ 10.5120/17399-7959,
author = { Suraiya Jabin },
title = { Stock Market Prediction using Feed-forward Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 9 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 4-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number9/17399-7959/ },
doi = { 10.5120/17399-7959 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:27:44.140982+05:30
%A Suraiya Jabin
%T Stock Market Prediction using Feed-forward Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 9
%P 4-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents computational approach for stock market prediction. Artificial Neural Network (ANN) forms a useful tool in predicting price movement of a particular stock. In the short term, the pricing relationship between the elements of a sector holds firmly. An ANN can learn this pricing relationship to high degree of accuracy and be deployed to generate profits with sufficiently large amounts of data, preferably in times of low volatility and over a short time period. Experimental results are presented to demonstrate the performance of the proposed system. The paper also aims to suggest about training algorithms and training parameters that must be chosen in order to fit time series kind of complicated data to a neural network model. The proposed model succeeded in prediction of the trends of stock market with 100% prediction accuracy.

References
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Index Terms

Computer Science
Information Sciences

Keywords

BPNN DAX nntool newff trainbr trainscg trainrp

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