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A Hybrid Intelligent Artificial Neural Network Model for Stock Market Index Prediction

Published on September 2015 by Ipsita Maharana, Sumanjit Das, M.r. Senapati
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International Conference on Emergent Trends in Computing and Communication
Foundation of Computer Science USA
ETCC2015 - Number 2
September 2015
Authors: Ipsita Maharana, Sumanjit Das, M.r. Senapati
7cf9042c-2b2b-47bf-aff7-51ca4448f237

Ipsita Maharana, Sumanjit Das, M.r. Senapati . A Hybrid Intelligent Artificial Neural Network Model for Stock Market Index Prediction. International Conference on Emergent Trends in Computing and Communication. ETCC2015, 2 (September 2015), 27-31.

@article{
author = { Ipsita Maharana, Sumanjit Das, M.r. Senapati },
title = { A Hybrid Intelligent Artificial Neural Network Model for Stock Market Index Prediction },
journal = { International Conference on Emergent Trends in Computing and Communication },
issue_date = { September 2015 },
volume = { ETCC2015 },
number = { 2 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 27-31 },
numpages = 5,
url = { /proceedings/etcc2015/number2/22340-4570/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Emergent Trends in Computing and Communication
%A Ipsita Maharana
%A Sumanjit Das
%A M.r. Senapati
%T A Hybrid Intelligent Artificial Neural Network Model for Stock Market Index Prediction
%J International Conference on Emergent Trends in Computing and Communication
%@ 0975-8887
%V ETCC2015
%N 2
%P 27-31
%D 2015
%I International Journal of Computer Applications
Abstract

Emergent trends in computing use hybrid approaches to solve optimization problems. Such hybrid model comprising of soft computing technique based on neuro-fuzzy approach and an optimization technique based on fire fly algorithm is proposed in this paper. Firstly, this paper describes some existing techniques on which it is based. Then the new technique, its algorithm, benefits, result and error is elaborated. In this paper we have proposed an efficient model to predict the closing index value of financial market. Comparison with other existing models shows better accuracy in predicted output. The mean absolute percentage error (MAPE) obtained using this model is 0. 0753.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Neuro Fuzzy Algorithm Firefly Algorithm Computational Intelligence Hybrid Models Prediction