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

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IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication
© 2015 by IJCA Journal
ETCC 2015 - Number 2
Year of Publication: 2015
Authors:
Ipsita Maharana
Sumanjit Das
M. R. Senapati

Ipsita Maharana, Sumanjit Das and M r Senapati. Article: A Hybrid Intelligent Artificial Neural Network Model for Stock Market Index Prediction. IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication ETCC 2015(2):27-31, September 2015. Full text available. BibTeX

@article{key:article,
	author = {Ipsita Maharana and Sumanjit Das and M.r. Senapati},
	title = {Article: A Hybrid Intelligent Artificial Neural Network Model for Stock Market Index Prediction},
	journal = {IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication},
	year = {2015},
	volume = {ETCC 2015},
	number = {2},
	pages = {27-31},
	month = {September},
	note = {Full text available}
}

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.

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