Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique

Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent....

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Main Authors:	Das, Debashish, Sadiq, Ali Safa, Mirjalili, Seyedali
Format:	Conference or Workshop Item
Language:	English
Published:	2017
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/20937/ http://umpir.ump.edu.my/id/eprint/20937/1/ICCSCM%20Paper-Stock%20Prediction-GWO-April2017.pdf

id	ump-20937
recordtype	eprints
spelling	ump-209372018-03-30T08:17:16Z http://umpir.ump.edu.my/id/eprint/20937/ Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique Das, Debashish Sadiq, Ali Safa Mirjalili, Seyedali QA75 Electronic computers. Computer science Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. 2017 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/20937/1/ICCSCM%20Paper-Stock%20Prediction-GWO-April2017.pdf Das, Debashish and Sadiq, Ali Safa and Mirjalili, Seyedali (2017) Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique. In: The 6th International Conference on Computer Science and Computational Mathematics (ICCSCM 2017), 4-5 May 2017 , Langkawi, Malaysia. pp. 1-8..
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Das, Debashish Sadiq, Ali Safa Mirjalili, Seyedali Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique
description	Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate.
format	Conference or Workshop Item
author	Das, Debashish Sadiq, Ali Safa Mirjalili, Seyedali
author_facet	Das, Debashish Sadiq, Ali Safa Mirjalili, Seyedali
author_sort	Das, Debashish
title	Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique
title_short	Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique
title_full	Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique
title_fullStr	Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique
title_full_unstemmed	Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique
title_sort	stock prediction by applying hybrid clustering-gwo-narx neural network technique
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/20937/ http://umpir.ump.edu.my/id/eprint/20937/1/ICCSCM%20Paper-Stock%20Prediction-GWO-April2017.pdf
first_indexed	2023-09-18T22:30:30Z
last_indexed	2023-09-18T22:30:30Z
_version_	1777416252998811648

Stock prediction by applying hybrid Clustering-GWO-NARX neural network technique

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