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dc.contributor.authorDas, Debashish
dc.contributor.authorSadiq, Ali Safa
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorNoraziah, A
dc.date.accessioned2017-11-27T02:13:41Z
dc.date.available2017-11-27T02:13:41Z
dc.date.issued2017
dc.identifier.issn1742-6588
dc.identifier.doi10.1088/1742-6596/892/1/012018
dc.identifier.urihttp://hdl.handle.net/10072/354131
dc.description.abstractPrediction 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. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Physics Publishing
dc.relation.ispartofpagefrom012018-1
dc.relation.ispartofpageto012018-14
dc.relation.ispartofjournalJournal of Physics: Conference Series
dc.relation.ispartofvolume892
dc.subject.fieldofresearchAnalysis of Algorithms and Complexity
dc.subject.fieldofresearchAtomic, Molecular, Nuclear, Particle and Plasma Physics
dc.subject.fieldofresearchCondensed Matter Physics
dc.subject.fieldofresearchOther Physical Sciences
dc.subject.fieldofresearchcode080201
dc.subject.fieldofresearchcode0202
dc.subject.fieldofresearchcode0204
dc.subject.fieldofresearchcode0299
dc.titleHybrid Clustering-GWO-NARX neural network technique in predicting stock price
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttps://creativecommons.org/licenses/by/3.0/
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2017. Published under licence in the Journal of Physics: Conference Series by IOP Publishing Ltd. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the Hybrid Clustering-GWO-NARX neural network technique in predicting stock price and DOI:10.1088/1742-6596/892/1/012018.
gro.hasfulltextFull Text
gro.griffith.authorMirjalili, Seyedali


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