dc.contributor.author | Das, Debashish | |
dc.contributor.author | Sadiq, Ali Safa | |
dc.contributor.author | Mirjalili, Seyedali | |
dc.contributor.author | Noraziah, A | |
dc.date.accessioned | 2017-11-27T02:13:41Z | |
dc.date.available | 2017-11-27T02:13:41Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 1742-6588 | |
dc.identifier.doi | 10.1088/1742-6596/892/1/012018 | |
dc.identifier.uri | http://hdl.handle.net/10072/354131 | |
dc.description.abstract | 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. 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.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Institute of Physics Publishing | |
dc.relation.ispartofpagefrom | 012018-1 | |
dc.relation.ispartofpageto | 012018-14 | |
dc.relation.ispartofjournal | Journal of Physics: Conference Series | |
dc.relation.ispartofvolume | 892 | |
dc.subject.fieldofresearch | Deep learning | |
dc.subject.fieldofresearch | Data structures and algorithms | |
dc.subject.fieldofresearch | Condensed matter physics | |
dc.subject.fieldofresearch | Other physical sciences | |
dc.subject.fieldofresearch | Physical sciences | |
dc.subject.fieldofresearchcode | 461103 | |
dc.subject.fieldofresearchcode | 461305 | |
dc.subject.fieldofresearchcode | 5104 | |
dc.subject.fieldofresearchcode | 5199 | |
dc.subject.fieldofresearchcode | 51 | |
dc.title | Hybrid Clustering-GWO-NARX neural network technique in predicting stock price | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dc.type.code | C - Journal Articles | |
dcterms.license | https://creativecommons.org/licenses/by/3.0/ | |
dc.description.version | Version 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.hasfulltext | Full Text | |
gro.griffith.author | Mirjalili, Seyedali | |