Forecasting stock price using wavelet neural network optimized by directed Artificial Bee Colony algorithm

Khuat, TT; Le, QC; Nguyen, BL; Le, MH

Forecasting stock price using wavelet neural network optimized by directed Artificial Bee Colony algorithm

Khuat, TT Le, QC Nguyen, BL Le, MH

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Publication Type:: Journal Article
Citation:: Journal of Telecommunications and Information Technology, 2016, 2016, (2), pp. 43-52
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Khuat, TT
dc.contributor.author	Le, QC
dc.contributor.author	Nguyen, BL
dc.contributor.author	Le, MH
dc.date.accessioned	2022-08-15T03:11:07Z
dc.date.available	2022-08-15T03:11:07Z
dc.date.issued	2016-01-01
dc.identifier.citation	Journal of Telecommunications and Information Technology, 2016, 2016, (2), pp. 43-52
dc.identifier.issn	1509-4553
dc.identifier.issn	1899-8852
dc.identifier.uri	http://hdl.handle.net/10453/160188
dc.description.abstract	Stock prediction with data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. This study proposes an integrated approach where Haar wavelet transform and Artificial Neural Network optimized by Directed Artificial Bee Colony algorithm are combined for the stock price prediction. The proposed approach was tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the prediction result was found satisfactorily enough as a guide for traders and investors in making qualitative decisions.
dc.language	en
dc.relation.ispartof	Journal of Telecommunications and Information Technology
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	Forecasting stock price using wavelet neural network optimized by directed Artificial Bee Colony algorithm
dc.type	Journal Article
utslib.citation.volume	2016
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-15T03:11:06Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	2016
utslib.citation.issue	2

Abstract:

Stock prediction with data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. This study proposes an integrated approach where Haar wavelet transform and Artificial Neural Network optimized by Directed Artificial Bee Colony algorithm are combined for the stock price prediction. The proposed approach was tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the prediction result was found satisfactorily enough as a guide for traders and investors in making qualitative decisions.

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http://hdl.handle.net/10453/160188