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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578540
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dc.contributor.authorChang Sim Vui (UMS)
dc.contributor.authorChin Kim On (UMS)
dc.contributor.authorGan Kim Soon (UMS)
dc.contributor.authorRayner Alfred (UMS)
dc.contributor.authorPatricia Anthony
dc.date.accessioned2023-11-06T03:03:20Z-
dc.date.available2023-11-06T03:03:20Z-
dc.date.issued2017-06
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:116006
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578540-
dc.descriptionThis paper investigates the accuracy of Feedforward Neural Network (FFNN) with different external parameters in predicting the closing price of a particular stock. Specifically, the feedforward neural network was trained using Levenberg-Marquardt backpropagation algorithm to forecast the CIMB stock’s closing price in the Kuala Lumpur Stock exchange (KLSE). The results indicate that the use of external parameters can improve the accuracy of the stock’s closing price.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-25-S-6
dc.rightsUKM
dc.subjectArtificial Neural Network (ANNS)
dc.subjectBackpropagation
dc.subjectFeedforward Neural Network (FFNN)
dc.subjectLevenberg-Marquardt algorithm
dc.subjectMacroeconomic parameters
dc.subjectStock market forecasting
dc.titleExternal constraints of neural cognition for cimb stock closing price prediction
dc.typeJournal Article
dc.format.volume25
dc.format.pages29-38
dc.format.issueSpecial Issue
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