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dc.contributor.authorDassanayake, Wajira
dc.date.accessioned2018-07-30T23:18:54Z
dc.date.available2018-07-30T23:18:54Z
dc.date.issued2017-10-05
dc.identifier.urihttps://hdl.handle.net/10652/4333
dc.description.abstractMy research intends to derive an intelligent multistep scalable hybrid model Integrating technical, fundamental and textual analyses. This dynamic multistep model would link the existing segregated models in the literature. The literature survey reveals that the existing models formulate marginally segregated subsets in the area of stock market price prediction. This research will be the first scientific exploration to combine the effects of historical factors, macroeconomic determinants and spontaneous events in a single multistep model to forecast the stock market prices.en_NZ
dc.language.isoenen_NZ
dc.rightsAll rights reserveden_NZ
dc.subjectstock marketsen_NZ
dc.subjectstock movement. predictionen_NZ
dc.subjectcorrelation analysisen_NZ
dc.subjectstock price analysisen_NZ
dc.titleA scalable multi-step hybrid model for stock index predictionen_NZ
dc.typeConference Contribution - Oral Presentationen_NZ
dc.date.updated2018-04-27T14:30:02Z
dc.rights.holderAuthoren_NZ
dc.subject.marsden1502 Other Banking, Finance and Investmenten_NZ
dc.subject.marsden080109 Pattern Recognition and Data Miningen_NZ
dc.identifier.bibliographicCitationDassanayake, W. (2017, October). A scalable multi-step hybrid model for stock index prediction. Paper presented at the Unitec Research Symposium, 2017, Unitec Institute of Technology, New Zealand.en_NZ
unitec.publication.titleUnitec Research Symposium, 2017en_NZ
unitec.conference.orgUnitec Institute of Technologyen_NZ
unitec.conference.locationUnitec Institute of Technology, Auckland, New Zealanden_NZ
unitec.conference.sdate2017-10-05
unitec.conference.edate2017-10-05
unitec.peerreviewedyesen_NZ
unitec.identifier.roms61318en_NZ
unitec.institution.studyareaAccounting and Financeen_NZ


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