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dc.contributor.authorDassanayake, Wajira
dc.date.accessioned2020-12-17T00:29:02Z
dc.date.available2020-12-17T00:29:02Z
dc.date.issued2020-12-08
dc.identifier.urihttps://hdl.handle.net/10652/5035
dc.description.abstractIntroduction Purpose Methodology Key findings Conclusion Research limitationen_NZ
dc.language.isoenen_NZ
dc.subjectNew Zealanden_NZ
dc.subjectstock marketsen_NZ
dc.subjectS&P/NZX50 Indexen_NZ
dc.subjectstock movementen_NZ
dc.subjectpredictionen_NZ
dc.subjectstock price analysisen_NZ
dc.subjectNew Zealand stock market indexen_NZ
dc.subjectcomputer modelingen_NZ
dc.subjectdeep-learning algorithmsen_NZ
dc.subjectalgorithmsen_NZ
dc.subjectLSTM (Long short-term memory)en_NZ
dc.titleEffectiveness of stock index forecasting using LSTM model : evidence from New Zealanden_NZ
dc.typeConference Contribution - Oral Presentationen_NZ
dc.date.updated2020-12-11T13:30:13Z
dc.rights.holderAuthoren_NZ
dc.subject.marsden150299 Banking, Finance and Investment not elsewhere classifieden_NZ
dc.subject.marsden080108 Neural, Evolutionary and Fuzzy Computationen_NZ
dc.identifier.bibliographicCitationDassanayake, W. (2020, December). Effectiveness of stock index forecasting using LSTM model : evidence from New Zealand. Paper presented at the Unitec Research Symposium, Te Puna Mt Albert Campus, Unitec Institute of Technology.en_NZ
unitec.publication.titleUnitec Research Symposium 2020en_NZ
unitec.conference.titleUnitec Research Symposium 2020en_NZ
unitec.conference.orgUnitec Institute of Technologyen_NZ
unitec.conference.locationMount Albert, Auckland, New Zealanden_NZ
unitec.conference.sdate2020-12-07
unitec.conference.edate2020-12-07
unitec.peerreviewedyesen_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
unitec.identifier.roms65209en_NZ
unitec.identifier.roms65208
unitec.institution.studyareaAccounting and Financeen_NZ


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