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dc.contributor.authorDassanayake, Wajira
dc.contributor.authorArdekani, Iman
dc.contributor.authorJayawardena, C.
dc.contributor.authorSharifzadeh, Hamid
dc.contributor.authorGamage, N.
dc.date.accessioned2020-11-10T01:15:16Z
dc.date.available2020-11-10T01:15:16Z
dc.date.issued2020
dc.identifier.issn1175-8007
dc.identifier.urihttps://hdl.handle.net/10652/5013
dc.description.abstractFinancial time series is volatile, dynamic, nonlinear, nonparametric, and chaotic. Accurate forecasting of stock market prices and indices is always challenging and complex endeavour in time series analysis. Accurate predictions of stock market price movements could bring benefits to different types of investors and other stakeholders to make the right trading strategies. Adopting a technical analysis perspective, this study examines the predictive power of Holt-Winters Exponential Smoothing (HWES) methodology by testing the models on the New Zealand stock market (S&P/NZX50) Index. Daily time-series data ranging from January 2009 to December 2017 are used in this study. The forecasting performance of the investigated models is evaluated using the root mean square error (RMSE], mean absolute error (MAE) and mean absolute percentage error (MAPE). Employing HWES on the undifferenced S&P/NZX50 Index (model 1) and HWES on the differenced S&P/NZX50 Index (model 2) we find that model 1 is the superior predictive algorithm for the experimental dataset. When the tested models are evaluated overtime of the sample period we find the supportive evidence to our original findings. The evaluated HWES models could be employed effectively to predict the time series of other stock markets or the same index for diverse periods (windows) if substantiate algorithm training is carried out.en_NZ
dc.language.isoenen_NZ
dc.publisherNew Zealand Journal of Applied Business Researchen_NZ
dc.subjectNew Zealanden_NZ
dc.subjectS&P/NZX50 Indexen_NZ
dc.subjectNew Zealand stock market indexen_NZ
dc.subjectHolt-Winters Exponential Smoothing modelsen_NZ
dc.subjecttechnical analysisen_NZ
dc.subjectstock movementen_NZ
dc.subjectpredictionen_NZ
dc.subjectcomputer modelingen_NZ
dc.subjectstock marketsen_NZ
dc.titleForecasting accuracy of Holt-Winters Exponential Smoothing : evidence from New Zealand.en_NZ
dc.typeJournal Articleen_NZ
dc.date.updated2020-11-04T13:30:06Z
dc.subject.marsden150299 Banking, Finance and Investment not elsewhere classifieden_NZ
dc.subject.marsden0802 Computation Theory and Mathematicsen_NZ
dc.identifier.bibliographicCitationDassanayake, W., Ardekani, I., Jayawardena, C., Sharifzadeh, H., & Gamage, N. (2020). Forecasting accuracy of Holt-Winters Exponential Smoothing: evidence from New Zealand. New Zealand Journal Of Applied Business Research, 17 (1), 11-30.en_NZ
unitec.publication.spage11en_NZ
unitec.publication.lpage30en_NZ
unitec.publication.volume17en_NZ
unitec.publication.issue1en_NZ
unitec.publication.titleNew Zealand Journal of Applied Business Researchen_NZ
unitec.peerreviewedyesen_NZ
unitec.identifier.roms65126en_NZ
unitec.identifier.roms65203
unitec.identifier.roms65204
unitec.publication.placeManukau, New Zealanden_NZ


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