Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1075
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dc.contributor.authorDassanayake, W.-
dc.contributor.authorArdekani, I.-
dc.contributor.authorGamage, N.-
dc.contributor.authorJayawardena, C.-
dc.contributor.authorSharifzadeh, H.-
dc.date.accessioned2022-02-09T09:25:22Z-
dc.date.available2022-02-09T09:25:22Z-
dc.date.issued2021-12-09-
dc.identifier.issn978-1-6654-0862-2/21-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1075-
dc.description.abstractTime series of stock market indices are dynamic, interdependent, and considered sensitive to many factors. Thus, the prediction of such indexes is always challenging. A comprehensive review carried out by the authors finds that no attempts have yet been carried out to test ARIMA models’ predictive efficacy applied to the New Zealand financial markets. Thus, technical analysis based ARIMA prediction models are developed and empirically tested on the New Zealand stock market (NZX50) index. Daily NZX50 index data are used, and the forecasting precision of the models is assessed based on Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE]. Our study finds that ARIMA (1, 1, 0) plus intercept is the best order forecasting model out of the models we examined. Once a substantiate algorithm training is implemented, formulated ARIMA models could be successfully employed to forecast the time series of other stock market indexes or the same index for varied periods. Future researchers could compare the forecasting efficiencies of ARIMA with a deep-learning model such as long short-term memory (LSTM). The presence of limited published research of ARIMA applied to the financial markets of New Zealand validates the need and the contribution of this paper.en_US
dc.language.isoenen_US
dc.publisher2021 3rd International Conference on Advancements in Computing (ICAC), SLIITen_US
dc.subjectTime series predictionen_US
dc.subjectARIMA modelsen_US
dc.subjectNZX50 indexen_US
dc.subjectNew Zealand stock market indexen_US
dc.subjecttechnical analysisen_US
dc.titleEffectiveness of Stock Index Forecasting using ARIMA model: Evidence from New Zealanden_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC54203.2021.9671132en_US
Appears in Collections:3rd International Conference on Advancements in Computing (ICAC) | 2021
Department of Computer systems Engineering-Scopes
Research Papers - Dept of Computer Systems Engineering
Research Papers - SLIIT Staff Publications

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