Forecasting accuracy of Holt-Winters Exponential Smoothing : evidence from New Zealand.
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Authors
Dassanayake, Wajira
Ardekani, Iman
Jayawardena, C.
Sharifzadeh, Hamid
Gamage, N.
Ardekani, Iman
Jayawardena, C.
Sharifzadeh, Hamid
Gamage, N.
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Date
2020
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Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
New Zealand
New Zealand Stock Exchange (NZX)
Holt-Winters Exponential Smoothing (HWES)
technical analysis
stock movement prediction
computer modelling
stock markets
New Zealand Stock Exchange (NZX)
Holt-Winters Exponential Smoothing (HWES)
technical analysis
stock movement prediction
computer modelling
stock markets
ANZSRC Field of Research Code (2020)
Citation
Dassanayake, 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.
Abstract
Financial 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.
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New Zealand Journal of Applied Business Research
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