Forecasting Accuracy of Holt-Winters Exponential Smoothing: Evidence From New Zealand

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Wajira Dassanayake
Iman Ardekani
Chandimal Jayawardena
Hamid Sharifzadeh
Narmada Gamage

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.

Article Details

How to Cite
Dassanayake, W., Ardekani, I., Jayawardena, C., Sharifzadeh, H., & Gamage, N. (2019). Forecasting Accuracy of Holt-Winters Exponential Smoothing: Evidence From New Zealand. New Zealand Journal of Applied Business Research , 17(1), 11–30. https://doi.org/10.34074/jabr.17102.02
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Plaudit

References

Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle.

Archibald, B. C. (1990). Parameter space of the Holt-Winters' model. International Journal of Forecasting, 6(2), 199-209.

Assimakopoulos,V., & Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International Journal of Forecasting, 16, 521–530.

Balaban, E., Bayar, A., & Faff, R. W. (2006). Forecasting stock market volatility: Further international evidence. The European Journal of Finance, 12(2), 171-188.

Bley, J., & Olson, D. (2008). Volatility forecasting performance with VIX: Implied volatility versus historical standard deviation and conditional volatility. Advances in Financial Planning and Forecasting, (3), 67-91.

Brown, R. G. (1959). Statistical forecasting for inventory control. McGraw/Hill.

Brown, R. G. (1963). Smoothing, forecasting and prediction of discrete time series. Englewood Cliffs, NJ7 Prentice-Hall.

Carreno, J., & Madinaveitia, J. (1990). A modification of time series forecasting methods for handling announced price increases. International Journal of Forecasting, 6(4), 479484.

Chen, N. F. (1991). Financial investment opportunities and the macroeconomy. The Journal of Finance, 46(2), 529-554.

Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal of business, 383-403.

Covel, M. (2004). Trend following: how great traders make millions in up or down markets. FT Press.

Dassanayake, W., & Jayawardena, C. (2017, January). Determinants of stock market index movements: Evidence from New Zealand stock market. In 2017 6th National Conference on Technology and Management (NCTM) (pp. 6-11). IEEE.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.

Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 1057-1072.

Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251-276.

Fama, E. F. (1991). Efficient capital markets: II. The journal of finance, 46(5), 1575-1617.

Faraway, J., & Chatfield, C. (1998). Time series forecasting with neural networks: a comparative study using the airline data. Journal of the Royal Statistical Society: Series C (Applied Statistics). 47(2), 231-250.

Gardner Jr, E. S. (2006). Exponential smoothing: The state of the art—Part II. International journal of forecasting, 22(4), 637-666.

Granger, C. J. (1986). Developments in the study of cointegrated economic variables. Oxford Bulletin of economics and statistics, 48(3), 213-228.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation. 9(8), 1735-1780

Holt, C. C. (1957). Forecasting seasonals and trends by exponentially weighted moving averages, Office of Naval Research. Research memorandum, 52.

Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International journal of forecasting, 20(1), 5-10.

Holt, C. C., Modigliani, F., Muth, J. F., & Simon, H. A. (1960). Planning production, inventories, and work force. Englewood Cliffs, NJ7 Prentice-Hall.

Hyndman, R. J., & Billah, B. (2003). Unmasking the Theta method. International Journal of Forecasting, 19, 287– 290.

Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state-space framework for automatic forecasting using exponential smoothing methods. International Journal of forecasting, 18(3), 439-454.

Kihoro, J., Otieno R., & C. Wafula. (2004). Seasonal time series forecasting: a comparative study of ARIMA and ANN models. African Journal of Science and Technology. 5 (2).

Kwon, C. S., & Shin, T. S. (1999). Cointegration and causality between macroeconomic variables and stock market returns. Global finance journal, 10(1), 71-81.

Lawton, R. (1998). How should additive Holt-Winters estimates be corrected? International Journal of Forecasting, 14(3), 393-403.

Leung, M. T., Daouk, H., & Chen, A. S. (2000). Forecasting stock indices: a comparison of classification and level estimation models. International Journal of Forecasting, 16(2), 173-190.

MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of applied econometrics, 11(6), 601-618.

Makridakis, S. (1993). Accuracy measures: theoretical and practical concerns. International Journal of Forecasting, 9(4), 527-529.

Maris, K., Pantou, G., Nikolopoulos, K., Pagourtzi, E., & Assimakopoulos, V. (2004). A study of financial volatility forecasting techniques in the FTSE/ASE 20 index. Applied Economics Letters, 11(7), 453-457.

McKenzie, E. (1986). Error analysis for Winters additive seasonal forecasting system. International Journal of Forecasting 2, no. 3 (1986): 373-382.

Muth, J. F. (1960). Optimal properties of exponentially weighted forecasts. Journal of the American statistical association, 55(290), 299-306.

Ord, J. K., Koehler, A. B., & Snyder, R. D. (1997). Estimation and prediction for a class of dynamic nonlinear statistical models. Journal of the American Statistical Association, 92(440), 1621-1629.

Pegels, C. C. (1969). Exponential forecasting: some new variations. Management Science, 311-315.

Pereira, R. (2004). Forecasting Portuguese stock market volatility. Journal of Forecasting, 18, 333-343.

Phillips, P. (1987). Time Series Regression with a Unit Root. Econometrica, 55(2), 277301. doi:10.2307/1913237

Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.

Poon, S. H., Hyung, N., & Granger, C. W. (2006). A source of long memory in volatility. Available at SSRN 904582.

Proceedings of IEEE international symposium on information theory. Awajan, A. M., Ismail, M. T., & Wadi, S. A. (2018). Improving forecasting accuracy for stock market data using EMD-HW bagging. PloS one, 13(7).

Roberts, S. A. (1982). A general class of Holt-Winters type forecasting models. Management Science, 28(7), 808-820.

Rosas, A. L., & Guerrero, V. M. (1994). Restricted forecasts using exponential smoothing techniques. International Journal of Forecasting, 10(4), 515-527.

Schwager, J. D. (1993). Market wizards: Interviews with top traders. Collins, New York.

Schwager, J. D. (1995). The new market wizards: Conversations with America's top traders. Wiley, New York.

Sharif, O., & Hasan, M. Z. (2019). Forecasting the Stock Price by using Holt’s Method. Indonesian Journal of Contemporary Management Research, 1(1), 15-24.

S&P/NZX50 Index. (2018). S&P Dow Jones Indices. https://www.spglobal.com/spdji/en/indices/equity/sp-nzx-50-index/#overview.

Statistics, O. E. C. D. (2013). Glossary of Statistical Terms. Competitiveness in international trade. http://stats.OECD.org/glossary/detail.asp.

Taylor, J. W. (2004). Volatility forecasting with smooth transition exponential smoothing. International Journal of Forecasting, 20(2), 273-286.

Tseng, K. C., Kwon, O., & Tjing, L. C. (2012). Time series and neural network forecasts of daily stock prices. Investment Management and Financial Innovations, (9, Iss. 1), 32-54.

Williams, D. W., & Miller, D. (1999). Level-adjusted exponential smoothing for modeling planned discontinuities. International Journal of Forecasting, 15(3), 273-289.

Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324-342.

Wongbangpo, P., & Sharma, S. C. (2002). Stock market and macroeconomic fundamental dynamic interactions: ASEAN-5 countries. Journal of Asian Economics, 13(1), 27-51.

Yu, J. (2002). Forecasting volatility in the New Zealand stock market. Applied Financial Economics, 12(3), 193-202.