Stock Indices Forecasting: A Comparison of Holt-Winters Seasonality and Dynamic Harmonic Regression

Abstract

This research aims to investigate the performance of various time-series forecasting approaches in predicting stock indices in Indonesia. This research compared the performance of additive Holt-Winters seasonality, multiplicative Holt-Winters seasonality, and Dynamic Harmonic regression. The stock indices being forecast are SRI-KEHATI, LQ45, and IHSG. Forecasting SRI-KEHATI index is the novelty in this research. SRI-KEHATI index contains all the companies that comply with the requirements regarding sustainability and concerns for the environmental impact of the companies operations. Decompositions of SRI-KEHATI, LQ45, and IHSG reveal that the trend and seasonality components are all existent within all indices. The results showed that Holt-Winters models are superior to Dynamic Harmonic Regression. Multiplicative Holt-Winters seasonality forecast best for SRI-KEHATI and LQ45. Additive Holt-Winters excelled at predicting IHSG. Although Dynamic Harmonic Regression had less accuracy, its performance was still very outstanding since its mean average percentage errors never exceeded 8%. The result signifies the excellence of the Holt-Winters model for predicting stock indices and also shows that Dynamic Harmonic Regression also scores high in accuracy. Both models validate the time variance notion of the stock market proposed by Boudreaux (1995). The practical benefit for Investors is that this research enables investors to forecast the stock indices in the future and make adjustments in their trading strategy thereof.