Jurnal Keuangan dan Perbankan Universitas Merdeka Malang
Stock Indices Forecasting: A Comparison of Holt-Winters Seasonality and Dynamic Harmonic Regression
Dublin Core
Title
Jurnal Keuangan dan Perbankan Universitas Merdeka Malang
Stock Indices Forecasting: A Comparison of Holt-Winters Seasonality and Dynamic Harmonic Regression
Stock Indices Forecasting: A Comparison of Holt-Winters Seasonality and Dynamic Harmonic Regression
Subject
Additive; Dynamic Harmonic Regression; Holt-Winters Seasonality; Multiplicative
Description
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. SRIKEHATI index ontains all the companies that comply with the equirements 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.
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.
Creator
Regi Muzio Ponziani
Source
DOI: 10.26905/jkdp.v26i2.6755
Publisher
Universitas Merdeka Malang
Date
April 2022
Contributor
Sri Wahyuni
Rights
ISSN: 2443-2687 (Online) ISSN: 1410-8089 (Print)
Format
PDF
Language
English
Type
Text
Coverage
Jurnal Keuangan dan Perbankan Universitas Merdeka Malang
Files
Collection
Citation
Regi Muzio Ponziani, “Jurnal Keuangan dan Perbankan Universitas Merdeka Malang
Stock Indices Forecasting: A Comparison of Holt-Winters Seasonality and Dynamic Harmonic Regression,” Repository Horizon University Indonesia, accessed March 13, 2025, https://repository.horizon.ac.id/items/show/4842.
Stock Indices Forecasting: A Comparison of Holt-Winters Seasonality and Dynamic Harmonic Regression,” Repository Horizon University Indonesia, accessed March 13, 2025, https://repository.horizon.ac.id/items/show/4842.