MODELING THE IDX30 STOCK INDEX USING STEP FUNCTION INTERVENTION ANALYSIS

  • Rais Rais Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tadulako, Indonesia https://orcid.org/0000-0001-8665-191X
  • Dini Aprilia Afriza Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tadulako, Indonesia https://orcid.org/0009-0005-6783-0716
  • Iman Setiawan Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tadulako, Indonesia https://orcid.org/0000-0002-2401-4007
  • Hartayuni Sain Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tadulako, Indonesia https://orcid.org/0000-0003-4698-3754
  • Fadjryani Fadjryani Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tadulako, Indonesia https://orcid.org/0009-0009-7276-0846
  • Junaidi Junaidi Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tadulako, Indonesia https://orcid.org/0000-0002-3891-3492
Keywords: ARIMA, IDX30, Intervention Analysis, MAP, Step Function

Abstract

The significant decline in the IDX30 stock index occurred due to an intervention, namely the COVID-19 pandemic, which affected market stability and investment decisions. This study aims to model and forecast the IDX30 stock index using intervention analysis with a step function, which is very suitable for capturing long-term external shocks. The methodology used includes the ARIMA (AutoRegressive Integrated Moving Average) model combined with step function intervention analysis to account for structural changes due to external disturbances. The data used is sourced from investing.com, consisting of weekly IDX30 stock index prices from January 2019 to December 2023. The results show that the COVID-19 pandemic significantly impacted the IDX30 index, causing a drastic decline. The best model identified is ARIMA (1,2,1) with intervention parameters b = 0, s = 0, and r = 1. The forecasting results range from Rp. 488 to Rp. 505, with a Mean Absolute Percentage Error (MAPE) of 1.9404%, which shows the forecasting results are very good, indicating high forecasting accuracy. These findings highlight the effectiveness of intervention analysis in modeling financial time series data affected by external disturbances.

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Published
2025-07-01
How to Cite
[1]
R. Rais, D. A. Afriza, I. Setiawan, H. Sain, F. Fadjryani, and J. Junaidi, “MODELING THE IDX30 STOCK INDEX USING STEP FUNCTION INTERVENTION ANALYSIS”, BAREKENG: J. Math. & App., vol. 19, no. 3, pp. 2057-2068, Jul. 2025.