Time Series Modeling with Intervention Analysis to Evaluate of COVID-19 Impact on the Stock Markets in Indonesia and Global

The COVID-19 pandemic began in December 2019 and led to significant disruptions in global financial markets. This study investigates the impact of the pandemic on stock indices in Indonesia (IHSG), the United States (DJI), and South Korea (KOSPI) using intervention analysis with a step function, which is designed to model permanent shifts in time series data following external shocks. Unlike traditional models such as ARIMA that assume data continuity, intervention models, particularly those using step functions, are highly suitable for assessing long-term economic disruptions and structural breaks caused by pandemics. This research uses daily stock price index data from January 10, 2019, to May 8, 2020, obtained from Yahoo Finance. The step function identifies the point of sustained change triggered by the initial COVID-19 outbreak and subsequent market reactions. The analysis shows that the pandemic caused significant and persistent declines across all observed indices. IHSG recorded its sharpest drop on March 26, 2020, while DJI and KOSPI experienced similar downward trends from March to April 2020. The forecasting performance of the intervention model was excellent, with Mean Absolute Percentage Error (MAPE) values of 0.72% for IHSG, 0.87% for DJI, and 0.82% for KOSPI, demonstrating high accuracy in modeling stock market behavior during crisis conditions.

Authors

Publication Details

Journal
Euler Jurnal Ilmiah Matematika Sains dan Teknologi
Published
2025-04-28
DOI
https://doi.org/10.37905/euler.v13i1.31081
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Time Series Modeling with Intervention Analysis to Evaluate of COVID-19 Impact on the Stock Markets in Indonesia and Global

Andrea Tri Rian Dani, Fachrian Bimantoro Putra
Euler Jurnal Ilmiah Matematika Sains dan Teknologi
Data Mining and Machine Learning Applications
article

Time Series Modeling with Intervention Analysis to Evaluate of COVID-19 Impact on the Stock Markets in Indonesia and Global

Andrea Tri Rian Dani, Fachrian Bimantoro Putra
article en

Abstract

The COVID-19 pandemic began in December 2019 and led to significant disruptions in global financial markets. This study investigates the impact of the pandemic on stock indices in Indonesia (IHSG), the United States (DJI), and South Korea (KOSPI) using intervention analysis with a step function, which is designed to model permanent shifts in time series data following external shocks. Unlike traditional models such as ARIMA that assume data continuity, intervention models, particularly those using step functions, are highly suitable for assessing long-term economic disruptions and structural breaks caused by pandemics. This research uses daily stock price index data from January 10, 2019, to May 8, 2020, obtained from Yahoo Finance. The step function identifies the point of sustained change triggered by the initial COVID-19 outbreak and subsequent market reactions. The analysis shows that the pandemic caused significant and persistent declines across all observed indices. IHSG recorded its sharpest drop on March 26, 2020, while DJI and KOSPI experienced similar downward trends from March to April 2020. The forecasting performance of the intervention model was excellent, with Mean Absolute Percentage Error (MAPE) values of 0.72% for IHSG, 0.87% for DJI, and 0.82% for KOSPI, demonstrating high accuracy in modeling stock market behavior during crisis conditions.

Euler Jurnal Ilmiah Matematika Sains dan TeknologiVol. 13(1)
Openalex Percentile: Top 88%
Data Mining and Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.