Melola Discovery

← Melola Discovery

Open access Journal

Modeling Determinants of Composite Stock Price Index Based on Multivariable Nonparametric Penalized Spline Regression Model alized Spline

Jurnal Matematika, Statistika dan Komputasi · Vol. 20 No. 3 (2024) · pp. 497–512

DOI
10.20956/j.v20i3.32145
ISSN
2614-8811, 1858-1382
Published
2024-05-15
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

English

The Composite Stock Price Index (IHSG) is a critical indicator in the Indonesian capital market, playing a central role as one of the key instruments influencing the dynamics of a country's economy. Modeling IHSG can provide a substantial contribution to stakeholders in the capital market, facilitating investment decision-making. Therefore, it is essential to obtain accurate and responsive estimates for IHSG data. The IHSG data used covers the period from January 2020 to December 2022 and tends to be fluctuating. Hence, a spline regression analysis with effective penalized spline estimation is applied to overcome the limitations of assumptions in the relationship between variables. The variables used in the modeling include inflation, exchange rates, interest rates, and IDJ. From the analysis results, optimal values based on the minimum GCV for each variable are sequentially 0.278, 0.904, 0.751, and 0.665. It is also known that these four variables collectively have a 92.1% influence, with inflation having varied impacts, exchange rates exhibiting a stronger negative effect at certain levels, interest rates showing opposite effects depending on their levels, and IDJ having a positive effect on IHSG movements. The significant variability of these impacts indicates that these variables make important contributions. In other words, IHSG fluctuations can be explained by variations in the values of inflation, exchange rates, interest rates, and IDJ.

Indonesian

Indeks Harga Saham Gabungan (IHSG) merupakan indikator kritis dalam pasar modal Indonesia dan memegang peran sentral sebagai salah satu instrumen kunci yang memengaruhi dinamika perekonomian suatu negara. Pemodelan IHSG dapat memberikan kontribusi substansial bagi pihak-pihak yang berkepentingan di dalam pasar modal serta mendorong pengambilan keputusan investasi. Oleh karena itu, penting untuk mendapatkan perkiraan yang tepat dan responsif terhadap data IHSG. Data IHSG yang digunakan berada dalam rentang periode Januari 2020 hingga Desember 2022 dan cenderung fluktuatif, sehingga diterapkan analisis regresi spline dengan estimasi penalized spline yang efektif untuk mengatasi keterbatasan asumsi dalam hubungan antar variabel. Dari hasil analisis, didapatkan nilai optimum berdasarkan GCV minimum untuk masing-masing variabel secara berturut-turut ialah 0.278, 0.904, 0.751, dan 0.665. Diketahui pula bahwa inflasi, kurs, tingkat suku bunga, dan IDJ secara bersama-sama memiliki pengaruh sebesar 92.1% terhadap pergerakan IHSG dengan tingkat inflasi yang memiliki dampak bervariasi, kurs dengan efek negatif yang lebih kuat pada tingkat tertentu, suku bunga dengan efek yang berlawanan tergantung pada tingkatannya, dan IDJ dengan efek positif pada IHSG. Variabilitas signifikan dari dampak ini menunjukkan bahwa variabel-variabel ini memberikan kontribusi penting atau dengan kata lain, fluktuasi IHSG dapat dijelaskan dengan variasi nilai-nilai inflasi, kurs, tingkat suku bunga, dan IDJ.

Keywords

Collaboration network of the authors

40 authors · 137 co-authorship links · 6 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Hasanuddin University
  • Cenderawasih University
  • IPB University
  • Sebelas Maret University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sri Astuti ThamrinHasanuddin University3
Anna IslamiyatiHasanuddin University3
Nirwan IlyasHasanuddin University3
Andi M. Alfin BasoHasanuddin University3
Sitti SahrimanHasanuddin University3
M. Zaky Hisyam GozhiHasanuddin University3
Andi Isna YunitaHasanuddin University2
Nurtiti SunusiHasanuddin University2
Georgina Maria TinungkiHasanuddin University2
Afifah Mutiah NurdinHasanuddin University2
Muh. Indirwan ArfanHasanuddin University2
Muhammad FadilAffiliation unknown2
Muhammad Ridzky DavalaHasanuddin University2
Yundy HafizriandaCenderawasih University1
Alfiana AntohCenderawasih University1
Fitri FitriAffiliation unknown1
Nasrah SirajangHasanuddin University1
Nurkamalia NurkamaliaAffiliation unknown1
Nurul IkhsaniAffiliation unknown1
Hajratul Ashwad KAffiliation unknown1
RaupongAffiliation unknown1
SuritmanAffiliation unknown1
Surya Cahyadi SyamAffiliation unknown1
Kevin Ekarinaldo PalunganAffiliation unknown1
Sitti SahrimanAffiliation unknown1
A. Muthiah Nur AngrianyHasanuddin University1
Rini JamalAffiliation unknown1
Andi FebriyantiAffiliation unknown1
Rahmiati RahimAffiliation unknown1
A. Elisha WulandariAffiliation unknown1
Belia Nurul FajriAffiliation unknown1
Abdullah PannuIPB University1
Alfian Akbar VallarinoAffiliation unknown1
Anugrah Nur Isnaeni RahmanAffiliation unknown1
Haksar HaksarAffiliation unknown1
Endang SulastriSebelas Maret University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Modeling Determinants of Composite Stock Price Index Based on Multivariable Nonparametric Penalized Spline Regression Model alized Spline</dc:title>
	<dc:creator xml:lang="en">Octavia, Dhita Hartanti</dc:creator>
	<dc:creator xml:lang="en">Auliarani, Asma</dc:creator>
	<dc:creator xml:lang="en">Siswanto, Siswanto</dc:creator>
	<dc:creator xml:lang="en">Kalondeng, Anisa</dc:creator>
	<dc:subject xml:lang="en">IHSG</dc:subject>
	<dc:subject xml:lang="en">Nonparametric</dc:subject>
	<dc:subject xml:lang="en">Penalized Spline</dc:subject>
	<dc:subject xml:lang="en">Regression</dc:subject>
	<dc:description xml:lang="en">The Composite Stock Price Index (IHSG) is a critical indicator in the Indonesian capital market, playing a central role as one of the key instruments influencing the dynamics of a country's economy. Modeling IHSG can provide a substantial contribution to stakeholders in the capital market, facilitating investment decision-making. Therefore, it is essential to obtain accurate and responsive estimates for IHSG data. The IHSG data used covers the period from January 2020 to December 2022 and tends to be fluctuating. Hence, a spline regression analysis with effective penalized spline estimation is applied to overcome the limitations of assumptions in the relationship between variables. The variables used in the modeling include inflation, exchange rates, interest rates, and IDJ. From the analysis results, optimal values based on the minimum GCV for each variable are sequentially 0.278, 0.904, 0.751, and 0.665. It is also known that these four variables collectively have a 92.1% influence, with inflation having varied impacts, exchange rates exhibiting a stronger negative effect at certain levels, interest rates showing opposite effects depending on their levels, and IDJ having a positive effect on IHSG movements. The significant variability of these impacts indicates that these variables make important contributions. In other words, IHSG fluctuations can be explained by variations in the values of inflation, exchange rates, interest rates, and IDJ.</dc:description>
	<dc:description xml:lang="id">Indeks Harga Saham Gabungan (IHSG) merupakan indikator kritis dalam pasar modal Indonesia dan memegang peran sentral sebagai salah satu instrumen kunci yang memengaruhi dinamika perekonomian suatu negara. Pemodelan IHSG dapat memberikan kontribusi substansial bagi pihak-pihak yang berkepentingan di dalam pasar modal serta mendorong pengambilan keputusan investasi. Oleh karena itu, penting untuk mendapatkan perkiraan yang tepat dan responsif terhadap data IHSG. Data IHSG yang digunakan berada dalam rentang periode Januari 2020 hingga Desember 2022 dan cenderung fluktuatif, sehingga diterapkan analisis regresi spline dengan estimasi penalized spline yang efektif untuk mengatasi keterbatasan asumsi dalam hubungan antar variabel. Dari hasil analisis, didapatkan nilai optimum berdasarkan GCV minimum untuk masing-masing variabel secara berturut-turut ialah 0.278, 0.904, 0.751, dan 0.665. Diketahui pula bahwa inflasi, kurs, tingkat suku bunga, dan IDJ secara bersama-sama memiliki pengaruh sebesar 92.1% terhadap pergerakan IHSG dengan tingkat inflasi yang memiliki dampak bervariasi, kurs dengan efek negatif yang lebih kuat pada tingkat tertentu, suku bunga dengan efek yang berlawanan tergantung pada tingkatannya, dan IDJ dengan efek positif pada IHSG. Variabilitas signifikan dari dampak ini menunjukkan bahwa variabel-variabel ini memberikan kontribusi penting atau dengan kata lain, fluktuasi IHSG dapat dijelaskan dengan variasi nilai-nilai inflasi, kurs, tingkat suku bunga, dan IDJ.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2024-05-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/32145</dc:identifier>
	<dc:identifier>10.20956/j.v20i3.32145</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 20 No. 3 (2024): May 2024; 497-512</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/32145/11492</dc:relation>
	<dc:relation>10.20956/j.v20i3.32145.g11492</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>