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COMPARING GAUSSIAN AND EPANECHNIKOV KERNEL OF NONPARAMETRIC REGRESSION IN FORECASTING ISSI (INDONESIA SHARIA STOCK INDEX)

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 1 (2022) · pp. 323–332

DOI
10.30598/barekengvol16iss1pp321-330
ISSN
2615-3017, 1978-7227
Published
2022-03-21
Publisher
PATTIMURA UNIVERSITY
Language
eng

Abstract

ISSI reflects the movement of sharia stock prices as a whole. It is necessary to forecast the share price to help investors determine whether the shares should be sold, bought, or retained. This study aims to predict the value of ISSI using nonparametric kernel regression. The kernel regression method is one of the nonparametric regression methods used to estimate conditional expectations using kernel functions. Kernel functions used in this study are gaussian and Epanechnikov kernel functions. The estimator used is the estimator Nadaraya-Watson. This study aims to compare the two kernel functions to predict the value of ISSI in the period from January 2016 to October 2019. The analysis results obtained the best method in predicting ISSI values, namely nonparametric kernel regression using Nadaraya-Watson estimator and Gaussian kernel function with the MAPE value of 15% and the coefficient of determination of 85%. Independent variables that significantly affect ISSI are interest rates, exchange rates, and inflation. Curve smoothing is done using bandwidth value (h) searched by the Silverman rule. The calculation result with the Silverman rule obtained a bandwidth value of 101832.7431.

Collaboration network of the authors

37 authors · 92 co-authorship links · 1 across different organisations

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  • UIN Sunan Ampel Surabaya
  • Politeknik Elektronika Negeri Surabaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Moh. HafiyusholehUIN Sunan Ampel Surabaya4
Achmad Teguh WibowoUIN Sunan Ampel Surabaya3
Hani KhaulasariUIN Sunan Ampel Surabaya3
Silvia Kartika SariUIN Sunan Ampel Surabaya2
Latifatun Nadya DesinainiUIN Sunan Ampel Surabaya2
Putroue Keumala IntanUIN Sunan Ampel Surabaya2
Aris FananiUIN Sunan Ampel Surabaya2
Luluk WulandariUIN Sunan Ampel Surabaya2
Nanida Jenahara ZaenUIN Sunan Ampel Surabaya2
Ghina Salsabila FirdausUIN Sunan Ampel Surabaya1
Ahmad Teguh WibowoAffiliation unknown1
Dian Candra Rini NovitasariUIN Sunan Ampel Surabaya1
Diah Devi PramestiUIN Sunan Ampel Surabaya1
Halimatus Sa’dyahPoliteknik Elektronika Negeri Surabaya1
Nilna AlmumtazahUIN Sunan Ampel Surabaya1
Muhammad Sahrul KiromiUIN Sunan Ampel Surabaya1
Husna Nur LailiUIN Sunan Ampel Surabaya1
Dian YuliatiUIN Sunan Ampel Surabaya1
Suyesti YusiUIN Sunan Ampel Surabaya1
Mayandah FarmitaUIN Sunan Ampel Surabaya1
Wika Dianita UtamiUIN Sunan Ampel Surabaya1
Shafa Fitria Aqilah KhansaUIN Sunan Ampel Surabaya1
Fery FirmansyahAffiliation unknown1
Yahya Vigo Tri SaputraUIN Sunan Ampel Surabaya1
Yanuwar ReinaldiUIN Sunan Ampel Surabaya1
Hani KhaulasariAffiliation unknown1
Yayan Luthfi KhoirinaAffiliation unknown1
Ery Fuji RisnawatiAffiliation unknown1
Siti AisyahAffiliation unknown1
Abdulloh HamidAffiliation unknown1
Lutfi HakimUIN Sunan Ampel Surabaya1
Elisa SyafaqohAffiliation unknown1
Jiphie Gilia IndriyaniAffiliation unknown1
Firda Yunita SariAffiliation unknown1

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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">COMPARING GAUSSIAN AND EPANECHNIKOV KERNEL OF NONPARAMETRIC REGRESSION IN FORECASTING ISSI (INDONESIA SHARIA STOCK INDEX)</dc:title>
	<dc:creator>Farida, Yuniar</dc:creator>
	<dc:creator>Purwanti, Ida</dc:creator>
	<dc:creator>Ulinnuha, Nurissaidah</dc:creator>
	<dc:description xml:lang="en-US">ISSI reflects the movement of sharia stock prices as a whole. It is necessary to forecast the share price to help investors determine whether the shares should be sold, bought, or retained. This study aims to predict the value of ISSI using nonparametric kernel regression. The kernel regression method is one of the nonparametric regression methods used to estimate conditional expectations using kernel functions. Kernel functions used in this study are gaussian and Epanechnikov kernel functions. The estimator used is the estimator Nadaraya-Watson. This study aims to compare the two kernel functions to predict the value of ISSI in the period from January 2016 to October 2019. The analysis results obtained the best method in predicting ISSI values, namely nonparametric kernel regression using Nadaraya-Watson estimator and Gaussian kernel function with the MAPE value of 15% and the coefficient of determination of 85%. Independent variables that significantly affect ISSI are interest rates, exchange rates, and inflation. Curve smoothing is done using bandwidth value (h) searched by the Silverman rule. The calculation result with the Silverman rule obtained a bandwidth value of 101832.7431.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2022-03-21</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/5127</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss1pp321-330</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan; 323-332</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol16iss1year2022</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/5127/4034</dc:relation>
</oai_dc:dc>