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Sustainable Stock Screening Based on Fundamental and Technical Indicators using Gaussian Naive Bayes Classifier

Jurnal Matematika, Statistika dan Komputasi · Vol. 22 No. 3 (2026) · pp. 587–601

DOI
10.20956/j.v22i3.49168
ISSN
2614-8811, 1858-1382
Published
2026-05-14
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

Investors increasingly require systematic methods for sustainable stock screening, particularly for ESG-focused benchmarks like Inodnesia's SRI-KEHATI Index. This Study addresses a gap by developing and evaluating a stock screening framework using a Gaussian Naive Bayes (GNB) classifier to integrate both fundamental and technical analysis. The model utilized quarterly data from 25 SRI-KEHATI stocks from Q1 2024 to Q2 2025, training on 11 indicators to predict future quarterly returns, classifying stocks as "Investable" (Label 1) or "Non-investable" (Label 0). The model achieved an average training accuracy of 73.6%. Feature importance analysis revealed that technical indicators, such as Average Log Return, Average MACD, Average RSI, and key fundamental ratios, PBV and ROA, were the most influential predictors. Model predictions were evaluated through a simple equal-weighted portfolio simulation for Q3 2025. The simulation results showed the model-selected "Investable" portfolio generated a 29.9% return, substantially outperforming and the "Non-investable" portfolio (3.56%). These findings demonstrate that the GNB classifier is an effective framework for sustainable stock screening, successfully identifying ESG-compliant stocks that also deliver superior financial returns and providing a practical tool for responsible investing in the Indonesian capital market.

Keywords

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10 authors · 27 co-authorship links

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Co-authorOrganisationJoint articles
Shantika MarthaAffiliation unknown2
Wirda AndaniAffiliation unknown2
Evy SulistianingsihAffiliation unknown2
Hendri AgustonoAffiliation unknown2
Rifki PebriyandiAffiliation unknown2
Geraldo OwenAffiliation 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://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Sustainable Stock Screening Based on Fundamental and Technical Indicators using Gaussian Naive Bayes Classifier</dc:title>
	<dc:creator xml:lang="en">Gunawan, Risky</dc:creator>
	<dc:creator xml:lang="en">Maharani, Cinta Priscillia</dc:creator>
	<dc:creator xml:lang="en">Fitriyana, Gita</dc:creator>
	<dc:creator xml:lang="en">Maharani, Dwi Indah</dc:creator>
	<dc:subject xml:lang="en">Screening</dc:subject>
	<dc:subject xml:lang="en">SRI-Kehati</dc:subject>
	<dc:subject xml:lang="en">Portfolio Performance</dc:subject>
	<dc:subject xml:lang="en">Investable Stocks</dc:subject>
	<dc:description xml:lang="en">Investors increasingly require systematic methods for sustainable stock screening, particularly for ESG-focused benchmarks like Inodnesia's SRI-KEHATI Index. This Study addresses a gap by developing and evaluating a stock screening framework using a Gaussian Naive Bayes (GNB) classifier to integrate both fundamental and technical analysis. The model utilized quarterly data from 25 SRI-KEHATI stocks from Q1 2024 to Q2 2025, training on 11 indicators to predict future quarterly returns, classifying stocks as "Investable" (Label 1) or "Non-investable" (Label 0). The model achieved an average training accuracy of 73.6%. Feature importance analysis revealed that technical indicators, such as Average Log Return, Average MACD, Average RSI, and key fundamental ratios, PBV and ROA, were the most influential predictors. Model predictions were evaluated through a simple equal-weighted portfolio simulation for Q3 2025. The simulation results showed the model-selected "Investable" portfolio generated a 29.9% return, substantially outperforming and the "Non-investable" portfolio (3.56%). These findings demonstrate that the GNB classifier is an effective framework for sustainable stock screening, successfully identifying ESG-compliant stocks that also deliver superior financial returns and providing a practical tool for responsible investing in the Indonesian capital market.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2026-05-14</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/49168</dc:identifier>
	<dc:identifier>10.20956/j.v22i3.49168</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 22 No. 3 (2026): May 2026; 587-601</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/49168/14412</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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