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Deep Learning Factor Investing in the Indonesian Stock Market

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 4 (2025) · pp. 451–460

DOI
10.22146/ijccs.112549
ISSN
2460-7258, 1978-1520
Published
2025-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Traditional linear factor models often fail to capture the complex, non-linear dynamics of emerging stock markets. This research designs and validates a novel Recurrence Plot (RP) matrices with β-VAE deep learning methodology to discover non-linear investment factors within the Indonesian context. We demonstrate that this framework is a systematically superior "factor factory" compared to a linear RP with PCA baseline, discovering twice as many high-quality factors (Sharpe > 0.3) and generating 7-fold more alpha on average. A key finding is the model's ability to disentangle high-frequency predictive signals (identified by SHAP) from more valuable, low-frequency profitable trends (validated by backtesting). The champion factor from this process yields a robust annualized alpha of 6.65% with a minimal max drawdown of -7.73% from 2018 to 2025. This study concludes that the RP -> β -VAE approach is a robust and resilient framework for discovering safer, non-linear sources of return unexplained by conventional models.

Keywords

Collaboration network of the authors

7 authors · 16 co-authorship links

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  • Universitas Dian Nuswantoro
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
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Co-authorOrganisationJoint articles
Defri KurniawanUniversitas Dian Nuswantoro1

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.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Deep Learning Factor Investing in the Indonesian Stock Market</dc:title>
	<dc:creator>Atha Rohmatullah, Fawwaz</dc:creator>
	<dc:creator>Alzami, Farrikh</dc:creator>
	<dc:creator>Rakhmat Sani, Ramadhan</dc:creator>
	<dc:creator>Novita Dewi, Ika</dc:creator>
	<dc:creator>Winarno, Sri</dc:creator>
	<dc:creator>Sulistyono, Teguh</dc:creator>
	<dc:subject xml:lang="en-US">factor investing; deep learning; asset pricing; Indonesian capital market; β-VAE; Recurrence Plot</dc:subject>
	<dc:subject xml:lang="en-US">factor investing; deep learning; asset pricing; Indonesian capital market; β-VAE; Recurrence Plot; non-linear dynamics; alpha generation; emerging markets</dc:subject>
	<dc:description xml:lang="en-US">Traditional linear factor models often fail to capture the complex, non-linear dynamics of emerging stock markets. This research designs and validates a novel Recurrence Plot (RP) matrices with β-VAE deep learning methodology to discover non-linear investment factors within the Indonesian context. We demonstrate that this framework is a systematically superior "factor factory" compared to a linear RP with PCA baseline, discovering twice as many high-quality factors (Sharpe &gt; 0.3) and generating 7-fold more alpha on average. A key finding is the model's ability to disentangle high-frequency predictive signals (identified by SHAP) from more valuable, low-frequency profitable trends (validated by backtesting). The champion factor from this process yields a robust annualized alpha of 6.65% with a minimal max drawdown of -7.73% from 2018 to 2025. This study concludes that the RP -&gt; β -VAE approach is a robust and resilient framework for discovering safer, non-linear sources of return unexplained by conventional models.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/112549</dc:identifier>
	<dc:identifier>10.22146/ijccs.112549</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 4 (2025): October; 451-460</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/112549/42795</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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