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Predicting Price and Risk ICBP Stocks Using GRU and VaR

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 1 (2025) · pp. 73–84

DOI
10.22146/ijccs.101974
ISSN
2460-7258, 1978-1520
Published
2025-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The economy plays a vital role in maintaining a country’s stability and progress, where stock investments serve as a primary financial instrument to enhance societal welfare. In Indonesia, interest in stock investments, especially in the essential food sector, continues to grow due to its long-term profit potential. This study combines stock price prediction with risk analysis using a Gated Recurrent Unit (GRU) model and Value at Risk (VaR) calculation based on historical simulation. The GRU model is selected for stock price prediction due to its ability to capture complex, fluctuating patterns and adapt to market changes, while VaR is used to measure potential maximum loss at a 95% confidence level. The findings indicate a potential loss of IDR 65.785, demonstrating that this approach can provide a risk estimate by combining future predicted prices with historical data. Thus, this approach offers guidance for investors in understanding potential profits and risks in stock assets. The integration of GRU-based predictions and historical simulation VaR is expected to support more informative and prudent investment decision-making, particularly in facing the dynamic and risky stock market conditions.

Keywords

Collaboration network of the authors

3 authors · 3 co-authorship links

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  • University of Pembangunan Nasional Veteran Jawa Timur
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  • Same organisation
  • Affiliation unknown
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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 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Predicting Price and Risk ICBP Stocks Using GRU and VaR</dc:title>
	<dc:creator>Ryan Dana, Alvin</dc:creator>
	<dc:creator>Trimono, Trimono</dc:creator>
	<dc:creator>Idhom, Mohammad</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Stock price prediction; Risk analytic; Blue chip stock; Gated Recurrent Unit (GRU); Value at Risk (VaR)</dc:subject>
	<dc:description xml:lang="en-US">The economy plays a vital role in maintaining a country’s stability and progress, where stock investments serve as a primary financial instrument to enhance societal welfare. In Indonesia, interest in stock investments, especially in the essential food sector, continues to grow due to its long-term profit potential. This study combines stock price prediction with risk analysis using a Gated Recurrent Unit (GRU) model and Value at Risk (VaR) calculation based on historical simulation. The GRU model is selected for stock price prediction due to its ability to capture complex, fluctuating patterns and adapt to market changes, while VaR is used to measure potential maximum loss at a 95% confidence level. The findings indicate a potential loss of IDR 65.785, demonstrating that this approach can provide a risk estimate by combining future predicted prices with historical data. Thus, this approach offers guidance for investors in understanding potential profits and risks in stock assets. The integration of GRU-based predictions and historical simulation VaR is expected to support more informative and prudent investment decision-making, particularly in facing the dynamic and risky stock market conditions.</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>2025-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/101974</dc:identifier>
	<dc:identifier>10.22146/ijccs.101974</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 1 (2025): January; 73-84</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/101974/40997</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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