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An Optimal Stock Market Portfolio Proportion Model Using Genetic Algorithm

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 12 No. 2 (2018) · pp. 171–180

DOI
10.22146/ijccs.36154
ISSN
2460-7258, 1978-1520
Published
2018-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

To reduce the amount of loss due to investment risk, an investor or stockbroker usually forms an optimal stock portfolio. This technique is done to get the maximum return of investment on shares to be purchased. However, in forming a stock portfolio required a fairly complex calculations and certain skills. This work aims to provide an alternative solution in the problem of forming the optimal and efficient stock portfolio composition by designing a system that can help decision making of investors or stockbrokers in preparing stock portfolio in accordance with the policy and risk investment. In this work, determination of optimal stock portfolio composition is constructed by using Genetic Algorithm. The data used in this work are the 4 selected stocks listed on the LQ45 index in 2017. Meanwhile, the calculation of profit and loss rate utilizes a single index model theory. The efficiency of the algorithm has been examined against the population size and crossover and mutation probabilities. The experimental results show that the proposed algorithm can be used as one of solutions to select the optimal stock portfolio.

Keywords

Collaboration network of the authors

40 authors · 65 co-authorship links · 5 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.

  • Universitas Gadjah Mada
  • University of Ulsan
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Reny Eka SariAffiliation unknown2
Agfianto Eko PutraUniversitas Gadjah Mada2
Joko HariyonoAffiliation unknown1
Agus HarjokoUniversitas Gadjah Mada1
Abdul KholikUniversitas Gadjah Mada1
Suprapto SupraptoUniversitas Gadjah Mada1
Yaqutina Marjani SantosaUniversitas Gadjah Mada1
Nur RokhmanUniversitas Gadjah Mada1
Riska Amalia PraptiwiUniversitas Gadjah Mada1
Aliyyah Nur AzhariUniversitas Gadjah Mada1
Cholis MahardhikaAffiliation unknown1
Aida Roihana ZuhroAffiliation unknown1
Amin YusufAffiliation unknown1
Yanuarius Seran FahikAffiliation unknown1
Tri SuminarAffiliation unknown1
Aninda Mawar IntanuariAffiliation unknown1
Siti NurnaningsihAffiliation unknown1
Laela AtikahAffiliation unknown1
Dian ArifianiAffiliation unknown1
Shella PrahastiAffiliation unknown1
Ayu ErawatiAffiliation unknown1
Elizah ElizahAffiliation unknown1
Nuratun ZakiyahAffiliation unknown1
Falilah FalilahAffiliation unknown1
S MartonoAffiliation unknown1
Ariesa PrihartantiAffiliation unknown1
Rusdarti RusdartiAffiliation unknown1
Indah MerakatiAffiliation unknown1
Joko WidodoAffiliation unknown1
Ika Maratus SholekhahAffiliation unknown1
Sucihatiningsih Dian Wisika PrajantiAffiliation unknown1
Mugiono MugionoAffiliation unknown1
Nanda Bella Fidanty ShahnazAffiliation unknown1
Rony Karunia RachmanAffiliation 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.ugm.ac.id/ijccs/oai, first harvested 26 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">An Optimal Stock Market Portfolio Proportion Model Using Genetic Algorithm</dc:title>
	<dc:creator>Wahyono, Wahyono</dc:creator>
	<dc:creator>Puspitasari, Chasandra</dc:creator>
	<dc:creator>Fauzi, Muhammad Dzulfikar</dc:creator>
	<dc:creator>Kasliono, Kasliono</dc:creator>
	<dc:creator>Mulyani, Wahyu Sri</dc:creator>
	<dc:creator>Kurnianggoro, Laksono</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">genetic algorithm; LQ45 index; stock market portfolio; single index model</dc:subject>
	<dc:description xml:lang="en-US">To reduce the amount of loss due to investment risk, an investor or stockbroker usually forms an optimal stock portfolio. This technique is done to get the maximum return of investment on shares to be purchased. However, in forming a stock portfolio required a fairly complex calculations and certain skills. This work aims to provide an alternative solution in the problem of forming the optimal and efficient stock portfolio composition by designing a system that can help decision making of investors or stockbrokers in preparing stock portfolio in accordance with the policy and risk investment. In this work, determination of optimal stock portfolio composition is constructed by using Genetic Algorithm. The data used in this work are the 4 selected stocks listed on the LQ45 index in 2017. Meanwhile, the calculation of profit and loss rate utilizes a single index model theory. The efficiency of the algorithm has been examined against the population size and crossover and mutation probabilities. The experimental results show that the proposed algorithm can be used as one of solutions to select the optimal stock portfolio.</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>2018-07-31</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/36154</dc:identifier>
	<dc:identifier>10.22146/ijccs.36154</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 12, No 2 (2018): July; 171-180</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/36154/21764</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 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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