Multi-period and multi-objective stock portfolio modeling considering cone constraints
Nowadays, one of the major concerns of investors is choosing a realistic stock portfolio and making proper decisions according to an individual's utility level. It is essential to consider two conflicting goals of return and risk for profitability; as a result, balancing the above goals has bee...
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| Format: | Article |
| Language: | English |
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Ayandegan Institute of Higher Education,
2025-03-01
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| Series: | International Journal of Research in Industrial Engineering |
| Subjects: | |
| Online Access: | https://www.riejournal.com/article_202215_ee83dc829f577f994dd20dbbe84dad48.pdf |
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| author | Masoomeh Zeinalnezhad Zohreh Ebrahimi Towhid Pourrostam |
| author_facet | Masoomeh Zeinalnezhad Zohreh Ebrahimi Towhid Pourrostam |
| author_sort | Masoomeh Zeinalnezhad |
| collection | DOAJ |
| description | Nowadays, one of the major concerns of investors is choosing a realistic stock portfolio and making proper decisions according to an individual's utility level. It is essential to consider two conflicting goals of return and risk for profitability; as a result, balancing the above goals has been identified as an investment concern. This paper modifies and optimizes a multi-objective and multi-period stock portfolio considering cone constraints and uncertain and stochastic discrete decisions. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to solve the model due to the issue's complexity. Two objective functions in the model could be explained by maximizing expected returns and minimizing investment risk. The Pareto chart of the problem was drawn, which allows investors to make decisions based on various levels of risk. Another result obtained in this study is calculating the percentage of optimal amounts assigned to each asset, providing a base for investors to avert investing in unsuitable assets and incurring losses. Finally, a sensitivity analysis of essential parameters was performed, which is critical in this issue. According to the results, increasing the number of problem constraints provides a base for the model reaction, and the optimal percentage allocated to each asset varies. Therefore, this prioritizes restrictions in different situations and according to the investors' choice. |
| format | Article |
| id | doaj-art-7882d36633f34aaabca669aa603944be |
| institution | Kabale University |
| issn | 2783-1337 2717-2937 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Ayandegan Institute of Higher Education, |
| record_format | Article |
| series | International Journal of Research in Industrial Engineering |
| spelling | doaj-art-7882d36633f34aaabca669aa603944be2025-01-30T15:10:51ZengAyandegan Institute of Higher Education,International Journal of Research in Industrial Engineering2783-13372717-29372025-03-0114112010.22105/riej.2024.455343.1441202215Multi-period and multi-objective stock portfolio modeling considering cone constraintsMasoomeh Zeinalnezhad0Zohreh Ebrahimi1Towhid Pourrostam2Department of Industrial Engineering, Engineering and Technical Faculty, West Tehran Branch, Islamic Azad University, Tehran, Iran.Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.Nowadays, one of the major concerns of investors is choosing a realistic stock portfolio and making proper decisions according to an individual's utility level. It is essential to consider two conflicting goals of return and risk for profitability; as a result, balancing the above goals has been identified as an investment concern. This paper modifies and optimizes a multi-objective and multi-period stock portfolio considering cone constraints and uncertain and stochastic discrete decisions. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to solve the model due to the issue's complexity. Two objective functions in the model could be explained by maximizing expected returns and minimizing investment risk. The Pareto chart of the problem was drawn, which allows investors to make decisions based on various levels of risk. Another result obtained in this study is calculating the percentage of optimal amounts assigned to each asset, providing a base for investors to avert investing in unsuitable assets and incurring losses. Finally, a sensitivity analysis of essential parameters was performed, which is critical in this issue. According to the results, increasing the number of problem constraints provides a base for the model reaction, and the optimal percentage allocated to each asset varies. Therefore, this prioritizes restrictions in different situations and according to the investors' choice.https://www.riejournal.com/article_202215_ee83dc829f577f994dd20dbbe84dad48.pdfgenetic algorithmoptimizationstock portfoliocone constraintsmulti-objective modelingdiscrete decisions |
| spellingShingle | Masoomeh Zeinalnezhad Zohreh Ebrahimi Towhid Pourrostam Multi-period and multi-objective stock portfolio modeling considering cone constraints International Journal of Research in Industrial Engineering genetic algorithm optimization stock portfolio cone constraints multi-objective modeling discrete decisions |
| title | Multi-period and multi-objective stock portfolio modeling considering cone constraints |
| title_full | Multi-period and multi-objective stock portfolio modeling considering cone constraints |
| title_fullStr | Multi-period and multi-objective stock portfolio modeling considering cone constraints |
| title_full_unstemmed | Multi-period and multi-objective stock portfolio modeling considering cone constraints |
| title_short | Multi-period and multi-objective stock portfolio modeling considering cone constraints |
| title_sort | multi period and multi objective stock portfolio modeling considering cone constraints |
| topic | genetic algorithm optimization stock portfolio cone constraints multi-objective modeling discrete decisions |
| url | https://www.riejournal.com/article_202215_ee83dc829f577f994dd20dbbe84dad48.pdf |
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