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Iranian Journal of Numerical Analysis and Optimization، جلد ۱۵، شماره Issue ۲، صفحات ۶۰۰-۶۲۴
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Multi-objective portfolio optimization using real coded genetic algorithm based support vector machines |
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| چکیده انگلیسی مقاله |
Investors need to grasp how liquidity affects both risk and return in order to optimize their portfolio performance. There are three classes of stocks that accommodate those criteria: Liquid, high-yield, and less-risky. Classifying stocks help investors build portfolios that align with their risk profiles and investment goals, in which the model was constructed using the one-versus-one support vector machines method with a radial basis function kernel. This model was trained using a combination of the Kompas100 index and the Indonesian industrial sectors stocks data. Single optimal portfolios were created using the real coded genetic algorithm based on different sets of objectives: Maximizing short-term and long-term returns, maximizing liquidity, and minimizing risk. In conclusion, portfolios with a balance on all these four investment objectives yielded better results compared to those focused on partial objectives. Furthermore, our proposed method for selecting portfolios of top-performing stocks across all criteria outperformed the approach of choosing top stocks based on a single criterion. |
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| کلیدواژه‌های انگلیسی مقاله |
Genetic algorithm,Liquidity,Multi-Objective Optimization,One-versus-one support vector machines,Radial basis functions |
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| نویسندگان مقاله |
B. Surja |
Center for Mathematics and Society, Department of Mathematics, Faculty of Science, Parahyangan Catholic University, Bandung, Indonesia.
L. Chin |
Center for Mathematics and Society, Department of Mathematics, Faculty of Science,
Parahyangan Catholic University, Bandung, Indonesia.
F. Kusnadi |
Center for Mathematics and Society, Department of Mathematics, Faculty of Science,
Parahyangan Catholic University, Bandung, Indonesia.
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| نشانی اینترنتی |
https://ijnao.um.ac.ir/article_46386_f4520351f87da448b113e86daa535120.pdf |
| فایل مقاله |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
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