پیوندهای مفید
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Designing and explaining the portfolio optimization model using censored models and meta-heuristic algorithm | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 14، دوره 15، شماره 7، مهر 2024، صفحه 135-152 اصل مقاله (683.62 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.30955.4526 | ||
| نویسندگان | ||
| Zeinab Kazemi1؛ Gholamreza Zomorodian* 1؛ Mirfeiz Fallah Shams Layalestani1؛ Farhad Hanifi2 | ||
| 1Department of Financial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| 2Department of Business Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| تاریخ دریافت: 15 اسفند 1401، تاریخ بازنگری: 25 خرداد 1402، تاریخ پذیرش: 28 خرداد 1402 | ||
| چکیده | ||
| One of the important topics discussed in the stock market, which should be considered by both natural and legal investors, is choosing an optimal investment portfolio. In this regard, investors are studied in order to select the best portfolio based on risk and return. However, traditional investment methods do not focus on portfolio optimization and only consider the highest return and lowest risk. This research addresses the gap in solving the problem of wide portfolio optimization by comparing answers using more effective and efficient metaheuristic optimization algorithms, thus reducing the probability of error. During this research, metaheuristic optimization methods are well-designed and studied, and then used to optimize the portfolio despite real market limitations. The developed algorithms are all implemented to solve the extended portfolio optimization problem. In this research, more effective and efficient metaheuristic optimization algorithms are used to solve the problem of wide portfolio optimization and by comparing the answers, the probability of error can be almost zero. The stock portfolios formed by the model based on censoring models have more returns and less risk (variance) than the invasive weed algorithm, showing the superiority of the proposed model in comparison to the invasive weed algorithm. The findings of the research have filled the research gap in investment portfolio valuation and demonstrate that the proposed model has effectively considered investment portfolio selection conditions and determined an optimal investment portfolio. | ||
| کلیدواژه‌ها | ||
| investment portfolio؛ portfolio optimization؛ censorship models؛ invasive weed algorithm | ||
| مراجع | ||
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[1] R. Aghamohammadi, R. Tehrani, and M. Khademi, Investigating the effect of study period selection on solving portfolio optimization based on different risk criteria using meta-heuristic algorithms, J. Financ. Manag. Persp. 12 (2022), no. 37. [2] M.A. Akbay, C.B. Kalayci, and O. Polat, A parallel variable neighborhood search algorithm with quadratic programming for cardinality constrained portfolio optimization, Knowledge-Based Syst. 198 (2020), 105944. [3] O. Ertenlice and C.B. Kalayci, A survey of swarm intelligence for portfolio optimization: Algorithms and applications, Swarm Evol. Comput. 39 (2018), 36–52. [4] N. Grishina, C.A. Lucas, and P. Date, Prospect theory–based portfolio optimization: An empirical study and analysis using intelligent algorithms, Quant. Finance 17 (2017), no. 3, 353–367. [5] C. John, High speed hill climbing algorithm for portfolio optimization, Tanzania J. Sci. 47 (2021), no. 3, 1236–1242. [6] C.B. Kalayci, O. Ertenlice, and M.A. Akbay, A comprehensive review of deterministic models and applications for mean-variance portfolio optimization, Expert Syst. Appl. 125 (2019), 345–368. [7] C.B. Kalaycı, O. Ertenlice, H. Akyer, and H. Aygoren, A review on the current applications of genetic algorithms in mean-variance portfolio optimization, Pamukkale Univ. J. Eng. Sci. 23 (2017), 470–476. [8] A.T. Khan, X. Cao, S. Li, B. Hu, and V.N. Katsikis, Quantum beetle antennae search: A novel technique for the constrained portfolio optimization problem, Sci. China Info. Sci. 64 (2021), no. 5, 1–14. [9] Y. Kim, D. Kang, M. Jeon, and C. Lee, GAN-MP hybrid heuristic algorithm for non-convex portfolio optimization problem, Eng. Econ. 64 (2019), no. 3, 196–226. [10] H.M. Markowitz, Foundations of portfolio theory, J. Finance 46 (1991), no. 2, 469–477. [11] S.S. Meghwani and M. Thakur, Multi-objective heuristic algorithms for practical portfolio optimization and rebalancing with transaction cost, Appl. Soft Comput. 67 (2018), 865–894. [12] B.A. Mercangoz and E. Eroglu, The genetic algorithm: an application on portfolio optimization, Research anthology on multi-industry uses of genetic programming and algorithms, IGI Global, 2021, pp. 790–810. [13] D.A. Milhomem and M.J.P. Dantas, Analysis of new approaches used in portfolio optimization: A systematic literature review, Production 30 (2020). [14] M.J. Naik and A.L. Albuquerque, Hybrid optimization search-based ensemble model for portfolio optimization and return prediction in business investment, Prog. Artif. Intell. 11 (2022), no. 4, 315–331. [15] M. Rahmani, M. Khalili Eraqi, and H. Nikoomaram, Portfolio optimization by means of meta heuristic algorithms, Adv. Math. Financ. Appl. 4 (2019), no. 4, 83–97. [16] I.B. Salehpoor and S. Molla-Alizadeh-Zavardehi, A constrained portfolio selection model at considering risk-adjusted measure by using hybrid meta-heuristic algorithms, Appl. Soft Comput. 75 (2019), 233–253. [17] E.P. Setiawan, Comparing bio-inspired heuristic algorithm for the mean-CVaR portfolio optimization, J. Phys.: Conf. Ser. IOP Pub. 1581 (2020), no. 1, 012014. [18] J. Wang, A novel firefly algorithm for portfolio optimization problem, IAENG Int. J. Appl. Math. 49 (2019), no. 1, 1–6. [19] Z. Wang, X. Zhang, Z. Zhang, and D. Sheng, Credit portfolio optimization: A multi-objective genetic algorithm approach, Borsa Istanbul Rev. 22 (2022), no. 1, 69–76. [20] M. Zanjirdar, Overview of portfolio optimization models, Adv. Math. Financ. Appl. 5 (2020), no. 4, 419–435. [21] W. Zhou, W. Zhu, Y. Chen, and J. Chen, Dynamic changes and multi-dimensional evolution of portfolio optimization, Econ. Res. Ekon. Istraz. 35 (2021), no. 1, 1–26. | ||
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