Combining the process of fuzzy hierarchical analysis process and multi-objective fuzzy ideal planning in order to form a stock portfolio
Subject Areas :
Hamidreza Rezaei
1
,
Abdollah Hadi-Vencheh
2
,
Hosein Arman
3
,
Mehrdad Nikbakht
4
1 - PhD Student, Department of Management, Na.C., Islamic Azad University, Najafabad, Iran.
2 - Full Professor, Department of Mathematics, Is.C., Islamic Azad University, Isfahan, Iran (Corresponding Author).
3 - Assistant Professor, Department of Management, Mo.C., Islamic Azad University, Mobarakeh, Isfahan, Iran
4 - Assistant Professor, Department of Industrial Engineering, Na.C., Islamic Azad University, Najafabad, Iran.
Keywords: stock portfolio, fuzzy hierarchical analysis process, fuzzy multi-objective programming, fuzzy sets,
Abstract :
Objective: The purpose of this research is to present a hybrid model for optimizing stock portfolios under conditions of uncertainty by integrating expert opinions and fuzzy multi-objective decision-making techniques.
Research Methodology: In this hybrid approach, six key indicators were first weighted using the Fuzzy Analytic Hierarchy Process (FAHP). Then, a multi-objective fuzzy goal programming model was designed and solved to determine the optimal share of each company in the portfolio.
Findings: From the experts’ perspective, P/E ratio, beta coefficient, and liquidity rank were the most important criteria. The model’s results showed that only two of the six companies studied should participate in the portfolio to maximize satisfaction.
Originality/Scientific Value Added: The novelty of this research lies in providing a practical model that, by combining qualitative expert opinions and mathematical optimization under fuzzy conditions, offers a transparent and scientific solution to the complex problem of portfolio selection.
Abolmakarem, S., Abdi, F., Khalili-Damghani, K., & Didehkhani, H. (2024). Futuristic portfolio optimization problem: wavelet based long short-term memory. Journal of Modelling in Management, 19(2), 523–555. https://doi.org/10.1108/JM2-09-2022-0232
Akbay, M. A., Kalayci, C. B., & Polat, O. (2020). A parallel variable neighborhood search algorithm with quadratic programming for cardinality constrained portfolio optimization. Knowledge-Based Systems, 198, 105944. https://doi.org/10.1016/j.knosys.2020.105944
Alexander, G. J., Baptista, A. M., & Yan, S. (2017). Portfolio selection with mental accounts and estimation risk. Journal of Empirical Finance, 41, 161–186. https://doi.org/10.1016/j.jempfin.2016.07.012
Amiri, M., Vanani, I. R., Hossein, S., & Agha, R. H. (2021). Development of Fuzzy Artificial Intelligence and Multi-Objective Planning Model to Optimize the Portfolio of Investment Companies. Journal Of Business Intelligence Management Studies, 9(36), 243–302. https://www.sid.ir/fileserver/jf/4040214003608.pdf.(In Persian).
Arman, H., Hadi‐Vencheh, A., Arman, A., & Moslehi, A. (2021). Revisiting the approximated weight extraction methods in fuzzy analytic hierarchy process. International Journal of Intelligent Systems, 36(4), 1644–1667. https://doi.org/10.1002/int.22355
Barbati, M., Figueira, J. R., Greco, S., Ishizaka, A., & Panaro, S. (2023). A multiple criteria methodology for priority based portfolio selection. Socio-Economic Planning Sciences, 88, 101595. https://doi.org/10.1016/j.seps.2023.101595
Behera, J., Pasayat, A. K., Behera, H., & Kumar, P. (2023). Prediction based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets. Engineering Applications of Artificial Intelligence, 120, 105843. https://doi.org/10.1016/j.engappai.2023.105843
Chen, Y., Zhao, X., & Hao, J. (2023). A novel MOPSO-SODE algorithm for solving three-objective SR-ES-TR portfolio optimization problem. Expert Systems with Applications, 233, 120742. https://doi.org/10.1016/j.eswa.2023.120742
De, M., Mangaraj, B. K., & Das, K. B. (2018). A fuzzy goal programming model in portfolio selection under competitive-cum-compensatory decision strategies. Applied Soft Computing, 73, 635–646. https://doi.org/10.1016/j.asoc.2018.09.006
Deep, K., Singh, K. P., Kansal, M. L., & Mohan, C. (2009). A fuzzy interactive approach for optimal portfolio management. Opsearch, 46, 69–88. https://doi.org/10.1007/s12597-009-0005-2
Deng, X., He, X., & Huang, C. (2021). A new fuzzy random multi-objective portfolio model with different entropy measures using fuzzy programming based on artificial bee colony algorithm. Engineering Computations, 39(2), 627–649. https://doi.org/10.1108/EC-11-2020-0654
Deng, X., & Pan, X. (2018). The research and comparison of multi-objective portfolio based on intuitionistic fuzzy optimization. Computers & Industrial Engineering, 124, 411–421. https://doi.org/10.1016/j.cie.2018.07.044
Didekhani, H., & Hojatiostani, S. (2017). Presenting a multi-objective planning model for stock selection considering the fuzzy value at risk, fuzzy credit theory approach. Financial Engineering and Portfolio Management, 32(8), 239–268. https://journals.iau.ir/article_533504.html (In Persian).
Farid, D., Dehghani Firizabadi, A., Andalib Ardakani, D., & Mirzaei, H. (2021). Analysis of the factors affecting the selection of the stock portfolio using the logarithmic fuzzy preference planning approach. Farda Management Journal, 66. https://www.sid.ir/paper/1039228/fa (In Persian).
Fu, T.-C., Chung, C.-P., & Chung, F.-L. (2013). Adopting genetic algorithms for technical analysis and portfolio management. Computers & Mathematics with Applications, 66(10), 1743–1757. https://doi.org/10.1016/j.camwa.2013.08.012
Guo, S., Yu, L., Li, X., & Kar, S. (2016). Fuzzy multi-period portfolio selection with different investment horizons. European Journal of Operational Research, 254(3), 1026–1035. https://doi.org/10.1016/j.ejor.2016.04.055
Gupta, P., Mehlawat, M. K., Yadav, S., & Kumar, A. (2019). A polynomial goal programming approach for intuitionistic fuzzy portfolio optimization using entropy and higher moments. Applied Soft Computing, 85, 105781. https://doi.org/10.1016/j.asoc.2019.105781
Jiang, M., Liu, W., Xu, W., & Chen, W. (2021). Improved multiobjective bat algorithm for the credibilistic multiperiod mean-VaR portfolio optimization problem. Soft Computing, 25, 6445–6467. https://doi.org/10.1007/s00500-021-05638-z
Khanjani Shiraz, R., Tavana, M., & Fukuyama, H. (2020). A random-fuzzy portfolio selection DEA model using value-at-risk and conditional value-at-risk. Soft Computing, 24, 17167–17186. https://doi.org/10.1007/s00500-020-05010-7
Koratamaddi, P., Wadhwani, K., Gupta, M., & Sanjeevi, S. G. (2021). Market sentiment-aware deep reinforcement learning approach for stock portfolio allocation. Engineering Science and Technology, an International Journal, 24(4), 848–859. https://doi.org/10.1016/j.jestch.2021.01.007
Li, Y., Wang, B., Fu, A., & Watada, J. (2020). Fuzzy portfolio optimization for time-inconsistent investors: a multi-objective dynamic approach. Soft Computing, 24, 9927–9941. https://doi.org/10.1007/s00500-019-04504-3
Liesiö, J., Salo, A., Keisler, J. M., & Morton, A. (2021). Portfolio decision analysis: Recent developments and future prospects. European Journal of Operational Research, 293(3), 811–825. https://doi.org/10.1016/j.ejor.2020.12.015
Lin, C.-C. (2004). A weighted max–min model for fuzzy goal programming. Fuzzy Sets and Systems, 142(3), 407–420. https://doi.org/10.1016/S0165-0114(03)00092-7
Liu, Y.-J., & Zhang, W.-G. (2021). Fuzzy multi-period portfolio selection model with time-varying loss aversion. Journal of the Operational Research Society, 72(4), 935–949. https://doi.org/10.1080/01605682.2019.1705191
Ma, X., Chen, J., Sun, Y., & Zhu, Z. (2021). Assistant reference point guided evolutionary algorithm for many-objective fuzzy portfolio selection. Swarm and Evolutionary Computation, 62, 100862. https://doi.org/10.1016/j.swevo.2021.100862
Markowitz, H. M. (1999). The early history of portfolio theory: 1600–1960. Financial Analysts Journal, 55(4), 5–16. https://doi.org/10.2469/faj.v55.n4.2281 Mehlawat, M. K., Kumar, A., Yadav, S., & Chen, W. (2018). Data envelopment analysis based fuzzy multi-objective portfolio selection model involving higher moments. Information Sciences, 460, 128–150. https://doi.org/10.1016/j.ins.2018.05.043
Nayebpur, H., & Nazem Bokaei, M. (2017). Portfolio selection with fuzzy synthetic evaluation and genetic algorithm. Engineering Computations, 34(7), 2422–2434. https://doi.org/10.1108/EC-03-2017-0084
Puerto, J., Rodríguez-Madrena, M., & Scozzari, A. (2020). Clustering and portfolio selection problems: A unified framework. Computers & Operations Research, 117, 104891. https://doi.org/10.1016/j.cor.2020.104891
Tsaur, R.-C., Chiu, C.-L., & Huang, Y.-Y. (2021). Guaranteed rate of return for excess investment in a fuzzy portfolio analysis. International Journal of Fuzzy Systems, 23, 94–106. https://doi.org/10.1007/s40815-020-00990-y
Wang, B., Li, Y., & Watada, J. (2017). Multi-period portfolio selection with dynamic risk/expected-return level under fuzzy random uncertainty. Information Sciences, 385, 1–18. https://doi.org/10.1016/j.ins.2016.12.033
Wang, Y., & Aste, T. (2023). Dynamic portfolio optimization with inverse covariance clustering. Expert Systems with Applications, 213, 118739. https://doi.org/10.1016/j.eswa.2022.118739
Witkowska, D., Kompa, K., & Staszak, M. (2021). Indicators for the efficient portfolio construction. The case of Poland. Procedia Computer Science, 192, 2022–2031. https://doi.org/10.1016/j.procs.2021.08.208
Xiao, H., Ren, T., & Ren, T. (2020). Estimation of fuzzy portfolio efficiency via an improved DEA approach. INFOR: Information Systems and Operational Research, 58(3), 478–510. https://doi.org/10.1080/03155986.2020.1734904
Yurtsal, A., Karaömer, Y., & Benzer, A. İ. (2022). A Swarm Intelligence Optimization Algorithm for Cryptocurrency Portfolio Optimization. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 10(1), 347–363. https://doi.org/10.18506/anemon.975505
Zare Mehrjerdi, Y., Shahmohammadi, M., & emami Maibodi, L. (2013). A Hybrid Intelligent Algorithm for Portfolio Selection using Fuzzy Mean-Variance-Skewness TT. IUST, 23(4), 447–458. https://www.iust.ac.ir/ijiefa/article-1-395-fa.pdf. (In Persian).
Zhang, H., Watada, J., & Wang, B. (2019). Sensitivity‐based fuzzy multi‐objective portfolio model with Value‐at‐Risk. IEEJ Transactions on Electrical and Electronic Engineering, 14(11), 1639–1651. https://doi.org/10.1002/tee.22986
Zhou, J., & Li, X. (2021). Multi-period mean-semi-entropy portfolio management with transaction costs and bankruptcy control. Journal of Ambient Intelligence and Humanized Computing, 12(1), 705–715. https://doi.org/10.1007/s12652-020-02053-4
Zhou, W., & Xu, Z. (2019). Hesitant fuzzy linguistic portfolio model with variable risk appetite and its application in the investment ratio calculation. Applied Soft Computing, 84, 105719. https://doi.org/10.1016/j.asoc.2019.105719
Zhou, X., Wang, J., Yang, X., Lev, B., Tu, Y., & Wang, S. (2018). Portfolio selection under different attitudes in fuzzy environment. Information Sciences, 462, 278–289. https://doi.org/10.1016/j.ins.2018.06.013
