Development of Optimal Stock Portfolio Selection Model in the Tehran Stock Exchange by Employing Markowitz Mean-Semivariance Model

Authors

  • Soheila Sadeghi University of the Incarnate Word
  • Taimoor Marjani University of Science and Culture
  • Ali Hassani University of Science and Culture
  • Jose Moreno University of the Incarnate Word

DOI:

https://doi.org/10.58886/jfi.v20i1.3061

Keywords:

Stock Portfolio, Interval TOPSIS, Optimization, Markowitz mean-semivariance model

Abstract

In an increasingly complex financial market, selecting the optimal stock portfolio has become a subject of intense debate. This study aims to develop a model for optimal stock portfolio selection. We apply Markowitz's mean-semivariance approach to determine the downside risk of portfolios, which reflects investors' intuitive perception of risk. In the first stage, the combination of the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with interval data is employed to identify and rank good quality stocks according to the recommended criteria by experts. After selecting qualified stocks, in the second phase, we create portfolios, and the weight invested in each stock is determined. Then, three portfolios are created for three groups of risk-averse, neutral to risk, and risk-taker investors. The mean-semivariance optimization model is used in this phase. The proposed approach in the paper is implemented in a real case study of the Tehran stock exchange (TSE). Three portfolios for three groups of investors were evaluated and compared to the market performance using sharp criteria. All three portfolios outperformed the market portfolio both in terms of risk and return. The proposed model of this study can be utilized as a decision support tool when forming an optimal stock portfolio by considering both experts’ opinions on stock evaluation and investor risk preferences simultaneously.

References

Aomar, R. A. (2010). A combined ahp-entropy method for deriving subjective and objective criteria weights.

Aruldoss, M., Lakshmi, T. M., & Venkatesan, V. P. (2013). A survey on multi criteria decision making methods and its applications. American Journal of Information Systems, 1(1), 31-43.

Barro, Robert J. 1996. Determinants of Economic Growth: A Cross-Country Empirical Study. National Bureau of Economic Research, Working Paper: 5698. DOI: https://doi.org/10.3386/w5698

Boasson, V., Boasson, E., & Zhou, Z. (2011). Portfolio optimization in a mean-semivariance framework. Investment management and financial innovations, (8, Iss. 3), 58-68.

Chakraborty, S., & Chakraborty, S. (2022). A scoping review on the applications of MCDM techniques for parametric optimization of machining processes. Archives of Computational Methods in Engineering, 1-22. DOI: https://doi.org/10.1007/s11831-022-09731-w

Chen, W., Wang, Y., Zhang, J., & Lu, S. (2017). Uncertain portfolio selection with high-order moments. Journal of Intelligent & Fuzzy Systems, 33(3), 1397-1411. DOI: https://doi.org/10.3233/JIFS-17369

Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to algorithms. MIT press.

Deng, X., Liu, Y., Zhuang, H., & Lin, Z. (2021). Fuzzy Portfolio Model under Investors’ Different Attitudes with Risk Adaptation Value Parameter Based on Possibility Theory. A a, 1(2), 0-1.

Ece, O., Uludag, A.S. (2017). Applicability of Fuzzy TOPSIS Method in Optimal Portfolio Selection and an Application in BIST. International Journal of Economics and Finance, 9 (10), 107–127. https://doi.org/10.5539/ijef.v9n10p107.

Eftekhari, B., & Satchell, S. E. (1996). Some problems with modelling asset returns using the elliptical class. Applied Economics Letters, 3(9), 571-572. DOI: https://doi.org/10.1080/135048596355970

Estrada, J. (2008) Mean-Semivariance Optimization: A Heuristic Approach, Journal of Applied Finance, 18, 57-72. DOI: https://doi.org/10.2139/ssrn.1028206

Evans, J. L., & Archer, S. H. (1968). Diversification and the reduction of dispersion: an empirical analysis. The Journal of Finance, 23(5), 761-767. DOI: https://doi.org/10.1111/j.1540-6261.1968.tb00315.x

Formisano, A., Castaldo, C., & Chiumiento, G. (2017). Optimal seismic upgrading of a reinforced concrete school building with metal-based devices using an efficient multi-criteria decision-making method. Structure and Infrastructure Engineering, 13(11), 1373-1389. DOI: https://doi.org/10.1080/15732479.2016.1268174

Giove, S. (2002), Interval TOPSIS for Multicriteria Decision Making, Wirn Vietri, 2486, 56-63. https://doi.org/10.1007/3-540-45808-5_5. DOI: https://doi.org/10.1007/3-540-45808-5_5

Grootveld, H., & Hallerbach, W. (1999). Variance vs downside risk: Is there really that much difference?. European Journal of operational research, 114(2), 304-319. DOI: https://doi.org/10.1016/S0377-2217(98)00258-6

Hilborn, R., & Walters, C. J. (Eds.). (2013). Quantitative fisheries stock assessment: choice, dynamics and uncertainty. Springer Science & Business Media.

Ince, M., Yigit, T., & Işık, A.H. (2017). AHP-TOPSIS Method for Learning Object Metadata Evaluation. International Journal of Information and Education Technology, 7, 884-887. DOI: https://doi.org/10.18178/ijiet.2017.7.12.989

Jahanshahloo, G.R., Hosseinzadeh, L. & Davoodi, A.R. (2009) Extension of TOPSIS for Decision-Making Problems with Interval Data: Interval efficiency, Mathematical and Computer Modeling, 49, 1137-1142. https://doi.org/10.1016/j.mcm.2008.07.009. DOI: https://doi.org/10.1016/j.mcm.2008.07.009

Jamei, R. (2020). Investigating the Mathematical Models (TOPSIS, SAW) to Prioritize the Investments in the Accepted Pharmaceutical, Advances in Mathematical Finance and Applications, 5(2), 215-https://doi.org/227. 10.22034/AMFA.2020.1880616.1312.

Lam, J. W. (2016). Robo-advisors: A portfolio management perspective. Senior thesis, Yale College, 20.

Leland, H. E. (1999). Beyond Mean–Variance: Performance Measurement in a Nonsymmetrical World (corrected). Financial analysts journal, 55(1), 27-36. DOI: https://doi.org/10.2469/faj.v55.n1.2239

Li, B., & Zhang, R. (2021). A new mean-variance-entropy model for uncertain portfolio optimization with liquidity and diversification. Chaos, Solitons & Fractals, 146, 110842. DOI: https://doi.org/10.1016/j.chaos.2021.110842

Li, X., Wu, X., & Zhou, W. (2017). Optimal stopping investment in a logarithmic utility-based portfolio selection problem. Financial Innovation, 3(1), 1-10. DOI: https://doi.org/10.1186/s40854-017-0080-y

Liu, Y. J., & Zhang, W. G. (2015). A multi-period fuzzy portfolio optimization model with mini-mum transaction lots. European Journal of Operational Research, 242(3), 933-941. DOI: https://doi.org/10.1016/j.ejor.2014.10.061

Makui, A., & Mohammadi, E. (2019). A MCDM-based approach using UTA-STAR method to discover behavioral aspects in stock selection problem. International Journal of Industrial Engineering & Production Research, 30(1), 93-103.

Malik, D. A. A., Yusof, Y., & Na’im Ku Khalif, K. M. (2021, July). A view of MCDM application in education. In Journal of Physics: Conference Series (Vol. 1988, No. 1, p. 012063). IOP Publishing. DOI: https://doi.org/10.1088/1742-6596/1988/1/012063

Mansini, R., Ogryczak, W., & Speranza, M. G. (2015). Portfolio optimization with transaction costs. In Linear and Mixed Integer Programming for Portfolio Optimization (pp. 47-62). Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-18482-1_3

Markowitz, H. M. (1959). Portfolio Selection, Cowles Foundation Monograph 16. New York, New York: JohnWiley and Sons, 1959.

Markowitz, H. (1952) Portfolio selection, J. Finance, 7, 77–91. DOI: https://doi.org/10.1111/j.1540-6261.1952.tb01525.x

Mehlawat, M. K. (2016). Credibilistic mean-entropy models for multi-period portfolio selection with multi-choice aspiration levels. Information Sciences, 345, 9-26. DOI: https://doi.org/10.1016/j.ins.2016.01.042

Michaud, R. O., & Michaud, R. O. (2008). Efficient asset management: a practical guide to stock portfolio optimization and asset allocation. Oxford University Press. DOI: https://doi.org/10.1093/oso/9780195331912.001.0001

Mirdamadian, B. S., Shirouyehzad, H., & Rajabi, M. (2016). Performance evaluation of top 50 companies of Tehran Stock Exchange based on identified indices in 2012. International Journal of Productivity and Quality Management, 18(1), 19-33. DOI: https://doi.org/10.1504/IJPQM.2016.075703

Oguzhan Ece & Ahmet Serhat Uludag,(2017), ”Applicability of Fuzzy TOPSIS Method in Optimal Portfolio Selection and an Application in BIST”, International Journal of Economics and Finance; Vol. 9, No. 10; ISSN 1916-971X E-ISSN 1916-9728. DOI: https://doi.org/10.5539/ijef.v9n10p107

Pahwa, K., & Agarwal, N. (2019, February). Stock market analysis using supervised machine learning. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (pp. 197-200). IEEE. DOI: https://doi.org/10.1109/COMITCon.2019.8862225

Park, C. (2013). Asian Capital Market Integration: Theory and Practice; ADB Economics Working Paper Series No. 285; Asian Development Bank: Manila, Philippines.

Park, C. (2017). Developing Local Currency Bond Markets in Asia. Emerging Markets Finance & Trade, 53(12), 2826-2844. DOI: https://doi.org/10.1080/1540496X.2017.1321539

Popović, M. (2021). An MCDM approach for personnel selection using the CoCoSo method. Journal of process management and new technologies, 9(3-4), 78-88. DOI: https://doi.org/10.5937/jouproman2103078P

Qu, B. Y., Zhou, Q., Xiao, J. M., Liang, J. J., & Suganthan, P. N. (2017). Large-scale portfolio optimization using multi-objective evolutionary algorithms and preselection methods. Mathematical Problems in Engineering, 2017. DOI: https://doi.org/10.1155/2017/4197914

Raei, M., & Jahromi M. B. (2012). Portfolio Optimization Using a Hybrid of Fuzzy ANP, VIKOR and TOPSIS, Management Science Letters, 2, 2473–2484. https://doi.org/10.5267/j.msl.2012.07.019. DOI: https://doi.org/10.5267/j.msl.2012.07.019

Roshandel, J., Miri-Nargesi, S. S., & Hatami-Shirkouhi, L. (2013). Evaluating and selecting the supplier in detergent production industry using hierarchical fuzzy TOPSIS. Applied mathematical modelling, 37(24), 10170-10181. DOI: https://doi.org/10.1016/j.apm.2013.05.043

Sepahyar, S., Vaziri, R., & Rezaei, M. (2019, December). Comparing Four Important Sorting Algorithms Based on Their Time Complexity. In Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence (pp. 320-327). DOI: https://doi.org/10.1145/3377713.3377808

Setiawan, C. H., Marciano, D., & Cayaban, C. J. G. (2022). Changes in investors risk-taking behavior during Indonesian economic recession due to the Covid-19 in 2020. Journal of Management and Business, 21(1), 1-12. DOI: https://doi.org/10.24123/jmb.v21i1.535

Singh, R. R., Sarva, M., & Sharma, M. (2020). Investment Behaviour and Risk-Taking Ability Among Retail Investor: Role of Demographic Factors. PalArch's Journal of Archaeology of Egypt/Egyptology, 17(9), 5902-5926.

Sotoudeh-Anvari, A., & Sadi-Nezhad, S. (2015). A new approach based on the level of reliability of information to determine the relative weights of criteria in fuzzy TOPSIS. International Journal of Applied Decision Sciences, 8(2), 164-178. DOI: https://doi.org/10.1504/IJADS.2015.069603

Sun, Y., Aw, G., Teo, K. L., Zhu, Y., & Wang, X. (2016). Multi-period portfolio optimization under probabilistic risk measure. Finance Research Letters, 18, 60-66. DOI: https://doi.org/10.1016/j.frl.2016.04.001

Tan, T., Mills, G., Papadonikolaki, E., & Liu, Z. (2021). Combining multi-criteria decision making (MCDM) methods with building information modelling (BIM): A review. Automation in Construction, 121, 103451. DOI: https://doi.org/10.1016/j.autcon.2020.103451

Velasquez M, Hester P (2013) An analysis of multi-criteria decision making methods. Int J Oper Res 10(2):56–66

Wentao, X. & Huan, Q. (2010) A Extended TOPSIS Method for the Stochastic Multicriteria Decision Making Problem Through Interval Estimation, IEEE, https://doi.org/10.1109/IWISA.2010.5473307. DOI: https://doi.org/10.1109/IWISA.2010.5473307

Wu, W., Chen, J., Xu, L., He, Q., & Tindall, M. L. (2019). A statistical learning approach for stock selection in the Chinese stock market. Financial Innovation, 5(1), 1-18. DOI: https://doi.org/10.1186/s40854-019-0137-1

Xidonas, P., Askounis, D., Psarras, J. & Res., J. O. (2009). Common Stock Portfolio Selection: a Multiple Criteria Decision Making Methodology and an Application to the Athens Stock Exchange, Oper Res Int J, 9, 55–79. https://doi.org/10.1007/s12351-008-0027-1 DOI: https://doi.org/10.1007/s12351-008-0027-1

Zhang, Y., Li, X., & Guo, S. (2018). Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature. Fuzzy Optimization and Decision Making, 17(2), 125-158. DOI: https://doi.org/10.1007/s10700-017-9266-z

Downloads

Published

2022-07-16

How to Cite

Sadeghi, Soheila, Taimoor Marjani, Ali Hassani, and Jose Moreno. 2022. “Development of Optimal Stock Portfolio Selection Model in the Tehran Stock Exchange by Employing Markowitz Mean-Semivariance Model”. Journal of Finance Issues 20 (1):47-71. https://doi.org/10.58886/jfi.v20i1.3061.

Issue

Section

Original Articles