Hybrid Portfolio Optimization using Analytic Hierarchy Process (AHP), Combined Compromise Solution (CoCoSo) and Markowitz Model (Case study of Tehran Stock Exchange)
Subject Areas :
Journal of Investment Knowledge
Nasimeh Abdi
1
,
mehdi Moradzadeh Fard
2
,
Hamid Ahmadzadeh
3
,
Mahmoud Khoddam
4
1 - Ph.D. Student in Financial Engineering, Department of Financial Management, Karaj Branch, Islamic Azad University, Karaj, Iran
2 - Associate Professor, Department of Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran
3 - Assistant professor Department of Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran
4 - Assistant professor, Department of Financial Management, Karaj Branch, Islamic Azad University, Karaj, Iran
Received: 2021-11-05
Accepted : 2022-02-06
Published : 2023-12-22
Keywords:
Combined Compromise Solution (CoCoSo),
portfolio optimization,
Markowitz Model,
Decision making,
Analytic Hierarchy Process (AHP),
Abstract :
Using effective and efficient criteria in choosing the investment portfolio can provide the most profitability for individual and institutional investors. Therefore, it seems necessary to choose a hybrid method to create a portfolio that shows better performance. The purpose of this study is to provide a model that can combine Multi-criteria decision-making techniques and Markowitz's mean-variance model, in different periods, to create an optimal portfolio that maximizes shareholder profits. The proposed model was implemented in three steps. In the first step, using the AHP technique, utilizing the opinion of experts, comparing different decision options based on the fundamental and technical criteria effective in decision making and prioritizing the mentioned criteria during the period from June 2016 to June 2021, among industries Activists in the Tehran Stock Exchange were selected as top industries. In the second step, from selected industries, three portfolios with one-month, six-month, and one-year periods were selected using the CoCoSo technique. In the third step, using the Markowitz model in the expressed time period, optimal portfolios were created on the efficient frontier. The results of this study showed that this hybrid proposed model will give more returns to investors according to the risk in different time periods.
References:
تاتایی، پیمان، نیکو مرام، هاشم، حافظ الکتب، اشکان (1400)، کاربرد نظریه بازیهای همکارانه در بهینهسازی انتخاب سبد سهام. دانش سرمایهگذاری، شماره 39، صص563-584.
سپهری، علی، جباری، حسین، قدرتی قزاآنی، پناهیان، حسین، تلفیق تصمیمگیری چند معیاره و بهینهسازی ریاضی، زمینهای برای تصمیمگیری سرمایهای، دانش سرمایهگذاری.
Ackermann, F., Pohl, W., & Schmedders, K. (2017). Optimal and Naive Diversification in Currency Markets. Management Science, 63(10), 3347-3360.
Ban, G.-Y., Karoui, N. E., & Lim, A. E. B. (2018). Machine Learning and Portfolio Optimization. Management Science, 64(3), 1136-1154.
Bolster, P., & Warrick, S. (2008). Matching Investors with Suitable, Optimal and Investable Portfolios. The Journal of Wealth Management, 10(4), 53.
Bolster, P. J., Janjigian, V., & Trahan, E. A. (1995). Determining Investor Suitability Using the Analytic Hierarchy Process. Financial Analysts Journal, 51(4), 63-75.
Çela, E., Hafner, S., Mestel, R., & Pferschy, U. (2021). Mean-variance portfolio optimization based on ordinal information. Journal of Banking & Finance, 122, 105989.
Markowitz. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Hagin, R. (1979). The Dow Jones-Irwin guide to modern portfolio theory: Irwin Professional Publishing.
Huang, X. (2008). Mean-semivariance models for fuzzy portfolio selection. Journal of Computational and Applied Mathematics, 217(1), 1-8.
Kim, J. H., Lee, Y., Kim, W. C., & Fabozzi, F. J. (2021). Mean–Variance Optimization for Asset Allocation. The Journal of Portfolio Management, 47(5), 24-40.
Lai, H., Liao, H., Wen, Z., Zavadskas, E. K., & Al-Barakati, A. (2020). An Improved CoCoSo Method with a Maximum Variance Optimization Model for Cloud Service Provider Selection. Engineering Economics, 31(4), 411-424.
Lynch Jr, J. G. (2011). Introduction to the journal of marketing research special interdisciplinary issue on consumer financial decision making. Journal of Marketing Research, 48(SPL), Siv-Sviii.
Meziani, Aboubaker Seddik. "Assessing the Effect of Investment Barriers on International Capital Flows Using an Expert‐Driven System." Multinational Business Review (2003).
Peng, X., & Luo, Z. (2021). Decision-making model for China’s stock market bubble warning: the CoCoSo with picture fuzzy information. Artificial Intelligence Review, 1-23.
Saaty, T. L. (1977). A Scaling Method for Priorities in Hierarchical Structures. Journal of Mathematical Psychology, 15, 234-281.
Saraoglu, H., & Detzler, M. L. (2002). A Sensible Mutual Fund Selection Model. Financial Analysts Journal, 58(3), 60-72.
Seddik Meziani, A. (2003). Assessing the Effect of Investment Barriers on International Capital Flows Using an Expert‐Driven System. Multinational Business Review, 11(2), 49-74.
Sivam, S., & Rajendran, R. (2020). On the Modelling of Integrated AHP and CoCoSo Approach for Robust Design of Multi-objective Optimization of thinning Parameters for Maximum thinning Rate and Determine Optimum Locations for Directionally-rolled Deep-drawn Cups using Scaling Laws.
Wu, H., Zeng, Y., & Yao, H. (2014). Multi-period Markowitz's mean–variance portfolio selection with state-dependent exit probability. Economic modeling, 36, 69-78.
Vásquez, J. A., Escobar, J. W., & Manotas, D. F. (2022). AHP–TOPSIS Methodology for Stock Portfolio Investments. Risks, 10(1), 4.
Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501-2519.
Ledoit, O., & Wolf, M. (2017). Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks. The Review of Financial Studies, 30(12), 4349-4388.
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.
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