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A More Human-Like Portfolio Optimization Approach: Using Utility Function to Find an Individualized Portfolio | ||
Advances in Industrial Engineering | ||
دوره 54، شماره 3، مهر 2020، صفحه 293-310 اصل مقاله (688.17 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/jieng.2021.324959.1769 | ||
نویسندگان | ||
Zahra Touni1؛ Ahmad Makui1؛ Emran Mohammadi* 2 | ||
1School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran. | ||
2School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran | ||
چکیده | ||
In this paper, a multi-objective model based on the decision maker’s (DM) utility function is proposed to find an optimized portfolio that fits with the desires of the DM. The proposed algorithm is developed in three stages. First, a fundamental analysis on accounting criteria is done using TOPSIS-DEA method. By this method, companies’ efficiency ranks according their fundamental reporting sheets are achieved. Second, the specific utility function of DM is found using the UTASTAR method. Third, a two-objective model is solved to find the stocks’ proportion for the individual investor. In this study, different criteria and decision making tools are used to make human-like decisions that meet investor’s expectations as well as possible. This approach is illustrated in this paper by a real-world case study concerning the evaluation of stocks in the Iran stock exchange. The suggested portfolio not only made a higher level of utility with the minimum level of risk but also is consistent with investor’s interests. | ||
کلیدواژهها | ||
Multiple Criteria Decision Making (MCDM)؛ Portfolio Optimization؛ Utility Function؛ Fundamental Analysis | ||
مراجع | ||
[1] H. Markowitz, (1952). Portfolio selection*, J. Finance 7, 77–91. [2] Xidonas, P., Mavrotas, G., and Psarras, J. (2010). A multiple criteria decision-making approach for the selection of stocks. Journal of the Operational Research Society, 61(8), 1273-1287. [3] Raei, R., and Jahromi, M. (2012). Portfolio optimization using a hybrid of fuzzy ANP, VIKOR and TOPSIS. Management Science Letters, 2(7), 2473-2484. [4] Bouri, A., Martel, J. M., and Chabchoub, H. (2002). A multi‐criterion approach for selecting attractive portfolio. Journal of Multi‐Criteria Decision Analysis, 11(4‐5), 269-277. [5] Ehrgott, M., Waters, C., Kasimbeyli, R., and Ustun, O. (2009). Multiobjective programming and multiattribute utility functions in portfolio optimization. INFOR: Information Systems and Operational Research, 47(1), 31-42. [6] Tamiz, M., and Azmi, R. A. (2019). Goal programming with extended factors for portfolio selection. International Transactions in Operational Research, 26(6), 2324-2336. [7] Ehrgott, M., Klamroth, K., and Schwehm, C. (2004). An MCDM approach to portfolio optimization. European Journal of Operational Research, 155(3), 752-770. [8] Morton, A. J., and Pliska, S. R. (1995). Optimal portfolio management with fixed transaction costs. Mathematical Finance, 5(4), 337-356. [9] Fei, W. (2007). Optimal consumption and portfolio choice with ambiguity and anticipation. Information Sciences, 177(23), 5178-5190. [10] Buckley, I., Saunders, D., and Seco, L. (2008). Portfolio optimization when asset returns have the Gaussian mixture distribution. European Journal of Operational Research, 185(3), 1434-1461. [11] Bodnar, T., Parolya, N., and Schmid, W. (2015). On the exact solution of the multi-period portfolio choice problem for an exponential utility under return predictability. European Journal of Operational Research, 246(2), 528-542. [12] Ma, G., Siu, C. C., and Zhu, S. P. (2019). Dynamic portfolio choice with return predictability and transaction costs. European Journal of Operational Research, 278(3), 976-988. [13] Yu, B. W. T., Pang, W. K., Troutt, M. D., and Hou, S. H. (2009). Objective comparisons of the optimal portfolios corresponding to different utility functions. European Journal of operational research, 199(2), 604-610. [14] Rather, A. M., Sastry, V. N., and Agarwal, A. (2017). Stock market prediction and Portfolio selection models: a survey. Opsearch, 54(3), 558-579. [15] Markowitz, H., Todd, P., Xu, G., and Yamane, Y. (1993). Computation of mean-semivariance efficient sets by the critical line algorithm. Annals of Operations Research, 45(1), 307-317. [16] Corazza, M., and Favaretto, D. (2007). On the existence of solutions to the quadratic mixed-integer mean–variance portfolio selection problem. European Journal of Operational Research, 176(3), 1947-1960. [17] Freitas, F. D., De Souza, A. F., and de Almeida, A. R. (2009). Prediction-based portfolio optimization model using neural networks. Neurocomputing, 72(10-12), 2155-2170. [18] Cesarone, F., Scozzari, A., and Tardella, F. (2013). A new method for mean-variance portfolio optimization with cardinality constraints. Annals of Operations Research, 205(1), 213-234. [19] Li, T., Zhang, W., and Xu, W. (2015). A fuzzy portfolio selection model with background risk. Applied Mathematics and Computation, 256, 505-513. [20] Sharpe, W. F. (1967). A linear programming algorithm for mutual fund portfolio selection. Management Science, 13(7), 499-510. [21] Chopra, V. K., and Ziemba, W. T. (1993). The Effect of Errors in Means, Variances, and Covariances on Optimal Portfolio Choice. The Journal of Portfolio Management, 19(2), 6-11. [22] Huang, X. (2007). Two new models for portfolio selection with stochastic returns taking fuzzy information. European Journal of Operational Research, 180(1), 396-405. [23] Rios, L. M., and Sahinidis, N. V. (2010). Portfolio optimization for wealth-dependent risk preferences. Annals of Operations Research, 177(1), 63-90. [24] Zhang, X., Zhang, W. G., and Xu, W. J. (2011). An optimization model of the portfolio adjusting problem with fuzzy return and a SMO algorithm. Expert Systems with Applications, 38(4), 3069-3074. [25] Sadjadi, S. J., Gharakhani, M., and Safari, E. (2012). Robust optimization framework for cardinality constrained portfolio problem. Applied Soft Computing, 12(1), 91-99. [26] Liu, Y. J., and Zhang, W. G. (2015). A multi-period fuzzy portfolio optimization model with minimum transaction lots. European Journal of Operational Research, 242(3), 933-941. [27] Leung, M. T., Daouk, H., and Chen, A. S. (2001). Using investment portfolio return to combine forecasts: a multiobjective approach. European Journal of Operational Research, 134(1), 84-102. [28] Armananzas, R., and Lozano, J. A. (2005, September). A multiobjective approach to the portfolio optimization problem. In 2005 IEEE Congress on Evolutionary Computation (Vol. 2, pp. 1388-1395). IEEE. [29] Chiam, S. C., Al Mamun, A., and Low, Y. L. (2007, September). A realistic approach to evolutionary multiobjective portfolio optimization. In 2007 IEEE Congress on Evolutionary Computation (pp. 204-211). IEEE. [30] Ammar, E. E. (2008). On solutions of fuzzy random multiobjective quadratic programming with applications in portfolio problem. Information Sciences, 178(2), 468-484. [31] Greco, S., Matarazzo, B., and Słowiński, R. (2013). Beyond Markowitz with multiple criteria decision aiding. Journal of Business Economics, 83(1), 29-60. [32] Rather, A. M., Sastry, V. N., and Agarwal, A. (2014, September). Portfolio selection using maximum-entropy gain loss spread model: a GA based approach. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 400-406). IEEE. [33] Zhao, S., Lu, Q., Han, L., Liu, Y., and Hu, F. (2015). A mean-CVaR-skewness portfolio optimization model based on asymmetric Laplace distribution. Annals of Operations Research, 226(1), 727-739. [34] Akian, M., Sulem, A., and Taksar, M. I. (2001). Dynamic Optimization of Long‐Term Growth Rate for a Portfolio with Transaction Costs and Logarithmic Utility. Mathematical Finance, 11(2), 153-188. [35] Ferland, R., and Watier, F. (2008). FBSDE approach to utility portfolio selection in a market with random parameters. Statistics and probability letters, 78(4), 426-434. [36] Yu, B. W. T., Pang, W. K., Troutt, M. D., and Hou, S. H. (2009). Objective comparisons of the optimal portfolios corresponding to different utility functions. European Journal of operational research, 199(2), 604-610. [37] Çanakoğlu, E., and Özekici, S. (2010). Portfolio selection in stochastic markets with HARA utility functions. European Journal of Operational Research, 201(2), 520-536. [38] Ma, Q. H., Yao, H. X., and Li, S. Y. (2012). Logarithm Utility Maximization Portfolio Engineering with Bankruptcy Control: a Nonparametric Estimation Framework. Systems Engineering Procedia, 5, 150-155. [39] Hurson, C., Mastorakis, K., and Siskos, Y. (2012). Application of a synergy of MACBETH and MAUT multicriteria methods to portfolio selection in Athens stock exchange. International Journal of Multicriteria Decision Making 7, 2(2), 113-127. [40] Lopes, Y. G., and de Almeida, A. T. (2015). Assessment of synergies for selecting a project portfolio in the petroleum industry based on a multi-attribute utility function. Journal of Petroleum Science and Engineering, 126, 131-140. [41] Touni, Z., Makui, A., and Mohammadi, E. (2019). A MCDM-based approach using UTA-STAR method to discover behavioral aspects in stock selection problem. International Journal of Industrial Engineering and Production Research, 30(1), 93-103. [42] Gupta, P., Mehlawat, M. K., and Saxena, A. (2010). A hybrid approach to asset allocation with simultaneous consideration of suitability and optimality. Information Sciences, 180(11), 2264-2285. [43] Barak, S., Abessi, M., and Modarres, M. (2013). Fuzzy turnover rate chance constraints portfolio model. European Journal of Operational Research, 228(1), 141-147. [44] Li, J., and Xu, J. (2013). Multi-objective portfolio selection model with fuzzy random returns and a compromise approach-based genetic algorithm. Information Sciences, 220, 507-521. [45] Acikalin, S., Aktas, R., and Unal, S. (2008). Relationships between stock markets and macroeconomic variables: an empirical analysis of the Istanbul Stock Exchange. Investment Management and Financial Innovations, 5(1), 8-16. [46] Peiro, A. (2016). Stock prices and macroeconomic factors: Some European evidence. International Review of Economics and Finance, 41, 287-294. [47] Owusu-Nantwi, V., and Kuwornu, J. K. (2011). Analyzing the effect of macroeconomic variables on stock market returns: Evidence from Ghana. Journal of Economics and International Finance, 3(11), 605-615. [48] Tirea, M., and Negru, V. (2014, September). Intelligent stock market analysis system-a fundamental and macro-economical analysis approach. In 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (pp. 519-526). IEEE. [49] Lam, M. (2004). Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decision support systems, 37(4), 567-581. [50] Siskos, Y., and Yannacopoulos, D. (1985). UTASTAR: An ordinal regression method for building additive value functions. Investigaçao Operacional, 5(1), 39-53. [51] Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465. [52] Greco, S., Figueira, J., and Ehrgott, M. (2016). Multiple criteria decision analysis. New York: Springer. [53] Mendonça, G. H., Ferreira, F. G., Cardoso, R. T., and Martins, F. V. (2020). Multi-attribute decision making applied to financial portfolio optimization problem. Expert Systems with Applications, 158, 113527. [54] Sukono, Sidi, P., Bon, A. T. B., and Supian, S. (2017, March). Modeling of Mean-VaR portfolio optimization by risk tolerance when the utility function is quadratic. In AIP Conference Proceedings (Vol. 1827, No. 1, p. 020035). AIP Publishing LLC. [55] Raeiszadeh, S., Dehghan Dehnavi, M., Bahrololoum, M., Peymany Foroushany, M. (2020). Portfolio Selection Optimization Problem Under Systemic Risks. Advances in Industrial Engineering, 54(2), 121-140. doi: 10.22059/jieng.2021.321882.1759. [56] Ebrahimi, S. (2016). Robust Estimation in Nonlinear Modeling of Volatility Transmission in Stock Market. Advances in Industrial Engineering, 50(2), 165-176. doi: 10.22059/jieng.2016.60722. [57] Aria, S., Torabi, S., Nayeri, S. (2020). A Hybrid Fuzzy Decision-Making Approach to Select the Best online-taxis business. Advances in Industrial Engineering, 54(2), 99-120. doi: 10.22059/jieng.2021.320051.1754. | ||
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