A Multi-Objective Approach to Portfolio Optimization Problem Using the Analytic Hierarchy Process (AHP) and Genetic Algorithm

Document Type : Original Article

Authors

1 industrial engineering/ Bu ali Sina university/ Hamedan- Iran

2 Associate Professor, Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

Abstract

This study analyzes the portfolio optimization model, by considering the financial management and investment science in order to evaluate risks and return in regard with restrictions such as buyers’ assets for purchasing per share. Accordingly, a novel model is designed as linear programming in order to optimize the investment portfolio, considering the expected rate of return, the minimum risk, and the buyer’s assets. After the introduction of the model as linear programming and expressing the related limitations, different types of investments which an investor can consider in order to form an investment portfolio were studied. Finally, an approach is proposed to solve the model by using the genetic algorithm, and is implemented and analyzed in regard with a real example. According to the results of this study, the new model reduced downside risk in comparison with previously proposed models, in a manner that its stair descent continues as the number of shares under study increases.

Keywords


Anagnostopoulos, K. & Mamanis, G. (2009). Multiobjective      evolutionary algorithms for complex portfolio optimization problems.  Springer-Verlag, 8(3): 259-279. DOI:10.1007/s10287-009-0113-8
Armananzas, R. & Lozano, J. A. (2005). A multiobjective approach to the portfolio optimization problem. IEEE congress on evolutionary computation, 2: 1388- 1395. DOI:10.1109/CEC.2005.1554852
Chang-Chun Lin, Yi-Ting Liu_Genetic algorithms for portfolio selection problemswith minimum transaction lots (2008).  DOI:10.1016/j.ejor.2006.12.024
Derakhshan, M., Golmakani, H. & Hanafizadeh, P. (2012). Multiobjective Portfolio Selection of Tehran Stock Exchange with the Metaheuristic Optimization Approach. International Journal of Indestrial Engineering and Production Management, 23(3): 318-331. (in Persian). Doi: 10.22091/jemsc.2019.1294
Fernandez, A. , Gomez, S. , "Portfolio Selection Using Neural Networks", Computers & Operations Research, No. 34, pp. 1177-1191, 2007.  DOI:10.1016/j.cor.2005.06.017
HosseinDastkhan, Naser Shams Gharneh, HamidRezaGolmakani. _ A linguistic-based portfolio selection model using weighted max–min operator and hybrid genetic algorithm (2011).  DOI:10.1016/j.eswa.2011.03.060
Irina Bolshakova, Mikhail Kovalev_portfolio optimization problems (2009).  Doi: 10.22091/jemsc.2019.1294
Khaleiji, M., Zeiaee, M., Tabei, A., Jahed-Motlagh, M.R. & Khaloozadeh, H. (2009).  Dynamically Weighted Continuous Ant Colony Optimization for Bi- Objective Portfolio Selection Using Value-at-Risk. Third Asian International Conference on Digital Object Identifier, 1(2): 230-235. Doi: 10.22091/jemsc.2019.1294
Li, H. & Zhang, Q. (2009). Multiobjective Optimization Problems with Complicated Pareto Sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Comutation, 13(2): 284-302. DOI:10.1109/TEVC.2008.925798
Lilian Noronha Nassif, João Carlos Santiago Filho, José Marcos Nogueira_ Project Portfolio Selection in Public Administration Using Fuzzy Logic (2013).  DOI:10.1016/j.sbspro.2013.03.036
Lin, C. M. , Gen, M. , "An Effective Decision-based Genetic Algorithm Approach to Multi-objective Portfolio Optimization Problem" , Applied Mathematical Sciences ,Vol. 1, No. 5, pp. 201-210, 2007. DOI:10.1016/j.amc.2003.10.057
Maciej Nowak_ Project Portfolio Selection Using Interactive Approach (2013). Doi:  10.1016/j.proeng.2013.04.103  
Markowitz, H, Todd, P, Xu, G, & Yamane, Y. (1993). Computation of mean-semi variance efficient sets by the critical line algorithm. Annals of Operations Research, 45, 307–317.  DOI:10.1007/BF02282055
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7, 77–91. Doi: 10.2307/2975974
Markowitz, H. (1956). The optimization of a quadratic function subject to linear constraints. Naval Research Logistics Quarterly, 3, 111–133.  Doi: 10.1002/nav.3800030110
Mishra, S.K., Panda, G. & Meher, S. (2009). Multi-objective particle swarm optimization approach to portfolio optimization. World Congress on Nature & Biologically Inspired Computing. DOI:10.1109/NABIC.2009. 5393659. DOI:10.1109/NABIC.2009.5393659
Raei, R.(2002). Creating stock portfolios for risky investors: Comparison of Neural Networks and Markovitz Models,2(2):77-96.(in persian) Doi: 10.22034/amfa.2019.1870129.1235  
Shahalizadeh, M & Memariani, A.(2002). Mathematical Framework Selection of stock portfolios with multiple goals.  Accounting and Audit review,10(23):83-110.(in persian) Doi: 10.22034/amfa.2016.527813
Skolpadungket, P., Dahal, K. & Harnpornchai, N. (2007). Portfolio optimization using multi-objective genetic algorithms. IEEE congress on evolutionary computation, CEC: 516-523. DOI: 10.1109/CEC.2007.4424514. DOI:10.1109/CEC.2007.4424514.
Speranza, M. Grazia. (1995). A Heuristics Algorithm for A Portfolio OptimizationModel Applied To the Milan Stock Market, Computer & Ops Res, 5,. 433-441.  Doi: 10.1016/0305-0548(95)00030-5
Tanaka, H. , Guo, P. , Turksen, I. B. , "Portfolio Selection Based on Fuzzy Probabilities and Possibility Distributions", Fuzzy sets and Systems, No. 111, pp. 387-397, 2000.  DOI:10.1016/S0165-0114(98)00041-4
Vafaei Jahan, M. , AkbarzadehTootonchi, M. R. , "Spin Glass Portfolio Selection," Proceeding of First Joint Congress on Fuzzy and Intelligent Systems, pp. 29-31, Aug 2007.  DOI: 10.22091/jemsc.2019.1294
Werner, J. C. , Fogarti, T. C. , "Genetic Control Applied to Asset Managements", EuroGP, LNCS, pp. 192-201,2002.  DOI:10.1007/978-3-540-89378-3_52
Yousef Kilani_ Comparing the performance of the genetic and local search algorithms for solvingthesatisfiability problems (2010). Doi:10.1016/j.asoc.2009.07.012
CAPTCHA Image