International Journal of Econometrics and Financial Management
ISSN (Print): 2374-2011 ISSN (Online): 2374-2038 Website: http://www.sciepub.com/journal/ijefm Editor-in-chief: Tarek Sadraoui
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International Journal of Econometrics and Financial Management. 2015, 3(2), 99-103
DOI: 10.12691/ijefm-3-2-7
Open AccessArticle

Optimal Portfolio Selection Using Multi-Objective Fuzzy-Genetic Method

Iman Goroohi Sardou1, , Ataa Nazari1, Esmaeil Ghodsi1 and Ehsan Bagherzadeh1

1Faculty of Electrical and Computer Engineering, ShahidBeheshti University, Tehran, Iran

Pub. Date: February 25, 2015

Cite this paper:
Iman Goroohi Sardou, Ataa Nazari, Esmaeil Ghodsi and Ehsan Bagherzadeh. Optimal Portfolio Selection Using Multi-Objective Fuzzy-Genetic Method. International Journal of Econometrics and Financial Management. 2015; 3(2):99-103. doi: 10.12691/ijefm-3-2-7

Abstract

The purpose of investors is to maximize the expected returnin an acceptable level of risk. A genetic algorithm (GA) based on multi-objective fuzzy approach is presented in this paper to solve the multi-objective problem of portfolio selection. The expected return maximization and the risk minimization are the objective functions of the proposed portfolio selection problem. Since GA does not require prespecified information of the problem, it has more flexibility rather than the other nonlinear methods. Furthermore, the GA is able to model the nonlinear manner of the objective functions of the problem. In the proposed fuzzy-genetic method the objective functions are transmitted to a fuzzy domain using a fuzzy membership function and after that the weighted sum method is employed to determine the total objective function. Besides, the Pareto front of the objectives of return and risk are obtained by varying the weighting coefficients and solving the new single-objective problems. To demonstrate the effectiveness of the proposed method, a case study including several active companies is studied.

Keywords:
portfolio selection asset return risk genetic algorithm multi-objective fuzzy approach

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References:

[1]  X. Sun, J. Li, L. Tang, and D. Wu. Identifying the risk-return tradeoff and exploring the dynamic risk exposure of country portfolio of the FSU's oil economies, Economic Modelling, 29. (2012). pp. 2494-2503.
 
[2]  J. MencĂ­a. Assessing the risk-return trade-off in loan portfolios, Journal of Banking & Finance, 36. (2012). pp. 1665-1677.
 
[3]  G. Palomba, Luca Riccetti. Portfolio frontiers with restrictions to tracking error volatility and value at risk, Journal of Banking & Finance, 36. (2012). pp. 2604-2615.
 
[4]  S. Kumar Mishra, G. Panda,R. Majhi.A comparative performance assessment of a set of multiobjective algorithms for constrained portfolio assets selection, Swarm and Evolutionary Computation, 16.(2014). pp. 38-51
 
[5]  Joel WeiqiangGoh, Kian Guan Lim, Melvyn Sim, Weina Zhang. Portfolio value-at-risk optimization for asymmetrically distributed asset returns, European Journal of Operational Research, 221. (2012). pp. 397-406.
 
[6]  R. J. Bianchi, G. Bornholt, M. E. Drew, M. F. Howard. Long-term U.S. infrastructure returns and portfolio selection, Journal of Banking & Finance, 42. (2014). pp. 314-325.
 
[7]  Wei. Zhang, Q. Mei, Q. Lu, W. Xiao. Evaluating methods of investment project and optimizing models of portfolio selection in fuzzy uncertainty, Computers & Industrial Engineering, 61. (2011). pp. 721-728.
 
[8]  C. Aranha, C. R.B. Azevedo, H. Iba. Money in trees: How memes, trees, and isolation can optimize financial portfolios, Information Sciences, 182. (2012). pp. 184-198.
 
[9]  Min Zhu, Return distribution predictability and its implications for portfolio selection, International Review of Economics & Finance, 27 (2013). pp. 209-223.
 
[10]  F. Makhatabrafiei, M.A. Fatahzadeh, Linear regression and multiobjective method to solve the portfolio selection problem, 9th international conference of industrial engineering, Tehran, Iran, 2013.
 
[11]  X. Li, Z. Qin, Interval portfolio selection models within the framework of uncertainty theory, Economic modeling, 41 (2014), pp. 338-344.
 
[12]  H. Yao, Z. Li, Sh. Chen, Continuous-time mean-variance portfolio selection with only risky assets, Economic modeling, 36 (2014), pp. 244-251.
 
[13]  F. He, R. Qu, A two-stage stochastic mixed-integer program modeling and hybrid solution approach to portfolio selection problems, Information Sciences, (2014), In press.