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JACIII Vol.19 No.5 pp. 619-623
doi: 10.20965/jaciii.2015.p0619
(2015)

Paper:

Portfolio Optimization of Financial Returns Using Fuzzy Approach with NSGA-II Algorithm

Jirakom Sirisrisakulchai*,†, Kittawit Autchariyapanitkul**, Napat Harnpornchai*, and Songsak Sriboonchitta*

*Faculty of Economics, Chiang Mai University
Chiang Mai 52000, Thailand

**Faculty of Economics, Maejo University
Chiang Mai 52090, Thailand

Corresponding author

Received:
September 29, 2014
Accepted:
May 9, 2015
Published:
September 20, 2015
Keywords:
fuzzy portfolio optimization, possibilistic mean, possibilistic semivariance, NSGA-II
Abstract
This paper applied possibilistic approaches to a portfolio selection model. We considered a return rate as fuzzy variables. Based on the concept of possibilistic moments of fuzzy numbers, fuzzy stock returns and market risks are quantified by possibilistic mean and lower possibilistic semivariance, respectively. The non-dominated sorting genetic algorithm II (NSGA-II) was used to obtain the pareto optimal investment strategies for the integrated oil and gas company stocks.
Cite this article as:
J. Sirisrisakulchai, K. Autchariyapanitkul, N. Harnpornchai, and S. Sriboonchitta, “Portfolio Optimization of Financial Returns Using Fuzzy Approach with NSGA-II Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.5, pp. 619-623, 2015.
Data files:
References
  1. [1] H. Markowitz, “Portfolio selection,” J. Finance, Vol.7, No.1, pp. 77-91, 1952.
  2. [2] X. Huang, “Mean-semivariance models for fuzzy portfolio selection,” J. of Computational and Applied Mathematics, Vol.217, No.1, pp. 1-8, 2008.
  3. [3] C. Carlsson, R. Fuller, and P. Majlender, “A possibilistic approach to selecting portfolios with highest utility score,” Fuzzy Sets and Systems, Vol.131, No.1, pp. 13-21, 2002.
  4. [4] R. Campbell, R. Huisman, and K. Koedijk, “Optimal portfolio selection in a Value-at-Risk framework,” J. of Banking and Finance, Vol.25, No.9, pp. 1789-1804, 2001.
  5. [5] M. T. Leung, H. Daouk and A. S. Chen, “Using investment portfolio return to combine forecast: a multiobjective approach,” European J. Oper. Res., Vol.134, No.1, pp. 84-102, 2001.
  6. [6] S. C. Liu, S. Y. Wang, and W. H. Qiu, “A mean-variance-skewness model for portfolio selection with transaction costs,” Internat. J. Systems Sci., Vol.34, No.4, pp. 255-262, 2003.
  7. [7] J. Estrada, “The cost of equity in internet stocks: a downside risk approach,” European J. Finance, Vol.10, pp. 239-254, 2004.
  8. [8] D. Lien and Y. K. Tse, “Hedging downside risk: futures vs options,” Internat. Rev. Econom. Finance, Vol.10, No.2, pp. 159-169, 2001.
  9. [9] H. Markowitz, “Portfolio selection: Efficient Diversification of Investment,” Wiley, New York, 1959.
  10. [10] X. Huang, “Portfolio selection with the new definition of risk,” European J. of Operational Research, Vol.186, No.1, pp. 351-357, 2008.
  11. [11] E. Vercher, J. D. Bermudez, and J. V. Segura, “Fuzzy portfolio optimization under downside risk measures,” Fuzzy Sets and Systems, Vol.158, No.7, pp. 769-782, 2007.
  12. [12] Y. J. Liu and W. G. Zhang, “Fuzzy portfolio optimization model under real constraints,” Insurance: Mathematics and Economics, Vol.53, No.3, pp. 704-711, 2013.
  13. [13] D. Dubois and H. Prade, “Fuzzy Sets and Systems: Theory and Application,” Academic Press, New York, 1980.
  14. [14] C. Carlsson and R. Fuller, “On possibilistic mean value and variance of fuzzy numbers,” Fuzzy Sets and Systems, Vol.122, No.2, pp. 315-326, 2001.
  15. [15] A. Saeidifar and E. Pasha, “The possibilistic moments of fuzzy numbers and their applications,” J. of Computational and Applied Mathematics, Vol.223, No.2, pp. 1028-1042, 2009.
  16. [16] W. G. Zhang, Y. J. Liu, and W. J. Xu, “A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs,” European J. of Operational Research, Vol.222, No.2, pp. 341-349, 2012.
  17. [17] P. Gupta, M. K. Mehlawat, M. Inuiguchi, and S. Chandra, “Fuzzy portfolio optimization,” Studies in fuzziness and soft computing, Vol.316, 2014.
  18. [18] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A Fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. on Evolutionary Computation, Vol.6, No.2, 2002.

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