FUZZY PORTFOLIO OPTIMIZATION USING QUADRATIC PROGRAMMING
DOI:
https://doi.org/10.61841/39619707Keywords:
Portfolio optimization, quadratic programming, fuzzy optimization, fuzzy portfolio selectionAbstract
This paper presents the arrangement of fuzzy portfolio advancement utilizing the Lagarrange multiplier technique. The definition of the nonlinear improvement model for the portfolio advancement issue and its answer are given. As information for portfolio improvement, the recorded returns of benefits and estimations of expected returns are taken. At the point when portfolio improvement issues are portrayed with vulnerability and adaptability in issue imperatives, the fuzzy sets are an appropriate portrayal for demonstrating this kind of enhancement issue. The detailing of the fluffy portfolio advancement issue and its answer are given. The level method is utilized to characterize vitality work for fuzzy portfolios and find ideal fuzzy estimations of the protections.
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References
[1] Harry Markowitz. “Portfolio Selection." The Journal of Finance, vol. VII, No. 1, March 1952.
[2] Konno, H., and H. Yamazaki (1991): “Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market," Management Science, 37, p. 519-531.
[3] R. Mansini and M.G. Speranza, Heuristic algorithms for the portfolio selection problem with minimum transaction lots, European Journal of Operational Research 114, p. 219–233, 1999.
[4] A.Cichocki and R.Unbehauen. Neural Networks for Optimization and Signal Processing, John Wiley & Sons, Inc., New York, NY, USA
[5] Frank S. Budnick. “Applied Mathematics for Business, Economics, and the Social Sciences'." McGraw-Hill International Edition, USA, 1993.
[6] K.K. Lai, S.Y. Wang, J.P. Xu, S.S. Zhu, and Y.Fang, “A Class of Linear Interval Programming Problems and Its Application to Portfolio Selection." IEEE Transactions on Fuzzy Systems, Vol. 10, No. 6, pp. 698–703, December 2002.
[7] H. J. Zimmermann. Description and optimization of fuzzy systems. International Journal of General Systems, 2:209–215, 1976.
[8] H. Ishibuchi, “A fuzzy neural network," in “Fuzzy Modelling: Paradigms and Practice," ed. Witold Pedryz, Kluwer Academic Publisher, Boston, 1996.
[9] Rahib Abiyev, Mustafa Menekay. Linear programming models for portfolio selection. Second International Conference on Softcomputing and Computing with Words in System Analysis, Decision, and Control. ICSCCW 2003.Dedicated to Prof. Zadeh. Antalya, Turkey, September 9-11, 2003, pp. 226-231.
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