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On Fuzzy Portfolio Selection Problems

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Abstract

The uncertainty of a financial market is traditionally dealt with probabilistic approaches. However, there are many non-probabilistic factors that affect the financial markets. A number of empirical studies showed limitation of using probabilistic approaches in characterizing the uncertainty of the financial markets. Fuzzy set is a powerful tool used to describe an uncertain environment with vagueness, ambiguity or some other type of fuzziness, which are always involved in not only the financial markets but also the behavior of the financial managers' decisions. In a financial optimization model using fuzzy approaches, quantitative analysis, qualitative analysis, the experts' knowledge and the managers' subjective opinions can be better integrated. In this paper, we give an overview on the development of fuzzy portfolio selection to date. Some related problems that might deserve further investigations are also discussed.

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References

  • Bellman, R. and L. A. Zadeh. (1970). “Decision Making in a Fuzzy Environment,” Management Science 17, 141–164.

    Google Scholar 

  • Deng, X. T., S. Y. Wang, and Y. S. Xia. (2000). “Criteria, Models and Strategies in Portfolio Selection,” Advanced Modeling and Optimization 2, 79–104.

    Google Scholar 

  • Dourra, H. and P. Siy. (2001). “Stock Evaluation Using Fuzzy Logic,” International Journal of Theoretical and Applied Finance 4, 585–602.

    Google Scholar 

  • Dubois, D. and H. Prade. (1988). Possibility Theory. New York: Plenum Press.

    Google Scholar 

  • Fang, Y., K. K. Lai, and S. Y. Wang. (2001). “A Model for Portfolio Selection with Interval Coefficients,” submitted to Fuzzy Optimization and Decision Making.

  • Inuiguchi, M. and J. Ramik. (2000). “Possibilistic Linear Programming: A Brief Review of Fuzzy Mathematical Programming and a Comparison with Stochastic Programming in Portfolio Selection Problem,” Fuzzy Sets and Systems 111, 3–28.

    Google Scholar 

  • Inuiguchi, M. and T. Tanino. (2000). “Portfolio Selection under Independent Possibilistic Information,” Fuzzy Sets and Systems 115, 83–92.

    Google Scholar 

  • Heilpern, S. (1992). “The Expected Value of a Fuzzy Number,” Fuzzy Sets and Systems 47, 81–86.

    Google Scholar 

  • Kataoka, S. (1963). “A Stochastic Programming Model,” Econometrica 31, 181–196.

    Google Scholar 

  • Konno, K. and H. Yamazika. (1991). “Mean Absolute Deviation Portfolio Optimization Model and Its Application to Tokyo Stock Market,” Management Science 37, 519–531.

    Google Scholar 

  • Lai, K. K., S. Y. Wang, J. H. Zeng, and S. S. Zhu. (2001a). “Portfolio Selection Models with Transaction Costs: Crisp Case and Interval Number Case.” In D. Li (ed.), Proceedings of the 5th International Conference on Optimization Techniques and Applications, Hong Kong, December, 943–950.

  • Lai, K. K., S. Y. Wang, J. P. Xu, S. S. Zhu, and Y. Fang. (2001b). “A Class of Linear Interval Programming Problems and Its Application to Portfolio Selection,” To appear in IEEE Transactions on Fuzzy Systems.

  • Leoń, T., V. Liern, and E. Vercher. (2000). “Fuzzy Mathematical Programming for Portfolio Management.” In M. Boilla, T. Casasus, and R. Sala (eds.), Financial Modelling. Heidelberg: Physica-Verlag, 241–256.

    Google Scholar 

  • Leoń, T., V. Liern, and E. Vercher. (2002). “Viability of Infeasible Portfolio Selection Problems: A Fuzzy Approach,” European Journal of Operational Research 139, 178–189.

    Google Scholar 

  • Liu, B. (2002). Theory and Practice of Uncertain Programming. Heidelberg: Physica-Verlag.

    Google Scholar 

  • Mansini, R. and M. G. Speranza. (1999). “Heuristic Algorithms for the Portfolio Selection Problem with Minimum Transaction Lots,” European Journal of Operational Research 114, 219–233.

    Google Scholar 

  • Markowitz, H. (1952). “Portfolio Selection,” Journal of Finance 7, 77–91.

    Google Scholar 

  • Östermark, R. (1996). “A Fuzzy Control Model (FCM) for Dynamic Portfolio Management,” Fuzzy Sets and Systems 78, 243–254.

    Google Scholar 

  • Parra, M. A., A. B. Terol, and M. V. R. Uría. (2001). “A Fuzzy Goal Programming Approach to Portfolio Selection,” European Journal of Operational Research 133, 287–297.

    Google Scholar 

  • Peters, E. E. (1996). Chaos and Orders in the Capital Markets. New York: John Wiley & Sons Inc.

    Google Scholar 

  • Philippe, J. (1997). Value at Risk: The New Benchmark for Controlling Derivatives Risk. McGraw-Hill.

  • Ramaswamy, S. (1998). Portfolio Selection Using Fuzzy Decision Theory, Working Paper of Bank for International Settlements, No.59.

  • Roy, A. D. (1952). “Safety-First and the Holding of Assets,” Econometrics 20, 431–449.

    Google Scholar 

  • Saaty, T. L. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill.

    Google Scholar 

  • Sharpe, W. (1964). “Capital Asset Prices: ATheory of Market Equilibrium under Conditions of Risk,” Journal of Finance 19, 425–442.

    Google Scholar 

  • Speranza, M. G. (1993). “Linear Programming Models for Portfolio Optimization,” Finance 14, 107–123.

    Google Scholar 

  • Sturm, J. (1999). “Using SeDuMi 1.02, a MATLAB Toolbox for Optimization Over Symmetric Cones,” Optimization Methods and Software 11, 625–653.

    Google Scholar 

  • Tanaka, H. and P. Guo. (1999). “Portfolio Selection Based on Upper and Lower Exponential Possibility Distributions,” European Journal of Operational Research 114, 115–126.

    Google Scholar 

  • Tanaka, H., P. Guo, and I. B. Türksen. (2000). “Portfolio Selection Based on Fuzzy Probabilities and Possibility Distributions,” Fuzzy Sets and Systems 111, 387–397.

    Google Scholar 

  • Tay, N. S. P. and S. C. Linn. (2001). “Fuzzy Inductive Reasoning, Expectation Formation and the Behavior of Security Prices,” Journal of Economic Dynamics & Control 25, 321–361.

    Google Scholar 

  • Vandenberghe, L. and S. Boyd. (1996). “Semidefinite Programming,” SIAM Review 38, 49–95.

    Google Scholar 

  • Watada, J. (2001). “Fuzzy Portfolio Model for Decision Making in Investment.” In Y. Yoshida (ed.), Dynamical Aspects in Fuzzy Decision Making. Heidelberg: Physica-Verlag, 141–162.

    Google Scholar 

  • Zadeh, L. A. (1965). “Fuzzy Sets,” Information and Control 8, 338–353.

    Google Scholar 

  • Zadeh, L. A. (1978). “Fuzzy Sets as a Basis for a Theory of Possibility,” Fuzzy Sets and Systems 1, 3–28.

    Google Scholar 

  • Zeng, J. H. (2002). Portfolio Selection Based on Probability Theory and Fuzzy Set Theory and Its Empirical Studies. PhD thesis, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing.

    Google Scholar 

  • Zimmermann, H. J. (1985). Fuzzy Set Theory and Its Applications. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Zmeskal, Z. (2001). “Application of the Fuzzy-Stochastic Methodology to Appraising the Firm Value as a European Call Option,” European Journal of Operational Research 135, 303–310.

    Google Scholar 

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Wang, S., Zhu, S. On Fuzzy Portfolio Selection Problems. Fuzzy Optimization and Decision Making 1, 361–377 (2002). https://doi.org/10.1023/A:1020907229361

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