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A possibilistic portfolio adjusting model with new added assets

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  • Zhang, Wei-Guo
  • Xiao, Wei-Lin
  • Xu, Wei-Jun

Abstract

In order to fit changes in financial markets, portfolio managers often need to revise an existing portfolio. This article analyzes the portfolio adjusting problem with new added assets. We propose a possibilistic portfolio adjusting model with transaction costs and bounded constraints on holdings of assets, which can be transformed into a linear programming problem. Both the lower bounds on holdings and the total investment constraints influence the optimal portfolio adjusting strategies. Furthermore, a numerical example of a portfolio adjusting problem is given to illustrate our proposed effective approaches. The numerical results show the case that investors do not need to invest total capital and to hold all assets in the portfolio for some required return levels.

Suggested Citation

  • Zhang, Wei-Guo & Xiao, Wei-Lin & Xu, Wei-Jun, 2010. "A possibilistic portfolio adjusting model with new added assets," Economic Modelling, Elsevier, vol. 27(1), pages 208-213, January.
  • Handle: RePEc:eee:ecmode:v:27:y:2010:i:1:p:208-213
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    References listed on IDEAS

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    12. Zhang, Wei-Guo & Zhang, Xi-Li & Xiao, Wei-Lin, 2009. "Portfolio selection under possibilistic mean-variance utility and a SMO algorithm," European Journal of Operational Research, Elsevier, vol. 197(2), pages 693-700, September.
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    Citations

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    Cited by:

    1. Li, Ting & Zhang, Weiguo & Xu, Weijun, 2013. "Fuzzy possibilistic portfolio selection model with VaR constraint and risk-free investment," Economic Modelling, Elsevier, vol. 31(C), pages 12-17.
    2. Liu, Yong-Jun & Zhang, Wei-Guo & Zhang, Pu, 2013. "A multi-period portfolio selection optimization model by using interval analysis," Economic Modelling, Elsevier, vol. 33(C), pages 113-119.
    3. Zhang, Wei-Guo & Liu, Yong-Jun & Xu, Wei-Jun, 2012. "A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs," European Journal of Operational Research, Elsevier, vol. 222(2), pages 341-349.

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