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An interval linear programming approach for portfolio selection model

Author

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  • P. Kumar
  • G. Panda
  • U.C. Gupta

Abstract

Uncertainty plays an important role in predicting the future earning of the assets in the financial market and it is generally measured in terms of probability. But in some cases, it would be a good idea for an investor to state the expected returns on assets in the form of closed intervals. Therefore, in this paper, we consider a portfolio selection problem wherein expected return of any asset, risk level and proportion of total investment on assets are in the form of interval, and obtain an optimum (best) portfolio. Such portfolio gives the total expected return and proportion of total investment on assets in the form of interval. The proposed portfolio model is solved by considering an equivalent linear programming problem, where all the parameters of the objective function and constraints as well as decision variables are expressed in form of intervals. The procedure gives a strongly feasible optimal interval solution of such problem based on partial order relation between intervals. Efficacy of the results is demonstrated by means of numerical examples.

Suggested Citation

  • P. Kumar & G. Panda & U.C. Gupta, 2016. "An interval linear programming approach for portfolio selection model," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 27(1/2), pages 149-164.
    • Handle: RePEc:ids:ijores:v:27:y:2016:i:1/2:p:149-164
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    Citations

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    Cited by:

    1. P. Kumar & A. K. Bhurjee, 2022. "Multi-objective enhanced interval optimization problem," Annals of Operations Research, Springer, vol. 311(2), pages 1035-1050, April.
    2. P. Kumar & Jyotirmayee Behera & A. K. Bhurjee, 2022. "Solving mean-VaR portfolio selection model with interval-typed random parameter using interval analysis," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 41-77, March.
    3. Elif Garajová & Milan Hladík & Miroslav Rada, 2019. "Interval linear programming under transformations: optimal solutions and optimal value range," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(3), pages 601-614, September.
    4. P. Kumar & G. Panda, 2017. "Solving nonlinear interval optimization problem using stochastic programming technique," OPSEARCH, Springer;Operational Research Society of India, vol. 54(4), pages 752-765, December.

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