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A portfolio selection model based on the knapsack problem under uncertainty

Author

Listed:
  • Fereshteh Vaezi
  • Seyed Jafar Sadjadi
  • Ahmad Makui

Abstract

One of the primary concerns in investment planning is to determine the number of shares for asset with relatively high net value of share such as Berkshire Hathaway on Stock market. Traditional asset allocation methods like Markowitz theorem gives the solution as a percentage and this ratio may suggest allocation of half of a share on the market, which is impractical. Thus, it is necessary to propose a method to determine the number of shares for each asset. This paper presents a knapsack based portfolio selection model where the expected returns, prices, and budget are characterized by interval values. The study determines the priority and importance of each share in the proposed model by extracting the interval weights from an interval comparison matrix. The resulted model is converted into a parametric linear programming model in which the decision maker is able to determine the optimism threshold. Finally, a discrete firefly algorithm is designed to find the near optional solutions in large dimensions. The proposed study is implemented for some data from the US stock exchange.

Suggested Citation

  • Fereshteh Vaezi & Seyed Jafar Sadjadi & Ahmad Makui, 2019. "A portfolio selection model based on the knapsack problem under uncertainty," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-19, May.
  • Handle: RePEc:plo:pone00:0213652
    DOI: 10.1371/journal.pone.0213652
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    References listed on IDEAS

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    1. Li, Han-Lin & Tsai, Jung-Fa, 2008. "A distributed computation algorithm for solving portfolio problems with integer variables," European Journal of Operational Research, Elsevier, vol. 186(2), pages 882-891, April.
    2. Sugihara, Kazutomi & Ishii, Hiroaki & Tanaka, Hideo, 2004. "Interval priorities in AHP by interval regression analysis," European Journal of Operational Research, Elsevier, vol. 158(3), pages 745-754, November.
    3. Giove, Silvio & Funari, Stefania & Nardelli, Carla, 2006. "An interval portfolio selection problem based on regret function," European Journal of Operational Research, Elsevier, vol. 170(1), pages 253-264, April.
    4. P. Bonami & M. A. Lejeune, 2009. "An Exact Solution Approach for Portfolio Optimization Problems Under Stochastic and Integer Constraints," Operations Research, INFORMS, vol. 57(3), pages 650-670, June.
    5. Francesco Cesarone & Andrea Scozzari & Fabio Tardella, 2013. "A new method for mean-variance portfolio optimization with cardinality constraints," Annals of Operations Research, Springer, vol. 205(1), pages 213-234, May.
    6. Corazza, Marco & Favaretto, Daniela, 2007. "On the existence of solutions to the quadratic mixed-integer mean-variance portfolio selection problem," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1947-1960, February.
    7. Yong-Jun Liu & Wei-Guo Zhang & Jun-Bo Wang, 2016. "Multi-period cardinality constrained portfolio selection models with interval coefficients," Annals of Operations Research, Springer, vol. 244(2), pages 545-569, September.
    8. Li, Xiang & Qin, Zhongfeng, 2014. "Interval portfolio selection models within the framework of uncertainty theory," Economic Modelling, Elsevier, vol. 41(C), pages 338-344.
    9. Duan Li & Xiaoling Sun & Jun Wang, 2006. "Optimal Lot Solution To Cardinality Constrained Mean–Variance Formulation For Portfolio Selection," Mathematical Finance, Wiley Blackwell, vol. 16(1), pages 83-101, January.
    10. Wang, Ying-Ming & Elhag, Taha M.S., 2007. "A goal programming method for obtaining interval weights from an interval comparison matrix," European Journal of Operational Research, Elsevier, vol. 177(1), pages 458-471, February.
    11. D. Goldfarb & G. Iyengar, 2003. "Robust Portfolio Selection Problems," Mathematics of Operations Research, INFORMS, vol. 28(1), pages 1-38, February.
    12. Pierre Bonami & Miguel A. Lejeune, 2009. "An Exact Solution Approach for Integer Constrained Portfolio Optimization Problems Under Stochastic Constraints," Post-Print hal-00421756, HAL.
    13. Dumas, Bernard & Luciano, Elisa, 1991. "An Exact Solution to a Dynamic Portfolio Choice Problem under Transactions Costs," Journal of Finance, American Finance Association, vol. 46(2), pages 577-595, June.
    14. Castro, F. & Gago, J. & Hartillo, I. & Puerto, J. & Ucha, J.M., 2011. "An algebraic approach to integer portfolio problems," European Journal of Operational Research, Elsevier, vol. 210(3), pages 647-659, May.
    15. Kolm, Petter N. & Tütüncü, Reha & Fabozzi, Frank J., 2014. "60 Years of portfolio optimization: Practical challenges and current trends," European Journal of Operational Research, Elsevier, vol. 234(2), pages 356-371.
    16. 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.
    17. MOSSIN, Jan, 1968. "Optimal multiperiod portfolio policies," LIDAM Reprints CORE 19, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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