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A New Fuzzy Approach for Multi-period Portfolio Optimization Under Uncertainty

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Progress in Intelligent Decision Science (IDS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1301))

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Abstract

Portfolio optimization is one of the most important issues in the financial world, so investors are trying to make decisions that are most in line with the real world. But the uncertainty in data and parameters, and the contradiction in the investor’s goals, adds to the complexity of the stock portfolio optimization problem, and the other hand because of the efficient market, it is necessary to use multi-period models that, unlike single-period models, allow the investor to review their wealth at the beginning of each period. In this paper We formulate a bi-objective mean- CVaR portfolio optimization model based on General Fuzzy theory. Then the proposed model is solved by the Epsilon constraint method. Finally, we use the data of 15 companies from different industries operating in the Iran Stock Exchange Market in 1398, we examine the validity of the model and its efficiency.

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Notes

  1. 1.

    Value at Risk.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Zahra Khandan

  2. Iran University of Science & Technology, Tehran, Iran

    Emran Mohammadi

Authors
  1. Zahra Khandan
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  2. Emran Mohammadi
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Corresponding author

Correspondence to Emran Mohammadi .

Editor information

Editors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey

    Tofigh Allahviranloo

  2. Faculty of Engineering and Natural Sciences, Bahçeşehir University, Istanbul, Turkey

    Soheil Salahshour

  3. Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey

    Nafiz Arica

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Khandan, Z., Mohammadi, E. (2021). A New Fuzzy Approach for Multi-period Portfolio Optimization Under Uncertainty. In: Allahviranloo, T., Salahshour, S., Arica, N. (eds) Progress in Intelligent Decision Science. IDS 2020. Advances in Intelligent Systems and Computing, vol 1301. Springer, Cham. https://doi.org/10.1007/978-3-030-66501-2_25

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