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Fuzzy Portfolio Selection Model Using Linear Programming

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13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018 (ICAFS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 896))

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Abstract

This paper development of crisp and fuzzy portfolio models using linear programming. Using Lagrange multiplier method the solution of linear programming is carried out. As a input data past gains of assets and values of expect gains are taken. The portfolio selection model, in the form of linear programming, based on the values of expected gains and the variance of securities is formulated. The gradient method is connected to discover weights values of assets. The α level method and interim number arithmetic is utilized to take care of fuzzy enhancement issue and locate the ideal fuzzy estimations of the securities.

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Author information

Authors and Affiliations

  1. Department of International Business, Near East University, Nicosia, Mersin 10, TRNC, Turkey

    Mustafa Menekay

Authors
  1. Mustafa Menekay
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Correspondence to Mustafa Menekay .

Editor information

Editors and Affiliations

  1. Joint MBA Program, Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Rafik A. Aliev

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  4. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA

    Mo. Jamshidi

  5. Department of Mechatronics, Near East University, Nicosia TRNC, Turkey

    Fahreddin M. Sadikoglu

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Menekay, M. (2019). Fuzzy Portfolio Selection Model Using Linear Programming. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_79

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