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Optimization of Financial Asset Neutrosophic Portfolios

Author

Listed:
  • Marcel-Ioan Boloș

    (Department of Finance and Banks, University of Oradea, 410087 Oradea, Romania)

  • Ioana-Alexandra Bradea

    (Department of Informatics and Cybernetics, Bucharest University of Economic Studies, 010374 Bucharest, Romania)

  • Camelia Delcea

    (Department of Informatics and Cybernetics, Bucharest University of Economic Studies, 010374 Bucharest, Romania)

Abstract

The purpose of this paper was to model, with the help of neutrosophic fuzzy numbers, the optimal financial asset portfolios, offering additional information to those investing in the capital market. The optimal neutrosophic portfolios are those categories of portfolios consisting of two or more financial assets, modeled using neutrosophic triangular numbers, that allow for the determination of financial performance indicators, respectively the neutrosophic average, the neutrosophic risk, for each financial asset, and the neutrosophic covariance as well as the determination of the portfolio return, respectively of the portfolio risk. There are two essential conditions established by rational investors on the capital market to obtain an optimal financial assets portfolio, respectively by fixing the financial return at the estimated level as well as minimizing the risk of the financial assets neutrosophic portfolio. These conditions allowed us to compute the financial assets’ share in the total value of the neutrosophic portfolios, for which the financial return reaches the level set by investors and the financial risk has the minimum value. In financial terms, the financial assets’ share answers the legitimate question of rational investors in the capital market regarding the amount of money they must invest in compliance with the optimal conditions regarding the neutrosophic return and risk.

Suggested Citation

  • Marcel-Ioan Boloș & Ioana-Alexandra Bradea & Camelia Delcea, 2021. "Optimization of Financial Asset Neutrosophic Portfolios," Mathematics, MDPI, vol. 9(11), pages 1-36, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1162-:d:559440
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    References listed on IDEAS

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    1. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    2. Fernando A. F. Ferreira & Sérgio P. Santos & Paulo M. M. Rodrigues & Ronald W. Spahr, 2014. "How to create indices for bank branch financial performance measurement using MCDA techniques: an illustrative example," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 15(4), pages 708-728, September.
    3. Hossein Dastkhan & Hamid Reza Golmakani & Naser Shams Gharneh, 2013. "How to obtain a series of satisfying portfolios: a fuzzy portfolio management approach," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 14(3), pages 333-351.
    4. F. M. Bandi & J. R. Russell, 2008. "Microstructure Noise, Realized Variance, and Optimal Sampling," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 75(2), pages 339-369.
    5. Lidiia Karpenko & Iryna Chunytska & Nataliia Oliinyk & Nataliia Poprozman & Olha Bezkorovaina, 2020. "Consideration of Risk Factors in Corporate Property Portfolio Management," JRFM, MDPI, vol. 13(12), pages 1-14, November.
    6. Irina Georgescu & Louis Aimé Fono, 2019. "A Portfolio Choice Problem in the Framework of Expected Utility Operators," Mathematics, MDPI, vol. 7(8), pages 1-16, July.
    7. Erick Delage & Yinyu Ye, 2010. "Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems," Operations Research, INFORMS, vol. 58(3), pages 595-612, June.
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