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Linguistic Z-number weighted averaging operators and their application to portfolio selection problem

Author

Listed:
  • Amir Hosein Mahmoodi
  • Seyed Jafar Sadjadi
  • Soheil Sadi-Nezhad
  • Roya Soltani
  • Farzad Movahedi Sobhani

Abstract

Z-numbers can generate a more flexible structure to model the real information because of capturing expert’s reliability. Moreover, various semantics can flexibly be reflected by linguistic terms under various circumstances. Thus, this study aims to model the portfolio selection problems based on aggregation operators under linguistic Z-number environment. Therefore, a multi-stage methodology is proposed and linguistic Z-numbers are applied to describe the assessment information. Moreover, the weighted averaging (WA) aggregation operator, the ordered weighted averaging (OWA) aggregation operator and the hybrid weighted averaging (HWA) aggregation operator are developed to fuse the input arguments under the linguistic Z-number environment. Then, using the max-score rule and the score-accuracy trade-off rule, three qualitative portfolio models are presented to allocate the optimal assets. These models are suitable for general investors and risky investors. Finally, to illustrate the validity of the proposed qualitative approach, a real case including 20 corporations of Tehran stock exchange market in Iran is provided and the obtained results are analyzed. The results show that combining linguistic Z-numbers with portfolio selection processes can increase the tendencies and capabilities of investors in the capital market and it helps them manage their portfolios efficiently.

Suggested Citation

  • Amir Hosein Mahmoodi & Seyed Jafar Sadjadi & Soheil Sadi-Nezhad & Roya Soltani & Farzad Movahedi Sobhani, 2020. "Linguistic Z-number weighted averaging operators and their application to portfolio selection problem," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-34, January.
    • Handle: RePEc:plo:pone00:0227307
    DOI: 10.1371/journal.pone.0227307
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    References listed on IDEAS

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    2. R. A. Aliev & O. H. Huseynov & R. Serdaroglu, 2016. "Ranking of Z-Numbers and Its Application in Decision Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1503-1519, November.
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    1. Du, Yan-dong & Zhou, Jia-le & Zhou, Xin-yue & Wu, Yang-wen & Lu, Qiang, 2024. "Investment risk assessment of anaerobic fermentation based hydrogen production project based on regret linguistic Z-number averaging Choquet integral operator," Renewable Energy, Elsevier, vol. 227(C).
    2. Rupkumar Mahapatra & Sovan Samanta & Madhumangal Pal & Tofigh Allahviranloo & Antonios Kalampakas, 2024. "A Study on Linguistic Z-Graph and Its Application in Social Networks," Mathematics, MDPI, vol. 12(18), pages 1-24, September.
    3. Pejman Peykani & Mojtaba Nouri & Mir Saman Pishvaee & Camelia Oprean-Stan & Emran Mohammadi, 2023. "Credibilistic Multi-Period Mean-Entropy Rolling Portfolio Optimization Problem Based on Multi-Stage Scenario Tree," Mathematics, MDPI, vol. 11(18), pages 1-23, September.

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