A fuzzy extension of MEREC method using parabolic measure and its applications

Authors

  • Monika Narang Department of Mathematics, D.S.B. Campus, Kumaun University, Nainital, India
  • Arun Kumar Department of Mathematics, Statistics and Computer Science, GBPUA&T, Pantnagar, India
  • Rajat Dhawan Department of Training and Placement, Six Sigma Institute of Technology & Science, Rudrapur, India

DOI:

https://doi.org/10.31181/jdaic10020042023n

Keywords:

MEREC, fuzzy MEREC, fuzzy TOPSIS, Stock portfolio selection, MCDM

Abstract

Human qualitative judgments are often characterized by uncertainty and predictability. Decision-makers tend to be more confident in making linguistic decisions than in crisp value judgments. MEREC is capable of achieving relative objective weights of several conflicting criteria. This paper contains two parts, first, the extension of MEREC method in fuzzy circumstances based on linguistic terms in which a parabolic measure has been used to calculate the overall performance of alternatives as it is able to work according the definition of TFNs and to show the applicability, a simple decision matrix is ​​analysed in a fuzzy environment. Second, a new hybrid ranking methodology Fuzzy MEREC-TOPSIS for multi criteria decision making. Further, to illustrate the credibility and effectiveness of the proposed hybrid ranking method, a real-world example of stock selection has been used. The portfolio is constructed using ranking of the stocks received through the proposed method and capital is allocated according to the order of preference of stocks. To validate the proposed ranking model, the next 30 days closing price of each stock is predicted by a deep recurrent neural network and the portfolio for future investments is analysed. The results of the future analysis validate the credibility of the portfolio.

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Published

20.04.2023

How to Cite

Narang, M., Kumar, A., & Dhawan, R. (2023). A fuzzy extension of MEREC method using parabolic measure and its applications. Journal of Decision Analytics and Intelligent Computing, 3(1), 33–46. https://doi.org/10.31181/jdaic10020042023n