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Bonferroni Weighted Logarithmic Averaging Distance Operator Applied to Investment Selection Decision Making

Author

Listed:
  • Victor G. Alfaro-Garcia

    (Facultad de Contaduría y Ciencias Administrativas, Universidad Michoacana de San Nicolas de Hidalgo, Gral. Francisco J. Múgica S/N, C.U., Morelia 58030, Mexico)

  • Fabio Blanco-Mesa

    (Facultad de Ciencias Económicas y Administrativas, Escuela de Administración de Empresas, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150001, Colombia)

  • Ernesto León-Castro

    (Faculty of Economics and Administrative Sciences, Universidad Católica de la Santísima Concepción, Av. Alonso de Ribera 2850, Concepción 4030000, Chile)

  • Jose M. Merigo

    (Department of Management Control and Information Systems, School of Economic and Business, University of Chile, Av. Diagonal Paraguay, 257, Santiago 8330015, Chile)

Abstract

Distance measures in ordered weighted averaging (OWA) operators allow the modelling of complex decision making problems where a set of ideal values or characteristics are required to be met. The objective of this paper is to introduce extended distance measures and logarithmic OWA-based decision making operators especially designed for the analysis of financial investment options. Based on the immediate weights, Bonferroni means and logarithmic averaging operators, in this paper we introduce the immediate weights logarithmic distance (IWLD), the immediate weights ordered weighted logarithmic averaging distance (IWOWLAD), the hybrid weighted logarithmic distance (HWLD), the Bonferroni ordered weighted logarithmic averaging distance (B-OWLAD) operator, the Bonferroni immediate weights ordered weighted logarithmic averaging distance (B-IWOWLAD) operator and the Bonferroni hybrid weighted logarithmic distance (HWLD). A financial decision making illustrative example is proposed, and the main benefits of the characteristic design of the introduced operators is shown, which include the analysis of the interrelation between the modelled arguments required from the decision makers and the stakeholders, and the comparison to an ideal set of characteristics that the possible companies in the example must portray. Moreover, some families, particular cases and brief examples of the proposed operators, are studied and presented. Finally, among the main advantages are the modeling of diverse perspectives, attitudinal characteristics and complex scenarios, through the interrelation and comparison between the elements with an ideal set of characteristics given by the decision makers and a set of options.

Suggested Citation

  • Victor G. Alfaro-Garcia & Fabio Blanco-Mesa & Ernesto León-Castro & Jose M. Merigo, 2022. "Bonferroni Weighted Logarithmic Averaging Distance Operator Applied to Investment Selection Decision Making," Mathematics, MDPI, vol. 10(12), pages 1-13, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2100-:d:840885
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    References listed on IDEAS

    as
    1. Sondra G. Beverly & Marianne A. Hilgert & Jeanne M. Hogarth, 2003. "Patterns of financial behaviors: implications for community educators and policymakers," Proceedings 883, Federal Reserve Bank of Chicago.
    2. Luis F. Espinoza-Audelo & Maricruz Olazabal-Lugo & Fabio Blanco-Mesa & Ernesto León-Castro & Victor Alfaro-Garcia, 2020. "Bonferroni Probabilistic Ordered Weighted Averaging Operators Applied to Agricultural Commodities’ Price Analysis," Mathematics, MDPI, vol. 8(8), pages 1-16, August.
    3. Rodrigo Gómez Monge & Evaristo Galeana Figueroa & Víctor G. Alfaro-García & José M. Merigó & Ronald R. Yager, 2021. "Variances and Logarithmic Aggregation Operators: Extended Tools for Decision-Making Processes," Mathematics, MDPI, vol. 9(16), pages 1-19, August.
    4. Fabio Blanco-Mesa & Ernesto León-Castro & Jorge Romero-Muñoz, 2021. "Pythagorean Membership Grade Aggregation Operators: Application in Financial knowledge," Mathematics, MDPI, vol. 9(17), pages 1-15, September.
    5. Alex Bennet & David Bennet, 2008. "The Decision-Making Process in a Complex Situation," International Handbooks on Information Systems, in: Handbook on Decision Support Systems 1, chapter 1, pages 3-20, Springer.
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