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ISSN (print) 1136-0593 · ISSN (online) 2445-4192


Victor G. Alfaro-García. Universidad Michoacana de San Nicolas de Hidalgo, Facultad de Contaduría y Ciencias Administrativas. Morelia, México. E-Mail: victor.alfaro@umich.mx

Ernesto León-Castro. Faculty of Economics and Administrative Sciences, Universidad Católica de la Santísima Concepción. Concepción. Chile. E-Mail: eleon@ucsc.cl

Fabio Blanco-Mesa. Universidad Pedagógica y Tecnológica de Colombia, Facultad de Ciencias Económicas y Administrativas. Boyacá, Colombia. E-Mail: fabio.blanco01@uptc.edu.co


This paper presents applications of computational intelligence tools in investment decision making processes. The aim is to build effective stock portfolios, for diverse risk-oriented individuals, including both, objective and subjective inputs. The proposed methods include the analysis of 5 different stock shares from companies that are in the Nasdaq 100 index, namely: Microsoft, Amazon.com Inc, MercadoLibre Inc, Intel Corporation and Facebook Inc using the ordered weighted average distance (OWAD) operator, the ordered weighted averaging adequacy coefficient (OWAAC) operator and the ordered weighted averaging index of maximum and minimum level (OWAIMAM) operator. Results show that a risk seeking investor would prefer stocks that display the behavior of the analyzed dataset of Amazon and a conservative investor would prefer Intel. The main advantage of the proposed methods is introducing multiple objective and subjective data in a single formulation that includes risk aversion, aptitude, expectations, and investment inputs, providing a wider representation of options and scenarios.

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