Using Compensatory Fuzzy Logic to Model an Investor’s Preference Regarding Portfolio Stock Selection within Markowitz’s Mean-Variance Framework

• Autores: Luis Cisneros, Raúl Porras, Gilberto Rivera, Rafael Alejandro Espín Andrade, Vicente García
• Localización: Computación y Sistemas (CyS), ISSN 1405-5546, ISSN-e 2007-9737, Vol. 28, Nº. 3, 2024, págs. 1349-1359
• Idioma: inglés
• DOI: 10.13053/cys-28-3-5187
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• Resumen

  • Abstract: We analyze the use of Compensatory Fuzzy Logic (CFL) applied to an optimization model to reflect an investor’s preferences regarding portfolio stock selection. CFL is a framework that allows the construction of fuzzy predicates using fuzzy parametrized linguistic variables. Although the potential of a CFL predicate to model preferences is high, to the best of our knowledge, this is the first use of this strategy to do so. Real data from the Mexican Stock Exchange was employed to create a test instance. Portfolios were obtained using the Particle Swarm Optimization algorithm. By maximising the degree of truth of the predicate representing the investor’s preferences, the model is able to reflect investor profiles regarding the return-risk relation of the portfolios. Three artificial investor profiles were defined during the experimentation; the model was able to reflect all of these preferences.

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