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Selección de cartera: un enfoque de sesgos de comportamiento

Author

Listed:
  • Francisco Vargas Serrano

    (Universidad de Sonora, México)

  • José Arturo Montoya

    (Universidad de Sonora, México)

  • José del Carmen Jiménez Hernández

    (Universidad Tecnológica de la Mixteca, México)

Abstract

El objetivo es proporcionar carteras financieras óptimas en función de una verosimilitud normal, una distribución a priori sobre los parámetros del modelo de valoración de activos y la opinión del inversionista sobre cómo ponderar la verosimilitud y la a priori en la construcción de la cartera. Se aplica la metodología de inferencia bayesiana sesgada a la selección de carteras de media-varianza, con diferentes configuraciones de sesgo o ponderación. Los resultados muestran una propuesta eficaz para encontrar carteras óptimas que reflejen ponderaciones hechas sobre la verosimilitud y las creencias a priori. Además, incluir sesgos en la selección de carteras puede ser relevante para la optimización de la cartera. La propuesta contribuye al campo de los sesgos de finanzas conductuales y se puede aplicar fácilmente a otros modelos financieros que han sido tratados desde un enfoque bayesiano. En conclusión, la propuesta proporciona ponderaciones óptimas para la cartera que reflejan tanto los datos como las creencias, y la inclusión de sesgos en la optimización de la cartera puede ayudar a construir carteras óptimas que incorporen preferencias de riesgo y objetivos de inversión.

Suggested Citation

  • Francisco Vargas Serrano & José Arturo Montoya & José del Carmen Jiménez Hernández, 2025. "Selección de cartera: un enfoque de sesgos de comportamiento," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 20(2), pages 1-21, Abril - J.
  • Handle: RePEc:imx:journl:v:20:y:2025:i:2:a:8
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    References listed on IDEAS

    as
    1. Meir Statman, 1999. "Behaviorial Finance: Past Battles and Future Engagements," Financial Analysts Journal, Taylor & Francis Journals, vol. 55(6), pages 18-27, November.
    2. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    3. Begoña Font, 2016. "Bootstrap estimation of the efficient frontier," Computational Management Science, Springer, vol. 13(4), pages 541-570, October.
    4. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    5. P. G. Bissiri & C. C. Holmes & S. G. Walker, 2016. "A general framework for updating belief distributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 1103-1130, November.
    6. Imad A. Moosa & Vikash Ramiah, 2017. "The Financial Consequences of Behavioural Biases," Springer Books, Springer, number 978-3-319-69389-7, March.
    7. Bodnar, Taras & Lindholm, Mathias & Niklasson, Vilhelm & Thorsén, Erik, 2022. "Bayesian portfolio selection using VaR and CVaR," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    8. Campbell Harvey & John Liechty & Merrill Liechty & Peter Muller, 2010. "Portfolio selection with higher moments," Quantitative Finance, Taylor & Francis Journals, vol. 10(5), pages 469-485.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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