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Optimización de Carteras de Renta Variable con Aprendizaje Automático

Author

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  • Alejandro Vargas Sánchez

    (Universidad Privada Boliviana)

  • André Nicolas Monje Prudencio

    (Universidad Privada Boliviana)

Abstract

Esta investigación propone aplicar técnicas de Aprendizaje Automático en la gestión de carteras de inversión de renta variable, con el fin de mejorar el proceso de estructuración de portafolios y generar resultados empíricos óptimos en comparación con técnicas tradicionales. Se utiliza la técnica de Clustering Affinity Propagation, complementada con el algoritmo Graphical Lasso y Multi-Dimensional Scaling, para identificar patrones de comportamiento similar entre empresas y optimizar la composición del portafolio.

Suggested Citation

  • Alejandro Vargas Sánchez & André Nicolas Monje Prudencio, 2023. "Optimización de Carteras de Renta Variable con Aprendizaje Automático," Investigación & Desarrollo, Universidad Privada Boliviana, vol. 23(2), pages 23-45.
    • Handle: RePEc:iad:wpaper:0223
    DOI: 10.23881/idupbo.023.2-2e
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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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