Modelación en riesgo financiero: aplicaciones con bibliometría y Latent Dirichlet Allocation
DOI:
https://doi.org/10.17268/sciendo.2025.030Palabras clave:
Riesgo financiero, modelación, Latent Dirichlet Allocation, big data, insolvenciaResumen
El presente tiene como objetivo explorar la importancia de la modelación en el riesgo financiero, un aspecto clave para la estabilidad de las instituciones financieras y las empresas en un entorno global caracterizado por incertidumbres económicas. Utilizando herramientas de bibliometría y Latent Dirichlet Allocation (LDA), se analizan los avances recientes en el campo, desde el uso de big data y algoritmos genéticos mejorados hasta la adopción de modelos predictivos avanzados. Este enfoque permite identificar patrones de investigación y tendencias clave, destacando el papel de la colaboración internacional y el crecimiento de la producción científica en la gestión del riesgo financiero. Los resultados subrayan la necesidad de seguir desarrollando modelos más sofisticados para predecir insolvencias y mejorar la capacidad de respuesta ante riesgos financieros en mercados cada vez más interconectados.
Citas
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Derechos de autor 2025 Alexander Fernando Haro Sarango, Sara Isabel Cabanillas Ñaño
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
