Sensibilidad de las calificaciones crediticias a elasticidades de las razones financieras respecto a variables macroeconómicas: un modelo de árboles de decisión clasificadores para las empresas mexicanas

• Ana Cecilia Parada Rojas ^[1] ; Jorge Omar Razo De Anda ^[1] ; Salvador Cruz Aké ^[1]
  1. [1] Instituto Politécnico Nacional

     Instituto Politécnico Nacional

     México

     
• Localización: Contaduría y administración, ISSN 0186-1042, ISSN-e 2448-8410, Vol. 66, Nº. 4, 2021
• Idioma: español
• Títulos paralelos:
  • Credit rating sensitivity to elasticities of financial ratios respect to macroeconomic variables: A classifier decision trees model for mexican firms
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Los factores que influyen en un cambio de calificación crediticia se desconocen porque el proceso de asignación depende de empresas privadas, por lo que identificar estos factores en combinación con determinadas situaciones macroeconómicas es fundamental para gestionar el riesgo crediticio, más allá del proceso privado de asignación de calificaciones. El objetivo de este artículo es determinar un conjunto de reglas que permitan a la gerencia de la empresa anticipar el cambio en la calificación crediticia de una empresa mexicana, considerando los niveles de elasticidad de sus razones financieras a variables macroeconómicas desde un enfoque de minería de datos, a través de un proceso iterativo de ajuste de regresión logística y un modelo de árbol de decisión clasificador utilizando datos públicos

  • English

    The factors that influence a change in credit rating are unknown because the allocation process depends on private companies, so identifying these factors in combination with certain macroeconomic situations is essential to manage the credit risk, beyond the private process of assigning grades. The objective of this paper is to determine a set of rules that allow to firm’s management to anticipate the change in the credit rating of a Mexican firm, considering the levels of elasticity of its financial ratios to macroeconomic variables from a data mining approach, through an iterative logit regression fitting process and a classification decision tree model using public data

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