proyecto final de grado.page.titleprefix Análisis de riesgo crediticio de empresas
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Date
2009
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Abstract
"En este escrito se presenta un análisis de riesgo crediticio para gestionar el otorgamiento de préstamos a empresas. El objetivo es predecir la insolvencia de las empresas. Con este fín, se desarrolla un modelo que las califica de manera tal que una entidad financiera pueda decidir si les otorgará o no un crédito.
En primera instancia, se presentan las metodologías que se utilizan tradicionalmente para gestionar el riesgo crediticio y se elige el método más adecuado para este estudio, presentando las justificaciones correspondientes.
Seguido de esto, se desarrolla el modelo utilizando regresión logística. Este método consiste en estimar la probabilidad de que ocurra la insolvencia en función de un conjunto de variables de comportamiento crediticio, que se consideran causales de la misma o que son manifestaciones tempranas que permiten anticiparla. Es así como se obtiene un algoritmo que permite precedir el atraso en el pago de las deudas de una empresa.
Una vez desarrollado el modelo, se lo valida con diferentes indicadores y ensayos. Los valores obtenidos fueron muy satisfactorios, con lo cual se pudo determinar que el algoritmo tiene un alto poder de predicción.
Finalmente, se presenta una aplicación práctica del modelo para maximizar el resultado de la entidad financiera. Esta incluye una simulación de la utilidad que puede obtenerse en función de la fluctuación de las tasas activa y pasiva".
"This essay presents a credit risk management analysis for business loans. The aim is to predict the insolvency of enterprises. To this end, a model that classifies businesses is developed so that a financial institution can decide whether or not to grant them credit. In the first place, the traditionally used methodologies to manage credit risk are presented and the most suitable method for this study is chosen, making the proper justifications. Following this, the model is developed using logistic regression. This method involves estimating the probability of occurrence of the insolvency according to a set of credit behavior variables, which are considered causative of it or are early manifests that allow its anticipation. In this way you generate an algorithm that predicts the delay in payment of a company's debt. Once developed, the model is validated with various indicators and tests. Given that the values obtained were very satisfactory, there is sufficient proof to conclude that the algorithm is highly predictive. Finally, there is a practical application of the model to maximize the profit of the financial institution. Furthermore, this essay includes a simulation of how the utility is affected when the interest rates and credit spreads fluctuate".
"This essay presents a credit risk management analysis for business loans. The aim is to predict the insolvency of enterprises. To this end, a model that classifies businesses is developed so that a financial institution can decide whether or not to grant them credit. In the first place, the traditionally used methodologies to manage credit risk are presented and the most suitable method for this study is chosen, making the proper justifications. Following this, the model is developed using logistic regression. This method involves estimating the probability of occurrence of the insolvency according to a set of credit behavior variables, which are considered causative of it or are early manifests that allow its anticipation. In this way you generate an algorithm that predicts the delay in payment of a company's debt. Once developed, the model is validated with various indicators and tests. Given that the values obtained were very satisfactory, there is sufficient proof to conclude that the algorithm is highly predictive. Finally, there is a practical application of the model to maximize the profit of the financial institution. Furthermore, this essay includes a simulation of how the utility is affected when the interest rates and credit spreads fluctuate".
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Keywords
ANALISIS FINANCIERO, ANALISIS DE REGRESION, GESTION DE RIESGOS, EMPRESAS, CREDITO