Aproximación predictiva al riesgo crediticio comercial en empresas alimenticias ecuatorianas

Gustavo Flores-Sánchez; Jorge Campoverde-Campoverde; Armando Romero-Galarza; Katherine Coronel-Pangol

doi:10.18046/j.estger.2021.160.4022

Aproximación predictiva al riesgo crediticio comercial en empresas alimenticias ecuatorianas

Translated title of the contribution: Predictive approach to commercial credit risk in ecuadorian food companies

Universidad de Cuenca

Research output: Contribution to journal › Article › peer-review

Abstract

This article aimed to analyze the probability of commercial credit risk of 650 Ecuadorian companies in the food sector, through inferential statistical analysis and implementation of a logistic regression model. In effect, three hypotheses were converged, indicating that liquidity, size, and location of the company influence the probability of credit risk. The results of the model significantly showed that companies located in the Sierra, large in size, and with high liquidity are those with the highest probability of risk. These derivations provide a predictive approximation of the credit risk of food companies, and a contribution to the discussion in business, academic, and scientific spaces.

Translated title of the contribution	Predictive approach to commercial credit risk in ecuadorian food companies
Original language	Spanish
Pages (from-to)	413-424
Number of pages	12
Journal	Estudios Gerenciales
Volume	37
Issue number	160
DOIs	https://doi.org/10.18046/j.estger.2021.160.4022
State	Published - 31 Aug 2021

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10.18046/j.estger.2021.160.4022

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title = "Aproximaci{\'o}n predictiva al riesgo crediticio comercial en empresas alimenticias ecuatorianas",

abstract = "This article aimed to analyze the probability of commercial credit risk of 650 Ecuadorian companies in the food sector, through inferential statistical analysis and implementation of a logistic regression model. In effect, three hypotheses were converged, indicating that liquidity, size, and location of the company influence the probability of credit risk. The results of the model significantly showed that companies located in the Sierra, large in size, and with high liquidity are those with the highest probability of risk. These derivations provide a predictive approximation of the credit risk of food companies, and a contribution to the discussion in business, academic, and scientific spaces.",

keywords = "Credit portfolio, Credit risk, Food sector, Logit",

author = "Gustavo Flores-S{\'a}nchez and Jorge Campoverde-Campoverde and Armando Romero-Galarza and Katherine Coronel-Pangol",

note = "Publisher Copyright: {\textcopyright} 2021 Universidad ICESI. Published by Universidad Icesi, Colombia.",

year = "2021",

month = aug,

day = "31",

doi = "10.18046/j.estger.2021.160.4022",

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T1 - Aproximación predictiva al riesgo crediticio comercial en empresas alimenticias ecuatorianas

AU - Flores-Sánchez, Gustavo

AU - Campoverde-Campoverde, Jorge

AU - Romero-Galarza, Armando

AU - Coronel-Pangol, Katherine

PY - 2021/8/31

Y1 - 2021/8/31

N2 - This article aimed to analyze the probability of commercial credit risk of 650 Ecuadorian companies in the food sector, through inferential statistical analysis and implementation of a logistic regression model. In effect, three hypotheses were converged, indicating that liquidity, size, and location of the company influence the probability of credit risk. The results of the model significantly showed that companies located in the Sierra, large in size, and with high liquidity are those with the highest probability of risk. These derivations provide a predictive approximation of the credit risk of food companies, and a contribution to the discussion in business, academic, and scientific spaces.

AB - This article aimed to analyze the probability of commercial credit risk of 650 Ecuadorian companies in the food sector, through inferential statistical analysis and implementation of a logistic regression model. In effect, three hypotheses were converged, indicating that liquidity, size, and location of the company influence the probability of credit risk. The results of the model significantly showed that companies located in the Sierra, large in size, and with high liquidity are those with the highest probability of risk. These derivations provide a predictive approximation of the credit risk of food companies, and a contribution to the discussion in business, academic, and scientific spaces.

KW - Credit portfolio

KW - Credit risk

KW - Food sector

KW - Logit

UR - https://www.scopus.com/pages/publications/85114945128

U2 - 10.18046/j.estger.2021.160.4022

DO - 10.18046/j.estger.2021.160.4022

M3 - Artículo

AN - SCOPUS:85114945128

SN - 0123-5923

VL - 37

SP - 413

EP - 424

JO - Estudios Gerenciales

JF - Estudios Gerenciales

IS - 160

ER -

Aproximación predictiva al riesgo crediticio comercial en empresas alimenticias ecuatorianas

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