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| Format: | Artículo científico |
| Language: | en |
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Universidad ICESI
2019
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| Online Access: | https://www.redalyc.org/articulo.oa?id=21262744002 https://www.redalyc.org/journal/212/21262744002/ https://www.redalyc.org/journal/212/21262744002/html/ https://www.redalyc.org/journal/212/21262744002/21262744002.epub https://www.redalyc.org/journal/212/21262744002/movil |
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Table of Contents:
- Analysis of credit risk faced by public companies in Brazil: an approach based on discriminant analysis, logistic regression and artificial neural networks José Willer do Prado Francisval de Melo Carvalho Gideon Carvalho de Benedicto André Luis Ribeiro Lima Administración y Contabilidad Brazil bankruptcy Credit risk financial indicators The aims of the present article are to identify the economic-financial indicators that best characterize Brazilian public companies through credit-granting analysis and to assess the most accurate techniques used to forecast business bankruptcy. Discriminant analysis, logistic regression and neural networks were the most used methods to predict insolvency. The sample comprised 121 companies from different sectors, 70 of them solvent and 51 insolvent. The conducted analyses were based on 35 economic-financial indicators. Need of working capital for net income, liquidity thermometer, return on equity, net margin, debt breakdown and equity on assets were the most relevant economic-financial indicators. Neural networks recorded the best accuracy and the Receiver Operating Characteristic Curves (ROC curve) corroborated this outcome.JEL Classification: G110, G210, G330. 2019 artículo científico 0123-5923 https://www.redalyc.org/articulo.oa?id=21262744002 https://www.redalyc.org/journal/212/21262744002/ https://www.redalyc.org/journal/212/21262744002/html/ https://www.redalyc.org/journal/212/21262744002/21262744002.epub https://www.redalyc.org/journal/212/21262744002/movil 10.18046/j.estger.2019.153.3151 en http://www.redalyc.org/revista.oa?id=212 Estudios Gerenciales application/pdf Universidad ICESI Estudios Gerenciales (Colombia) Num.153 Vol.35