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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 () and André Luis Ribeiro Lima ()

Estudios Gerenciales, 2019, vol. 35, issue 153, 347-360

Abstract: 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.

Keywords: credit risk; bankruptcy; Brazil; financial indicators (search for similar items in EconPapers)
JEL-codes: G11 G21 G33 (search for similar items in EconPapers)
Date: 2019
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Handle: RePEc:col:000129:017761