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.
Highlights
Decisions about whether to grant credit or not play a crucial role in lending institutions
The aims of this research are to identify the economic-financial indicators that best contribute to improving the accuracy of credit granting analyses applied to Brazilian public companies, and to assess the most accurate techniques used in business bankruptcy forecasting
27 insolvent companies, more than half of the sample, were classified as Type V Financial Structure ‘very bad’ (24 companies, negative Working Capital (WC), Need of Working Capital value (NWC) and BT) and as Type VI ‘high risk’ (3 companies, negative WC and NWC, and positive BT) and it justified the negative NWC values recorded for insolvent companies (-0.077 or -0.114)
Summary
Decisions about whether to grant credit or not play a crucial role in lending institutions. Insight into factors leading to business failure and solvency indications can make the difference between profit or loss. Both studies on significant economic-financial indicators for credit analysis or on methodologies to help provide credit are economically and socially important. Managers must take into consideration the risks involved when there is a high volume of credit operations. Risks concern constant costs in credit operations; they are worth quantifying. Financial operations involving credit must be protected against constant operational risks or, at least, turn uncertainty into measurable risks (Silva, 1983)
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