Risco de crédito: uma abordagem utilizando análise discriminante, regressão logística e redes neurais artificiais

Considering the relevance of researches concerning credit risk, model diversity and the existent indicators, this thesis aimed at verifying if the Fleuriet Model contributes in discriminating Brazilian open capital companies in the analysis of credit concession. We specifically intended to i) identify the economic-financial indicators used in credit risk models; ii) identify which economic-financial indicators best discriminate companies in the analysis of credit concession; iii) assess which techniques used (discriminant analysis, logistic regression and neural networks) present the best accuracy to predict company bankruptcy. To do this, the theoretical background approached the concepts of financial analysis, which introduced themes relative to the company evaluation process; considerations on credit, risk and analysis; Fleuriet Model and its indicators, and, finally, presented the techniques for credit analysis based on discriminant analysis, logistic regression and artificial neural networks. Methodologically, the research was defined as quantitative, regarding its nature, and explanatory, regarding its type. It was developed using data derived from bibliographic and document analysis. The financial demonstrations were collected by means of the Economática ® and the BM$FBOVESPA website. The sample was comprised of 121 companies, being those 70 solvents and 51 insolvents from various sectors. In the analyses, we used 22 indicators of the Traditional Model and 13 of the Fleuriet Model, totalizing 35 indicators. The economic-financial indicators which were a part of, at least, one of the three final models were: X1 (Working Capital over Assets), X3 (NCG over Assets), X4 (NCG over Net Revenue), X8 (Type of Financial Structure), X9 (Net Thermometer), X16 (Net Equity divided by the total demandable), X17 (Asset Turnover), X20 (Net Equity Profitability), X25 (Net Margin), X28 (Debt Composition) and X31 (Net Equity over Asset). The final models presented setting values of: 90.9% (discriminant analysis); 90.9% (logistic regression) and 97.8% (neural networks). The modeling in neural networks presented higher accuracy, which was confirmed by the ROC curve. In conclusion, the indicators of the Fleuriet Model presented relevant results for the research of credit risk, especially if modeled by neural networks.

Determinação da densidade amostral ótima para a geração de mapas temáticos na cafeicultura de precisão

Among the crops commercially exploited in Brazil, the coffee has a great economic importance, especially in the states of Minas Gerais and Espirito Santo. In the search for higher yield and lower environmental impact, farmers and researchers seek to develop new technologies that result in greater efficiency in various production processes of the coffee. For this, the adoption of precision agriculture in the management of operations in coffee crops, called precision coffee, has shown results that justify its use, by identifying the spatial variability of several variables, allowing its localized management and in the proper intensity. Unlike conventional management that is based on the average of observations in an area, precision agriculture uses a more detailed sampling, based on a sampling grid, which allows to represent in greater detail the reality of farming. Many previous studies have identified the spatial variability of the production of coffee system variables, but without worrying about the quality of information obtained due to the sampling grid used as precision and accuracy. Given the above, the objective of this study was to evaluate the quality of four different sampling grids for different variables and three times, in order to identify the most appropriate grid for use in precision coffee. Also aimed to compare the results between the precision coffee and conventional, according to reference values.