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.

Metodologias para a avaliação do potencial fisiológico de sementes de tabaco

Despite tobacco being a culture propagated by seeds, there is little information concerning tests that allow the distinction of similar germination lots in different levels of vigor. The diversity of cultivars available in the market, and a few peculiarities of the species, such as uneven maturation of the flowers, fruits and seeds, small size and seed dormancy, are considered obstacles for obtaining lots of tobacco of high physiological potential. Thus, this research was developed with the objective of adapting feasibility and vigor tests for evaluating the physiological potential of tobacco seed lots. We used nine lots of tobacco seeds of cultivar CSC 447 and nine lots of seeds of cultivar BAT 2101, belonging to variety groups Virginia and Burley, respectively. Initially, germination test was conducted to characterize the profile of the lots. For determining the feasibility and vigor of the tobacco seeds, germination tests were conducted in distinct temperatures, controlled emergence conditions, electric conductivity, artificial aging and in tetrazolium. For determining the isoenzymatic marker for seed quality, analyses were conducted with enzymes catalase, esterase, malate dehydrogenase and alcohol dehydrogenase. In conclusion, the emergence tests at 25oC and artificial aging at 41oC for 72 hours, are efficient in discriminating the lots of tobacco seeds in different levels of vigor. The electric conductivity and germination tests in different temperatures have distinct responses in relation to the genotype of the tobacco seeds. The tetrazolium test using the methodology with pre-conditioning in 3.5% sodium hypochlorite solution and subsequent emersion in 1.0% tetrazolium solution for 18 hours is efficient for the quick evaluation of the feasibility of tobacco seeds. The analysis of the profiles of enzymes catalase, esterase, malate dehydrogenase and alcohol dehydrogenase is efficient as markers for tobacco seed quality.