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.

Aspectos nutricionais do baru (Dipteryx alata Vog.) em casa de vegetação e em campo

With the objective of evaluating the response of baru (Dipteryx alata Vog.) to nutrient limitation and to the different levels of fertilization, seven experiments were conducted. Experiment 1: Nutritional limitation in greenhouse. We employed 12 treatments in a completely randomized design with eight replicates. Experiment 2: Levels of liming and P in greenhouse. The experimental design was completely randomized in a factorial scheme with four levels of liming (V23.2% (natural soil), V45%, V65% and V85%) and four doses of P (0, 100, 300 and 500 mg kg -1 of P). Experiment 3: Doses of N in greenhouse. We used seven treatments (0, 75, 150, 225, 300, 375 and 450 mg kg -1 of N) in a completely randomized design. Experiment 4: Doses of K in greenhouse. We used seven treatments (0, 75, 150, 225, 300, 375 and 450 mg kg -1 ) in a completely randomized design. Experiment 5: Levels of liming under field conditions. We used four treatments (V6.7% (natural soil), V35%, V55% and V75%) in a randomized blocks design. Experiment 6: doses of P under field conditions. We used five treatments (0, 23.67, 53.34, 106.67 and 213.36 kg ha -1 of P 2O5) in a randomized blocks design. Experiment 7: Doses of N under field conditions. We used five treatments (0, 30, 60, 120 and 240 kg ha -1 of N) in Latin square. In greenhouse, the evaluations were conducted at eight months (for experiments 1 and 2) and 12 months (for experiments 3 and 4) after seeding, when the measurements of height and root collar diameter of the seedlings. Subsequently, the plants were harvested and separated into shoot and root system, for weighing and evaluating dry biomass gain. In the field, the evaluations were conducted at six, 12, 18, 24 and 30 months (for experiments 5 and 6) and at six, 12 and 18 months (for experiment 7). In these experiments, we evaluated the survival of the seedlings, height of the plants and diameter of the stem at soil height. The data obtained were submitted to analysis of variance, mean tests and regression analysis. In conclusion, during the phase of seedling formation, the species is little demanding in S and B, negatively responds to liming, positively responds to phosphate fertilization and does not respond to nitrogen and potassium fertilization. In the field, in general, the species does not respond to the application of P or to liming, and is negatively influenced by the application of elevated doses of nitrogen.