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.

Análise do teor de cocaína em amostras apreendidas pela polícia utilizando-se a técnica de cromatografia liquida de alta eficiência com detector UV-Vis

The HPLC technique with UV-Vis detection was employed in the analysis of cocaine content in apprehended samples of cocaine and crack. A peak signal for cocaine was obtained in 3.5 minutes run by using acetonitrile/water (95:5v/v) as a mobile phase. Optimized spectrophotometric signal was obtained at a wavelength of 224 nm. The analytical curve from 1.0 to 40.0 ppm of cocaine was obtained, showing a linear correlation coefficient of 0.9989, with detection and quantification limits of 0.75 ppm and 3.78 ppm, respectively. This methodology was employed at the dosage of confiscated samples of cocaine and crack in the Scientific Police Laboratory of Ribeirão Preto-SP city.

Eficiência agronômica da ureia estabilizada com inibidores de urease e nitrificação na cultura do milho

Nitrogen (N) is the most required nutrient for corn plants and, in order to supply this demand in highly productive crops, mineral fertilizers are used, especially urea. The disadvantage of urea is the loss of N-NH3 to atmosphere. To reverse this situation, some technologies have been developed, such as nitrification and urease inhibitors, which are used as additives to urea. This work aimed at evaluating the agronomic efficiency of urea stabilized with urease and nitrification inhibitors applied to cover the 2013/2014 corn crop. We evaluated 11 nitrogen fertilizer applied in coverage: urea + PA (41.6% N, 3% Cu); urea + PA (41.6% N, 1.5% Cu); urea + PA (41.6% N, 3% Zn); urea + PA (41.6% N, 1.5% Zn); urea + PA (41.6% N, 0.34% Cu, 0.94% B); urea + PA (41.6% N, 0.25% Cu, 0.68% B); urea + PA (41.6% N); urea (44.3% N, 0.15% Cu, 0.4% B); urea (43% N, 0.1% Cu, 0.3% B, 0.05% Mo); pearled urea (46% N); urea + 0,8% DMPP (45% N) and the control, which did not receive nitrogen topdressing. The evaluations were: Nitrogen losses through volatilization, content and accumulation of N, boron (B), copper (Cu) and zinc (Zn) to the dry matter of aerial parts, grains, and in straw and grain productivity. Fertilizers stabilized with urease and nitrification inhibitors did not reduce the volatilization of ammonia volatilization, when compared to pearled urea. Urea with 0.8% of DMPP nitrification inhibitor (3,4-dimethylpyrazole phosphate) provided higher loss by volatilization, lower productivity and agronomic efficiency compared to pearled urea. The coating of urea with Cu, B and Zn did not increase the accumulation of these nutrients in grains and MSPA plants. The use of fertilizers stabilized and coated with micronutrients did not increase the productivity and agronomic efficiency compared to conventional urea.