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.

Acidovorax citruli: genetic analyses and protocol for its detection in seeds

Bacterial fruit blotch of cucurbits (BFB), caused by the seed borne Gramnegative bacterium Acidovorax citrulli is a serious threat to cucurbit industry worldwide. Since late 1980`s after devastating outbreaks in watermelon fields in southern United States, BFB has spread worldwide and has been reported in other cucurbit crops such as melon, pumpkin, cucumber and squash. To date, there is evidence for the existence of at least two genetically and pathogenically distinct populations of A. citrulli. In Brazil, the first report of BFB was in 1991, in a watermelon field in São Paulo. Although widespread in the country, BFB has been a major problem to melon production. More precisely, BFB has caused significant yield losses to melon production in northeastern Brazil, which concentrates > 90% of the country`s melon production. Despite the management efforts and the recent advances in A. citrulli research, BFB is still a continuous threat to the cucurbit industry, including seed producers, growers and transplant nurseries. To better understand the population structure of A. citrulli strains in Brazil, and to provide a basis for the integrated management of BFB, we used pulsed-field gel electrophoresis (PFGE), multilocus sequence analysis (MLSA) of housekeeping and virulence-associated genes and pathogenicity tests on different cucurbit seedlings to characterize a Brazilian population of A. citrulli strains from different hosts and regions. Additionally, we conducted for the first time a comparative analysis of the A. citrulli group I and II population at genomic level and showed that these two groups differ on their genome sizes due to the presence of eight DNA segments, which are present in group II and absent in group I genomes. We also provide the first evidence to suggest that temperature might be a driver in the ecological adaptation of A. citrulli populations under nutrient-rich or -depleted conditions. Finally, in order to improve the routine detection of A. citrulli on melon seedlots, we designed a new primer set that is able to detect the different Brazilian haplotypes, thus minimizing the risk of false-negatives on PCR-based seed health testing.