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.

A sucessão ecológica em ambientes florestais em restauração: estrutura e dinâmica da regeneração natural

Ecological restoration aims to restore the self-sustainability potential of degraded environments. The successional trajectory of areas in restoration may differ from the reference ecosystem on a number of factors, especially the disturbance history of the area and management techniques applied in order to reverse the degradation scenario. In this sense, the objective of this study was to characterize the dynamics of natural regeneration and the structure of the regenerating community in an area of ecological restoration for 25 years on the right bank of the Rio Grande in the Power Station in Camargos that belongs to the Companhia Energetica de Minas Gerais - CEMIG, Nazareno, MG, in order to identify the current state of environmental regeneration. In November 2014, there was the first community inventory of seedlings existing in regeneration subplots (2 x 2 m) within the inclusion criteria (height ≥ 10 cm and DBH <5 cm), divided into 44 subplots (11 plots) in two areas, one area in restoration (REF) and a fragment of native vegetation (FRAG). In October 2015, the second sampling was carried out, in which individuals were resampled and recruits and dead individuals quantified. The parameters abundance, floristic similarity, density, dominance, importance value, equability, richness and diversity were analyzed in both samples. The dynamics was evaluated for the number of individuals and basal area in the period of 2014-2015 and individuals distributed according to height. The history of usage area provoked different behavior between the study environments; the patterns of the FRAG and REF communities along the years allowed the distinction between environments and demonstrated great environmental diversity, with differences in the abundance of species, floristic similarity, richness, diversity, vertical structure and dynamics parameters. In the REF area, there was a considerable increase in the density of individuals along the years and the establishment of different species. In this sense, the evaluation of natural regeneration in REF has shown that resilience is gradually being resumed, a necessary condition to support the potential for environmental self-sustainability. However, given the disturbance history, it can be concluded that the environment moves to an alternative state, which is possibly different from that prior to degradation.