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 da paisagem para o planejamento e a gestão da conservação da Mata Atlântica

Landscape is dynamic, having complex nature, with tangible and intangible dimensions, presenting a continuous evolution process. The aim of this research were based on the identification and classification of landscapes in units and subunits, from the ownership by individuals; the development of a methodology to assist in the planning and management, conciliating conservation of natural areas with anthropic activities; and, from the information gathered, evaluate the different social groups aiming to design a landscape from the sustainable development perspective; thus better understanding both cultural and forest fragmentation processes, in the city of Ouro Preto, Minas Gerais. The research analyzed the current landscape and its historical evolution, distinguishing between material and immaterial dimensions. Information was raised from field trips, soil types, relief, slope, drainage, conservation units, administrative zoning, urban areas, natural resources, economy, tax raising, transport and building infrastructure, satellite images, types of management applied to the preservation or conservation of forests and fields, and semi-structured interviews with the various actors that modify and transform the territory, thus making a balance between the built landscape and the demands of the society and ecosystems. Results were composed by a map of land use in 2011; a map of landscape units and their subunits, with their appropriate definitions; a map with five levels of activities intensity, with their respective descriptions; and raising barriers to improving the welfare of the actors and the integrity of ecosystems. The number of generated ecosystem services are difficult to measure, but its benefits are useful for everyone. The physical changes are a reflection of the economy, which caused environmental impacts, mainly related to mining activities, tourism, agriculture and conservation of natural areas, all requiring ideally a shared management. In this sense, the landscape needs a management to create sustainable alternatives to anthropic activities. The dynamics of the landscape has been shaped by a slow evolution, set by mining activities due to the high financial revenues, there were areas of revegetation after clearcuts in the past, and now tourism lacks structure. The city has great potential for development projects with payments for environmental services, however, gaps for shared management exists.