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.

Modelos duplo sigmoidais para o crescimento de frutos do cafeeiro

Coffee is one of the main products of Brazilian agriculture, the country is currently the largest producer and exporter. Knowing the growth pattern of a fruit can assist in the development of culture indicating for example, the times of increased fruit weight and its optimum harvest, essential to improve the management and quality of coffee. Some authors indicate that the growth curve of the coffee fruit has a double sigmoid shape. However, it consists of just a visual observation without exploring the use of regression models. The aims of this study were: i) determine if the growth pattern of the coffee fruit is really double sigmoidal; ii) to propose a new approach in weighted importance re-sampling to estimate the parameters of regression models and select the most suitable double sigmoidal model to describe the growth of coffee fruits; iii) to study the spatial distribution effect of the crop in the growth curve of coffee fruits. In the first article the aim was determine if the growth pattern of the coffee fruit is really double sigmoidal. The models double Gompertz and double Logistic showed significantly superior fit to models of simple sigmoid confirming that the standard of coffee fruits growth is really double sigmoidal. In the second article we propose to consider an approximation of the likelihood as the candidate distribution of the weighted importance resampling, aiming to facilitate the process of obtaining samples of marginal distributions of each parameter. This technique was effective since it provided parameters with practical interpretation and low computational effort, therefore, it can be used to estimate parameters of double sigmoidal growth curves. The nonlinear model double Logistic was the most appropriate to describe the growth curve of coffee fruits. In the third article aimed to verify the influence of different planting alignments and sun exposure faces in the fruits growth curve. A difference between the growth rates in the two stages of fruit development was identified, regardless the side. Although it has been proven differences in productivity and quality of coffee, there was no difference between the growth curves in the different planting alignments herein studied.