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.

Relação entre governança corporativa e gerenciamento de resultados: um estudo em empresas brasileiras listadas na BM&FBOVESPA

The objective of this work was to identify a possible relation between corporate governance, through the structure of the boards of directors and the levels of earnings management of Brazilian public companies. The study is characterized as a descriptive, of a hypothetical-deductive nature, with quantitative approach guided by the estimation model proposed by Kang and Sivaramakrishnan (1995). The sample was comprised by 56 companies, analyzed in the period from 2011 to 2014. The information on the companies were extracted from Economatica databank. For the data analysis, we used multivariate techniques, such as Pearson correlation and panel data in POLS, Fixed Effects and Random Effects approaches. Furthermore, we applied the mean comparison test ANOVA. The results obtained show that, generally, the organizations tend to follow the profile of boards of directors recommended by the codes of corporative governance. However, the characteristics of the composition of the board, regarding its size and the duality of positions that are not yet sufficient to be considered capable of inhibiting the discretionary practice of the studied companies. Relative the control variables, only size and return on equity presented no significant relation with result management. It is important to highlight that literature point many factors that explain the different impacts caused by the formation of the administration board in different regions or countries. Among the factors pointed, we highlight the legal system of the country, the economic and political development, the importance of the capital market, and the level of accounting education.

Electrochemical feasibility study of methyl parathion determination on graphite-modified basal plane pyrolytic graphite electrode

A low-cost electrochemical method was developed for the determination of trace-level of methyl parathion (MP) based on the properties of graphite-modified basal plane pyrolytic graphite electrode (graphite-bppg). A combination of graphite-bppg with square-wave voltammetric (SWV) analysis resulted in an original, sensitive and selective electrochemical method for determination of MP pesticide in drinking water. The electrode was constructed and the electrochemical behavior of MP was studied. Immobilization is achieved via film modification from dispersing graphite powder in deionized water and through pipeting a small volume onto the electrode surface allowing the solvent to volatilize. The strong affinity of the graphite modifier for the phosphorous group of the MP allowed the deposition of a significant amount of MP in less than 60 seconds. The cyclic voltammetric results indicate that the graphite-bppg electrode can enhance sensitivity in current intensity towards the quasi-reversible redox peaks of the products of the cathodic reduction of the nitro group at negative potential (peak I = 0.077 V and peak II = –0.062 V) and that the cathodic irreversible peak (peak III = –0.586 V) in comparison with bare bppg electrode and is also adsorption controlled process. Under optimized conditions, the concentration range and detection limit for MP pesticide are respectively 79.0 to 263.3 mmol L-1 and 3.00 mmol L-1. The proposed method was successfully applied to MP determination in drinking water and the performance of this electrochemical sensor has been evaluated in terms of analytical figures of merit.