705 resultados para Contrat de concession
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Inclut la bibliographie
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Incluye Bibliografía
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Pós-graduação em Engenharia Elétrica - FEIS
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Pós-graduação em Direito - FCHS
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Pós-graduação em Direito - FCHS
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Pós-graduação em Direito - FCHS
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em História - FCHS
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Pós-graduação em Ciências Sociais - FFC
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Concessions have become an important mechanism in Latin America for attracting financing and private management to the highway sector. Highways are one of the areas of transport infrastructure in which this concept in long-term investment in road conservation and management has been widely applied and the concession-holder's costs are recouped through tolls and other complementary mechanisms.After a brisk start in the 1990s, the pattern of road concessions has proved to be less dynamic in the current decade. Nevertheless, highway concessions have expanded significantly and now account for 1% of the total inter-city road network. The international seminar entitled "Concessions for the provision of transport infrastructure: challenges for Latin America" was organized jointly by the Economic Commission for Latin America and the Caribbean (ECLAC) and the Agency for the Promotion of Private Investment of Peru (PROINVERSION) and held in Lima, Peru, on 13 and 14 November 2003.
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The Transport Unit has developed a project evaluation methodology whereby benefits may be broken down by target groups. To date, this approach has been used in three highway concession projects in Argentina, Chile and Colombia. It is also applicable to other projects where it is important to know how benefits are to be distributed as well as what the overall benefits will be. For general inquiries and information on this methodology, please contact ithomson@eclac.cl.
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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.