Modelos computacionais e probabilísticos em riscos de crédito

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dc.contributor.advisorBasso, Leonardo Fernando Cruzpt_BR
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/1866154361601651por
dc.contributor.authorBarboza, Flavio Luiz de Moraespt_BR
dc.creator.Latteshttp://lattes.cnpq.br/4204955149040832por
dc.date.accessioned2016-03-15T19:31:11Z
dc.date.accessioned2020-05-28T18:02:49Z
dc.date.available2015-09-21pt_BR
dc.date.available2020-05-28T18:02:49Z
dc.date.issued2015-02-06pt_BR
dc.description.abstractThis dissertation studies credit risk to promove a discussion about the breadth of scientific literature and two highlighted topics: regulatory capital and bankruptcy prediction modelling. These issues are divided among three essays. The first one is a review of literature in nature. The main studies on credit risk were classified and coded, and a citation-based approach was used to determine its relevance and contributions. Interesting omissions of knowledge are found in this work, which give us motivation to develop two subjects. The second essay discusses the influence of the desirefor higher rating positions for financial instituitons strategies when aiming to minimize economic capital, considering the borrower s credit rating and target rating itself. Using a probabilistic distribution model to simulate loss-given default (LGD), our results show that the use of credit ratings in the guidance for calculating minimum capital requirements can be an alternative to the banks. Yet, we find it possible to get better rankings to lend to some small intervals of LGD. The third study shows a comparative analysis in the performance of computational models, which are widely used to solve classification problems, and traditional methods applied to predict failures one year before the event. The models are formulated by machine learning techniques (support vector machines, bagging, boosting and random forest). Applying data from U.S. companies from 1985 to 2013, we compare the results of these innovative methods with neural networks, logistic regression, and discriminant analysis. The major result of this part of the study is a substantial improvement in predictive power by using machine learning techniques, when, besides the original variable Z-Score from Altman (1968), six metrics (or constructs) selected from Carton e Hofer (2006) are included as explanatory variables. The analysis shows that the bagging and the random forest models outperform other techniques; all predictions are improved when the suggested constructs are included in the survey.eng
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.formatapplication/pdfpor
dc.identifier.citationBARBOZA, Flavio Luiz de Moraes. Modelos computacionais e probabilísticos em riscos de crédito. 2015. 118 f. Tese (Doutorado em Administração) - Universidade Presbiteriana Mackenzie, São Paulo, 2015.por
dc.identifier.urihttp://dspace.mackenzie.br/handle/10899/23234
dc.languageporpor
dc.publisherUniversidade Presbiteriana Mackenziepor
dc.rightsAcesso Abertopor
dc.subjectrisco de créditopor
dc.subjectcapital econômicopor
dc.subjectratingpor
dc.subjectfalênciapor
dc.subjectaprendizagem de máquinaspor
dc.subjectcredit riskeng
dc.subjecteconomic capitaleng
dc.subjectratingeng
dc.subjectbankruptcyeng
dc.subjectmachine learningeng
dc.subject.cnpqCNPQ::CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO::ADMINISTRACAO DE EMPRESASpor
dc.thumbnail.urlhttp://tede.mackenzie.br/jspui/retrieve/3097/Flavio%20Luiz%20de%20Moares%20Barboza_%20Portuguesprot.pdf.jpg*
dc.titleModelos computacionais e probabilísticos em riscos de créditopor
dc.typeTesepor
local.contributor.board1Forte, Denispt_BR
local.contributor.board1Latteshttp://lattes.cnpq.br/0075062531510292por
local.contributor.board2Sobreiro, Vinicius Amorimpt_BR
local.contributor.board2Latteshttp://lattes.cnpq.br/3150339971293179por
local.contributor.board3Jucá, Michele Nascimentopt_BR
local.contributor.board3Latteshttp://lattes.cnpq.br/6770985264140454por
local.contributor.board4Kimura, Herbertpt_BR
local.contributor.board4Latteshttp://lattes.cnpq.br/2048706172366367por
local.publisher.countryBRpor
local.publisher.departmentAdministraçãopor
local.publisher.initialsUPMpor
local.publisher.programAdministração de Empresaspor
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