Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/87097
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Barboza, Flavio | por |
dc.contributor.author | de Frias Barbosa, Jorge Henrique | por |
dc.contributor.author | Kimura, Herbert | por |
dc.contributor.author | Santos, Gustavo Carvalho | por |
dc.contributor.author | Cortez, Paulo | por |
dc.date.accessioned | 2023-10-25T18:32:58Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Barboza, F., d, J. H., Barbosa, . e F., Kimura, H., Santos, G. C., & Cortez, P. (2023). Early warning system for preventing bank distress in Brazil. International Journal of Business and Systems Research. Inderscience Publishers. http://doi.org/10.1504/ijbsr.2023.130632 | - |
dc.identifier.issn | 1751-200X | - |
dc.identifier.uri | https://hdl.handle.net/1822/87097 | - |
dc.description.abstract | The global financial crisis in 2007/2008 showed how important is to be prudent with events related to the banking sector, illustrating emphatically the contagion in the financial system caused by distress in one or more banks. This issue goes beyond competitiveness and the interrelationship among its members, requiring at least signs or warnings of potential problems in such institutions. Thus, the present study presents some early warning system models for bank crises and bank distress, which are empirically tested for Brazilian banks. In addition to the traditional logit, we analyse two machine learning techniques are: random forest (RF) and support vector machine (SVM). The database of Brazilian banks covers 179 events considered as unsound bank. Our findings suggest that RF and SVM underperform the logit model. Moreover, RF models presented greater predictive capacity with the time windows of 32 and 34 months, proving adequate to the regulators’ needs. | por |
dc.description.sponsorship | - (undefined) | por |
dc.language.iso | eng | por |
dc.publisher | Inderscience | por |
dc.rights | restrictedAccess | por |
dc.subject | banking crisis | por |
dc.subject | Brazil | por |
dc.subject | distress prediction | por |
dc.subject | early warning system | por |
dc.subject | EWS | por |
dc.subject | machine learning techniques | por |
dc.title | Early warning system for preventing bank distress in Brazil | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.inderscienceonline.com/doi/abs/10.1504/IJBSR.2023.130632 | por |
oaire.citationStartPage | 326 | por |
oaire.citationEndPage | 346 | por |
oaire.citationIssue | 3 | por |
oaire.citationVolume | 17 | por |
dc.date.updated | 2023-10-25T16:58:07Z | - |
dc.identifier.eissn | 1751-2018 | - |
dc.identifier.doi | 10.1504/IJBSR.2023.130632 | por |
dc.date.embargo | 10000-01-01 | - |
dc.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | por |
sdum.export.identifier | 12869 | - |
sdum.journal | International Journal of Business and Systems Research | por |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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2023-IJBRS.pdf Acesso restrito! | 167,98 kB | Adobe PDF | Ver/Abrir |