Author(s): Barboza, Flavio ; de Frias Barbosa, Jorge Henrique ; Kimura, Herbert ; Santos, Gustavo Carvalho ; Cortez, Paulo
Date: 2023
Persistent ID: https://hdl.handle.net/1822/87097
Origin: RepositóriUM - Universidade do Minho
Subject(s): banking crisis; Brazil; distress prediction; early warning system; EWS; machine learning techniques; Ciências Naturais::Ciências da Computação e da Informação
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.
