| Título |
Financial Soundness Prediction Using a Multi-classification Model: Evidence from Current Financial Crisis in OECD Banks |
| Autores |
GUERRERO ARIAS, ISMAEL, LÓPEZ MARTÍN, MARÍA DEL CARMEN, MONTERO ROMERO, Mª TERESA, MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS |
| Publicación externa |
No |
| Medio |
Comput. Econ. |
| Alcance |
Article |
| Naturaleza |
Científica |
| Cuartil JCR |
3 |
| Cuartil SJR |
2 |
| Impacto JCR |
1.18500 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015617440&doi=10.1007%2fs10614-017-9676-6&partnerID=40&md5=a36e8a29ba89907fb434b84340e74948 |
| Fecha de publicacion |
01/06/2018 |
| ISI |
000435355300013 |
| Scopus Id |
2-s2.0-85015617440 |
| DOI |
10.1007/s10614-017-9676-6 |
| Abstract |
The paper aims to develop an early warning model that separates previously rated banks (337 Fitch-rated banks from OECD) into three classes, based on their financial health and using a one-year window. The early warning system is based on a classification model which estimates the Fitch ratings using Bankscope bank-specific data, regulatory and macroeconomic data as input variables. The authors propose a "hybridization technique" that combines the Extreme learning machine and the Synthetic Minority Over-sampling Technique. Due to the imbalanced nature of the problem, the authors apply an oversampling technique on the data aiming to improve the classification results on the minority groups. The methodology proposed outperforms other existing classification techniques used to predict bank solvency. It proved essential in improving average accuracy and especially the performance of the minority groups. |
| Palabras clave |
Bankruptcy prediction; Artificial neural networks; Extreme learning machine; Early warning models; Computational intelligence; Financial crisis |
| Miembros de la Universidad Loyola |
|
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