Artículo
Hybrid model using logit and nonparametric methods for predicting micro-entity failure
Autor/es | Blanco Oliver, Antonio Jesús
Irimia Diéguez, Ana Isabel Oliver Alfonso, María Dolores Vázquez Cueto, María José |
Departamento | Universidad de Sevilla. Departamento de Economía Financiera y Dirección de Operaciones Universidad de Sevilla. Departamento de Economía Aplicada III |
Fecha de publicación | 2016 |
Fecha de depósito | 2018-12-07 |
Publicado en |
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Resumen | Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper
by combining parametric and non-parametric approaches.To this end, the variables with the highest ... Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction. |
Cita | Blanco Oliver, A.J., Irimia Diéguez, A.I., Oliver Alfonso, M.D. y Vázquez Cueto, M.J. (2016). Hybrid model using logit and nonparametric methods for predicting micro-entity failure. Investment Management and Financial Innovations, 13 (3), 35-46. |
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