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Hybrid model using logit and nonparametric methods for predicting micro-entity failure

 

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Opened Access Hybrid model using logit and nonparametric methods for predicting micro-entity failure
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Author: Blanco Oliver, Antonio Jesús
Irimia Diéguez, Ana Isabel
Oliver Alfonso, María Dolores
Vázquez Cueto, María José
Department: Universidad de Sevilla. Departamento de Economía Financiera y Dirección de Operaciones
Universidad de Sevilla. Departamento de Economía Aplicada III
Date: 2016
Published in: Investment Management and Financial Innovations, 13 (3), 35-46.
Document type: Article
Abstract: 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.
Cite: 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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URI: https://hdl.handle.net/11441/80840

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