A. Irimia-Dieguez
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Hybrid model using logit and nonparametric methods for predicting micro-entity failure
A. Blanco-Oliver , A. Irimia-Dieguez , M.D. Oliver-Alfonso , M.J. Vázquez-Cueto doi: http://dx.doi.org/10.21511/imfi.13(3).2016.03Investment Management and Financial Innovations Volume 13, 2016 Issue #3 pp. 35-46
Views: 933 Downloads: 286 TO CITEFollowing 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
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