Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/174244 
Year of Publication: 
2016
Citation: 
[Journal:] Revista de Métodos Cuantitativos para la Economía y la Empresa [ISSN:] 1886-516X [Volume:] 22 [Publisher:] Universidad Pablo de Olavide [Place:] Sevilla [Year:] 2016 [Pages:] 3-18
Publisher: 
Universidad Pablo de Olavide, Sevilla
Abstract (Translated): 
The importance of micro-entities due to their generation of employment and propelling economic activity, together with the fact of their particularities, implies the need to design appropriate methods that anticipate their bankruptcies. For that purpose, a hybrid model by combining parametric and nonparametric approaches is developed in this paper. First, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, a non-parametric method, namely regression trees and classification (CART), is then applied to companies classified as "bankruptcy" or "non-bankruptcy". Our results show that this model provides a better result than when it is implemented in isolation, which joins its easier interpretation and faster convergence. Moreover, we demonstrate that the introduction of non-financial and macroeconomic variables complement the financial ratios for bankruptcy prediction. Findings are based on a data set of micro-entities (MEs), as recently defined by the European Union.
Subjects: 
micro-entities
bankruptcy models
hybrid methods
nonparametric methods
decision trees
micro-entities
JEL: 
G33
G41
G21
G32
Creative Commons License: 
cc-by-sa Logo
Document Type: 
Article

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