Design of a Specific Model for Predicting Micro-Entities Failure
DOI:
https://doi.org/10.46661/revmetodoscuanteconempresa.2336Keywords:
modelos de quiebra, métodos híbridos, métodos no paramétricos, árboles de decisión, micro-entities, bankruptcy models, hybrid methods, nonparametric methods, decision treesAbstract
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.
Downloads
References
Altman E.I. & Sabato, G. (2007). Modeling credit risk for SMEs: Evidence from US market. A Journal of Accounting, Finance and Business Studies (ABACUS), 43(3), 332-357.
Altman, E.I., Sabato, G. & Wilson, N. (2010). The Value of Non Financial Information in Small and Medium-Sized Enterprise Risk Management. Journal of Credit Risk, 6(2), 95-127.
BIS (2013). Department for Business, Innovations and Skills. Simpler financial reporting for micro-entities: The UK’s proposal to implement the ‘micros directive’, https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/86259/13-626-simpler-financial-reporting-for-micro-entities-consultation.pdf. Accessed on 24 September 2014.
Breiman, L. (1988). Submodel selection and evaluation in regression: the x-fixed case and little bootstrap. Technical Report No. 169, Statistics Department, U.C. Berkeley.
Breiman, L., Friedman, J., Olshen, R. & Stone, C. (1984). Classification and regression trees. Wadsworth, Belmont, CA.
Berger A.N. & Frame, S.W. (2007). Small business credit scoring and credit availability. Journal of Small Business Management, 45(1), 5-22.
Caro, N, Díaz, M. & Porporato, M. (2013). Predicción de quiebras empresariales en economías emergentes: uso de un modelo logístico mixto. Journal of Quantitative Methods for Economics and Business Administration, 16, 200-215.
Carter, R. & Van Auken, H. (2006). Small firm bankruptcy. Journal of Small Business Management, 44(4), 493-512.
Ciampi, F. & Gordini, N. (2013). Small enterprise default prediction modeling through artificial neural networks: An empirical analysis of Italian small enterprises. Journal of Small Business Management, 51(1), 23-45.
Claessens, S., Djankov, S. & Lang, L.H. (2000). The separation of ownership and control in East Asian corporations. Journal of Financial Economics, 58, 81-112.
Crone, S.F. & Finlay, S. (2012). Instance sampling in credit scoring: An empirical study of sample size and balancing. International Journal of Forecasting, 28(1), 224-238.
Engelmann, B., Hayden, L.E. & Tasche, D. (2003). Testing rating accuracy. Risk, 16, 82-86.
García-Gallego, A. & Mures-Quintana, M.J. (2013). The Sample of Firms in Business Failure Prediction Models: Influence on Classification Results. Journal of Quantitative Methods for Economics and Business Administration, 14, 131-150
Grunert, J., Norden, L. & Weber, M. (2005). The role of non-financial factors in internal credit ratings. Journal of Banking and Finance, 29(2), 509-531.
Hastie, T., Tibshirani, R. & Friedman, J.H. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer Series in Statistics. New York.
Wilson, N., Wright, M. & Scholes, L. (2013). Family business survival and the role of boards. Entrepreneurship Theory and Practice, 37(6), 1369-1389.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2016 Journal of Quantitative Methods for Economics and Business Administration
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Submission of manuscripts implies that the work described has not been published before (except in the form of an abstract or as part of thesis), that it is not under consideration for publication elsewhere and that, in case of acceptance, the authors agree to automatic transfer of the copyright to the Journal for its publication and dissemination. Authors retain the authors' right to use and share the article according to a personal or instutional use or scholarly sharing purposes; in addition, they retain patent, trademark and other intellectual property rights (including research data).
All the articles are published in the Journal under the Creative Commons license CC-BY-SA (Attribution-ShareAlike). It is allowed a commercial use of the work (always including the author attribution) and other derivative works, which must be released under the same license as the original work.
Up to Volume 21, this Journal has been licensing the articles under the Creative Commons license CC-BY-SA 3.0 ES. Starting from Volume 22, the Creative Commons license CC-BY-SA 4.0 is used.
