Estimación de la probabilidad de riesgo de quiebra en las empresas colombianas a partir de un modelo para eventos raros.
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abr 10, 2018
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Resumen
Para discriminar en riesgo de quiebra y no quiebra a las empresas colombianas que reportaron sus estados financieros a la Superintendencia de Sociedades de Colombia para el periodo 2011-2015, este trabajo considera la quiebra como un evento raro y emplea un modelo logístico, un modelo aditivo generalizado, un modelo de valor extremo generalizado y un modelo binario aditivo de valor extremo generalizado (BGEVA). En términos comparativos, el modelo BGEVA presenta mejor desempeño predictivo con respecto a los otros al asumir una distribución de valor extremo en la función link y estructuras semi-paramétricas en las estimaciones, permitiendo así determinar la relación existente entre la probabilidad de default y las variables explicativas.
Keywords
Quiebra, eventos raros, modelos de predicción.
References
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2. Altman, E.; Haldeman, R. y Narayanan, P. (1977). Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporations, Journal of Banking & Finance, 1 (1), 29-54.
3. Altman, E. y Sabato, G. (2006). Modeling Credit Risk for SMEs: Evidence from the US Market, ABACUS, 43 (3), 716-723.
4. Anandarajan, M., Lee, P. y Anandarajan, A. (2001). Bankruptcy Prediction of Financially Stressed Firms: An Examination of the Predictive Accuracy of Artificial Neural Networks, International Journal of Intelligent Systems in Accounting, Finance & Management, 10 (2), 69-81.
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8. Berg, D. (2007), Bankruptcy Prediction by Generalized Additive Models, Applied Stochastic Models in Business and Industry, 23 (2), 129-143.
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15. Gu, C. (2002). Smoothing Spline ANOVA Models, New York, USA, Springer.
16. Hosmer, D., Lemeshow, S. y Sturdivant, R. (2013). Applied Logistic Regression, Hoboken, New Jersey, USA: Third Edition, John Wiley & Sons, Inc.
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18. Laitinen, E. y Laitinen, T. (2000). Bankruptcy Prediction: Application of the Taylor's Expansion in Logistic Regression, International Review of Financial Analysis, 9 (4), 327-349.
19. Martínez, Ó. (2003). Determinantes de Fragilidad en las Empresas Colombianas, Borradores de Economía, Banco de la Republica, (259), 1-24.
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21. Narváez, L. (2010). Análisis de la Aplicación de los Modelos de Predicción de Quiebras en Colombia, Tesis pregrado en contaduría pública, Universidad Autónoma de Occidente, Cali, Colombia.
22. Ohlson, J. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy, Journal of Accounting Research, 18 (1), 109-131.
23. O'Leary, D. (1998). Using Neural Networks to Predict Corporate Failure, International Journal of Intelligent Systems in Accounting, Finance and Management, 7, 187–197.
24. Pérez, J., González, K. y Lopera, M. (2013). Modelos de Predicción de la Fragilidad Empresarial: Aplicación al Caso Colombiano Para El Año 2011, Perfil de Coyuntura Económica, 22, 205-228.
25. R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
26. Ravi Kumar, P. y Ravi, V. (2007). Bankruptcy Prediction in Banks and Firms via Statistical and Intelligent Techniques – A Review, European Journal of Operational Research, 180 (1), 1-28.
27. Rosillo, J. (2002). Modelo de Predicción de Quiebras de las Empresas Colombianas, Innovar, revista de ciencias administrativas y sociales, 12, 109-124.
28. Santos, M., Cortez, P., Pereira, J. y Quintela, H. (2006). Corporate Bankruptcy Prediction Using Data Mining Techniques, WIT Transactions on Information and Communication Technologies, 37 (1), 349-357.
29. Sobehart, J., Keenan, S. y Stein R. (2000). Validation Methodologies for Default Risk Models, Default Risk, (Mayo), 51-56.
30. Tsay, R. (2010). Analysis of Financial Time Series, Hoboken, New Jersey, USA: Third Edition, John Wiley & Sons, Inc.
31. Vozzella, P. y Gabbi, G. (2010). Default and Asset Correlation: An Empirical Study for Italian SMEs. Working Paper.
32. Whaba, G. (1990). Spline Models or Observational Data, Philadelphia, Pennsylvania, USA, Society for industrial and applied mathematics.
33. Wood, S. (2006). Generalized Additive Models: An Introduction with R, Boca Raton, Florida, USA, First Edition, Chapman & Hall/CRC.
34. Wood, S. (2017). mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML Smoothness Estimation, R package version, 1.8-17.
35. Zibanezhad, E., Foroghi, D. y Monadjemi, A. (2011). Applying Decision Tree to Predict Bankruptcy, Proceedings of IEEE International Conference on Computer Science and Automation Engineering, 4, 165-169.
2. Altman, E.; Haldeman, R. y Narayanan, P. (1977). Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporations, Journal of Banking & Finance, 1 (1), 29-54.
3. Altman, E. y Sabato, G. (2006). Modeling Credit Risk for SMEs: Evidence from the US Market, ABACUS, 43 (3), 716-723.
4. Anandarajan, M., Lee, P. y Anandarajan, A. (2001). Bankruptcy Prediction of Financially Stressed Firms: An Examination of the Predictive Accuracy of Artificial Neural Networks, International Journal of Intelligent Systems in Accounting, Finance & Management, 10 (2), 69-81.
5. Aoki, S. y Hosonuma, Y. (2004). Bankruptcy Prediction Using Decision Tree, en The Application of Econophysics, Proceedings of the Second Nikkei Econophysics Symposium, pp. 299-302. Springer Japan, Tokyo.
6. Beaver, W. (1966). Financial Ratios as Predictors of Failure, Journal of Accounting Research, 4, 71-111.
7. Beirlant, J., Goegebeur, Y., Segers, J. y Teugels J. (2004). Statistics of Extremes: Theory and Applications, Chichester, England: First Edition, John Wiley & Sons, Ltd.
8. Berg, D. (2007), Bankruptcy Prediction by Generalized Additive Models, Applied Stochastic Models in Business and Industry, 23 (2), 129-143.
9. Bernhardsen, E. (2001). A Model of Bankruptcy Prediction, Working Paper, Norges Bank. 10.
10. Brédart, X., (2014). Bankruptcy Prediction Model: The Case of the United State, International Journal of Economics and Finance, 6 (3), 1-7.
11. Calabrese, R., Marra, G. y Osmetti, S. (2016). Bankruptcy Prediction of Small and Medium Enterprises Using a Flexible Binary Generalized Extreme Value Model, Journal of the Operational Research Society, 67 (4), 604-615.
12. Calabrese, R. y Osmetti, S. A. (2013). Modelling Small and Medium Enterprise Loan Defaults as Rare Events: The Generalized Extreme Value Regression Model, Journal of Applied Statistics, 40 (6), 1172-1188.
13. Charitou, A., Neophytou, E. y Charalambous, C. (2004) Predicting Corporate Failure: Empirical Evidence for the UK, European Accounting Review, 13 (3), 465-497.
14. Ciampi, F. y Gordini, N. (2008). Using Economic–Financial Ratios for Small Enterprise Default Prediction Modeling: An Empirical Analysis. In 2008 Oxford business and economics conference proceedings, Oxford, UK. 1-21.
15. Gu, C. (2002). Smoothing Spline ANOVA Models, New York, USA, Springer.
16. Hosmer, D., Lemeshow, S. y Sturdivant, R. (2013). Applied Logistic Regression, Hoboken, New Jersey, USA: Third Edition, John Wiley & Sons, Inc.
17. Jenkinson, A. (1955). The frequency distribution of the annual maximum (or minimum) values of meteorological events. Quarterly Journal of the Royal Meteorological Society, 81 (348), 158-172.
18. Laitinen, E. y Laitinen, T. (2000). Bankruptcy Prediction: Application of the Taylor's Expansion in Logistic Regression, International Review of Financial Analysis, 9 (4), 327-349.
19. Martínez, Ó. (2003). Determinantes de Fragilidad en las Empresas Colombianas, Borradores de Economía, Banco de la Republica, (259), 1-24.
20. Marra, G. y Radice, R. (2017). GJRM: Generalized Joint Regression Modelling, R package version 0.1-1.
21. Narváez, L. (2010). Análisis de la Aplicación de los Modelos de Predicción de Quiebras en Colombia, Tesis pregrado en contaduría pública, Universidad Autónoma de Occidente, Cali, Colombia.
22. Ohlson, J. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy, Journal of Accounting Research, 18 (1), 109-131.
23. O'Leary, D. (1998). Using Neural Networks to Predict Corporate Failure, International Journal of Intelligent Systems in Accounting, Finance and Management, 7, 187–197.
24. Pérez, J., González, K. y Lopera, M. (2013). Modelos de Predicción de la Fragilidad Empresarial: Aplicación al Caso Colombiano Para El Año 2011, Perfil de Coyuntura Económica, 22, 205-228.
25. R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
26. Ravi Kumar, P. y Ravi, V. (2007). Bankruptcy Prediction in Banks and Firms via Statistical and Intelligent Techniques – A Review, European Journal of Operational Research, 180 (1), 1-28.
27. Rosillo, J. (2002). Modelo de Predicción de Quiebras de las Empresas Colombianas, Innovar, revista de ciencias administrativas y sociales, 12, 109-124.
28. Santos, M., Cortez, P., Pereira, J. y Quintela, H. (2006). Corporate Bankruptcy Prediction Using Data Mining Techniques, WIT Transactions on Information and Communication Technologies, 37 (1), 349-357.
29. Sobehart, J., Keenan, S. y Stein R. (2000). Validation Methodologies for Default Risk Models, Default Risk, (Mayo), 51-56.
30. Tsay, R. (2010). Analysis of Financial Time Series, Hoboken, New Jersey, USA: Third Edition, John Wiley & Sons, Inc.
31. Vozzella, P. y Gabbi, G. (2010). Default and Asset Correlation: An Empirical Study for Italian SMEs. Working Paper.
32. Whaba, G. (1990). Spline Models or Observational Data, Philadelphia, Pennsylvania, USA, Society for industrial and applied mathematics.
33. Wood, S. (2006). Generalized Additive Models: An Introduction with R, Boca Raton, Florida, USA, First Edition, Chapman & Hall/CRC.
34. Wood, S. (2017). mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML Smoothness Estimation, R package version, 1.8-17.
35. Zibanezhad, E., Foroghi, D. y Monadjemi, A. (2011). Applying Decision Tree to Predict Bankruptcy, Proceedings of IEEE International Conference on Computer Science and Automation Engineering, 4, 165-169.
Cómo citar
Pérez García, J. I., Lopera Castaño, M., & Vásquez Bedoya, F. A. (2018). Estimación de la probabilidad de riesgo de quiebra en las empresas colombianas a partir de un modelo para eventos raros. Cuadernos De Administración, 30(54), 7–38. https://doi.org/10.11144/Javeriana.cao30-54.eprqe
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