Financial ratios as a powerful instrument to predict insolvency; a study using boosting algorithms in Colombian firms

Authors

  • Diego Andrés Correa-Mejía Professor, Departamento de Ciencias Contables, Universidad de Antioquia, Medellín, Colombia. https://orcid.org/0000-0002-1319-0451
  • Mauricio Lopera-Castaño Professor, Departamento de Estadística y Matemáticas, Universidad de Antioquia, Medellín, Colombia.

DOI:

https://doi.org/10.18046/j.estger.2020.155.3588

Keywords:

insolvency prediction, bankruptcy, financial analysis, financial ratios, boosting algorithm

Abstract

This study is motivated by the importance of accurately predicting insolvency before it happens. The paper aims to develop an insolvency prediction model for Colombian firms with one, two and three years of anticipation through financial ratios, keeping sample structures and taking into account insolvency-related regulation. This research contributes to the literature because unlike many studies, it takes legislation into account, explains the different types of financial ratios, and uses boosting algorithms without biasing the sample. Data from 11,812 Colombian companies covering the period 2012-2016 was used. The results show accuracy above 70% for insolvency predic­tion with one, two and three years of anticipation.

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References

Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.2307/2978933

Altman, E., Iwanicz-Drozdowska, M., Laitinen, E., & Suvas, A. (2017). Financial Distress Prediction in an International Context: A Review and Empirical Ana-lysis of Altman’s Z-Score Model. Journal of International Financial Management and Accounting, 28(2), 131-171. https://doi.org/10.1111/jifm.12053

Amendola, A., Giordano, F., Parrella, M., & Restaino, M. (2017). Variable selection in high-dimensional regression: a nonparametric procedure for business failure prediction. Applied Stochastic Models in Business and Industry, 33(4), 355-368. https://doi.org/10.1002/asmb.2240

Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417. https://doi.org/10.1016/j.eswa.2017.04.006

Bauer, J., & Agarwal, V. (2014). Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test. Journal of Banking and Finance, 40(1), 432-442. https://doi.org/10.1016/j.jbankfin.2013.12.013

Beaver, W. H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4(71), 71-111. https://doi.org/10.2307/2490171

Ben, S. (2017). Bankruptcy prediction using Partial Least Squares Logistic Regression. Journal of Retailing and Consumer Services, 36, 197-202. https://doi.org/10.1016/j.jretconser.2017.02.005

Box, G. E., & Cox, D. R. (1964). An Analysis of Transformations. Journal of the Royal Statistical Society, 26(2), 211-252. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x

Calabrese, R., & 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. https://doi.org/10.1080/02664763.2013.784894

Calabrese, R., & Osmetti, S. A. (2015). Improving forecast of binary rare events data: A gam-based approach. Journal of Forecasting, 34(3), 230-239. https://doi.org/10.1002/for.2335

Charitou, A., Dionysiou, D., Lambertides, N., & Trigeorgis, L. (2013). Alternative bankruptcy prediction models using option-pricing theory. Journal of Banking and Finance, 37(7), 2329-2341. https://doi.org/10.1016/j.jbankfin.2013.01.020

Cultrera, L., & Brédart, X. (2016). Bankruptcy prediction: The case of Belgian SMEs. Review of Accounting and Finance, 15(1), 101-119. https://doi.org/10.1108/RAF-06-2014-0059

De Mooij, R., & Hebous, S. (2018). Curbing Corporate Debt Bias: Do Limitations to Interest Deductibility Work? Journal of Banking & Finance, 96, 368-378. https://doi.org/10.1016/j.jbankfin.2018.07.013

do Prado, J. W., Carvalho, F. de M., Benedicto, G. C. de, & Lima, A. L. R. (2019). Análisis del riesgo de crédito que enfrentan las empresas de capital abierto en Brasil: un enfoque utilizando análisis discriminante regresión logística y redes neuronales artificiales. Estudios Gerenciales, 35(153), 347-360. https://doi.org/10.18046/j.estger.2019.153.3151

Du Jardin, P. (2015). Bankruptcy prediction using terminal failure processes. European Journal of Operational Research, 242(1), 286-303. https://doi.org/10.1016/j.ejor.2014.09.059

Fedorova, E., Gilenko, E., & Dovzhenko, S. (2013). Bankruptcy prediction for Russian companies: Application of combined classifiers. Expert Systems with Applications, 40(18), 7285-7293. https://doi.org/10.1016/j.eswa.2013.07.032

Fonseca, S. (2007). Régimen de insolvencia empresarial: Propuesta de unificación de los privilegios concursales para los países miembros de la comunidad andina de naciones. Estado del arte. Civilizar. Ciencias So-ciales y Humanas, 7(13), 173-191. https://doi.org/10.22518/16578953.772

Freund, Y., & Schapire, R. E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119-139. https://doi.org/10.1144/GSL.SP.2005.240.01.16

Hastie, T., Tibshirani, R., & Friedman, J. (2008). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer.

Hernandez, M., & Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394-419. https://doi.org/10.1016/j.irfa.2013.02.013

Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks. Journal of Business Finance and Accounting, 44(1-2), 3-34. https://doi.org/10.1111/jbfa.12218

Kim, M., Kang, D., & Bae, H. (2015). Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction. Expert Systems with Applications, 42(3), 1074-1082. https://doi.org/10.1016/j.eswa.2014.08.025

Kim, T., & Ahn, H. (2015). A Hybrid Under-sampling Approach for Better Bankruptcy Prediction. Journal of Intelligence and Information Systems, 21(2), 173-190. https://doi.org/10.13088/jiis.2015.21.2.173

Lartey, V. C., Antwi, S., & Boadi, E. K. (2013). The Relationship between Liquidity and Profitability of Listed Banks in Ghana. International Journal of Business and Social Science, 4(3), 48-56.

Le, T., Son, L. H., Vo, M. T., Lee, M. Y., & Baik, S. W. (2018). A cluster-based boosting algorithm for bankruptcy prediction in a highly imbalanced dataset. Symmetry, 10(7), 1-12. https://doi.org/10.3390/sym10070250

Ley 1116. (2006). Diario Oficial No. 46.494 de 27 de diciembre de 2006, Colombia.

Liang, D., Lu, C. C., Tsai, C. F., & Shih, G. A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252(2), 561-572. https://doi.org/10.1016/j.ejor.2016.01.012

López, F. J., & Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks. Expert Systems with Applications, 42(6), 2857-2868. https://doi.org/10.1016/j.eswa.2014.11.025

Manly, B. F. J. (1976). Exponential Data Transformations. Journal of the Royal Statistical Society, 25(1), 37-42. https://doi.org/10.2307/2988129

Mongrut, S., Fuenzalida, D., Alberti, F., & Akamine, M. (2011). Determinantes de la insolvencia empresarial en el Perú. Revista Latinoamericana de Administración, (47), 126-139.

Mora, M. (2014). Declive organizativo, fracaso y reestructuración organizacional en empresas colombianas. Contaduría y Administración, 59(3), 235-260. https://doi.org/10.1016/S0186-1042(14)71271-9

Mu, C., Wang, A., & Yang, J. (2017). Optimal capital structure with moral hazard. International Review of Economics and Finance, 48, 326-338. https://doi.org/10.1016/j.iref.2016.12.006

Ng, A. C., & Rezaee, Z. (2015). Business sustainability performance and cost of equity capital. Journal of Corporate Finance, 34, 128-149. https://doi.org/10.1016/j.jcorpfin.2015.08.003

Nishihara, M., & Shibata, T. (2016). Asset sale, debt restructuring, and liquidation. Journal of Economic Dynamics and Control, 67, 73-92. https://doi.org/10.1016/j.jedc.2016.03.011

Nissim, D., & Penman, S. (2003). Financial Statement Analysis of Leverage and How It Informs About Probability and Price-to-Book Ratio. Journal of Chemical Information and Modeling, 8, 531-560. https://doi.org/10.1017/CBO9781107415324.004

Ochoa, Y., Toro, D., Betancur, L., & Correa, J. (2009). El indicador Z, una forma de evaluar el riesgo de continuidad. Contaduría Universidad de Antioquia, (54), 225-255.

Olson, D. L., Delen, D., & Meng, Y. (2012). Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, 52(2), 464-473. https://doi.org/10.1016/j.dss.2011.10.007

Pérez, J., Lopera, M., & Vásquez, F. (2017). 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

Rodríguez, J. (2008). El derecho concursal colombiano a la luz de la constitución. E-Mercatoria, 7(2), 1-53.

Romero, F., Melgarejo, Z., & Vera, M. (2015). Fracaso empresarial de las pequeñas y medianas empresas (pymes) en Colombia. Suma de Negocios, 6(13), 29-41. https://doi.org/10.1016/j.sumneg.2015.08.003

Roumani, Y. F., Nwankpa, J. K., & Tanniru, M. (2019). Predicting firm failure in the software industry. Artificial Intelligence Review. https://doi.org/10.1007/s10462-019-09789-2

Vo, X. V. (2017). Determinants of capital structure in emerging markets: Evidence from Vietnam. Research in International Business and Finance, 40, 105-113. https://doi.org/10.1016/j.ribaf.2016.12.001

Wang, G., Ma, J., & Yang, S. (2014). An improved boosting based on feature selection for corporate bankruptcy prediction. Expert Systems with Applications, 41(5), 2353-2361. https://doi.org/10.1016/j.eswa.2013.09.033

Wilches, R. (2008). Vacíos e inconsistencias estructurales del nuevo régimen de insolvencia empresarial colombiano. Identificación y propuestas de solución. Universitas, (117), 197-218.

Wilches, R. (2009). La insolvencia transfronteriza en el derecho colombiano. Revista de Derecho, (32), 162-198.

Yazdanfar, D., & Öhman, P. (2015). Debt financing and firm performance: an empirical study based on Swedish data. The Journal of Risk Finance, 16(1), 102-118. https://doi.org/10.1108/JRF-06-2014-0085

Yeo, I.-K., & Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry. Biometrika, 87(4), 954-959.

Published

2020-03-04

How to Cite

Financial ratios as a powerful instrument to predict insolvency; a study using boosting algorithms in Colombian firms. (2020). Estudios Gerenciales, 36(155), 229-238. https://doi.org/10.18046/j.estger.2020.155.3588