Indicadores financeiros como poderoso instrumento para prever insolvência. Um estudo usando o algoritmo boosting em empresas colombianas

Autores

  • 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

Palavras-chave:

previsão de insolvencia, falencia, análise financeira, indicadores financeiros, algoritmo boosting

Resumo

Esta pesquisa é motivada pela importância de ter uma boa previsão de insolvência com antecedência. O objetivo deste artigo é desenvolver um modelo preditivo para as empresas colombianas com um, dois e três anos de antecedência, utilizando indicadores financeiros, preser­vando a estrutura original da amostra e levando em consideração o regulamento de insolvência. Este artigo contribui com a literatura, pois, diferentemente dos estudos tradicionais, são levados em consideração aspectos como legislação, explicando os diferentes tipos de indica­dores financeiros, e o algoritmo boosting é utilizado sem influenciar a amostra inicial. Para o desenvolvimento deste estudo, considerou-se uma amostra de 11.812 empresas colombianas durante o período 2012-2016. Os resultados mostram uma precisão superior a 70% na previsão da insolvência com um, dois e três anos de antecedência.

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Referências

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.

Publicado

2020-03-04

Como Citar

Indicadores financeiros como poderoso instrumento para prever insolvência. Um estudo usando o algoritmo boosting em empresas colombianas. (2020). Estudios Gerenciales, 36(155), 229-238. https://doi.org/10.18046/j.estger.2020.155.3588