Anticipating corporate’s distresses

Flávio Luiz de Moraes Barboza; Denize Lemos Duarte; Michele Aparecida Cunha

doi:10.5585/exactaep.2021.17494

Autores

Flávio Luiz de Moraes Barboza Federal University of Uberlândia Professor of Finance at School of Business and Management - Federal University of Uberlândia https://orcid.org/0000-0002-3449-5297
Denize Lemos Duarte Federal University of Uberlândia Professor at the Faculty of Accounting Sciences https://orcid.org/0000-0002-5074-3385
Michele Aparecida Cunha Federal University of Uberlândia Master's degree in Business Administration from the Faculty of Business https://orcid.org/0000-0003-1546-0504

DOI:

https://doi.org/10.5585/exactaep.2021.17494

Palavras-chave:

Risco de Crédito, Dificuldade financeira corporativa, Inteligência artificial, Extreme gradient boosting, Importância de variáveis.

Resumo

O objetivo deste estudo é apresentar um modelo de previsão de Dificuldades Financeiras (DF) a partir da perspectiva das técnicas de aprendizado de máquina (TAMs). Aplicamos e comparamos os modelos XGBoost, Random Forest e Regressão Logística usando indicadores financeiros para buscar melhores previsões das DFs um ano antes do evento em empresas latino-americanas no período de 2000 a 2017. Nossos resultados mostraram que as TAMs superam o modelo de logit, atingindo uma precisão geral de 96 % (XGboost). Além disso, cinco indicadores foram relevantes para o seu sucesso. O estudo amplia o conhecimento e as discussões ao enfocar o poder preditivo na comparação entre os modelos, destacando os benefícios do uso de algoritmos aplicados à pesquisa financeira. Auxilia na gestão de riscos, na prevenção de perdas, permitindo maior equilíbrio e saúde para o sistema financeiro’, que contribui para o desenvolvimento econômico, social e sustentável de uma sociedade.

Downloads

Não há dados estatísticos.

Biografia do Autor

Flávio Luiz de Moraes Barboza, Federal University of Uberlândia Professor of Finance at School of Business and Management - Federal University of Uberlândia

Professor of Finance at School of Business and Management - Federal University of Uberlândia. Bachalor's degree in Math - UNESP (2003), MSc. in Physics - UNESP (2007), and PhD in Finance - Mackenzie (2015). Experience in Math applied to Finance, focusing on computational modelling for Risk Management, Credit Risk, Financial Performance, and Investment Analysis.

Denize Lemos Duarte, Federal University of Uberlândia Professor at the Faculty of Accounting Sciences

Experience in Finance, Management, Controllership, Administrative, Logistics, Systems and Methods, Tax and Accounting, FP&A (Tactical and Strategic), Costs, computational modeling for Risk Management, Credit Risk, Financial Performance, and Investment Analysis, Budget and Commercial Management in large and medium companies, with a degree in Accounting, MBA in Finance and Planning Strategic by the Institute of Economics and Master in Administration.

Michele Aparecida Cunha, Federal University of Uberlândia Master's degree in Business Administration from the Faculty of Business

Graduated in Business Administration and Accounting from the University Center of Patos de Minas (UNIPAM), with a specialization in Business Strategy Management and an MBA in People Management. He is currently a Master's student in Administration at the Faculty of Management and Business (FAGEN) of the Federal University of Uberlândia (UFU). Acts as a substitute professor at the Faculty of Management and Business at the Federal University of Uberlândia, teaching classes at the Undergraduate level. His research interests involve: Third Sector, Organizational Behavior, Consumer Behavior, Business Consulting and Risk Management.

Referências

Abdou, H. A. (2009). Genetic programming for credit scoring: The case of egyptian public sector banks. Expert systems with applications, 36, 11402–11417. https://doi.org/10.1016/j.eswa.2009.01.076

Alfaro, E., García, N., Gámez, M., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of adaboost and neural networks. Decision Support Systems, 45, 110–122. https://doi.org/10.1016/j.dss.2007.12.002

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bunkrupty “. The Journal of Finance, 23, 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x

Bae, J. K. (2012). Predicting financial distress of the south korean manufacturing industries.

Expert Systems with Applications, 39, 9159–9165. https://doi.org/10.1016/j.eswa.2012.02.058

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

Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, (pp. 71–111). https://www.jstor.org/stable/2490171

Beaver, W. H., McNichols, M. F., & Rhie, J.-W. (2005). Have financial statements become less informative? evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting studies, 10, 93–122. https://doi.org/10.1007/s11142-004-6341-9

BIS (2006). Basel II: International Convergence of Capital Measurement and Capital Standards: A Revised Framework – Comprehensive Version. Retrieved from: http://www.bis.org/publ/bcbs128.pdf

Blanco, A., Pino-Mejías, R., Lara, J., & Rayo, S. (2013). Credit scoring models for the microfinance industry using neural networks: Evidence from peru. Expert Systems with applications, 40, 356–364. https://doi.org/10.1016/j.eswa.2012.07.051

Breiman, L. (1996). Bagging predictors. Machine learning, 24, 123–140. https://doi.org/10.1007/BF00058655

Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39, 3446–3453. https://doi.org/10.1016/j.eswa.2011.09.033

Campos, A. L. S., & Nakamura, W. T. (2015). Rebalanceamento da estrutura de capital: endividamento setorial e folga financeira. Revista de Administração Contemporânea, 19, 20–37. https://doi.org/10.1590/1982-7849rac20151789

Carmona, P., Climent, F., & Momparler, A. (2019). Predicting failure in the us banking sector: An extreme gradient boosting approach. International Review of Economics & Finance, 61, 304–323. https://doi.org/10.1016/j.iref.2018.03.008

Chang, T.-M., & Hsu, M.-F. (2018). Integration of incremental filter-wrapper selection strategy with artificial intelligence for enterprise risk management. International Journal of Machine Learning and Cybernetics, 9, 477–489. https://doi.org/10.1007/s13042-016-0545-8

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321–357. https://doi.org/10.1613/jair.953

Chen, H.-L., Yang, B., Wang, G., Liu, J., Xu, X., Wang, S.-J., & Liu, D.-Y. (2011). A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method.

Knowledge-Based Systems, 24, 1348–1359. https://doi.org/10.1016/j.knosys.2011.06.008

Chen, T., & Guestrin, C. (2015). XGBoost: reliable large-scale tree boosting system. In Proceedings of the 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA (pp. 13-17). Available: http://ml-pai-learn.oss-cn-beijing.aliyuncs.com/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%B5%84%E6%96%99/LearningSys_2015_paper_32.pdf

Cho, S., Hong, H., & Ha, B.-C. (2010). A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the mahalanobis distance: For bankruptcy prediction. Expert Systems with Applications, 37, 3482–3488. https://doi.org/10.1016/j.eswa.2009.10.040

Chuang, C.-L., & Huang, S.-T. (2011). A hybrid neural network approach for credit scoring.

Expert Systems, 28, 185–196. https://doi.org/10.1111/j.1468-0394.2010.00565.x

Climent, F., Momparler, A., & Carmona, P. (2019). Anticipating bank distress in the eurozone: An extreme gradient boosting approach. Journal of Business Research, 101, 885–896. https://doi.org/10.1016/j.jbusres.2018.11.015

Danenas, P., & Garsva, G. (2015). Selection of support vector machines based classifiers for credit risk domain. Expert Systems with Applications, 42, 3194–3204. https://doi.org/10.1016/j.eswa.2014.12.001

Domingos, P. (1999). Metacost: A general method for making classifiers cost-sensitive. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 155–164). https://dl.acm.org/doi/pdf/10.1145/312129.312220

Finlay, S. (2011). Multiple classifier architectures and their application to credit risk assessment. European Journal of Operational Research, 210, 368–378. https://doi.org/10.1016/j.ejor.2010.09.029

Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning. Springer series in statistics New York.

Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, (pp. 1189–1232). https://www.jstor.org/stable/2699986

Friedman, J. H. (2002). Stochastic gradient boosting. Computational statistics & data analysis, 38, 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2

Gepp, A., Kumar, K., & Bhattacharya, S. (2010). Business failure prediction using decision trees. Journal of forecasting, 29, 536–555. https://doi.org/10.1002/for.1153

Gujarati, D. N., & Porter, D. C. (2011). Econometria Básica-5. Amgh Editora.

Guo, Y., Zhou, W., Luo, C., Liu, C., & Xiong, H. (2016). Instance-based credit risk assessment for investment decisions in p2p lending. European Journal of Operational Research, 249, 417–426. https://doi.org/10.1016/j.ejor.2015.05.050

Harris, T. (2015). Credit scoring using the clustered support vector machine. Expert Systems with Applications, 42, 741–750. https://doi.org/10.1016/j.eswa.2014.08.029

He, H., Zhang, W., & Zhang, S. (2018). A novel ensemble method for credit scoring: Adaption of different imbalance ratios. Expert Systems with Applications, 98, 105–117. https://doi.org/10.1016/j.eswa.2018.01.012

Hens, A. B., & Tiwari, M. K. (2012). Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method. Expert Systems with Applications, 39, 6774–6781. https://doi.org/10.1016/j.eswa.2011.12.057

Heo, J., & Yang, J. Y. (2014). Adaboost based bankruptcy forecasting of korean construction companies. Applied soft computing, 24, 494–499. https://doi.org/10.1016/j.asoc.2014.08.009

Hsieh, T.-J., Hsiao, H.-F., & Yeh, W.-C. (2012). Mining financial distress trend data using penalty guided support vector machines based on hybrid of particle swarm optimization and artificial bee colony algorithm. Neurocomputing, 82, 196–206. https://doi.org/10.1016/j.neucom.2011.11.020

Huang, Y.-P., & Yen, M.-F. (2019). A new perspective of performance comparison among machine learning algorithms for financial distress prediction. Applied Soft Computing, 83, 105663. https://doi.org/10.1016/j.asoc.2019.105663

Iquipaza, R. A., Lamounier, W. M., & Amaral, H. F. (2008). Assimetric information and dividends payout at the São Paulo stock exchange (bovespa). Ad. Sci. appl. Account. 1(1), 1001-14. Available: https://www.researchgate.net/publication/241765395_Asymmetric_information_and_dividends_payout_at_the_Sao_Paulo_stock_exchange

Iturriaga, F. J. L., & Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks: A study of us commercial banks. Expert Systems with applications, 42, 2857– 2869. https://doi.org/10.1016/j.eswa.2014.11.025

Japkowicz, N. (2000). The class imbalance problem: Significance and strategies. In Proc. of the Int’l Conf. on Artificial Intelligence. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.35.1693&rep=rep1&type=pdf

Jeong, C., Min, J. H., & Kim, M. S. (2012). A tuning method for the architecture of neural network models incorporating gam and ga as applied to bankruptcy prediction. Expert Systems with Applications, 39, 3650–3658. https://doi.org/10.1016/j.eswa.2011.09.056

Jones, S., Johnstone, D., & Wilson, R. (2015). An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes. Journal of Banking & Finance, 56, 72–85. https://doi.org/10.1016/j.jbankfin.2015.02.006

Kim, M.-J., & Kang, D.-K. (2010). Ensemble with neural networks for bankruptcy prediction.

Expert systems with applications, 37, 3373–3379. https://doi.org/10.1016/j.eswa.2009.10.012

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

Kim, S. Y. (2011). Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis. The Service Industries Journal, 31, 441–468. https://doi.org/10.1016/j.eswa.2014.08.025

Kim, S. Y., & Upneja, A. (2014). Predicting restaurant financial distress using decision tree and adaboosted decision tree models. Economic Modelling, 36, 354–362. https://doi.org/10.1016/j.econmod.2013.10.005

Korol, T. (2013). Early warning models against bankruptcy risk for central european and latin american enterprises. Economic Modelling, 31, 22–30. https://doi.org/10.1016/j.econmod.2012.11.017

Lai, K. K., Yu, L., Wang, S., & Zhou, L. (2006). Credit risk analysis using a reliability-based neural network ensemble model. In International Conference on Artificial Neural Networks (pp. 682–690). Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_71

Lee, T.-S., Chiu, C.-C., Lu, C.-J., & Chen, I.-F. (2002). Credit scoring using the hybrid neural discriminant technique. Expert Systems with applications, 23, 245–254. https://doi.org/10.1016/S0957-4174(02)00044-1

Li, H., Huang, H.-B., Sun, J., & Lin, C. (2010a). On sensitivity of case-based reasoning to optimal feature subsets in business failure prediction. Expert Systems with Applications, 37, 4811–4821. https://doi.org/10.1016/j.eswa.2009.12.034

Li, H., Lee, Y.-C., Zhou, Y.-C., & Sun, J. (2011). The random subspace binary logit (rsbl) model for bankruptcy prediction. Knowledge-Based Systems, 24, 1380–1388. https://doi.org/10.1016/j.knosys.2011.06.015

Li, H., & Sun, J. (2010). Business failure prediction using hybrid2 case-based reasoning (h2cbr). Computers & Operations Research, 37, 137–151. https://doi.org/10.1016/j.cor.2009.04.003

Li, H., & Sun, J. (2011). Empirical research of hybridizing principal component analysis with multivariate discriminant analysis and logistic regression for business failure prediction. Expert Systems with Applications, 38, 6244–6253. https://doi.org/10.1016/j.eswa.2010.11.043

Li, H., & Sun, J. (2012). Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples–evidence from the chinese hotel industry. Tourism Management, 33, 622–634. https://doi.org/10.1016/j.tourman.2011.07.004

Li, H., Sun, J., & Sun, B.-L. (2009). Financial distress prediction based on or-cbr in the principle of k-nearest neighbors. Expert Systems with Applications, 36, 643–659. https://doi.org/10.1016/j.eswa.2007.09.038

Li, H., Sun, J., & Wu, J. (2010b). Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods. Expert Systems with Applications, 37, 5895–5904. https://doi.org/10.1016/j.eswa.2010.02.016

Li, Z., Tian, Y., Li, K., Zhou, F., & Yang, W. (2017). Reject inference in credit scoring using semi-supervised support vector machines. Expert Systems with Applications, 74, 105–114. https://doi.org/10.1016/j.eswa.2017.01.011

Malhotra, R., & Malhotra, D. K. (2002). Differentiating between good credits and bad credits using neuro-fuzzy systems. European journal of operational research, 136, 190–211. https://doi.org/10.1016/S0377-2217(01)00052-2

Manzaneque, M., Priego, A. M., & Merino, E. (2016). Corporate governance effect on financial distress likelihood: Evidence from spain. Revista de Contabilidad, 19, 111–121. https://doi.org/10.1016/j.rcsar.2015.04.001

Martinez-Villa, B. A., & Machin-Mastromatteo, J. D. (2016). Four theories to improve justice in latin america. Information Development, 32, 1284–1288. https://doi.org/10.1177/0266666916658588

Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T., & Brunk, C. (1994). Reducing misclassification costs. In Machine Learning Proceedings 1994 (pp. 217–225). Elsevier, (pp. 217-225). Morgan Kaufmann. https://doi.org/10.1016/B978-1-55860-335-6.50034-9

Pindado, J., Rodrigues, L., & De La Torre, C. (2008). Estimating financial distress likelihood.

Journal of Business Research, 61, 995–1003. https://doi.org/10.1016/j.jbusres.2007.10.006

Piramuthu, S. (1999). Financial credit-risk evaluation with neural and neuro fuzzy systems.

European Journal of Operational Research, 112, 310–321. https://doi.org/10.1016/S0377-2217(97)00398-6

Sanz, L. J., & Ayca, J. (2006). Financial distress costs in Latin America: A case study. Journal of Business Research, 59, 394–395. https://doi.org/10.1016/j.jbusres.2005.09.014

Sun, J., Fujita, H., Chen, P., & Li, H. (2017). Dynamic financial distress prediction with concept drift based on time weighting combined with adaboost support vector machine ensemble. Knowledge-Based Systems, 120, 4–14. https://doi.org/10.1016/j.knosys.2016.12.019

Sun, J., Jia, M.-Y., & Li, H. (2011). Adaboost ensemble for financial distress prediction: An empirical comparison with data from chinese listed companies. Expert Systems with Applications, 38, 9305–9312. https://doi.org/10.1016/j.eswa.2011.01.042

Sung, T. K., Chang, N., & Lee, G. (1999). Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction. Journal of Management Information Systems, 16, 63–85. https://doi.org/10.1080/07421222.1999.11518234

Tang, X., Li, S., Tan, M., & Shi, W. (2020). Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods. Journal of Forecasting. https://doi.org/10.1002/for.2661

Tsai, C.-F., Hsu, Y.-F., & Yen, D. C. (2014). A comparative study of classifier ensembles for bankruptcy prediction. Applied Soft Computing, 24, 977–984. https://doi.org/10.1016/j.asoc.2014.08.047

Tsai, C.-F., & Wu, J.-W. (2008). Using neural network ensembles for bankruptcy prediction and credit scoring. Expert systems with applications, 34, 2639–2649. https://doi.org/10.1016/j.eswa.2007.05.019

Wang, G., & Ma, J. (2011). Study of corporate credit risk prediction based on integrating boosting and random subspace. Expert Systems with Applications, 38, 13871–13878. https://doi.org/10.1016/j.eswa.2011.04.191

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

Wang, J., Veugelers, R., & Stephan, P. (2017). Bias against novelty in science: A cautionary tale for users of bibliometric indicators. Research Policy, 46, 1416–1436. https://doi.org/10.1016/j.respol.2017.06.006

Wang, Y., Wang, S., & Lai, K. K. (2005). A new fuzzy support vector machine to evaluate credit risk. IEEE Transactions on Fuzzy Systems, 13, 820–831. https://doi.org/10.1109/TFUZZ.2005.859320

West, D. (2000). Neural network credit scoring models. Computers & Operations Research, 27, 1131–1152. https://doi.org/10.1016/S0305-0548(99)00149-5

Xia, Y., Liu, C., Da, B., & Xie, F. (2018). A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Systems with Applications, 93, 182–199. https://doi.org/10.1016/j.eswa.2017.10.022

Xia, Y., Liu, C., Li, Y., & Liu, N. (2017). A boosted decision tree approach using bayesian hyper-parameter optimization for credit scoring. Expert Systems with Applications, 78, 225–241. https://doi.org/10.1016/j.eswa.2017.02.017

Yu, L., Yang, Z., & Tang, L. (2016). A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment. Flexible Services and Manufacturing Journal, 28, 576–592. https://doi.org/10.1007/s10696-015-9226-2

Zhang, D., Zhou, X., Leung, S. C., & Zheng, J. (2010). Vertical bagging decision trees model for credit scoring. Expert Systems with Applications, 37, 7838–7843. https://doi.org/10.1016/j.eswa.2010.04.054

Zhang, L., Priestley, J., & Ni, X. (2018). Influence of the event rate on discrimination abilities of bankruptcy prediction models. arXiv preprint arXiv:1803.03756. https://doi.org/10.5121/ijdms.2018.10101

Zhao, Z., Xu, S., Kang, B. H., Kabir, M. M. J., Liu, Y., & Wasinger, R. (2015). Investigation and improvement of multi-layer perceptron neural networks for credit scoring. Expert Systems with Applications, 42, 3508–3516. https://doi.org/10.1016/j.eswa.2014.12.006

Zie˛ba, M., Tomczak, S. K., & Tomczak, J. M. (2016). Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Systems with Applications, 58, 93–101. https://doi.org/10.1016/j.eswa.2016.04.001