Antecipando dificuldades financeiras nas organizações

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

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.


Palavras-chave


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

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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




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

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