Avaliação de falências de empresas por meio de florestas causais

Bittencourt, Wanderson Rocha; Albuquerque, Pedro H. M.

doi:10.1590/1808-057x202010360

Wanderson Rocha Bittencourt

Universidade de Brasília, Faculdade de Administração, Contabilidade, Economia e Gestão de Políticas Públicas, Departamento de Administração, Brasília, DF, Brasil

https://orcid.org/0000-0003-3417-2225

Pedro H. M. Albuquerque

Universidade de Brasília, Faculdade de Administração, Contabilidade, Economia e Gestão de Políticas Públicas, Departamento de Administração, Brasília, DF, Brasil

https://orcid.org/0000-0002-1415-716X

E-mail:
wandersonrochab@yahoo.com.br

E-mail:
pedroa@unb.br

Editora Associada:

Fernanda Finotti Cordeiro Perobelli

Endereço para correspondência:

Wanderson Rocha Bittencourt. Universidade de Brasília, Faculdade de Administração, Contabilidade, Economia e Gestão de Políticas Públicas, Departamento de Ciências Contábeis e Atuariais. Campus Universitário Darcy Ribeiro, Bloco A-2 - CEP 70910-900. Asa Norte - Brasília - DF - Brasil

Modelo genérico	Modelo específico	Alguns autores que utilizaram
Análise discriminante	Básico	FitzPatrick (1932FitzPatrick, P. J. (1932). A comparison of the ratios of successful industrial enterprises with those of failed companies. Retrieved from https://www.worldcat.org/title/comparison-of-the-ratios-of-successful-industrial-enterprises-with-those-of-failed-companies/oclc/6284198 https://www.worldcat.org/title/compariso... )
Análise discriminante	Multivariado	Altman (1968Altman, E. I. (1968). Financianl ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.), Lennox (1999Lennox, C. (1999). Identifying failing companies: A re-evaluation of the logit, probit and DA approaches. Journal of Economics and Business, 51, 347-364.), Min e Lee (2005Min, J. H., & Lee, Y. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603-614. https://doi.org/10.1016/j.eswa.2004.12.008 https://doi.org/10.1016/j.eswa.2004.12.0... ), Cho, Kim e Bae (2009Cho, S., Kim, J., & Bae, J. K. (2009). An integrative model with subject weight based on neural network learning for bankruptcy prediction. Expert Systems With Applications, 36(1), 403-410. https://doi.org/10.1016/j.eswa.2007.09.060 https://doi.org/10.1016/j.eswa.2007.09.0... ), Lee e Choi (2013Lee, S., & Choi, W. S. (2013). A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis. Expert Systems with Applications, 40(8), 2941-2946. https://doi.org/10.1016/j.eswa.2012.12.009 https://doi.org/10.1016/j.eswa.2012.12.0... ), Barboza et al. (2017Barboza, 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 https://doi.org/10.1016/j.eswa.2017.04.0... ), García, Marqués, Sánchez e Ochoa-Domínguez (2017García, V., Marqués, A. I., Sánchez, J. S., & Ochoa-Domínguez, H. J. (2017). Dissimilarity-based linear models for corporate bankruptcy prediction. Computational Economics, 53, 1019-1031. https://doi.org/10.1007/s10614-017-9783-4 https://doi.org/10.1007/s10614-017-9783-... )
Logit	Básico	Ohlson (1980Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395 https://doi.org/10.2307/2490395... ), Lennox (1999Lennox, C. (1999). Identifying failing companies: A re-evaluation of the logit, probit and DA approaches. Journal of Economics and Business, 51, 347-364.), Min e Lee (2005Min, J. H., & Lee, Y. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603-614. https://doi.org/10.1016/j.eswa.2004.12.008 https://doi.org/10.1016/j.eswa.2004.12.0... ), Cho et al. (2009Cho, S., Kim, J., & Bae, J. K. (2009). An integrative model with subject weight based on neural network learning for bankruptcy prediction. Expert Systems With Applications, 36(1), 403-410. https://doi.org/10.1016/j.eswa.2007.09.060 https://doi.org/10.1016/j.eswa.2007.09.0... ), Premachandra, Bhabra e Sueyoshi (2009Premachandra, I. M., Bhabra, G. S., & Sueyoshi, T. (2009). DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique. European Journal of Operational Research, 193(2), 412-424. https://doi.org/10.1016/j.ejor.2007.11.036 https://doi.org/10.1016/j.ejor.2007.11.0... ), Tseng e Hu (2010Tseng, F., & Hu, Y. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems With Applications, 37(3), 1846-1853. https://doi.org/10.1016/j.eswa.2009.07.081 https://doi.org/10.1016/j.eswa.2009.07.0... ), Antunes et al. (2017Antunes, F., Ribeiro, B., & Pereira, F. (2017). Probabilistic modeling and visualization for bankruptcy prediction. Applied Soft Computing Journal, 60, 831-843. https://doi.org/10.1016/j.asoc.2017.06.043 https://doi.org/10.1016/j.asoc.2017.06.0... ), Barboza et al. (2017Barboza, 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 https://doi.org/10.1016/j.eswa.2017.04.0... ), García et al. (2017García, V., Marqués, A. I., Sánchez, J. S., & Ochoa-Domínguez, H. J. (2017). Dissimilarity-based linear models for corporate bankruptcy prediction. Computational Economics, 53, 1019-1031. https://doi.org/10.1007/s10614-017-9783-4 https://doi.org/10.1007/s10614-017-9783-... )
Logit	Logit de intervalo quadrático	Tseng e Hu (2010Tseng, F., & Hu, Y. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems With Applications, 37(3), 1846-1853. https://doi.org/10.1016/j.eswa.2009.07.081 https://doi.org/10.1016/j.eswa.2009.07.0... )
Probit	Básico	Zmijewski (1984Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 83-86. https://doi.org/10.2307/2490860 https://doi.org/10.2307/2490860... ), Lennox (1999Lennox, C. (1999). Identifying failing companies: A re-evaluation of the logit, probit and DA approaches. Journal of Economics and Business, 51, 347-364.)
Redes neurais	Básico	Pendharkar (2005Pendharkar, P. C. (2005). A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Computers & Operations Research, 32(10), 2561-2582. https://doi.org/10.1016/j.cor.2004.06.023 https://doi.org/10.1016/j.cor.2004.06.02... ), Chauhan, Ravi e Chandra (2009Chauhan, N., Ravi, V., & Chandra, D. K. (2009). Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks. Expert Systems With Applications, 36(4), 7659-7665. https://doi.org/10.1016/j.eswa.2008.09.019 https://doi.org/10.1016/j.eswa.2008.09.0... ), Cho et al. (2009Cho, S., Kim, J., & Bae, J. K. (2009). An integrative model with subject weight based on neural network learning for bankruptcy prediction. Expert Systems With Applications, 36(1), 403-410. https://doi.org/10.1016/j.eswa.2007.09.060 https://doi.org/10.1016/j.eswa.2007.09.0... ), Tseng e Hu (2010Tseng, F., & Hu, Y. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems With Applications, 37(3), 1846-1853. https://doi.org/10.1016/j.eswa.2009.07.081 https://doi.org/10.1016/j.eswa.2009.07.0... ), Tsai et al. (2014Tsai, C. F., Hsu, Y. F., & Yen, D. C. (2014). A comparative study of classifier ensembles for bankruptcy prediction. Applied Soft Computing Journal, 24, 977-984. https://doi.org/10.1016/j.asoc.2014.08.047 https://doi.org/10.1016/j.asoc.2014.08.0... ), Barboza et al. (2017Barboza, 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 https://doi.org/10.1016/j.eswa.2017.04.0... )
	Propagação reversa	Lee e Choi (2013Lee, S., & Choi, W. S. (2013). A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis. Expert Systems with Applications, 40(8), 2941-2946. https://doi.org/10.1016/j.eswa.2012.12.009 https://doi.org/10.1016/j.eswa.2012.12.0... )
	Multicamada	Zmijewski (1984Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 83-86. https://doi.org/10.2307/2490860 https://doi.org/10.2307/2490860... ), Lennox (1999Lennox, C. (1999). Identifying failing companies: A re-evaluation of the logit, probit and DA approaches. Journal of Economics and Business, 51, 347-364.)
	Rede de função de base radial	Tseng e Hu (2010Tseng, F., & Hu, Y. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems With Applications, 37(3), 1846-1853. https://doi.org/10.1016/j.eswa.2009.07.081 https://doi.org/10.1016/j.eswa.2009.07.0... )
	Wavelet treinada em evolução	Chauhan et al. (2009Chauhan, N., Ravi, V., & Chandra, D. K. (2009). Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks. Expert Systems With Applications, 36(4), 7659-7665. https://doi.org/10.1016/j.eswa.2008.09.019 https://doi.org/10.1016/j.eswa.2008.09.0... )
	Modelo interativo com peso*	Cho et al. (2009Cho, S., Kim, J., & Bae, J. K. (2009). An integrative model with subject weight based on neural network learning for bankruptcy prediction. Expert Systems With Applications, 36(1), 403-410. https://doi.org/10.1016/j.eswa.2007.09.060 https://doi.org/10.1016/j.eswa.2007.09.0... )
	Variação limiar	Pendharkar (2005Pendharkar, P. C. (2005). A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Computers & Operations Research, 32(10), 2561-2582. https://doi.org/10.1016/j.cor.2004.06.023 https://doi.org/10.1016/j.cor.2004.06.02... )
Árvore de decisão	Básico	Min e Lee (2005Min, J. H., & Lee, Y. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603-614. https://doi.org/10.1016/j.eswa.2004.12.008 https://doi.org/10.1016/j.eswa.2004.12.0... ), Cho et al. (2009Cho, S., Kim, J., & Bae, J. K. (2009). An integrative model with subject weight based on neural network learning for bankruptcy prediction. Expert Systems With Applications, 36(1), 403-410. https://doi.org/10.1016/j.eswa.2007.09.060 https://doi.org/10.1016/j.eswa.2007.09.0... ), Tsai et al. (2014Tsai, C. F., Hsu, Y. F., & Yen, D. C. (2014). A comparative study of classifier ensembles for bankruptcy prediction. Applied Soft Computing Journal, 24, 977-984. https://doi.org/10.1016/j.asoc.2014.08.047 https://doi.org/10.1016/j.asoc.2014.08.0... )
Máquina de suporte vetorial	Básico	Min e Lee (2005Min, J. H., & Lee, Y. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603-614. https://doi.org/10.1016/j.eswa.2004.12.008 https://doi.org/10.1016/j.eswa.2004.12.0... ), Yang et al. (2011Yang, Z., You, W., & Ji, G. (2011). Using partial least squares and support vector machines for bankruptcy prediction. Expert Systems With Applications, 38(7), 8336-8342. https://doi.org/10.1016/j.eswa.2011.01.021 https://doi.org/10.1016/j.eswa.2011.01.0... ), Tsai et al. (2014Tsai, C. F., Hsu, Y. F., & Yen, D. C. (2014). A comparative study of classifier ensembles for bankruptcy prediction. Applied Soft Computing Journal, 24, 977-984. https://doi.org/10.1016/j.asoc.2014.08.047 https://doi.org/10.1016/j.asoc.2014.08.0... ), Antunes et al. (2017Antunes, F., Ribeiro, B., & Pereira, F. (2017). Probabilistic modeling and visualization for bankruptcy prediction. Applied Soft Computing Journal, 60, 831-843. https://doi.org/10.1016/j.asoc.2017.06.043 https://doi.org/10.1016/j.asoc.2017.06.0... ), García et al. (2017García, V., Marqués, A. I., Sánchez, J. S., & Ochoa-Domínguez, H. J. (2017). Dissimilarity-based linear models for corporate bankruptcy prediction. Computational Economics, 53, 1019-1031. https://doi.org/10.1007/s10614-017-9783-4 https://doi.org/10.1007/s10614-017-9783-... )
	Linear	Barboza et al. (2017Barboza, 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 https://doi.org/10.1016/j.eswa.2017.04.0... )
	Radial	Barboza et al. (2017Barboza, 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 https://doi.org/10.1016/j.eswa.2017.04.0... )
Análise envoltória de dados	Básico	Cielen, Peeters e Vanhoof (2004Cielen, A., Peeters, L., & Vanhoof, K. (2004). Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research, 154(2), 526-532. https://doi.org/10.1016/S0377-2217(03)00186-3 https://doi.org/10.1016/S0377-2217(03)00... ), Premachandra et al. (2009Premachandra, I. M., Bhabra, G. S., & Sueyoshi, T. (2009). DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique. European Journal of Operational Research, 193(2), 412-424. https://doi.org/10.1016/j.ejor.2007.11.036 https://doi.org/10.1016/j.ejor.2007.11.0... ), Premachandra, Chen e Watson (2011Premachandra, I. M., Chen, Y., & Watson, J. (2011). DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment. Omega, 39(6), 620-626. https://doi.org/10.1016/j.omega.2011.01.002 https://doi.org/10.1016/j.omega.2011.01.... )
Processo gaussiano	Básico	Antunes et al. (2017Antunes, F., Ribeiro, B., & Pereira, F. (2017). Probabilistic modeling and visualization for bankruptcy prediction. Applied Soft Computing Journal, 60, 831-843. https://doi.org/10.1016/j.asoc.2017.06.043 https://doi.org/10.1016/j.asoc.2017.06.0... )

Variáveis endógenas	Autores
Capital circulante líquido/AT	Beaver (1966Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171 https://doi.org/10.2307/2490171... ), Altman (1968Altman, E. I. (1968). Financianl ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.), Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... ), Altman, Haldeman e Narayanan (1977Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETA analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6 https://doi.org/10.1016/0378-4266(77)900... )
Lucros retidos/AT	Altman (1968Altman, E. I. (1968). Financianl ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.), Altman et al. (1977Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETA analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6 https://doi.org/10.1016/0378-4266(77)900... ), Ohlson (1980Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395 https://doi.org/10.2307/2490395... )
EBITDA/AT	Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... )
EBIT/AT	Altman (1968Altman, E. I. (1968). Financianl ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.), Altman et al. (1977Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETA analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6 https://doi.org/10.1016/0378-4266(77)900... ), Giordani et al. (2014Giordani, P., Jacobson, T., Schedvin, E. Von, & Villani, M. (2014). Taking the Twists into account: Predicting firm bankruptcy risk with splines of financial ratios. Journal of Financial and Quantitative Analysis, 49(4), 1071-1099. https://doi.org/10.1017/S0022109014000623 https://doi.org/10.1017/S002210901400062... )
Valor de mercado do PL/VCP	Altman (1968Altman, E. I. (1968). Financianl ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.)
Vendas/AT	Altman (1968Altman, E. I. (1968). Financianl ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.)
Taxa líquida/AT	Beaver (1966Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171 https://doi.org/10.2307/2490171... ), Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... )
Passivo total/AT	Beaver (1966Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171 https://doi.org/10.2307/2490171... ), Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... ), Ohlson (1980Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395 https://doi.org/10.2307/2490395... ), DeYoung (2003DeYoung, R. (2003). The failure of new entrants in commercial banking markets: A split-population duration analysis. Review of Financial Economics, 12(1), 7-33. https://doi.org/10.1016/S1058-3300(03)00004-1 https://doi.org/10.1016/S1058-3300(03)00... ), Jostarndt e Sautner (2010Jostarndt, P., & Sautner, Z. (2010). Out-of-court restructuring versus formal bankruptcy in a non-interventionist bankruptcy setting. Review of Finance, 14(4), 623-668. https://doi.org/10.1093/rof/rfp022 https://doi.org/10.1093/rof/rfp022... ), Giordani et al. (2014Giordani, P., Jacobson, T., Schedvin, E. Von, & Villani, M. (2014). Taking the Twists into account: Predicting firm bankruptcy risk with splines of financial ratios. Journal of Financial and Quantitative Analysis, 49(4), 1071-1099. https://doi.org/10.1017/S0022109014000623 https://doi.org/10.1017/S002210901400062... )
Ativo circulante/AT	Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... )
Capital de giro/AT	Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... ), Ohlson (1980Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395 https://doi.org/10.2307/2490395... ), Cole e Gunther (1995Cole, R. A., & Gunther, J. W. (1995). Separating the likelihood and timing of bank failure. Journal of Banking and Finance, 19(6), 1073-1089. https://doi.org/10.1016/0378-4266(95)98952-M https://doi.org/10.1016/0378-4266(95)989... )
Caixa/AT	Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... )
Fluxo de caixa/AT	Beaver (1966Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171 https://doi.org/10.2307/2490171... )
Ativo circulante/PC	Beaver (1966Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171 https://doi.org/10.2307/2490171... ), Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... ), Altman et al. (1977Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETA analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6 https://doi.org/10.1016/0378-4266(77)900... ), Ohlson (1980Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395 https://doi.org/10.2307/2490395... )
Ativo circulante líquido/PC	Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... ), Giordani et al. (2014Giordani, P., Jacobson, T., Schedvin, E. Von, & Villani, M. (2014). Taking the Twists into account: Predicting firm bankruptcy risk with splines of financial ratios. Journal of Financial and Quantitative Analysis, 49(4), 1071-1099. https://doi.org/10.1017/S0022109014000623 https://doi.org/10.1017/S002210901400062... )
Caixa/Passivo circulante	Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... )
Ativo circulante/Vendas	Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... )
Ativo circulante líquido/Vendas	Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... )
Caixa/Vendas	Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... )
Capital de giro/Vendas	Deakin (1972Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accountin Research, 10(1), 167-179. Retrieved from http://www.jstor.org/stable/2490225. http://www.jstor.org/stable/2490225.... )
Provisões de fundos/AT	Ohlson (1980Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395 https://doi.org/10.2307/2490395... )
Variáveis exógenas
Tamanho	Altman et al. (1977Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETA analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6 https://doi.org/10.1016/0378-4266(77)900... ), Ohlson (1980Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395 https://doi.org/10.2307/2490395... ), Cole e Gunther (1995Cole, R. A., & Gunther, J. W. (1995). Separating the likelihood and timing of bank failure. Journal of Banking and Finance, 19(6), 1073-1089. https://doi.org/10.1016/0378-4266(95)98952-M https://doi.org/10.1016/0378-4266(95)989... ), Strömberg (2000Strömberg, P. (2000). Conflicts of interest and market illiquidity in bankruptcy auctions: Theory and tests. Journal of Finance, 55(6), 2641-2692. https://doi.org/10.1111/0022-1082.00302 https://doi.org/10.1111/0022-1082.00302... ), DeYoung (2003DeYoung, R. (2003). The failure of new entrants in commercial banking markets: A split-population duration analysis. Review of Financial Economics, 12(1), 7-33. https://doi.org/10.1016/S1058-3300(03)00004-1 https://doi.org/10.1016/S1058-3300(03)00... ), Jostarndt e Sautner (2010Jostarndt, P., & Sautner, Z. (2010). Out-of-court restructuring versus formal bankruptcy in a non-interventionist bankruptcy setting. Review of Finance, 14(4), 623-668. https://doi.org/10.1093/rof/rfp022 https://doi.org/10.1093/rof/rfp022... ), Giordani et al. (2014Giordani, P., Jacobson, T., Schedvin, E. Von, & Villani, M. (2014). Taking the Twists into account: Predicting firm bankruptcy risk with splines of financial ratios. Journal of Financial and Quantitative Analysis, 49(4), 1071-1099. https://doi.org/10.1017/S0022109014000623 https://doi.org/10.1017/S002210901400062... )
Produto interno bruto	Giordani et al. (2014Giordani, P., Jacobson, T., Schedvin, E. Von, & Villani, M. (2014). Taking the Twists into account: Predicting firm bankruptcy risk with splines of financial ratios. Journal of Financial and Quantitative Analysis, 49(4), 1071-1099. https://doi.org/10.1017/S0022109014000623 https://doi.org/10.1017/S002210901400062... )
Idade	Jostarndt e Sautner (2010Jostarndt, P., & Sautner, Z. (2010). Out-of-court restructuring versus formal bankruptcy in a non-interventionist bankruptcy setting. Review of Finance, 14(4), 623-668. https://doi.org/10.1093/rof/rfp022 https://doi.org/10.1093/rof/rfp022... ), Giordani et al. (2014Giordani, P., Jacobson, T., Schedvin, E. Von, & Villani, M. (2014). Taking the Twists into account: Predicting firm bankruptcy risk with splines of financial ratios. Journal of Financial and Quantitative Analysis, 49(4), 1071-1099. https://doi.org/10.1017/S0022109014000623 https://doi.org/10.1017/S002210901400062... )

Nome	Sigla	Média	DP	Mínimo	Mediana	3-quantis	Máximo
Patrimônio líquido	TE_I	0,38	3,06	-272,70	0,61	0,78	9,28
Passivo total	TL_I	0,67	6,36	-0,09	0,39	0,60	730,98
Passivo longo prazo	TLTD_I	0,13	0,42	0,00	0,02	0,16	25,97
Total recebíveis líquidos	TRN_I	0,17	0,15	-0,21	0,14	0,23	10,81
Rendimentos totais	TR_I	0,33	0,86	-6,58	0,27	0,41	134,18
Equipamentos	PPETN_I	0,22	0,23	-0,17	0,15	0,31	11,79
Lucros retidos	RE_AD_I	-3,53	95,27	-16.217,69	-0,06	0,32	4,69
Ativo total	LN_TA	17,76	1,78	5,01	17,90	18,98	33,96
Ativo circulante	TCA_I	0,63	5,86	0,00	0,62	0,78	756,76
Passivo circulante	TCL_I	0,43	2,93	0,00	0,22	0,36	273,70
Débito total	TD_I	0,24	1,14	0,00	0,08	0,27	77,76
Lucro bruto	GP_I	0,11	0,26	-8,50	0,09	0,14	32,79
Lucro após impostos	NIAT_I	-0,07	1,03	-76,59	0,00	0,02	15,27
Vendas líquidas	NS_I	0,33	0,86	-6,58	0,26	0,41	134,18
Dívidas de curto prazo	NPSTD_I	0,05	0,65	0,00	0,00	0,00	59,14
Lucro operacional	OI_I	-0,05	0,86	-76,82	0,01	0,03	15,82
Custos dos produtos	CR_I	0,21	0,65	-5,69	0,15	0,27	101,39
EBIT	EBIT_I	-0,06	1,74	-175,93	0,01	0,03	15,82
EBITDA	EBITDA_I	-0,04	1,67	-165,73	0,02	0,04	15,82
Contas a pagar	AP_I	0,13	0,81	0,00	0,06	0,12	92,98
Despesas provisionadas	AE_I	0,11	0,65	-21,24	0,06	0,10	73,80
Caixas e equivalentes	CSTI_I	0,23	0,23	-0,01	0,15	0,37	3,61
Estoque	CST_I	0,23	0,23	-0,01	0,15	0,37	3,61
Dummies setoriais
Tecnologia	D_T	0,24	0,43	0,00	0,00	0,00	1,00
Materiais básicos	D_BM	0,05	0,23	0,00	0,00	0,00	1,00
Consumo cíclico	D_CC	0,18	0,39	0,00	0,00	0,00	1,00
Consumo não cíclico	D_CNC	0,05	0,22	0,00	0,00	0,00	1,00
Energia	D_E	0,06	0,25	0,00	0,00	0,00	1,00
Financeiro	D_F	0,02	0,14	0,00	0,00	0,00	1,00
Hospitalar	D_H	0,17	0,37	0,00	0,00	0,00	1,00
Indústria	D_I	0,19	0,39	0,00	0,00	0,00	1,00
Telecomunicações	D_TS	0,01	0,12	0,00	0,00	0,00	1,00
Utilidades	D_U	0,01	0,08	0,00	0,00	0,00	1,00

Variáveis	Estimativas	Erro padrão	Pr(>\|t\|)	Variáveis	Estimativas	Erro padrão	Pr(>\|t\|)
(Intercepto)	1,11451	0,00129	0,00000	I(TE_I * W)	0,11359	0,00176	0,00000
TL_I	0,00088	0,00010	0,00000	I(TRN_I * W)	-0,01963	0,00547	0,00033
TLTD_I	0,15660	0,00199	0,00000	I(PPETN_I * W)	0,06758	0,00502	0,00000
PPETN_I	0,21366	0,00350	0,00000	I(LN_TA * W)	-0,01647	0,00013	0,00000
RE_AD_I	0,00021	0,00001	0,00000	I(TCA_I * W)	0,00220	0,00023	0,00000
TCA_I	-0,00169	0,00015	0,00000	I(TCL_I * W)	0,12330	0,00187	0,00000
OI_I	0,00277	0,00163	0,09050	I(GP_I * W)	-0,05196	0,00370	0,00000
CR_I	-0,00059	0,00139	0,67372	I(NPSTD_I * W)	0,02562	0,00109	0,00000
EBITDA_I	0,00006	0,00048	0,90396	I(OI_I * W)	-0,01134	0,00189	0,00000
AP_I	-0,01409	0,00108	0,00000	I(AE_I * W)	-0,00835	0,00219	0,00013
D_BM	0,56272	0,00166	0,00000	I(CSTI_I * W)	0,12206	0,00382	0,00000
D_CC	0,11772	0,00148	0,00000
D_CNC	0,13174	0,00248	0,00000
D_TS	0,13751	0,00470	0,00000

Folha	Estimativa	Folha	Estimativa	Folha	Estimativa	Folha	Estimativa
Folha_1	-1	Folha_40	-0,89796	Folha_79	-0,39655	Folha_118	0,38961
Folha_2	-0,99813	Folha_41	-0,89305	Folha_80	-0,34615	Folha_119	0,39011
Folha_3	-0,99448	Folha_42	-0,89286	Folha_81	-0,34211	Folha_120	0,40705
Folha_4	-0,99375	Folha_43	-0,88889	Folha_82	-0,3125	Folha_121	0,45554
Folha_5	-0,99058	Folha_44	-0,88462	Folha_83	-0,30303	Folha_122	0,53782
Folha_6	-0,98944	Folha_45	-0,88413	Folha_84	-0,30189	Folha_123	0,54167
Folha_7	-0,98936	Folha_46	-0,875	Folha_85	-0,29412	Folha_124	0,56061
Folha_8	-0,98924	Folha_47	-0,87097	Folha_86	-0,27778	Folha_125	0,6
Folha_9	-0,98221	Folha_48	-0,86957	Folha_87	-0,27451	Folha_126	0,62372
Folha_10	-0,98077	Folha_49	-0,86538	Folha_88	-0,2525	Folha_127	0,63333
Folha_11	-0,97857	Folha_50	-0,86207	Folha_89	-0,25	Folha_128	0,66885
Folha_12	-0,97619	Folha_51	-0,86	Folha_90	-0,22222	Folha_129	0,74868
Folha_13	-0,97464	Folha_52	-0,85185	Folha_91	-0,2151	Folha_130	0,78481
Folha_14	-0,97297	Folha_53	-0,84783	Folha_92	-0,14474	Folha_131	0,79081
Folha_15	-0,96985	Folha_54	-0,84328	Folha_93	-0,14444	Folha_132	0,79094
Folha_16	-0,9697	Folha_55	-0,84	Folha_94	-0,05294	Folha_133	0,8
Folha_17	-0,96769	Folha_56	-0,83871	Folha_95	-0,04819	Folha_134	0,8125
Folha_18	-0,96636	Folha_57	-0,81731	Folha_96	-0,04762	Folha_135	0,81818
Folha_19	-0,96592	Folha_58	-0,78571	Folha_97	-0,03509	Folha_136	0,82467
Folha_20	-0,96552	Folha_59	-0,78182	Folha_98	-0,02817	Folha_137	0,82524
Folha_21	-0,96226	Folha_60	-0,75177	Folha_99	-0,02542	Folha_138	0,82927
Folha_22	-0,96104	Folha_61	-0,75	Folha_100	-0,01471	Folha_139	0,83888
Folha_23	-0,95808	Folha_62	-0,67701	Folha_101	-0,01407	Folha_140	0,84615
Folha_24	-0,95288	Folha_63	-0,67059	Folha_102	-0,01316	Folha_141	0,86194
Folha_25	-0,94767	Folha_64	-0,66667	Folha_103	-0,00855	Folha_142	0,86517
Folha_26	-0,94643	Folha_65	-0,66365	Folha_104	-0,00131	Folha_143	0,86842
Folha_27	-0,9449	Folha_66	-0,65625	Folha_105	-0,00031	Folha_144	0,89836
Folha_28	-0,93023	Folha_67	-0,65476	Folha_106	-0,00136	Folha_145	0,90398
Folha_29	-0,9292	Folha_68	-0,65385	Folha_107	0,00201	Folha_146	0,91525
Folha_30	-0,92593	Folha_69	-0,64706	Folha_108	0,00678	Folha_147	0,92011
Folha_31	-0,92126	Folha_70	-0,62805	Folha_109	0,00797	Folha_148	0,92537
Folha_32	-0,92	Folha_71	-0,6156	Folha_110	0,01109	Folha_149	0,94
Folha_33	-0,91824	Folha_72	-0,58696	Folha_111	0,01667	Folha_150	0,94231
Folha_34	-0,91701	Folha_73	-0,58283	Folha_112	0,02041	Folha_151	0,95187
Folha_35	-0,91667	Folha_74	-0,56604	Folha_113	0,0303	Folha_152	0,9575
Folha_36	-0,91463	Folha_75	-0,53061	Folha_114	0,05085	Folha_153	0,96364
Folha_37	-0,91183	Folha_76	-0,43836	Folha_115	0,15094	Folha_154	0,96923
Folha_38	-0,9108	Folha_77	-0,42	Folha_116	0,24316	Folha_155	0,975
Folha_39	-0,90691	Folha_78	-0,40789	Folha_117	0,30556	Folha_156	1

Nome	Sigla	Média	Nome	Sigla	Média
Patrimônio líquido	TE_I	-0,031596154	EBIT	EBIT_I	-0,068320513
Passivo total	TL_I	1,085089744	EBITDA	EBITDA_I	-0,050916667
Passivo longo prazo	TLTD_I	0,233576923	Contas a pagar	AP_I	0,188685897
Total recebíveis líquidos	TRN_I	0,14500641	Despesas provisionadas	AE_I	0,133032051
Rendimentos totais	TR_I	0,388935897	Caixas e equivalentes	CSTI_I	0,120948718
Equipamentos	PPETN_I	0,309858974	Estoque	CST_I	0,120948718
Lucros retidos	RE_AD_I	-3,572378205	Tecnologia	D_T	0,102634615
Ativo total	LN_TA	15,31235897	Materiais básicos	D_BM	0,191153846
Ativo circulante	TCA_I	0,488692308	Consumo cíclico	D_CC	0,312897436
Passivo circulante	TCL_I	0,697269231	Consumo não cíclico	D_CNC	0,097602564
Débito total	TD_I	0,518230769	Energia	D_E	0,029730769
Lucro bruto	GP_I	0,108211538	Financeiro	D_F	0,004371795
Lucro após impostos	NIAT_I	-0,120929487	Hospitalar	D_H	0,091384615
Vendas líquidas	NS_I	0,389647436	Indústria	D_I	0,148961538
Dívidas de curto prazo	NPSTD_I	0,118884615	Telecomunicações	D_TS	0,020955128
Lucro operacional	OI_I	-0,094794872	Utilidades	D_U	0,000269231
Custos dos produtos	CR_I	0,281365385

Média geral do CATE da amostra de teste
	Média	DP	Mínimo	1º quartil	Mediana	3º quartil	Máximo
Predições	0	0,04	-1,48	0	0	0	0,68
Variância estimada	0	0,01	0,00	0	0	0	2,98
Erro viesado	0	0,00	0,00	0	0	0	0,27
Média geral do CATE da amostra de validação
Predições	0	0,04	-1,28	0	0	0	0,68
Variância estimada	0	0,01	0,00	0	0	0	0,64

Variável	Frequência	Variável	Frequência	Variável	Frequência
GP_I	0,26701	OI_I	0,01747	AE_I	0,00589
AP_I	0,14584	TL_I	0,01334	NS_I	0,00516
EBIT_I	0,09351	RE_AD_I	0,01318	D_BM	0,00255
EBITDA_I	0,07489	TCA_I	0,01260	D_I	0,00209
TLTD_I	0,05646	CR_I	0,00979	D_TS	0,00130
PPETN_I	0,04446	CST_I	0,00881	D_CC	0,00038
TE_I	0,04394	NPSTD_I	0,00849	D_T	0,00012
TRN_I	0,04311	CSTI_I	0,00780	D_CNC	0,00012
LN_TA	0,04266	TD_I	0,00709	D_E	0,00000
D_H	0,03818	TR_I	0,00678	D_F	0,00000
NIAT_I	0,02083	TCL_I	0,00614	D_U	0,00000

Y_{i}^{o b s} {= Y}_{i} (W_{i}) = \{\begin{matrix} Y_{i} (0) s e W_{i} = 0 \\ Y_{i} (1) s e W_{i} = 1 \end{matrix}

\hat{μ} (x) = \frac{1}{|\{i : W_{i} = 1 \in L (x)\}|} \sum_{\{i : W_{i} = 1 \in L (x)\}} Y_{i} - \frac{1}{|\{i : W_{i} = 0 \in L (x)\}|} \sum_{\{i : W_{i} = 0 \in L (x)\}} Y_{i}

[1] E-mail:
wandersonrochab@yahoo.com.br

Brasil

Brasil

Avaliação de falências de empresas por meio de florestas causais

RESUMO