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...
) |
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 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...
) |