Avaliação de Falências de Empresas por meio de Florestas Causais

ID:
59173
Resumo:
Esta pesquisa buscou analisar as variáveis que podem influenciar a falência das empresas. Durante vários anos, as principais pesquisas sobre falência reportaram as metodologias convencionais visando à sua predição. Em suas análises, a utilização de variáveis contábeis predominou maciçamente. Porém, ao aplicá-las, as variáveis contábeis eram consideradas homogêneas, ou seja, para os modelos tradicionais, presumia-se que em todas as empresas o comportamento dos indicadores era similar, ignorando a heterogeneidade entre elas. Observa-se, ainda, a relevância da crise financeira ocorrida no final de 2007, causando grande colapso financeiro mundial, tendo efeitos diferentes nos mais diversos setores e empresas. Nesse cenário, pesquisas que visam identificar problemas como a heterogeneidade entre as empresas e analisar as diversidades entre elas ganham relevância, haja vista que as características setoriais de estrutura de capital, porte, dentre outras, variam de acordo com as empresas. A partir disso, novas abordagens aplicadas à modelagem de previsão de falência devem considerar a heterogeneidade entre as empresas, buscando aprimorar ainda mais as modelagens utilizadas. Foram utilizadas a árvore e a floresta causais com dados contábeis trimestrais e setoriais de 1.247 empresas, sendo 66 falidas, das quais 44 depois de 2008 e 22 antes. Os resultados mostraram que existe heterogeneidade não observada quando se analisam os processos de falência das empresas, colocando em cheque os modelos tradicionais como, por exemplo, análise discriminante e logit, dentre outros. Por conseguinte, com o elevado volume em dimensões, observou-se que pode haver uma forma funcional capaz de explicar a falência das empresas, porém essa não é linear. Destaca-se, ainda, que existem setores mais propensos a crises financeiras, agravando o processo de falência.
Citação ABNT:
BITTENCOURT, W. R.; ALBUQUERQUE, P. Avaliação de Falências de Empresas por meio de Florestas Causais. Revista Contabilidade & Finanças, v. 31, n. 84, p. 542-559, 2020.
Citação APA:
Bittencourt, W. R., & Albuquerque, P. (2020). Avaliação de Falências de Empresas por meio de Florestas Causais. Revista Contabilidade & Finanças, 31(84), 542-559.
DOI:
10.1590/1808-057x202010360
Link Permanente:
https://www.spell.org.br/documentos/ver/59173/avaliacao-de-falencias-de-empresas-por-meio-de-florestas-causais/i/pt-br
Tipo de documento:
Artigo
Idioma:
Português
Referências:
Acharya, V. V., & Mora, N. (2015). A crisis of banks as liquidity providers. Journal of Finance, 70(1), 1-43. https://doi. org/10.1111/jofi.12182

AF-170203 Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395

Altman, E. I. (1968). Financianl ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.

Altman, 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

Altman, E. I., & Hotchkiss, E. (2007). Corporate financial distress and bankruptcy (3a ed.). Hoboken, NJ: John Wiley & Sons, Inc. https://doi.org/10.1002/9781118267806

Antunes, 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

Athey, S. (2015). Machine learning and causal inference for policy evaluation. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD’15 (p. 5-6). New York, NY. https://doi. org/10.1145/2783258.2785466

Athey, S. (2019). CausalTree. Recuperado de https://github.com/ susanathey/causalTree

Athey, S., & Imbens, G. (2015). Machine learning methods in economics and econometrics: A measure of robustness to misspecification. American Economic Review, 105(5), 476-480. https://doi.org/10.1257/aer.p20151020

Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360. https://doi. org/10.1073/pnas.1510489113

Athey, S., Imbens, G., Pham, T., & Wager, S. (2017). Estimating average treatment effects: Supplementary analyses and remaining challenges. American Economic Review, 107(5), 278-281. https://doi.org/10.1257/aer.p20171042

Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. The Annals of Statistics, 47(2), 1148-1178. https://doi. org/10.1214/18-AOS1709

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

Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi. org/10.2307/2490171

Belloni, A., Chernozhukov, V., & Hansen, C. (2014a). Highdimensional methods and inference on structural and treatment effects. Journal of Economic Perspectives, 28(2), 29-50. https://doi.org/10.1257/jep.28.2.29

Belloni, A., Chernozhukov, V., & Hansen, C. (2014b). Inference on treatment effects after selection among high-dimensional controls. Review of Economic Studies, 81(2), 608-650. https:// doi.org/10.1093/restud/rdt044

Benmelech, E., & Bergman, N. K. (2011). Bankruptcy and the collateral channel. Journal of Finance, 66(2), 337-378. https:// doi.org/10.1111/j.1540-6261.2010.01636.x

Boot, A. W. A., & Thakor, A. V. (1997). Financial system architecture. Review of Financial Studies, 10(3), 693-733. https://doi.org/10.1093/rfs/10.3.693

Brogaard, J., Li, D., & Xia, Y. (2017). Stock liquidity and default risk. Journal of Financial Economics, 124(3), 486-502. https:// doi.org/10.1016/j.jfineco.2017.03.003

Chauhan, 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

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(1), 321-357. https://doi.org/10.1613/jair.953

Cho, 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

Cielen, 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

Cole, 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

Deakin, 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

DeMarzo, P. M., & Fishman, M. J. (2007). Optimal long-term financial contracting. Review of Financial Studies, 20(6), 20792128. https://doi.org/10.1093/rfs/hhm031

DeSpiegeleer, J., Madan, D. B., Reyners, S., & Schoutens, W. (2018). Machine learning for quantitative finance: Fast derivative pricing, hedging and fitting. Quantitative Finance, 18(10), 16351643. https://doi.org/10.1080/14697688.2018.1495335

DeYoung, 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

FitzPatrick, P. J. (1932). A comparison of the ratios of successful industrial enterprises with those of failed companies. Recuperado de https://www.worldcat.org/title/comparison-ofthe-ratios-of-successful-industrial-enterprises-with-those-offailed-companies/oclc/6284198

García, V., Marqués, A. I., Sánchez, J. S., & Ochoa-Domínguez, H. J. (2017). Dissimilarity-based linear models for corporate bankruptcy prediction. Computational Economics, 53, 10191031. https://doi.org/10.1007/s10614-017-9783-4

Giordani, 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

Helwege, J., & Zhang, G. (2016). Financial firm bankruptcy and contagion. Review of Finance, 20(4), 1321-1362. https://doi. org/10.1093/rof/rfv045

Hertzel, M. G., Li, Z., Officer, M. S., & Rodgers, K. J. (2008). Interfirm linkages and the wealth effects of financial distress along the supply chain. Journal of Financial Economics, 87(2), 374387. https://doi.org/10.1016/j.jfineco.2007.01.005

Hertzel, M. G., & Officer, M. S. (2012). Industry contagion in loan spreads. Journal of Financial Economics, 103(3), 493-506. https://doi.org/10.1016/j.jfineco.2011.10.012

Hochreiter, S., & Schimidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735-1780. https://doi. org/10.1162/neco.1997.9.8.1735

Ivashina, V., Iverson, B., & Smith, D. C. (2016). The ownership and trading of debt claims in Chapter 11 restructurings. Journal of Financial Economics, 119(2), 316-335. https://doi. org/10.1016/j.jfineco.2015.09.002

Johnson, C. G. (1970). Ratio Stability and corporate failure. The Journal of Finance, 25(5), 1166-1168. https://doi. org/10.2307/2325590

Jorion, P., & Zhang, G. (2007). Good and bad credit contagion: Evidence from credit default swaps. Journal of Financial Economics, 84(3), 860-883. https://doi.org/10.1016/j. jfineco.2006.06.001

Jostarndt, 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

Kalay, A., Singhal, R., & Tashjian, E. (2007). Is Chapter 11 costly? Journal of Financial Economics, 84(3), 772-796. https://doi. org/10.1016/j.jfineco.2006.04.001

Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767-2787. https://doi. org/10.1016/j.jbankfin.2010.06.001

Lang, L. H. P., & Stulz, R. (1992). Contagion and competitive intra-industry effects of bankruptcy announcements. An empirical analysis. Journal of Financial Economics, 32(1), 4560. https://doi.org/10.1016/0304-405X(92)90024-R

Lee, 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

Lennox, C. (1999). Identifying failing companies: A re-evaluation of the logit, probit and DA approaches. Journal of Economics and Business, 51, 347-364.

Ludwig, R. S., & Piovoso, M. J. (2005). A comparison of machinelearning classifiers for selecting money managers. Intelligent Systems in Accounting, Finance and Management, 13(3), 151164. https://doi.org/10.1002/isaf.262

Mensah, Y. M. (1984). An examination of the stationarity of multivariate bankruptcy prediction models: A methodological study. Journal of Accounting Research, 22(1), 380. https://doi. org/10.2307/2490719

Min, 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

Montenegro, M. R., & Albuquerque, P. H. M. (2017). Wealth management: Modeling the nonlinear dependence. Algorithmic Finance, 6(1-2), 51-65. https://doi.org/10.3233/

Park, C. (2000). Monitoring and structure of debt contracts. The Journal of Finance, 55(5), 2157-2195. https://doi. org/10.1111/0022-1082.00283

Pendharkar, 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

Premachandra, 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

Premachandra, 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

Rodano, G., Serrano-Velarde, N., & Tarantino, E. (2016). Bankruptcy law and bank financing. Journal of Financial Economics, 120(2), 363-382. https://doi.org/10.1016/j. jfineco.2016.01.016

Strömberg, 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

Taffler, R. J. (1984). Empirical models for the monitoring of UK corporations. Journal of Banking and Finance, 8(2), 199-227. https://doi.org/10.1016/0378-4266(84)90004-9

Tsai, 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

Tseng, 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

Vapnik, V. N. (2000). The nature of statistical learning theory (2a ed.). New York, NY: Springer-Verlag. https://doi. org/10.1007/978-1-4757-3264-1

Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28. https://doi. org/10.1257/jep.28.2.3

Varian, H. R. (2016). Intelligent technology. Finance & Development, 53(3), 6-9.

Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228-1242. https://doi.org/10.1080/01621459.2017.1319839

Yang, 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

Yaohao, P., & Albuquerque, P. H. M. (2019). Non-linear interactions and exchange rate prediction: Empirical evidence using support vector regression. Applied Mathematical Finance, 26(1), 69-100. https://doi.org/10.1080/135048 6X.2019.1593866

Yaohao, P., Albuquerque, P. H. M., Camboim de Sá, J. M., Padula, A. J. A., & Montenegro, M. R. (2018). The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with support vector regression. Expert Systems with Applications, 97, 177-192. https://doi. org/10.1016/j.eswa.2017.12.004

Zhang, M., & Zhou, Z. (2007). ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7), 2038-2048. https://doi.org/10.1016/j.patcog.2006.12.019

Zmijewski, 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