FORECAST OF INSOLVENCY IN THE BASIC MATERIALS SECTOR APPLYING DATA MINING
DOI:
https://doi.org/10.4270/ruc.2015343-62Keywords:
Prediction of insolvency. Selection of accounting variables. Data mining. Sector of basic material - Brazil.Abstract
This study aims to select variables in the sample of companies in the basic materials sector by applying data mining techniques in insolvency forecasting problems using database balancing techniques and attributes selection selection of attributes. From these variables an analysys for the financial implications to explain about the discontinuity of these companies is determined. This is an applied research research with quantitative approach; the aims, is descriptive. The database used was derived from financial statements of companies listed on BM&FBOVESPA between 1996 and 2012. This sector was chosen for their relevance to the Brazilian economy in terms of competitiveness and billing. The selected variables were: EOCpOT, EOAT, GAF, MB, EBITDA, MO and TERFIN. The results showed that companies in this sector become insolvent not only because they lose the ability (financial) to borrow, but also because they lose operational ability to generate cash.
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ALTMAN, E. I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, v. 23, n. 4, p. 589-609, 1968. http://dx.doi.org/10.1111/j.1540-6261.1968.tb00843.x
______; HALDEMAN, R. G.; NARAYANAN, P. Zeta Analysis: A new model to identify bankruptcy risk of corporations, Journal of Banking and Finance, v. 1, p. 29–54, 1977. http://dx.doi.org/10.1016/0378-4266(77)90017-6
______; BAIDYA, T. K. N.; DIAS, L. M. R. Previsão de problemas financeiros em empresas. Revista de Administração de Empresas, v. 19, jan./mar., p. 17-28, 1979. http://dx.doi.org/10.1590/S0034-75901979000100002
______; GIANCARLO, M.; VARETTO, F. Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance, v. 18, n. 3, p. 505-529, 1994. http://dx.doi.org/10.1016/0378-4266(94)90007-8
ATIYA, A. F. Bankruptcy prediction for credit risk using neural network: a survey and new results. IEEE transactions on neural networks, v. 12, n. 4, p. 929-935, 2001. http://dx.doi.org/10.1109/72.935101
BALCAEN, S.; OOGHE, H. 35 Years of studies on business failure: on overview of the classical statistical methodologies and their related problems. The British Accounting Review, v. 38, n. 1, p. 63-93, 2006. http://dx.doi.org/10.1016/j.bar.2005.09.001
BRAGA-NETO, U.; HASHIMOTO, R.; DOUGHERTY, E. R.; NGUYEN, D. V.; CARROLL, R. J. Is cross-validation better than resubstitution for ranking genes? Bioinformatics, v. 20, n. 2, p. 253-258, 2004. http://dx.doi.org/10.1093/bioinformatics/btg399
CANBAS S, A.; CABUK, S.B.; KILIC, Prediction of commercial bank failure via multivariate statistical analysis of financial structure: The Turkish case, European Journal of Operational Research, v. 166, p. 528–546, 2005. http://dx.doi.org/10.1016/j.ejor.2004.03.023
CHAWLA, N. V.; BOWYER, K. W.; HALL, L. O.; KEGELMEYER, W. P. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, v. 16, p. 321-357, 2002. http://dx.doi.org/10.1613/jair.953
______; JAPKOWICZ, N.; KOLCZ, A. Editorial: Special Issue on Learning from Imbalanced Datasets. SIGKDD Explorations, v. 6, n. 1, p. 1-6, 2004. http://dx.doi.org/10.1145/1007730.1007733
DAMODARAN, Aswath. Finanças corporativas aplicadas. Tradução Jorge Ritter. Porto Alegre: Bookman, 2002.
DING, Y.; SONG, X., ZEN,Y. Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Systems with Applications, v. 34, n. 4, p. 3081-3089, 2008. http://dx.doi.org/10.1016/j.eswa.2007.06.037
ELIZABETSKY, R. Um modelo matemático para decisão no banco comercial. (Trabalho apresentado ao Departamento de Engenharia de Produção da Escola Politécnica da USP). São Paulo: USP, 1976.
GARY M. Weiss. Mining with Rarity: A Unifying Framework, SIGKDD Explorations, v. 6, Issue 1, 2004, p.7-19.
HAN, J.; KAMBER, M.; PEI, J. Data mining: concepts and techniques. 3rd ed. Waltham: Morgan Kaufmann, 2011. 744 p.
HORTA, Rui Américo Mathiasi. Utilização de indicadores contábeis na previsão de insolvência: Análise empírica de uma amostra de empresas comerciais e industriais brasileiras. 2001. 108 p. Dissertação Mestrado em Ciências Contábeis – Faculdade de Ciências Contábeis da Universidade Estadual do Rio de Janeiro.
HORTA, Rui Américo Mathiasi. Uma metodologia de mineração de dados para a previsão de insolvência de empresas brasileiras de capital aberto. 2010. 152 p. Doutorado em Engenharia Civil – COPPE - Universidade Federal do Rio e Janeiro.
HORTA R.A.M., DE LIMA B.S.L.P., BORGES C.C.H. A semi-deterministic ensemble strategy for imbalanced datasets (SEID) applied to bankruptcy prediction. In: Data mining IX: data minig, protection, detection and other security technologies. WIT transactions on information and communication technologies, v. 40, Spain, 2008, p. 205–213.
HORTA, Rui Américo Mathiasi; Carlos Cristiano Hasenclever Borges; JORGE, M. J. Descontinuidade de empresas brasileiras do setor de consumo cíclico: Uma metodologia para balanceamento de base de dados utilizando técnicas de data mining. Revista Ambiente Contábil, v. 6, p. 99-121, 2014.
HORTA, Rui Américo Mathiasi; JORGE, M. J.; ALVES, F. J. Descontinuidade de empresas brasileiras do setor de consumo não cíclico: Uma metodologia para balanceamento de base de dados utilizando técnicas de data mining. Revista de Informação Contábil (UFPE), v. 07, p. 63-83, 2013.
HUA, Zhongsheng; WANG, Yu; XU, Xiannoyan; ZHANG, Bin; LIANG, Liang. Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications, v. 33, Issue 2, p. 434-440, 2007. http://dx.doi.org/10.1016/j.eswa.2006.05.006
HUNG, Chihli; CHEN, Jing-Hong. A selective ensemble based on expected probabilities for bankruptcy prediction. Expert systems with applications, v. 36, Issue 3, p. 3297-5309, 2009. http://dx.doi.org/10.1016/j.eswa.2008.06.068
IUDÍCIBUS, S. de. Análise de Balanços. 9. ed. São Paulo: Atlas, 2008. 258 p.
JAPKOWICZ N.; STEPHEN, S. The Class Imbalance Problem: A Systematic Study. Intelligent Data Analysis, v. 6, Issue 5, p. 429-449, 2002.
JOSHI, M. V. Learning Classier Models for Predicting Rare Phonemena. PhD thesis, University of Minnesota, Twin Cites, Minnesota, USA, 2001.
KANITZ, Stephen Charles. Como prever falências. São Paulo: Mc Graw-Hill do Brasil, 1978.174 p.
KÄUCK, H. Bayesian formulations of multiple instance learning with applications to general object recognition. Master's thesis, University of British Columbia, Vancouver, BC, Canada, 2004.
KIM, Hong Sik; SOHN, So Young. Support vector machines for default prediction of SMEs based on technology credit. European Journal of Operational Research, v. 201, Issue 3, p. 838-846, 2010. http://dx.doi.org/10.1016/j.ejor.2009.03.036
KOHAVI, R..; JOHN, G. H. Wrappers for feature subset selection. Artif. Intell., v.97, 1997. p. 273-324. http://dx.doi.org/10.1016/S0004-3702(97)00043-X
LI HUI, JIE SUN. Majority voting combination of multiple case-based reasoning for financial distress prediction. Expert Systems with Applications, v.36, p. 4363-4373, apr, 2009. http://dx.doi.org/10.1016/j.eswa.2008.05.019
LOPES, Alexsandro Broedel; MARTINS, Eliseu. Teoria da contabilidade: uma nova abordagem – 2ª. Reimpressão – São Paulo: Atlas, 2007.
MARTIN, D. Early warning of bank failure: A logit regression approach, Journal of Banking and Finance, v.1, p. 249–276, 1977. http://dx.doi.org/10.1016/0378-4266(77)90022-X
MATARAZZO, Dante Carmine. Análise financeira de balanços: abordagem básica e gerencial. 6ª Ed. São Paulo: Atlas, 2003.
MATIAS, Alberto Borges. Contribuição às técnicas de análise financeira: um modelo de concessão de crédito. (Trabalho apresentado ao Departamento de Administração da Faculdade de Economia e Administração da USP.) São Paulo: [s.n.], 1978, p. 82, 83, 90.
MIN,Sung-Hwan.; LEE, Jumin,;HAN. Ingoo. Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications, v. 31, Issue 3, p. 652-660, oct. 2006. http://dx.doi.org/10.1016/j.eswa.2005.09.070
MOROZINI, João Francisco; OLINQUEVITCH, José Leônidas; HEIN, Nelson. Seleção de índices na análise de balanços: uma aplicação da técnica estatística ‘ACP’. Revista Contabilidade & Finanças USP. São Paulo Vol. 2 Número 41, Maio/Agosto 2006. http://dx.doi.org/10.1590/S1519-70772006000200007
NANNI, Loris,; LUMINI, Alessandra. An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications, v. 36, Issue 2, Part 2, p. 3028-3033, mar. 2009. http://dx.doi.org/10.1016/j.eswa.2008.01.018
OHLSON, J.A. Financial ratios and the probabilisitic prediction of bankruptcy. Journal of Accounting Research, v. 18, p.109-131, 1980. http://dx.doi.org/10.2307/2490395.
OLSON, David L. DELEN, Dursun, MENG, Yanyan. Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, v. 52, n. 2, January 2012, p. 464-473. http://dx.doi.org/10.1016/j.dss.2011.10.007
PIRAMUTHU S. On preprocessing data for financial credit risk evaluation. Expert Systems with Applications, v. 30, p.489-497, 2006. http://dx.doi.org/10.1016/j.eswa.2005.10.006
RAVI, V.; KURNIAWAN, H.; THAI, Peter Nwee Kok,; KUMAR, P. Ravi. Soft computing system for bank performance predition. Applied Soft Computing, v. 8, p. 305-315, jan. 2008. http://dx.doi.org/10.1016/j.asoc.2007.02.001
ROSS, Stephen A.; WESTERFIELD, Randolph, W. Jaffe; JORDAN, Bradford; LAMB, Roberto. Fundamentos de Administração Financeira – Porto Alegre: Editora Mc Graw Hill, 9ª edição, 2013.
SANVICENTE, Antônio Zoratto:, MINARDI, Andréa Maria A. F. Identificação de indicadores contábeis significativos para previsão de concordata de empresas. Disponível: <http://www.risktech.br/artigos/artigostécnicos/index.html>. Acesso em: 23/10/2005.
SHIN, Kyung-Shik; LEE, Yong-Joo; KIM, Hyun-Jung. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, v. 28, Issue 1, p. 127-135, jan. 2005.
SILVA BRITO, Giovani Antônio; ASSAF NETO, Alexandre; CORRAR, Luiz João. Sistemas de classificação de risco de crédito: uma aplicação a companhias abertas no Brasil. Revista Contabilidade & Finanças USP. São Paulo, v. 20, n. 51, p. 28-43, Setembro/Dezembro, 2009.
SILVA, José Pereira da. Gestão e análise de risco de crédito. 5. ed. São Paulo: Atlas, 2006. 448 p.
SOMOL P.; BAESENS B.; PUDIL P.; VANTHIENEN J., Filter-versus Wrapper-based Feature Selection for Credit Scoring, International Journal of Intelligent Systems, v. 20, Number 10, p. 985-999, 2005.
SUN, Jie; LI, Hui; HUANG, Qing-Hua; HE, Kai-Yu. Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, v. 57, p. 41-56, February 2014.
TINOCO, Mario Hernandez, WILSON Nick. Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, v. 30, p. 394-419, December 2013.
TSAI, C. F. Feature selection in bankruptcy prediction. Knowledge-Based Systems, v. 22, n. 2, p. 120-127, mar. 2009. http://dx.doi.org/10.1016/j.knosys.2008.08.002
______; WU J. W. Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with applications, v. 34, n. 4, p. 2639-2649, May 2008.
______; HSU, Yu-Feng; YEN, David C. A comparative study of classifier ensembles for bankruptcy prediction. Applied Soft Computing, v. 24, p. 977-984, November 2014.
VERIKAS, Antanas; KALSYTE, Zivile; BACAUSKIENE, Marija; GELZINIS, Adas. Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey. Soft Comput v.14, p. 995-1010, 2010. http://dx.doi.org/10.1007/s00500-009-0490-5
WEISS, G. M.; McCARTHY, K.; BIBI, Zabar. Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?, In: Proceedings of the 2007 International Conference on Data Mining, Fordham University, Bronx, NY, USA, SREA Press, p. 35-41, 2007.
WEST, David; DELLANA, Scott; QIAN, Jingxia. Neural network ensemble strategies for financial decision applications. Computers & Operations Research, v. 32, n. 10, p. 2543-2559, oct. 2005. http://dx.doi.org/10.1016/0378-4266(85)90021-4
WEST, R. C, A factor analytic approach to bank condition, Journal of Banking and Finance, v. 9, p. 253-266, jun.1985.
WITTEN, Ian .H.; FRANK, Eibe. Data Mining: Practical Machine Learning Tools and Techniques. The Morgan Kaufmann Series in Data Management Systems, 3rd ed. 2011. 630 p.
WU, Y.; GAUNT, C. GRAY, S. A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics, v. 6, n. 1, p. 34-45, June 2010. http://dx.doi.org/10.1016/j.jcae.2010.04.002
YU, L. WAUNG; LAI, K. K. Credit risk assessment with a multistage neural network ensemble learning approach. Expert Systems with Applications, v. 34, p. 1434-1444, fev. 2008. http://dx.doi.org/10.1016/j.eswa.2007.01.009
ZHOU, Ligang. Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods. Knowledge-Based Systems, v. 41, p. 16-25, mar. 2013. http://dx.doi.org/10.1016/j.knosys.2012.12.007
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