Insolvency prediction from fuzzy classification and financial projection
Este artículo propone un método de referencia basado en la teoría de conjuntos difusos y en el algoritmo de clasificación difusa c-means para detectar y prever la insolvencia empresarial. El método consiste en un conjunto de pasos para comparar los resultados obtenidos en un proceso de clasificación...
- Autores:
-
Castiblanco, Fabian
Sánchez Villamil, Deisy Nohemí
Franco Gómez, Yuly Andrea
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2021
- Institución:
- Universidad Cooperativa de Colombia
- Repositorio:
- Repositorio UCC
- Idioma:
- OAI Identifier:
- oai:repository.ucc.edu.co:20.500.12494/49106
- Acceso en línea:
- https://hdl.handle.net/20.500.12494/49106
https://www.sigef.net/2014-09-26-07-16-23/summaries-and-abstracts/item/678-insolvency-prediction-from-fuzzy-classification-and-financial-projection
- Palabra clave:
- Solvencia financiera
Clasificación difusa
Ratios financieros
Proyección financiera
Financial solvency
Fuzzy classification
Financial ratios
Financial projection
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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Insolvency prediction from fuzzy classification and financial projection |
| title |
Insolvency prediction from fuzzy classification and financial projection |
| spellingShingle |
Insolvency prediction from fuzzy classification and financial projection Solvencia financiera Clasificación difusa Ratios financieros Proyección financiera Financial solvency Fuzzy classification Financial ratios Financial projection |
| title_short |
Insolvency prediction from fuzzy classification and financial projection |
| title_full |
Insolvency prediction from fuzzy classification and financial projection |
| title_fullStr |
Insolvency prediction from fuzzy classification and financial projection |
| title_full_unstemmed |
Insolvency prediction from fuzzy classification and financial projection |
| title_sort |
Insolvency prediction from fuzzy classification and financial projection |
| dc.creator.fl_str_mv |
Castiblanco, Fabian Sánchez Villamil, Deisy Nohemí Franco Gómez, Yuly Andrea |
| dc.contributor.author.none.fl_str_mv |
Castiblanco, Fabian Sánchez Villamil, Deisy Nohemí Franco Gómez, Yuly Andrea |
| dc.subject.none.fl_str_mv |
Solvencia financiera Clasificación difusa Ratios financieros Proyección financiera |
| topic |
Solvencia financiera Clasificación difusa Ratios financieros Proyección financiera Financial solvency Fuzzy classification Financial ratios Financial projection |
| dc.subject.other.none.fl_str_mv |
Financial solvency Fuzzy classification Financial ratios Financial projection |
| description |
Este artículo propone un método de referencia basado en la teoría de conjuntos difusos y en el algoritmo de clasificación difusa c-means para detectar y prever la insolvencia empresarial. El método consiste en un conjunto de pasos para comparar los resultados obtenidos en un proceso de clasificación difusa con la información contable proyectada. El aspecto central del método es la comparación entre la situación actual y futura de un conglomerado de empresas a partir de un conjunto específico de ratios financieros. La situación futura se establece a partir de las proyecciones macroeconómicas de analistas locales y extranjeros. Para validar la efectividad y precisión de nuestra propuesta, se realiza su aplicación en un sector económico específico de Colombia y se comparan los resultados con el modelo Altman Z2. |
| publishDate |
2021 |
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2021-07-01 |
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2023-04-10T21:42:06Z |
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2023-04-10T21:42:06Z |
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Artículos Científicos |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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1136-0593 |
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https://hdl.handle.net/20.500.12494/49106 https://www.sigef.net/2014-09-26-07-16-23/summaries-and-abstracts/item/678-insolvency-prediction-from-fuzzy-classification-and-financial-projection |
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Castiblanco, F., Sánchez Villamil, D. N. y Franco Gómez, Y. A. (2021). Insolvency prediction from fuzzy classification and financial projection. Fuzzy economic review, 26(1), 49-73. https://www.sigef.net/2014-09-26-07-16-23/summaries-and-abstracts/item/678-insolvency-prediction-from-fuzzy-classification-and-financial-projection |
| identifier_str_mv |
1136-0593 Castiblanco, F., Sánchez Villamil, D. N. y Franco Gómez, Y. A. (2021). Insolvency prediction from fuzzy classification and financial projection. Fuzzy economic review, 26(1), 49-73. https://www.sigef.net/2014-09-26-07-16-23/summaries-and-abstracts/item/678-insolvency-prediction-from-fuzzy-classification-and-financial-projection |
| url |
https://hdl.handle.net/20.500.12494/49106 https://www.sigef.net/2014-09-26-07-16-23/summaries-and-abstracts/item/678-insolvency-prediction-from-fuzzy-classification-and-financial-projection |
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https://www.sigef.net/2014-09-26-07-16-23/summaries-and-abstracts/item/678-insolvency-prediction-from-fuzzy-classification-and-financial-projection |
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Fuzzy economic review |
| dc.relation.references.none.fl_str_mv |
Aldazábal, J., & Napán, A. (2014). Análisis discriminante aplicado a modelos de prediccíon de quiebra. Quipukamayoc - Revista de la Facultad de Ciencias Contables, 22(42), 53-59. Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 568-609. Altman, E. (2005). An emerging market credit scoring system for corporate bonds. Emerging Markets Review, 6, 311-323. http://pages.stern.nyu.edu/~ealtman/Corp-Distress.pdf Altman, E., & Hotchkiss, E. (2006). Corporate Financial Distress and Bankruptcy. Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt. J. Wiley & Sons, Inc., Hoboken. Argenti, J. (1976). Corporate Collopse: the causes and symptoms. Holsted Press. McGraw-Hill. Bank of the Republic of Colombia (2018). Proyecciones macroeconómicas de analistas locales y extranjeros (desde 06/2004 hasta 04/2020). http://www.banrep.gov.co/es/encuesta-proyecciones-macroeconomicas Beaver, W. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71-111. Beaver, W. H. (1968). Alternative Accounting Measures as predictors of failure. The Accounting Review, 43(1), 113 – 122. Bell, T.B., Ribar, G.S., & Verchio, J. (1990). Neural Nets Versus Logistic Regression: A Comparison of Each Model’s Ability to Predict Commercial Bank Failures. In R.P. Srivastava (ed). Proceedings of the 1990 Deloitte & Touche/University of Kansas Symposium in Auditing Problems, (pp. 29-53). Misisipi: Estados Unidos Bezdek, J., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10(2-3), 191-203. Castiblanco, F. (2014). Una mirada al presupuesto anual de ventas de Rautenstrauch & Villers a partir de los números borrosos: el manejo de la incertidumbre y la subjetividad. Criterio Libre, 12(20), 199-222. Castiblanco, F. (2016). La teoria de los subconjuntos borrosos en el proceso presupuestario de las organizaciones. Bogotá: Editorial universitaria de la Universidad La Gran Colombia. Castiblanco, F., Franco, C., Rodriguez, J., & Montero, J. (2020). Evaluation of the Quality and Relevance of a Fuzzy Partition. Journal of Intelligent & Fuzzy Systems, 39(3), 4211-4226. Castiblanco, F., Franco, C., Rodríguez, J., & Montero, J. (2021). Degree of Global Covering and Global Overlapping in Solvency Fuzzy Classification. In E. León-Castro, F. Blanco-Mesa, A. M Gil-Lafuente, J. Merigó, & J. Kacprzyk, Intelligent and Complex Systems in Economics and Business. Advances in Intelligent Systems and Computing (pp. 21-32). Cham: Springer Castiblanco, F., Montero, J., Rodríguez, J. T., & Gómez, D. (2017). Quality assessment of fuzzy classification: An application to solvency analysis. Fuzzy economic review, 22(1), 19-31. Chen, H., Yang, B., Wang, G., Liu, J., Xu, X., Wang, S., & Liu, D. (2001). A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method. Knowledge-Based Systems, 24(8), 1348-1359. Cielen, A., & Vanhoof, K. (2004). Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research, 2(154), 526-532. Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(1) 167-179. Dimitras, A., Slowinski, R., Susmaga, R., & Zopounidis, C. (1999). Business failure prediction using rough sets. European Journal of Operational Research, 2(114), 263-280. Fitzpatrick, P. (1932). A comparison of ratios of successful industrial enterprises with those of failed companies. The Certified Public Accountant (October, November, December), 2(8), 598-605, 656-662, and 727-731, respectively. Frydman, H., Altman, E., & Kao, D. (1985). Introducing recursive partitioning for classification: The case of financial distress. Journal of Finance, 1(40), 269-291. Hair, J. J., Anderson, R., Tatham, R., & Black, W. (1999). Análisis Multivariante. Prentice Hall Iberia. Hanss, M. (2005). Applied Fuzzy Arithmetic. New York: Springer-Verlag Berlin Heidelberg. Kaufmann, A., & Gil Aluja, J. (1987). Técnicas operativas de gestión para el tratamiento de la incertidumbre, Barcelona: Hispano Europea Keasey, K., Watson, R., & Wynarczyk, P. (1988). The small company audit qualification: a preliminary investigation. Accounting and Business Research, 18, 323-333. Lam, M. (2004). Neural networks techniques for financial performance prediction: Integrating fundamental and technical analysis. Decision Support Systems, 4(34), 567-581. Leshno, M., & Spector, Y. (1996). Neural network prediction analysis: The bankruptcy case. Neurocomputing, 2(10), 125-147. Marais, M., Pattell, J., & Wolfson, M. (1984). The experimental design of classification models: an application of recursive partitioning and bootstrapping to commercial bank loan classifications. Journal of Accounting Research, 22(1), 87-118 Martin, D. (1977). Early Warning of Bank Failure. Journal of Banking and Finance, 1(3), 249-276. Min, J., & Jeong, C. (2009). A binary classification method for bankruptcy prediction. Expert Systems with Applications, 36(3), 5256-5263. Min, J., & Lee, Y. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 4(28), 603-614. Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 1(18), 109-131. Park, C., & Han, I. (2002). A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 3(23), 255-264. Pessoa de Oliveira, A. (2016). Análisis de solvencia de las empresas: modelo estático vs modelo dinámico. Thesis PhD. Facultad de Ciencias Económicas y Empresariales. Universidad de Zaragoza https://zaguan.unizar.es/record/48319/files/TUZ_0863_pessoa_analisis.pdf Sansalvador, M., Reig, J., & Trigueros, J. (2000). Lógica borrosa y sus aplicaciones a la contabilidad. Revista Española de Financiación y Contabilidad, 29(103), 83-106. Shin, K., & Lee, Y. (2002). A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications, 3(23), 321-328. Tascón, M., & Castaño, F. (2012). Variables y modelos para la identificación y predicción del fracaso empresarial: revisión de la investigación empírica reciente. Revista de Contabilidad - Spanish Accounting Review, 15(1), 7-58. Villamil, H. (2004). Modelos multivariados para la predicción de insolvencia empresarial. Una aplicación al caso colombiano. Bogotá: Universidad Piloto de Colombia. Yang, Z., Platt, M., & Platt, H. (1999). Probability neural network in bankruptcy prediction. Journal of Business Research, 1999, 44(2), 67-74. Yim, J., & Mitchell, H. (2005). A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis. Nova Economia, 15(1), 73-93. Zimmermann, H. (1996). Fuzzy set theory and its applications. London: Kluwer Academic Publishers. Zmijewski, M. (1984). Methodological issues related to the estimation of financial. Journal of Accounting Research, 22, 58-82. |
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Castiblanco, FabianSánchez Villamil, Deisy NohemíFranco Gómez, Yuly AndreaVol. 26/No. 12023-04-10T21:42:06Z2023-04-10T21:42:06Z2021-07-011136-0593https://hdl.handle.net/20.500.12494/49106https://www.sigef.net/2014-09-26-07-16-23/summaries-and-abstracts/item/678-insolvency-prediction-from-fuzzy-classification-and-financial-projectionCastiblanco, F., Sánchez Villamil, D. N. y Franco Gómez, Y. A. (2021). Insolvency prediction from fuzzy classification and financial projection. Fuzzy economic review, 26(1), 49-73. https://www.sigef.net/2014-09-26-07-16-23/summaries-and-abstracts/item/678-insolvency-prediction-from-fuzzy-classification-and-financial-projectionEste artículo propone un método de referencia basado en la teoría de conjuntos difusos y en el algoritmo de clasificación difusa c-means para detectar y prever la insolvencia empresarial. El método consiste en un conjunto de pasos para comparar los resultados obtenidos en un proceso de clasificación difusa con la información contable proyectada. El aspecto central del método es la comparación entre la situación actual y futura de un conglomerado de empresas a partir de un conjunto específico de ratios financieros. La situación futura se establece a partir de las proyecciones macroeconómicas de analistas locales y extranjeros. Para validar la efectividad y precisión de nuestra propuesta, se realiza su aplicación en un sector económico específico de Colombia y se comparan los resultados con el modelo Altman Z2.This paper proposes a benchmark method based on fuzzy set theory and the c-means fuzzy classification algorithm to detect and forecast business insolvency. The method consists of a set of steps to compare the results obtained in a fuzzy classification process with the projected accounting information. The central aspect of the method is the comparison between the current and future situation of a conglomerate of companies based on a specific set of financial ratios. The future situation is established from the macroeconomic projections of local and foreign analysts. To validate the effectiveness and precision of our proposal, its application is carried out in a specific economic sector of Colombia and the results are compared with the Altman Z2 model.https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001632816https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000014769https://orcid.org/0000-0002-4057-0687https://orcid.org/0000-0002-7458-1915https://orcid.org/0000-0003-2938-9331https://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000005640deisy.sanchezv@campusucc.edu.cofabianalberto.castiblanco@ugc.edu.coyuly.franco@uniagustiniana.edu.cohttps://scholar.google.com/citations?user=wTBs26sAAAAJ&hl=eshttps://scholar.google.es/citations?user=9-89dDIAAAAJ&hl=eshttps://scholar.google.es/citations?user=0leB9rsAAAAJ&hl=es49-73Universidad Cooperativa de Colombia, Facultad de Ciencias Económicas, Administrativas y Contables, Contaduría Pública, BogotáContaduría PúblicaBogotáhttps://www.sigef.net/2014-09-26-07-16-23/summaries-and-abstracts/item/678-insolvency-prediction-from-fuzzy-classification-and-financial-projectionFuzzy economic reviewAldazábal, J., & Napán, A. (2014). Análisis discriminante aplicado a modelos de prediccíon de quiebra. Quipukamayoc - Revista de la Facultad de Ciencias Contables, 22(42), 53-59.Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 568-609.Altman, E. (2005). An emerging market credit scoring system for corporate bonds. Emerging Markets Review, 6, 311-323. http://pages.stern.nyu.edu/~ealtman/Corp-Distress.pdfAltman, E., & Hotchkiss, E. (2006). Corporate Financial Distress and Bankruptcy. Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt. J. Wiley & Sons, Inc., Hoboken.Argenti, J. (1976). Corporate Collopse: the causes and symptoms. Holsted Press. McGraw-Hill.Bank of the Republic of Colombia (2018). Proyecciones macroeconómicas de analistas locales y extranjeros (desde 06/2004 hasta 04/2020). http://www.banrep.gov.co/es/encuesta-proyecciones-macroeconomicasBeaver, W. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71-111.Beaver, W. H. (1968). Alternative Accounting Measures as predictors of failure. The Accounting Review, 43(1), 113 – 122.Bell, T.B., Ribar, G.S., & Verchio, J. (1990). Neural Nets Versus Logistic Regression: A Comparison of Each Model’s Ability to Predict Commercial Bank Failures. In R.P. Srivastava (ed). Proceedings of the 1990 Deloitte & Touche/University of Kansas Symposium in Auditing Problems, (pp. 29-53). Misisipi: Estados UnidosBezdek, J., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10(2-3), 191-203.Castiblanco, F. (2014). Una mirada al presupuesto anual de ventas de Rautenstrauch & Villers a partir de los números borrosos: el manejo de la incertidumbre y la subjetividad. Criterio Libre, 12(20), 199-222.Castiblanco, F. (2016). La teoria de los subconjuntos borrosos en el proceso presupuestario de las organizaciones. Bogotá: Editorial universitaria de la Universidad La Gran Colombia.Castiblanco, F., Franco, C., Rodriguez, J., & Montero, J. (2020). Evaluation of the Quality and Relevance of a Fuzzy Partition. Journal of Intelligent & Fuzzy Systems, 39(3), 4211-4226.Castiblanco, F., Franco, C., Rodríguez, J., & Montero, J. (2021). Degree of Global Covering and Global Overlapping in Solvency Fuzzy Classification. In E. León-Castro, F. Blanco-Mesa, A. M Gil-Lafuente, J. Merigó, & J. Kacprzyk, Intelligent and Complex Systems in Economics and Business. Advances in Intelligent Systems and Computing (pp. 21-32). Cham: SpringerCastiblanco, F., Montero, J., Rodríguez, J. T., & Gómez, D. (2017). Quality assessment of fuzzy classification: An application to solvency analysis. Fuzzy economic review, 22(1), 19-31.Chen, H., Yang, B., Wang, G., Liu, J., Xu, X., Wang, S., & Liu, D. (2001). A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method. Knowledge-Based Systems, 24(8), 1348-1359.Cielen, A., & Vanhoof, K. (2004). Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research, 2(154), 526-532.Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(1) 167-179.Dimitras, A., Slowinski, R., Susmaga, R., & Zopounidis, C. (1999). Business failure prediction using rough sets. European Journal of Operational Research, 2(114), 263-280.Fitzpatrick, P. (1932). A comparison of ratios of successful industrial enterprises with those of failed companies. The Certified Public Accountant (October, November, December), 2(8), 598-605, 656-662, and 727-731, respectively.Frydman, H., Altman, E., & Kao, D. (1985). Introducing recursive partitioning for classification: The case of financial distress. Journal of Finance, 1(40), 269-291.Hair, J. J., Anderson, R., Tatham, R., & Black, W. (1999). Análisis Multivariante. Prentice Hall Iberia.Hanss, M. (2005). Applied Fuzzy Arithmetic. New York: Springer-Verlag Berlin Heidelberg.Kaufmann, A., & Gil Aluja, J. (1987). Técnicas operativas de gestión para el tratamiento de la incertidumbre, Barcelona: Hispano EuropeaKeasey, K., Watson, R., & Wynarczyk, P. (1988). The small company audit qualification: a preliminary investigation. Accounting and Business Research, 18, 323-333.Lam, M. (2004). Neural networks techniques for financial performance prediction: Integrating fundamental and technical analysis. Decision Support Systems, 4(34), 567-581.Leshno, M., & Spector, Y. (1996). Neural network prediction analysis: The bankruptcy case. Neurocomputing, 2(10), 125-147.Marais, M., Pattell, J., & Wolfson, M. (1984). The experimental design of classification models: an application of recursive partitioning and bootstrapping to commercial bank loan classifications. Journal of Accounting Research, 22(1), 87-118Martin, D. (1977). Early Warning of Bank Failure. Journal of Banking and Finance, 1(3), 249-276.Min, J., & Jeong, C. (2009). A binary classification method for bankruptcy prediction. Expert Systems with Applications, 36(3), 5256-5263.Min, J., & Lee, Y. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 4(28), 603-614.Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 1(18), 109-131.Park, C., & Han, I. (2002). A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 3(23), 255-264.Pessoa de Oliveira, A. (2016). Análisis de solvencia de las empresas: modelo estático vs modelo dinámico. Thesis PhD. Facultad de Ciencias Económicas y Empresariales. Universidad de Zaragoza https://zaguan.unizar.es/record/48319/files/TUZ_0863_pessoa_analisis.pdfSansalvador, M., Reig, J., & Trigueros, J. (2000). Lógica borrosa y sus aplicaciones a la contabilidad. Revista Española de Financiación y Contabilidad, 29(103), 83-106.Shin, K., & Lee, Y. (2002). A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications, 3(23), 321-328.Tascón, M., & Castaño, F. (2012). Variables y modelos para la identificación y predicción del fracaso empresarial: revisión de la investigación empírica reciente. Revista de Contabilidad - Spanish Accounting Review, 15(1), 7-58.Villamil, H. (2004). Modelos multivariados para la predicción de insolvencia empresarial. Una aplicación al caso colombiano. Bogotá: Universidad Piloto de Colombia.Yang, Z., Platt, M., & Platt, H. (1999). Probability neural network in bankruptcy prediction. Journal of Business Research, 1999, 44(2), 67-74.Yim, J., & Mitchell, H. (2005). A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis. Nova Economia, 15(1), 73-93.Zimmermann, H. 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