Pronóstico de insolvencia empresarial en Colombia a través de indicadores financieros.

Autores:: Correa Mejía, Diego Andrés
Lopera Castaño, Mauricio

Tipo de recurso:: Article of journal

Fecha de publicación:: 2019

Institución:: Universidad de Cartagena

Repositorio:: Repositorio Universidad de Cartagena

Idioma:: spa

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dc.title.spa.fl_str_mv	Pronóstico de insolvencia empresarial en Colombia a través de indicadores financieros.
dc.title.translated.eng.fl_str_mv	Forecast of business insolvency in Colombia through financial indicators.
title	Pronóstico de insolvencia empresarial en Colombia a través de indicadores financieros.
spellingShingle	Pronóstico de insolvencia empresarial en Colombia a través de indicadores financieros. Insolvency Financial indicators Financial analysis Boosting algorithm Logistic regression Insolvencia empresarial Indicadores financieros Análisis financiero Algoritmo boosting Regresión logística Insolvabilité des entreprises Indicateurs financiers Analyse financière Algorithme de boosting Régression logistique
title_short	Pronóstico de insolvencia empresarial en Colombia a través de indicadores financieros.
title_full	Pronóstico de insolvencia empresarial en Colombia a través de indicadores financieros.
title_fullStr	Pronóstico de insolvencia empresarial en Colombia a través de indicadores financieros.
title_full_unstemmed	Pronóstico de insolvencia empresarial en Colombia a través de indicadores financieros.
title_sort	Pronóstico de insolvencia empresarial en Colombia a través de indicadores financieros.
dc.creator.fl_str_mv	Correa Mejía, Diego Andrés Lopera Castaño, Mauricio
dc.contributor.author.spa.fl_str_mv	Correa Mejía, Diego Andrés Lopera Castaño, Mauricio
dc.subject.eng.fl_str_mv	Insolvency Financial indicators Financial analysis Boosting algorithm Logistic regression
topic	Insolvency Financial indicators Financial analysis Boosting algorithm Logistic regression Insolvencia empresarial Indicadores financieros Análisis financiero Algoritmo boosting Regresión logística Insolvabilité des entreprises Indicateurs financiers Analyse financière Algorithme de boosting Régression logistique
dc.subject.spa.fl_str_mv	Insolvencia empresarial Indicadores financieros Análisis financiero Algoritmo boosting Regresión logística Insolvabilité des entreprises Indicateurs financiers Analyse financière Algorithme de boosting Régression logistique
publishDate	2019
dc.date.accessioned.none.fl_str_mv	2019-04-01 00:00:00
dc.date.available.none.fl_str_mv	2019-04-01 00:00:00
dc.date.issued.none.fl_str_mv	2019-04-01
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.doi.none.fl_str_mv	10.32997/2463-0470-vol.27-num.2-2019-2639
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dc.relation.ispartofjournal.spa.fl_str_mv	Panorama Económico
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dc.relation.citationedition.spa.fl_str_mv	Núm. 2 , Año 2019
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dc.relation.references.spa.fl_str_mv	Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x Amendola, A., Giordano, F., Parrella, M., y Restaino, M. (2017). Variable selection in high-dimensional regression: a nonparametric procedure for business failure prediction. Applied Stochastic Models in Business and Industry, 33(4), 355-368. https://doi.org/10.1002/asmb.2240 Barboza, F. , Kimura, H., y 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. (1966). Financial Ratios As Predictors of Failure. Journal of Accounting Research, 4(1966), 71-111. https://doi.org/10.2307/2490171 Ben, S. (2017). Bankruptcy prediction using Partial Least Squares Logistic Regression. Journal of Retailing and Consumer Services, 36(November 2016), 197-202. https://doi.org/10.1016/j.jretconser.2017.02.005 Bredart, X., Vella, V., y Bonello, J. (2018). Machine Learning Models for Predicting Financial Distress. Journal of Research in Economics, 2(2), 174-185. https://doi.org/10.24954/JORE.2018.22 Calabrese, R., y Osmetti, S. A. (2013). Modelling small and medium enterprise loan defaults as rare events: The generalized extreme value regression model. Journal of Applied Statistics, 40(6), 1172-1188. https://doi.org/10.1080/02664763.2013.784894 Calabrese, R., y Osmetti, S. A. (2015). Improving forecast of binary rare events data: A gam-based approach. Journal of Forecasting, 34(3), 230-239. https://doi.org/10.1002/for.2335 Correa-García, J. A. (2005). De la partida doble al análisis financiero. Contaduría Universidad de Antioquia, (46), 169-194. Correa-García, J. A., Gómez, S., y Londoño, F. (2018). Indicadores financieros y su eficiencia en la explicación de la generación de valor en el sector cooperativo. Rev.fac.cienc.econ., XXVI(2), 129-144. https://doi.org/10.18359/rfce.3859 Correa, D., Laura, M., Camila, R., y Alejandra, Y. (2018). Los indicadores de costos: una herramienta para gestionar la generación de valor en las empresas industriales colombianas. Estudios Gerenciales, 34(147), 190-199. https://doi.org/10.18046/j.estger.2018.147.2643 Correa, J., Pulgarín, A., Muñoz, L., y Álvarez, M. (2010). Marco normativo y antecedentes de la revelación contable en Colombia. Contaduría Universidad de Antioquia, (56), 269-292. Cultrera, L., y Brédart, X. (2016). Bankruptcy prediction: The case of Belgian SMEs. Review of Accounting and Finance, 15(1), 101-119. https://doi.org/10.1108/RAF-06-2014-0059 Dinca, G., Baba, M. C., Dinca, M. S., Dauti, B., y Deari, F. (2017). Insolvency risk prediction using the logit and logistic models: Some evidences from Romania. Economic Computation and Economic Cybernetics Studies and Research, 51(4), 139-157. Eling, M., y Jia, R. (2018). Business failure, efficiency, and volatility: Evidence from the European insurance industry. International Review of Financial Analysis, 59, 58-76. https://doi.org/10.1016/j.irfa.2018.07.007 Gupta, J., Gregoriou, A., y Ebrahimi, T. (2018). Empirical comparison of hazard models in predicting SMEs failure. Quantitative Finance, 18(3), 437-466. https://doi.org/10.1080/14697688.2017.1307514 Hastie, T., Tibshirani, R., Friedman, J. (2017). The Elements of Statistical Learning. Data Mining, Inference, and Prediction, Standford, California. Hebous, S., y De Mooij, R. (2018). Curbing Corporate Debt Bias: Do Limitations to Interest Deductibility Work? Journal of Banking y Finance, 1-11. https://doi.org/10.1016/j.jbankfin.2018.07.013 Jabeur, S. Ben, y Fahmi, Y. (2018). Forecasting financial distress for French firms: a comparative study. Empirical Economics, 54(3), 1173-1186. https://doi.org/10.1007/s00181-017-1246-1 Jayasekera, R. (2018). Prediction of company failure: Past, present and promising directions for the future. International Review of Financial Analysis, 55, 196-208. https://doi.org/10.1016/j.irfa.2017.08.009 Jones, S., Johnstone, D., y Wilson, R. (2017). Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks. Journal of Business Finance and Accounting, 44(1-2), 3-34. https://doi.org/10.1111/jbfa.12218 Jovanovik, D., Todorovic, M., y Grbic, M. (2017). Financial Indicators as Predictors of Illiquidity. Romanian Journal of Economic Forecasting, 20(1), 128-149. Kovacova, M., y Kliestik, T. (2017). Logit and Probit application for the prediction of bankruptcy in Slovak companies. Equilibrium-Quarterly Journal of Economics and Economic Policy, 12(4), 775-791. https://doi.org/10.24136/eq.v12i4.40 Le, T., Son, L. H., Vo, M. T., Lee, M. Y., y Baik, S. W. (2018). A cluster-based boosting algorithm for bankruptcy prediction in a highly imbalanced dataset. Symmetry, 10(7), 1-12. https://doi.org/10.3390/sym10070250 Ley 1116. (2006). Diario Oficial No. 46.494 de 27 de diciembre de 2006, Colombia, diciembre 27 de 2006. Lyandres, E., y Zhdanov, A. (2013). Investment opportunities and bankruptcy prediction. Journal of Financial Markets, 16(3), 439-476. https://doi.org/10.1016/j.finmar.2012.10.003 Nyitrai, T., y Virág, M. (2018). The effects of handling outliers on the performance of bankruptcy prediction models. Socio-Economic Planning Sciences, 1-9. https://doi.org/10.1016/0304-3762(82)90059-1 Pérez, J., Lopera, M., y Vásquez, F. (2017). Estimación de la probabilidad de riesgo de quiebra en las empresas colombianas a partir de un modelo para eventos raros. Cuadernos de Administración, 30(54), 7-38. https://doi.org/10.11144/Javeriana.cao30-54.eprqe Rodríguez, J. (2007). Nuevo régimen de insolvencia. Bogotá, Colombia: Universidad Externado de Colombia. Romero, F., Melgarejo, Z., y Vera, M. (2015). Fracaso empresarial de las pequeñas y medianas empresas (pymes) en Colombia. Suma de Negocios, 6(13), 29-41. https://doi.org/10.1016/j.sumneg.2015.08.003 Tian, S. , y Yu, Y. (2017). Financial ratios and bankruptcy predictions: An international evidence. International Review of Economics and Finance, 51, 510-526. https://doi.org/10.1016/j.iref.2017.07.025 Upegui, A. y Londoño, Á. (2011). Comentarios al régimen de insolvencia empresarial. Bogotá, Colombia: Legis Editores. Vélez, L. (2011). ¿Qué tan bueno es el sistema de insolvencia empresarial colombiano? Revista Supersociedades, 2, 5-6. Wang, G., Ma, J., y Yang, S. (2014). An improved boosting based on feature selection for corporate bankruptcy prediction. Expert Systems with Applications, 41(5), 2353-2361. https://doi.org/10.1016/j.eswa.2013.09.033 Wilches, R. (2008). Vacíos e inconsistencias estructurales del nuevo régimen de insolvencia empresarial colombiano. Identificación y propuestas de solución. Vniversitas, (117), 197-218. Wilches, R. (2009). La insolvencia transfronteriza en el derecho colombiano. Revista de Derecho, (32), 162-198. Yazdanfar, D., y Öhman, P. (2015). Debt financing and firm performance: an empirical study based on Swedish data. The Journal of Risk Finance, 16(1), 102-118. https://doi.org/10.1108/JRF-06-2014-0085
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spelling	Correa Mejía, Diego Andrésa7c3040cb2043d4bd4e1686100bcab05500Lopera Castaño, Mauricio9efe5333721a20c4de25bbf632075cac2019-04-01 00:00:002019-04-01 00:00:002019-04-010122-8900https://hdl.handle.net/11227/1387710.32997/2463-0470-vol.27-num.2-2019-26392463-0470https://doi.org/10.32997/2463-0470-vol.27-num.2-2019-2639application/pdfspaUniversidad de CartagenaPanorama Económicohttps://revistas.unicartagena.edu.co/index.php/panoramaeconomico/article/download/2639/2220Núm. 2 , Año 2019526251027Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.xAmendola, A., Giordano, F., Parrella, M., y Restaino, M. (2017). Variable selection in high-dimensional regression: a nonparametric procedure for business failure prediction. Applied Stochastic Models in Business and Industry, 33(4), 355-368. https://doi.org/10.1002/asmb.2240Barboza, F. , Kimura, H., y Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417. https://doi.org/10.1016/j.eswa.2017.04.006Beaver, W. (1966). Financial Ratios As Predictors of Failure. Journal of Accounting Research, 4(1966), 71-111. https://doi.org/10.2307/2490171Ben, S. (2017). Bankruptcy prediction using Partial Least Squares Logistic Regression. Journal of Retailing and Consumer Services, 36(November 2016), 197-202. https://doi.org/10.1016/j.jretconser.2017.02.005Bredart, X., Vella, V., y Bonello, J. (2018). Machine Learning Models for Predicting Financial Distress. Journal of Research in Economics, 2(2), 174-185. https://doi.org/10.24954/JORE.2018.22Calabrese, R., y Osmetti, S. A. (2013). Modelling small and medium enterprise loan defaults as rare events: The generalized extreme value regression model. Journal of Applied Statistics, 40(6), 1172-1188. https://doi.org/10.1080/02664763.2013.784894Calabrese, R., y Osmetti, S. A. (2015). Improving forecast of binary rare events data: A gam-based approach. Journal of Forecasting, 34(3), 230-239. https://doi.org/10.1002/for.2335Correa-García, J. A. (2005). De la partida doble al análisis financiero. Contaduría Universidad de Antioquia, (46), 169-194.Correa-García, J. A., Gómez, S., y Londoño, F. (2018). Indicadores financieros y su eficiencia en la explicación de la generación de valor en el sector cooperativo. Rev.fac.cienc.econ., XXVI(2), 129-144. https://doi.org/10.18359/rfce.3859Correa, D., Laura, M., Camila, R., y Alejandra, Y. (2018). Los indicadores de costos: una herramienta para gestionar la generación de valor en las empresas industriales colombianas. Estudios Gerenciales, 34(147), 190-199. https://doi.org/10.18046/j.estger.2018.147.2643Correa, J., Pulgarín, A., Muñoz, L., y Álvarez, M. (2010). Marco normativo y antecedentes de la revelación contable en Colombia. Contaduría Universidad de Antioquia, (56), 269-292.Cultrera, L., y Brédart, X. (2016). Bankruptcy prediction: The case of Belgian SMEs. Review of Accounting and Finance, 15(1), 101-119. https://doi.org/10.1108/RAF-06-2014-0059Dinca, G., Baba, M. C., Dinca, M. S., Dauti, B., y Deari, F. (2017). Insolvency risk prediction using the logit and logistic models: Some evidences from Romania. Economic Computation and Economic Cybernetics Studies and Research, 51(4), 139-157.Eling, M., y Jia, R. (2018). Business failure, efficiency, and volatility: Evidence from the European insurance industry. International Review of Financial Analysis, 59, 58-76. https://doi.org/10.1016/j.irfa.2018.07.007Gupta, J., Gregoriou, A., y Ebrahimi, T. (2018). Empirical comparison of hazard models in predicting SMEs failure. Quantitative Finance, 18(3), 437-466. https://doi.org/10.1080/14697688.2017.1307514Hastie, T., Tibshirani, R., Friedman, J. (2017). The Elements of Statistical Learning. Data Mining, Inference, and Prediction, Standford, California.Hebous, S., y De Mooij, R. (2018). Curbing Corporate Debt Bias: Do Limitations to Interest Deductibility Work? Journal of Banking y Finance, 1-11. https://doi.org/10.1016/j.jbankfin.2018.07.013Jabeur, S. Ben, y Fahmi, Y. (2018). Forecasting financial distress for French firms: a comparative study. Empirical Economics, 54(3), 1173-1186. https://doi.org/10.1007/s00181-017-1246-1Jayasekera, R. (2018). Prediction of company failure: Past, present and promising directions for the future. International Review of Financial Analysis, 55, 196-208. https://doi.org/10.1016/j.irfa.2017.08.009Jones, S., Johnstone, D., y Wilson, R. (2017). Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks. Journal of Business Finance and Accounting, 44(1-2), 3-34. https://doi.org/10.1111/jbfa.12218Jovanovik, D., Todorovic, M., y Grbic, M. (2017). Financial Indicators as Predictors of Illiquidity. Romanian Journal of Economic Forecasting, 20(1), 128-149.Kovacova, M., y Kliestik, T. (2017). Logit and Probit application for the prediction of bankruptcy in Slovak companies. Equilibrium-Quarterly Journal of Economics and Economic Policy, 12(4), 775-791. https://doi.org/10.24136/eq.v12i4.40Le, T., Son, L. H., Vo, M. T., Lee, M. Y., y Baik, S. W. (2018). A cluster-based boosting algorithm for bankruptcy prediction in a highly imbalanced dataset. Symmetry, 10(7), 1-12. https://doi.org/10.3390/sym10070250Ley 1116. (2006). Diario Oficial No. 46.494 de 27 de diciembre de 2006, Colombia, diciembre 27 de 2006.Lyandres, E., y Zhdanov, A. (2013). Investment opportunities and bankruptcy prediction. Journal of Financial Markets, 16(3), 439-476. https://doi.org/10.1016/j.finmar.2012.10.003Nyitrai, T., y Virág, M. (2018). The effects of handling outliers on the performance of bankruptcy prediction models. Socio-Economic Planning Sciences, 1-9. https://doi.org/10.1016/0304-3762(82)90059-1Pérez, J., Lopera, M., y Vásquez, F. (2017). Estimación de la probabilidad de riesgo de quiebra en las empresas colombianas a partir de un modelo para eventos raros. Cuadernos de Administración, 30(54), 7-38. https://doi.org/10.11144/Javeriana.cao30-54.eprqeRodríguez, J. (2007). Nuevo régimen de insolvencia. Bogotá, Colombia: Universidad Externado de Colombia.Romero, F., Melgarejo, Z., y Vera, M. (2015). Fracaso empresarial de las pequeñas y medianas empresas (pymes) en Colombia. Suma de Negocios, 6(13), 29-41. https://doi.org/10.1016/j.sumneg.2015.08.003Tian, S. , y Yu, Y. (2017). Financial ratios and bankruptcy predictions: An international evidence. International Review of Economics and Finance, 51, 510-526. https://doi.org/10.1016/j.iref.2017.07.025Upegui, A. y Londoño, Á. (2011). Comentarios al régimen de insolvencia empresarial. Bogotá, Colombia: Legis Editores.Vélez, L. (2011). ¿Qué tan bueno es el sistema de insolvencia empresarial colombiano? Revista Supersociedades, 2, 5-6.Wang, G., Ma, J., y Yang, S. (2014). An improved boosting based on feature selection for corporate bankruptcy prediction. Expert Systems with Applications, 41(5), 2353-2361. https://doi.org/10.1016/j.eswa.2013.09.033Wilches, R. (2008). Vacíos e inconsistencias estructurales del nuevo régimen de insolvencia empresarial colombiano. Identificación y propuestas de solución. Vniversitas, (117), 197-218.Wilches, R. (2009). La insolvencia transfronteriza en el derecho colombiano. Revista de Derecho, (32), 162-198.Yazdanfar, D., y Öhman, P. (2015). Debt financing and firm performance: an empirical study based on Swedish data. The Journal of Risk Finance, 16(1), 102-118. https://doi.org/10.1108/JRF-06-2014-0085Panorama Económico - 2019https://creativecommons.org/licenses/by-nc-sa/4.0http://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.https://revistas.unicartagena.edu.co/index.php/panoramaeconomico/article/view/2639InsolvencyFinancial indicatorsFinancial analysisBoosting algorithmLogistic regressionInsolvencia empresarialIndicadores financierosAnálisis financieroAlgoritmo boostingRegresión logísticaInsolvabilité des entreprisesIndicateurs financiersAnalyse financièreAlgorithme de boostingRégression logistiquePronóstico de insolvencia empresarial en Colombia a través de indicadores financieros.Forecast of business insolvency in Colombia through financial indicators.Artículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleJournal articleOREORE.xmltext/xml2577https://repositorio.unicartagena.edu.co/bitstream/11227/13877/1/ORE.xmlcccffa7b2cffd45d598f095296979bfaMD51open access11227/13877oai:repositorio.unicartagena.edu.co:11227/138772023-06-13 20:10:09.497An error occurred on the license name.\|\|\|https://creativecommons.org/licenses/by-nc-sa/4.0metadata only accessBiblioteca Digital Universidad de Cartagenabdigital@metabiblioteca.com

Pronóstico de insolvencia empresarial en Colombia a través de indicadores financieros.

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