Probability of business bankruptcy in theconstruction sector of Ecuador: Period 2011 – 2020

Authors

DOI:

https://doi.org/10.17981/econcuc.44.2.2023.Econ.2

Keywords:

Bankruptcy, Construction, Logistic regression, Probi, Predictive capacity

Abstract

In business decisions, it is necessary to determine which are the variables that explain the probability of bankruptcy in order to make predictions about them in a second stage. The objective of this research work is to determine the probability of failure of companies in the construction sector in Ecuador. In order to achieve the goal, the logistic regression model and the Probit model were applied, which are binary discrete choice models. Among the important findings, it can be said that the variables that explain the probability of business bankruptcy in the sector are the size of the company, the level of indebtedness, liquidity, profitability and net income. In addition, the predictive capacity of the model was verified under different metrics such as sensitivity, specificity and later the ROC curve. In general, the Probit model gives a better predictive capacity of the model.

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Author Biographies

Iván Felipe Orellana Osorio, Universidad del Azuay

Is Public Accountant and Commercial Engineer from the University of Azuay. He is a Specialist in University Teaching and obtained his Master's Degree in Business Administration MBA from the University of Azuay, in addition, he is a graduate of the INCAE Senior Management Program, and is studying the Doctorate Program in Administration at the National University of Rosario in Argentina. Professor since 2006 of undergraduate and postgraduate courses in management, projects and finance at the University of Azuay and the University of Cuenca.

 

Luis Gabriel Pinos Luzuriaga, Universidad del Azuay

Is Economist, Master in Insurance and Financial Risks, in the academic field he has served as a teacher in the chairs of Statistics, Econometrics, Actuarial Calculation and Financial Risk Management in undergraduate and postgraduate courses at the University of Azuay and the University of Cuenca. In recent years, he has been linked to research groups at the University of Azuay performing support tasks in the quantitative area.

 

 

Marco Antonio Reyes Clavijo, Universidad del Azuay

Is a commercial engineer graduated from the Universidad del Azuay. He obtained his Master's Degree in Business Administration with a Mention in Finance at the Universidad del Azuay. He currently works as a teacher in the area of statistics and as a financial technician and researcher at the Business Observatory of the Universidad del Azuay. His research has focused on the analysis and management of financial risk, applied to different economic sectors of Ecuador. 

Estefanía del Rocío Cevallos Rodríguez, Universidad del Azuay

Is Master in resource management sciences, at the Academy he has worked as a teacher of the chairs of Environmental Audit and Environmental Impact Assessment at the Faculty of Administration Sciences of the University of Azuay. In the last three years, she has been linked to the Business Observatory of the same University, carrying out information management tasks and writing scientific articles and knowledge dissemination documents.

Luis Bernardo Tonon Ordóñez, Universidad del Azuay

EIs conomist from the Universidad del Azuay. Higher Diploma in Finance, Stock Market and Fiduciary Businesses, Higher Diploma in International Negotiation, and obtained his Master's Degree in Business Administration from the Universidad del Azuay. Professor since 2003 at the Universidad del Azuay in the areas of Economics and Finance. He has participated in various research groups and is currently part of the UDA Business Observatory

 

 

References

Ahn, B. S., Cho, S. S. & Kim, C. Y. (2000). Integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 18(2), 65–74. https://doi.org/10.1016/S0957-4174(99)00053-6

Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.2307/2978933

Altman, E., Haldeman, R. & Narayanan, P. (1977). ZETATM 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

Ariza Dau, M., Acosta Rueda, K. y Altamar, L. (2016). Aplicación de los Modelos de Respuesta Binaria a los Determinantes de la Demanda de Postgrado en Colom­bia. Escenarios, 14(1), 7–18. https://doi.org/10.15665/esc.v14i1.874

Beaver, W. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171

Bermeo, D. y Armijos, J. (2021). Predicción de quiebra bajo el modelo Z2 Altman en empresas de construcción de edificios residenciales de la provincia del Azuay. Revista Economía, 33, 48–63. https://doi.org/10.25097/rep.n33.2021.03

Bermúdez, N. y Bravo, A. (2019). Modelo Predictivo de los Determinantes del Cierre Empresarial de las MIPYMES en el Ecuador Período 2007-2016. X - Pedientes Económicos, 3(5), 78–93. Disponible en https://ojs.supercias.gob.ec/index.php/X-pedientes_Economicos/article/view/32

Bravo, F. y Pinto, C. (2008). Modelos predictivos de la probabilidad de insolvencia en microempresas chilenas. Contaduría Universidad de Antioquia, (53), 13–52. https://doi.org/10.17533/udea.rc.2175

Cortes, M., Saavedra, M. & Palacios, P. (2021). Análisis del fracaso de las MiPyME franquiciantes en México. Un crecimiento cuestionable del sector. Revista Pers­pectiva Empresarial, 7(2), 36–52. https://doi.org/10.16967/23898186.651

Cruz, S., Gavira, N. & García, R. (2017). Eficiencia de los modelos Poisson y Lo­gístico en la asignación de probabilidades de incumplimiento a empresas mine­ras mexicanas. Revista Mexicana de Economía y Finanzas, 12(1), 1–21. http://dx.doi.org/10.21919/remef.v12i1.9

Cueva, D. F., Cortes, S., Tapia, R., Tabi, W., Torres, J., Maza, C., Uyaguari, K. y González, M. (2017, junio). Fragilidad Financiera de las Empresas - Esti­mación de un Modelo probabilístico LOGIT y PROBIT: Caso Ecuatoriano. Conferencia presentada en la XII Conferencia Ibérica de Sistemas y Tecno­logías de la Información, CISTI, Lisbon, Portugal. https://doi.org/10.23919/CISTI.2017.7975927

Deakin, E. (1972). Research Reports A Discriminant Analysis of Predictors of Business Failure. Journal of Accountin Research, 10(1), 167–179. https://doi.org/10.2307/2490225

Dupleix, M. (2021). La teoría efectual y el fracaso empresarial. Innovar, 31(81), 139–154. https://doi.org/10.15446/innovar.v31n81.95581

FitzPatrick, P. (1932). Average Ratios of Twenty Representative Industrial Failures *. The Certified Public Account, 13–18.

Gujarati, D. y Porter, D. (2010). Econometría (5 ed.). México, D.F.: McGraw-Hill.

Kliestik, T., Misankova, M., Valaskova, K. & Svabova, L. (2018). Bankruptcy Prevention: New Effort to Reflect on Legal and Social Changes. Science and Engineering Ethics, 24(2), 791–803. https://doi.org/10.1007/s11948-017-9912-4

Krishnasami, J. (2012). Financial Risk: Impact on Debt-Equity Mix. SCMS Jour­nal of Indian Management, 9(1), 43–59. Available from https://www.scms.edu.in/uploads/journal/January%20-%20March%202012.pdf

Kücher, A., Mayr, S., Mitter, C., Duller, C. & Feldbauer-Durstmüller, B. (2020). Firm age dynamics and causes of corporate bankruptcy: age dependent explanations for business failure. Review of Managerial Science, 14(3), 633–661. https://doi.org/10.1007/s11846-018-0303-2

Lennox, C. (1999). Identifying failing companies: A reevaluation of the Logit, Pro­bit and DA approaches. Journal of Economics and Business, 51(4), 347–364. https://doi.org/10.1016/s0148-6195(99)00009-0

Lin, T.-H. (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, Logit, Probit and neural networks models. Neurocomputing, 72(16–18), 3507–3516. https://doi.org/10.1016/j.neucom.2009.02.018

Martínez, H., Cazallo, A., Meñaca, I. y Uribe, C. (2020). Desempeño financiero de las empresas minoristas de alimentos y bebidas en Barranquilla - Colombia. Revista de Ciencias Sociales, 26(1), 144–158. https://doi.org/10.31876/rcs.v26i1.31316

Mittal, S. & Lavina. (2018). The Determinants of Financial Distress in Indian real estate and Construction industry. Gurukul Business Review, 14(2), 6–11. Avail­able: https://gurukulbusinessreview.in/past-issues/

Navarrete, G. (2022). Organizaciones inteligentes y su incipiente incursión en la esfera turística. Una aproximación al estado del conocimiento. Telos Revista de Estudios Interdisciplinarios en Ciencias Sociales, 24(1), 100–122. https://doi.org/10.36390/telos241.07

Ogachi, D., Ndege, R., Gaturu, P. & Zoltan, Z. (2020). Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya. Jour­nal of Risk and Financial Management, 13(3), 1–14. https://doi.org/10.3390/jrfm13030047

Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395

Pérez, G., González, C. y Lopera, C. (2013). Modelos de predicción de la fragilidad empresarial: aplicación al caso colombiano para el año 2011. Perfil de Coyuntu­ra Económica, (22), 205–228. Disponible en https://revistas.udea.edu.co/index.php/coyuntura/article/view/20539

Rodríguez, C., Maté, M. y López, F. (2020). La proximidad geográfica en el conta­gio del fracaso empresarial en la pyme: Una aplicación empírica con el modelo Probit espacial. Estudios de Economía Aplicada, 34(3), 629–648. https://doi.org/10.25115/eae.v34i3.3063

Romero, F. (2013). Variables financieras determinantes del fracaso empresarial para la pequeña y mediana empresa en Colombia: análisis bajo modelo Logit. Pensamiento & Gestión, (34), 235–277. Disponible en https://rcientificas.uni­norte.edu.co/index.php/pensamiento/article/view/5647

Romero, F., Melgarejo, Z. & Vera-Colina, 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

Singh, B. P. & Mishra, A. K. (2016). Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies. Finan­cial Innovation, 2(1), 1–28. https://doi.org/10.1186/s40854-016-0026-9

Solomon, D. C. & Muntean, M. (2012). Assessment of Financial Risk in Firm’s Prof­itability Analysis. Economy Transdisciplinarity Cognition, 15(2), 58–67. Avail­able from https://www.ugb.ro/etc/etc2012no2/11_Solomon,_Muntean_final.pdf

Spekman, R. E. & Davis, E. W. (2004). Risky business: Expanding the discussion on risk and the extended enterprise. International Journal of Physical Distribution and Logistics Management, 34(5), 414–433. https://doi.org/10.1108/09600030410545454

Supercias. (2020). Portal de información. Superintendencia de compañías valores y seguros. Disponible en https://appscvsmovil.supercias.gob.ec/portalInforma­cion/sector_societario.zul

Svabova, L., Michalkova, L., Durica, M. & Nica, E. (2020). Business failure predic­tion for Slovak small and medium-sized companies. Sustainability (Switzer­land), 12(11), 1–14. https://doi.org/10.3390/su12114572

Támara, A. y Villegas, G. (2021). Influencia del entorno financiero, el entorno ma­croeconómico, la estructura organizacional y la transparencia en la quiebra em­presarial. Contaduria y Administracion, 66(2), 1–23. https://doi.org/10.22201/fca.24488410e.2021.2618

Wu, W. W. (2010). Beyond business failure prediction. Expert Systems with Applica­tions, 37(3), 2371–2376. https://doi.org/10.1016/j.eswa.2009.07.056

Zambrano, F., Sánchez, M. y Correa, S. (2021). Análisis de rentabilidad, endeuda­miento y liquidez de microempresas en Ecuador. Retos, 11(22), 235–249. https://doi.org/10.17163/ret.n22.2021.03

Zmijweski, M. E. (1984). Methodological Issues Related to the Estimation of Finan­cial Distress Prediction Models. Journal of Accounting Research, 22, 59–82. https://doi.org/10.2307/2490859

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Published

2023-04-25

How to Cite

Orellana Osorio, I. F., Pinos Luzuriaga, L. G., Reyes Clavijo, M. A., Cevallos Rodríguez, E. del R., & Tonon Ordóñez, L. B. (2023). Probability of business bankruptcy in theconstruction sector of Ecuador: Period 2011 – 2020. ECONÓMICAS CUC, 44(2), 9–32. https://doi.org/10.17981/econcuc.44.2.2023.Econ.2

Issue

Vol. 44 No. 2 (2023): January – June, 2023

Section

Articles: Economy and Finance

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Copyright (c) 2023 Marco Reyes Clavijo, Iván Orellana, Luis Pinos, Estefanía Cevallos, Luis Tonon

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