Risk of business failure in the C23 manufacturing sector in Ecuador
Main Article Content
Abstract
The analysis of business failure is important, considering that companies are the engine of a country's economy. In the present research work, the risk of failure of companies in the manufacturing sector of other non-metallic mineral products in Ecuador (ISIC C23) is studied. The data consists of an average of 183 companies in the period 2009-2019. Starting from the Ohlson model (1980), logit and probit econometric models are proposed to calculate the probability of failure of companies in the sector. In the logit model, the probability of failure is between 3,67% and 8,42%, while in the probit, it is between 3,79% and 7,75%. It is highlighted that as the business size increases, the risk of failure is reduced and that the year 2017 presents less risk. In addition, the logit model has greater predictive capacity.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
When an article is approved, the author or authors keep the rights or authorship and cede to PODIUM the right to be the first able to edit it, reproduce it, exhibit it and communicate it by printed or electronica media.
In order to reinforce our open access policy, PODIUM journal is published under a license named “Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)”. This license allows sharing (copying and redistributing the material in any means or format) and adapting (re-mixing, transforming, and creating starting from the material). Corresponding credits must be given and no commercial use of the materials is allowed.
Partial or complete reproduction of articles published in PODIUM is authorized, as long as the author is appropriately cited as the source and the reproduction has no commercial purposes.
References
Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23, 589-609. https://doi.org/10.2307/2978933
Altman, E., Baidya, T., & Ribeiro, L. (1979). Assessing Potential Financial Problems for firms in Brazil. Journal of International Business Studies, 10, 9–24. https://doi.org/10.1057/palgrave.jibs.8490787
Altman, E., Hartzell, J., & Peck, M. (1995). Emerging market corporate bonds — a scoring system. Salomon Brothers Inc, New York University, 391–400. https://doi.org/10.1007/978-1-4615-6197-2_25
Balaguer, P. (2009). Una explicación del rendimiento estudiantil universitario mediante modelos de regresión logística. Visión Gerencial, 0(2), 415-427.
Banco Central del Ecuador. (2020). Anuario estadístico 40. https://contenido.bce.fin.ec/documentos/PublicacionesNotas/Catalogo/Anuario/Boletinanuario.htm
Beaver, W. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71-111. https://doi.org/https://doi.org/10.2307/2490171
Belyaeva, E. (2014). On a new logistic regression model for bankruptcy prediction in the IT branch. En Uppsala Universitet (Número December).
Boritz, E., & Kennedy, D. (1995). Effectiveness of neural network types for prediction of business failure. Expert Systems With Applications, 9(4), 503-512. https://doi.org/10.1016/0957-4174(95)00020-8
Camm, J., Cochran, J., Fry, M., Ohlmann, J., & Anderson, D. (2019). Business Analytics: descriptive, predictive, prescriptive. Cengage Learning.
Castro, Y., Huertas, C., Obando, C., & Valencia, C. (2019). Análisis de supervivencia para predicción de bancarrota: Caso de las industrias minoristas en Colombia. Espacios, 40(1), 18.
Chiriboga, B., Daniela, C., Cordero, A., & Johanna, C. (2021). Predicción de quiebra bajo el modelo Z2 Altman en empresas de construcción de edificios residenciales de la provincia del Azuay. Revista Economía y Política, 33.
Cox, D. R. (1972). Regression Models and Life-Tables. Imperial College, London, 34(2), 187-202. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x
FitzPatrick, P. (1932). Average Ratios of Twenty Representative Industrial Failures *. The certified public account, 13-18.
Gregova, E., Valaskova, K., Adamko, P., Tumpach, M., & Jaros, J. (2020). Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods. Sustainability, 12(10), 3954. https://doi.org/10.3390/su12103954
Gujarati, D., & Porter, D. (2010). Econometría (McGraw-Hill (ed.); 5.a ed.).
Horrigan, J. (1965). Some Empirical Bases of Financial Ratio Analysis. American Accounting Association, 40(3), 558-568.
Jackendoff, N. (1962). A Study of Published Industry Financial and Operating Ratios. En Temple University, Bureau of Economic and Business Research.
Kristanti, F., & Herwany, A. (2017). Corporate Governance, Financial Ratios, Political Risk and Financial Distress: A Survival Analysis. Accounting & Finance Review, 2(2), 26-34. https://doi.org/10.35609/afr.2017.2.2(4)
Kuběnka, M., & Myšková, R. (2019). Obvious and hidden features of corporate default in bankruptcy models. Journal of Business Economics and Management, 20(2), 368–383. https://doi.org/10.3846/jbem.2019.9612
Kubíčková, D. K., & Nulíček, V. (2014). Predictors of Financial Distress and Bankruptcy Model Construction. International Journal of Management Science and Business Administration, 2(6), 34–42. https://doi.org/10.18775/ijmsba.1849-5664-5419.2014.26.1003
Lane, W. R., Looney, S. W., & Wansley, J. W. (1986). An application of the cox proportional hazards model to bank failure. Journal of Banking and Finance, 10(4), 511-531. https://doi.org/10.1016/S0378-4266(86)80003-6
Mares, A. I. (2001). Análisis de las dificultades financieras de las empresas en una economía emergente: Las bases de datos y las variables independientes en el sector hotelero de la bolsa mexicana de valores ( Tesis doctoral). En Universitat Autonoma de Barcelona. Universitat autónoma de Barcelona, Barcelona.
Martin, D. (1977). Early warning of bank failure. A logit regression approach. Journal of Banking and Finance, 1(3), 249-276. https://doi.org/10.1016/0378-4266(77)90022-X
Merwin, C. (1942). Financing Small Corporations in Five Manufacturing Industries , 1926-36 . Journal of the American Statistical Association, 39(225), 129-130.
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
Orellana, I., Tonon, L., Reyes, M., Pinos, L., & Cevallos, E. (2020). Riesgos financieros en el sector manufacturero del Ecuador (1.a ed.). Casa editora Universidad del Azuay.
Pascale, R. (1988). A Multivariate Model To Predict Firm Financial Problems: the Case of Uruguay. Studies in Banking and Finance, 7, 171–182.
Pece, M., Acosta, M., Saavedra, S., & Bruno, C. (2012). Aplicación de la regresión logística en un estudio de emergencia de plántulas de Algarrobo blanco (Prosopis alba Griseb.) en vivero, bajo diferentes concentraciones salinas. Quebracho, 20(2), 78-84.
Santomero, A., & Vinso, D. (1977). Estimating the Probability of Failure for Commercial Banks and the Banking System. Journal of Banking and Finance, 185-205. https://doi.org/https://doi.org/10.1016/0378-4266(77)90006-1
Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1), 101-124. https://doi.org/10.2139/ssrn.171436
Siekelova, A., Kliestik, T., & Adamko, P. (2018). Predictive Ability of Chosen Bankruptcy Models: A Case Study of Slovak Republic. Economics and Culture, 15(1), 105–114. https://doi.org/10.2478/jec-2018-0012
Smith, R., & Winakor, A. (1935). Changes in the Financial Structure of Unsuccessful Corporations. Bureau of Business Research, bulletin number 51.
Superintendencia de Compañías, Valores y Seguros. (2016). Reglamento sobre inactividad, disolución, liquidación, reactivación y cancelación de compañías nacionales y sucursales extranjeras. Lexis.
Superintendencia de Compañías, Valores y Seguros. (2020). Portal de información. https://appscvsmovil.supercias.gob.ec/portalInformacion/sector_societario.zul
Swanson, E., & Tybout, J. (1988). Industrial bankruptcy determinants in Argentina. Journal of Banking and Finance, 7, 1–25.
Tascón, M. T., Castaño, F. J., & Castro, P. (2018). A new tool for failure analysis in small firms: frontiers of financial ratios based on percentile differences (PDFR). Revista Espanola de Financiacion y Contabilidad, 47(4), 433–463. https://doi.org/10.1080/02102412.2018.1468058
White, R., & Turnbull, M. (1975a). The Probability of Bankruptcy: American Railroads. Working paper, Institute of Finance and Accounting,London University Graduate School of Business.
White, R., & Turnbull, M. (1975b). The Probability of Bankruptcy for American Industrial Firms. Working paper.
Zadeh, Lotfi. (1965). Fuzzy Sets. Journal of Plant Pathology, 8, 338-353. https://doi.org/https://doi.org/10.1016/S0019-9958(65)90241-X
Zadeh, Lofti. (1968). Fuzzy algorithms. Information and Control, 12(2), 94-102. https://doi.org/10.1016/S0019-9958(68)90211-8
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