Estrés financiero en el sector manufacturero de Ecuador

Resumen

El presente artículo clasifica a las empresas manufactureras del Ecuador en empresas con estrés financiero (ECE) y sin estrés financiero (ESE). Para tal efecto, se clarifica el significado de estrés financiero y el criterio bajo el cual una empresa sería clasificada como ECE o ESE. Además, se recurre a dos modelos ampliamente utilizados en el medio: el análisis discriminante múltiple y la regresión logística, basados en los trabajos previos de Altman y Ohlson, respectivamente. El estudio se enfoca en las empresas del sector manufacturero ecuatoriano durante el periodo 2014-2018. Se destaca que uno de los hallazgos principales es que, en algunos casos, los signos de los coeficientes de los modelos estimados difieren de los modelos originales de Altman y Ohlson. Sin embargo, en ambos casos, las tasas de precisión de este estudio son mayores que las de los modelos originales. Finalmente, se encontró que las microempresas son las que
presentan mayor estrés en sentido financiero.

Palabras clave:

Análisis discriminante múltiple Ecuador Estrés financiero Manufactura Regresión logística

Citas

Abeles, M., Cimoli, M. y avarello, P. (2017). Manufactura y cambio estructural. Santiago de Chile: Comisión Económica para América Latina y el Caribe (CEPAL).

Agarwal, V. y Taffler, R. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance, 32(8), 1541-1551. https:/doi.org/10.1016/j.jbankfin.2007.07.014

Ahmad, A. H. (2019). What factors discriminate reorganized and delisted distressed firms: Evidence from Malaysia. Finance Research Letters, 29, 50-56. https:/doi. org/10.1016/j.frl.2019.03.010

Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O. y Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94, 164-184. https:/doi.org/10.1016/j.eswa.2017.10.040

Altman, E. I. (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

Altman, E. I. (2000). Predicting financial distress of companies: revisiting the Z-Score and ZETA® models. Handbook of Research Methods and Applications in Empirical Finance, (September, 1968), 428-456. https:/doi.org/10.4337/9780857936097.00027

Altman, E. I. y Hotchkiss, E. (2005). Corporate financial distress and bankruptcy: Predict and avoid bankruptcy analyze and invest in distressed debt (3.ª ed.). Nueva York: John Wiley & Sons. https:/doi.org/10.1002/9781118267806.ch11

Altman, E. I. y Hotchkiss, E. (2006). Corporate Financial Distress and Bankruptcy (3.ª ed.). New Jersey: Wiley Finance Series.

Altman, E. I., Danovi, A. y Falini, A. (1988). Z-Score Models’ Application to Italian companies subject to extraordinary administration. Journal of Applied Finance. Formerly Finance Practice and Education, 23(1), 10.

Altman, E. I., Laitinen, E. K. y Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman’s Z-Score Model. Journal of International Financial Management & Accounting, 28(2), 131-172. https:/doi.org/10.1111/jifm.12053

Altman, I. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance, 18(3), 505-529. https:/doi.org/10.1016/0378-4266(94)90007-8

Arellano, A. S., Gil, J. A. y Martínez, A. H. (2003). El análisis discriminante en la previsión de la insolvencia en las empresas de seguros de no vida. Revista Española de Financiación y Contabilidad, 32(116), 183-233 https:/doi.org/10.1080/02102412.2003.10779487

Back, B., Laitinen, T., Sere, K. y van Wezel, M. (2009). Choosing bankruptcy predictors using discriminant analysis, logit analysis and genetic algorithms. Turku, Centre for Computer Science, 40, 214. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.604.2813&rep=rep1&type=pdf

Bae, J. K. (2012). Predicting financial distress of the South Korean manufacturing industries. Expert Systems with Applications, 39(10), 9159-9165. https:/doi.org/10.1016/j.eswa.2012.02.058

Baidya, T., Ribeiro, L. y Altman, E. I. (1979). Assessing potential financial problems for firms in Brazil. Journal of International Business Studies, 10(2), 9-24. https:/doi.org/10.1057/palgrave.jibs.8490787

Balcaen, S. y Ooghe, H. (2006). 35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems. British Accounting Review, 38(1), 63-93. https:/doi.org/10.1016/j.bar.2005.09.001

Banco Central del Ecuador (2019). Información Estadística Mensual 2006 [abril]. https://contenido.bce.fin.ec/home1/estadisticas/bolmensual/IEMensual.jsp

Banco Mundial (2017). Políticas procíclicas vs. Políticas contracíclicas. https://www.bancomundial.org/es/news/infographic/2017/10/12/politicas-prociclicas-politicas-contraciciclas.

Bartoloni, E. y Baussola, M. (2014). Financial performance in manufacturing firms: A comparison between parametric and non-parametric approaches. Business Economics, 49(1), 32-45. https://ideas.repec.org/a/pal/buseco/v49y2014i1p32-45.html

Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4(71). https:/doi.org/10.2307/2490171

Beaver, W., McNichols, M. y Rhie, J. W. (2005). Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies, 10(1), 93-122. https:/doi.org/10.1007/s11142-004-6341-9

Bhattacharya, H. (2007). Total management by ratios (2.ª ed.). Sage Publications India.

Blum, M. (1974). Failing company discriminant analysis. Journal of Accounting Research, 12(1), 1. https:/doi.org/10.2307/2490525

Brealey, R. A., Myers, S. C. y Allen, F. (2011). Principles of corporate finance (10.ª ed.). Nueva York: McGraw Hill.

Campbel, J. Y., Hilscher, J. y Szilagyi, J. (2008). In search of distress risk. Journal of Finance, 63(6), 2899-2939.

Carmichael, D. R. (1972). The Auditor’s reporting obligation: The meaning and implementation of the fourth standard of reporting. Auditing research monograph (8.ª ed.). Guides, Handbooks and Manuals.

Chan, K. C. y Chen, N. F. (1991). Structural and return characteristics of small and large firms. The Journal of Finance, 46(4), 1467-1484.

Chen, Y., Zhang, L. y Zhang, L. (2013). Financial Distress Prediction for Chinese Listed Manufacturing Companies. Procedia Computer Science, 17, 678-686. https:/doi.org/10.1016/j.procs.2013.05.088

Chin, B. y Yap, F. (2012). Evaluating company failure in malaysia using financial ratios and logistic regression. Asian Journal of Finance & Accounting, 4(1), 330-344. https:/doi.org/10.5296/ajfa.v4i1.1752

Comisión Económica para América Latina y el Caribe (CEPAL) (2017). CEPALSTAT. Perfil económico ALC. https://estadisticas.cepal.org/cepalstat/Perfil_Regional_Economico.html?idioma=spanish

Comisión Económica para América Latina y el Caribe (CEPAL) (2008). Estudio económico de América Latina y el Caribe. https://www.cepal.org/es/publicaciones/1068-estudio-economico-america-latina-caribe-2008-2009-politicas-la-generacion-empleo

Danenas, P. y Garsva, G. (2012). Credit Risk modeling of USA manufacturing companies using linear SVM and Sliding Window Testing Approach. Business Information Systems, 15, 249-259. https:/doi.org/10.1007/978-3-642-20511-8

Davig, T. y Hakkio, C. (2010). What is the effect of financial stress on economic activity. Economic Review. Federal Reserve Bank of Kansas City, 95(Q II), 35-62. http://ideas.repec.org/a/fip/fedker/y2010iqiip35-62nv.95no.2.html

Deakin, E. B. (1972). A Discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(1), 167. https:/doi.org/10.2307/2490225

Dimitras, A. I., Zanakis, S. H. y Zopounidis, C. (1996). A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90(3), 487-513. https:/doi.org/10.1016/0377-2217(95)00070-4

Dirección de Estadísticas Económicas (DIEE) (2018). Directorio de Empresas y Establecimientos, 2017. Boletín técnico, 1-2018. Quito: DIEE.

Dirección Nacional de Investigación y Estudios (DNIYE) (Coord.) (2018). Panorama de la industria manufacturera en Ecuador, 2013-2017. https://investigacionyestudios.supercias. gob.ec/wp-content/uploads/2018/09/Panorama-de-la-Industria-Manufactureraen-

el-Ecuador-2013-2017.pdf

Dudley, E. y Ellie, Q. (2018). Financial distress, refinancing, and debt structure. Journal of Banking and Finance, 94, 185-207. https:/doi.org/10.1016/j.jbankfin.2018.07.004

Edmister, R. O. (1972). an empirical test of financial ratio analysis for small business failure prediction. The Journal of Financial and Quantitative Analysis, 7(2), 1477. https:/doi.org/10.2307/2329929

Ehrhardt, M. C. y Brigham, E. F. (2007). Finanzas corporativas (2.ª ed.). Ciudad de México: CENGAGE Learning.

Eom, Y. H., Kim, D. W. y Altman, E. I. (1998). Failure prediction: Evidence from Korea. Journal of International Financial Management and Accounting, 6.

Espinel, K. (2016). Riesgo de quiebra empresarial en el Ecuador durante 2009 a 2012. Universidad de Las Américas.

Etheridge, H. L. y Sriram, R. S. (1997). A comparison of the relative costs of financial distress models: artificial neural networks, logit and multivariate discriminant analysis. International Journal of Intelligent Systems in Accounting, Finance & Management, 6(3), 235-

https:/doi.org/10.1002/(sici)1099-1174(199709)6:3<235::aid-isaf135>3.0.co;2-n

Fama, E. F. y French, K. R. (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2), 427-465. https:/doi.org/10.1017/CBO9781107415324.004

Fama, E. F. y French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56. https:/doi.org/10.1016/0304 -405X(93)90023-5

Fernández-Gámez, M. Á., Soria, J. A. C., Santos, J. A. y Alaminos, D. (2020). European country heterogeneity in financial distress prediction: An empirical analysis with macroeconomic and regulatory factors. Economic Modelling, 88(September), 398-407. https:/doi.org/10.1016/j.econmod.2019.09.050

Foulke, R. A. (1968). Practical financial statement analysis. Nueva York: McGraw-Hill Company.

Frydman, H., Altman, E. I. y Kao, D. L. (1985). Introducing recursive partitioning for financial classification: The case of financial distress. The Journal of Finance, 40(1), 269-291.

García, M., Ollague, J. y Capa, L. (2018). La realidad crediticia para las pequeñas y medianas empresas ecuatorianas. http://scielo.sld.cu/scielo.php?script=sci_arttext&pid =S2218-36202018000200040#B4

Gitman, L. (1981). Fundamentos de administración financiera. Ciudad de México: Harper & Row Latinoamerican.

Gordon, M. J. (1964). Postulates, principles and research in accounting. The Accounting Review, 39(2), 251-263.

Gregova, E., Valaskova, K., Adamko, P., Tumpach, M. y Jaros, J. (2020). Predicting financial distress of slovak enterprises: Comparison of selected traditional and learning algorithms methods. Sustainability, 12(10), 1-17. https:/doi.org/10.3390/SU12103954

Guresen, E. y Kayakutlu, G. (2011). Definition of artificial neural networks with comparison to other networks. Procedia Computer Science, 3, 426-433. https:/doi.org/10.1016/j.procs.2010.12.071

Hair, J. F., Black, W. C., Babin, B. J. y Anderson, R. E. (2014). Multivariate data analysis (7.a ed.). Nueva York: Pearson.

Herbert, H. (1985). Economic Aspects Bankruptcy Law. Journal of Institutional and Theoretical Economics, 141, 80-98.

Hernández Ramírez, M. (2014). A financing guideline for the detection of bankruptcy with the aid of a multiple discriminating analysis. Intersedes: Revista de las Sedes Regionales, 15(32), 4-19. http://www.redalyc.org/pdf/666/66633023001.pdf

Horváthová, J. y Mokrišová, M. (2020). Comparison of the results of a data envelopment analysis model and logit model in assessing business financial health. Information, 11(3), 1-20. https:/doi.org/10.3390/info11030160

Hua, Z., Wang, Y., Xu, X., Zhang, B. y Liang, L. (2007). Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications, 33(2), 434-440. https:/doi.org/10.1016/j.eswa.2006.05.006

Hyz, A. (2019). SME finance and the economic crisis: The case of Greece. Routledge

Ibarra, A. (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. Barcelona: Universitat Autonoma de Barcelona. http://www.

eumed.net/tesis/2010/aim/index.htm

Isik, O., Jones, M. y Sidorova, A. (2012). Using neural nets to combine information sets in corporate bankruptcy prediction. Intelligent Systems in Accounting, Finance and Management, 19, 90-101. https:/doi.org/10.1002/isaf

Ko, Y. C., Fujita, H. y Li, T. (2017). An evidential analysis of Altman Z-score for financial predictions: Case study on solar energy companies. Applied Soft Computing Journal, 52, 748-759. https:/doi.org/10.1016/j.asoc.2016.09.050

Koller, T., Goedhart, M. y Wesseles, D. (2020). Valuation, measuring and managing the value of companies. (McKinsey y Company, ed.) (7.ª ed.). Hoboken: John Wiley & Sons.

Kolodner, J. (1993). Case-based reasoning. San Mateo: Morgan Kaufmann Publisher.

Laitinen, E. K. y Laitinen, T. (1998). Cash management behavior and failure prediction. Journal of Business Finance and Accounting, 25(7-8), 893-919. https:/doi.org/10.1111/1468-5957.00218

Lev, B. (1969). Industry averages as targets for financial ratios. Journal of Accounting Research, 7(2), 290-299.

Liang, D., Lu, C., Tsai, C. y Shih, G. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252(2), 561-572. https:/doi.org/10.1016/j.ejor.2016.01.012

Lin, T. H. (2009). A cross model study of corporate financial distress prediction 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

Männasoo, K. (2008). Patterns of firm survival in Estonia. Eastern European Economics, 46(4), 27-42. https:/doi.org/10.2753/EEE0012-8775460402

Margaine, M., Schlosser, M., Vernimmen, P. y Altman, E. I. (1974). Financial and statistical analysis for commercial loan evaluation: A French experience. Journal of Financial and Quantitative Analysis, 9(02), 195-211.

Maricica, M. y Georgeta, V. (2012). Business failure risk analysis using financial ratios. Procedia. Social and Behavioral Sciences, 62, 728-732. https:/doi.org/10.1016/j.sbspro.2012.09.123

Molina, C. (2017). ¿Por qué se endeudan las empresas latinoamericanas? Debates IESA, (2014), 46-49.

Mora, A. (1994). Limitaciones metodológicas de los trabajos empíricos sobre la predicción del fracaso empresarial. Revista Española de Financiación y Contabilidad, 24(80), 709-732.

Mselmi, N., Lahiani, A. y Hamza, T. (2017). Financial distress prediction: The case of French small and medium-sized firms. International Review of Financial Analysis, 50, 67-80. https:/doi.org/10.1016/j.irfa.2017.02.004

Müller, A. C. y Guido, S. (2016). Introduction to Machine Learning with Python. A guide for data scientists. Sebastopol: O’Reilly Media.

Nandi, A., Sengupta, P. P. y Dutta, A. (2019). Diagnosing the financial distress in oil drilling and exploration sector of india through discriminant analysis. Vision, 1-10. https:/doi.org/10.1177/0972262919862920

Nyitrai, T. y Virág, M. (2019). The effects of handling outliers on the performance of bankruptcy prediction models. Socio-Economic Planning Sciences, 67, 34-42. https:/doi.org/10.1016/j.seps.2018.08.004

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., Reyes, M. y Cevallos, E. (2018). Análisis de insolvencia del sector alimenticio de la ciudad de Cuenca. Observatorio empresarial. Universidad del Azuay 73-92. https:/doi.org/10.1017/CBO9781107415324.004

Oude Avenhuis, J. (2013). Testing the generalizability of the bankruptcy prediction of Altman, Ohlson and Zmijewski for Dutch listed and large non-listed firms. Universidad of Twente. The School of Management and Governance.

Palepu, K. G., Healy, P. M. y Bernard, V. L. (2003). Business Analysis & Valuation: Using Financial Statements (3.ª ed.). Nueva York: South-Western College Pub.

Park, C. S. y Han, I. (2002). A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 23(3), 255-264. https:/doi.org/10.1016/S0957-4174(02)00045-3

Pham Vo Ninh, B., Do Thanh, T. y Vo Hong, D. (2018). Financial distress and bankruptcy prediction: An appropriate model for listed firms in Vietnam. Economic Systems, 42(4), 616-624. https:/doi.org/10.1016/j.ecosys.2018.05.002

Pillajo, V., Salas, S. y Palacios, J. (2018). Modelo Z de Altman: predictor de quiebras.

Pindado, J., Rodrigues, L. y De la Torre, C. (2016). How does financial distress affect small firm’s financial structure? Small Business Economics, 26(4), 377-391.

Pongsatat, S., Ramage, J. y Lawrence, H. (2004). Bankruptcy Prediction for Large and Small Firms in Asia : A Comparison of Ohlson and Altman. Journal of Accounting and Corporate Governance, 1, 1-13.

Pozzoli, M. y Paolone, F. (2017). Corporate Financial Distress: A Study of the Italian Manufacturing Industry. Cham, Switzerland: Springer. http://search.ebscohost. com/login.aspx?direct=true&db=edsebk&AN=1594211&amp%0Alang=pt-pt&site=eds-live&authtype=sso

Ramayah, T., Ahmad, N. H., Halim, H. A., Rohaida, S., Zainal, M. y Lo, M. (2010). Discriminant analysis: An illustrated example. African Journal of Business Management, 4(9), 1654-1667.

Rifqi, M. y Kanazaki, Y. (2016). Predicting financial distress in indonesian manufacturing industry. Sendai: Tohoku Management & Accounting Research Group.

Ross, S. A., Westerfield, R. y Jordan, B. D. (2017). Essentials of corporate finance (9.ª ed.). Nueva York: McGraw-Hill Education.

Ross, S., Westerfield, R. y Jordan, B. (2010). Fundamentos de finanzas corporativas (9.ª ed.). México: McGraw Hill.

Sayari, N. y Mugan, C. S. (2017). Industry specific financial distress modeling. BRQ Business Research Quarterly, 20(1), 45-62. https:/doi.org/10.1016/j.brq.2016.03.003

Serrano, C., Gutiérrez, B. y Bernate, M. (2019). The use of accounting anomalies indicators to predict business failure. European Management Journal, 37(3), 353-375. https:/doi.org/10.1016/j.emj.2018.10.006

Shilpa, N. C. y Amulya, M. (2017). Corporate financial distress: Analysis of Indian automobile industry. SDMIMD Journal of Management, 8(1), 85. https:/doi.org/10.18311/sdmimd/2017/15726

Subramanyam, K. R. y Wild, J. J. (2009). Financial Statement Analysis (10.ª ed.). Nueva York: McGraw-Hill/Irwin.

Sun, J., Li, H., Huang, Q. H. y He, K. Y. (2014). Predicting financial distress and corporate Failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56. https:/doi.org/10.1016/j.

knosys.2013.12.006

Superintendencia de Bancos. (2020). Estudios y Análisis. Portal estadístico. https://estadisticas.superbancos.gob.ec/portalestadistico/portalestudios/?page_id=1054#1508173151736-7ddba919-ba8c

Swanson, E. V. y Tybout, J. R. (1988). Industrial bankruptcy determinants in Argentina. International Business Failure Prediction Models, 7.

Takahashi, K., Kurokawa, Y. y Watase, K. (1984). Corporate bankruptcy prediction in Japan. Journal of Banking and Finance, 8(2), 229-247. https:/doi.org/10.1016/0378-4266(84)90005-0

Trueck, S. y Rachev, S. (2009). Rating and scoring techniques. Rating Based Modeling of Credit Risk. https:/doi.org/10.1016/b978-0-12-373683-3.00003-8

Úsuga Manco, O. C. y Patiño Rodríguez, C. E. (2008). Análisis discriminante no métrico y regresión logística en el problema de clasificación. TecnoLógicas, (21), 13-29.

https:/doi.org/10.22430/22565337.249

Valencia Cárdenas, M., Trochez González, J., Vanegas López, J. G. y Restrepo Morales, J. A. (2016). Modelo para el análisis de la quiebra financiera en pymes agroindustriales antioqueñas. Apuntes del Cenes, 35(62), 147-168. https:/doi.org/10.19053/22565779.4310

Veganzones, D. y Severin, E. (2020). Corporate failure prediction models in the twenty-first century: a review. European Business Review. https:/doi.org/10.1108/EBR-12-2018-0209

Wang, H., Jiang, Y. y Wang, H. (2009). Stock return prediction based on Baggingdecision tree. 2009 IEEE International Conference on Grey Systems and Intelligent Services, (GSIS 2009), 1575-1580. https:/doi.org/10.1109/GSIS.2009.5408165

Wang, Y. y Campbell, M. (2010). Financial ratios and the prediction of bankruptcy: The Ohlson Model applied to Chinese Publicly Traded Companies. Proceedings of ASBBS, 17(January).

Yazdipour, R. (2011). Advances in entrepreneurial finance. Nueva York: Springer. https:/doi.org/10.1007/978-1-4419-7527-0

Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22(1984), 59-82.

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