Testing Altman’s Z’’-Score to assess the level of accuracy of the model in Mexican companies
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Keywords

bankruptcy prediction
Z’’-Score
ratios
financial distress
model accuracy
emerging countries
Mexican firms
Mexican stock exchange
economy
finance
stable economic
manufacturing company
financial ratios
evaluation of creditworthiness
credit lenders
credit-rating agencies
investors predicción de quiebra
Puntuación Z’’
razones
problemas financieros
precisión del modelo
países emergentes
empresas mexicanas
bolsa mexicana de valores
economía
finanzas
empresas manufactureras
estabilidad económica
bancarrota
ratios financieros
evaluación de la solvencia
oferentes de crédito
agencias de calificación crediticia
inversores

How to Cite

Pantoja Aguilar, M. P., Pizano Ramírez, G. de M., Lerma Torres, B., & Zavala Vargas, M. Ángel . (2021). Testing Altman’s Z’’-Score to assess the level of accuracy of the model in Mexican companies. Nova Scientia, 13(27). https://doi.org/10.21640/ns.v13i27.2881

Abstract

Introduction: in 1968, Altman developed a multivariable predictive Z-score model to assess the probability of a public manufacturing company going to bankruptcy based on financial ratios. Later, Altman (1983) re-stated a more improved Z’’-Score model designed to apply in public or private, manufacturing, or non-manufacturing firms, but also in emerging countries. Prediction of the updated model proved to be highly efficient. This research was conducted to prove the level of accuracy of the Z’’-Score model applied to firms listed in the Mexican Stock Exchange (MSE) since there is little relevant research on this subject.                    

Method: this research was conducted under a quantitative approach as a census and its scope was situational with a non-experimental and longitudinal research design. The period covered by this research was 2012-2019 since the data was available for those years under a somehow stable economic situation without significant economic ups and downs. This research considered the integration of a large financial database and the design of a typology to classify and analyze 155 firms based on a standard deviation and average results of 837 Z’’-scores. A second analysis was conducted to prove if the predicted situation (area) by the Z’’-Score corresponded to the real situation in the marketplace for every company.

Results: the results showed that the accuracy level of the Altman model decreased when applied to Mexican firms. The error of the model applied to Mexican companies related to those classified in the bankruptcy prediction area was 75 % of misclassification cases. The total error of the model included all areas, or cases, was 18 % of misclassification cases. This model is supposed to be effective within a time frame of two years before a possible bankruptcy. Even considering a longer time frame, the companies located in the bankruptcy prediction area continued having misclassifications representing 57 % of error. The error for the model considering all cases and all areas, was 14 % of misclassification cases. This represented a high level of inefficiency of the model applied to an emerging country companies, such as Mexico.

Discussion or conclusion: the model is certainly effective while predicting companies in the areas of non-bankrupt sector and grey, but it was inefficient when predicting the possibility of bankruptcy. It was also demonstrated that the time frame of two years is no longer effective when applying the model to Mexican companies. As a result, more research cases are needed to update the model to perform efficiently in emerging countries including country-specific conditions and considering a different time frame to predict bankruptcy.

https://doi.org/10.21640/ns.v13i27.2881
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References

AlAli, M. (2018). Predicting financial distress for mobile telecommunication companies listed in Kuwait Stock Exchange using Altman’s model. Journal of Economics, Finance and Accounting, 5(3), 242-248. http://doi.org/10.17261/Pressacademia.2018.933

Altman, E.I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance. September, 189-209. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x

Altman, E.I. (1969). Corporate bankruptcy potential, stockholder returns and share evaluation. The Journal of Finance, December, 24(5), 887-900. https://doi.org/10.1111/j.1540-6261.1969.tb01700.x

Altman, E.I., Haldeman, R.G. & Narayanan, P. (1977). ZETA tm ANALYSIS, A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 29-54. https://doi.org/10.1016/0378-4266(77)90017-6

Altman, E.I. (1983). Corporate financial distress: A complete guide to predicting, avoiding, and dealing with bankruptcy. John Wiley and Sons. ISBN 0-471-08707-6

Altman, E.I. (2005). An emerging market credit scoring system for corporate bonds. Emerging Markets Review, (6), 311-323. https://doi.org/10.1016/j.ememar.2005.09.007

Altman, E.I., & Hotchkiss. E. (2006). Corporate financial distress and bankruptcy. Predict and avoid bankruptcy and Invest in distressed debt, (3rd Ed.) John Wiley & Sons Inc. https://login.e-revistas.ugto.mx/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=147866&lang=es&site=eds-live&ebv=EB&ppid=pp_230

Altman, E.I., Iwanicz, M., Laitinen, E.K. & 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), 131-171. https://doi.org/10.1111/jifm.12053

Altman, E. I. (2018). Applications of distress prediction models: What have we learned after 50 years from the Z-Score models? International Journal of Financial Studies, 6(3), 70. https://doi.org/10.3390/ijfs6030070

Anuj, C.S., Narayanan, A., Nandan, S. & Thangjam, R. (2018). The relevance of Altman Z-score analysis. International Journal of Research and Analytical Reviews (IJRAR), 5(4), 233-240. https://www.ijrar.org/papers/IJRAR190I024.pdf

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

Bauer, J., & Agarwal, V. (2014). Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive Test. Journal of Banking and Finance, 40, 432-442. https://doi.org/10.1016/j.jbankfin.2013.12.013

Beaver, W.H. (1966). Financial ratios as predictors of failure. Empirical Research in Accounting: Selected Studies, 4, 71-111. https://doi.org/10.2307/2490171

Beaver, W.H. (1968). Alternative accounting measures as predictors of failure. The Accounting Review, January, 113-122. https://www.jstor.org/stable/i211316

Begovic, S.V., Bonic, L., & Jovin, S. (2020). A comparison of the bankruptcy prediction models on a sample of Serbian companies. Teme - Časopis za Društvene Nauke. Central and Eastern European Online Library. XLIV (2), 503-518. https://www.ceeol.com/search/article-detail?id=885245

Bermeo, D. C., & Armijos, J. 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), 48-63. https://doi.org/10.25097/rep.n33.2021.03

Calkins, F. (1948). Corporate reorganization under chapter X: A Postmortem. Journal of Finance. June, 3, 19-28. https://doi.org/10.1111/j.1540-6261.1948.tb01510.x

Diogenes, A., Martins, M., Sampaio, G. A., & Trindade, J. (2020). Corporate Reputation and Bankruptcy Risk. BAR – Brazilian Administration Review, 17(2), 1-22. http://dx.doi.org/10.1590/1807-7692bar2020180159

Fito, M., Plana-Erta, D., & Llobet, J. (2018). Usefulness of Z scoring models in the early detection of financial problems in bankrupt Spanish companies. Intangible Capital, 14(1), 162-170. http://dx.doi.org/10.3926/ic.1108

Fitz, P.J. (1932) A Comparison of ratios of successful industrial enterprises with those of failed firm. Certified Public Accountant, 6, 727-731.

https://openlibrary.org/books/OL6298050M/A_comparison_of_the_ratios_of_successful_industrial_enterprises_with_those_of_failed_companies

Gao, P., Parsons, C. A., & Shen, J. (2018). Global relation between financial distress and equity returns. Review of Financial Studies, 31(1), 239-277. https://doi.org/10.1093/rfs/hhx060

Grice, J.S. & R.W. Ingram (2001). Tests of the generalizability of Altman’s bankruptcy prediction model. Journal of Business Research, 54, 53–61. https://doi.org/10.1016/S0148-2963(00)00126-0

Habib, A., Costa, M. D., Huang, H. J., Bhuiyan, M. B. U. & Sun, L. (2020). Determinants and consequences of financial distress: Review of the empirical literature. Accounting and Finance, 60(S1), 1023-1075. https://doi.org/10.1111/acfi.12400.

Horrigan, J.O. (1968). A short history of financial ratio analysis. The Accounting Review, 43(2), 284-294. https://www.jstor.org/stable/243765

Instituto Nacional de Estadística, Geografía e Informática. (2019, March 29). Censos Económicos 2019. https://www.inegi.org.mx/programas/ce/2019/

Jackson, R.H., & Wood, A. (2013). The performance of insolvency prediction and credit risk models in the UK: A comparative study. British Accounting Review, 45, 183-202. https://doi.org/10.1016/j.bar.2013.06.009

Kumar, P., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review. European Journal of Operational Research, 180, 1-28. https://doi.org/10.1016/j.ejor.2006.08.043

Lizarzaburu, E. R., Burneo, K., & Berggrun, L. (2021). Risk of insolvency and return of shares: Empirical analysis of Altman’s Z-score in the Peruvian mining sector between 2008 and 2018. Universidad & Empresa, 23(40), 1-33. https://doi.org/10.12804/revistas.urosario.edu.co/empresa/a.8558

Manaseer, S. R., & Oshaibat, S. D. (2018). Validity of Altman Z-Score Model to Predict Financial Failure: Evidence from Jordan. International Journal of Economics and Finance, 10(8), 181-189. https://doi.org/10.5539/ijef.v10n8p181

Mejía, M. B., & Flores, J. A. (2020). Application of Altman ́s Z-Score Model to classify levels of financial failure in the commercial sector in the province of Manabí – Ecuador. 593 Digital Publisher CEIT, 5(5-1), 26-39. https://doi.org/10.33386/593dp.2020.5-1.318

Mervin, C.L. (1942). Financing small corporations: In five manufacturing industries, 1926-1936. National Bureau Economic research. ISBN: 0-870-14130-9. https://www.nber.org/books/merw42-1

Moreno, E., & Bravo, F. (2018). Análisis de la probabilidad de quiebra de las empresas cotizadas españolas. Revista de Estudios Empresariales. Segunda Época, 2, 57-72. https://dx.doi.org/10.17561/ree.v2018n2.3

Panigrahi, A. (2019). Validity of Altman´s “Z” score model in predicting financial distress of pharmaceutical companies. NMIMS Journals of Economics and Public Policy, 4(1), 65-73. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3326312

Prasetiyani, E., & Sofyan, M. (2020). Bankruptcy analysis using Altman Z-score model and Springate model in retail trading company listed in Indonesia Stock Exchange. Ilomata International Journal of Tax & Accounting, 1(3), 139-144. https://doi.org/10.5281/zenodo.3979881

Ross, S.A., Westerfield, R.W., Jaffe, J.F., & Jordan, B.D. (2018). Finanzas corporativas 11ED. McGraw-Hill. ISBN 9781456260873.

Vimrová, H. (2015). Financial analysis tools, from traditional indicators through contemporary instruments to complex performance measurement and management systems in the Czech business practice. Procedia Economics and Finance, 25, 166-175. https://doi.org/10.1016/S2212-5671(15)00725-X

Winakor, A. & Smith, R.F. (1935). Changes in Financial structure of unsuccessful industrial companies. Bureau of Business Research, 51. University of Illinois Press. https://searchworks.stanford.edu/view/79468

Xu, M. & C. Zhang (2010). Bankruptcy prediction: The case of Japanese listed companies. Review of accounting studies, 14, 554-558. https://doi.org/10.1007/s11142-008-9080-5

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