Selection of Variables in Small Business Failure Analysis: Mean Selection vs. Median Selection // Selección de variables en el análisis de fracaso de empresas pequeñas: selección de medias frente a selección de medianas

Autores/as

  • María T. Tascón Departamento de Dirección y Economía de la Empresa Universidad de León
  • Francisco J. Castaño Departamento de Dirección y Economía de la Empresa Universidad de León

Palabras clave:

small business failure, variable selection, discriminant analysis, probit, logit, financial ratios, qualitative information, fracaso en pequeñas empresas, selección de variables, análisis discriminante, ratios financieros, información cualitativa

Resumen

This paper focuses on one of the most determinant processes in business failure assessment: Variable selection. After a preselection of variables based on previous empirical literature, we perform a statistical variable selection on a sample of small firms using both mean and median differences. As the resulting variables differ in each test, we have performed a varied group of business failure assessment methods (linear discriminant analysis, quadratic discriminant analysis, logistic discriminant analysis, k-th nearest-neighbor discriminant analysis, logit, and probit) to identify the implications of using one test or the other. Our results show that the nature of the sample determines not only the statistical variable selection test, but the most appropriate methods to assess business failure, which constitutes our main contribution. Additionally, we contribute new evidence on the addition of qualitative information (payment incidents), with previous evidence for SMEs being scarce.

------------------------------------

Este trabajo se ocupa de uno de los procesos más determinantes en la evaluación del fracaso empresarial: la selección de variables. Tras una preselección de variables basada en los resultados empíricos de la literatura previa, llevamos a cabo una selección estadística de variables sobre una muestra de empresas pequeñas, utilizando tanto diferencias en medias como diferencias en medianas. Como las variables resultantes difieren con el test, hemos utilizado un variado grupo de métodos de evaluación de fracaso empresarial (LDA, QDA, LogDA, KNNDA, logit y probit) con el fin de identificar las implicaciones de usar uno u otro test. Nuestros resultados muestran que la naturaleza de la muestra determina no solo el test de selección estadística de variables, sino también los métodos más apropiados para evaluar el fracaso empresarial, lo que constituye nuestra principal contribución. Además, el trabajo proporciona nueva evidencia sobre la adición de información cualitativa (incidencias de pago), siendo escasa la evidencia previa para pymes.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Albert, A. and Lesaffre, E. (1986): “Multiple group logistic discrimination”, Computers and Mathematics with Applications, 12A(2), 209–224.

Altman, E.I. (1968): “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy”, Journal of Finance, 23(4), 589–609.

Altman, E.I. (1993): Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting and Avoiding Distress and Profiting from Bankruptcy. New York: John Wiley & Sons.

Altman, E. I.; Sabato, G. and Wilson, N. (2008): “The value of qualitative information in SME risk management”, Journal of Credit Risk, 6(2), 95–127.

Aziz, M.A. and Dar, H.A., (2006): “Predicting corporate bankruptcy: Where we stand”, Corporate Governance, 6, 18–33.

Balcaen, S. and 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.

Baum, C.F. (2006): An introduction to modern Econometrics using Stata. Texas: Stata Press.

Bellovary, J.L.; Giacomino, D.E. and Akers, M.D. (2007): “A review of bankruptcy prediction studies: 1930 to present, Journal of Financial Education, 33, 1–43.

Berkson, J. (1944): “Application of the logistic function to bio-assay”, Journal of the American Statistical Association, 39, 357–365.

Bliss, C. I. (1934): “The method of probits”, Science, 79, 38–39.

Collins, R.A. and Green, R.D. (1982): “Statistical methods of bankruptcy forecasting”, Journal of Economics and Business; 32(4), 349–352.

Dambolena, I.G. and Khoury, S.J. (1980): “Ratio stability and corporate failure”, Journal of Finance, 35(4), 1017–1026.

Daubie, M. and Meskens, N. (2002): “Business failure prediction: A review and analysis of the Literature”. En C. Zopounidis (ed.): New Trends in Banking Management. New York: Physica-Verlag, pp. 71–86.

Eisenbeis, R. (1977): “Pitfalls in the application of discriminant analysis in business, finance and economics”, Journal of Finance, 32, 875–900.

Fisher, R.A. (1936): “The use of multiple measurements in taxonomic problems”, Annals of Eugenics, 7, 179–188.

García-Ayuso, M. (1995): “La necesidad de llevar a cabo un replanteamiento de la investigación en materia de análisis de la información financiera”, Análisis financiero, 66, 36–61.

Grice, S. and Ingram, R. (2001): “Tests of the generalizability of Altman’s bankruptcy prediction model”, Journal of Business Research, 54, 53–61.

Hair, J.F.; Anderson, R.E.; Tatham, R.L. and Black, W.C. (1999): Análisis multivariante, Madrid: Prentice-Hall.

Jin, J.Y.; Kanagaretnam, K. and Lobo, G.J. (2011): “Ability of accounting and audit quality variables to predict bank failure during the financial crisis”, Journal of Banking and Finance, 35(11), 2811–2819.

Jones F.L. (1987): “Current techniques in bankruptcy prediction”, Journal Accounting Literature, 6, 131–164.

Keasey, K. and Watson, R. (1987): “Non-financial symptoms and the prediction of small company failure: a test of Argenti’s hypothesis”, Journal of Business, Finance and Accounting, 14(3), 335–354.

Keasey, K. and Watson, R. (1991): “Financial distress prediction models: A review of their usefulness”, British Journal of Management, 2(2), 89–102.

Laitinen, T. and Kankaanpää, M. (1999): “Comparative analysis of failure prediction methods: The Finnish case”. The European Accounting Review, 8(1), 67–92.

Martin, D. (1977): “Early warning of bank failure: A logit regression approach”, Journal of Banking & Finance, 1(3), 249–276.

McLachlan, G. J. (2004): Discriminant Analysis and Statistical Pattern Recognition. New York: Wiley.

Ohlson, J.A. (1980): “Financial ratios and the probabilistic prediction of bankruptcy”, Journal of Accounting Research, 18(1), 109–131.

Peel, M.J. and Peel, D.A. (1987): “Some further empirical evidence on predicting private company failure”, Accounting and Business Research, 18(69), 57–66.

Peel, M.J.; Peel, D.A. and Pope, P.F. (1986): “Predicting corporate failure- Some results for the UK corporate sector”, Omega. International Journal of Management Science, 14(1) 5–12.

Platt, H.D. and Platt, M.B. (1990): “Development of a class of stable predictive variables: The case of bankruptcy prediction”, Journal of Business, Finance & Accounting, 17(1), 31–51.

Premachandra, I.M.; Bhabra, G.S. and Sueyoshi, T. (2009): “DEA as a toll for bankrupcy assessment: A comparative study with logistic regression technique”, European Journal of Operational Research, 193, 412–424.

Ravi Kumar, P. and Ravi, V. (2007): “Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review”, European Journal of Operational Research, 180(1), 1–28.

Scott, J. (1981): “The probability of bankruptcy”, Journal of Banking and Finance, 5, 317–344.

Smith, C.A.B. (1947): “Some examples of discrimination”, Annals of Eugenics, 13, 272–282.

Somoza López, A. (2002): “Modelos de predicción de la insolvencia: la incorporación de otro tipo de variables”. En F. Doldán M. Rodríguez, M. (eds.): La gestión del riesgo de crédito. Madrid, AECA, pp. 139–173.

Sueyoshi, T. and Goto, M. (2009a): “Can R&D expenditure avoid corporate bankruptcy? Comparison between Japanese machinery and electric equipment industries using DEA-discriminant analysis”, European Journal of Operational Research, 199, 576–594.

Sueyoshi, T. and Goto, M. (2009b): “Methodological comparison between DEA (data envelopment analysis) and DEA-DA (discriminant analysis) from the perspective of bankruptcy assessment”, European Journal of Operational Research, 199, 561–575.

Sueyoshi, T. and Goto, M. (2009c): “DEA-DA for bankruptcy-based performance assessment: misclassification analysis of Japanese construction industry”, European Journal of Operational Research, 199, 576–594.

Sun, J.; Li, H.; Huang, Q.-H. and 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.

Taffler, R.J. (1982): “Forecasting company failure in the UK using discriminant analysis and finance ratio data”, Journal of the Royal Statistical Society, Series A, 145(3), 342–358.

Tascón, M.T. and Castaño, F.J. (2012): “Variables and models for the identification and prediction of business failure: Revision of recent empirical research advances”, Spanish Accounting Review, 15(1), 7–58.

Zavgren, C.V. (1983): “The prediction of corporate failure: The state of the art”, Journal of Accounting Literature, 2(1), 1–38.

Zavgren, C.V. (1985): “Assessing the vulnerability of failure of American industrial firms: A logistic analysis”, Journal of Banking and Finance, 12(1), 19-45.

Publicado

2017-12-20

Cómo citar

Tascón, M. T., & Castaño, F. J. (2017). Selection of Variables in Small Business Failure Analysis: Mean Selection vs. Median Selection // Selección de variables en el análisis de fracaso de empresas pequeñas: selección de medias frente a selección de medianas. Revista De Métodos Cuantitativos Para La Economía Y La Empresa, 24, Páginas 54 a 88. Recuperado a partir de https://www.upo.es/revistas/index.php/RevMetCuant/article/view/2880

Número

Sección

Artículos