A Statistical Analysis to Predict Financial Distress
ABSTRACT
The aim of this study is to apply the statistical inference to identify if a firm is likely to become financially distressed in the short term. To do this, we decided to collect data from the firms’ financial statements. The analyses performed were based on a group of 45 financial ratios observed from a sample of 86 firms operating in Argentina. First, we used the principal component analysis to turn the information in the 45 original ratios into two new global variables named as ?Risk and ?Return. In this way, we can easily represent and compare in a graph the firms’ risk and return variations. By the computation of these new variables it is possible to quickly financially categorize a certain firm based on the risk the company has with regard to the nature of its business and the risk involved in the amount of debt it has taken in comparison to the profits that were generated during the last two fiscal years. Second, we performed a logistic regression analysis to estimate the probability that a firm becomes financially distressed in the short term. The model finally selected managed to successfully identify 85% of the companies from the sample and it explains 65% of the total sample variability. The model is represented by the following variables: 1) Current Debt Ratio, 2) Total Cost of Debt, 3) Operating Profit Margin, and 4) ?ROE. The outcomes from this study are two tools that were developed based on the statistical inference from which we can quickly asses the financial status of a firm based on its risks and return’s variation as well as to estimate the probability that a firm becomes financially distressed in the short term. There are different ways of taking these tools into practice such as: 1) to control and follow up the financial performance of a company, 2) to support the decision of lending money to a company, 3) to support the decision of investing money or the decision of merging with a company, 4) to support market analysis from a financial perspective, and 5) to support actions or decisions related to the financial assessment of a company that declares itself to be financially distressed.
The aim of this study is to apply the statistical inference to identify if a firm is likely to become financially distressed in the short term. To do this, we decided to collect data from the firms’ financial statements. The analyses performed were based on a group of 45 financial ratios observed from a sample of 86 firms operating in Argentina. First, we used the principal component analysis to turn the information in the 45 original ratios into two new global variables named as ?Risk and ?Return. In this way, we can easily represent and compare in a graph the firms’ risk and return variations. By the computation of these new variables it is possible to quickly financially categorize a certain firm based on the risk the company has with regard to the nature of its business and the risk involved in the amount of debt it has taken in comparison to the profits that were generated during the last two fiscal years. Second, we performed a logistic regression analysis to estimate the probability that a firm becomes financially distressed in the short term. The model finally selected managed to successfully identify 85% of the companies from the sample and it explains 65% of the total sample variability. The model is represented by the following variables: 1) Current Debt Ratio, 2) Total Cost of Debt, 3) Operating Profit Margin, and 4) ?ROE. The outcomes from this study are two tools that were developed based on the statistical inference from which we can quickly asses the financial status of a firm based on its risks and return’s variation as well as to estimate the probability that a firm becomes financially distressed in the short term. There are different ways of taking these tools into practice such as: 1) to control and follow up the financial performance of a company, 2) to support the decision of lending money to a company, 3) to support the decision of investing money or the decision of merging with a company, 4) to support market analysis from a financial perspective, and 5) to support actions or decisions related to the financial assessment of a company that declares itself to be financially distressed.
KEYWORDS
Financial Distress, Financial Risk, Principal Component Analysis, Logistic Regression Analysis
Financial Distress, Financial Risk, Principal Component Analysis, Logistic Regression Analysis
Cite this paper
nullN. Monti and R. Garcia, "A Statistical Analysis to Predict Financial Distress," Journal of Service Science and Management, Vol. 3 No. 3, 2010, pp. 309-335. doi: 10.4236/jssm.2010.33038.
nullN. Monti and R. Garcia, "A Statistical Analysis to Predict Financial Distress," Journal of Service Science and Management, Vol. 3 No. 3, 2010, pp. 309-335. doi: 10.4236/jssm.2010.33038.
References
[1] I. Guzmán, “Factores explicativos del reparto de dividendos a cuenta en las empresas espa?olas,” Instituto Valenciano de Investigaciones Económicas, 2004. http:// www.ivie.es/downloads/docs/wpasec/wpasec-2004-09.pdf [2] M. L.Heine, “Predicting Financial Distress of Companies: Revisting the Z-Score and ZETA Models,” Stern School of Business, New York University, 2000. http://www.stern. nyu.edu/~ealtman/Zscores.pdf [3] J. De la Torre Martínez, R. Navarro, A. Azofra and J. C. Pe?a, “Aplicación de los modelos SEPARATE y regresión logística para determinar la relevancia de la información contable en el mercado de capitales: una comparación,” Universidad de Granada, 2002. [4] M. Kahl, “Financial Distress as a Selection Mechanism: Evidence from the United States,” Paper 16-01, Anderson School of Management, University of California, Los Angeles, 2001. http://repositories.cdlib.org/anderson/fin/ 16-01 [5] W. H. Beaver, “Financial Ratios as Predictors of Failure,” Empirical Research in Accounting, selected studies (in supplement to the Journal of Accounting Research, January, 1967), 1966, pp. 71-111. [6] E. I. Altman, “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” The Journal of Finance, Vol. 23, No. 4, 1968, pp. 589-609. [7] S. S. Alexander, “The Effect of Size of Manufacturing Corporation on the Distribution of the Rate of Return,” Review of Economics and Statistics, Vol. 31, No. 3, 1949, pp. 229-235. [8] D. Pe?a, “Análisis de Datos Multivariantes,” McGraw- Hill, 2002. [9] D. Johnson, “Métodos Multivariados Aplicados Al Análisis de Datos,” Thomson, 2000. [10] D. W. Hosmer and S. Lemeshow, “Applied Logistic Regression,” 2nd Edition, John Wiley & Sons Inc., 2000. [11] R. M. García, “Inferencia Estadística y Dise?o de Experimentos,” Eudeba, 2004. [12] L. García, “Las Empresas Más Rentables,” Revista Mercado, 2004, pp. 76-80. [13] L. García, “Las Empresas Más Rentables,” Revista Mercado, 2005, pp. 110-114. http://www.ax5.com/antonio/publicaciones/delatorre2002aplicacion.pdf [14] L. García, “Las 100 Empresas Más Rentables,” Revista Mercado, 2006, pp. 134-138.
[1] I. Guzmán, “Factores explicativos del reparto de dividendos a cuenta en las empresas espa?olas,” Instituto Valenciano de Investigaciones Económicas, 2004. http:// www.ivie.es/downloads/docs/wpasec/wpasec-2004-09.pdf [2] M. L.Heine, “Predicting Financial Distress of Companies: Revisting the Z-Score and ZETA Models,” Stern School of Business, New York University, 2000. http://www.stern. nyu.edu/~ealtman/Zscores.pdf [3] J. De la Torre Martínez, R. Navarro, A. Azofra and J. C. Pe?a, “Aplicación de los modelos SEPARATE y regresión logística para determinar la relevancia de la información contable en el mercado de capitales: una comparación,” Universidad de Granada, 2002. [4] M. Kahl, “Financial Distress as a Selection Mechanism: Evidence from the United States,” Paper 16-01, Anderson School of Management, University of California, Los Angeles, 2001. http://repositories.cdlib.org/anderson/fin/ 16-01 [5] W. H. Beaver, “Financial Ratios as Predictors of Failure,” Empirical Research in Accounting, selected studies (in supplement to the Journal of Accounting Research, January, 1967), 1966, pp. 71-111. [6] E. I. Altman, “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” The Journal of Finance, Vol. 23, No. 4, 1968, pp. 589-609. [7] S. S. Alexander, “The Effect of Size of Manufacturing Corporation on the Distribution of the Rate of Return,” Review of Economics and Statistics, Vol. 31, No. 3, 1949, pp. 229-235. [8] D. Pe?a, “Análisis de Datos Multivariantes,” McGraw- Hill, 2002. [9] D. Johnson, “Métodos Multivariados Aplicados Al Análisis de Datos,” Thomson, 2000. [10] D. W. Hosmer and S. Lemeshow, “Applied Logistic Regression,” 2nd Edition, John Wiley & Sons Inc., 2000. [11] R. M. García, “Inferencia Estadística y Dise?o de Experimentos,” Eudeba, 2004. [12] L. García, “Las Empresas Más Rentables,” Revista Mercado, 2004, pp. 76-80. [13] L. García, “Las Empresas Más Rentables,” Revista Mercado, 2005, pp. 110-114. http://www.ax5.com/antonio/publicaciones/delatorre2002aplicacion.pdf [14] L. García, “Las 100 Empresas Más Rentables,” Revista Mercado, 2006, pp. 134-138.