A new tool for failure analysis in small firmsfrontiers of financial ratios based on percentile differences (PDFR)

  1. María T. Tascón 1
  2. Francisco J. Castaño 1
  3. Paula Castro 1
  1. 1 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Aldizkaria:
Revista española de financiación y contabilidad

ISSN: 0210-2412

Argitalpen urtea: 2018

Alea: 47

Zenbakia: 4

Orrialdeak: 433-463

Mota: Artikulua

DOI: 10.1080/02102412.2018.1468058 DIALNET GOOGLE SCHOLAR lock_openBULERIA editor

Beste argitalpen batzuk: Revista española de financiación y contabilidad

Garapen Iraunkorreko Helburuak

Laburpena

This paper proposes an innovative methodology based on the use of differences between percentiles to compute the scores and distances to failure of a specific firm or group of firms. This approach is based on significant differences between the group of failed firms and the population to which the failed firms belong (meaning the same industry, period and geographical zone selected) and eliminates the effects of correlation between the factors selected to compute the scores. The use of accounting ratios, which can be computed using data available in the mandatory financial statements, and the homogenisation of these variables using percentiles make percentile difference frontier of ratios a tool specially oriented to small and medium-sized enterprises (SMEs). Our results for the selection of the most discriminant variables are consistent with those of previous studies, and the hit rates of failed and non-failed firms outperform those of the commonly used traditional methodologies. In addition, the proposed methodology enables us to compute distances to failure of both individual firms and groups of firms. Finally, this methodology identifies which of the financial drivers used are strengths or weaknesses for the specific firm or group of firms under study for purposes of a potential reorganisation.

Finantzaketari buruzko informazioa

Finantzatzaile

Erreferentzia bibliografikoak

  • Alfaro, E., Gámez, M., García, N., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45, 110–122.
  • Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 568–609.
  • Altman, E. I., & Sabato, G. (2005). Effects of the new Basel capital accord on bank capital requirements for SMEs. Journal of Financial Services Research, 28(1–3), 15–42.
  • Altman, E. I., & Sabato, G. (2007). Modeling credit risk for SMEs: Evidence from the U.S. market. Abacus, 43(3), 332–357.
  • Altman, E. I., Sabato, G., & Wilson, N. (2008). The value of qualitative information in SME risk management. Working Paper. Leonard N. Stern School of Business, New York University.
  • Aziz, M. A., & Dar, H. A. (2006). Predicting corporate bankruptcy: Where we stand? Corporate Governance, 6(1), 18–33.
  • Baixauli, J. S., & Módica-Milo, A. (2010). The bias of unhealthy SMEs in bankruptcy prediction models. Journal of Small Business and Enterprise Development, 17(1), 60–77.
  • Banks, W. J., & Prakash, L. A. (1994). On the performance of linear programming heuristics applied on a quadratic transformation in the classification problem. European Journal of Operational Research, 72(1), 23–28.
  • Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. January.
  • Behr, P., & Güttler, A. (2007). Credit risk assessment and relationship lending: An empirical analysis of German small and medium-sized enterprises. Journal of Small Business Management, 45(2), 194–213. April.
  • Bell, T. B., Ribar, G. S., & Verchio, J. (1990). Neural nets versus logistic regression: A comparison of each model’s ability to predict commercial bank failures, en Srivastava, R.P. (ed.) Auditing Symposium on Auditing Problems, 29–53.
  • Bellovary, J. L., Giacomino, D. E., & Akers, M. D. (2007). A review of bankruptcy prediction studies: 1930 to present. Journal of Financial Education, 33(Winter), 1–43.
  • Bharath, S. T., & Shumway, T. (2008). Forecasting default with the Merton distance to default model. The Review of Financial Studies, 21(3), 1339–1369.
  • Bryant, S. M. (1997). A case-based reasoning approach to bankruptcy prediction modeling. Intelligent Systems in Accounting, Finance and Management, 6, 195–214.
  • Castaño, F. J., & Tascón, M. T. (2012, May 9–11). Selection of variables in business failure analysis: Mean selection vs. median selection. European Accounting Association 35th Annual Congress, Ljubljana (Slovenia). Retrieved from: http://ssrn.com/abstract=2775258
  • Cochran, B. (1986). Small business mortality rates: A review of the literature. Journal of Small Business Management, 19(4), 50–59.
  • Cook, G. A. S., Pandit, N. R., & Milman, D. (2012). A resource-based analysis of bankruptcy law, SMEs and corporate recovery. International Small Business Journal, 30(3), 275–293.
  • Dannreuther, C., & Kessler, O. (2010). Small firm finance and the political economy of risk. London: Routledge.
  • Daubie, M., & Meskens, N. (2002). Business failure prediction: A review and analysis of the literature. In C. Zopounidis (Ed.), New trends in banking management (pp. 71–86). Heidelberg: Physica-Verlag.
  • Davydenko, S. A., & Franks, J. R. (2008). Do bankruptcy codes matter? A study of defaults in France, Germany, and the U.K. The Journal of Finance, 63(2), 565–608. April.
  • Dietsch, M., & Petey, J. (2002). The credit risk in SME loans portfolios: Modeling issues, pricing, and capital requirements. Journal of Banking and Finance, 26(2–3), 303–322.
  • Dietsch, M., & Petey, J. (2004). Should SME exposures be treated as retail or corporate exposures? A comparative analysis of default probabilities and asset correlations in French and German SMEs. Journal of Banking and Finance, 28(4), 773–788.
  • Dimitras, A., Zanakis, S., & Zopounidis, C. (1996). A survey of business failures with an emphasis on failure prediction methods and industrial applications. European Journal of Operational Research, 90(3), 487–513.
  • Du Jardin, P. (2015). Bankruptcy prediction using terminal failure processes. European Journal of Operational Research, 242(1), 286–303.
  • Du Jardin, P., & Séverin, E. (2012). Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time. European Journal of Operational Research, 221(2), 378–396.
  • Edmister, R. O. (1972). An empirical test of financial ratio analysis for small business failure prediction. Journal of Financial and Quantitative Analysis, 7(2), 1477–1493. March.
  • Franks, J. R., & Torous, W. N. (1992). Lessons from a comparison of US and UK insolvency codes. Oxford Review of Economic Policy, 8(3), 70–82.
  • Gill De Albornoz, B., & Giner, B. (2010). El fracaso empresarial en los sectores inmobiliario y de la construcción. 2008-2009. In B. Gill de Albornoz Dir., J. Fernández de Guevara, B. Giner, & L. Martínez (En.), Las empresas del sector de la construcción e inmobiliario en España: Del boom a la recesión económica (pp. 173–231). Madrid: Funcas.
  • Headd, B. (2003). Redefining business success: Distinguishing between closure and failure. Small Business Economics, 21(1), 51–61.
  • Jessen, C., & Lando, D. (2015). Robustness of distance-to-default. Journal of Banking & Finance, 50(1), 493–505.
  • Labatut, G., Pozuelo, J., & Veres, E. J. (2009). Time modelling of the accounting ratios for detection of management failure in Spanish small and medium size entreprises. Spanish Journal of Finance and Accounting, 38(143), 423–448.
  • Laitinen, E. K. (2008). Data system for assessing probability of failure in SME reorganization. Industrial Management and Data Systems, 108(7), 849–866.
  • Lincoln, M. (1984). An empirical study of the usefulness of accounting ratios to describe levels of insolvency risk. Journal of Banking and Finance, 8(2), 321–340.
  • López, J., Gandía, J. L., & Molina, R. (1998). La suspensión de pagos en las pymes: Una aproximación empírica. Spanish Journal of Finance and Accounting, 27(94), 71–97.
  • Mar Molinero, C., & Ezzamel, M. (1991). Multidimensional scaling applied to corporate failure. Omega, 19(4), 259–274.
  • Marais, M., Patell, J., & Wolfson, M. (1984). The experimental design of classification models: An application of recursive partitioning and bootstrapping to commercial bank loan classifications. Journal of Accounting Research, 22(1), 87–118.
  • Martin, D. (1977). Early warning of bank failure. Journal of Banking and Finance, 1(3), 249–276.
  • Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance, 29(2), 449–470.
  • Paradi, J. C., Asmild, M., & Simak, P. C. (2004). Using DEA and worst practice DEA in credit risk evaluation. Journal of Productivity Analysis, 21(2), 153–165. March.
  • Park, C. S., & 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.
  • Pompe, P. P. M., & Bilderbeek, J. (2005). The prediction of bankruptcy of small and medium sized industrial firms. Journal of Business Venturing, 20(6), 847–868. November.
  • Premachandra, I. M., Bhabra, G. S., & Sueyoshi, T. (2009). DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique. European Journal of Operational Research, 193(2), 412–424.
  • Psillaki, M., Tsolas, I. E., & Margaritis, D. (2010). Evaluation of credit risk based on firm performance. European Journal of Operational Research, 201(3), 873–881.
  • Ravi 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), 1–28.
  • Serrano Cinca, C. (1996). Self organizing neural networks for financial diagnosis. Decision Support Systems, 17(3), 227–238.
  • Shin, K. S., & Lee, Y. J. (2002). A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications, 23(3), 321–328.
  • Sueyoshi, T., & Goto, M. (2009). DEA–DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry. European Journal of Operational Research, 199(2), 576–594.
  • Sun, J., Li, H., Huang, Q.-H., & 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.
  • Sun, L., & Shenoy, P. P. (2007). Using Bayesian networks for bankruptcy prediction: Some methodological issues. European Journal of Operational Research, 180(2), 738–753.
  • Tascón, M. T., & 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.
  • Watson, J., & Everett, J. (1996). Do small business have high failure rates? Journal of Small Business Management, 34(4), 45–52.