1. bookVolume 5 (2014): Issue 2 (September 2014)
Journal Details
First Published
19 Sep 2012
Publication timeframe
2 times per year
© 2020 Sciendo

Inability of Gearing-Ratio as Predictor for Early Warning Systems

Published Online: 10 Sep 2014
Page range: 23 - 45
Received: 02 Feb 2014
Accepted: 18 May 2014
Journal Details
First Published
19 Sep 2012
Publication timeframe
2 times per year
© 2020 Sciendo

Background: Research in business failure and insolvency prediction provides numerous potential variables, which are in the position to differentiate between solvent and insolvent firms. Nevertheless, not all of them have the same discriminatory power, and therefore their general applicability as crisis indicators within early warning systems seems questionable. Objectives: The paper aims to demonstrate that gearing-ratio is not an appropriate predictor for firm failures/bankruptcies. Methods/Approach: The first and the second order derivatives for the gearing-ratio formula were computed and mathematically analysed. Based on these results an interpretation was given and the suitability of gearing-ratio as a discriminator within business failure prediction models was discussed. These theoretical findings were then empirically tested using financial figures from financial statements of Austrian companies for the observation period between 2008 and 2010. Results: The theoretical assumptions showed that gearing-ratio is not a suitable predictor for early warning systems. This finding was confirmed with empirical data. Conclusions: The inclusion of gearing-ratio within business failure prediction models is not able to provide early warning signals and should therefore be ignored in future model building attempts.



1. Ansoff, H. I., Sullivan, P. A. (1993), “Optimizing profitability in turbulent environment: A formula for strategic success”, Long Range Planning, Vol. 26, No. 5, pp. 11-23.Search in Google Scholar

2. Altman, E. I., Sabato, G., Wilson, N. (2010), “The value of non-financial information in small and medium-sized enterprise risk management”, The Journal of Credit Risk, Vol. 6, No. 2, pp. 1-33.Search in Google Scholar

3. Altman, E. I., Haldeman, R. G., Narayanan, P. (1977), “ZETATM Analysis: A new model to identify bankruptcy risk of corporations”, Journal of Banking and Finance, Vol. 1, No. 1, pp. 29-54.Search in Google Scholar

4. Altman, E. I. (1968), “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy”, The Journal of Finance, Vol. 23, No. 4, pp. 589-609.Search in Google Scholar

5. Anandarajan, M., Lee, P., Anandarajan, A. (2001), “Bankruptcy prediction of financially stressed firms: An examination of the predictive accuracy of artificial neural networks”, International Journal of Intelligent Systems in Accounting, Finance & Management, Vol. 10, No. 2, pp. 69-81.Search in Google Scholar

6. Barniv, R., McDonald, J. B. (1992), “Identifying financial distress in the insurance industry: A synthesis of methodological and empirical issues”, The Journal of Risk and Insurance, Vol. 59, No. 4, pp. 543 - 573.Search in Google Scholar

7. Barniv, R., Raveh, A. (1989), “Identifying financial distress: A new nonparametric approach”, Journal of Business Finance & Accounting, Vol. 16, No. 3, pp. 361-383.Search in Google Scholar

8. Beaver, W. H. (1966), “Financial ratios as predictors of failure”, Journal of Accounting Research, Vol. 4, pp. 71-111.Search in Google Scholar

9. Begley, J., Ming, J., Watts, S. (1996), “Bankruptcy classification errors in the 1980s: An empirical analysis of Altman’s and Ohlson’s models”, Review of Accounting Studies, Vol. 1, No. 4, pp. 267-284.Search in Google Scholar

10. Boritz, J. E., Kennedy, D. B., de Miranda e Albuquerque, A. M. (1995), “Predicting corporate failure using a neural network approach”, Intelligent Systems in Accounting, Finance and Management, Vol. 4, No. 2, pp. 95-111.Search in Google Scholar

11. Brouthers, K. D., Roozen, F. A. (1999), “Is it time to start thinking about strategic accounting?” Long Range Planning, Vol. 32, No. 3, pp. 311-322.Search in Google Scholar

12. Casey, C., Bartczak, N. (1985), “Using operating cash flow data to predict financial distress: Some extensions”, Journal of Accounting Research, Vol. 23, No. 1, pp. 384-401.Search in Google Scholar

13. Charitou, A., Neophytou, E., Charalambous, C. (2004), “Predicting corporate failure: Empirical evidence for the UK”, European Accounting Review, Vol. 13, No. 3, pp. 465-497.Search in Google Scholar

14. Chava, S., Jarrow, R. A. (2004), “Bankruptcy prediction with industry effects”, Review of Finance, Vol. 8, No. 4, pp. 537-569.Search in Google Scholar

15. Chen, J., Marshall, B. R., Zhang, J., Ganesh, S. (2006), “Financial distress prediction in China”, Review of Pacific Basin Financial Markets and Policies, Vol. 9, No. 2, pp. 317-336.Search in Google Scholar

16. Chen, W. S., Du, Y. K. (2009), “Using neural networks and data mining techniques for the financial distress prediction model”, Expert Systems with Applications, Vol. 36, No. 2, pp. 4075-4086.Search in Google Scholar

17. Chi, L. C., Tang, T. C. (2006), “Bankruptcy prediction: Application of logit analysis in export credit risks”, Australian Journal of Management, Vol. 31, No. 1, pp. 17-27.Search in Google Scholar

18. Creditreform Wirtschaftsforschung Austria (2011), “Insolvenzen in Europa: Jahr 2010/11”, available at: http://www.creditreform.at/fileadmin/user_upload/Oesterreich/Downloads/Insolv enz/Insolvenzen_in_Europa_2010.pdf (13 January 2014).Search in Google Scholar

19. Dambolena, I. G., Khoury, S. J. (1980), “Ratio stability and corporate failure”, The Journal of Finance, Vol. 35, No. 4, pp. 1017-1026.Search in Google Scholar

20. Dietrich, J., Arcelus, F. J., Srinivasan, G. (2005), “Predicting financial failure: Some evidence from New Brunswick agricultural co-ops”, Annals of Public and Cooperative Economics, Vol. 76, No. 2, pp. 179-194.Search in Google Scholar

21. Du Jardin, P. (2009), “Bankruptcy prediction models: How to choose the most relevant variables?”, Bankers, Markets & Investors, No. 98, pp. 39-46.Search in Google Scholar

22. Edmister, R. O. (1972), „An empirical test of financial ratio analysis for small business failure prediction”, Journal of Financial and Quantitative Analysis, Vol. 7, No. 2, pp. 1477-1493.Search in Google Scholar

23. Exler, M. W., Situm, M. (2014), „Indikatoren zur Früherkennung von Unternehmenskrisen in der Beraterpraxis: Ansatzpunkte zur Etablierung eines internen Frühwarnsystems”, Krisen-, Sanierungs- und Insolvenzberatung, Vol. 10, No. 2, pp. 53-59.Search in Google Scholar

24. Exler, M. W., Situm, M. (2013), “Früherkennung von Unternehmenskrisen: Systematische Einteilung von Krisenfrüherkennungsindikatoren zu den unterschiedlichen Krisenphasen eines Unternehmens”, Krisen-, Sanierungs- und Insolvenzberatung, Vol. 9, No. 4, pp. 161-166.Search in Google Scholar

25. Fanning, K. M., Cogger, K. O. (1994), “A comparative analysis of artificial neural networks using financial distress prediction”, Intelligent Systems in Accounting, Finance and Management, Vol. 3, No. 4, pp. 241-252.Search in Google Scholar

26. Fawcett, T. (2006), “An introduction to ROC analysis”, Pattern Recognition Letters, Vol. 27, No. 8, pp. 861-874.Search in Google Scholar

27. Frydman, H., Altman, E. I., Kao, D. L. (1985), “Introducing recursive partitioning for financial classification: The case of financial distress”, The Journal of Finance, Vol. 40, No. 1, pp. 269-291.Search in Google Scholar

28. Gepp, A., Kumar, K. (2008), “The role of survival analysis in financial distress prediction”, International Research Journal of Finance and Economics, Vol. 16, pp. 13-34.Search in Google Scholar

29. Gombola, M. J., Haskins, M. E., Ketz, E. J., Williams, D. D. (1987), “Cash flow in bankruptcy prediction”, Financial Management, Vol. 16, No. 4, pp. 55-65.Search in Google Scholar

30. Grunert, J., Norden, L., Weber, M. (2005), “The role of non-financial factors in internal credit ratings”, Journal of Banking & Finance, Vol. 29, No. 2, pp. 509-531.Search in Google Scholar

31. Hackbarth, D., Miao, J., Morellec, E. (2006), “Capital structure, credit risk, and macroeconomic conditions”, Journal of Financial Economics, Vol. 82, pp. 519–550.Search in Google Scholar

32. Hauser, R. P., Booth, D. (2011), “Predicting bankruptcy with robust logistic regression”, Journal of Data Science, Vol. 9, pp. 565-584.Search in Google Scholar

33. Hennessy, C. A., Whited, T. M. (2005), “Debt dynamics”, The Journal of Finance, Vol. 60, No 3, pp. 1129-1165.Search in Google Scholar

34. Hopwood, W., McKeown, J., Mutchler, J. (1988), “The sensitivity of financial distress prediction models to departures from normality”, Contemporary Accounting Research, Vol. 5, No. 1, pp. 284-298.Search in Google Scholar

35. Houghton, K. A., Woodliff, D. R. (1987), “Financial ratios: The prediction of corporate ‘success’ and failure”, Journal of Business Finance & Accounting, Vol. 14, No. 4, pp. 537-554.Search in Google Scholar

36. Hwang, R. C., Cheng, K. F., Lee, J. C. (2007), “A semiparametric method for predicting bankruptcy”, Journal of Forecasting, Vol. 26, No. 5, pp. 317-342.Search in Google Scholar

37. Keasey, K., Watson, R. (1991), “Financial distress prediction models: A review of their usefulness”, British Journal of Management, Vol. 2, No. 2, pp. 89-102.Search in Google Scholar

38. lazzolino, G., Migliano, G., Gregorace, E. (2013), “Evaluating intellectual capital for supporting credit risk assessment: An empirical study”, Investment Management and Financial Innovations, Vol. 10, No. 2, pp. 44-54.Search in Google Scholar

39. Jones, S., Hensher, D. A. (2004), “Predicting firm financial distress: A mixed logit model”, The Accounting Review, Vol. 79, No. 4, pp. 1011-1038.Search in Google Scholar

40. Klecka, W. R. (1980). “Discriminant analysis”, Newbury Park: Sage.Search in Google Scholar

41. Laitinen, E. K., Laitinen, T. (2000), “Bankruptcy prediction: Application of the Taylor’s expansion in logistic regression”, International Review of Financial Analysis, Vol. 9, No. 4, pp. 327-349.Search in Google Scholar

42. Lau, A. H. L. (1987), “A five-state financial distress prediction model”, Journal of Accounting Research, Vol. 25, No. 1, pp. 127-138.Search in Google Scholar

43. Leland, H. E., Toft, K. B. (1996), “Optimal capital structure, endogenous bankruptcy, and the term structure of credit spreads”, The Journal of Finance, Vol. 51, No. 3, pp. 987-1019.Search in Google Scholar

44. Li, H., Sun, J. (2011), “Predicting business failure using forward ranking-order case-based reasoning”, Expert Systems with Applications, Vol. 38, No. 4, pp. 3075-3084.Search in Google Scholar

45. Lin, F., Liang, D., Chen, E. (2011), “Financial ratio selection for business crisis prediction”, Expert Systems with Applications, Vol. 38, No. 12, pp. 15094-15102.Search in Google Scholar

46. Liou, D. K., Smith, M. (2007), “Macroeconomic variables and financial distress”, Journal of Accounting, Business & Management, Vol. 14, pp. 17-31.Search in Google Scholar

47. Madrid-Guijarro, A., Garcia-Pèrez-de-Lema, D., van Auken, H. (2011), “An analysis of non-financial factors associated with financial distress”, Entrepreneurship & Regional Development, Vol. 23, No. 3-4, pp. 159-186.Search in Google Scholar

48. Muller, G. H., Steyn-Bruwer, B. W., Hamman, W. D. (2009), “Predicting financial distress of companies listed on JSE - A comparison of techniques”, South African Journal of Business & Management, Vol. 40, No. 1, pp. 21-32.Search in Google Scholar

49. Müller-Stewens, G. (2007). “Früherkennungssysteme”, in Köhler, R., Küpper, H. U., Pfingsten, A. (ed.), „Handwörterbuch der Betriebswirtschaft”, Schaeffer-Pöschl, Stuttgart, pp. 558-580.Search in Google Scholar

50. Nam, C. W. et al. (2008), “Bankruptcy prediction using a discrete-time duration model incorporating temporal macroeconomic dependencies”, Journal of Forecasting, Vol. 27, No. 6, pp. 493-506.Search in Google Scholar

51. Neves, J. C., Vieira, A. (2006), “Improving bankruptcy prediction with hidden layer learning vector quantization”, European Accounting Review, Vol. 15, No, 2, pp. 253-271.Search in Google Scholar

52. Ohlson, J. A. (1980), “Financial ratios and the probabilistic prediction of bankruptcy”, Journal of Accounting Research, Vol. 18, No. 1, pp. 109-131.Search in Google Scholar

53. Ooghe, H., Spaenjers, C. (2009), “A note on performance measures for business failure prediction models”, Applied Economics Letter, Vol. 17, No. 1, pp. 67-70.Search in Google Scholar

54. Pacey, J. W., Pham, T. M. (1990), “The predictiveness of bankruptcy models: Methodological problems and evidence”, Australian Journal of Management, Vol. 15, No. 2, pp. 315-337.Search in Google Scholar

55. Pervan, I., Kuvek, T. (2013), “The relative importance of financial ratios and nonfinancial variables in predicting of insolvency”, Croatian Operational Research Review, Vol. 4, No. 1, pp. 187-198.Search in Google Scholar

56. Pervan, I., Pervan, M., Vukoja, B. (2011), “Prediction of company bankruptcy using statistical techniques - Case of Croatia”, Croatian Operational Research Review, Vol. 2, No. 1, pp. 158-167.Search in Google Scholar

57. Pervan, M., Visic, J. (2012), “Influence of firm size on its business success”, Croatian Operational Research Review, Vol. 3, No. 1, pp. 213-223.Search in Google Scholar

58. Platt, H. D., Platt, M. B. (2002), “Predicting corporate financial distress: Reflections on choice-based sample bias”, Journal of Economics and Finance, Vol. 26, No. 2, pp. 184-199.Search in Google Scholar

59. Pohar, M., Blas, M., Turk, S. (2004), “Comparison of logistic regression and linear discriminant analysis: A simulation study”, Metdološki Zvezki, Vol. 1, No. 1, pp. 143–161.Search in Google Scholar

60. Pompe, P. P. M., Bilderbeek, J. (2005), “Bankruptcy prediction: The influence of the year prior to failure selected for model building and the effects in a period of economic decline”, Intelligent Systems in Accounting, Finance and Management, Vol. 13, No. 2, pp. 95-112.Search in Google Scholar

61. Prasad, D., Puri, Y. R. (2005), “Does combining alternate bankruptcy prediction models improve forecasting accuracy?”, The International Journal of Finance, Vol. 17, No. 3, pp. 3581-3602.Search in Google Scholar

62. Press, J. S., Wilson, S. (1978), “Choosing between logistic regression and discriminant analysis”, Journal of American Statistical Association, Vol. 73, No. 364, pp. 699-705.Search in Google Scholar

63. Pretorius, M. (2008), “Critical variables of business failure: A review and classification framework”, South African Journal of Economic and Management Sciences, Vol. 11, No. 4, pp. 408-430.Search in Google Scholar

64. Saunders, A., Cornett, M. (2011). “Financial institutions management: A risk management approach”, 7th edition, New York: Mc-Graw-Hill.Search in Google Scholar

65. Schmidt, R., Terberger, E. (1996). „Grundzüge der Investitions- und Finanzierungstheorie”, 3rd edition, Wiesbaden: Gabler.Search in Google Scholar

66. Sharma, D. S. (2001), “The role of cash flow information in predicting corporate failure: The state of the literature”, Managerial Finance, Vol. 27, No. 4, pp. 3-28.Search in Google Scholar

67. Silva, A. P. D., Stam, A., Neter, J. (2002), “The effects of misclassification costs and skewed distributions in two-group classification”, Communications in Statistics -Simulation and Computation, Vol. 31, No. 3, pp. 401-423.Search in Google Scholar

68. Skogsvik, K., Skogsvik, S. (2013), “On the choice based sample bias in probabilistic bankruptcy prediction”, Investment Management and Financial Innovations, Vol. 10, No. 1, pp. 29-37.Search in Google Scholar

69. Subhash, S. (1996). “Applied multivariate techniques”, New York: John Wiley & Sons.Search in Google Scholar

70. Thomas, L. C., Edelman, D. B., Crook, J. N. (2002). “Credit scoring and its applications”, Philadelphia: Society for Industrial and Applied Mathematics.Search in Google Scholar

71. Tsai, B. H. (2013), “An early warning system of financial distress using multinomial logit models and a bootstrapping approach”, Emerging Markets Finance & Trade, Vol. 49, No. 2, pp. 43-69.Search in Google Scholar

72. Yim, J., Mitchell, H. E. (2007), “Predicting financial distress in the Australian financial service industry”, Australian Economic Papers, Vol. 46, No. 4, pp. 375-388.Search in Google Scholar

73. Zenzerovic, R. (2011), “Credit scoring models in estimating the creditworthiness of small and medium and big enterprises”, Croatian Operational Research Review, Vol. 2, No. 1, pp. 143-157.Search in Google Scholar

74. Zmijewski, M. E. (1984), “Methodological issues related to the estimation of financial distress prediction models”, Journal of Accounting Research, Vol. 22, pp. 59-82.Search in Google Scholar

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