Inability of Gearing-Ratio as Predictor for Early Warning Systems

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. 11-23.10.1016/0024-6301(93)90073-OSearch 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. 1-33.10.21314/JCR.2010.110Search 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. 29-54.10.1016/0378-4266(77)90017-6Search 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. 589-609.10.1111/j.1540-6261.1968.tb00843.xSearch 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. 69-81.10.1002/isaf.199Search 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 - 573.10.2307/253344Search 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. 361-383.10.1111/j.1468-5957.1989.tb00024.xSearch in Google Scholar

8. Beaver, W. H. (1966), “Financial ratios as predictors of failure”, Journal of Accounting Research, Vol. 4, pp. 71-111.10.2307/2490171Search 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. 267-284.10.1007/BF00570833Search 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. 95-111.10.1002/j.1099-1174.1995.tb00083.xSearch 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. 311-322.10.1016/S0024-6301(99)00035-7Search 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. 384-401.10.2307/2490926Search 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. 465-497.10.1080/0963818042000216811Search in Google Scholar

14. Chava, S., Jarrow, R. A. (2004), “Bankruptcy prediction with industry effects”, Review of Finance, Vol. 8, No. 4, pp. 537-569.10.1093/rof/8.4.537Search 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. 317-336.10.1142/S0219091506000744Search 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. 4075-4086.10.1016/j.eswa.2008.03.020Search 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. 17-27.10.1177/031289620603100102Search 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. 1017-1026.10.1111/j.1540-6261.1980.tb03517.xSearch 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. 179-194.10.1111/j.1370-4788.2005.00275.xSearch 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. 1477-1493.10.2307/2329929Search 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. 241-252.10.1002/j.1099-1174.1994.tb00068.xSearch in Google Scholar

26. Fawcett, T. (2006), “An introduction to ROC analysis”, Pattern Recognition Letters, Vol. 27, No. 8, pp. 861-874.10.1016/j.patrec.2005.10.010Search 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. 269-291.10.1111/j.1540-6261.1985.tb04949.xSearch 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. 55-65.10.2307/3666109Search 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. 509-531.10.1016/j.jbankfin.2004.05.017Search 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. 519–550.10.1016/j.jfineco.2005.10.003Search in Google Scholar

32. Hauser, R. P., Booth, D. (2011), “Predicting bankruptcy with robust logistic regression”, Journal of Data Science, Vol. 9, pp. 565-584.10.6339/JDS.201110_09(4).0006Search in Google Scholar

33. Hennessy, C. A., Whited, T. M. (2005), “Debt dynamics”, The Journal of Finance, Vol. 60, No 3, pp. 1129-1165.10.1111/j.1540-6261.2005.00758.xSearch 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. 284-298.10.1111/j.1911-3846.1988.tb00706.xSearch 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. 537-554.10.1111/j.1468-5957.1987.tb00111.xSearch 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. 317-342.10.1002/for.1027Search 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. 89-102.10.1111/j.1467-8551.1991.tb00019.xSearch 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. 1011-1038.10.2308/accr.2004.79.4.1011Search in Google Scholar

40. Klecka, W. R. (1980). “Discriminant analysis”, Newbury Park: Sage.10.4135/9781412983938Search 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. 327-349.10.1016/S1057-5219(00)00039-9Search 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. 127-138.10.2307/2491262Search 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. 987-1019.10.1111/j.1540-6261.1996.tb02714.xSearch 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. 3075-3084.10.1016/j.eswa.2010.08.098Search 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. 15094-15102.10.1016/j.eswa.2011.05.035Search 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. 159-186.10.1080/08985620903233911Search 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. 21-32.10.4102/sajbm.v40i1.532Search 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. 493-506.10.1002/for.985Search 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. 253-271.10.1080/09638180600555016Search 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. 109-131.10.2307/2490395Search 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. 67-70.10.1080/13504850701719769Search 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. 315-337.10.1177/031289629001500206Search 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. 184-199.10.1007/BF02755985Search 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. 143–161.10.51936/ayrt6204Search 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. 95-112.10.1002/isaf.259Search 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. 699-705.10.1080/01621459.1978.10480080Search 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.10.1007/978-3-663-14776-3Search 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. 3-28.10.1108/03074350110767114Search 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. 401-423.10.1081/SAC-120003849Search 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.10.1137/1.9780898718317Search 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. 43-69.10.2753/REE1540-496X4902S203Search 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. 375-388.10.1111/j.1467-8454.2007.00326.xSearch 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. 59-82.10.2307/2490859Search in Google Scholar