Financial Ratios as Predictors of Failure: Evidence from ... - ERIM

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ROE increases the odds of default 1.021 times, or an extra ROE increases the odds of default by 2.1%. This positive relation is in support of the classic risk-return tradeoff promoted by Black et al (1972) and Chava and Purnanandam (2008). As predicted by Hypothesis Five, the ability to generate cash/income should negatively influence corporate failure. The representative variable for the first sample is NITA, and it is negatively and significantly correlated with odds of default at 1% level. Each additional NITA decreases the odds of default by 20.3% ((0.797-1)*100), controlling for other variables in the model. This is consistent with the results in previous studies such as Deakin (1972), and Ohlson (1980), among others. Free cash flow is not found to have any significant impact on probability of default. This is different from Altman (1968) and Zeitun et al (2007)’s results, since they concluded that companies with high free cash flow measured by RETA have higher default risk. Most scholars applied bankruptcy research on single industry. However, others (Zeitun, 2007) who believed financial ratios vary dramatically cross industries and used industry dummies found significant results. In this analysis, time and industry dummies do not seem to be significant, meaning that no particular industry or year is significantly different from others in predicting bankruptcy. Hol et al (2002) did not find industry dummies significantly different from zero either, and they gave a possible explanation that these variables mostly capture the inter-industry differences in default probability. The Omnibus test in Table 7 illustrates that the model is significant at the 1% level, meaning that at least one of the independent variables is significantly correlated with the dependent variable, and all the variables in the model jointly are capable of predicting the dependent variable. The Hosmer and Lemeshow chi-square test of goodness of fit as well as the classification tables which will be introduced in Section 5.3 assess the models’ fit. A finding of non-significance, as can be seen in Table 7, indicates that the model adequately fits the data. It is less straight forward to interpret the ratios under Model 21
Summary. -2 log likelihood (-2LL) is crucial when comparing different logistic models, but it cannot be used directly in significance test and thus is not very informative in assessing a single model. SPSS in Logistic regression also outputs R 2 like that in OLS regression. However the Cox & Snell R 2 and Nagelkerke R 2 can only be seen as approximations to OLS R 2 , not as the actual percentage of variance explained. Thus, they are not informative in indicating model fit. For Sample One, the Cox & Snell R 2 and Nagelkerke R 2 are 42.6% and 56.9% respectively, which are reasonably high. In the second sample, after adding observations in non-default companies and without controlling factors such as firm size and time, coefficients of individual variables become more significant (see Table 8). Interestingly, when randomly picking non-default companies, we see that size seems to be a significant factor in predicting default. Its negative coefficient with odds of default is also consistent with Westgaard and Wijst (2001)’s findings that small firms have limited access to capital markets and are more likely to fail. It can be concluded that each additional Size (measured by log10(total assets)) decreases the odds of default by a factor of 0.488, controlling for other variables in the model. Ratios such as NITA and TDTA are significant at least at 5% level in sample two as well. However, unlike the result in Table 7, ROE does not have a significant relationship with odds of default despite of its positive sign. RETA, SATA, EQTC are not found to be significant either. Empirical results for two years and three years before default in both samples are presented in Table 9 and 10 respectively (see Appendix Four to Seven). The main conclusion is that TDTA seems to be positively and significantly related with odds of default over all three years in both matched and non-matched sample. Another observation is that in Table 9 (Appendix Four), the coefficients (β) of 2001 under two and three years before default are significant. The exponential values are 0.034 (e -3.371 ) and 22
Page 1 and 2: 17 Nov 2008 Financial Ratios as Pre
Page 3 and 4: Abstract This paper presents some e
Page 5 and 6: 1. Introduction One year after the
Page 7 and 8: However he did not find substantial
Page 9 and 10: interest in predicting bankruptcy b
Page 11 and 12: atios he employed in the research w
Page 13 and 14: there are specific statistical requ
Page 15 and 16: “returns”, “values” and “
Page 17 and 18: Table 2 Explanatory Variables Varia
Page 19 and 20: which results in high standard erro
Page 21: Table 7 Variables in the Equation (
Page 25 and 26: predicted to be default, it is call
Page 27 and 28: estimation procedure. However it is
Page 29 and 30: Ohlson (1980)). In the second sampl
Page 31 and 32: Reference Altman, E. I., 1968, “F
Page 33 and 34: Hong Kong Society of Accountants, 1
Page 35 and 36: MDA Appendix One Table 1 Literature
Page 37 and 38: Appendix Two Table 4 Descriptive St
Page 39 and 40: Appendix Four Table 9 Coefficients
Page 41 and 42: Appendix Six Table 10 Coefficients
Page 43 and 44: Appendix Eight Table 12 Classificat

Financial Ratios as Predictors of Failure: Evidence from ... - ERIM

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