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

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to the definition of default (see section two) in this study, 30 default companies with complete financial data have been found over 2001-2007. They are in ten different industries 15 . For each default company, the first three fiscal year-end financial data before its default announcement is used. The first sample selection is similar to Beaver (1966) and Altman et al (1977)’s. In order to isolate the characteristics of default companies, each default company is matched with a non-default company from the same industry group, with similar asset size, and in the same year. The purpose of matching is that there could be a potential bias in certain of the ratios. For example, some financial ratios could vary dramatically cross industries. Therefore, industry and time dummies are added in the first sample to control the bias. However, the “matching procedures” tend to be somewhat arbitrary (Ohlson, 1980). It is also interesting to see the effect of excluding matching. In the second sample, I pool cross-sectional and time-series data for all the companies over the period 2001-2007. 71 16 non-failed companies are randomly selected for the second sample over the same time period. Similar to sample one, non-default companies are chosen from the same ten industries, but without controlling time and firm size. 4.3 Explanatory Variables and Hypotheses The variables used in this study are summarized and presented in Table 2 at the end of this section. The variables selected for the regression model are related to “cash flow”, 15 Industries (number of observations): Capital Goods (3), Consumer Durables & Apparel (7), Diversified Financials (2), Food Beverage & Tobacco (3), Materials (3), Real Estate (2), Software & Services (2), Technology & Hardware Equipment (4), Telecommunication Services (2), Transportation (2) 16 Due to the limitation of collecting information of default companies, in the second sample the number of default companies is still 30. However we cannot unilaterally and infinitely increase the number of non-default companies. It would lead to a biased result. Therefore, randomly picked 71 non-default firms are included in sample two. 13
“returns”, “values” and “debt obligations”. The sign of the coefficients of the different ratios is based on previous studies. The regression model can also be specified as Li = ln [pi/(1-pi)] = β1TDTAi - β2EQTCi + β3RETAi + β4ROEi - β5CFTDi – β6NITAi – β7SATAi – β8WCTAi – β9CRi – β10Sizei + dummy (year) + dummy (industry) + ui. Hypothesis 1: Highly leveraged firms are more likely to fail. Firms with heavy debt obligation have higher distress cost, thus are more likely to go bankrupt. Empirical studies proved the positive relationship between total debt to total assets (TDTA) and probability of default (Martin (1977), and Hol et al (2002)). A company’s financial leverage can also be measured by solidity. It estimates the extent by which the company’s assets are funded by equity (EQTC). Westgaard and Wijst (2001) found a negative and significant relationship between solidity and default probabilities. Hypothesis 2: Those Companies with more free cash flow have higher probability to fail. Managers probably would rather invest in projects with negative NPV instead of paying back companies’ shareholders. Free cash flow is estimated by retained earnings to total assets (RETA). It is hypothesized to have a positive correlation with probability of default (Altman (1968), Altman et al (1977), and Zeitun et al (2007)). Hypothesis 3: Abnormal and high equity returns may indicate high default risk. Stock returns can be used to classify firms’ potential failure. A positive risk premium is required when investors detect potential high risks (Black et al (1972), and Chava and Purnanandam (2008)). Therefore equity returns (ROE) are supposed to be positively related to default probabilities. Hypothesis 4: A company’s cash flow is negatively related to bankruptcy. 14
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: there are specific statistical requ
Page 17 and 18: Table 2 Explanatory Variables Varia
Page 19 and 20: which results in high standard erro
Page 21 and 22: Table 7 Variables in the Equation (
Page 23 and 24: Summary. -2 log likelihood (-2LL) i
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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