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Financial Ratios as Predictors of Failure:

Evidence from Hong Kong using Logit Regression

17 Nov 2008

Student: Weiying Guo 292341

Coach: Dr. Ben Tims

Co-reader: Drs. Johannes Meuer

Finance and Investment

Rotterdam School of Management

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Preface

The author1 declares that the text and work presented in this master thesis is original and

that no sources other than those mentioned in the text and its references have been used in

creating the master thesis.

The copyright of the master thesis rests with the author. The author is responsible for its

contents. RSM Erasmus University is only responsible for the educational coaching and

beyond that cannot be held responsible for the content.

1 The author would like to thank Dr. Ben Tims, and Drs. Johannes Meuer for valuable suggestions and helpful comments. All remaining errors are the author’s responsibility.

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Abstract

This paper presents some empirical results of predicting corporate failure by using

various financial ratios. It aims to identify the characteristics that distinguish default and

non-default companies. Two samples (matched and non-matched) of Hong Kong based

companies are used in this research over the period 2001-2007. By using logistic

regression, it shows that level of debt, and return on equity increase corporate failure,

whereas bankruptcy decreases with firm size and profitability. The results are in support

of capital structure theory and risk-return tradeoff. The predictive power of the logit

model is reasonably high in three years prior to default.

Keywords: Risk Management, Probability of default, bankruptcy, logistic regression

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Table of Contents

1. Introduction..................................................................................................................... 4 2. Probability of Default and Bankruptcy ........................................................................... 5 3. Literature Review............................................................................................................ 6

3.1 Theories..................................................................................................................... 7

3.2 Multi factors in predicting PD and the methodologies ............................................. 9

3.2.1 Multiple Discriminant Analysis.......................................................................... 9

3.2.2 Logistic Regression .......................................................................................... 10

4. Data and Methodology.................................................................................................. 11 4.1 Methodology ........................................................................................................... 11

4.2 Data and Sample...................................................................................................... 12

4.3 Explanatory Variables and Hypotheses................................................................... 13

5. Empirical Results .......................................................................................................... 16 5.1 Descriptive Results.................................................................................................. 16

5.2 Estimation results of the Logit model ..................................................................... 19

5.3 Predictive power of the model ................................................................................ 23

5.4 Robustness Test....................................................................................................... 25

6. Conclusion .................................................................................................................... 27 Reference .......................................................................................................................... 30 Appendix One ................................................................................................................... 34 Appendix Two................................................................................................................... 36 Appendix Three ................................................................................................................ 37 Appendix Four .................................................................................................................. 38 Appendix Five................................................................................................................... 39 Appendix Six .................................................................................................................... 40 Appendix Seven ................................................................................................................ 41 Appendix Eight ................................................................................................................. 42 Appendix Nine .................................................................................................................. 43

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1. Introduction

One year after the onset of the credit crunch, most Americans have still been suffering

from losing jobs, skyrocketing commodity prices, weakening dollar, all in all, a bad

economy. The downward trend has unfortunately crept to countries in Europe and Asia,

and fear of potential financial markets meltdown has reached its new level ever before

after the 1997 Asian financial crisis and 9/11. At the same time, credibility and risk

management have probably never caught such attention from investors and policymakers

in history. The credit crunch is prompting a new age of risk management, requiring risk

managers to measure risk in real time and help prevent future crises. The new Basel

framework, Basel II, intends to further strengthen the soundness and stability of the

international banking system (Basel committee, 2004) by promoting stronger risk

management practices in the banking industry. Under the Internal Rating Approach,

banks are given opportunities to estimate counterparties’ default probabilities by

themselves. Probability of default (PD) plays an important role not only because it is the

cornerstone when calculating regulatory capital requirements, but also when it comes to

making tough loan decisions it helps banks discriminate good borrowers from bad

borrowers.

For banks, one of the best ways to examine a company’s financial health is by looking at

its financial ratios. The objective of this paper is to explore the possibilities to predict

probability of default and to identify the characteristics distinguishing default companies

from non-default ones by using logistic regression. Relying on existing theories, such as

risk-return tradeoff, and capital structure theory, the study examines the relationship

between PD and different financial ratios in various industries in Hong Kong. Many

scholars have conducted default research in the United States and Western Europe (see

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Beaver (1966), Marais (1979), Altman and Lavallee (1981) and Westgaard and Wijst

(2001) and others). Nevertheless, there has been little similar bankruptcy research in Asia.

One possible explanation could be that Asian financial markets just emerged in the last

decade, whereas most of the default analyses were conducted over 20 years ago. So far

there has been no comprehensive bankruptcy analysis performed in the context of Hong

Kong. However, being one of the first banking industries who adopted the new banking

regulation, Hong Kong deserves extra attention. Compared to most empirical studies in

the 70s and 80s, the data set used in this research (from 2001 to 2007) is rather

contemporary. Therefore, whether previous empirical results still stand in a modern

context is under question.

The rest of the paper is structured as follows. In the next section, I briefly introduce

probability of default and bankruptcy. Section 3 reviews literature. In section 4, I explain

the data, sample selection, and methodology used in the analysis. Section 5 presents the

empirical results. Section 6 concludes.

2. Probability of Default and Bankruptcy

Legally speaking, bankruptcy is described as a debtor not being able to meet its debt

obligations as they fall due. In an accounting sense, when the sum of the realized cash

flow and expected future cash flow is less than the debt obligations, bankruptcy occurs.

PD is used to measure the likelihood that a firm defaults under its debt obligations.

Different researchers have different definitions of failure2. Operationally, Beaver (1968

(1)) defined a failed firm when any of the following events has occurred: bankruptcies,

bond default, overdrawn bank account, or nonpayment of a preferred stock dividend.

2 See a summary of the definitions of failure in major empirical default analyses, by Castagna and Matolcsy (1981)

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However he did not find substantial differences in the empirical results even under this

boarder definition. Deakin (1972) only included those firms which experienced

bankruptcy, insolvency, or which were otherwise liquidated for the benefit of creditors in

his default analysis. I define default companies as those delisted (except for companies

that are taken over by others3) from the Hong Kong Stock Exchange4, or as those listed

as “ceased place of business”, or “winding up” according to the Integrated Companies

Registry Information System (ICRIS)5. Therefore, the date of default is either the date of

delisting from the HKEx, or the date of winding up.

Bankruptcy of a company generates both direct and indirect costs. Assets of the firm are

usually being sold at a price well below the one that would be realized before the

bankruptcy announcement. Accountants and lawyers can cost huge amounts of money.

The companies’ brand name and long established reputation are often ruined. Of course,

failure of a company is costly to suppliers of capital, which in most cases are banks. It is

therefore in banks’ interest to predict borrowers’ probability of default when making loan

decisions.

3. Literature Review

Bankruptcies have been mostly explained by capital structure, risk-return tradeoff, cash

flow, and agency theory. Some scholars also tried to combine different proxy variables

that are derived from accounting data into their models for predicting corporate default.

These studies vary from time, countries, and industries. However they all proved that

3 Acquired companies will not be included in the sample selection. HKEx offers relevant takeover information (last updated on 22 June 2007) regarding announcement date, name of offeror, name of offeree, and offer type, etc. 4 This definition is also used by Izan (1984) for Australian companies, and Zeitun, Tian, & Keen (2007) for Jordanian companies. 5 Official website offered by Hong Kong government. http://www.icris.cr.gov.hk/csci/

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financial ratios can be used to predict PD.

3.1 Theories

In a world without tax and bankruptcy, there is no optimal capital structure under the

classic Modigliani-Miller irrelevance theorem 6 (Modigliani & Miller 1958, 1963).

Nevertheless, in the real world, most companies have to choose between tax advantages

and bankruptcy costs (trade-off theory, Kraus and Litzenberger, 1973). That is, debt

brings tax shields for a company, but at the same time it increases distress or bankruptcy

costs. The optimal amount of debt should produce the lowest weighted average cost of

capital. Kim (1978) claimed that the market value of a company decreases as financial

leverage becomes extreme, thus it should finance less debt than its debt capacity (the

optimum7). The transfer of ownership from shareholders to debtholders also encourages

risk taking behavior, because shareholders have the limited downside risk while enjoying

unlimited upside potential, further reinforcing the conflict of interests between various

stakeholders in the company. Therefore, higher debt in one’s capital structure should be

associated with higher PD.

From external to internal financing, cash flow has been an important determinant of

bankruptcy. Scott (1981) claimed that cash flow variables involve estimates of the firm’s

future cash flow distribution, and that past and present cash flow should be able to predict

PD. Financial Accounting Standards Board (1981) stated that “the greater the amount of

future net cash inflows from operations, the greater the ability of the enterprise to

withstand adverse changes in operating conditions”. Other scholars have also shown

6 The total value of a firm will not change because of its capital structure. In other words, no capital structure is better or worse than any other capital structure for the firm’s shareholders. 7 The breakeven point is where the marginal benefit of the tax shield equals the marginal cost of financial distress.

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interest in predicting bankruptcy by incorporating cash flow characteristics in their

predictive models (see e.g. Casey and Bartczak (1985), Gentry et al (1985), Gombola et

al (1987), and Aziz (1988), among others). Cash rich companies are assumed to be better

able to diversify their risks and therefore less likely to go bankrupt. Deakin (1972) used

several cash flow ratios (cash flow/total debt, cash/total assets, cash/current liabilities,

cash/sales) to test their coefficients with bankruptcy. The signs of the coefficients were

negative but did not seem to be consistent in all five years before companies’

bankruptcies. Recently, Zeitun et al (2007) demonstrated the negative relationship

between cash flow and default risk, however the results were not significant. Similar to

cash flow, a shortage of liquidity could also trigger corporate failure. Cash, the most

liquid form of assets is crucial to a company disregarding its size or industry type.

Furthermore, it also matters how quickly a company is able to covert other assets into

cash with no or little price discount. Therefore, the availability and convertibility of a

company’s assets are extremely important especially in crises. Becchetti and Sierra

(2003), among others used liquidity ratios for their bankruptcy analyses.

There are always trade-offs between internal and external financing. According to the

pecking order theory (Myers, 1984), a company should use relatively costless internal

financing over external financing. However internal financing is not without problem. If a

company’s cash flow cannot be distributed to its shareholders or debtholders, managers

are more likely to misuse retained earnings. Internal financing better sources free cash

flow (measured as operating cash flow minus the capital expenditures) and thereby

increases agency costs (Jensen, 1986) due to the asymmetric information between

managers and shareholders. A high level of free cash flow seems to destroy corporate

value, so it is expected to be positively related to bankruptcy.

Changes in market prices of stocks can also be used to predict failure (Beaver 1968 (2),

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Altman and Brenner, 1981). According to the classic Capital Asset Pricing Model

(CAPM)8, investors would expect higher returns for bearing more risk. The first and most

well-know empirical tests can be traced back to over 30 years ago. For example, Black et

al (1972) provided empirical tests and additional insights based on the original CAPM

model. They confirmed that the beta factor (firm-specific risk) is important in

determining security returns using cross-sectional tests. More recently, Chava and

Purnanandam (2008) uncovered the risk-return relation by extending their sample period,

and suggested that expected returns are positively correlated with bankruptcy.

Consequently, there should be a positive relation between return on equity and default

risk.

3.2 Multi factors in predicting PD and the methodologies

3.2.1 Multiple Discriminant Analysis

Many scholars have incorporated various factors in terms of financial ratios into their

predicting model in order to discriminate default and non-default companies. Beaver

(1966) and Altman (1968) pioneered the studies. By using univariate analysis, Beaver

pairwisely compared failed and non-failed companies and found that ratios like cash

flow/total assets, net income/total assets, total debt/total assets, and cash flow/total debt9

in particular were important indicators of failure. Later, based on his research, Altman

(1968) developed the classic Multiple Discriminant Analysis (MDA) and Z-score model.

Being more advanced than univariate analysis, MDA examines the entire variable profile

simultaneously instead of sequentially testing individual variables. The five accounting

8 E(R)=Rf+β*(Rm- Rf), E(R) is the expected return of the capital asset, Rf is the risk-free rate of interest, β is the sensitivity of the asset returns to market returns, Rm is the expected return of the market, (Rm- Rf) is the difference between expected return on market and risk-free rate, or also known as risk premium. Since most investors are diversified, the expected return on a security should be positively related to its beta. The model was introduced by Treynor (1962), Sharpe (1964), Lintner (1965), and Mossin (1966). 9 Cash flow/total debt has predictive power up to five years before bankruptcy.

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ratios he employed in the research were working capital/total assets, retained

earnings/total assets, earning before interest and taxes/total assets, market value

equity/book value of total debt, and sales/total assets. The model proved very accurate

when tested on a sample of US manufacturing firms, and the predictive value of the

model for the first two years prior to bankruptcy was quite high (correct prediction: 95%

for year one and 72% for year two). Following his previous research, Altman et al (1977)

constructed a second-generation model, called ZETA. ZETA model was effective in

classifying bankrupt companies up to five years prior to failure on a sample of

corporations consisting of manufacturers and retailers. Many other scholars have also

applied multidimensional models in their studies (see for example, Deakin (1972) and

Sinkey (1975)).

3.2.2 Logistic Regression

Later, new econometric methodology of logit and probit analysis has been introduced into

this field. There is no fundamental difference between logit and probit models, except

that the conditional probability p approaches zero or one at a slower rate in logit than in

probit. In practice many researchers choose logit model because of its comparative

mathematical simplicity (Gujarati, 2003). Comparing to quantitative explanatory

variables in normal regression, dependent variables in logistic regression are normally

qualitative (or dummy). Martin (1977) first intended to build an early warning model for

predicting future bank failure based on current period’s balance sheet and income

statement by using logistic regression. Ohlson (1980) tested the 1970-1976 industrial

sample data, and found that the predictive power of the default risk model (two years

before default) based on financial ratios seemed to be robust. Meanwhile, he found four

basic factors being significant with probability of failure (within one year), namely firm

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size, financial structure, performance10, and current liquidity.

In their empirical analysis, Westgaard and Wijst (2001) used the 1996 accounting data

and the 1998 bankruptcy information (2-year prior to default) and illustrated that

financial ratios (cash flow to debt, financial coverage, liquidity, and equity ratio) were

negatively and significantly correlated with PD in a corporate bank portfolio in Norway.

One year later, Westgaard and Wijst together with Hol (2002) did another research for

Norwegian limited liability companies, but with different proxy variables and time

horizon (1995-2000). Their main finding was that leverage and cash flow standard

deviation had a significantly positive effect on default probability, while cash flow had a

significant negative effect. Similarly, Zeitun et al (2007) proved that firms’ cash flow

decreased corporate failure in Jordanian companies. Their main contribution, however,

was that they addressed the issue of free cash flow11 and default risk. They concluded

that firms’ PD increased with firms’ free cash flow, which also seemed to be consistent

with agency theory. The predictive power of their models (three years prior to default) for

both matched and non-matched is very high (91.5%, 80%, and 81%). A summary of the

previous researches is presented in Table One in Appendix One.

4. Data and Methodology

4.1 Methodology

Multiple Discriminant Analysis has been widely used in default research. Nevertheless

the usefulness of MDA is quite limited, since such a technique only provides qualitative

differentiation among counterparties, and does not produce probabilities. Furthermore,

10 Measured as net income to total assets 11 Free cash flow was measured as retained earning to total assets (also see Dhumale, 1998).

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there are specific statistical requirements under this approach. For example, it assumes

predictors have normal distributions which would restrict the use of dummy independent

variables (Ohlson, 1980).

Probability of default is characterized as a non-linear S-shaped cumulative distribution

function with probabilities varying from 0 to 1, therefore a logit regression suits best.

Logit regression can specify a dichotomous dependent variable as a function of various

explanatory variables. More importantly, logit solves the problem with linear probability

model that is inherently unbounded. Different counterparties can be mapped into the

regression model within a boundary between 0 and 1. By taking the natural log of the

odds ratio (p/(1-p))12, the logit model is linear in X and in parameters, facilitating the

interpretation of coefficients. The model can be written as Li=ln[pi/(1-pi)]=βXi+ui, with

two states, L=1 if the firm defaults, L=0 otherwise. As p goes from 0 to 1, the logit L

goes from -∞ to +∞. The models are estimated by using the SPSS software. Early

examples of the use of logit regression are for instance, Martin (1977), Ohlson (1980),

Westgaard and Wijst (2001), and Zeitun et al (2007) as mentioned in section 3.2.2.

4.2 Data and Sample

The companies in this study are publicly traded and listed on the Hong Kong Stock

Exchange (HKEx)13 over the period 2001-2007. Their accounting data (for three years

prior to default) is collected by Thomson One Banker (TOB). TOB provides basic

company information such as company name, industry code, income statements and

balance sheets. However, TOB does not specify a company’s financial health (default or

non-default). Instead it only states whether the company is active or inactive14. According

12 P as in probability 13 http://www.hkex.com.hk/index.htm 14 Although inactive companies are usually default.

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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

industries15. 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. 7116

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.

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“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.

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Cash flow over total debt (CFTD) measures the ability of a company to pay back its

debtholders. Westgaard and Wijst (2001), and Zeitun et al (2007) used the same ratio and

found a negative relation between these two variables (although the empirical result from

Zeitun et al was not statistically significant).

Hypothesis 5: The ability to generate income helps prevent bankruptcies.

Net income to total assets (NITA) and sales to total assets (SATA) are used to measure

profitability (see Altman (1968), Deakin (1972), Martin (1977), and Ohlson (1980)).

Profitable firms seem to be associated with low probability of default as they have great

flexibility in allocating money and diversifying their investment.

Hypothesis 6: Companies’ liquidity has a negative relationship with PD.

Bankruptcies ought to depend on how fast a company can generate cash. Liquidity risk is

measured by working capital to total assets (WCTA) and current ratio (CA/CL) in this

study. Altman (1968) and Becchetti & Sierra (2003) used WCTA to distinguish default

and non-default firms, and they found it negatively and significantly impacted on PD.

Current ratio also indicates a negative correlation with PD, but the coefficient was not

significant in Deakin (1972)’s study.

Hypothesis 7: Large firms tend to survive compared with small firms.

A company’s size is expected to be negatively related to probability of default. In many

previous studies, researchers used the base 10 logarithm of a company’s total assets when

estimating firm size (Altman (1984), Westgaard and Wijst (2001), and Manzoni (2004)),

and they confirmed this negative correlation.

Table 2 Explanatory Variables Variables Description Expected Sign TDTA Total Debt to Total Assets + EQTC Equity to Total Capital - RETA Retained Earnings to Total Assets + ROE Return on Equity + CFTD Cash Flow (Net Income+Depreciation) to Total Debt - NITA Net Income to Total Assets - SATA Sales to Total Assets - WCTA Working Capital (Current Assets-Current Liabilities) to Total Assets - CR Current Ratio (Current Assets/Current Liabilities) - Size The base 10 logarithm of the Total Assets -

5. Empirical Results

5.1 Descriptive Results

Table 3(a) illustrates informative descriptive statistics for default firms first year17 prior

to bankruptcy and their matched non-default firms. Comparing both types of firms

(default vs. non-default), the majority of the mean values supports the expected signs of

explanatory independent variables as shown in Table 2 (e.g. NITA for non-bankrupt

companies is higher than that of the default firms. TDTA is the other way around). In

general, the financial ratios of the default companies tend to fluctuate more than those of

the non-default ones. It means that for the default companies, their financial figures are

more likely to be different from each others’. This result is not surprising because

extreme values seem to be easily found on the failed companies’ balance sheets and

income statements, especially right before their failures. However extreme values can

still be detected in non-default companies (Table 3(a) right, Sample One, 30 companies),

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17 For the comparisons between bankrupt and non-bankrupt companies two and three years before bankruptcy events, see Table 4 and 5 in Appendix Two and Three.

such instability in terms of high standard deviation is reduced to some extent by including

more observations (Table 3(b) right, Sample Two, 71 companies).

Table 3(a) Descriptive Statistics (Sample One, 1st year before default) Default Companies Non-Default Companies

Minimum Maximum Mean Std.

Deviation Variance Minimum Maximum Mean Std.

Deviation Variance

CFTD1 -1.96 11.95 0.26 2.36 5.56 -0.74 327.10 14.18 59.73 3567.81 RETA1 -20.10 0.42 -1.00 3.70 13.67 -3.81 0.71 0.01 0.89 0.79 WCTA1 -8.77 0.40 -0.47 1.65 2.71 -0.34 0.58 0.20 0.21 0.04 CR1 0.01 2.95 1.03 0.80 0.64 0.06 26.10 2.54 4.55 20.68 NITA1 -554.77 15.47 -34.97 103.61 10735.72 -12.67 57.48 7.97 12.82 164.35 TDTA1 0.60 897.53 65.22 160.72 25830.92 0.03 45.75 15.74 11.72 137.40 SATA1 0.06 5.10 0.81 1.01 1.02 0.01 3.95 0.95 0.88 0.77 Size1 1.37 4.17 2.89 0.71 0.50 1.32 5.78 3.10 0.86 0.74 EQTC1 9.60 100.00 76.69 26.04 678.16 39.34 100.00 83.80 15.85 251.28 ROE1 -1017.34 65.71 -76.30 215.30 46353.62 -176.03 47.23 5.01 41.56 1727.08

Table 3(b) Descriptive Statistics (Sample Two, 1st year before default) CFTD1 -1.96 11.95 0.26 2.36 5.56 -0.49 111.30 5.54 15.75 247.91 RETA1 -20.10 0.42 -1.00 3.70 13.67 -3.81 0.71 0.08 0.72 0.52 WCTA1 -8.77 0.40 -0.47 1.65 2.71 -0.34 0.78 0.19 0.24 0.06 CR1 0.01 2.95 1.03 0.80 0.64 0.06 15.54 2.28 2.48 6.15 NITA1 -554.77 15.47 -34.97 103.61 10735.72 -7.00 65.74 9.58 11.73 137.62 TDTA1 0.60 897.53 65.22 160.72 25830.92 0.07 52.54 17.79 13.60 185.03 SATA1 0.06 5.10 0.81 1.01 1.02 0.01 3.95 0.86 0.77 0.59 Size1 1.37 4.17 2.89 0.71 0.50 1.32 5.78 3.40 0.83 0.69 EQTC1 9.60 100.00 76.69 26.04 678.16 27.28 100.00 83.14 16.61 276.00 ROE1 -1017.34 65.71 -76.30 215.30 46353.62 -176.03 3894.62 68.00 461.46 212947.93

Unlike ordinary least square (OLS) method, the variance inflation factors (VIF)18 cannot

be computed in logistic regression as there is no direct counterpart to R2. Nevertheless,

the problem of multicollinearity19 potentially exists in either OLS or logistic regression

18 VIF shows how the variance of an estimator is inflated by the presence of multicollinearity. As the extent of collinearity increases, the variance of an estimator increases, and in the limit it can become infinite (Gujarati, 2003.)

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19 Multicollinearity is the phenomenon that one independent variable is highly and linearly correlated with

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which results in high standard errors of coefficients β and thus leads to an unreliable

interpretation of final results. In logistic regression, large standard errors signal the

possibility of multicollinearity (Studenmund, 2000, and Gujarati, 2003). Multicollinearity

cannot be eliminated entirely, however it should be reduced as much as possible.

Therefore before selecting variables for the model, it seems necessary to look at the

correlations between individual variables (see Table 6(a) and (b)). Variables that show

high correlation with the others might be dropped out of the model. For example, RETA

and WCTA are highly correlated with other seven variables respectively. However it

needs to be kept in mind that correlations only indicate the connections between two

single variables, instead of a single variable and the rest of the variables. The elimination

procedure based on the correlation table therefore is rather subjective. Another

observation from Table 6(a) and (b) is that the correlations between individual variables

decline as more companies are included in the sample, implying that multicollinearity

decreases with sample size.

Table 6(a) Correlations (Sample One, 1st year before default)

CFTD1 RETA1 WCTA1 CR1 NITA1 TDTA1 SATA1 Size1 EQTC1 ROE1

CFTD1 1.000 0.068 0.094 0.961** 0.058 -0.062 -0.036 -0.073 0.141 0.055

RETA1 0.068 1.000 0.949** 0.127 0.955** -0.953** 0.000 0.388** 0.284* 0.299*

WCTA1 0.094 0.949** 1.000 0.188 0.965** -0.969** -0.003 0.322* 0.311* 0.354**

CR1 0.961** 0.127 0.188 1.000 0.127 -0.118 -0.044 -0.073 0.172 0.116

NITA1 0.058 0.955** 0.965** 0.127 1.000 -0.233 -0.041 0.342** 0.262* 0.262*

TDTA1 -0.062 -0.953** -0.969** -0.118 -0.233 1.000 0.026 -0.307* -0.323* -0.312*

SATA1 -0.036 0.000 -0.003 -0.044 -0.041 0.026 1.000 -0.223 0.057 -0.233

Size1 -0.073 0.388** 0.322* -0.073 0.342** -0.307* -0.223 1.000 -0.024 0.257*

EQTC1 0.141 0.284* 0.311* 0.172 0.262* -0.323* 0.057 -0.024 1.000 0.318*

ROE1 0.055 0.299 0.354** 0.116 0.431** -0.312** -0.233 0.257* 0.318* 1.000

other independent variables. Serious multicollinearity endangers the reliability of the estimators. However, multicollinearity is not a serious problem when the purpose is prediction only (see for example, Geary, 1963)

Table 6(b) Correlations (Sample Two, 1st year before default)

CFTD1 RETA1 WCTA1 CR1 NITA1 TDTA1 SATA1 Size1 EQTC1 ROE1

CFTD1 1.000 0.100 0.113 0.219* 0.087 -0.104 0.038 0.044 0.218* 0.015

RETA1 0.100 1.000 0.930** 0.167 0.927** -0.940** -0.003 0.352** 0.257** 0.032

WCTA1 0.113 0.930** 1.000 0.281** 0.944** -0.956** -0.021 0.281** 0.287** 0.092

CR1 0.219* 0.167 0.281** 1.000 0.175 -0.163 -0.128 0.070 0.215* -0.009

NITA1 0.087 0.927** 0.944** 0.175 1.000 -0.212 -0.043 0.293** 0.238* 0.249*

TDTA1 -0.104 -0.940** -0.956** -0.163 -0.212 1.000 0.033 -0.266** -0.313** -0.108

SATA1 0.038 -0.003 -0.021 -0.128 -0.043 0.033 1.000 -0.215* 0.090 -0.075

Size1 0.044 0.352** 0.281** 0.070 0.293** -0.266** -0.215* 1.000 -0.139 -0.032

EQTC1 0.218* 0.257** 0.287** 0.215* 0.238* -0.313** 0.090 -0.139 1.000 0.104

ROE1 0.015 0.032 0.092 -0.009 0.249 -0.108 -0.075 -0.032 0.104 1.000

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

5.2 Estimation results of the Logit model

Similar to linear regression, logistic regression also gives estimation for the coefficient of

each parameter and its relevant significance (based on t-ratios) to the dependent variable

(PD). But the interpretation of logit regression is different, since it assumes a non-linear

relationship between probability and the independent variables. Remember that

Li=ln[pi/(1-pi)], after taking the antilog of the estimated logit, we get pi/(1-pi) (that is, the

odds ratio). In this case, since p represents probability of default, pi/(1-pi) can also be

called as odds of default. Therefore, instead of looking at parameter β (which is used to

explain the ln(odds of default)), Exp(β) should be considered the equivalent value when

interpreting odds of default directly. Table 7 shows the logit results by using matched

samples (sample one). The variables included in the model (RETA, NITA, TDTA, and

ROE) are those which jointly make the model valid and maximize the predictive power

of the model.

19

20

Table 7 Variables in the Equation (Sample One, 1st year before default)

B S.E. Wald df Sig. Exp(B) RETA1 0.979 0.775 1.597 1.000 0.206 2.662 NITA1 -0.227** 0.079 8.164 1.000 0.004 0.797** TDTA1 0.041* 0.020 4.083 1.000 0.043 1.042* ROE1 0.020* 0.010 4.005 1.000 0.045 1.021* RE -0.346 1.763 0.039 1.000 0.844 0.707 TH -0.011 1.190 0.000 1.000 0.993 0.989 MT -0.570 1.409 0.164 1.000 0.686 0.565 CD 0.713 0.938 0.578 1.000 0.447 2.041 DF 0.784 1.446 0.294 1.000 0.588 2.191 CG -1.312 1.263 1.080 1.000 0.299 0.269 TS 0.466 1.575 0.087 1.000 0.767 1.593 2001 -0.941 1.269 0.549 1.000 0.459 0.390 2002 -1.968 1.571 1.570 1.000 0.210 0.140 2003 -0.632 1.159 0.297 1.000 0.586 0.532 2006 -0.719 1.351 0.283 1.000 0.595 0.487 2007 -1.795 1.313 1.868 1.000 0.172 0.166

Omnibus Tests of Model Coefficients

Hosmer and Lemeshow Test

Model Summary

Chi-square 33.352 Chi-square 6.732 -2 Log Likelihood 49.825 df 16.000 df 8.000 Cox & Snell R2 0.426 significance 0.007** significance 0.566 Nagelkerke R2 0.569

Notes: *, and ** significant at 5, and 1 percent level respectively. The sample includes 60 companies over period

2001-2007. Industry and time dummies are included in the model, however they are not statistically significant.

RE: Real Estate; TH: Technology & Hardware Equipment; MT: Materials; CD: Consumer Durables & Apparel; DF:

Diversified Financials; CG: Capital Goods; TS: Telecommunication Services

For Hypothesis One, highly leveraged companies are more prone to go bankrupt.

According to the logit results, one additional TDTA increases the odds of default by about

4.2%. To put it another way, the odds of default are 1.042 times as large for companies

with high TDTA as for those with low ratio. The result is consistent with the trade-off

theory that PD increases as more debt in a company’s capital structure (Beaver (1966),

Martin (1977), Hol (2002)). The ratio, ROE, has seldom been used in previous MDA or

logit regression empirical analyses. In this study, ROE is at least at 5% significance level,

and it has a positive relationship with odds of default. Specifically, one unit change in

21

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

22

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 R2 like that in OLS

regression. However the Cox & Snell R2 and Nagelkerke R2 can only be seen as

approximations to OLS R2, not as the actual percentage of variance explained. Thus, they

are not informative in indicating model fit. For Sample One, the Cox & Snell R2 and

Nagelkerke R2 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

23

0.0856 (e-2.458) respectively. It means that the odds for bankruptcy in 2001 could be 3.4%

(or 8.56%) higher than that in the other years.

Table 8 Variables in the Equation (Sample Two, 1st year before default) B S.E. Wald df Sig. Exp(B) NITA1 -0.232** 0.079 8.629 1.000 0.003 0.793** Size1 -0.717* 0.332 4.681 1.000 0.031 0.488* RETA1 0.976 0.594 2.702 1.000 0.100 2.653 TDTA1 0.039* 0.019 4.079 1.000 0.043 1.040* SATA1 -0.694 0.794 0.764 1.000 0.382 0.500 EQTC1 0.016 0.014 1.430 1.000 0.232 1.016 ROE1 0.003 0.003 1.031 1.000 0.310 1.003 CD 1.206 0.951 1.607 1.000 0.205 3.340 DF 1.137 1.197 0.902 1.000 0.342 3.117 MT -0.895 1.121 0.638 1.000 0.424 0.409 SS -1.126 1.562 0.520 1.000 0.471 0.324 TPT 1.439 1.134 1.610 1.000 0.204 4.217 TH -0.455 1.225 0.138 1.000 0.710 0.635



Model Summary

Chi-square 71.835 Chi-square 16.268 -2 Log Likelihood 68.181 df 13.000 df 8.000 Cox & Snell R2 0.509 significance 0.000*** significance 0.066 Nagelkerke R2 0.679

Notes: *, **, and *** significant at 10, 5, and 1 percent level respectively. The sample includes 101 companies over

period 2001-2007. Industry dummies are included in the model, however they are not statistically significant.

CD: Consumer Durables & Apparel; DF: Diversified Financials; MT: Materials; SS: Software & Services; TPT:

Transportation; TH: Technology & Hardware Equipment.

5.3 Predictive power of the model

As one year prior to default in sample one, the predictive success according to table 11

comprises 86.7% correct prediction of non-default companies and 76.7% default

companies, with an overall 81.7% accuracy. Table 11 also displays two types of

prediction error, Type I and Type II errors. In this case, Type I error (n=7) occurs when a

default company is misclassified as non-default, and when a non-default company is

24

predicted to be default, it is called Type II error (n=4). The overall predictive success in

sample two (86.1%, see Table 12 in Appendix Eight) is relatively higher than that in

sample one. However, as mentioned before, unilaterally increasing the number of

observations (in this case, non-default companies) could somehow bias the interpretation

of the results 20 . Consequently, the number of compared observations (default &

non-default) in each sample should not be different from each other too much.

Table 11 Classification (Sample One, 1st year before default)

Predicted

Default

Observed 0 1 Percentage Correct

Default 0 26 4 86.7 1 7 23 76.7 Overall Percentage 81.7

The cut value is .500

An alternative way to look at the prediction is through the histogram of predicted

probabilities (Figure 1, representing sample one). The x axis represents the probability

from 0 (non- default) to 1 (default). The y axis is the frequency of the cases. Ideally,

failed (non-failed) companies should be clustered on the right (left) side of the x axis.

Moreover a U-shaped distribution with well differentiated predictions is more desirable

over normal distribution. Because a model where predictions are close to 0 or 1 provides

more information than one with predictions all cluster around the cut value 0.5. This

U-shaped distribution might be less obvious in Figure 1, mostly because the sample size

is rather small. As more observations are included in the model, a more desirable

distribution can be clearly seen in Figure 2 (representing sample two) in Appendix Eight.

20 The overall accuracy of the logit in Sample One equals 86.7%*0.5+76.7% *0.5=81.7%, because the default and non-default companies represent half of the total observations respectively. In Sample Two, the overall predictive success=95.8%*(71/101)+63.3%*(30/101)=86.1%. The increase in predictive power is mostly due to the uneven weights distribution.

In this study, the predictive success of the models for all three years prior to default in

both samples is reasonably high (all above 70%). In general, models in sample two have

higher predictive power than those in sample one. Within each sample, the further the

distance is to bankruptcy, the less powerful the predictive power becomes. Although the

difference between year two and year three is quite small, this result is consistent with

Zeitun et al (2007)’s. In their matched sample, the percent of predictive successes in year

two and year three are 80% and 81% respectively.

5.4 Robustness Test

The robustness of the model depends on if it can be applied in multi-period. That is, the

longer the accuracy of the model could be maintained, the better the model becomes. In

this study, as the predictive powers of the model in all three years prior to bankruptcy are

above 70% in both samples, we conclude that the model seem to be robust across

25

26

estimation procedure. However it is interesting to check what impact of outliers to the

model prediction would be. After excluding observations with standardized residuals

greater than 2 (Table 13), the predictive power of the entire model improves, and Type I

(n=3) and Type II errors (n=3) decrease at the same time (Table 14).

Table 13 Casewise Listb (Sample One, 1st year, outliers) Observed Temporary Variable Case Selected

StatusaDefault

Predicted Predicted Group Resid ZResid

34 S 0** 0.859 1 -0.859 -2.469 37 S 1** 0.243 0 0.757 1.764 41 S 1** 0.190 0 0.810 2.067 55 S 1** 0.194 0 0.806 2.038

a. S = Selected, U = Unselected cases, and ** = Misclassified cases. b. Cases with studentized residuals greater than 2.000 are listed.

Table 14 Classification (Sample One, 1st year, excluding outliers) Predicted

Default




Nonetheless, such improvements are not costless. The results generated by the new model

(excluding outliers) do not seem to be as stable as the original results. Especially for the

industry and time dummies, their standard errors slightly increase (see Table 15). This is

probably because of the limited number of companies in sample one. Elimination of

outliers could cause large fluctuations in the estimation of the parameters. When more

observations are used in the sample (see Appendix Nine), the model is robust after

excluding outliers.

27

Table 15 Variables in the Equation (Sample One, 1st year, excluding outliers) B S.E. Wald df Sig. Exp(B) RETA1 1.453 1.008 2.076 1.000 0.150 4.276 NITA1 -0.473*** 0.173 7.464 1.000 0.006 0.623 TDTA1 0.116** 0.048 5.892 1.000 0.015 1.123 ROE1 0.047** 0.020 5.817 1.000 0.016 1.048 RE -1.877 2.157 0.757 1.000 0.384 0.153 TH 1.575 1.602 0.967 1.000 0.325 4.831 MT -2.110 2.186 0.932 1.000 0.334 0.121 CD -0.944 1.484 0.405 1.000 0.525 0.389 DF 0.662 1.663 0.159 1.000 0.690 1.939 CG -2.431 1.525 2.542 1.000 0.111 0.088 TS 0.429 6.419 0.004 1.000 0.947 1.535 2001 -1.875 1.628 1.327 1.000 0.249 0.153 2002 -2.207 2.386 0.855 1.000 0.355 0.110 2003 -0.333 1.468 0.051 1.000 0.820 0.717 2006 -1.563 3.371 0.215 1.000 0.643 0.209 2007 -6.996** 3.404 4.223 1.000 0.040 0.001

6. Conclusion

The article focused on predicting bankruptcies for Hong Kong based companies. It

examines the relationship between default risk and various financial ratios. Logistic

regression is the main methodology in this research for testing different theories and

hypotheses. The result is in support of capital structure theory and risk-return tradeoff. To

be specific, TDTA, representing capital structure, has a positive and significant impact on

bankruptcy, which is consistent with the results of Martin (1977) and Hol et al (2002).

Shareholders’ expected return, as reflected by ROE, is also an important determinant of

corporate failure. ROE has not been used extensively in previous MDA or logit analyses.

But its positive relationship with PD as estimated in the study is consistent with the

classic risk-return tradeoff. Moreover, profitability, as measured by NITA, is significantly

and negatively related to default probabilities in both samples (Altman (1968), and

28

Ohlson (1980)). In the second sample, it has been proved that firm size plays an

important role in predicting PD. Small firms are more prone to go bankrupt because of

the limited access to capital market. Westgaard and Wijst (2001) and others illustrate the

same result. Other variable, such as free cash flow (as measured by RETA), does not

seem to be related to default risk for companies in Hong Kong, since it is not significant

in both samples. Contrary to previous empirical results, such as Zeitun et al (2007), this

inconsistency should be explained by a Hong Kong specific factor. Because most of the

Hong Kong companies (even large publicly traded) are family controlled21, agency

problem raised by free cash flow does not seem to be severe in such context (without the

principal and agent relationship). In that case, free cash flow, measured by RETA, should

not threaten a company’s default probability. Industry dummies do not explain

bankruptcy in this study. The result is consistent with Hol (2002)’s, meaning that industry

variables capture the inter-industry differences in default probability. The majority of the

time dummies are not significantly correlated with bankruptcy, except for the year 2001

for two and three years before bankruptcy in sample one. The result may imply that firms

in Hong Kong have higher probabilities to go bankrupt in 2001 compared with those in

other years. Interestingly, in 2001 the real GDP increase by percentage was the lowest

over 2000-2007 according to Census and Statistics Department (Hong Kong) 22 .

Intuitively, this indicates that macroeconomic factors could influence default probability.

The predictive power for all three years before bankruptcy in both samples is reasonably

high, and generally increases as time approaching to the bankruptcy event. The result is

21 70% of Hong Kong listed companies were majority controlled by a family or an individual (Hong Kong Society of Accountants, 1996). 53% of all listed companies had one shareholder or one family group of shareholders that owned 50% or more of the entire issued capital (Hong Kong Society of Accountants, 1997). Also see publications of Ho (2003), and the presentation at International Financial Corporation IO Training Session on 30 April 2004, “Corporate governance in Hong Kong”, by Patrick Contoy, Hong Kong Exchanges and Clearing. 22 Official website: http://www.censtatd.gov.hk/hong_kong_statistics/statistical_tables/2000+: Real GDP increases by percentage: 2000 (7.95), 2001 (0.50), 2002 (1.84), 2003 (3.01), 2004 (8.46), 2005 (7.12), 2006 (6.75), 2007 (6.83)

http://www.censtatd.gov.hk/hong_kong_statistics/statistical_tables/

29

useful in describing default risk in the context of Hong Kong. As a risk manager of a

bank, he or she would first look at borrowers (companies)’ capital structure, profitability,

firm size, and equity returns over other characters, such as retained earnings, or industry

type according to the empirical results in this study.

The limitations of the research need to be taken into consideration. One of the limitations

is that the multicollinearity problem cannot be fully dealt with. Two main solutions

adopted in the research are reducing explanatory variables in the models and increasing

sample size, ensuring that multicollinearity is to a large extent under control. Another

limitation is from the dataset. Only 30 publicly traded companies have been defined as

default in the study (due to mostly missing value, and the definition of bankruptcy) over

period 2001 to 2007. This results in a relatively small sample size in both matched and

non-matched sample. However, because bankruptcies are scarce activities, the problem of

limited sample observations also exists in previous studies23.

The models in this study will only be viewed as a static and myopic way in predicting

bankruptcies in short term. In future research, macroeconomic risk factors should also be

incorporated in estimating corporate default (see e.g., Fama (1986)). Macroeconomic

conditions have an impact not only on firms’ default risk but also on their financial

decisions (Hackbarth et al, 2006). The model would then provide more insights in

predicting future default under different scenarios. Meanwhile, with more and more

countries are adopting Basel II, similar bankruptcy analyses should also be encouraged in

Asia, where risk management practices are relatively weak compared with those in

western countries.

23 For example, Altman (1968)’s sample comprises 66 corporations with 33 firms in each of the two groups. Castagna and Matolcsy (1981) analyzed 21 failed and non-failed firms respectively in their MDA analysis. In Zeitun et al (2007)’s first sample, it is composed of 29 failed and 30 non-failed firms.

30

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34

Appendix One

Table 1 Literature Summary

Author Year Ratios

Beaver 1966 cash flow/total assets, net income/total assets, total debt/total assets, and cash flow/total debt

Altman 1968

working capital/total assets, retained earnings/total assets, earning before interest and taxes/total assets, market value equity/book value of total debt, and sales/total assets

Deakin 1972

Working capital/Total assets, current assets/current liabilities, total debt/total assets, quick assets/sales, quick assets/total assets, cash/total assets, cash/sales, current assets/total assets, cash/current liabilities, net income/total assets, current assets/sales, cash flow/total debt

Sinkey 1975

Other expenses as % of revenue, loans as % of revenue, operating expense/operating income, loans/(capital + reserve), state and local obligations as % of revenue, (cash + U.S. treasury securities)/total assets, loans/total assets, provision for loan losses/operating expense, U.S. treasury securities as % of revenue, interest paid on deposits as % in revenue

MDA

Altman et al 1977

Return on assets, stability of earnings (measured by a normalized measure of the standard error of estimate around a ten-year trend, debt service (measured by the familiar interest coverage ratio, cumulative profitability (measured by the firm’s retained earnings), liquidity, capitalization, size

35

Appendix One (continued)

Author Year Ratios Significant & Positive

Significant & Negative

Martin 1977

Net income/total assets, gross charge-offs/net operating income, expenses/operating revenues, loans/total assets, commercial loans/total loans, loss provision/loans+securities, net liquid assets/total assets, gross capital/risk assets

expenses/operating revenues, loans/total assets

gross capital/risk assets

Ohlson 1980

Size, total liabilities to total assets, working capital to total assets, current liabilities to current assets, OENEG (one if total liabilities exceeds total assets, zero otherwise), net income to total assets, funds provided by operations to total liabilities, INTWO (one if net income was negative for the last two years, zero otherwise)

current liabilities to current assets, INTWO

working capital to total assets

Westgaard and Wijst

2001

cash flow to debt, financial coverage, liquidity, and equity ratio

all

Hol et al 2002

total debt/total assets, tax/earnings before interest and tax, cash flow (net income+depreciation/total assets), standard deviation of cash flow, bankruptcy cost (ln(sales))

total debt/total assets, standard deviation of cash flow, and bankruptcy cost

cash flow

Logistic regression

Zeitun et al 2007

Net income to total liabilities, cash flow to total debt, sales to total assets, current assets minus current liabilities to total assets, , long-term liabilities to total assets, total debt to total equity, firms age in years, log the assets of the firm, retained earning to total assets

retained earning to total assets

sales to total assets, net income to total liabilities

36

Appendix Two

Table 4 Descriptive Statistics (Sample One, 2nd and 3rd year) Default Companies Non-Default Companies

Minimum Maximum Mean STDV Variance Minimum Maximum Mean STDV Variance CFTD2 -513.12 125.90 -12.35 97.35 9476.50 -11.80 19.23 1.64 5.36 28.77 CFTD3 -4.57 9.29 0.36 2.09 4.39 -10.68 36.35 2.28 8.23 67.73 RETA2 -24.60 0.55 -0.95 4.50 20.29 -38.82 0.67 -1.38 7.15 51.08 RETA3 -8.89 0.51 -0.44 1.83 3.34 -29.47 0.78 -0.98 5.44 29.55 WCTA2 -1.21 0.54 -0.10 0.43 0.18 -0.43 0.61 0.12 0.23 0.05 WCTA3 -0.94 0.99 0.06 0.38 0.14 -0.44 0.73 0.17 0.25 0.06 CR2 0.03 3.89 1.20 0.88 0.77 0.15 3.47 1.56 0.85 0.71 CR3 0.16 67.21 4.37 12.76 162.69 0.16 6.52 1.75 1.19 1.41 NITA2 -257.04 31.96 -17.25 62.04 3848.38 -85.28 41.94 -0.06 24.72 610.83 NITA3 -205.59 28.30 -10.87 45.93 2109.58 -124.05 27.01 -3.19 28.17 793.38 TDTA2 0.01 113.25 33.34 28.42 807.88 0.34 56.01 16.40 13.42 180.00 TDTA3 0.22 277.39 37.82 49.67 2467.28 0.00 71.25 17.43 15.82 250.40 SATA2 0.02 2.84 0.89 0.77 0.59 0.04 3.11 1.00 0.78 0.61 SATA3 0.02 2.84 0.88 0.68 0.46 0.03 4.55 0.96 0.86 0.73 Size2 1.60 4.07 2.93 0.61 0.38 1.14 5.69 2.97 0.89 0.79 Size3 -1.08 3.98 2.86 0.94 0.88 1.34 5.60 2.93 0.86 0.74 EQTC2 30.79 100.00 77.24 22.48 505.56 37.35 100.00 83.18 16.85 283.81 EQTC3 18.89 100.00 74.56 22.36 499.77 -652.16 100.00 56.89 135.77 18434.32 ROE2 -610.38 84.34 -31.73 127.36 16220.59 -1593.75 68.05 -52.14 295.40 87261.91 ROE3 -2537.76 58.16 -110.31 476.83 227364.26 -424.07 93.25 -11.49 91.22 8321.36

37

Appendix Three

Table 5 Descriptive Statistics (Sample Two, 2nd and 3rd year) Default Companies Non-Default Companies

Minimum Maximum Mean STDV Variance Minimum Maximum Mean STDV Variance

CFTD2 -513.12 125.90 -12.35 97.35 9476.50 -4.55 158.32 6.50 24.00 575.98 CFTD3 -4.57 9.29 0.36 2.09 4.39 -10.68 64.07 4.51 13.14 172.57 RETA2 -24.60 0.55 -0.95 4.50 20.29 -3.94 0.73 -0.02 0.91 0.82 RETA3 -8.89 0.51 -0.44 1.83 3.34 -3.87 0.78 -0.02 0.83 0.69 WCTA2 -1.21 0.54 -0.10 0.43 0.18 -0.43 0.71 0.17 0.25 0.06 WCTA3 -0.94 0.99 0.06 0.38 0.14 -0.42 0.66 0.16 0.23 0.05 CR2 0.03 3.89 1.20 0.88 0.77 0.15 23.32 2.42 3.18 10.10 CR3 0.16 67.21 4.37 12.76 162.69 0.01 11.26 2.16 2.12 4.47 NITA2 -257.04 31.96 -17.25 62.04 3848.38 -85.28 41.94 3.96 16.72 279.46 NITA3 -205.59 28.30 -10.87 45.93 2109.58 -124.05 41.34 4.42 20.76 431.18 TDTA2 0.01 113.25 33.34 28.42 807.88 0.08 66.50 20.85 16.12 259.76 TDTA3 0.22 277.39 37.82 49.67 2467.28 0.00 60.24 21.30 16.04 257.37 SATA2 0.02 2.84 0.89 0.77 0.59 0.01 3.18 0.87 0.72 0.52 SATA3 0.02 2.84 0.88 0.68 0.46 0.00 3.55 0.86 0.72 0.51 Size2 1.60 4.07 2.93 0.61 0.38 1.14 5.69 3.34 0.84 0.71 Size3 -1.08 3.98 2.86 0.94 0.88 1.34 5.60 3.29 0.82 0.67 EQTC2 30.79 100.00 77.24 22.48 505.56 23.55 100.00 80.55 18.17 330.29 EQTC3 18.89 100.00 74.56 22.36 499.77 10.39 100.00 80.10 19.98 399.03 ROE2 -610.38 84.34 -31.73 127.36 16220.59 -202.77 68.05 5.64 39.61 1568.70 ROE3 -2537.76 58.16 -110.31 476.83 227364.26 -196.74 93.25 11.46 39.46 1556.94

38

Appendix Four

Table 9 Coefficients two or three years before default (Sample One)

Variables Two years before default Three years before default RETA 0.447 1.214** (0.206) (0.006) NITA -0.074 0.156* (0.339) (0.019) TDTA 0.097** 0.084** (0.007) (0.001) ROE 0.023 -0.089* (0.509) (0.011) FBT -1.558 dropped (0.274) TPT 1.540 dropped (0.293) MT dropped -2.267 (0.159) CD dropped -0.583 (0.469) CG dropped -0.451 (0.683) TS dropped -1.128 (0.422) 2001 -3.371* -2.458* (0.041) (0.043) 2002 -1.651 -1.183 (0.170) (0.334) 2003 -1.501 -1.521 (0.060) (0.083) 2004 dropped dropped 2005 -1.577 -0.962 (0.108) (0.302) 2006 -2.275 -0.834 (0.052) (0.406) 2007 -2.475* -1.810 (0.050) (0.099)

39

Appendix Five

Table 9 (continued) Model Summary

Percentage Correct 73.333 73.333 -2 Log likelihood 62.294 58.940 Cox & Snell R Square 0.294 0.332 Nagelkerke R Square 0.392 0.443


Chi-square 20.883 24.237 df 12.000 14.000 sig 0.050* 0.043*


Chi-square 3.654 9.892 df 8.000 8.000 sig 0.887 0.273

40

Appendix Six

Table 10 Coefficients two or three years before default (Sample Two)

Variables Two years before default Three years before default

NITA -0.068 0.036 -0.077 -0.316 Size -0.604* -0.609 (0.026) -0.067 ROE 0.018 -0.029 -0.147 -0.122 TDTA 0.037** 0.043** (0.006) (0.010) SATA -0.367 -0.67 -0.349 -0.183 EQTC 0.004 -0.001 -0.741 -0.956 RETA 0.247 0.464 -0.344 -0.382 CD 1.391 1.745* -0.055 (0.049) MT -0.199 -0.094 -0.825 -0.924 RE -0.35 0.146 -0.77 -0.915 TH -0.016 dropped

-0.984

DF -0.057 dropped

-0.96

CG dropped 1.099 -0.252 FBT dropped 0.669 -0.513 SS dropped -0.15 -0.898 TS dropped 0.314 -0.779 TPT dropped 0.117 -0.916

41

Appendix Seven

Table 10 (continued) Model Summary

Percentage Correct 78.218 79.208 -2 Log likelihood 101.122 97.363 Cox & Snell R Square 0.320 0.344 Nagelkerke R Square 0.426 0.459


Chi-square 38.894 42.653 df 12.000 15.000 sig 0.000** 0.000**


Chi-square 4.774 4.556 df 8.000 8.000 sig 0.781 0.804

Appendix Eight

Table 12 Classification (Sample Two, 1st year before default)

Predicted

Default




42

43

Appendix Nine

Table 16 Casewise Listb (Sample Two, 1st year, outliers) Observed Temporary Variable Case Selected

StatusaDefault

Predicted Predicted Group Resid ZResid

67 S 1** 0.020 0 0.980 6.992 99 S 1** 0.013 0 0.987 8.757

a. S = Selected, U = Unselected cases, and ** = Misclassified cases. b. Cases with studentized residuals greater than 2.000 are listed.

Table 17 Classification (Sample Two, 1st year, excluding outliers)

Predicted

Default




Table 18 Variables in the Equation (Sample Two, 1st year, excluding outliers) B S.E. Wald df Sig. Exp(B) NITA1 -0.488** 0.164 8.866 1.000 0.003 0.614** Size1 -1.227* 0.484 6.433 1.000 0.011 0.293* RETA1 2.130 1.208 3.107 1.000 0.078 8.412 TDTA1 0.095** 0.032 8.650 1.000 0.003 1.099** SATA1 -3.280* 1.530 4.594 1.000 0.032 0.038* EQTC1 0.033 0.019 3.118 1.000 0.077 1.033 ROE1 0.008 0.005 2.424 1.000 0.119 1.008 CD 3.654* 1.583 5.330 1.000 0.021 38.613* DF 2.086 1.604 1.692 1.000 0.193 8.057 MT -1.440 1.388 1.076 1.000 0.300 0.237 SS -2.411 1.891 1.625 1.000 0.202 0.090 TPT 3.198* 1.612 3.937 1.000 0.047 24.479* TH -1.324 2.330 0.323 1.000 0.570 0.266

financial ratios as predictors of failure: evidence from ......4 1. introduction one year after the...

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