Download Contemporary Logistics The Research on Credit Risk of Manufacturing Listed Companies

Contemporary Logistics 14 (2014) 1838-739X
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Contemporary Logistics
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The Research on Credit Risk of Manufacturing Listed Companies
in China Based on Logistic Model
Tianlin ZHAO, Jia DU
School of Economics and Management, Beijing Jiaotong University, 100044, P.R.China
KEYWORDS
ABSTRACT
Manufacturing listed companies,
Logistic model,
Credit risk,
Default probability,
Financial indicators
Currently, three commonly used ways to research credit risk of listed company is KMV
model, CreditMetrics model and CreditRisk + model. But the three models are established
based on data of capital market and credit rating. Consider China’s special circumstance, the
development of capital market and rating system is not mature. So the three models are not
so applicable in China. But logistic model is based on financial data, so the result is more
reliable. According to the characteristics of manufacturing industry, this paper choose 6
financial indicators to predict the default probability of manufacturing listed companies and
construct a logistic model through principal component analysis. Empirical result shows that
the predictive accuracy of logistic model is 81.3%, two principal components have
significant effect on default probability. Compared with other models, logistic model has
good predictive effect, can not only provide a standard for company’s risk warning, but also
provide a standard for commercial bank loans.
© ST. PLUM-BLOSSOM PRESS PTY LTD
1 Introduction
Manufacture is a pillar industry of China, manufacturing companies can’t develop without the support of credit funds from
commercial banks. In order to improve the security of bank’s credit funds, it’s necessary to research the credit risk of manufacturing
companies. As the development of China’s capital market is not mature, the data of capital market is single and can’t reflect the real
value of a company. So the three traditional measurement models of credit risk are not well applicable in our country. But logistic
model is based on financial data, financial data is real and easy to get. So, this paper use logistic model to research the credit risk of
manufacturing listed companies. Taking the default probability of manufacturing companies as dependent variable and the financial
data as independent variable, the paper aim to build a binary logistic regression model through SPSS analysis and then use the
empirical results to test the model’s predictive effect.

Corresponding author.
E-mail: [email protected]
English edition copyright © ST. PLUM-BLOSSOM PRESS PTY LTD
DOI:10.5503/J.CL.2014.14.019
102
2 The Application of Logistic Model on Research of Domestic and Foreign
Credit Risk
It was earliest start from abroad when logistic regression model was applied to research in company’s credit risk. As early as in 1977,
Scholar Martin use logistic model to predict company’s bankruptcy and default probability. He defined and selected 58 troubled
banks from 5,700 Federal Reserve member banks from 1970-1977, and selected 8 financial ratios from 25 financial indicators to
predict company’s bankruptcy and default probability and then established logistic regression model. He also compared the
predictive ability of Z-Score model, ZETA model and logistic model, it turned out that logistic regression model is better than
Z-Score model and ZETA model. Later, Scholar Ohlson also used logistic model to analyze credit risk. In 1986, Scholar Madalla
used logistic model to distinguish default and non-default loan applicants, the result of his research shows that when default
probability P>0.551, the loan is risk loan, when P<0.551, the loan is non-risk loan.
Qi Zhiping, Yu Miaozhi selected 164 listed companies from Shanghai and Shenzhen Stock Exchange, use linear model, logistic
regression model and logistic model including quadratic term and cross term to predict two years in advance of sample data, the
result shows that logistic model including quadratic term and cross term has the highest predictive accuracy of data for the previous
year, is 83.3%, the predictive accuracy of logistic regression model is 66.67%, and the predictive accuracy of linear model is 56.67%.
Scholar Wu Shinong selected all the ST companies in A share market in 1998-2000, totally 70 and also selected 70 non-ST
companies as matched sample, use statistical methods including profile analysis, univariate analysis, LPM, fisher two types of linear
judgment and logit model to research and predict financially distressed companies. Among them, logit model’s predictive accuracy of
data for the previous year is 93.53%; both fisher discriminance and LPM’s accuracy is 89.93%.
3 The Theoretical Significance of Logistic Model
In logistic model, the dependent variable is a binary variable, which can take only two values, 0 and 1, stands for a thing happens or
not. Independent variable can be continuous variable, discrete variable or dummy variable. When logistic model is applied to
research in company’s credit risk, the prediction of default probability is regarded as a problem of dummy variable. When the
variable values 1, means the company defaults, when the variable values 0, means the company doesn’t default. In this model, the
bigger P’s value is, the more possibility the company will default. Logistic model assumes that the probability of the occurrence of
dependent variable has non-linear relationship with other influencing factors, as shown in formula (1):
p=
1
1+e- k
n
k =a0 + ai xi
(1)
i =1
P is the unknown default probability, x is independent variables, when k varies from -∞ to +∞, P varies from 0 to 1. After
transformation, there is linear regression model, as shown in formula (2):
ln(
n
p
)=a0 + ai xi
1-p
i =1
(2)
The corresponding curve is shown in Figure 1:
Figure 1 The graph of logistic model
The most important factor that affects the possibility of default is the company’s financial position, and a company’s financial
position is usually reflected by financial information. Therefore, this paper selects several financial indicators from financial
statement of listed companies as independent variables to analyze.
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4 Modeling
4.1 The selection of sample data
Most of the foreign scholars regard companies applied for bankruptcy or named bankruptcy as default company samples. Domestic
literatures commonly use listed company’s data as research samples. Generally there are two methods to determine default samples.
The first method is using option pricing model to determine corporate default rates, the model based on option pricing theory regard
the repayment of debt or not as a selective option, if the market value of assets exceeds the total debt, the company choose to repay, if
the market value of assets is less than the total debt, the company choose to default. The second method to determine a company’s
default is the traditional method: generally believed that company with ST shares is default companies, while company with non-ST
shares is normal companies. ST shares refer to stock with special treatment, because the domestic listed company losses for two
consecutive years.
In domestic commercial banks, the information of internal default client is incomplete and hard to get, make it difficult to become the
object of the research. So this paper uses the second method. Classified by SFC industry segments, by 2012 China has a total of
1,224 manufacturing listed companies. This paper randomly selects 80 of them as a sample to study. Among them, 40 are ST shares,
as the default samples; 40 are non-ST shares, as the normal samples. The number of normal samples and default samples meet the 1:1
traditional matching principle. And select first quarter financial statements of 2012 to analyze. All the financial information and data
used in this paper is are from ifeng website.
4.2 The determine of financial indicators
Financial indicators in financial statements are mainly divides into four categories: credit capacity indicators, operation capacity
indicators, profitability indicators and development capacity indicators. These four categories of indicators reflect a company’s
financial position in a better, more comprehensive way from four different angles. This paper selects a total of 6 financial indicators
from the four categories, 1 or 2 from each category. The indicators are shown in Table 1:
Table 1 Financial indicators
Category
Credit capacity indicators
Operation capacity indicators
Profitability indicators
Development capacity indicators
Name
Current ratio
Quick ratio
Inventory turnover ratio
Return on equity
EPS growth rate
Net profit growth rate
Code
X1
X2
X3
X4
X5
X6
Among them, current ratio and quick ratio are short-term credit capacity indicators. It may take a relatively long time for a
manufacturing company to make products. When products are sold, there will be cash flows. But if a company lacks of liquidity in
short-term, it may have default risk. The inventory turnover ratio measures the ability of manufacturing company to turnover
products. If the products are unsalable and the inventory turnover is too slow, then the company may have default risk. The return on
equity is a very important profitability indicator for a manufacturing company. If shareholders can’t get return from the company,
there may be a great chance for the company to default. The EPS growth rate and net profit growth rate measure the development
capacity of the company, they compared current financial position with the last period. If the development prospect of the company is
not optimistic, there may be default risk. It can be seen that theoretically, the six indicators is very applicable to research credit risk of
manufacturing listed companies.
4.3 Empirical analysis
4.3.1 The logistic regression of raw data
Taking whether the company default or not as dependent variable, the six financial indicators in 4.2 as independent variables in
regression analysis, we get the regression results as shown in Table 2 and Table 3:
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Table 2 The predictive efficiency of logistic model
Through the comparison of observed value and predicted value, the predictive accuracy is 85%, the predictive effect is pretty good.
Table 3 The regression results of logistic model
From the significant level results, only return on equity X4’s P-value is less than significant level 0.05, while the other five financial
indicators don’t have a significant relationship with the dependent variable. The reason for this result is probably the chosen 6
indicators have much repeated information, that is, they have multicollinearity.
4.3.2 The test of multicollinearity
There are many ways to test multicollinearity, this paper uses variance inflation factor method to judge if there is multicollinearity.
Variance inflation factor VIF is the reciprocal of independent variable’s tolerance, stands for the correlation between one dependent
variable and the other dependent variables. The greater VIF’s value is, multicollinearity will be more serious. Experience judgement
shows: when 0<VIF<10, there is no multicollinearity; when 10≤VIF<100, there is strong multicollinearity; when VIF≥100, there is
serious multicollinearity.
Table 4 Multicollinearity statistics
The calculated results of VIF are shown in Table 4. We can see that VIF value of X1, X2, X5, X6 are more than 10, so there is strong
multicollinearity among dependent variables.
4.3.3 Eliminate multicollinearity
There are many ways to eliminate multicollinearity. For example, increasing sample observations; deleting unimportant explanatory
variables; transforming the form of model; stepwise regression and so on. This paper uses Principal Component Analysis to eliminate
multicollinearity. Because Principal Component Analysis can eliminate positive correlations among primitive indicators, in the
meantime reducing dimensions.
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Table 5 Variance explained
Through Principal Component Analysis, the cumulative variance proportion of eigenvalue 1, 2, 3 is 85.626%, as shown in Table 5. It
means the first three principal components basically contain the information of all the indicators which has met the requirement of
the principal component analysis. So we choose the first three eigenvalues to construct principal components. The calculated
eigenvectors after varimax rotation are shown in Table 6:
Table 6 Rotated component matrix
Constructing three principal components through eigenvectors, the expressions are shown as formula (3), formula (4) and formula
(5):
F1 =0.981X1 +0.983X 2 +0.021X 3 +0.339X 4 +0.032X 5 +0.035X 6
(3)
F2 =0.033X1 +0.008X 2 +0.048X 3 +0.266 X 4 +0.989X 5 +0.988X 6
(4)
F3 =-0.085X1 -0.006 X 2 +0.947 X 3 -0.392 X 4 -0.037X 5 -0.028X 6
(5)
In the first principal component, the coefficient of X1 and X2 are bigger than others, namely, current ratio and quick ratio, they are all
credit capacity indicators. In the second principal component, the coefficient of X5 and X6 are bigger than others, namely, EPS growth
rate and net profit growth rate, they are all development capacity indicators. In the third principal component, the coefficient of X3 are
bigger than others, namely, inventory turnover ratio, it is an operation capacity indicator. The coefficient of X4 has little difference
among three principal components, namely, return on equity, it is a profitability indicator.
4.3.4 The logistic regression of principal components
Calculate the value of three principal components of each sample according to the expression of each principal component. Then
taking whether the company default or not as dependent variable, the three principal components as dependent variables in regression
analysis, the results are shown in Table 7:
Table 7 The regression results of logistic model
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From the table, we can see only principal component F2 pass the significant test. The Sig value of F1 and F3 are bigger than 0.05. To
improve the significant level of variables, we should do two more regression analysis, the first time delete F1 and the second time
delete F3. The result shows that when delete principal component F3, principal component F1 and F2 and the constant all become
significant. The regression results are shown in Table 8:
Table 8 The regression results of logistic model
We can see that all the variables and constant pass the significant test. The cumulative variance proportion of F1’s eigenvalue and
F2’s eigenvalue is 67.950%, while the variance proportion of the deleted variable F3’s eigenvalue is 17.676%, far less than the first
two principal components’ variance proportion, so it don’t have great impact on information coverage of original indicators.
Therefore, the first two principal components can basically reflect the impact on dependent variable of all the financial indicators.
Now the expression of the model is shown in formula (6):
ln(
p
)=1.571-0.4F1 +0.012F2
1-p
(6)
4.3.5 The predictive efficiency of the model
Figure out the expression of P-value from the expression of the model, as shown in formula (7):
P=
exp(1.571-0.4F1 +0.012F2 )
1+exp(1.571-0.4F1 +0.012F2 )
(7)
P-value is a company’s default probability; the critical value of P is 0.5. When the P-value of a listed company is bigger than 0.5, the
company can be judged as ST company and has a high default probability; when the P-value of a listed company is smaller than 0.5,
the company can be judged as normal company and has a low default probability. This method provides a good standard for
commercial banks to offer loans. Compared the calculated P-value with the actual condition, we can test the model’s predictive
efficiency:
Table 9 The predictive efficiency of logistic model
From Table 9 we can see, in all the 40 ST companies, the model judge 6 of them as normal companies; in all the 40 normal
companies, the model judge 9 of them as ST companies. The comprehensive accuracy of the judgment is 81.3%. So the model has a
good predictive efficiency on manufacturing listed companies’ default.
5 Conclusion
The empirical analysis results shows that the predictive accuracy of the logistic model constructed in this paper is 81.3% for China’s
manufacturing listed companies, the predictive efficiency is pretty good. Principal component analysis can be an effective way to
solve the problem of financial data’s multicollinearity. In the final model, principal component F1 and F2 have a significant impact on
dependent variable. The two principal components cover the information of these financial indicators: current ratio, quick ratio, and
return on equity, EPS growth rate and net profit growth rate. So these financial indicators should be the key considerations in
manufacturing listed company’s risk warning, they should also be the key considerations when commercial banks offer loans. It has
been proved that the 6 financial indicators this paper selected are very applicable to China’s manufacturing listed companies, both
theoretically and empirically. The model has a better predictive efficiency than it will be when choosing other indicators. Compared
with KMV model, CreditMetrics model and CreditRisk + model, logistic model has a relatively better predictive accuracy, and it can
guarantee the authenticity and reliability of the results.
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