Logistic Financial Crisis Early-warning Model Subjoining Nonfinancial Indexes
for Listed Companies
Shao-fang Ding 1, Ying-chao Hou2, Pei-pei Hou3
1
Beijing Polytechnic, Beijing, China
Department of Economics and Management, NCUT, Beijing, China
3
Department of Foreign Languages, Xi’an Jiaotong University, Xi’an, China
(1dshf@ncut.edu.cn, 2houyingchao@live.cn, 3qiaomai2006www@126.com )
2
Abstract - The occurrence of financial crisis is related
with financial factors. Many nonfinancial ones also contain
important information relevant to the occurrence of
financial crisis. If merely financial factors are taken into
consideration, much useful information will be lost. Thus,
the early-warning capacity of the model will be reduced.
What’s more, we will fail to learn the cause for the
occurrence of financial crisis at a more profound level. It is
imperative to draw nonfinancial index into the study of
financial crisis early-warning and build a more effective and
more complete financial crisis early-warning model. The
paper introduces not only financial index, but also
nonfinancial index including enterprise ownership structure,
corporate governance, and major item, etc, while it takes a
preliminary identification and screening about the study
sample, paired sample and early-warning indicators. Then
we set up enterprise’s financial crisis early-warning model to
complete the warning index system.
Keywords - Financial crisis early-warning; Nonfinancial
indexes;, Logistic regression; factor analysis
companies by the ratio of 1:1 as paired samples. To
guarantee the consistency and comparability with the
original sample data, the paired samples are in the same or
similar industry and in the similar asset size with the
original ones while the same last three years’ information
is used as study object. Sample data come from Wind,
CSMAR and RESSET Databases.
This paper classifies early-warning indexes into
financial ones and nonfinancial ones. On the basis of
previous research, 31 indexes are chosen, according to the
principle of sensitivity, accuracy, representativeness and
comprehensiveness. Among those indexes, there are 16
financial indexes, selected in accordance with debt-paying
ability, operating capacity, earning power and development
capacity. The other 15 indexes are nonfinancial ones,
selected by shareholding structure, corporate governance,
significant matters and other factors. See TABLE I.
TABLE I
I.
Early-warning Index
INTRODUCTION
Index Classification
In previous papers, in the application of nonfinancial
index, scholars study more about relationship between
nonfinancial index and company performance. Even in
financial crisis early-warning model including nonfinancial
index, they apply mainly the financial indexes without a
comprehensive nonfinancial index system, while they
introduce just a few nonfinancial indexes, as there is only a
single ownership structure or corporate governance.
However, this paper introduces ownership structure,
corporate governance situation, and major item into the
early-warning model, according to different samples, data
characteristics, to achieve better warning effect.
II. THE SELECTION OF FINANCIAL CRISIS EARLYWARNING SAMPLE AND INDEX
A. The preliminary determination of samples and data
This paper will take listed companies who have
received ST for operation in China securities market Ashares as samples. 87 companies in total in Shanghai Stock
Exchange and Shenzhen Stock Exchange are chosen: 25
ones who were the first to receive ST in 2007, 34 ones who
received ST in 2008, and 28 ones who received ST in
2009. The financial and nonfinancial index information of
those listed companies in the three years before ST is used
to forecast whether they are financial crisis companies.
In order to find out the early-warning index which has
an impact on ST companies by comparing ST companies
with non-ST companies, this paper also chooses 87 non-ST
Evaluation
Items
Financial Index
Debt-paying
Ability
Index
Current Ratio
X1
Quick Ratio
X2
Debt Asset
Ratio X3
Operating
Capacity
Earning Power
Working
Capital Total
Assets Ratio
X4
Receivables
Turnover
Ratio X5
Inventory
Turnover
Ratio X6
Current Assets
Turnover
Ratio X7
Total Assets
Turnover
Ratio X8
Main Business
Profit Rate X9
Net Profit Rate
to Total Assets
X10
Net Profit
Margin on
Sales X11
Profit Margin
Computational
Formula
Current Assets／
Current Liabilities
(Current Assets—
Inventory)／
Current Liabilities
Total
Indebtedness／
Total Assets
Working Capital
/Average Total
Assets
Main Business
Income/Average
Receivables
Cost of Goods
Sold／Average
Inventory
Main Business
Income / Average
Current Assets
Main Business
Income／Average
Total Assets
Main Business
Profit / Main
Business Income
Net Profit /
Average Total
Assets
Net Profit / Main
Business Income
Net Profit /
Development
Capacity
on Net Assets
X12
main
business's
increasing rate
of income X13
Rate of Capital
Accumulation
X14
increasing rate
of Net Assets
X15
increasing rate
of Total Assets
X16
Nonfin
ancial Index
Shareholding
Structure[1]
Shareholding
Proportion of
the
Controlling
Shareholder
Y1
Herdindhal_5
Index Y2
Z IndexY3
CR_5 Index
Y4
Corporate
Governance[2][3]
Ratio of
Independent
Director Y5
Ratio of State
Shares Y6
Ratio of Upper
Management
Shares Y7
Position set of
Chairman of
the Board and
Average Net
Assets
(Main Business
Income of This
Year— Main
Business Income
of last year)／
Main Business
Income of last
year
Growth of
Owner's Equities
This Year／
Owner's Equities
at the Beginning
of the Year
(Net Assets of
This Period—Net
Assets of Last
Period)/ Net
Assets of Last
Period)
(Total Assets of
This Period—
Total Assets of
Last Period)/ Total
Assets of Last
Period
The Ratio of the
Shares of the
Controlling
Shareholder to the
Total Shares of
the Company
Sum of the
Squares of the
First Five
Substantial
Shareholders in
the Company’s
Shareholding
Proportion
Shareholding
Proportion of the
First Substantial
Shareholder /
Shareholding
Proportion of the
Second
Substantial
Shareholder
Sum of the
Shareholding
Proportion of the
First Five
Substantial
Shareholders in
the Company
Independent
Director /All
Director
The Amount of
State Shares /
capitalization
(Shareholding of
Board of directors
+Shareholding of
Management
Layer
)/ capitalization
If the Chairman of
the Board and the
general manager is
general
manager Y8
Significant
Matters[4]
Other
Factors[5][6]
Whether
Involving
Related Party
Transaction
Y9
Whether
having
Violation
Record Y10
Whether
involved in
Lawsuit or
Arbitration
Y11
Whether
Involving
External
Guarantees
Y12
Whether
having
changed
Accounting
Firms Y13
Whether
having altered
abbreviation
Y14
Type of Audit
Opinion Y15
the same person,
score 1; otherwise,
score 0.
If yes, score 1;
otherwise, score 0.
If yes, score 1;
otherwise, score 0.
If yes, score 1;
otherwise, score 0.
If yes, score 1;
otherwise, score 0.
If yes, score 1;
otherwise, score 0.
If yes, score 1;
otherwise, score 0.
If the auditor
presents standard
clean opinions,
score 1; otherwise,
score 0.
B. The preliminary selection of early-warning indexes
Normal distribution inspection
First, we take normal distribution test of these
primary early-warning indexes. By means of the K-S test
method in SPSS statistical software, we test the 31 primary
indexes selected from the samples of two group.
K statistic=max(|S( )-F( )|)
In the formula, S( ) is the actual cumulative
probability value of each different observing samples, of
which F( ) is the theoretical value. Under significant level
of ɑ=0.05, the bilateral progressive probability’s P value of
,
,
, , , ,
is greater than 0.05, which
means the 7 of them pass the inspection and overall accord
with the normal distribution. By two independent samples
test method of significant test, the rest 24 indexes do not
accord with normal distribution, so we use a nonparametric
test, Mann-Whitney test, to test their significance.
a. T-test of two independent samples
The equation of T[7] statistic:
The results are as follows:
Under significant level of ɑ=0.05, ,
, , pass
the T test, which means that these 4 indexes have
significant differences, while
, ,
does not, which
means they have no significant differences.
b. U-test of two independent samples
This paper selected the most effective alternative
method of parameter test, Mann-Whitney test.
U-test equation[8] is as followed:
rotated factor matrix
factor
,
,
The test results are as followed: the indexes’ values of
, , , , ,
,
,
,
,
, , ,
,
,
, , , 17 in all, are smaller than the significant level,
while the other 7 indexes do not pass the significant test.
In general, a total of 21 indexes pass the significant
test.
C. A further integration of early-warning index
The tests of significance above identify 21 earlywarning indexes, including 12 financial indexes and 9
nonfinancial indexes. Both non- and financial indexes
reflect a company’s financial performance. Accounting for
that these early-warning indexes may be relevant between
each other, the paper convert the multiple observable
variables into a few uncorrelated integrated indexes, by the
method of factor analysis, to best simplified the high
dimension data. Because some of the nonfinancial earlywarning indexes are virtual variables, of which the data is
not continuous, they can not be integrated.
a. KMO test[9]
We take a KMO test before factor analysis to
determine whether the financial ratios involved are suitable
for it.
TABLE II
KMO test
KMO and Bartlett test
enough sampling Kaiser-Meyer-Olkin test
Bartlett sphericity test
.729
chi-square 744.202
df
66
Sig.
.000
By
KMO test, the results show 0.729 of KMO test coefficient,
indicating a high relevant between the indexes, so they are
suitable for factor analysis. The 744,202 of Bartlett chisquare value and 0.000<0.05 of P value show the 12
financial indexes are not independent and there is a certain
relationship between them.
F1 F2 F3 F4
liquidity ratioX1
.043 .943 .057 -.049
quick ratioX2
.064 .910 .065 -.062
asset liability ratioX3
-.292 -.672 .396 .072
working capital to total asset ratioX4 -.044 .733 -.096 .026
total assets turnover ratioX8
.789 .232 .273 .107
main business profit rateX9
.890 .161 -.196 .029
total net asset profit rateX10
.859 .091 .298 -.061
sales net profit rateX11
.942 .107 .033 -.033
net assets income rateX12
.231 .187 .594 .090
the growth rate of main businessX13
.256 -.098 .112 .645
capital accumulation rateX14
.075 -.017 .006 .962
net asset growth rateX15
.383 .212 .733 .166
extraction method: principal component analysis method
rotation method: Kaiser standardized orthogonal rotation method
From the factor loading matrix after rotation above,
we can see that the 4 factor variances respectively yield
high load capacity in different index variables. According
to the factors’ load distribution, we can make a further
analysis as followed:
(1). Index factor load capacity of
on
is far greater than that of other indexes. It
shows the company’s operating profit level and the ability.
(2). Index factor load capacity of on
is far greater than that of other indexes. It shows the
company’s solvency.
(3). Index factor load capacity of on
is far
greater than that of other indexes. It shows the company’s
profitability and growth ability.
(4). Index factor load capacity of on
is far
greater than that of other indexes. It shows the company’s
ability to grow.
By calculating the coefficients in the linear
combination of common factors, as dependent factors, and
initial index variables, as the independent factors, we get
the initial linear expression as followed:
b. Factor analysis[10]
We screen out 12 financial indexes by significance
test above: , ,
, , ,
,
,
,
,
,
.
Then we take factor analysis on these 12 indexes, and find
that, the characteristic values of first 4 common factor are
greater than 1, and their accumulated contribution rates
reach 84.819%, recorded as , ,
. To explain them
reasonable, we need to get the correlation coefficients
between the 4 common factor and the 12 initial financial
indexes. So the paper uses orthogonal rotation maximum
variance method to do the conversion, and gets the factor
loading matrix as followed:
TABLE III
factor loading matrix
III. THE CONSTRUCTION OF LOGISTIC FINANCIAL
CRISIS EARLY-WARNING MODEL
A. The construction of Logistic model based on financial
indexes alone
In the construction of Logistic financial crisis earlywarning model based on financial indexes alone, the
previous three years’ data of the 44 ST companies and 44
non-ST companies are taken as original data and F1, F2,
F3, F4
as dependent variables. Multiple Logistic
regression is employed to do the analysis. The regression
results are presented in TABLE IV.
TABLE IV
the Logistic regression results based on financial indexes alone
Variables in Equation
B
Step 1a
S.E, Wald df Sig. Exp (B)
F1
-4.261 1.069 15.884 1 .000
.014
F2
-.748 .343 4.749 1 .029
.473
F3
-.400 .369 1.172 1 .079
.670
F4
-.687 .624 1.212 1 .071
.503
Constant -.730 .363 4.042 1 .044
.482
a. inputting Variables F1, F2, F3, F4 in step 1
The above chart illustrates that the coefficient of
every explanatory variable is significant when it is  =0.1,
which implies that the model fits well. Hence, the
company’s Logistic financial crisis early-warning model
based on financial indexes alone in the year T is:
B. The construction of Logistic
nonfinancial indexes[11][12]
model
injecting
Conduct regression analysis with the four common
factors F1, F2, F3, F4 obtained by factor analysis and the
nine nonfinancial index variablesY1 、 Y2 、 Y4 、 Y8 、
Y10、Y11、Y13、Y14、Y15, which have been through
parameter T test and non-parameter U test. Through
forward gradual selection variables method, the synthetical
early-warning model based on both financial and
nonfinancial indexes is constructed. The regression results
are presented in TABLE V.
TABLE V
the regression results of the Logistic synthetical model injecting nonfinancial
indexes
Variables in Equation
B
Step
3a
S.E, Wald df Sig.
Exp
(B)
F1
1.094 7.390 1 .003 .039
3.219
F2
1.168 3.908 1 .023 .121
2.114
F3
1.612 6.367 1 .014 .122
2.103
F4
1.701 9.948 1 .005 .202
1.601
Shareholding
Proportion
of the
3.437 1.806 6.312 1 .006 31.094
Controlling
Shareholder
Y1
CR_5 Index
1.236 7.836 1 .018 .121
Y4
2.108
Whether
having
3.262 1.155 7.975 1 .005 26.102
Violation
Record Y10
Whether
involved in
Lawsuit or 3.285 1.019 10.404 1 .001 26.709
Arbitration
Y11
Whether
having
altered
3.923 1.201 10.678 1 .001 50.552
abbreviation
Y14
Type of
Audit
1.098 6.915 1 .009 .056
2.888
Opinion Y15
Constant
1.130 1.024 1.217 1 .007 3.095
The above chart illustrates that the coefficient of
every explanatory variable is significant when it is ɑ=0.05,
which implies that the model fits well. Through the
coefficients of the variables in the chart above, the Logistic
financial crisis synthetical early-warning model injecting
nonfinancial indexes is obtained:
From the above synthetical early-warning model, one
can see that it is positive correlation between the
nonfinancial index variable Shareholding Proportion of the
Controlling Shareholder Y1 and the occurrence of
financial crisis probability P, which implies that the higher
the shareholding proportion of the controlling shareholder,
the greater the probability of financial crisis. It is negative
correlation between the nonfinancial index variable CR_5
index Y4 and P, which indicates that the higher the
shareholding proportion of the first five substantial
shareholders and the ownership concentration, the less the
probability of financial crisis. Meanwhile, if the company
is involved in violation record, lawsuit or attribution and
abbreviation alteration, the probability of financial crisis
will be further greater.
IV. THE TEST OF THE EARLY-WARNING MODEL
A. The test of Logistic early-warning model based on
financial indexes alone
Since the ratio between the original samples and
the paired samples is 1:1, hence 1 is to represent
companies with financial crisis while 0 is to represent
companies without financial crisis. P=0.5 is taken as
discriminating section ratio. If P>0.5, it is marked as
company with financial crisis; if P<0.5, it is marked as
company with normal financial condition.
Input the index variable data of the 86 companies in
the testing samples, consisting of 43 ST listed companies
and 43 non-ST listed companies, into the early-warning
model based on financial indexes alone to test the model’s
veracity. Testing results are illustrated in TABLE VI.
TABLE VI
Testing results of the Logistic model based on financial indexes alone
Classification Tablea
Predicted Value
Observed Value
Group
Group
Accuracy
ST
Non-ST
Company Company Rate（%）
misjudgment
rate （%）
ST
Company
32
11
74.42
25.58
Non-ST
Company
8
35
81.39
18.61
77.91
22.09
Total Percentage
a. Discriminant Piont.500
The above chart shows that the constructed earlywarning model based only on financial indexes is able to
discriminate accurately 32 ST companies and 35 non-ST
companies, taking P=0.5 as predicted discriminating point
and the actual 43 ST listed companies and 43 non-ST ones
as testing samples. In other words, the accuracy rates of the
early-warning model based only on financial indexes to the
prediction for the ST companies and non-ST ones
respectively are 74.42% and 81.39%. the average
percentage is 77.91%.
Non-ST
Company
Input the index variable data of the 86 companies in
the testing samples, consisting of 43 ST listed companies
and 43 non-ST listed companies, into the Logistic
synthetical early-warning model based both on financial
and nonfinancial indexes to test the model’s veracity and
compare the testing results of the two models. Testing
results are illustrated in TABLE VII.
TABLE VII
Testing results of the Logistic synthetical early-warning model injecting
nonfinancial indexes
Observed Value
Classification Tablea
Predicted Value
ST
Non-ST Accuracy misjudgment
Company Company Rate（%） rate （%）
Group
ST
Company
35
8
81.39
18.61
86.05
13.95
83.72
16.28
a. Discriminant Piont.500
From the above chart one can see that the constructed
Logistic synthetical early-warning model injecting
nonfinancial indexes is able to discriminate accurately 35
ST companies and 37 non-ST companies, taking P=0.5 as
predicted discriminating point and the actual 43 ST listed
companies and 43 non-ST ones as testing samples. Thus,
the accuracy rates of the Logistic synthetical early-warning
model injecting nonfinancial indexes to the prediction for
the ST companies and non-ST ones respectively are
81.39% and 86.05%. The average predicting percentage is
83.72%.
By comparing the testing results of the two models,
one can see that after drawing nonfinancial index variables
in, model’s accuracy rate increases by 5.81%, which
manifests that it enhances effectively the predicting
accuracy rate of the model to draw nonfinancial index into
the study of financial crisis early-warning.
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