Empirical Analysis on Influencing Factors to the Capital Structure of GEM
Board Listed Companies in China
Shi-chang Lu , Xue Luo
1
College of Business Administration, Liaoning Technical University, Huludao, China
(lorna881123@126.com)
Abstract- Taking the GEM (Growth Enterprises Market)
Board listed company in China as research subjects, using
factor analysis and regression analysis methods, this article
analyzes influencing factors of the capital structures. The
result shows that: there is negative relationship between the
operation situation, operation security, operation efficiency
and capital structure, that is, the profitability, cash flow,
debt-paying ability and operating ability of the company are
negatively related to capital structure; while there is positive
relationship between operating prospect, operating ability
and capital structure, that is, the scale, growth potential and
mortgage assets are positively related to capital structure.
Keywords- Capital structure, factor analysis, GEM
Board, influencing factors, regression analysis
I. INTRODUCTION
The purpose of establishing GEM Board in China is
to promote the development of enterprises which have
potentials and are technically innovative. As one layer of
China’s stock market, it has common features and
functions [1]. Meanwhile, as a relatively independent
market, it plays a special role. Therefore, choosing GEM
Board listed companies as research subjects and
analyzing the influencing factors of capital structure have
realistic meaning and research value.
II. LITERATURE REVIEW
The factors which influence the capital structure are
as follows: the scale, profitability, growth potential, asset
structure and internal resources of the company. There are
three points of view on the relationship between company
scale and capital structure: Zuo-ping Xiao (2004) holds
the idea that they are positively related [2], Zheng-fei Lu
and Yu Xin (1998) think the relationship is not obvious [3],
JuanWang and Feng-lin Yang (2002) think they are
negatively related [4]. As to the relationship between
profitability and capital structure, Xi-xi Hong, Yi-fen
Shen (2000) and Nian-cheng Tong (2010) agree they are
positively related [5] while Zheng-fei Lu and Yu Xin
(1998), Min-jian Hu and Qing-zhen Zhu (2011) think they
are negatively related [6]. To the relationship between
growth potential and capital structure, Zhe-bin Zhang,
Shao-xing Zhu [7], Jian-zhong Wu (2000) and Wei Wang
(2011) think they are positively related [8], Xi-xi Hong,
Yi-feng Shen (2000) and Shao-biao Yuan (2011) [9] agree
the relationship is not obvious, Gen-xiang Shen and
Ping-fang Zhu (1999) [10], Zuo-ping Xiao and Shi-nong
Wu (2002) [11] think they are negatively related. To the
relationship between asset structure and capital structure,
Zheng-fei Lu and Yu Xin (1998), Rui-yu Liu and Fei Ye
(2011) think they are positively related [12]. To the
relationship between internal resources and capital
structure, Jing-jing Zhang (2004) and Nian-cheng Tong
(2011) think they are negatively related [13].
The above results indicate that scholars’ opinions
vary. Therefore, this article aims to find out the factors
which influence the capital structure of China’s GEM
Board listed companies so as to promote their
development.
III. ANALYSIS ON INFLUENCING FACTORS
A. Sample Choice and Data Source
On October 30, 2009, China’s first group companies
in GEM went on the market in Shenzhen Stock Exchange.
By February 24, 2012, 291 companies have been listed in
GEM.We choose 100 companies at random as our
research subjects, among which one company has been
ruled out because of illegal operation and eight companies
are eliminated because of losing parts of the data.
Therefore, 91 companies are left to be research subjects.
B. Variable Illustration
Based on the scholars’ researches, combined with
features of these companies and their capital structures,
we analyze the influencing factors according to the
following financial index [14].
TABLE I
VARIABLE DESIGH
Variable
Dependent
Variable
Independent
Variable
Definition Method
Capital
Assets-liabilities
Structure
Ratio
Natural
Logarithm of
Total Assets
Company
Natural
Scale
Logarithm
of Main Business
Revenue
Main Business
Profit Margins
Total Assets
Profitability
Profit Margins
Net Assets Profit
Margins
Total Assets
Growth Rate
Growth
Main Business
Potential
Revenue Growth
Rate
Debt-paying
Liquidity Ratio
Y
Calculation Formula
Annual Total Debts in the end/Annual Total
Assets in the end
X1
Ln(Total Assets)
X2
Ln(Main Business Revenue)
X3
Main Business Profit / Main Business Revenue
X4
Net Profit/Total Assets
X5
Net Profit/Net Assets
X6
X7
X8
Annual Asset Growth/Last Year’s Total Assets
in the end
(Annual Main Business Revenue-Last Year’s
Main Business Revenue)Main Business
Revenue in the end
Current Asset in the end / Current Liability in
the end
Ability
Quick Ratio
Return Rate of
Operating Cash
Net Operating
Cash Flow for
Per-share
Inventory
Turnover Ratio
Total Assets
Turnover Ratio
Accounts
Receivable
Turnover Ratio
Fixed Assets
Ratio
Cash Flow
Operation
Ability
Asset
Structure
Mortgage Assets
Ratio
X9
(Current Asset-Stock in the end)/ Current
Liability in the end
X10
Net Flow of Operating Cash /Total Assets
X11
Net Operating Cash Flow /Number of Common
Stock
X12
Annual Main Business Cost/
X13
Annual Main Business Revenue /Annual
Average Total Assets
X14
Annual Main Business Revenue /Annual
Accounts Receivable Balance
X15
Fixed Assets in the end/Total Assets in the end
X16
(Fixed Assets in the end +Inventory)/ Total
Assets in the end
C. Empirical Research
a. Factor Analysis
(a) Determine if the variables are suitable for factor
analysis
12
0.085
0.532
99.253
13
0.065
0.406
99.659
14
0.043
0.266
99.925
15
0.011
0.071
99.996
16
0.001
0.004
100.000
From the table, we can see eigenvalues of 5 factors
are bigger than one and their cumulative contribution
rates of variance are bigger than 80%. So we can explain
it using 5 factors instead of 16.
(c) Factor Variable Naming Explained
TABLE IV
ROTATED COPONENT MATRIX
TABLE II
KMO AND BARTLETT’S TEST
Variable
Kaiser-Meyer-Olkin Test
0.539
Approximate Chi Square
1766.962
df
120
Sig.
0.000
Bartlett Sphericity Test
From the above table, based on Kaiser’s test, it
seems that it isn’t suitable for factor analysis because its
KMO is 0.539. But its obvious probability of BST is
0.000, which is 0.05 lower than obvious level. It is
suitable for this analysis. Therefore, we think the 16
indicators are suitable for factor analysis.
(b) Establish Factor Variable
TABLE III
TOTAL VARIANCE EXPLAINED
Initial Eigenvalues
Extraction of Square and Loading
Component
Total
Variance
Contribution
Rate%
Cumulative
Contribution
Rate%
Variance
Contribution
Rate %
Cumulative
Contribution
Rate%
1
3.755
23.470
23.470
3.451
21.567
21.567
2
3.464
21.653
45.123
2.781
17.378
38.945
3
2.669
16.682
61.805
2.644
16.524
55.469
4
1.459
9.119
70.924
2.409
15.058
70.527
5
1.306
8.164
79.088
1.370
8.562
79.088
6
0.907
5.668
84.756
7
0.756
4.725
89.481
8
0.621
3.884
93.365
9
0.422
2.638
96.003
10
0.311
1.946
97.949
11
0.123
0.772
98.720
Total
Factor
1
2
3
4
5
Ln Total Assets
0.122
0.813
0.074
-0.489
-0.060
Ln Main Business Revenue
0.048
0.858
-0.283
-0.139
-0.030
Main Business Profit Margins
0.716
-0.168
0.461
-0.092
-0.180
Total Assets Profit Margins
0.911
0.125
-0.055
0.013
-0.165
Net Assets Profit Margins
0.812
0.311
-0.230
0.023
-0.186
Total Assets Growth Rate
-0.033
0.778
0.189
-0.173
0.036
Main Business Revenue Growth Rate
0.270
0.795
0.022
0.075
0.004
Liquidity Ratio
0.068
-0.285
0.884
-0.034
-0.004
Quick Ratio
0.076
-0.286
0.887
-0.038
0.000
Return Rate of Operating Cash
0.809
-0.070
0.056
0.077
0.441
Net Operating Cash Flow for Per-share
0.737
-0.114
0.213
0.013
0.387
Inventory Turnover Ratio
-0.128
-0.139
2.533E-5
-0.039
0.649
Total Assets Turnover Ratio
-0.301
0.528
-0.173
0.019
0.683
Accounts Receivable Turnover Ratio
0.187
0.299
-0.019
0.073
0.704
Fixed Assets Ratio
0.092
0.017
-0.078
0.964
0.030
Mortgage Assets Ratio
0.009
0.101
-0.114
0.962
-0.035
From the table, we can draw the following
conclusions and name each factor as follows:
Factor 1 offers information about main business
profit margins, total assets profit margins, net assets profit
margins, return rate of operating cash, net operating cash
flow for per-share, namely, the operating situation of a
company.
Factor 2 offers information about ln total assets, ln
main business revenue growth rate, total assets growth
rate, namely, the operating prospect of a company.
Factor 3 offers information about liquidity ratio and
quick ratio which reflect the debt-paying ability and
liquidity of the company, namely, the operating security
of a company.
Factor 4 offers information about fixed assets ratio
and mortgage assets ratio, namely, the operating power of
a company [15].
Factor 5 offers information about inventory turnover
ratio, accounts receivable turnover ratio, total assets
turnover ratio, namely the operating efficiency of a
company.
b. Establishment of the Regression Model
TABLE V
COEFFICIENTS
Nonstandardized coefficients
Model
1
[1]
Standardized
coefficients
t
Sig.
15.948
0.000
-0.251
-3.170
0.002
0.380
4.795
0.000
0.009
-0.500
-6.313
0.000
0.003
0.009
0.027
0.345
0.731
-0.011
0.009
-0.096
-1.210
0.229
B
Standard Error
(Constant)
0.142
0.009
F1
-0.028
0.009
F2
0.043
0.009
F3
-0.057
F4
F5
Beta
The above table indicates that the P value of T test
for constancy, factor 1, factor 2 and factor 3 are lower
than 0.05, which means their coefficients are bigger or
smaller than 0. Factor 1, factor 2 and factor 5 have
negative relationship with debt ratio. While factor 2 and
factor 4 have positive relationship with debt ratio. But
their P value of T test is higher than 0.05, which means
their coefficients are near 0. So we can get the regression
equation as follows:
[2]
[3]
[4]
[5]
[6]
Y  0.142  0.028F1  0.043F2  0.057 F3  0.003F4  0.011F5
[7]
IV. CONCLUSION
Conclusion 1: Compared to traditional companies,
90% of the companies in GEM have great growth
potential and high profitability and are technologically
innovative, so they can satisfy their capital needs and
lower the chances of running into debt by reserving
surplus, hence affecting the capital structures of these
companies.
Conclusion 2: Most of the companies in GEM are
technologically innovative companies; they have more
floating assets but less fixed assets or real estate, so their
liquidity ratio and quick ratio are high. Therefore, when
they are in great need of fund, they can change assets into
cash and lower the chances of running into debt.
Conclusion 3: The negative relationship between
operating ability and debt ratio is not obvious because the
100 companies we choose as research subjects are not
classified. This results in the obscure regression results.
Conclusion 4: The GEM provides good financing
ways for small and medium-sized enterprises. Their
scales are relatively small, fixed assets and real estate are
less. It is hard for them to get loan from the bank so they
have less mortgage assets and lower debt ratio.
Conclusion 5: As most of these companies are
technologically innovative, they have less mortgage
assets. Their fewer chances to get bank loan lower their
debt ratio. All these factors have affected the capital
structure of these companies.Because of lacking
mortgaged property, they have lower assets debt ratio. So
their debt will not change with the mortgage assets.
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