Modern Economy, 2012, 3, 508-517
http://dx.doi.org/10.4236/me.2012.35066 Published Online September 2012 (http://www.SciRP.org/journal/me)
Equity Financing Regulation and the Optimal Capital
Structure: Evidence from China*
Zhengwei Wang1,2, Wuxiang Zhu1
1Tsinghua University, Beijing, China
2Beijing Normal University, Beijing, China
Received October 30, 2011; revised November 20, 2011; accepted November 29, 2011
ABSTRACT
The “Supply-side effect” on financial management caused by market imperfection has increasingly been concerned.
During the transition period, there is strict securities regulation in China’s capital market, which brings the supply-side
constraints to corporate financing. Using the data of listed companies those take secondary equity offerings between
1993-2007 in China’s A-share market, the paper examines how the change of regulation policies on SEOs affects cor-
porate financing decisions. Our result shows that regulation policy is a significant factor to the amount of refinancing
and the optimal capital structure. This result provides important evidence on how the equity regulation environment
affects corporate financial management.
Keywords: Equity Financing Regulation; Supply-Side Effect; Capital Structure
1. Introduction
Since the pioneering work by Modigliani and Miller [1],
the study on capital structure has continued for more than
half a century. However, there hasn’t been a consistent
theory for capital structure. All existing theories have
their own assumptions of environmental conditions and
concentrate on some factors that influence the financing
decision and capital structure of the firm. These factors
are very important for some firms under corresponding
circumstances, which, however, are not necessarily the
case under other circumstances.
The inference that capital structure has nothing to do
with the enterprise value of MM is built on the perfect
assumption of capital market while considering the op-
eration decision making as exogenesis. Nonetheless the
condition of MM theory is considered to be too harsh and
then the conclusion is unclear. Later on, financial schol-
ars made much deeper research which is closer to reality
of firm’s capital structure by broadening the assumptions
of MM Theory. For example, with the understanding of
the tax [2] and bankruptcy costs [3,4], the trade-off the-
ory was then derived. The rapid development of the In-
formation Asymmetry Theory in the 1970s led to the
“Pecking order” theory with a broadened assumption on
Information symmetry of MM [5,6]. The arguments of
Jensen & Meckling [7] and Myers [8] developed the
agent issues of MM, and the Agency theory on capital
structure then formed.
On the contrary, the capital structure research has not
accounted for the impact of “market conditions”1 [9] until
1990s. In fact, the financing condition change in capital
market obviously influences the enterprise’s financing
choices. With the questioning and demonstration on the
Efficient Markets Hypothesis (EMH), Behavioral Finance
came out and its development pushed the rise of Behav-
ioral Corporate Finance. Behavioral Corporate Finance
started to pay attention to the impact on enterprise’s in-
vestment and financing decisions and capital structure of
market’s Non-efficiency while giving up Semi-strong effi-
cient market assumption. Stein [10] presents a model on
firm investment and financing decisions when market is
inefficient and managers are rational. His model shows,
in a non-efficient market, the manager can make best use
of the inefficiency of the market to create value by rea-
sonably making the financing decisions. This conclusion
derives the market timing theory of financing, which is:
with the price changes in stock market, the enterprise has
the best financing opportunities or Window of Opportu-
nity, firms can select stock offering after it goes up.
The market timing hypothesis has been supported
by many empirical results. In the Graham and Harvey’s
anonymous survey of CFOs of public corporations, two-
thirds state that “the amount by which our stock is
undervalued or overvalued was an important or very
*This paper is sponsored by National Natural Science Fund (71002074).
1Titman argues that “market conditions”, which are determined by the
p
references of individuals and institutions that supply capital, can have
an im
p
ortant effect on how firms raise ca
p
ital.
C
opyright © 2012 SciRes. ME

Z. W. WANG, W. X. ZHU 509
important consideration” in equity issuance [11]. The sur-
vey made by Brau and Fawcett also shows that the over-
all circumstance on in stock market is the most important
factor (82.94% of CFO selected this factor) when made
IPO decisions [12]. Similarly many other researches also
find that the amount of equity issued in IPO and in SEO
is typically correlated with the prior stock price [13,14].
The market timing effect is also reflected in capital struc-
ture choice. This market timing theory of capital struc-
ture is developed and tested in Baker and Wurgler [15].
In an effort to capture the historical coincidence of mar-
ket valuations and the demand for external finance in a
single variable, they construct an “external finance wei-
ghted average” of a firm’s past market-to-book ratios.
For example, a high value implies that the firm raises the
bulk of its external finance, equity or debt, when its mar-
ket-to-book was high. If market timing has a persistent
impact on capital structure, Baker and Wurgler argue that,
this variable will have a negative cross-sectional rela-
tionship to the debt-to-assets ratio, even in regressions
that control for the current market-to-book ratio. In a broad
Compustat sample from 1968 to 1999, a strong negative
relationship is apparent.
Although the market timing theory has paid attention
to the impact on the capital structure of the capital mar-
ket condition, the capital market condition is not limited
to the market timing obviously but it includes many other
aspects, for example: the breadth and depth of the capital
market development. Up to now, each existing capital
structure theory has a common implicit assumption, that
is, the capital market can provide so many financial prod-
ucts that firms can choose means of financing freely. In
fact, the impact on capital structure of development in
capital market cannot be neglected. Even in US capital
market, the firm can not entirely freely choose the chan-
nel of financing. It is even more pronounced that the fi-
nancing decisions of China’s listed firms are constrained
by Chinese capital market condition. For example, there
are various threshold limits to equity financing (including
IPO, SEO, and convertible bond issuance…) of listed
firms over time and the equity financing channel could
even be shut down in special time periods (for example,
in the period of reform of non-tradable shares in 2006,
any kind of public equity issuing is prohibited). As a re-
sult, we usually regard China’s capital market as a mar-
ket with considerable immaturity and imperfection. These
immaturity and imperfection make firms hard to freely
choose their means of financing. Maybe it is the case that
a firm wants to raise funds for its investment, combined
with its own capital structure needs, equity financing is a
dominant choice. In contrast, the firm may be forced to
float a loan, merely because of equity financing constrains
due to market conditions.
The immaturity and imperfection of China’s capital
market bring financing frictions to listed firms in stock
market (When talking about market friction, the previous
literature mainly refers to the tax, transaction cost, in-
formation cost, agent cost, and so on. However, we refer
to market frictions in this paper with a broader definition,
that is, any restriction in financing caused by certain rea-
sons. They not only include all kinds of costs mentioned
above but also include financing restrictions brought by
any financing policies and regulations). Is this kind of
friction an important factor that influences the firm’s
capital structure? We think it is necessary to study the
impact on the optimal capital structure of the financing
frictions in China’s stock market. This paper will focus
on the relationship between optimal capital structure and
the changes of financing policies and regulations envi-
ronment, which proxies the changes of financing fric-
tions in China’s A-share market. Besides, for the impor-
tance of the market timing theory in recent years, this
paper will examine the applicability of the market timing
theory in China’s market at the same time.
2. Sample and Variables
2.1. Sample
Now that we want to check the impact on the optimal
capital structure caused by the change of financing fric-
tions in China A-share market, we must firstly identify
the explanatory variables on firm’s optimal capital struc-
ture. However, as mentioned above, firms cannot finance
freely. Thus we cannot ensure that firms can timely ad-
just their capital structure to the optimal level, especially
when it is higher than their optimal capital structure.
Consequently, in a dynamic economy with frictions the
leverage of most firms, most of the time, is likely to de-
viate from the “optimal leverage” [16].
A direct consequence of the deviation from the “opti-
mal leverage” is the non-observability of the optimal
capital structure. Much of the existing empirical litera-
ture that tests capital structure usually views observed
debt ratios as “optimal”, and this may cause serious bias
[17] because of the large gap between observed debt ratio
and the “optimal”.
To observe the optimal capital structure, a useful method
is to identify the time when the firm can freely choose its
financing policies An example is that if the equity fi-
nancing constrain, the most important financing constrain
that the firm faces in China, is released, or the firm can
choose its financing policies freely, then, it can be con-
sidered that the firm’s capital structure is in the “optimal
state” after its free financing policies.
For the above reasons, this paper restricts the sample
to those firms that made Seasonal Equity Offering (SEO)
between 1993 and 2007 in China A-share market. During
the sample period there are 1.078 SEOs including 946

Z. W. WANG, W. X. ZHU
510
China’s stock market are different from those in markets
of western countries and other emerging markets, with
particular emphasis on high degrees of unity of the stock
market norms, development and market capacity. The
stock issuing regulatory policy (including IPO and SEO)
is a most typical aspect. From issuing condition, applica-
tion auditing and final issuing, a series of requirements
are set (i.e. financing amount, financing channel, issuing
and auditing, issuing P/E ratio and issuing timing, etc).
The following figures summarized the changes of regu-
latory policies of two re-financing methods of rights of-
fering and additional share offering.
rights offerings and 343 additional share offerings (in-
cluding directional add-issuance). We further exclude fina-
ncial firms and firms with data missing according to the
variable we need. The data end with 1.024 SEOs, includ-
ing 890 rights offerings and 314 additional share offer-
ings. Most data of this paper is from the Wind Database.
2.2. Background of SEO in China
Because our research focuses on Secondary Equity Of-
fering firms in A-share market, we first briefly introduce
the evolution of SEO in China.
Since the establishment of Shanghai Securities Exchange
on December 1990, China’s stock market has achieved
rapid development. Nonetheless China’s stock market
has a different regulation environment compared with
other countries. In fact, the stock market in China is not a
Market-oriented mechanism. In contrast, it is established
by government and regulated by a complex regulatory
system comprising of the State Council, China’s Securi-
ties Regulatory Commission, ministries and commissions
and local government with the related rights. In history,
the issuing and reviewing was not in the charge of Secu-
rities Regulatory Commission. At the very beginning of
the establishment of the securities market, the right was
exercised by the local governments and exchanges. Learn-
ing from the lesson of “8.10” event in 1992, the state
decided to grant the authority of the issuing and auditing
rights to Securities Regulatory Commission. Hence the
issuing and auditing system at present is built at the base
of the past experience.
Figures 1 and 2 show that both the rights offering
policy and the additional share offering policy are vary-
ing over time, which leads the change of financing thres-
hold. In addition, the comparison of Figure 1 and Figure
2 shows that the time segment of rights offering policy is
not the same as additional share offering policy.
2.3. Proxies for Variables
Capital structure: as we are interested in the influencing
factors of optimal capital structure, the main variable of
interest is capital structure in our paper. There are two
kinds of measuring methods: one is in book value and the
other is in market value. As the data window in the paper
includes the reform of non-tradable shares period in
China, the market value measuring method of the firm
has experienced major changes. For sake of stability of
the data, this paper selects the book debt ratio (BDR) to
be the object of study defined as book debt divided by
total assets, where the book debt is the interest-bearing
debt defined as the sum of short-term debt, long-term
liabilities due within one year and long-term liabilities.
Under this system, except the basic regulatory system
(i.e. limits on information disclose, insider dealing, etc.),
the regulatory philosophy, policy and developing route of
Figure 1. The main policy thresholds evolvement of the rights offering in China.

Z. W. WANG, W. X. ZHU 511
Figure 2. The main policy thresholds evolvement of the additional share offering in China.
Financing proceeds: the market timing argues that
proceeds of equity issuing are influenced by the stock
market valuation [15,18]. The above discussion implies
that the financing friction in the stock market may be an
important factor influencing the financing. To compare
the applicability of market timing and financing friction,
this paper considers financing proceeds as one of the
objects of study, Proceeds/A definition as the amount of
proceeds divided by total assets at the end of the year,
where the amount of proceeds is defined as (right offer-
ing price) × (share issuing) for rights offering, or (addi-
tional share offering price) × (share issuing) for addi-
tional share offering.
Equity financing friction: as described above, the eq-
uity financing friction, especially how difficult to access
capan important factor influencing the financing of a
firm. However, it is hardly to be observed, and thus we
need to find a proxy. Refer back to the refinancing regu-
latory policies in Figures 1 and 2, with the changes of the
equity financing policies, the thresholds to enter the stock
market are also varying, which means that the difficulty
level is varying. As a result, dummy variables can be set
in the different policy periods to proxy changes of financ-
ing friction. According to the changes of the refinancing
policy in the above text, we can set policy dummy vari-
ables as Table 1 below.
If the results of our empirical study support our analy-
sis, we expect these dummies have significant explana-
tory power to firm’s financing policies.
Growth opportunities: Theoretical studies generally sug-
gest that growth opportunities are negatively related with
leverage, which is also supported by many empirical
studies [19-23]. There are also different proxies for growth
Table 1. Policy dummies.
DummyDefinition
P1 For time between 1993.12 and 1994.9, P1 = 1, otherwise, P1 = 0
P2 For time between 1994.10 and 1996.1, P2 = 1, otherwise, P2 = 0
P3 For time between 1996.2 and 1998.5, P3 = 1, otherwise, P3 = 0
P4 For time between 1998.6 and 1999.3, P4 = 1, otherwise, P4 = 0
P5 For time between 1999.4 and 2000.5, P5 = 1, otherwise, P5 = 0
P6 For time between 2000.6 and 2001.3, P6 = 1, otherwise, P6 = 0
P7 For time between 2001.4 and 2002.7, P7 = 1, otherwise, P7 = 0
P8 For time between 2002.8 and 2006.5, P8 = 1, otherwise, P8 = 0
P9 For time after 2006.5, P9 = 1, otherwise, P9 = 0
opportunities. Wald [21] uses a 5-year average of sales
growth. Rajan and Zingales [21] use Tobin’s Q and
Booth et al. [19] use market-to-book ratio of equity to
measure growth opportunities. We argue that sales
growth rate is the past growth experience, while mar-
ket-to-book ratio better proxies future growth opportuni-
ties; therefore, market-to-book ratio of total assets is em-
ployed to measure growth opportunities in our study.
It is worth mentioning that in the study of Baker and
Wurgler [15], market-to-book ratio is mainly used to build
the factor measuring the market timing. It is suspected,
however, that the control used by Baker and Wurgler is
likely to be very noisy [18,24]. In addition to growth
prospects, the market-to-book ratio is affected by a num-
ber of other factors, such as the current state of the
economy or the capital intensiveness of the firm’s tech-
nology. As a result, two firms with identical market-
to-book ratios may differ substantially in their growth
potential. If one of these firms has a repeated history of

Z. W. WANG, W. X. ZHU
512
raising capital at high market-to-book ratios, it is more
likely to be a growth firm, as the past financing activity
is consistent with a growth trend. Even if the firm’s cur-
rent investment prospects are dim, such a firm may keep
its leverage ratio low in order to maintain financial flexi-
bility for the future. Kayhan and Titman [25] documents
that the persistence result of Baker and Wurgler is mainly
driven by the persistence of the average market-to-book
ratio rather than the covariance between the market-to-
book ratio and the financing deficit. For these criticism,
this paper uses market-to-book ratio to proxy growth
opportunities but not market timing, and the market-to-
book ratio (M/B) is defined as total market value divided
by total book assets, where the market value is defined as
market value of tradable shares plus non-tradable shares
times net assets per share plus book value of debt.
Earnings Volatility: a series of studies have shown that
the optimal capital structure is decreasing in earnings vola-
tility [4,19,23,26,27]. Among a number of proxies, we
adopt the measure introduced by Booth et al. [19], namely
Volatility = STD (profit of EBIT/total capital), where
STD is the standard deviation for the recent three years.
Market Timing: the market timing theory suggests that
valuation in the capital market has an impact on the firm’s
capital structure. As a response to the skeptic of Baker
and Wurgler, Alti [18] measures market timing by the
number of IPOs. His results are consistent with the no-
tion that more IPOs are taking place during hot market
than during cold market and thereby leverages are re-
duced. Wang et al. [28] use this measure to investigate
the market timing of seasoned equity offering (SEO
hereafter) of Chinese listed firms. They find that market
time has pronounced influence on the SEO decision.
However, they also point that SEO in china is regulated by
CRSC, which suggests that the number of SEOs reflects
the regulator’s view of market timing rather than that of
the listed firms. Since this paper endeavors to disentangle
the impact of market timing and financing friction, it is
inappropriate to employ Alti’s methodology here. As a
matter of fact, Alti and Wang et al. both document a posi-
tive correlation between the activeness of stock market
and market index. Having said that, we measure market
timing using stock market index.
Profitability: the relationship between profitability and
capital market is still under debate. Some researchers
believe high profitability will increase the firm’s retained
earnings, leading to a low leverage. Others argue that the
leverage will increase in the case that the firm has better
investment opportunity as well as sufficient capacity to
borrow. Accordingly, the sign of its impact is not clear
yet. We use EBIT/A as the proxy, defined as earnings
before interest and tax divided by total assets.
Size: Fama and Jensen [29] conjecture that the bigger
the firm size, the more information the firm can provide
to debtors. In other words, the information asymmetry
between large firms and banks are lesser than that be-
tween small firms and banks. Subsequent most empirical
studies confirm this. Hence, we expect a positive impact
of firm size on optimal capital structure. We define firm
size as the natural logarithm of total assets.
Fixed Assets: it is well recognized that the firm with
more fixed assets can collateralize more assets and thus
are capable to borrow more. Accordingly, we expect a
positive impact of fixed assets measured by fixed assets
over total assets.
Depreciation: because depreciation and interest ex-
penses can be substitutes of the “tax shield” effect, higher
depreciation firms are expected to be lowlier levered. We
define DEP/A = depreciation divided by total assets.
Since listed firms in China did not disclose statement of
cash flows until 1998, we are not able to observe their
depreciation level. Having taken this into account, we
conduct a separate test for sample after 1998.
Ownership: Agency theory [7,30] proposes that firms
with different ownership structure will face different
agency problem and different capital structure consequently.
According to the specific institutional background of China,
private firms and state owned firms are facing different
resource constraints. In particular, state owned firms are
easier to access bank loan. We list ownership property as
one of the major determinants of capital structure and use
ownership proportion of state share as the proxy. It is
defined as STATE = state share divided by total shares
outstanding.
2.4. Descriptive Statistics
In Table 2, we provide the descriptive statistics for the
variables. It shows that there are 1204 observations for
all of them except DEP/A for which 914 observations are
available. It can be seen from the statistics that the mean
and the median of each variable are close, indicating that
the skewness is not a big issue of our sample.
In Table 3, we slice our sample into 10 subsamples
based on our prior review of regulations in the time se-
quence. Nine regulation dichotomy variables are created
accordingly. The table reveals the capital structure (BDR)
and SEO proceeds (Proceeds/A)’s mean and variation in
each subsample. It can be seen that among subsamples,
mean varies a lot in BDR and Proceed/A.
The last line of Table 3 lists the results of variation
analysis. In this table the ANOVA analysis2 of BDR
shows F statistics is 7.94 significant at 1% level, indicat-
ing that the mean value is different among ten subsam-
ples; the corresponding F statistics of Proceeds/A is 6.16
2T The null hypothesis of ANOVA analysis is mean is H0: the same
among subsamples; the alternative analysis is H1: no less than one
subsample has a different mean.

Z. W. WANG, W. X. ZHU 513
and significant at 1% level, indicating a large difference
among ten subsamples. These results preliminarily show
that the revolution of SEO regulations (or as indicated as
the change in friction of equity refinancing) does heavily
affect the proceeds of firms’ SEO decisions as well as
optimal capital structure. Nonetheless, they are only the
results from ANOVA analysis. It needs further confirma-
tion based on following multivariate tests.
3. Empirical Results
As mentioned previously, the change in friction of equity
refinancing can have impact on firms’ financing activi-
ties and further on optimal capital structure. On the other
hand, based behavior financial theory, if the market tim-
ing is right, financing proceeds would become greater
and the optimal capital structure would be affected as well.
In this section, we investigate the proceeds of equity
refinancing and optimal capital structure respectively. The
analysis includes detecting their determinants and further
the influence of financing friction and market timing.
Table 2. Descriptive statistics.
Variables obs mean S.D. min max Median
BDR 1204 0.2844 0.1725 0.0000 1.00000.2819
Proceeds/A 1204 0.1868 0.1314 0.0207 1.67210.1580
M/B 1204 1.6121 0.5797 0.9529 7.84631.4650
Volatility 1204 0.0251 0.0460 0.0002 0.20760.0182
Mktidx 1204 1868 1232 333 59541535
EBIT/A 1204 0.0696 0.0715 −0.2134 2.03710.0631
Size 1204 5.0570 1.0765 1.6760 9.58074.9769
FA/A 1204 0.3396 0.1897 0.0004 0.93120.3174
DEP/A 914 0.0203 0.0147 0.0000 0.12450.0175
STATE 1204 0.2458 0.2496 0.0000 0.88580.2112
Table 3. Variance analysis.
BDR Proceeds/A
obs mean S.D. mean S.D.
Subsample 1 27 0.26390.1886 0.23850.1420
Subsample 2 32 0.20770.1569 0.20900.1535
Subsample 3 84 0.28950.1768 0.11630.0678
Subsample 4 191 0.27330.1623 0.17220.1430
Subsample 5 132 0.26720.1814 0.20780.1689
Subsample 6 150 0.24130.1444 0.18710.0896
Subsample 7 208 0.26650.1586 0.21450.1497
Subsample 8 73 0.26020.1601 0.20480.1036
Subsample 9 101 0.30950.1583 0.15040.0995
Subsample 10 206 0.36390.1912 0.18880.1209
ANOVA Test in subsamples F Value：7.94*** F Value：6.16***
***mean statistically different from zero at the 1%.
3.1. Proceeds of Equity Refinancing
As mentioned previously, the change in friction of equity
refinancing can have impact on firms’ financing activi-
ties and further on optimal capital structure. On the other
hand, based behavior financial theory, if the market tim-
ing is right, financing proceeds would become greater and
the optimal capital structure would be affected as well.
In this section, we investigate the proceeds of equity
refinancing and optimal capital structure respectively. The
analysis includes detecting their determinants and further
the influence of financing friction and market timing.
01 121
3415
617 18
91
tt t
tt
tt
tt
ProceedsAcc MBcVolatility
c Mktidxc EBITAc SIZE
cFAAcDEPAcGYG
cBDR


1
1t





 
 

(1)
As some variables are unobservable when refinancing,
we adopt their value in prior year instead, denoted by
subscript t-1; Mktidx is the comprehensive index of A
share market when firms are refinancing, proxying for
market timing. ε is the error term.
In addition, based on the analysis of models in this
paper and its numerical solution, friction in stock market
is also a potential determinant of firms’ financing deci-
sion. To account for this effect, we expand the model as
follows,
01 121
3415
617 18
91
tt t
tt
tt
tt
ProceedsAcc MBc Volatility
cMktidxcEBITAcSIZE
cFAAcDEPA cGYG
cBDR P 

1
1
t





 
 

(2)
P denotes regulation indicator vector and β denotes other
coefficient vector.
Whether it is worth expanding model (1) into model (2)
or whether the regulation indicator vector is jointly sig-
nificant can be tested by F test.
The null hypothesis H0: no need to add regulation in-
dicators (that is, no need to establish model (2)); alterna-
tive hypothesis H1: need to add regulation indicators
(that is, need to establish model (2)). F statistics is im-
puted as,

 

1
ru
u
SSE SSET
FSSEN Tk


 (3)
in which SSEr and SS Eu denotes the square sum of the
residual square constraint model (without regulation in-
dicators ) and non-constraint model (with regulation in-
dicators) respectively; T is the number of regulation
changes, N is the number of observations, and k denotes
the number of other explanatory variables.
The regression results of model (1) and model (2) are
shown in Table 4. The first two columns illustrate the

Z. W. WANG, W. X. ZHU
514
regression results of the whole sample. From the results
of model (1), the adjusted R square is 13.25%. All vari-
ables but fixed assets proportion (FA/A) are significant. It
validates our selection of explanatory variables. Note that
the coefficient of Mktidx is significantly positive at 1%
level. Because this variable proxies market timing, we
can infer that under model (1) market timing is one of the
determinants of refinancing proceeds. In particular, the
higher the stock market index, the greater the proceeds of
firms’ refinancing.
Table 4. Empirical test of refinancing proceeds.
Full sample Subsample after 1998
Model (1) Model (2)Model (1) Model (2)
Intercept 0.27
(12.46***)
0.30
(9.29***)
0.24
(7.31***)
0.23
(6.27***)
M/Bt–1 0.01
(4.48***)
0.01
(3.52***)
0.04
(4.72***)
0.04
(4.23***)
Volatilityt–1 0.46
(2.71***)
0.48
(2.89***)
0.73
(3.92***)
0.72
(3.83***)
Mktidx 2E–5
(5.50***)
6E–6
(1.08)
8E–06
(2.39**)
2E–06
(0.42)
EBIT/At–1 0.15
(1.73*)
0.22
(2.54**)
0.03
(0.32)
0.07
(0.68)
Sizet–1 –0.03
(–7.23***)
–0.04
(–8.51***)
–0.03
(–5.31***)
–0.03
(–5.54***)
FA/At–1 0.01
(0.40)
–0.01
(–0.24)
–0.02
(–1.54)
–0.04
(–1.54)
DEP/ t–1 / /
0.61
(2.24**)
0.63
(2.03**)
STATEt–1 –0.03
(–2.56**)
–0.03
(–2.42**)
–0.03
(–2.14**)
–0.03
(–2.12**)
BDRt–1 –0.06
(–2.44**)
–0.04
(–1.87*)
–0.04
(–1.38)
–0.04
(–1.37)
P1 / 0.00
(0.14) / /
P2 / –0.07
(–2.63***) / /
P3 / 0.01
(0.21) / /
P4 / 0.03
(1.26) / 0.04 (2.00**)
P5 / 0.01
(0.55) / 0.01 (0.50)
P6 / 0.06
(2.15**) / 0.05 (2.31**)
P7 / 0.05
(1.81*) / 0.04
(1.53)
P8 / 0.03
(0.98) / 0.02 (0.88)
P9 / 0.05
(1.56) / 0.05 (1.81*)
Adjusted R2 0.1325 0.1703 0.1536 0.1612
SSE 17.836 16.931 12.517 12.321
obs 1024 1024 914 914
***, **, and * mean statistically different from zero at the 1%, 5% and 10%
level, respectively.
The second column illustrates the regression results of
model (2). Before analyzing the coefficients, we test if it
is necessary to establish model (2). As to the results of
two regressions, the square sum of the residual of con-
straint model SSEr = 17.836, the square sum of the re-
sidual of non-constraint model SSEr = 16.931, the num-
ber of observations N = 1204, the number of regulation
changes T = 9, the number of other variables k = 8. Get-
ting them into formular (3) leads to F = 7.93 and F0.01(8,
1187) = 2.51 according to F distribution, significant at
1% level. It indicates that regulation interval indicators
are jointly significant, suggesting that it is necessary to
establish model (2). Namely, there is need to introduce
regulation variables and equity refinancing friction has
explanatory power in the proceeds. The results of model
(2) show that after introducing regulation interval indi-
cators, adjusted R square increases to 17.03%, which is
greater than model (1). Furthermore, variable P2, P6, P7
are all significant, implying that among different regula-
tion intervals, the change in equity refinancing friction
significantly affect the proceeds. In addition, the coeffi-
cient of Mktidx is no longer significant under model (2).
This result suggests that the effect of market timing is
merely a “pseudo” phenomenon. In other words, its ef-
fect is not based on market valuation but instead due to
refinancing friction. It is gone after we account for regu-
lation change.
The last two columns list the results after year 1998. In
this subsample, we introduce a new explanatory variable
DEP/A. From the results of model (1), all variables are
significant except FA/A and EBIT/A. the coefficient of
Mktidx is significantly positive, consistent with the re-
sults of whole sample. Again, in model (2) we introduce
regulation interval indicators (note that after 1998 there
are 7 regulation intervals and thus 6 indicators are needed).
First, we put the statistics into formular (3) and get F
statistics of 2.86. Since F0.01
(8, 1187) = 2.51, it implies
the indicators are jointly significant and establishing model
(2) is necessary. After introducing regulation interval
indicators, the adjusted R square of model reaches to
16.12%, greater than 15.36% in model (1). Moreover,
variable P2, P6, P7 are all significant. Once more, it im-
plies that among different regulation intervals, the change
in equity refinancing friction significantly affect the pro-
ceeds. The coefficient of Mktidx also becomes insignifi-
cant under model (2) contrast to model (1). Its effect is
not based on market valuation but instead due to refinanc-
ing friction.
3.2. The Empirical Analysis of Optimal Capital
Structure
In order to examine the determinants of optimal capital
structure, we construct the regression model as follows,

Z. W. WANG, W. X. ZHU 515
01 2
34 5
67 8
ttt
tt
ttt
BDRcc MBcVolatility
c MktidxcEBITAc SIZE
cFAA cDEPA cGYGt

 
 
 
(4)
Because we are testing the capital structure at the year-
end of refinancing, we select the corresponding variables
at yearend as the explanatory variables, denoted by sub-
script t. Still Mktidx is the comprehensive index of A
share market when firms are refinancing, proxying for
market timing. ε is the error term.
In addition, we are mainly interested whether the fric-
tion of stock marker refinancing is a major determinant
of optimal capital structure. To this end, we expand the
model as follows,
01 2
34 5
67 8
ttt
tt
ttt
BDRcc MBcVolatility
c MktidxcEBITAc SIZE
cFAA cDEPA cGYGPt


 
 
 
= 9.08. Since F0.01 (8, 1188) = 2.51 < 9.08 according to F
(5)
in which, P denotes regulation interval indicators and β
represents its coefficient vector.
Relative to model (4), is vector P joint significant? Is it
necessary to establish extended model (5)? These issues
can be tested through F test in Formula (3). Null hy-
pothesis H0: no need to include regulation indicator (i.e.
unnecessary to establish extended model (5)); alternative
hypothesis: there’s need to include regulation indicator
(i.e. necessary to establish extended model (5)).
The results of model (4) and model (5) are shown in
Table 5. The first two columns of Table 5 demonstrate
the regression results based on whole sample. As can be
seen from the results of model (1), the adjusted R square
is 26.94%, indicating that the model fits well. First, the
coefficient of Mktidx is positive but insignificant. Since
this variable proxies market timing, we believe under
model (4), market timing has no significant effect on capital
structure. This result is inconsistent with Baker & Wurgler,
Alti and Wang et al. Second, we are interested in the
coefficient of M/B, which proxies firm growth. The re-
sults show that this coefficient is significantly negative,
which is consistent with the numerical solution on firm
growth. Besides, earnings volatility, profitability, size and
fix assets proportion are significant factors influencing
optimal capital structure, though ownership property
doesn’t load.
The second column lists the results of model (5). Still,
we test the necessity of establishing model (5) before
analyzing the coefficients. From the regression results,
the square sum of the residual of constraint model SSEr =
25.928, the square sum of the residual of unconstraint
model SSEu = 24.185, number of observation N = 1204,
number of regulation indicators T = 9, number of other
variables k = 7. Getting them into Formula (3) leads to F
Table 5. Empirical test of optimal capital structure.
Full sample Subsample after 1998
Model el (5) M(4) Mododel (4) Model (5)
Intercept (5 ) (4) (2
0.18
.72***
0.21
.87***
0.04
(1.17)
0.13
.40**)
M/B
t
Volatility1(( (
t–1.5 ***)(( (
t(8 ) )
FA t)
DEP t( (
t
–01 0.
1(
2
3(
–0 3
4
5(
6
7
8
9
Ad 20.294 0.3079 0.3246
–0.02
(–2.01**)
–0.00
(–0.00)
0.00
(0.35)
0.01
(1.10)
–0.45
(–2.35**)
–0.55
–) 2.92***
–0.36
–1.68*)
–0.39
–1.82*)
Mktidx 5E–6
(1.04)
–3E–6
(–0.48)
1E–5
(2.18**)
–5E–6
(–0.75)
EBIT/A0 (–14.7–1.72
–) 16.7***
–1.60
–) 13.1***
–1.72
–13.8***)
Size 0.04
.84***
0.06
(1 ) 0.36***
0.06
(9.90***
0.06
(9.28***)
/A0.05
(2.31**)
0.07
(2.90***
0.20
(6 ) .23***
0.19
(5.84***)
/A/ /
–2.08
–) 4.96***
–1.94
–4.62***)
STATE .0
(–0.34)
01
(0.41)
–0.02
(–1.12)
–0.00
(–0.16)
P / –0.07
*
–1.84 ) / /
P / –0.02
(–0.47) / /
P / –0.07
**
–2.22) / .0 (–1.08)
P / –0.08
(– ) 2.63*** / –0.04 (–1.34)
P / –0.12
(– ) 4.02*** / –0.07
–2.96***)
P / –0.13
(– ) 4.24*** / –0.08 (–3.77***)
P / –0.15
(– ) 4.50*** / –0.10 (–3.73***)
P / –0.13
(– ) 3.79*** / –0.06 (–2.51**)
P / –0.06
(–1.48) / /
j-R 60.3133
SSE 25.928 24.185 18.780 18.206
obs 1024 1024 914 914
*** * mestically d zere 1%, 5%10%
distribution it is significant at 1% level. It thus indicates
, **, and an statiifferent fromo at th and
level, respectively.
that regulation interval indicators are jointly significant
and introducing into regulation indicators is necessary.
From another aspect it shows that refinancing friction
change is an important determinant of optimal capital
structure. From the results of model (5), after introducing
regulation interval indicators, adjusted R square reaches
to 31.33%, slightly higher than that in model (4). More-
over, variable P1, P3, P4, P5, P6, P7, P8 are all significant,
indicating that during different regulation intervals equity
refinancing friction change significantly impact optimal
capital structure. Again, it is worth noting that the coeffi-
cient of Mktidx is still insignificant. It illustrates that in
our sample and model, market timing factor has no in-
fluence on optimal capital structure. Furthermore, the coef-

Z. W. WANG, W. X. ZHU
516
ficient of M/B is not significant under model (5). Because
Baker and Wurgler (2002) interpret this coefficient as a
measure of market timing, our results show that even
though M/B is a measure of market timing, it is not a
major determinant of optimal capital structure. In addi-
tion, earnings volatility, profitability, size and fix assets
proportion are still significant but ownership property
continues to be insignificant.
The last two columns show the statistics after 1998. In
th
4. Conclusion Remarks
ine the influence of stock
un
wi
firms, “mar-
ke
mple of equity refinancing firms, firm
gr
preciation, earnings vola-
tili
ctors which
ha
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