foreign institutional ownership and stock liquidity in...
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Foreign Institutional Ownership and Stock Liquidity in China
1
Foreign Institutional Ownership and Stock Liquidity in China
Abstract
The literature widely documents the negative liquidity impact of foreign participation in firms
that permit high foreign institutional ownership. This paper employs a unique setting for the limited
participation of qualified foreign institutional investors (QFIIs) in China`s A-share market and
examines how these impacts on stock liquidity in emerging markets. Contrary to the findings in the
literature, foreign investor participation helps enhance the liquidity of affected stocks by promoting
trade activities in increasing trading volume. The improvement in liquidity is more significant in small
firms compared to large firms. Our findings are robust to endogeneity and the possible influence of the
stock market shock, industry effects and the stock exchange. Further, the liquidity improving effects
of QFII are even stronger when the analysis is performed on a subsample of QFII firms.
Key words: liquidity, foreign institutional investor, QFII
1. Introduction
From the view of globalization, the general trend of financial liberalization and capital flows have
become an inevitable tendency and China is increasingly integrated with the global economy. China
allows foreigners to invest in A-share stocks through the QFII(qualified foreign institutional investor)
system instituted on December 1,2002. Nevertheless, the approach taken by the Chinese government
to liberalize the financial market is a cautious and conservative one. More and more foreign investors
invest in China’s listed company, which have a great impact and stock market. Our study focuses on
the influence of foreign institutional participation on stock market liquidity under China’s QFII scheme.
Liquidity is an important indicator of the efficiency of the stock market. Adequate liquidity facilities
investors to complete purchase requests for stocks faster and is also an important guarantee for
corporate fund raisers to conduct listing financing and refinancing.
A well-known stream of literature holds different arguments about the relationship between the
foreign institutional investor ownership and stock liquidity. On the one hand, foreign institutional
investors usually have information advantage, rational concept and much capital. They can help
investors to form rational investment concept and then improve market efficiency (Grinblatt and
Keloharju,2000; Froot, O’Connell and Seasholes, 2001; Seasholes,2004). Alternatively, the
introduction of foreign investment can improve the quality of information disclosure of the company,
which can reduce the potential risk or transactional cost. Hence it positively influences the liquidity
(Bae et al,2006; Li et al,2011). On the other hand, the presence of large shareholders could reduce the
number of shares available to the public for trading, thus reducing stock liquidity by lowering trading
activity (Demsetz, 1968; Bolton and Thadden, 1998; Rubin, 2007; Brockman, Chung and Yan, 2009;
Deng,2018). Or these talented foreign institutional investors could be information traders, thus bring
more sever information asymmetry which negatively influences the liquidity (Bhide,1993;
Agarwal,2007).
China’s QFII scheme is restrictive: the total shares held by each (all) QFII in one listed company
Foreign Institutional Ownership and Stock Liquidity in China
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is not permitted to exceed 10% (20%) of the total outstanding shares of the company. Thus, it`s possible
to identify the influence of foreign institutional investment on stock liquidity in a low ownership frame
work.
Our study will employ panel regression model with firm and time fixed effect to examine the
influence of foreign institutional (QFII) ownership on stock liquidity of A share listed firms. At the
same time, the study will also try to use different measures to consider market liquidity (illiquidity
ratio and trading volume). In addition, we also try to discuss the heterogeneity in different firm size
and use different measures for robustness test.
The rest of the paper is structured as follows. Section 2 is the literature review which develops
our hypotheses. Section 3 explains the measurement of liquidity for individual stocks: this section also
develops the econometric specifications of our panel data regressions. Section 4 describes the study’s
data sources and provides descriptive statistics for liquidity measures, firm characteristics, and
ownership variables. Section 5 reports the results of our main panel data regressions. Section 6
examines the endogeneity of foreign institutional ownership and causality. Section 7 presents the
results of various robustness checks. The final section summarizes the study and provides concluding
remarks.
2. Literature review and Hypothesis development
2.1. Liquidity
Liquidity, as one of the core concepts of market microstructure theory, is one of the important
variables that affect the long-term healthy operation of the market. Moderate liquidity can promote
market transactions, improve market efficiency and reduce financing costs. Keynes put forward the
concept of liquidity in 1930, saying that assets with better liquidity are assets that are easier to realize.
According to the definition of Amihud and Mendelson (1986), liquidity is the time or cost required
to complete the transaction within a certain period of time, or the time or cost required to find an ideal
price. According to Goyenkoetal (2009)[5], liquidity includes transaction cost and transaction speed.
Although the definition of liquidity includes multiple dimensions, the use of this word in the
literature is often limited to specific indicators. For example, the bid-ask spread (Amihud and
Mendelson ,1986), the turnover rate (Chordiaetal ,2001), or the Amihud measure (Amihud ,2002) and
so on.
Macroeconomic environment, policies, industry characteristics, enterprise characteristics, asset
characteristics and so on will affect liquidity. Generally speaking, there is a negative correlation
between equity concentration and liquidity. The larger shareholders, or the higher the proportion of
shares held by a certain shareholder, will lead to a much decrease in liquidity. In addition, the level of
information in the market will also significantly affect the level of liquidity, Heflin (2001)[7] found that
for companies with more adequate information disclosure and more transparent information, the
effective spread is lower. On the other hand, the companies with different sizes, different growth rate,
are different in information transmission and disclosure, which determines the difference in the mastery
of information. As a result, the impact of foreign investors on liquidity has been magnified or reduced
to a certain extent.
Foreign Institutional Ownership and Stock Liquidity in China
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2.2. QFII and liquidity
Studies have shown that foreign investors as informed traders may improve the liquidity of stocks.
Informed trader refers to the party who has relatively more information according to the differences in
the understanding of information among traders in the stock market, as opposed to the concept of
uninformed trader.
First of all, foreign investors as informed traders, the identity of the information advantage is easy
to cause herding effect in the market.
Seasholes (2001) [4]points out that foreign investors have more resources, more experience in
international capital markets and more effective investment strategies. Based on QFII's heavy holdings
in the mainland stock market, Sun Li and Lin Li (2006) [11]found that the capital performance of QFII
reflects the strong adaptability of foreign investors to China's economic development and better grasp
the development cycle of the industry. This significant investment advantage is likely to attract
investors' attention, attract more money and increase liquidity. Tong Yuansong and Wang Guangwei
(2001)[12] pointed out that qualified foreign investors, especially mature overseas institutions, are likely
to adopt the strategy of rational investment, which is reflected in the preference of performance stocks
in the stock market. The higher the proportion of foreign ownership, the more attention will be paid to
the stock of the listed company by other investors. With the increase of the amount of investment and
the number of transactions, the bid-ask spread of the corresponding stock decreases, which not only
increases liquidity, but also weakens the volatility of the stock price.
Second, as informed traders, foreign investors compete with other information traders in the
market.
Grossman (1980)[6] pointed out that in a market with asymmetric information, the competition
and monopoly behavior of informed traders will have an impact on the integration of private
information into the stock price. Subrahmanyam (1991) [10]believes that not only foreign investors are
informed traders, but also relatives of foreign company managers and some institutional investors are
also informed traders, and their competitive behavior will make this information included in the stock
price. Thus, the information efficiency of the stock is increased, and then the liquidity of the stock is
increased. By analyzing a non-competitive market model, Subrahmanyam (1991) [10]concludes that
liquidity is non-monotonous in terms of the number of informed traders and the accuracy of
information, and that the competition between them in the market will lead to more information into
trading. The improvement of information efficiency brings the increase of stock liquidity.
In addition, qualified foreign institutional investors lead to a significant improvement in the
information disclosure of the company, and the improvement of the quality of information disclosure
can reduce the potential risks and transaction costs, and improve the liquidity of stocks (Bae, 2006 and
Lietal, 2011[2] ).
Stulz (1999)[9] believes that when foreign investors invest in domestic stocks to become
shareholders of listed companies, they have a stronger and more professional ability to supervise the
company than small and medium-sized retail investors. Therefore, it will effectively improve the level
of corporate governance of domestic listed companies, improve the level and quality of information
disclosure to a greater extent, and then reduce the degree of information asymmetry of stocks.
Therefore, the stock price can better reflect the effective information related to the company, then
reduce the information advantage of the private information holder, so that for investors who do not
have the information advantage, the risk premium required to invest in the stock is reduced and finally
Foreign Institutional Ownership and Stock Liquidity in China
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enhance the liquidity of stocks.
Ding (2017)[3] believes that the information advantage of foreign institutional investors will be
particularly critical in developing countries, especially in China, because the problems of information
transparency and information asymmetry in China have always been very serious.
Many scholars have also discussed it from other angles. Amihud and Mendelson (2002) [1]believes
that after the introduction of foreign investors, domestic listed companies can enrich the equity
structure and allocate certain risks to foreign investors, and the diversification of shareholders increases
the liquidity of stocks. Subrahamanyam (1991)[10] in the study of stock information feedback
mechanism found that the better the stock liquidity, the more informed traders participate, and the cost
of communication and coordination between stakeholders and management is reduced. Then through
the feedback mechanism the ability of management of the company to make efficient decisions
promote and the information contents of the stock improve. Mendelson and Tunca (2004)[8] looking at
trading activities in an incomplete efficient market found that long-term information asymmetry among
traders leads to when more information is contained in a stock, a reduction in trading risk is
accompanied by a decrease in liquidity costs. That is, the liquidity of stocks increases.
Based on these, we propose the following two assumptions:
H1: The higher the QFII shareholding ratio is, the greater the stock trading volume is, and the
better the stock liquidity of the company is.
H2: Other conditions remain unchanged, the smaller the size of the company, the more obvious
the role of QFII in promoting stock liquidity
3. Measurement of variables and model specification
Due to the latent nature of liquidity and its multiple dimensions, a single measure cannot capture
all its features. We apply measures commonly used in the liquidity literature to reflect liquidity. The
control variables included in the panel data regressions are chosen mainly to conform to the literature.
3.1. Measurement of dependent variables
3.1.1. Illiquidity
To measure stock liquidity, we start by defining stock liquidity from the angle of price impact. We
use the illiquidity indicator Illiq proposed by Amihud (2002) to proxy for a stock's liquidity (Karolyi
et al., 2012). Illiq is the (Amihud) ratio of the absolute return to dollar trading volume, which can
effectively measure the price impacts of the unit trading volume (in dollars) in international stock
markets (Kang and Zhang, 2010; Fong et al., 2017).
𝐼𝑙𝑙𝑖𝑞𝑖,𝑡 = |𝑅𝑒𝑡𝑢𝑟𝑛𝑖,𝑡|
𝑉𝑜𝑙𝑢𝑚𝑒𝑖,𝑡
Where 𝑅𝑒𝑡𝑢𝑟𝑛𝑖,𝑡 is stock i`s average quarterly return in time t, 𝑉𝑜𝑙𝑢𝑚𝑒𝑖,𝑡 is quarterly trading
volume. The higher the Amihud ratio Illiq is, the higher the stock`s illiquidity, the lower the stock`s
liquidity.
Foreign Institutional Ownership and Stock Liquidity in China
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3.1.2. Trading Volume
TV represents trading volume that we had used in the calculation of Illiquidity ratio. Intuitively
the higher the trading volume is, the higher the stock`s liquidity.
3.2. Measurement of control variables
Previous studies indicate that firm size, stock price volatility, share price and turnover are
associated with liquidity. (Benston and Hagerman, 1974; Stoll and Whaley, 1983; Agarwal, 2007;
Brockman, Chung and Yan, 2009). For firm size, Stoll and Whaley (1983) argue that trading smaller
stocks is more expensive because less relevant information about these firms is available. According
to Chordia, Roll and Subarhmanyam (2001), volatility increases the market makers’ inventory risk and
the risk of unintentionally engaging in short-term speculative trades. Previous studies show that the
spread could be correlated with price nonlinearly; hence, it is standard practice to take the natural
logarithm of share price (Brockman, Chung and Yan, 2009; Chung, Elder and Kim, 2010). Agarwal
(2007) argues that high turnover may reflect belief dispersion induced by information differences
among investors.
In our panel data regressions, we control for these effects by including stock return volatility
(VOL) estimated by the standard deviation of daily stock returns, firm size measured by the book value
of a firm (SIZE), the natural logarithm of the share price (LNP), and the turnover rate (TO) as
explanatory variables. In addition, the degree of leverage (LEV) is included because the security design
literature has recognized that a firm’s capital structure can affect the degree of information disclosure
(Diamond and Verrecchia, 1991). We also control for ownership by domestic institutional investor (DI)
by including the percentage ownership of the five largest domestic institutions (i.e., open-end funds,
security, insurance, trust companies, and pension funds).
Finally, we include two control variables unique to China’s stock market: a state-owned enterprise
(SOE) dummy (STATE) and the nontradable share ratio (NT). China’s stock market is characterized
by the dominance of SOEs, and previous studies find that whether a firm is state=owned does matter
for stock liquidity due to its links to the government (Ding, 2015). Therefore, we control for SOE
dummy as a proxy for political connections, by defining a dummy variable taking the value of 1 if a
firm is state-owned. To improve the structure of corporate governance and market liquidity, a split-
share structure reform was introduced by the Chinese government in 2005 to dismantle the dual-share
structure by converting nontradable shares into tradable shares. Before the reform, the split-share
structure and the associated overhang of the nontradable shares presumably impaired liquidity (Jiang,
Foreign Institutional Ownership and Stock Liquidity in China
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Foreign Institutional Ownership and Stock Liquidity in China
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Foreign Institutional Ownership and Stock Liquidity in China
8
Laurenceson and Tang, 2008; Beltratti, Bortolotti and Caccavaio, 2012). We use a firm’s NT (i.e., the
number of nontradable shares divided by the total number of shares) to control for the split-share
structure. Many explanatory variables are available only on a quarterly basis; we mearsure these
variables in the beginning of each quarter (indicated by subscript t-1 in the regression). The remaining
variables are measured as the average over each quarter (indicated by subscript t in the regression).
3.3. Specification of panel regression models
Equation (1) specifies the panel data model for examining the relationship between the QFII
participation and stock market liquidity. The dependent variable measuring liquidity is the illiquidity
ratio and trading volume.
We follow Rubin (2007), Brockman, Chung and Yan (2009) and Chung, Elder and Kim (2010)
and estimate the following main panel regression for firm i and time t:
𝐿𝐼𝑄𝑖,𝑡 = 𝛼0 + 𝑎1𝑄𝐹𝐼𝐼𝑖,𝑡−1 + 𝛼2𝐷𝐼𝑖,𝑡−1 + 𝛼3𝑆𝐼𝑍𝐸𝑖,𝑡−1 + 𝛼4𝑆𝑇𝐴𝑇𝐸𝑖,𝑡−1 + 𝛼5𝐿𝐸𝑉𝑖,𝑡−1 +
𝛼6𝑉𝑂𝐿𝑖,𝑡 + 𝛼7𝐿𝑁𝑃𝑖,𝑡 + 𝛼8𝑇𝑂𝑖,𝑡 + 𝛼9𝑁𝑇𝑖,𝑡−1 + ∑ 𝛽𝑞𝐷𝑞 + 𝜀𝑖,𝑡𝑞 (1)
where LIQ = Illiq or TV, and QFII is the percentage ownership by qualified foreign institutional
investors. Due to the high skewness and kurtosis, we transform all dependent variables by taking the
natural logarithm. The control variables, namely domestic institutional ownership (DI), firm size
(SIZE), SOE dummy (STATE), leverage ratio (LEV), stock return volatility (VOL), the natural
logarithm of the share price (LNP), turnover rate (TO), and the nontradable share ratio (NT). The
quarterly time dummies (D) capture common shocks and potential time trends. For each measure of
liquidity, we run two panel regressions. The first regression has QFII ownership as an explanatory
variable, and the second has both QFII ownership and domestic institutional ownership as explanatory
variables (all regressions include the remaining control variables). If QFII ownership is positively
related to liquidity, the coefficient on QFII should have a negative sign for the illiquidity and a positive
sign for TV.
4. Data
4.1. Data sources and data filtering
The data for the QFII ownership which is our independent variable is from CSMAR. The
dependent variables which measure the liquidity of stocks are from REESET. The Illiq and TV are
quarterly averaged across all trading days for each stock in each year.
There are several control variables in our paper. Agarwal (2007) argues that high turnover may
reflect belief dispersion induced by information differences among investors. The turnover rate is
calculated as the total trading volume in a year divided by shares outstanding. Volatility is calculated
as the standard deviation of daily stock returns over quarters and according to Chordia, Roll and
Subrahmanyam (2001), volatility increases the market makers’ inventory risk and the risk of
unintentionally engaging in short-term speculative trades. The security design literature has recognized
that a firm’s capital structure can affect the degree of information disclosure (Diamond and Verrecchia,
Foreign Institutional Ownership and Stock Liquidity in China
9
1991) so consequently, the capital structure can be associated with market liquidity through the
informational channel. The leverage ratio is measured as the company’s total debt divided by its total
assets. The firm age is measured by current year deducting the established year. We also control for
ownership by domestic institutional investors (DI) by including the percentage ownership of the five
largest domestic institutions. Return on assets is measured by net profit divided by total assets. Finally,
because China’s stock market is characterized by the dominance of SOEs, and previous studies find
that whether a firm is state-owned does matter for stock liquidity due to its links to the government
(Ding, 2015). Therefore, we control for SOE dummy as a proxy for political connections, by defining
a dummy variable taking the value of 1 if a firm is state-owned.
4.2. Descriptive statistics and preliminary analysis
In this section, we present the descriptive statistics of the dependent variables, foreign and
domestic institutional ownership variables, and control variables. We proceed to a preliminary
univariate analysis comparing firms with participating foreign institutional investors and firms without
participating foreign institutional investors.
4.2.1. Summary statistics
Table 1 presents the summary statistics of the variables used in this study. We see that, on average,
QFIIs hold approximately 1.6% of a firm’s outstanding shares, while domestic institutions, in aggregate,
hold approximately 3.7% (open-end funds, security, insurance, trust companies, and pension funds).
The average Illiquidity ratio is approximately -21.919. For trading volume, the mean is 0.137 million
shares per day.
4.2.2. Correlation coefficient
Table 2(a) is the correlation coefficient. We can have a preliminary idea that Illiq is negatively
correlated to QFII ownership, and TV is positively correlated to QFII ownership.
4.2.3. Preliminary univariate analysis
To provide a visual impression of the dynamic relationship between liquidity and ownership, we
plot the total value of QFIIs’ shareholdings each quarter and the two average quarterly liquidity
measures, the Illiq and TV, as seen in Figure 1. The right vertical axis shows QFII participation, and
the left vertical axis shows liquidity over the sample period (2004Q3–2019Q1). The visual evidence in
Figure 1 suggests a negative relationship between QFII holdings and market illiquidity over time.
Figure 2 presents a visual evident of positive relationship between QFII holdings and trading volume.
We perform preliminary univariate tests on the two liquidity measures, Illiq, TV and the two
trading activity measures for two subsamples of firms: those with QFIIs and those without (non-QFII
firms).
We assume that, if QFII participation increase liquidity, we expect to find different (average)
values of the various measures for the two groups of firms. The evidence in Table 2(b) indeed suggests
Foreign Institutional Ownership and Stock Liquidity in China
10
that this is the case, and all differences are statistically significant at the 5% level.
Figure 1
Figure 2
Foreign Institutional Ownership and Stock Liquidity in China
11
Table 2 (b)
Preliminary analysis of the relationship between liquidity and QFII
variables Mean for QFII firms
Observations=10642
Mean for non-QFII firms
Observations=119701
p-Value
Illiquidity
-22.475 -21.870 0.000
Trading Volume
(in Billions, per day)
18.356 17.856 0.000
5. Empirical results
We begin our empirical analysis by investigating whether foreign institutional investors (QFIIs)
are associated with liquidity on the Chinese stock market using the panel regression in Equation (1).
Next, we analyze whether there is time effect. In the following section, we include large ownership by
strategic foreign institutional investors as an additional explanatory variable in addition to QFII
ownership to differentiate the link to liquidity between institutions with smaller and larger investments.
Table 3
QFII relationship with liquidity
The table shows the panel regressions using the two liquidity measures regressed on lagged ownership of
QFIIs. The two measures of liquidity are the Illiquidity ratio in Amihud(2002) and the trading volume
(TV).DI denotes the lagged domestic institutional ownership in the top 10 outstanding shareholders of a
firm. Remaining control variables refer to firm and share characteristic: firm size measured by the log of
book value(SIZE),degree of leverage(LEV), the log volatility of stock returns(VOL),the log of share
price(LNP) and the log of turnover rate of shares traded(TO). We also control for the nontradable ratio(NT)
and the state dummy(State),with the dummy variable equal to 1 if a firm is state-owned. The period of study
if from 2004Q1 to 2019Q1. Driscoll-Kraay standard errors reported in parentheses are robust to correlation
across residuals within a firm over time and across firms in the same year and different firms.
Dependent variables
Independent
variables
(1)
Illiq
(2)
Illiq
(3)
TV
(4)
TV
QFIIi,t -0.0111*
(0.0063)
-0.0113*
(0.0063)
0.0105*
(0.0055)
0.0103*
(0.0054)
DIi,t
0.0030***
(0.0009)
0.0027***
(0.0009)
TOi,t -0.1402***
(0.0044)
-0.1391***
(0.0044)
0.2186***
(0.0057)
0.2196***
(0.0058)
SIZEi,t-1 -0.6729***
(0.0176)
-0.6710***
(0.0176)
0.5760***
(0.0157)
0.5777***
(0.0158)
LEVi,t-1 -1.0718***
(0.0670)
-1.0714***
(0.0670)
1.0483***
(0.0632)
1.0487***
(0.0632)
VOLi,t 1.5607***
(0.4898)
1.5538***
(0.4877)
0.8061**
(0.3694)
0.7998**
(0.3678)
Foreign Institutional Ownership and Stock Liquidity in China
12
(Continued)
NTi,t-1 0.0220***
(0.0004)
0.0219***
(0.0004)
-0.0220***
(0.0004)
-0.0221***
(0.0004)
LNPi,t-1 -0.7480***
(0.0137)
-0.7597***
(0.0141)
0.7270***
(0.0135)
0.7164***
(0.0141)
State Yes Yes Yes Yes
N 57865 57865 57865 57865
adj. R2 0.695 0.695 0.730 0.730
F 3703.9643 3224.0997 4289.7284 3768.3899
Standard errors in parentheses (* p<0.1, ** p<0.05, *** p<0.01)
5.1. Foreign institutional ownership and stock market liquidity
Table 3 shows the results of the first panel data regressions. Our focus is on the link between
foreign institutional ownership and market liquidity. The first two regressions (columns) show the
results for the illiquidity ratio. We find clear evidence that increased foreign institutional ownership is
associated with a lower illiquidity ratio, which means that increased foreign institutional ownership is
associated with higher stock market liquidity. The inclusion of domestic institutions in the regressions
does not alter the relationship between foreign institutional ownership and the illiquidity ratio. The
coefficient estimate on foreign institutional ownership (QFII) is statistically highly significant and
stable at –0.0111 and –0.0113 in the two regressions. The numerical magnitudes of the coefficient
estimates indicate that a 10% higher foreign institutional ownership is associated with an
approximately -0.0111 lower Illiquidity ratio.
Table 4
Fixed effect model
The table shows the fixed effects panel regressions of the Illiquidity (Illiq) and the trading volume (TV) on
the QFII ownership. The period of study is from 2004Q3 to 2019Q1. Driscoll-Kraay standard errors reported
in parentheses are robust to correlation across residuals within a firm over time and across firms in the same
year and different quarters.
Dependent variables
Independent
variables
(1)
Illiq
(2)
Illiq
(3)
TV
(4)
TV
QFIIi,t-1 -0.0091***
(0.0032)
-0.0077**
(0.0032)
0.0145***
(0.0053)
0.0126**
(0.0052)
DIi,t
-0.8629***
(0.0396)
1.1495***
(0.0914)
SIZEi,t-1 -0.3233***
(0.0045)
-0.3211***
(0.0045)
0.2910***
(0.0115)
0.2881***
(0.0114)
LEVi,t-1 -0.5500***
(0.0208)
-0.5377***
(0.0207)
0.6351***
(0.0459)
0.6187***
(0.0450)
VOLi,t 1.1389***
(0.0628)
1.1616***
(0.0626)
0.8658**
(0.3445)
0.8355**
(0.3353)
Foreign Institutional Ownership and Stock Liquidity in China
13
(Continued)
NTi,t-1 0.0081***
(0.0003)
0.0082***
(0.0003)
-0.0105***
(0.0005)
-0.0106***
(0.0005)
LNPi,t -0.5528***
(0.0053)
-0.5155***
(0.0056)
0.5168***
(0.0114)
0.4672***
(0.0120)
TOi,t -0.1057***
(0.0011)
-0.1091***
(0.0011)
0.1649***
(0.0041)
0.1694***
(0.0042)
State Yes Yes Yes Yes
Time dummy Yes Yes Yes Yes
N 64654 64654 64654 64654
adj. R2 0.780 0.781 0.800 0.803
F 11563.6160 11119.6109 4096.9190 3934.5918
Standard errors in parentheses (* p<0.1, ** p<0.05, *** p<0.01)
The next two regressions show the results for TV (with and without domestic institutional
ownership as a control). Our evidence shows that foreign institutional ownership is significantly and
positively related to trading volume, with coefficient estimates 0.0105 and 0.0103 (i.e., foreign
institutional ownership is again linked to a higher liquidity, as with the spread). The numerical
magnitude of the coefficient estimate indicates that a 10% increase in foreign institutional ownership
is associated with an approximately 1% increase in the trading volume.
5.2. Fixed effect model
Unobservable time-invariant factors may simultaneously affect both the left-hand side and the
right-hand side of the regression in Equation (1). If so, the regression can suffer from omitted variable
bias. We estimate a time fixed effects regression in an attempt to control for possible omitted variables.
We report the results of the firm fixed effects regression in Table 4.
We find that the positive relationship between the QFII ownership and liquidity measures dose
not altered when the fixed effects model is applied. The results for the fixed effects estimation
nonetheless again strengthen our previous finding that the positive relationship between foreign
institutional ownership and liquidity is mainly due to increased trading volume.
Table 5
Different firm size
The table shows the fixed effects panel regressions of the Illiquidity (Illiq) and the trading volume (TV) on
the QFII ownership in subsample divided by the medium of firm size. The period of study is from 2004Q3
to 2019Q1. Driscoll-Kraay standard errors reported in parentheses are robust to correlation across residuals
within a firm over time and across firms in the same year and different quarters.
Dependent variables
Large firms Small firms
Independent
variables
(1)
Illiq
(2)
TV
(3)
Illiq
(4)
TV
QFIIi,t-1 -0.0022
(0.0059)
0.0068
(0.0062)
-0.0106**
(0.0052)
0.0202***
(0.0044)
Foreign Institutional Ownership and Stock Liquidity in China
14
(Continued)
DIi,t-1 -0.6926***
(0.1181)
0.9246***
(0.1197)
-0.4560***
(0.1164)
0.9676***
(0.1179)
SIZEi,t-1 -0.3514***
(0.0192)
0.3114***
(0.0189)
-0.2750***
(0.0181)
0.2677***
(0.0174)
LEVi,t -0.2907***
(0.0712)
0.3530***
(0.0731)
-0.6550***
(0.0568)
0.7860***
(0.0600)
VOLi,t 1.3563
(0.8703)
0.4012
(0.5039)
1.1775***
(0.3743)
1.0405***
(0.3212)
NTi,t-1 0.0057***
(0.0007)
-0.0090***
(0.0007)
0.0086***
(0.0007)
-0.0103***
(0.0006)
LNPi,t -0.5693***
(0.0174)
0.5203***
(0.0182)
-0.4798***
(0.0159)
0.4087***
(0.0155)
TOi,t -0.1383***
(0.0081)
0.2177***
(0.0108)
-0.0978***
(0.0037)
0.1524***
(0.0043)
State Yes Yes Yes Yes
Time dummy Yes Yes Yes Yes
N 32814 32814 31840 31840
adj. R2 0.783 0.799 0.769 0.801
F 1610.4196 1840.2160 1514.0860 1554.2132
Standard errors in parentheses (* p<0.1, ** p<0.05, *** p<0.01)
5.3. Different firm size
We investigate whether the positive relationship between QFII participation and liquidity is
prevalent in both the large and small firm. We therefore perform a panel regression analysis of the
model in Equation (1) by splitting the total sample into two subsamples of stocks listed by different
firm size. Table 5 shows that the coefficient of QFII ownership for the Illiq (the TV) is negative
(positive) and significant only on small firms. We find that the magnitudes of the coefficient estimate
for Illiq and TV on the small firms (–0.0106 and 0.0202, respectively) are similar to the full sample
estimates; however, the influence of QFII ownership is insignificant on large firms. One possible
reason for this difference is that large firms themselves are more regulated, the influence of QFII
ownership is then less obvious than in small firms
6. Endogeneity of foreign institutional ownership
Another intrinsic explanation for the positive correlation between QFII ownership is that QFII is
more likely to choose stocks with high liquidity, which may lead to reverse causality. Therefore, in this
section, we will study endogeneity to reduce this concern about causality and endogeneity.
Foreign Institutional Ownership and Stock Liquidity in China
15
6.1. Endogeneity test
We perform an endogeneity test developed by Wu (1973) and Hausman (1978) to examine
whether the QFII ownership or the illiquidity variables are endogenous. We conduct two-stage least
squares (2SLS) regressions, where the first-stage regression includes the same control variables as in
the main regression (Table 3), together with a set of new explanatory variables. We rely on previous
work on the investment preferences of foreign funds by Kang and Stulz (1997), Heflin and Shaw (2000),
Dahlquist and Robertsson (2001), Rubin (2007), and Liu, Bredin, Wang and Yi (2014) and use the
following additional explanatory variables in the first-stage regression: the return on assets (net profit
divided by total assets, ROA), the firm age (AGE), and an ownership concentration index, the
Herfindahl 10 index (OC)1.We also include industry fixed effect dummies (IND) that are equal to 1 if
the firm operates in a given industry2. The first-stage regression is:
𝑄𝐹𝐼𝐼𝑖,𝑡 = 𝜔0 + 𝜔1𝐿𝐼𝑄𝑖,𝑡−1 + 𝜔2𝐷𝐼𝑖,𝑡−1 + 𝜔3𝑆𝐼𝑍𝐸𝑖,𝑡−1
+𝜔4𝑆𝑇𝐴𝑇𝐸𝑖.𝑡−1 + 𝜔5𝐿𝐸𝑉𝑖,𝑡−1 + 𝜔6𝑉𝑂𝐿𝑖,𝑡 + 𝜔7𝐿𝑁𝑃𝑖,𝑡 + 𝜔8𝑇𝑂𝑖,𝑡
+𝜔9𝑁𝑇𝑖,𝑡−1 + ∑ 𝛽𝑞𝐷𝑞
𝑞
+ 𝜔10𝑅𝑂𝐴𝑖,𝑡−1 + 𝜔11𝐴𝐺𝐸𝑖,𝑡−1 + 𝜔12𝑂𝐶𝑖,𝑡−1
+ ∑ 𝜋𝑚𝐼𝑁𝐷𝑚,𝑖,𝑡−1𝑚 + 𝜇𝑖,𝑡 (2)
The second stage estimates the baseline regression in the main regression (Table 3) by replacing
the actual QFII holding with its lagged residual value (R_QFII) from the first-stage regression. If QFII
is endogenous, the coefficient on R_QFII in the second-stage regression is statistically different from
zero; if QFII is not endogenous, the coefficient is not statistically different from zero. The results are
shown in Table 6.
Table 6
Endogeneity test
The table shows a 2SLS regression analysis. The QFII ownership is the dependent variable in the first stage
estimation described in (first stage), while the second stage estimates the baseline model in the main
regression (Table 3), by replacing the actual QFII ownership with lagged value of residuals R_QFII from
the first stage estimation. We use two illiquidity measures, the Illquidity from Amihud(2002)and the
trading volume (TV). In addition to the control variables in the main regression (Table 3), we add new
control variables to the first stage estimations: the return on assets (ROA), the firm age (AGE), an ownership
concentration index (Herfindahl 10 index, OC) and industry fixed effect dummies that are equal to 1 if the
firm operates in a given industry. The period of study if from 2004Q1 to 2019Q1. Driscoll-Kraay standard
errors reported in parentheses are robust to correlation across residuals within a firm over time and across
firms in the same year and different firms. There are 63592 and 63594 observations in each 2SLS analysis.
Dependent variables
Independent
variables
(1)
QFII
(2)
Illiq
(3)
QFII
(4)
TV
Illiqi.t-1
-0.0011
(0.0051)
1 The Herfindahl 10 index measures the degree of ownership dispersion in the top 10 shareholder structure. 2 The industry classification is released by the CSRC, and the data are provided in the CCER database. There are 13
different industries at the level of classification used in equation 2.
Foreign Institutional Ownership and Stock Liquidity in China
16
(Continued)
TVi,t-1
-0.0022
(0.0052)
R_QFIIi,t-1
-0.0085*
(0.0045)
0.0100**
(0.0045)
SIZEi,t 0.0003
(0.0131)
-0.3168***
(0.0122)
0.0013
(0.0131)
0.3046***
(0.0116)
LEVi,t-1 -0.0337
(0.0484)
-0.6046***
(0.0454)
-0.0324
(0.0484)
0.6229***
(0.0449)
VOLi,t 0.0252
(0.3649)
9.5309***
(0.4206)
0.0585
(0.3594)
21.4262***
(0.3942)
NTi,t-1 0.0008
(0.0009)
0.0043***
(0.0006)
0.0008
(0.0009)
-0.0052***
(0.0005)
LNPi,t 0.0589***
(0.0123)
-0.5640***
(0.0113)
0.0607***
(0.0123)
0.4784***
(0.0108)
TOi,t -0.0027
(0.0025)
-0.1396***
(0.0029)
-0.0026
(0.0025)
0.1478***
(0.0029)
ROAi,t 0.0008
(0.0008)
0.0008
(0.0008)
AGEi,t 0.0124**
(0.0060)
0.0126**
(0.0060)
COi,t -0.0404
(0.1278)
-0.0466
(0.1256)
State Yes Yes Yes Yes
Time dummy Yes Yes Yes Yes
Industry dummy Yes Yes Yes Yes
N 63592 63592 63594 63594
adj. R2 0.004 0.806 0.004 0.831
F 2.7906 2185.1637 2.8181 2653.1149
Standard errors in parentheses (* p<0.1, ** p<0.05, *** p<0.01)
We find that the coefficient estimates on R_QFII, in column 2 for the Illiquidity (Illiq) and in
column 4 for the trading volume (TV), are not statistically different from zero. These results suggest
that our findings about the relationship between the QFII ownership and liquidity do not suffer from
endogeneity bias.
6.2. First difference model
We estimated the panel model in the main regression (Table 3) in first difference form and results
are reported in Table 7. We find that the change in foreign ownership is significantly negatively
associated with the change of the Illiquidity. However, consistent with the results for the level of the
trading volume (TV), there is no significant association between the change in foreign ownership and
Foreign Institutional Ownership and Stock Liquidity in China
17
change in the trading volume (TV). These results strengthen our previous finding that the positive
relationship between foreign institutional ownership and liquidity.
Table 7
First Difference model
The table shows the panel regressions of changes in the Illiquidity and the trading volume (TV) on changes
in the QFII ownership. The period of study if from 2004Q1 to 2019Q1. Driscoll-Kraay standard errors
reported in parentheses are robust to correlation across residuals within a firm over time and across firms in
the same year and different firms. There are 64654 observations in each regression.
Dependent variables
Independent
variables
(1)
ΔIlliq
(2)
ΔTV
ΔQFIIi,t-1 -0.0004**
(0.0002)
0.0001
(0.0002)
ΔDIi,t-1 -0.0063***
(0.0020)
-0.0107***
(0.0021)
ΔSIZEi,t-1 -0.0011
(0.0007)
0.0008
(0.0007)
ΔLEVi,t-1 -0.0006
(0.0027)
-0.0023
(0.0025)
ΔVOLi,t -0.1135**
(0.0500)
0.0243
(0.0159)
ΔNTi,t-1 -0.0005***
(0.0001)
-0.0007***
(0.0000)
ΔLNPi,t 0.0305***
(0.0006)
0.0320***
(0.0007)
ΔTOi,t 0.0067***
(0.0002)
0.0115***
(0.0003)
State Yes Yes
Time dummy Yes Yes
N 47532 47532
adj. R2 0.459 0.614
F 1323.6975 1420.5827
Standard errors in parentheses (* p<0.1, ** p<0.05, *** p<0.01)
The magnitude of the estimated coefficients of the change in foreign ownership on the change of
the Illiquidity is smaller compared to the estimations in level for the same variable. The economic
implication is that foreign institutions’ shorter-term net purchases have a much smaller impact on
liquidity than changes in their longer-term holdings, which are presumably governed by their longer-
term strategic requirements. However, the levels of statistical significance for the Illiquidity and trading
volume in the first difference regressions are much reduced as well. Even though our first difference
regression results are consistent with our level regression results, a plausible interpretation of the
reduced magnitude and statistical significance is that QFII ownership for many firms displays only
limited variation over time but varies substantially across firms. Taking first differences, then,
substantially reduces the cross-sectional variation and leaves only shorter-term time series information
Foreign Institutional Ownership and Stock Liquidity in China
18
with which to identify the coefficients of interest in the econometric model.
7. Additional robustness checks
We estimate three alternative specifications of the panel regression in the main regression (Table
3). First, the sample is divided into three subperiods: 2004Q3–2007Q4, 2008Q1–2014Q4 and
2015Q1-2019Q1. Second, we divide the sample into firms listed on the SZSE and GEM. Third, we
investigate whether including industry effects influence our previous results. Finally, we consider the
use of QFII firms dummy and domestic institutional firms dummy to capture their effects on liquidity.
7.1. Influence of the market shock
China's financial reform process is based on its own characteristics drawing lessons from foreign
experiences and adjusting financial regulatory framework from separate mode to functional mode, thus
enhance macro prudential supervision and regulatory coordination more effectively. Modern
supervision concept is adopted, constantly complete market and product construction so as to have
more coordinated efficient financial system.
Table 8
Influence of the market shock
The table shows panel regressions of the Illiquidity (Illiq) and the trading volume (TV) on the QFII
ownership. The period of study is from 2004Q3–2009Q1. The sample 1 period is from 2004Q3 to 2007Q4.
The sample 2 period is from 2008Q1 to 2015Q1 and sample 3 is defined from 2016Q1 to 2019Q1. Driscoll-
Kraay standard errors reported in parentheses are robust to correlation across residuals within a firm over
time and across firms in the same quarter and different years.
Dependent variables
2004Q3-2008Q4 2009Q1-2015Q4 2016Q1-2019Q1
Independent
variables
(1)
Illiq
(2)
TV
(3)
Illiq
(4)
TV
(5)
Illiq
(6)
TV
QFIIi,t-1 -0.0032
(0.0063)
0.0156**
(0.0063)
0.0128*
(0.0069)
-0.0123*
(0.0066)
-0.0228***
(0.0075)
0.0248***
(0.0066)
DIi,t-1 -.4725***
(0.0948)
0.3195***
(0.0923)
-0.9199***
(0.1491)
0.7727***
(0.1247)
SIZEi,t -.1694***
(0.0209)
0.2133***
(0.0233)
-.3475***
(0.0177)
0.3145***
(0.0179)
-0.1024***
(0.0353)
0.0431**
(0.0190)
LEVi,t-1 0.1528*
(0.0838)
-0.0692
(0.0896)
-.7854***
(0.0604)
0.9211***
(0.0667)
-0.0964
(0.1093)
0.1014
(0.0810)
VOLi,t 3.4516**
(1.3420)
-1.6049*
(0.9085)
0.7778**
(0.3146)
0.8626**
(0.3787)
23.3099***
(0.6993)
12.1785***
(0.7334)
NTi,t-1 0.0031***
(0.0005)
-.0036***
(0.0005)
0.0087***
(0.0007)
-.0133***
(0.0007)
0.0248***
(0.0044)
-0.0294***
(0.0067)
LNPi,t -.7110***
(0.0150)
0.5880***
(0.0137)
-.5570***
(0.0140)
0.5700***
(0.0145)
-0.5719***
(0.0312)
0.5209***
(0.0290)
(Continued)
Foreign Institutional Ownership and Stock Liquidity in China
19
TOi,t -.2233***
(0.0150)
0.2926***
(0.0161)
-.1012***
(0.0037)
0.1586***
(0.0046)
-0.1564***
(0.0080)
0.1670***
(0.0080)
State Yes Yes Yes Yes Yes Yes
N 11078 11078 42860 42860 10716 10716
adj. R2 0.815 0.872 0.538 0.704 0.481 0.674
F 1423.6520 2073.7114 1858.7740 3670.5704 234.1961 552.7097
Standard errors in parentheses (* p<0.1, ** p<0.05, *** p<0.01)
Like many developing countries, China has enjoyed a booming stock market. However, the
Chinese stock market was not immune to the Global Financial Crisis of 2007–2008 and the market
shock in 2015. The stock market in China essentially crashed. Furthermore, the total value of foreign
institutional holdings dropped dramatically from the beginning of 2008 until the end of that year, before
again increasing to pre-crisis levels in the second half of 2009. Based on the changing behavior of
foreign institutions at the beginning of 2008, presumably caused by the GFC, we perform a subperiod
analysis using the panel regression in the main regression to investigate whether the link between QFII
ownership and liquidity has changed over time, by dividing the sample into two subsamples: sample
1, defined as the period 2004Q3–2007Q4, sample 2, defined as the period 2008Q1–2015Q4 and
sample 3, defined as the period 2016Q1-2019Q1. The results are reported in Table 8.
We find that the association between the participation of QFIIs and Illiquidity remains strong
before 2008 and after 2015, yielding a negative link between ownership and Illiquidity and a positive
link between ownership and trading volume. The coefficients for the Illiquidity and trading volume are
much more statistically significant and greater than them before 2008, which implies an more and more
important role QFII played in recent market-oriented reform. For domestic institutions, the subperiod
results are again consistent with the full sample results.
7.2. Industry effects
We estimate the model in the main regression (Table 3) controlling for industry effects by
including industry fixed effect dummies that are equal to 1 if the firm operates in a given industry. The
results show that controlling for industry effects does not alter the association between QFII ownership
and liquidity. The results are shown in Table 9.
Table 9
Industry effects
The table shows the results from regressions including industry fixed effects. Based on the CSRC
classification, there are in total 13 industries. The period of study is from 2004Q3 to 2019Q1. Driscoll-
Kraay standard errors reported in parentheses are robust to correlation across residuals within a firm over
time and across firms in the different quarters. There are 65654 observations in each regression.
Dependent variables
Independent
variables
(1)
Illiq
(2)
Illiq
(3)
TV
(4)
TV
QFIIi,t-1 -0.0086*
(0.0049)
-0.0113***
(0.0031)
0.0140***
(0.0053)
0.0121**
(0.0051)
(Continued)
Foreign Institutional Ownership and Stock Liquidity in China
20
SIZEi,t-1 -0.3205***
(0.0121)
-0.3938***
(0.0039)
0.2914***
(0.0115)
0.2892***
(0.0113)
LEVi,t-1 -0.5435***
(0.0447)
-0.6973***
(0.0186)
0.6322***
(0.0462)
0.6174***
(0.0452)
VOLi,t 1.1395***
(0.3936)
1.0670***
(0.0637)
0.8680**
(0.3438)
0.8382**
(0.3346)
NTi,t-1 0.0081***
(0.0005)
0.0079***
(0.0003)
-0.0105***
(0.0005)
-0.0106***
(0.0005)
LNPi,t -0.5538***
(0.0112)
-0.4451***
(0.0054)
0.5193***
(0.0113)
0.4699***
(0.0119)
TOi,t -0.1058***
(0.0032)
-0.1033***
(0.0011)
0.1650***
(0.0041)
0.1695***
(0.0042)
DIi,t-1
-1.0657***
(0.0395)
1.1507***
(0.0910)
State Yes Yes Yes Yes
Time dummy Yes Yes Yes Yes
Industry dummy Yes Yes Yes Yes
N 64654 64654 64654 64654
adj. R2 0.789 0.801 0.803
F 2006.7448 2199.0699 2184.8097
Standard errors in parentheses (* p<0.1, ** p<0.05, *** p<0.01)
7.3. The Shanghai and the Shenzhen stock exchanges
We investigate whether the positive relationship between QFII participation and liquidity is
prevalent in both the SZSE and the GEM. We therefore perform a panel regression analysis of the
model in the main regression (Table 3) by splitting the total sample into two subsamples of stocks listed
on the respective exchanges. Table 10 (a) shows that the coefficient of QFII ownership for the
Illiquidity(trading volume)is negative(positive) and consistent on both stock exchanges. We find that
the estimates are significant only in SZSE. One possible reason for this difference is induced by distinct
inherent factors in the two markets that govern this investment behavior. For example, SHSE attracts
more large firms and state-owned firms, SZSE attracts more small and medium-sized firms, while
GEM attracts more small-sized and growing-up firms.
Table 10 (a)
Different Stock Exchange
The table shows panel regressions of the Illiquidity (Illiq) and the trading volume (TV) on the QFII
ownership estimated for the Shenzhen Stock Exchange (SZSE) and Growth Enterprises Market (GEM)
separately. The period of study is from 2004Q3 to 2019Q1. Driscoll-Kraay standard errors reported in
parentheses are robust to correlation across residuals within a firm over time and across firms in the same
quarter and different quarters. There are 30199 and 6789 observations in each regression for the SHSE and
SZSE, respectively.
Dependent variables
Foreign Institutional Ownership and Stock Liquidity in China
21
SZSE GEM
Independent
variables
(1)
Illiq
(2)
TV
(3)
Illiq
(4)
TV
QFIIi,t-1 -0.0175**
(0.0072)
0.0196***
(0.0073)
-0.0049
(0.0077)
0.0143
(0.0089)
DIi,t-1 -0.9952***
(0.1112)
1.3600***
(0.1200)
-0.3498*
(0.1913)
0.3252*
(0.1785)
SIZEi,t-1 -0.3174***
(0.0185)
0.2732***
(0.0172)
-0.3805***
(0.0361)
0.3402***
(0.0359)
LEVi,t-1 -0.5515***
(0.0601)
0.5972***
(0.0612)
-0.8397***
(0.1305)
0.7340***
(0.1263)
OLi,t 0.6800**
(0.2649)
0.4562**
(0.1991)
16.1430***
(1.4879)
6.0802***
(1.8683)
NTi,t-1 0.0074***
(0.0007)
-0.0098***
(0.0006)
0.0021
(0.0015)
-0.0064***
(0.0013)
LNPi,t -0.4838***
(0.0163)
0.4404***
(0.0168)
-0.3645***
(0.0239)
0.3533***
(0.0217)
TOi,t -0.1001***
(0.0047)
0.1584***
(0.0059)
-0.1175***
(0.0067)
0.1169***
(0.0063)
State Yes Yes Yes Yes
Time dummy Yes Yes Yes Yes
N 30199 30199 6789 6789
adj. R2 0.775 0.792 0.666 0.787
F 1661.4011 1850.5718 419.9410 640.4883
Standard errors in parentheses (* p<0.1, ** p<0.05, *** p<0.01)
7.4. QFII dummy variable effects
Table 2(b) indicates that about 10% of the whole sample comprises QFII firms while the other
90% are non-QFII firms. Our analysis thus far has been based on the whole sample. There is the
possibility that the small percentage of QFII firms in the sample might bias the slope coefficient
estimation. For robustness check, we reestimate the models in Tables 3 and 4 using a dummy variable
for QFIIs that takes the value 1 if a firm has QFIIs’ participation. The results reported in Table 11 are
related to the Illiquidity and trading volume. The results significant and consistent with the findings
reported in Table 3 and 4. QFIIs’ participation improves stock liquidity.
These results further support our findings and, if at all, strengthen the evidence that greater foreign
institutional participation is positively associated with greater stock market liquidity. For a necessary
condition, it requires further analysis of the difference in liquidity before and after a firm received QFII
investment. However, this is outside the scope of our study and is left for future research.
Table 10 (b)
Different firm size in SZSE
The table shows the panel regressions of the Illiquidity (Illiq) and the trading volume (TV) on the QFII
Foreign Institutional Ownership and Stock Liquidity in China
22
ownership in SZSE divided by the medium of firm size. The period of study is from 2004Q3 to 2019Q1.
Driscoll-Kraay standard errors reported in parentheses are robust to correlation across residuals within a
firm over time and across firms in the same year and different quarters.
Dependent variables
Large firms Small firms
Independent
variables
(1)
Illiq
(2)
TV
(3)
Illiq
(4)
TV
QFIIi,t-1 -0.0086
(0.0080)
0.0138*
(0.0078)
-0.0340**
(0.0169)
0.0409**
(0.0162)
DIi,t-1 -0.9029***
(0.1376)
1.2037***
(0.1542)
-0.4404**
(0.1724)
0.8790***
(0.1769)
SIZE -0.3928***
(0.0302)
0.3452***
(0.0307)
-0.2787***
(0.0306)
0.2584***
(0.0286)
LEVi,t-1 -0.4369***
(0.0950)
0.4970***
(0.0998)
-0.5917***
(0.0832)
0.6607***
(0.0860)
VOLi,t 0.8088
(0.5269)
0.0047
(0.2193)
0.7767***
(0.2923)
0.6882***
(0.2245)
NTi,t-1 0.0057***
(0.0010)
-0.0103***
(0.0010)
0.0081***
(0.0010)
-0.0098***
(0.0008)
LNPi,t -0.5147***
(0.0238)
0.4674***
(0.0259)
-0.4857***
(0.0232)
0.4157***
(0.0222)
TOi,t -0.1247***
(0.0112)
0.2050***
(0.0155)
-0.0947***
(0.0055)
0.1467***
(0.0065)
State Yes Yes Yes Yes
Time dummy Yes Yes Yes Yes
N 14349 14349 15850 15850
adj. R2 0.761 0.790 0.749 0.783
F . . 840.9017 898.1890
Standard errors in parentheses (* p<0.1, ** p<0.05, *** p<0.01)
Table 11
QFII relationship with liquidity using interaction with dummy variables
Panel regression results using the three measures of liquidity regressed on lagged value of QFIIDUM, with
a dummy variable equal to 1 if a firm has QFII participation. The two measures of liquidity are the Illiquidity
(Illiq) and the trading volume (TV). The remaining control variables are the same as the ones used in Table
3 and 4. The period of study is from 2004Q3 to 2019Q1. Driscoll-Kraay standard errors reported in
parentheses are robust to correlation across residuals within a firm over time and across firms in the same
year and different firms. There are 64654 observations in each regression.
Dependent variables
Independent
variables
(1)
Illiq
(2)
TV
Foreign Institutional Ownership and Stock Liquidity in China
23
QFIIDUMi,t-1 -0.0175**
(0.0086)
0.0218**
(0.0085)
DIi,t-1 -0.8707***
(0.0399)
1.2136***
(0.0394)
SIZEi,t-1 -0.3122***
(0.0044)
0.2786***
(0.0044)
LEVi,t -0.5211***
(0.0205)
0.6060***
(0.0202)
VOLi,t 1.1185***
(0.0620)
0.8370***
(0.0613)
NTi,t-1 0.0078***
(0.0003)
-0.0101***
(0.0002)
LNPi,t -0.5227***
(0.0056)
0.4738***
(0.0055)
TOi,t -0.1073***
(0.0011)
0.1677***
(0.0011)
State Yes Yes
Time dummy Yes Yes
N 64654 64654
adj. R2 0.786 0.798
F 10002.0131 10776.3372
Standard errors in parentheses (* p<0.1, ** p<0.05, *** p<0.01)
8. Summary and concluding remarks
With the continuous opening of the capital market and the entry of foreign capital into the stock
market, there are many controversies about its influence, which has become a hot topic in the academia.
This paper employs a unique setting for the limited participation of qualified foreign institutional
investors (QFIIs) in China`s A-share market and examines how these impacts on stock liquidity in
emerging markets.
Contrary to the findings in the literature, our results reveal that greater foreign institutional
participation is positively associated with stock market liquidity. This positive relationship operates
mainly through promoting trade activities by increasing trading volume. We also find that there exist
heterogeneity in the effect of the QFII ownership on different size of firms. The improvement in
liquidity is more significant in small firms compared to large firms. Our findings are robust to
endogeneity and the possible influence of the stock market shock, industry effects and the stock
exchange. Further, the liquidity improving effects of QFII are even stronger when the analysis is
performed on a subsample of QFII firms.
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