Stock liquidity 1

Journal of Accounting,
Auditing & Finance
1–29
ÓThe Author(s) 2018
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DOI: 10.1177/0148558X18798231
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The Effect of Stock Liquidity
on Corporate Risk-Taking
Charles Hsu1
, Zhiming Ma2
, Liansheng Wu2
,
and Kaitang Zhou3
Abstract
This study examines the effect of stock liquidity on corporate risk-taking behavior. We find
that stock liquidity has a positive and significant effect on corporate risk-taking. We find
consistent results when we use the split share structure reform (SSSR) in China as an exo-
genous shock to stock liquidity. We also investigate the channels through which stock
liquidity affects risk-taking and find that increases in stock liquidity lower the cost of capital
and increase the pay-for-performance sensitivity of managers. Finally, we conduct cross-sec-
tional tests to rule out privatization as an alternative explanation for our results. Our study
sheds light on the real effects of stock liquidity and contributes to the understanding of cap-
ital market development.
Keywords
stock liquidity, risk-taking, cost of capital, pay-for-performance sensitivity, split share
structure reform
Introduction
Prior studies have shown that corporate risk-taking, generally defined as the undertaking of
risky but value-enhancing investments by corporates, is an important factor in stimulating
long-term economic growth (e.g., Acemoglu & Zilibotti, 1997; DeLong & Summers, 1991;
John, Litov, & Yeung, 2008). In this article, we examine the effect of stock liquidity on
corporate risk-taking behavior. Stock liquidity is one of the most important firm character-
istics in the capital market (Fang, Noe, & Tice, 2009; Holmstrom & Tirole, 1993), and it
can be altered by capital market regulations and securities laws. Investigating the effect of
stock liquidity on corporate risk-taking can shed light on how to use capital markets to
improve economic welfare, especially in developing countries. However, to date, no study
has examined whether and how stock liquidity affects corporate risk-taking behavior. This
1
Hong Kong University of Science and Technology, Kowloon, Hong Kong
2
Peking University, Beijing, China
3
Wuhan University, Wuhan, China
Corresponding Author:
Charles Hsu, Department of Accounting, Hong Kong University of Science and Technology, Clear Water Bay,
Kowloon, Hong Kong.
Email: achsu@ust.hk
Conference Submission

may be due to the difficulty in finding an ideal setting to investigate the causal effect of
stock liquidity on corporate risk-taking.1
To examine the effect of stock liquidity on corporate risk-taking behavior, we use a quasi-
natural experimental setting, the split share structure reform (SSSR) in China. Beginning
from 2005, the SSSR eliminated selling restrictions on nontradable shares that accounted for
two thirds of market capitalization in 2004 (Li, Wang, Cheung, & Jiang, 2011). Thus, the
enactment of the SSSR produced a large and exogenous shock to firm stock liquidity.
Moreover, in this setting, the shock was permanent. This permanence allows us to better
identify the effects of liquidity on long-term risk-taking behavior. By taking the SSSR as the
experimental setting for our analysis, we are also able to examine the effects of dynamic
firm-level variations in liquidity on corporate risk-taking. Although the reform only removed
selling restrictions, the liquidity level is ultimately determined by the market.
Stock liquidity likely has conflicting effects on risk-taking. Increases in stock liquidity
may increase the information content of stock prices, lower transaction costs, and thus
lower the cost of capital. Because firms make investment decisions by comparing a proj-
ect’s returns (or risk) with the associated cost of capital (Bolton, Chen, & Wang, 2011;
Copeland, Koller, & Murrin, 2000), a decrease in the cost of capital might ease a firm’s
financial constraints, increasing its tolerance for failure and its likelihood of investing in
riskier projects (e.g., Bruno & Shin, 2014; Edmans, Fang, & Zur, 2013; Fang et al., 2009;
Paligorova & Joao, 2017; Tian & Wang, 2014). Bruno and Shin (2014), for instance, find
that a greater increase in liquidity relaxes firms’ financial constraints and motivates them to
undertake riskier corporate investments. They also find that liquidity impacts corporate
risk-taking more in firms that are more dependent on external financing. Moreover,
increases in liquidity may also affect managerial compensation such that managers are
more willing to take risks (e.g., Fang et al., 2009; Jayaraman & Milbourn, 2012). For
example, studies show that greater stock liquidity shifts the composition of executive com-
pensation away from cash-based compensation and toward stock-based compensation (e.g.,
Jayaraman & Milbourn, 2012). This shift results in higher pay-for-performance sensitivity
(PPS), which may encourage managers to undertake more risky projects to increase the
probability of higher stock prices down the line.2
On the contrary, increases in stock liquidity may lead to decreases in risk-taking. Higher
stock liquidity can increase the probability of hostile takeover attempts (Fang, Tian, &
Tice, 2014), which results in managerial myopia and reduction in long-term risky projects.
In addition, high PPS that results from increased liquidity may give managers incentives to
reduce their firms’ risk because managers are undiversified with respect to firm-specific
wealth (e.g., Armstrong, Larcker, Ormazabal, & Taylor, 2013; Coles, Daniel, & Naveen,
2006; Efendi, Srivastava, & Swanson, 2007). These alternative effects of increases in stock
liquidity can cause managerial myopia and lead to lower levels of long-term risk-taking,
such as investment in R&D or innovation projects (Fang et al., 2014).
The effects of liquidity discussed above are drawn mainly from studies in the U.S. set-
ting. Although the capital market in China is different from those in the United States and
other developed countries, increased liquidity can generate similarly mixed effects in the
Chinese setting. Although state-owned enterprises (SOEs) make up a large part of all listed
firms in the Chinese market, Chinese firms’ investment behavior is also sensitive to the
cost of capital (Xu & Tian, 2013). Detailed option data are not available, but some stock
options or restricted stocks are granted to managers. Managers in China are generally com-
pensated based on firm performance (Cao, Pan, & Tian, 2011; Conyon & He, 2011; Firth,
Fung, & Rui, 2006; Wang & Xiao, 2011); some of them also own firm shares, which are
2 Journal of Accounting, Auditing & Finance

sensitive to stock prices. Hence, whether stock liquidity affects corporate risk-taking and if
so, through which channel(s), are essentially empirical questions.
We begin our analysis using ordinary least squares (OLS) models with a full sample.
Our results show that firms with more liquid stocks are associated with higher levels of
future risk-taking. This effect is both statistically and economically significant. Our results
are robust to the inclusion of numerous controls, the use of alternative measures of stock
liquidity and risk-taking, and the inclusion of firm fixed effects to control for time-invariant
factors. To establish the causality of liquidity on risk-taking, we next use a difference-in-
differences (DID) approach using an SSSR sample. The results of this approach support
our previous conclusion: Firms experiencing higher increases in liquidity during the SSSR
exhibit higher levels of risk-taking than do firms experiencing no increase in liquidity.
We then perform two additional tests to reinforce our conclusion. First, to mitigate the
possible omitted variables concern, we follow Fang et al. (2014) and use a dynamic change
model for SSSR firms to investigate whether larger liquidity increases lead to greater cor-
porate risk-taking. We find that firms experiencing a larger liquidity increase after the
SSSR exhibit higher future levels of risk-taking. Second, to ensure that there are no obser-
vable differences between trends in risk-taking outcomes between our treatment and control
groups prior to the SSSR, we use a propensity-score matching (PSM) approach. Following
Bertrand and Mullainathan (2003) and Fang et al. (2014), we construct treatment and con-
trol groups and conduct our analysis using the PSM sample. The results show that our con-
clusions are robust to this analysis.
We then explore possible underlying mechanisms through which stock liquidity affects
risk-taking in firms. We find that increases in liquidity lead to lower costs of capital. Our
earlier discussion suggests that decreases in cost of capital increase risk-taking, where the
effect is stronger in firms that are more dependent on external financing. The results of our
cross-sectional analysis also confirm that the effect of liquidity on the cost of capital is
stronger when the level of financial constraint is higher. In addition, we find evidence con-
sistent with the conjecture that increased liquidity leads to higher PPS, which is consistent
with the findings in Jayaraman and Milbourn (2012).3
Taken together, our findings suggest
that stock liquidity affects risk-taking through its influence on both the cost of capital and
managerial incentives.
Finally, we conduct cross-sectional tests to rule out privatization as an alternative expla-
nation for our results. Privatization is an effect generated by the SSSR, as the reform
allows previously nontradable shares, including the nontradable SOE shares, to be freely
traded on the Chinese stock markets. Prior studies suggest that privatization leads to
improved firm profitability, productivity, investment, and innovation (e.g., Gupta, 2005;
Liao, Liu, & Wang, 2014; Tan, Tian, Zhang, & Zhao, 2015). If our main findings above
were caused by privatization, we should expect to find a stronger effect for SOEs than for
non-SOEs. However, our empirical results do not support this prediction.
Our study contributes to the literature in several ways. First, to the best of our knowl-
edge, our study is the first to investigate the causal effect of stock liquidity on corporate
risk-taking. Our analysis is made possible by use of the quasi-natural experimental setting
of the SSSR in China. Second, we find that stock liquidity increases future corporate risk-
taking by decreasing the cost of capital and increasing PPS. Our article thus sheds light on
how the capital market can be used to stimulate long-term economic growth. Third, our
study contributes to the understanding the effects of the SSSR in China. Studies in this area
focus on the privatization effect (Liao et al., 2014; Tan et al., 2015) and the corporate gov-
ernance improvement effect (Q. Chen, Chen, Schipper, Xu, & Xue, 2012; Hope, Wu, &
Hsu et al. 3

Zhao, 2017) of the SSSR.4
By eliminating a significant source of market friction, however,
the reform also brought about an exogenous shock to firm stock liquidity, which has its
own effect on corporate risk-taking behavior.
The rest of the article is organized as follows: In the section ‘‘Hypothesis
Development,’’ we develop our hypotheses. In the section ‘‘Research Design, Sample, and
Descriptive Statistics,’’ we discuss the research design and our sample. We present the
empirical results in sections ‘‘Empirical Analyses’’ and ‘‘The Channels’’ and conclude in
section ‘‘Conclusion.’’
Hypothesis Development
The Positive Effect of Stock Liquidity on Corporate Risk-Taking
There are several mechanisms through which stock liquidity might enhance corporate risk-
taking. First, studies show that higher stock liquidity decreases the risk of investment in the
secondary market. Specifically, stock liquidity stimulates the entry of informed investors,
who make stock prices more informative for stakeholders (Fang et al., 2009; Khanna &
Sonti, 2004). Thus, higher liquidity can increase the information content of a stock price,
making it such that investors bear lower risk and require less return. It follows that higher
liquidity lowers the cost of capital by reducing secondary market investment risk (e.g.,
Edmans et al., 2013; Fang et al., 2009).
Second, less liquid stocks are associated with higher issuing and transaction costs.
Investors demand compensation not only for the risks they bear but also for the transaction
costs they incur when buying and selling shares of their stocks. Stoll and Whaley (1983)
note that stock transaction costs need to be considered when valuing equity investments.
They suggest that higher stock transaction costs may explain the higher required rate of
return on small stocks, being relatively illiquid. Subsequent studies find that firms with
lower liquidity have higher implicit costs of external financing, including higher investment
banking fees (Butler, Grullon, & Weston, 2005) and higher costs of equity (Lipson &
Mortal, 2009). Taken together, these studies suggest that higher liquidity leads to a lower
cost of capital. Because firms facing external financing costs make investment decisions by
comparing a project’s returns (risk) with the associated cost of capital (Bolton et al., 2011;
Copeland et al., 2000), a decrease in the cost of capital can increase the firm’s tolerance
for failure and its likelihood of investing in riskier projects. The literature confirms that
less financially constrained firms have a greater tolerance for failure and are thus more
willing to take on risky projects (e.g., Kuang & Qin, 2014; Tian & Wang, 2014). For exam-
ple, Tian and Wang (2014) show that initial public offerings (IPO) firms backed by more
failure-tolerant venture capital (VC) investors invest more in riskier innovations and that
capital constraints can negatively distort a VC firm’s failure tolerance. Kuang and Qin
(2014) suggest that firms troubled by their credit ratings tend to decrease the managerial
incentives for risk-taking. The literature also shows that a lower cost of capital leads to
higher levels of risk-taking along different specifications (e.g., Bruno & Shin, 2014;
Moshirian, Tian, Wang, & Zhang, 2018). For example, Bruno and Shin (2014) show that
accommodative credit conditions are associated with greater risk-taking by way of lower
risk-adjusted lending rates. Moshirian et al. (2018) propose that financial liberalization sti-
mulates innovation through the relaxation of financial constraints for the reason that inno-
vative firms usually rely heavily on external financing. Focusing on a specific industry,
Paligorova and Joao (2017) present evidence that banks take more risks (e.g., charge risky
4 Journal of Accounting, Auditing & Finance

borrowers lower loan spreads compared with safe borrowers) in periods of easing monetary
policy than they do in periods of tightening. Based on the findings in these studies, we
expect that the overall level of risk-taking will increase with liquidity.
Xiong and Su (2014) investigate the relation between stock liquidity and corporate capi-
tal allocation efficiency in China and find that greater stock liquidity helps to improve
investment efficiency. In another study, Xu and Tian (2013) find that firms in China’s
emerging economy are sensitive to cost of capital when making investment decisions. Hao
and Liu (2008) find that companies generally increase investment when they can raise
more money through equity financing. High liquidity in the stock market plus increased
liquidity after the SSSR might help companies get equity financing at a lower cost and
may in turn increase the level and overall risk of their investments. Taken together, these
studies suggest that even in China, where there are many SOEs, investment and risk-taking
behaviors are affected by the cost of capital.
Third, increases in stock liquidity can affect managerial compensation such that manag-
ers are more willing to take risks. For example, Jayaraman and Milbourn (2012) show that
greater stock liquidity shifts the composition of executive compensation in favor of stock-
based compensation. More specifically, their study shows that as stock liquidity goes up,
the proportion of equity-based compensation in total compensation increases, while the pro-
portion of cash-based compensation decreases. As a result, managerial PPS with respect to
stock prices increases with liquidity (e.g., Fang et al., 2009; Jayaraman & Milbourn, 2012).
Studies also show that managerial incentives can encourage managerial risk-taking (e.g.,
Armstrong et al., 2013; Armstrong & Vashishtha, 2012; Coles et al., 2006; Efendi et al.,
2007; Gormley, Matsa, & Milbourn, 2013; Hayes, Lemmon, & Qiu, 2012; Low, 2009). For
example, Low (2009), Hayes et al. (2012), and Gormley et al. (2013) document that
increased equity-based compensation and PPS can result in greater managerial risk-taking.
In sum, these studies suggest that when liquidity is high, managers have more incentive to
implement riskier investments promising greater compensation. This in turn forecasts a pos-
itive association between stock liquidity and managerial risk-taking.
In China, in 2003, the State-Owned Assets Supervision and Administration Commission
of the State Council (SASAC) issued its ‘‘Interim Regulations on the Evaluation of the Top
Executive Operating Performance’’ for SOEs affiliated with the central government, stating
clearly that ‘‘top executive pay should be aligned to total profits and sales’’ (SASAC,
2003). In 2007 and 2008, the SASAC announced two supplementary provisions of this reg-
ulation, making further efforts toward aligning SOE executive pay to firm performance. In
2006 and 2010, the SASAC updated this regulation with additional rules concerning such
things as ‘‘the punishment of top executives when they were underperforming’’ (SASAC,
2006, 2010). In 2005, the China Securities Regulatory Commission (CSRC) issued the
‘‘Trial Regulation for the Stock Options Grants in Public Firms,’’ providing a framework
for introducing equity incentives for listed firms, and introduced a new rule that ‘‘allowed
publicly traded firms that have successfully completed stock split structural reforms to
offer restricted stocks or stock options plans to their top management members’’ (CSRC,
2005). Studies confirm that executive compensation is positively correlated to firm perfor-
mance in China (Cao et al., 2011; S. Chen, Lin, Lu, & Zhang, 2015; Conyon & He, 2011;
Firth et al., 2006; Wang & Xiao, 2011). These regulations and the evidence of prior studies
suggest that in China, increased stock liquidity, an outcome of the SSSR, might have a pos-
itive impact on executive compensation and managerial risk-taking.
Hsu et al. 5

The Negative Effect of Stock Liquidity on Corporate Risk-Taking
Stock liquidity may impede corporate risk-taking for at least two reasons. First, in the pres-
ence of information asymmetry between managers and investors, takeover pressure could
induce managers to sacrifice long-term performance for current profits to prevent the stock
from becoming undervalued (Stein, 1988). Because high-liquidity increases the probability
of a hostile takeover attempt, it can also exacerbate managerial myopia and lead to lower
levels of investment in long-term projects that are both risky and value-enhancing, such as
innovations (e.g., Fang et al., 2014).5
Second, high PPS due to increased liquidity makes managers’ wealth more closely tied
to firm performance. As managers also invest their human capital to the firm and are
unable to diversify their portfolio, they are risk averse by nature. High PPS may make man-
agers even more risk averse, which results in less risk taking (Armstrong et al., 2013; Coles
et al., 2006; Efendi et al., 2007).
In sum, both whether and how increases in stock liquidity impact a firm’s risk-taking
are empirical questions. We thus propose our main hypothesis in an alternative format:
Hypothesis 1.1: There is a positive association between stock liquidity and corporate
risk-taking.
Hypothesis 1.2: There is a negative association between stock liquidity and corporate
risk-taking.
Research Design, Sample, and Descriptive Statistics
To examine whether market liquidity influences corporate risk-taking, we estimate the fol-
lowing model using data from a set of firms that completed the SSSR:
Risk-taking = a0 + a1Liquidity +
X
aiControlsi + e: ð1Þ
Following John et al. (2008) and Faccio, Marchica, and Mura (2011), we use two mea-
sures to proxy for corporate risk-taking. Our primary measure of corporate risk-taking is
the volatility of industry-adjusted earnings, which is equal to
RiskT =
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1
T 1
X
T
t = 1
adj ROAit
1
T
X
T
t = 1
adj ROAit
!2
v
u
u
t jT = 5,
where
adj ROAit =
EBITit
ASSETSit

1
Nd, t
X
Nd, t
k = 1
EBITk, d, t
ASSETSk, d, t
:
The second measure we use is the industry-adjusted earnings range, which is equal to
RiskT2 = max adj ROAit
ð Þ min adj ROAit
ð Þ:
6 Journal of Accounting, Auditing Finance

In both measures, EBITit is the earnings before interest and taxes of firm i at year t;
ASSETSit is the total assets of firm i at year t; ROAit is the ratio of earnings before interests
and taxes to the total assets of firm i at year t; adj_ROAit is the industry-adjusted ROA for
firm i at year t; Nd, t is the number of firms within industry d at year t; and T represents 5-
year overlapping windows (0 to + 4, + 1 to + 5, + 2 to + 6, + 3 to + 7, + 4 to + 8, and so
on).
We use two measures to characterize stock liquidity. Following Jayaraman and
Milbourn (2012), our first measure of stock liquidity is the tradable turnover ratio
(TOVER), defined as the average daily turnover ratio (the total shares traded in a day
divided by total tradable shares) for a firm throughout the year. Our second measure of
liquidity is the Amivest liquidity ratio (LR), following Amihud, Mendelson, and Lauterbach
(1997) and Amihud (2002). The LR is defined as follows:
LRit =
P
Dit
d = 1
Vitd
P
Dit
d = 1
jRitdj
0
B
B
B
@
1
C
C
C
A
3103
,
where Ritd and Vitd are stock i’s return and dollar volume (in millions), respectively, on day
d at year t. Dit is the total number of trading days for stock i at year t. If increases to stock
liquidity lead to a higher level of risk-taking, we expect a1 to be positive in Equation 1.
Following John et al. (2008), Faccio et al. (2011), and Li et al. (2011), we control for a
variety of factors that have been found to affect corporate risk-taking behavior. Among
these, we include firm size (Size), measured as the natural logarithm of total assets; lever-
age (Leverage), measured as the ratio of total debt to total assets; Tobin’s Q (Tobin’s Q),
defined as the sum of the market value of tradable shares, the book value of nontradable
shares, and liabilities scaled by the book value of total assets; profitability (ROA), defined
as earnings before interest and taxes divided by total assets; and firm age (Ln_age), defined
as the natural log of (1 + the number of years since IPO). We also include several vari-
ables (State, Ownership, and NTS) to control for the effect of ownership. State is an indica-
tor variable equaling one for SOEs and zero otherwise. Ownership is the total cash flow
rights of controlling shareholders on record with the company. NTS equals the number of
nontradable shares divided by the total number of shares outstanding before the reform. All
variables are defined in the appendix. All control variables are measured at the end of the
first year of the sample period over which the volatility of earnings is measured. We
include industry and year fixed effects and use standard errors that are robust to heteroske-
dasticity and clustered at the firm level in the regression.
Our sample period begins in 2003 and ends in 2009 because controlling shareholder
data are not available before 2003 and measurements of risk-taking require 5-year overlap-
ping periods. We obtain financial and stock price data and ownership information from the
China Stock Market and Accounting Research (CSMAR) database. Our initial sample
includes all Chinese A-share companies that completed the SSSR and were listed on the
Shenzhen and Shanghai stock exchanges. Because the SSSR started in 2005 and we require
the firms in our sample to have at least 1 year of observation prior to the SSSR, we require
each firm to have been listed before December 31, 2004. We also exclude financial firms
from the sample. Our final sample consists of an unbalanced panel of 1,284 firms, with a
total of 7,987 firm-year observations (total sample hereafter).
Hsu et al. 7

Table 1, Panel A presents the distribution of our sample by year, showing that our
sample firms are distributed almost evenly across the sample period. Panel B of Table 1
presents the industry distribution and shows that manufacturing firms account for the
greater part of the sample (56.13 %). Panel C of Table 1 reports the descriptive statistics of
our main variables. To mitigate the undue influences of outliers, we winsorize all continu-
ous variables at the bottom and top one percentiles. The means of RiskT and RiskT2 are
0.050 and 0.110, with interquartile ranges of 0.040 and 0.095, respectively. The averages
of TOVER and LR are 0.027 and 2.042, respectively. On average, 46.8% of the sample
firms’ shares are nontradable (NTS), and 69.9% of sample firms are SOEs (State). The
mean firm size (Size) is 21.410 (about RMB 1,987.21 million). Typical firms in the sample
are not highly leveraged, with an average (median) leverage ratio of 53.5% (52.3%). The
average return on assets (ROA) is 4.6%, indicating that the sample firms are in relatively
good financial condition. The mean of Ownership is 32.95%, suggesting that the ownership
structure in our sample is highly concentrated. In general, the values of these variables are
reasonably distributed, and the descriptive statistics are comparable with what have been
documented in prior studies (Hope et al., 2017; Li et al., 2011; Liao et al., 2014). In an
untabulated correlation analysis, the correlation coefficients show that our stock liquidity
measures (TOVER, LR) are positively associated with our risk-taking measures (RiskT,
RiskT2).
Empirical Analyses
Baseline Regressions
Table 2 reports the regression results of Equation 1, which examines the effect of stock
liquidity on risk-taking. Columns 1 and 3 show the effects of TOVER on RiskT and RiskT2,
respectively. The coefficients on TOVER are both positive and significant, 0.095, t = 2.84
in column 1; 0.235, t = 2.93 in column 3, indicating that firms with higher stock liquidity
will take more risks in the future. The effect of stock liquidity on risk-taking is also eco-
nomically significant. The results in columns 1 and 3 indicate that a one-standard-deviation
increase in stock liquidity (TOVER) increases RiskT by 9.5% and RiskT2 by 13.45%.
Columns 2 and 4 show the results for LR on RiskT and RiskT2, respectively. The coeffi-
cients on LR are both positive and significant, 0.001, t = 6.20 in column 2; 0.003, t = 6.02
in column 4, supporting Hypothesis 1.1 that firms with higher stock liquidity will take
more risks in the future. The coefficients on the control variables are generally consistent
with those in prior studies (John et al., 2008; Li et al., 2011). For instance, both large firms
and more profitable firms are associated with lower levels of risk-taking.
Although the baseline specification model includes a list of common determinants of
risk-taking, it may still omit some unknown firm characteristics that could explain the
observed results. To ease this concern, we run fixed-effect regressions to control for the
influence of unknown firm-level factors. We report the results of controlling for firm fixed
effects in Table 3. These results are consistent with those derived from the baseline specifi-
cation model. Both measures of liquidity (TOVER and LR) are significantly and positively
related to risk-taking, suggesting that the baseline regression results are not seriously pla-
gued by any omitted firm-level factors.
Identifying the underlying causal relation is critical to the study of the effect of stock
liquidity on corporate risk-taking behavior. One might argue that omitted variables could
simultaneously affect both stock liquidity and risk-taking behavior. For example, studies

show that better corporate governance could lead both to higher risk-taking (John et al.,
2008) and higher stock liquidity (e.g., Chung, Elder, Kim, 2010; Edmans et al., 2013).
To establish causality, we exploit a quasi-natural experiment setting, the SSSR, enforced in
Table 1. Summary Statistics.
Panel A: Sample Distribution by Year.
Year n %
2003 815 10.20
2004 1,179 14.76
2005 1,209 15.14
2006 1,209 15.14
2007 1,197 14.99
2008 1,199 15.01
2009 1,179 14.76
Total 7,987 100.00
Panel B: Sample Distribution by Industry.
CSRC industry code n %
A: Agriculture, forestry, and fishing 143 1.79
B: Mining 241 3.03
C: Manufacturing 4,483 56.13
D: Electric, gas, and sanitary services 428 5.36
E: Construction 147 1.84
F: Transportation and storage 356 4.46
G: Information technology 275 3.44
H: Wholesale and retail trades 618 7.74
J: Real estate 706 8.84
K: Public administration 262 3.28
L: Communication and culture 67 0.84
M: Miscellaneous 261 3.27
Total 7,987 100.00
Panel C: Descriptive Statistics.
Variable Observation M SD P25 P50 P75
RiskT 7,969 0.050 0.050 0.020 0.030 0.060
RiskT2 7,987 0.110 0.108 0.041 0.072 0.136
TOVER 7,987 0.027 0.018 0.012 0.023 0.038
LR 7,987 2.042 2.980 0.377 0.927 2.438
NTS 7,987 0.468 0.208 0.348 0.510 0.625
State 7,987 0.699 0.459 0.000 1.000 1.000
Size 7,987 21.410 1.122 20.670 21.330 22.070
Leverage 7,987 0.535 0.266 0.380 0.523 0.652
ROA 7,987 0.046 0.081 0.025 0.048 0.079
Tobin’s Q 7,987 2.108 1.506 1.228 1.616 2.385
Ln_age 7,987 2.089 0.560 1.792 2.197 2.485
Ownership 7,987 32.950 17.730 19.100 30.310 45.980
Note. See appendix for the variable definitions. Panel A and Panel B report the sample distribution by year and
industry, respectively. Panel C reports the descriptive statistics of our main variables. CSRC = China Securities
Regulatory Commission.
Hsu et al. 9

China in 2005, which mandatorily converts nontradable shares on stock exchanges into
freely tradable shares. The SSSR provides us with a plausibly exogenous variation in
liquidity with which to evaluate the above endogeneity problem. The reform did not take
place at the same time for every firm; it ranged over the period from 2005 to 2009 and was
concentrated in the years 2005, 2006, and 2007.6
This enables us to identify a treatment
group and a control group. We use the firms that completed the reform in 2005 as our treat-
ment group and the firms that completed it in 2007 or later as our benchmark group.7
We
then estimate the following regression using the data that consist of both treatment and
benchmark firms from 2004 (i.e., prereform) and 2006 (i.e., postreform) (SSSR sample
hereafter):
Risk-taking = b0 + b1Treat + b2Post + b3Treat3Post + bkControlsit + eijt, ð2Þ
where Treat is an indicator variable that equals 1 if the reform occurred in year 2005 and 0
if the reform occurred in year 2007 or later and Post is a time indicator that equals 1 for
the year 2006 and 0 for 2004.8
All of the other variables are defined as in Equation 1. We
Table 2. The Effect of Stock Liquidity on Risk-Taking.
RiskT RiskT RiskT2 RiskT2
Variable (1) (2) (3) (4)
TOVER 0.095*** 0.235***
(2.84) (2.93)
LR 0.001*** 0.003***
(6.20) (6.02)
State –0.006*** –0.006*** –0.015*** –0.015***
(–6.08) (–6.08) (–6.24) (–6.23)
Size –0.005*** –0.008*** –0.013*** –0.019***
(–11.73) (–13.97) (–11.63) (–13.80)
Leverage 0.036*** 0.038*** 0.083*** 0.087***
(20.48) (21.26) (19.44) (20.20)
Tobin’s Q 0.006*** 0.005*** 0.013*** 0.011***
(16.56) (13.88) (15.47) (12.88)
ROA –0.194*** –0.194*** –0.455*** –0.454***
(–32.36) (–32.39) (–31.38) (–31.41)
Ln_age 0.005*** 0.005*** 0.012*** 0.012***
(5.91) (5.82) (5.90) (5.79)
NTS 0.003 0.005* 0.005 0.011
(0.95) (1.88) (0.65) (1.58)
Ownership –0.000** –0.000** –0.000** –0.000**
(–2.41) (–2.27) (–2.43) (–2.30)
Constant 0.134*** 0.185*** 0.329*** 0.451***
(12.81) (15.35) (13.03) (15.46)
Industry fixed effect YES YES YES YES
Year fixed effect YES YES YES YES
Observation 7,969 7,969 7,987 7,987
R2
.378 .381 .363 .365
Note. See appendix for variable definitions. The t statistics are reported in parentheses. Standard errors are
heteroskedasticity robust and clustered at the firm level.
*significance at 10% level. **significance at 5% level. ***significance at 1% level.

include industry fixed effects and use standard errors that are robust to heteroskedasticity
and clustered at the firm level in the regression.
Table 4, Panel A presents the detailed distribution of our SSSR sample. As shown in
Panel A, we have 228 treatment firms with 445 observations and 169 control firms with
338 observations. Panel B reports the summary statistics for the variables used in our DID
analysis. Panel C shows the change in stock liquidity around the SSSR. Specifically, the
change is calculated as the difference in stock liquidity proxies (TOVER and LR) between
the prereform and postreform values. The results in Panel C show a large increase in liquid-
ity after the reform, suggesting that the SSSR does indeed create a shock in market liquid-
ity. Panel D presents the change in risk-taking from before to after the reform for the
control and treatment groups. Our results indicate an increase in risk-taking for treatment
firms after the reform along with a parallel decrease in risk-taking for control firms. The
differences in temporal change to the risk-taking variable between the treatment group and
the control group are significant for both risk-taking measures.
Table 5 reports the estimation results of Equation 2.9
The coefficients on Treat and Post
are all negative and significant, consistent with those reported in Panel D of Table 4. The
Table 3. The Effect of Stock Liquidity on Risk-Taking: Firm Fixed Effect.
Variable (1) (2) (3) (4)
TOVER 0.049* 0.134**
(1.94) (2.19)
LR 0.001** 0.001**
(2.52) (2.37)
State 0.002* –0.002 0.005 –0.006
(1.66) (–1.09) (1.33) (–1.27)
Size 0.003*** –0.005*** 0.007*** –0.011***
(3.30) (–4.53) (3.44) (–4.43)
Leverage –0.004** 0.012*** –0.009* 0.029***
(–2.06) (3.05) (–1.81) (2.92)
Tobin’s Q 0.000 0.001** 0.001 0.002**
(1.50) (2.17) (1.47) (2.22)
ROA –0.128*** –0.134*** –0.305*** –0.319***
(–28.56) (–14.63) (–27.87) (–13.95)
Ln_age 0.002 0.005*** 0.006 0.012***
(1.36) (3.41) (1.45) (3.38)
NTS –0.021*** –0.010*** –0.052*** –0.025***
(–8.30) (–3.07) (–8.37) (–3.23)
Ownership –0.000*** –0.000*** –0.000*** –0.000***
(–5.20) (–3.41) (–5.10) (–3.40)
Constant 0.006 0.145*** 0.008 0.344***
(0.37) (7.20) (0.19) (7.18)
Firm fixed effect YES YES YES YES
Observation 7,969 7,969 7,987 7,987
R2
.160 .305 .155 .293
heteroskedasticity robust and clustered at the firm level.
Hsu et al. 11

coefficients on the interaction term (b3) are all positive and significant from column 1 to
column 6, suggesting that the increase in liquidity caused by the reform leads treatment
firms to become more risk-taking than firms that do not experience such a shock. That is,
Table 4. The Effect of Stock Liquidity on Risk-Taking: The DID Approach.
Panel A: Distribution of the SSSR Sample.
Number of observations
Type of group Number of firms Prereform (2004) Postreform (2006) Total
Treated 228 217 228 445
Control 169 169 169 338
Panel B: Summary Statistics of the SSSR Sample.
Variable Observation M SD P25 P50 P75
RiskT 779 0.060 0.060 0.020 0.036 0.078
RiskT2 783 0.145 0.142 0.049 0.089 0.185
State 783 0.619 0.486 0.000 1.000 1.000
Size 783 21.120 1.186 20.370 20.960 21.810
Leverage 783 0.596 0.382 0.389 0.540 0.678
Growth 783 0.259 0.686 –0.012 0.172 0.385
ROA 783 0.016 0.100 0.008 0.033 0.062
Ln_age 783 1.810 0.709 1.386 2.079 2.303
NTS 783 0.570 0.137 0.483 0.600 0.676
M_ownership 783 0.029 0.103 0.000 0.000 0.000
Ownership 783 34.310 18.530 20.330 31.410 47.920
Index 646 8.062 2.003 6.120 8.190 9.810
Incentive 644 0.085 0.279 0.000 0.000 0.000
Panel C: Univariate Tests of Change in Stock Liquidity Surrounding SSSR.
Variable Prereform Postreform Difference
TOVER 0.017 0.044 0.027***
(45.06)
LR 0.607 3.327 2.720***
(26.80)
Panel D: Univariate Tests of Risk-Taking Surrounding SSSR.
Prereform Postreform
DID
Variable Control Treated Difference Control Treated Difference
RiskT 0.100 0.036 –0.064 0.083 0.037 –0.046 0.018**
(2.33)
RiskT2 0.239 0.088 –0.151 0.197 0.091 –0.107 0.044**
(2.41)
Note. See appendix for variable definitions. Panel A reports the sample distribution of our SSSR sample. Panel B
presents descriptive statistics of our main variables for the SSSR sample. Panel C reports univariate tests of stock
liquidity surrounding the implementation of SSSR. Panel D presents univariate tests of risk-taking under the DID
design. DID = difference-in-differences; SSSR = split share structure reform.

we document a relative increase in risk taking following the reform in the treatment group
as compared with the control group. Our findings are also economically significant. For
example, the estimated coefficients in column 2 and column 5 suggest that the SSSR leads
to 2.53% (e0.025
– 1) and 5.54% (e0.054
– 1) increases in corporate risk-taking, respectively.
Robustness Checks
The change model. To mitigate the possible omitted variables concern, following Fang
et al. (2014), we also use a dynamic change model tracking only SSSR firms to test
whether larger liquidity increases lead to greater risk-taking. Specifically, we compare the
Table 5. Regression Results Using the DID Design.
Variable
RiskT RiskT RiskT RiskT2 RiskT2 RiskT2
(1) (2) (3) (4) (5) (6)
Treat –0.061*** –0.030*** –0.030*** –0.143*** –0.070*** –0.071***
(–9.42) (–5.74) (–5.74) (–9.30) (–5.57) (–5.58)
Post –0.017*** –0.021*** –0.021*** –0.042*** –0.047*** –0.048***
(–3.83) (–3.97) (–4.02) (–4.04) (–3.84) (–3.89)
Treat 3 Post 0.018*** 0.025*** 0.025*** 0.044*** 0.054*** 0.054***
(3.81) (4.20) (4.24) (4.04) (3.99) (4.03)
State –0.008 –0.009 –0.020 –0.021
(–1.46) (–1.56) (–1.48) (–1.58)
Size –0.007*** –0.007*** –0.016*** –0.016***
(–3.31) (–3.32) (–3.35) (–3.35)
Leverage 0.041*** 0.041*** 0.095*** 0.095***
(4.52) (4.51) (4.61) (4.59)
Growth –0.003 –0.003 –0.005 –0.005
(–1.57) (–1.57) (–1.20) (–1.20)
ROA –0.180*** –0.180*** –0.453*** –0.454***
(–5.38) (–5.43) (–6.27) (–6.33)
Ln_age 0.007** 0.008** 0.018** 0.020**
(2.12) (2.23) (2.18) (2.27)
NTS 0.014 0.014 0.022 0.022
(0.80) (0.79) (0.51) (0.50)
M_ownership 0.004 0.004 0.015 0.015
(0.24) (0.23) (0.37) (0.36)
Ownership 0.000 0.000 0.000 0.000
(0.86) (0.89) (0.94) (0.97)
Index –0.000 0.000 0.000 0.001
(–0.04) (0.03) (0.10) (0.17)
Incentive 0.010 0.022
(1.33) (1.26)
Constant 0.157*** 0.191*** 0.193*** 0.374*** 0.460*** 0.464***
(10.16) (4.51) (4.51) (10.87) (4.52) (4.51)
Industry fixed effect YES YES YES YES YES YES
Observation 779 646 644 783 646 644
R2
.277 .506 .509 .269 .512 .514
heteroskedasticity robust and clustered at the firm level. DID = difference-in-differences.
Hsu et al. 13

level of risk-taking in the prereform year and the postreform year for each SSSR firm,
requiring that there be observations for each firm in both years. The change model results
are shown in Table 6. The coefficients on change of liquidity (D.TOVER and D.LR) are
positive and significant in all columns, treating changes in risk-taking (D. RiskT and D.
RiskT2, respectively) as dependent variables. Thus, our main results are robust to this alter-
native approach.
The PSM approach. To verify that there are no observable different trends in risk-taking
outcomes between the treatment group and control group prior to the SSSR, we use a PSM
approach. Following Bertrand and Mullainathan (2003) and Fang et al. (2014), we use the
PSM approach to construct treatment and control groups and conduct the analysis within
the PSM sample. We estimate a logistic regression using Treat as the dependent variable
and include all control variables used in the baseline OLS regressions before the reform.
The logistic regression estimates the likelihood that a firm completes the reform in a given
year. Specifically, a firm is defined as a treatment firm (Treat = 1) in year t if the firm
Table 6. Dynamic Change Model with SSSR Firms.
Variable
D. RiskT D. RiskT2 D. RiskT D. RiskT2
(1) (2) (3) (4)
D.TOVER 0.056** 0.115**
(2.21) (2.74)
D.LR 0.001* 0.002**
(1.77) (2.12)
State 0.000 0.001 0.001 0.003
(0.45) (0.40) (1.14) (1.04)
D. Size 0.001 0.001 0.001 0.001
(0.76) (0.13) (0.72) (0.13)
D. Leverage –0.014*** –0.025* –0.014*** –0.023*
(–3.18) (–1.81) (–2.94) (–1.92)
D. Tobin’s Q 0.001* 0.001* 0.001** 0.001**
(1.87) (1.91) (2.17) (2.00)
D.ROA –0.056*** –0.118** –0.053*** –0.112***
(–8.68) (–2.42) (–8.14) (–6.60)
D. Ln_age 0.003 –0.001 –0.003 –0.014
(0.41) (–0.19) (–0.44) (–0.86)
D.NTS –0.008 –0.012 –0.008 –0.013
(–1.51) (–1.13) (–1.57) (–0.99)
D. Ownership –0.000 –0.000 –0.000 –0.000
(–0.29) (–0.37) (–0.45) (–0.32)
Constant 0.001 0.002 0.004 0.010
(0.17) (0.62) (0.96) (0.87)
Observation 1,154 1,155 1,157 1,158
R2
0.110 0.089 0.102 0.085
Note. We take the change value for all variables (both dependent and independent variables) in Table 2 expect
State. See appendix for variable definitions. The t statistics are reported in parentheses. Standard errors are
heteroskedasticity robust and clustered at the firm level. SSSR = split share structure reform.

completes the reform in that year. Otherwise, it is defined as a control firm (Treat = 0) in
year t. Using the predicted propensity score from this logistic regression, we then match
each treatment firm with a control firm in year t using the closest propensity score. For
both treatment and control firms in year t, we retain their observations from 1 year before
(year t – 1) and 1 year after the event year (year t + 1) to create the PSM sample. We also
ensure that each control firm in year t does not have the SSSR event in year t – 1 and year
t + 1 to ensure that the observations in the control group are not affected by the SSSR
event. Similar to Tables 4 and 5, we do not include the event year (i.e., year t) in the PSM
sample. We get 3,228 observations in this sample. As discussed, the reform did not take
place at the same time for every firm. The year of reform varied from 2005 to 2009, and
most reforms took place in 2005, 2006, and 2007. This helps to avoid the common identifi-
cation challenge that omitted variables can coincide with a single shock and directly affect
risk-taking.
We present the results of the PSM method in Table 7. Column 1 and column 2 show the
efficiency of the matching process. We report the logistic model results for the prematched
sample in column 1. We then reestimate the logistic model using the postmatched sample
and report the estimation results in column 2. As shown in column 2, there is no significant
difference in the key characteristics between firms in the treatment and control groups, sug-
gesting that the matching process is efficient. The regression results in columns 3 and 4
indicate that the coefficients on the interaction term (Treat 3 Post) are positive and signifi-
cant at the 5% level, 0.009, t = 2.17 in column 3; 0.020, t = 2.04 in column 4, using RiskT
and RiskT2, respectively, as dependent variables. These findings suggest that firms affected
by the SSSR (treatment firms) take more risks after the reform compared with matched
control firms unaffected by the SSSR.
Note that our PSM sample in Table 7 is drawn within the total sample to improve the
matching efficiency.10
The increase in matching efficiency helps rule out omitted trends
that are correlated with liquidity and risk taking in both the treatment and the control
groups. We also repeat our DID analysis by drawing the sample from within the SSSR
sample used in Tables 4 and 5. Our results, untabulated, are similar to those reported in
Table 7.
Alternative measures. In a different set of robustness tests, we reestimate our models in
Tables 2 and 5 using alternative measures of risk-taking and stock liquidity. First, we use
two alternative measures of risk-taking, RiskT3 and RiskT4, following John et al. (2008)
and Faccio et al. (2011). Specifically, RiskT3 is the standard deviation of industry-adjusted
firm-level profitability over a given 5-year period, where profitability is measured as a
firm’s earnings before interest, tax, depreciation, and amortization (EBITDA) divided by
total assets. RiskT4 is the difference between the minimum and maximum EBITDA/Assets
over the 5-year period. Our results (untabulated) are similar to those reported in Tables 2
and 5. Second, we also use the standard deviation of market-adjusted stock returns and the
range of market-adjusted stock returns as two alternative return-based risk-taking measures,
following John et al. (2008) and Faccio et al. (2011). Our main results continue to hold.
Third, we use the percentage of zero returns during the fiscal year to measure stock liquid-
ity following Lesmond (2005). The untabulated results show that our main results are
robust.11
In our DID analyses, we use earnings volatility from year t to t + 4 to measure corpo-
rate risk-taking following prior literature. One concern about this construct is that some
postreform data are used in calculating prereform risk-taking. However, to the extent the
Hsu et al. 15

reform leads to an increase in risk-taking, using some postreform data in calculating prere-
form risk-taking likely works against us finding the significant results in our DID analysis.
This is because the overlap of the two periods likely reduces the difference in risk taking
between the two periods, especially in the treatment group. Nonetheless, we conduct two
additional tests to address this concern. First, we use 3-year earnings volatility (t to t + 2)
as an alternative risk-taking proxy and reestimate our regressions in Table 5. Using 3-year
instead of 5-year window to calculate risk-taking measure reduces the overlap between pre-
and postreform periods. The untabulated results show that our results are unaffected.
Second, we use the 2000 to 2004 period to measure the prereform year’s (i.e., 2004s) risk
taking and find similar results. This is a reasonable approach if firms’ risk taking behavior
is reasonably stable in the prereform period.
Table 7. The Effect of Stock Liquidity on Risk-Taking: The PSM Approach.
Variable
Treat Treat
Variable
RiskT RiskT2
(1) (2) (3) (4)
TOVER 26.901*** –3.686 Treat –0.009*** –0.021***
(4.69) (–0.75) (–4.07) (–3.82)
State –1.051*** –0.097 Post –0.012*** –0.029***
(–6.10) (–0.59) (–5.04) (–5.00)
Size 0.701*** 0.155 Treat 3 Post 0.009** 0.020**
(8.84) (1.14) (2.17) (2.04)
Leverage –1.596*** –1.685** State –0.005*** –0.014***
(–5.11) (–1.96) (–3.70) (–3.94)
Tobin’s Q –0.023 –0.231 Size –0.004*** –0.010***
(–0.46) (–0.27) (–6.09) (–6.03)
ROA 1.520 0.861 Leverage 0.046*** 0.108***
(1.39) (0.83) (17.04) (16.54)
Ln_age –1.675*** –0.346** Tobin’s Q 0.005*** 0.013***
(–9.77) (–2.14) (13.68) (13.50)
NTS –8.008*** –4.688*** ROA –0.162*** –0.394***
(–12.43) (–7.60) (–15.89) (–16.07)
Ownership 0.036*** 0.005 Ln_age 0.004** 0.010***
(6.62) (0.96) (2.44) (2.69)
Constant –29.219 2.509 NTS 0.001 0.004
(–0.06) (1.49) (0.28) (0.34)
Ownership –0.000 –0.000
(–0.87) (–0.97)
Constant 0.048*** 0.117***
(2.83) (2.84)
Year fixed effect YES YES Year fixed effect YES YES
Industry fixed effect YES YES Industry fixed effect YES YES
Observation 3,503 1,614 Observation 3,228 3,228
Pseudo-R2
.682 .169 R2
.271 .270
Note. This table presents the results of PSM analysis. Columns 1 and 2 report the logistic regression results of the
likelihood that a firm is in treatment group for the prematched sample and the postmatched sample, respectively.
Results in these two columns show the efficiency of our PSM matching process. Columns 3 and 4 report the risk-
taking regression results of the postmatched sample. See appendix for variable definitions. The z statistics are
reported in parentheses in columns 1 and 2. The t statistics are reported in parentheses in columns 3 and 4.
Standard errors are heteroskedasticity robust and clustered at the firm level. PSM = propensity-score matching.

The Channels
Cost of Capital
Next, we explore some potential underlying mechanisms through which stock liquidity
increases corporate risk-taking. If an increase in market liquidity can decrease risk level
and decrease the transaction costs of a firm’s stock, we expect that the cost of capital will
decrease. As discussed earlier, this effect will be greater for firms facing more stringent
financial constraints. We first reestimate Equations 1 and 2 with the cost of capital as the
dependent variable using our total sample and the SSSR sample, respectively. We define
the cost of capital (Cost of capital) as the firm-specific cost of equity capital under the
price/earnings to growth ratio (PEG ratio) approach following Easton (2004) and H. Chen,
Chen, Lobo, and Wang (2011). The results are shown in Table 8.12
In Panel A, our results
based on the total sample show that high-liquidity firms are generally associated with a low
cost of capital. In Panel B, our results based on the DID design show that the coefficients
on Treat 3 Post are both negative and significant, 20.082, t = 22.87 in column 1;
20.062, t = 22.30 in column 2, suggesting that the cost of capital for treatment firms
decreases after the shock compared with benchmark firms.
We then examine whether the effect of market liquidity on cost of capital is more pro-
nounced for firms that face more stringent financial constraints. Earlier studies suggest that
non-SOEs and firms located in lower marketization regions face more severe financial con-
straints and have more difficulty obtaining external financing (Hope et al., 2017; Li, Yue,
Zhao, 2009; Liao et al., 2014). Accordingly, we partition our total sample (as well as the SSSR
sample) into two subsamples based on whether the firms are SOEs or non-SOEs or whether
the firms come from high- or low-marketization regions. We then reestimate the regressions
(Equations 1 and 2) within each subsample. Table 9 presents the cross-sectional results by
financial constraints. Panel A and Panel B show the results from the total sample and SSSR
sample, respectively. The results in Panel A (Panel B) show that the effect of the liquidity
(SSSR) on the cost of capital is larger for non-SOEs and for firms coming from low-
marketization regions, which is consistent with the above conjecture. We thus conclude that the
reduction of the cost of capital is a channel through which liquidity affects risk-taking behavior.
Management Incentives
We also investigate whether managerial incentive is a mechanism through which liquidity
affects corporate risk-taking. In our sample period, some stock options and restricted stocks
were granted to managers, although such practices are not popular in China. Managers also
own firm shares that are sensitive to stock prices. Because detailed data on option-based
compensation plans are not available before 2007,13
we cannot test the effect of liquidity
on managerial option-based compensation directly. As an alternative, we investigate the
effect of liquidity on management incentive by examining the effect of liquidity on the sen-
sitivity of cash-based compensation for firm performance. Jayaraman and Milbourn (2012)
show that PPS (cash-based compensation for performance) increases (decreases) with stock
liquidity. We estimate the following regression:
Ln Salary
ð Þ = g0 + g1Return + g2HIGHLIQ + g3HIGHLIQ3Return
+
X
giControlsi + e,
Hsu et al. 17

Table 8. The Effect of Stock Liquidity on Cost of Capital.
Panel A: Total Sample.
Variable
Cost of capital
(1) (2) (3) (4)
TOVER –0.293** –0.243***
(–2.15) (–2.72)
LR –0.002** –0.002**
(–2.01) (–2.40)
State 0.006 0.006 0.001 –0.005
(1.47) (1.46) (0.10) (–0.76)
Size 0.023*** 0.027*** 0.016*** –0.010**
(12.30) (11.70) (6.81) (–2.21)
Leverage –0.059*** –0.061*** –0.027*** 0.019*
(–7.97) (–8.25) (–3.10) (1.80)
Tobin’s Q 0.008*** 0.009*** 0.006*** 0.008***
(5.62) (6.24) (5.27) (5.13)
ROA 0.794*** 0.796*** 0.546*** 0.452***
(31.88) (31.96) (24.17) (19.42)
Ln_age –0.020*** –0.018*** –0.012*** –0.013
(–5.47) (–5.25) (–2.86) (–1.53)
NTS –0.039*** –0.044*** –0.004 0.036***
(–3.26) (–3.75) (–0.44) (2.84)
Ownership 0.000*** 0.000*** 0.001*** 0.001***
(4.24) (4.23) (5.66) (6.68)
Constant –0.475*** –0.559*** –0.330*** 0.145
(–11.05) (–11.23) (–6.70) (1.60)
Industry fixed effect YES YES NO NO
Firm fixed effect NO NO YES YES
Observation 7,924 7,924 7,924 7,924
R2
.248 .248 .068 .083
Panel B: SSSR Sample.
Cost of capital
Variable (1) (2)
Treat 0.192*** 0.111***
(9.18) (4.94)
Post 0.060*** 0.036*
(2.78) (1.72)
Treat 3 Post –0.082*** –0.062**
(–2.87) (–2.30)
State 0.023
(1.48)
Size 0.012
(1.62)
Leverage 0.017
(0.73)
Tobin’s Q 0.015**
(continued)

where Ln(Salary) is the natural logarithm of cash compensation (the sum of base cash
salary and bonus) for a firm’s Top 3 highest-paid executives. Return is a stock-based per-
formance measure defined as the annual stock return over the fiscal year following Firth
et al. (2006).14
HIGHLIQ is an indicator variable that equals one if the liquidity of the
stock (TOVER and LR) is higher than the annual sample median and zero otherwise. The
idea behind our design is that if managerial PPS is greater when liquidity is higher, the
association between firm performance and managerial cash compensation should be lower
when stock liquidity is higher (i.e., g3 in Equation 3 should be negative).
Table 10 shows the results. We report the total sample results in columns 1 and 3 and
the SSSR sample results in columns 2 and 4. The estimated coefficients of the interaction
term HIGHLIQ 3 Return are negative and significant in all columns.15
Our results indicate
that the sensitivity of managerial cash-based compensation (i.e., Salary) to performance
(i.e., Return) is lower when stock liquidity is higher. These results show that the sensitivity
of managerial compensation to stock-based performance is positively associated with stock
liquidity.
Ruling Out Privatization as an Alternative Explanation
We also conduct tests to rule out privatization as an alternative explanation for our results.
The privatization perspective regards the SSSR as part of China’s share issue privatization
(SIP) process for SOEs (Liao et al., 2014; Tan et al., 2015). If privatization is responsible
for our main results, we should find the effect to be more pronounced for SOEs, as there is
no privatization effect for non-SOEs. Thus, we partition our SSSR sample into two subsam-
ples according to firm ownership before the reform.16
We then reestimate Equation 2
within each subsample. Columns 1 and 3 present the non-SOE subsample results (treating
Table 8. (continued)
Cost of capital
Variable (1) (2)
(2.37)
ROA 0.818***
(9.50)
Ln_age –0.003
(–0.28)
Index 0.006
(1.50)
Constant –0.268** –0.502***
(–2.36) (–2.84)
Industry fixed effect YES YES
Observation 772 758
R2
.167 .292
Note. This table reports the estimation results of the effect of liquidity on Cost of capital. Panel A show the results
using the total sample while Panel B presents the results using the SSSR sample. See appendix for variable
definitions. The t statistics are reported in parentheses. Standard errors are heteroskedasticity robust and
clustered at the firm level. SSSR = split share structure reform.
Hsu et al. 19

Table
9.
The
Effect
of
Stock
Liquidity
on
Cost
of
Capital
Conditional
on
Financial
Constraints.
Panel
A:
Total
Sample.
Variable
Cost
of
capital
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
SOE
Non-SOE
SOE
Non-SOE
High-marketization
Low-marketization
High-marketization
Low-marketization
TOVER
–0.206
–0.452**
–0.228
–0.501***
(–1.65)
(–2.20)
(–1.01)
(–3.34)
LR
–0.001
–0.003*
0.001
–0.005***
(–1.33)
(–1.76)
(0.60)
(–3.51)
State
0.003
0.012*
0.003
0.013**
(0.34)
(1.87)
(0.35)
(1.98)
Size
0.019***
0.032***
0.019***
0.035***
0.020***
0.023***
0.020***
0.034***
(5.26)
(7.53)
(4.41)
(8.13)
(4.87)
(7.98)
(3.99)
(9.28)
Leverage
–0.023
–0.093***
–0.037*
–0.106**
–0.028
–0.088***
–0.027
–0.099***
(–0.93)
(–6.91)
(–1.80)
(–2.68)
(–1.13)
(–7.93)
(–1.11)
(–8.88)
Tobin’s
Q
0.010***
0.006**
0.008***
0.008**
0.008**
0.006***
0.008**
0.013***
(2.80)
(2.46)
(2.80)
(2.45)
(2.09)
(3.04)
(2.09)
(5.44)
ROA
0.886***
0.647***
0.579***
0.398***
0.668***
0.861***
0.672***
0.856***
(12.75)
(13.98)
(10.09)
(5.00)
(7.63)
(24.35)
(7.66)
(24.22)
Ln_age
–0.021***
–0.009
–0.013**
–0.006
–0.029***
–0.008
–0.028***
–0.009*
(–3.91)
(–1.45)
(–2.47)
(–1.67)
(–4.40)
(–1.55)
(–4.20)
(–1.68)
NTS
–0.039**
0.007
–0.035**
0.011
–0.042**
–0.016
–0.044**
–0.033*
(–2.18)
(0.37)
(–1.97)
(0.55)
(–2.11)
(–1.04)
(–2.22)
(–1.72)
Ownership
0.000**
0.001**
0.000**
0.001***
0.000
0.001***
0.000
0.001***
(2.43)
(2.49)
(2.21)
(4.32)
(1.13)
(3.82)
(1.19)
(3.94)
Constant
–0.407***
–0.644***
–0.358***
–0.715***
–0.360***
–0.475***
–0.358***
–0.734***
(–5.71)
(–6.86)
(–4.17)
(–9.05)
(–4.49)
(–7.55)
(–3.78)
(–9.42)
Industry
fixed
effect
YES
YES
YES
YES
YES
YES
YES
YES
Year
fixed
effect
YES
YES
YES
YES
YES
YES
YES
YES
Observation
5,532
2,392
5,529
2,395
4,028
3,896
4,028
3,896
R
2
.269
.219
.121
.120
.195
.296
.194
.302
(continued)
20

RiskT and RiskT2 as the dependent variable, respectively), and columns 2 and 4 present the
SOE subsample results. The results shown in Table 11 indicate that the coefficients of the
interaction term (Treat 3 Post) are significant only in non-SOE subsamples. In sum, the
results in Table 11 do not support the competing story described above, giving us more
confidence in our conclusion regarding the causal effect of liquidity on corporate risk-
taking.
Conclusion
This study examines the effect of stock liquidity on corporate risk-taking. First, we use a
conventional OLS approach to find that firms with more liquid stocks are associated with
greater risk-taking. Next, we exploit the SSSR in China as an exogenous event and use a
Table 9. (continued)
Cost of capital
SOE Non-SOE High-marketization Low-marketization
Variable (1) (2) (3) (4)
Treat 0.079*** 0.180*** 0.118*** 0.120***
(3.21) (3.81) (3.51) (3.59)
Post 0.010 0.085** 0.032 0.046
(0.44) (1.98) (1.03) (1.51)
Treat 3 Post –0.033 –0.119** –0.044 –0.080*
(–1.08) (–2.28) (–1.16) (–1.90)
State 0.021 0.044*
(1.00) (1.81)
Size 0.011 0.012 0.002 0.034**
(1.42) (0.75) (0.29) (2.49)
Leverage 0.011 –0.034 0.029 –0.016
(0.39) (–0.74) (0.84) (–0.44)
Tobin’s Q 0.006 0.030*** 0.017** 0.025**
(0.79) (2.69) (2.11) (2.35)
ROA 0.959*** 0.726*** 0.677*** 0.898***
(8.77) (4.95) (5.42) (7.05)
Ln_age –0.007 0.013 –0.011 0.006
(–0.47) (0.61) (–0.74) (0.28)
Index 0.003 0.005
(0.63) (0.78)
Constant –0.213 –0.543 –0.291 –0.777***
(–1.08) (–1.64) (–1.47) (–2.66)
Observation 466 292 373 374
R2
.333 .341 .266 .351
Note. This table reports the estimation results of the effect of liquidity on Cost of capital conditional on financial
constraints. Panel A shows the results using the total sample while Panel B presents the results using the SSSR
sample. See appendix for variable definitions. The t statistics are reported in parentheses. Standard errors are
heteroskedasticity robust and clustered at the firm level. SOE = state-owned enterprise; SSSR = split share
structure reform.
Hsu et al. 21

DID approach to probe the causal relation between liquidity and risk-taking. The DID
results confirm that stock liquidity has a positive and significant effect on corporate risk-
taking. Our additional analyses reveal that increases in liquidity decrease the cost of capital
and that this effect is more pronounced for firms facing more stringent financial constraints.
Our results also suggest that liquidity can affect managerial risk-taking by influencing PPS
Table 10. The Effect of Liquidity on PPS.
Variable
Ln(Salary)
Total sample SSSR sample Total sample SSSR sample
(1) (2) (3) (4)
Return 0.009 0.056 –0.028** 0.071
(0.36) (1.64) (–2.59) (1.58)
HIGHTOVER 0.012 0.093
(0.48) (1.31)
HIGHTOVER 3 Return –0.090*** –0.089**
(–3.76) (–2.25)
HIGHLR 0.006 0.033
(0.24) (0.51)
HIGHLR 3 Return –0.014* –0.005*
(–1.78) (–1.88)
State 0.033 –0.130 –0.011 –0.143**
(0.91) (–1.51) (–0.31) (–2.07)
Size 0.357*** 0.442*** 0.382*** 0.375***
(20.47) (16.46) (21.82) (10.22)
Leverage –0.225*** –0.334*** –0.219*** –0.232**
(–3.39) (–3.41) (–4.14) (–2.38)
Tobin’s Q 0.072*** 0.114*** 0.088*** 0.070***
(6.49) (3.84) (10.84) (2.96)
ROA 1.774*** 0.784** 1.809*** 0.540**
(10.00) (2.29) (10.80) (2.03)
Ln_age –0.060* –0.066 0.029 –0.133**
(–1.71) (–0.72) (0.50) (–2.15)
NTS –0.119 –0.052 –0.594*** –0.273
(–1.39) (–0.16) (–8.23) (–1.24)
Ownership –0.006*** –0.008*** –0.005*** –0.006***
(–5.92) (–5.20) (–4.45) (–2.82)
Constant 5.236*** 4.186*** 5.117*** 5.622***
(13.73) (7.10) (12.34) (6.41)
Year fixed effect YES NO YES NO
Observation 7,748 647 7,748 647
R2
.376 .366 .345 .393
Note. This table reports the estimation results of the effect of liquidity on PPS. HIGHLIQ (HIGHTOVER and HIGHLR)
is an indicator variable equal to one if the liquidity of the stock is higher than annual sample median and zero
otherwise. Return is the stock-based performance measure, defined as the annual stock return over the fiscal year.
See appendix for other variable definitions. The t statistics are reported in parentheses. Standard errors are
heteroskedasticity robust and clustered at the firm level. PPS = pay-for-performance sensitivity; SSSR = split share
structure reform.

and management incentives. Finally, we conduct tests to rule out the possibility that our
results can be explained by privatization, an effect of SSSR. Our study sheds light on the
real effects of stock liquidity and contributes to the understanding of financial
development.
Table 11. Ruling Out the Privatization Explanation.
Variable
Non-SOE SOE Non-SOE SOE
(1) (2) (3) (4)
Treat –0.043** –0.021*** –0.098** –0.050***
(–2.49) (–3.10) (–2.36) (–3.12)
Post –0.025** –0.012* –0.056* –0.033**
(–2.00) (–1.67) (–1.94) (–2.01)
Treat 3 Post 0.036** 0.011 0.083** 0.028
(2.35) (1.35) (2.33) (1.53)
Size –0.012** –0.004 –0.030** –0.009*
(–2.43) (–1.64) (–2.59) (–1.68)
Leverage 0.021 0.058*** 0.055 0.132***
(1.37) (3.74) (1.50) (3.72)
Growth 0.004 –0.006* 0.014* –0.012
(1.19) (–1.70) (1.67) (–1.60)
ROA –0.209*** –0.081 –0.503*** –0.267**
(–3.56) (–1.48) (–3.69) (–2.22)
Ln_age 0.006 0.002 0.021 0.004
(0.68) (0.52) (0.96) (0.38)
NTS 0.008 0.038* 0.025 0.081*
(0.28) (1.80) (0.35) (1.67)
M_ownership 0.004 0.023 0.017 0.108
(0.15) (0.10) (0.31) (0.19)
Constant 0.226** 0.014 0.577*** 0.046
(2.59) (0.25) (2.70) (0.34)
Observation 178 347 178 350
R2
.587 .459 .585 .461
Note. This table reports the risk-taking estimation results based on the SOE and Non-SOE subsamples. Columns 1
and 3 show the results within the non-SOE subsample with RiskT and RiskT2, respectively, as dependent variables,
while columns 2 and 4 show the results within the SOE subsample. See appendix for variable definitions. The t
statistics are reported in parentheses. Standard errors are heteroskedasticity robust and clustered at the firm
level. SOE = state-owned enterprise.
Hsu et al. 23

Appendix Variable Definitions.
Variable Definition
RiskT Industry-adjusted earnings volatility which is equal to
RiskT =
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1
T1
P
T
t = 1
adj ROAit 1
T
P
T
t = 1
adj ROAit
2
s
jT = 5;
adj ROAit = EBITit
ASSETSit
1
Nd, t
P
Nd, t
k = 1
EBITk, d, t
ASSETSk, d, t
,
where EBITit is the earnings before interest and taxes of firm i in year t; ASSETSit
is the total assets of firm i in year t; ROAit is the ratio of earnings before
interests and taxes to total assets for firm i at year t; adj_ROAit is industry-
adjusted ROA for firm i at year t. Nd, t is the number of firms within industry d
and year t; T over (0 to + 4, + 1 to + 5, + 2 to + 6, + 3 to + 7, + 4 to + 8,
etc.)
RiskT2 Industry-adjusted earnings range, calculated as
RiskT2 = max adj ROAit
ð Þ min adj ROAit
ð Þ
RiskT3 Standard deviation of industry-adjusted firm level profitability over a given 5-year
period, where profitability is measured as a firm’s EBITDA/Assets
RiskT4 Difference between the maximum and minimum EBITDA/Assets over the 5-year
period
TOVER Tradable turnover ratio, which is the average daily turnover ratio (total shares
traded in a day divided by total tradable shares) for a firm during the year.
LR The liquidity ratio defined as follows:
LRit =
P
Dit
d = 1
Vitd

P
Dit
d = 1
jRitdj

3103
,
where Ritd and Vitd are stock i’s return and dollar volume (in millions) on day d
in year t, respectively. Dit is equal to the total number of days traded for stock i
in year t.
Treat Indicator variable which equals one if the reform happens in 2005 and zero
otherwise.
Post Indicator variable which equals one for year 2006, and zero for year 2004.
NTS Number of nontradable shares divided by the total number of shares outstanding
before the reform.
Incentive Indicator variable which equals one if the firm granted stock-based incentive
compensation plan, including stock options or restricted stock, to managers in
the reform, and zero otherwise.
Cost of capital Firm-specific cost of equity capital estimated using the PEG ratio approach
following Easton (2004), which is measured as the square root of the inverse
of price-earnings-growth ratio.
SEO Indicator variable which equals one if the firm undertakes seasoned equity
offerings (SEO) in a certain year, and zero otherwise.
InvIneff Following Richardson (2006), we use the residuals from the expected investment
model as the firm-level proxy for investment inefficiency.
State Indicator variable which equals one for state-owned enterprises, and zero
otherwise.
Ownership The total cash flow rights of the controlling shareholder on record with the
company following Faccio, Marchica, and Mura (2011).
Deviation The separation between cash flow rights and voting rights of the controlling
shareholder on record with the company following Faccio et al. (2011).
M_ownership Percentage of shares held by the executives.
Size The natural logarithm of total assets.
(continued)

Authors’ Note
Kaitang Zhou’s is now affiliated with Wuhan University, Wuhan, China.
Acknowledgments
We are grateful to two anonymous reviewers, C. S. Agnes Cheng (associate editor), Tarun Chordia
(associate editor), and workshop participants at Xiamen University for their valuable comments and
suggestions.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/
or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or
publication of this article: Kaitang Zhou acknowledges financial support from the School of
Economics and Management at Wuhan University.
Notes
1. The current literature on stock liquidity uses the setting of stock price decimalization (e.g., Fang,
Noe, Tice, 2009; Fang, Tian, Tice, 2014) and brokerage merger or closure (e.g.,
Balakrishnan, Billings, Kelly, Ljungqvist, 2014; Kelly Ljungqvist, 2012) to single out exo-
genous shocks to stock liquidity. However, as indicated by Back, Li, and Ljungqvist (2015), the
shocks created by these factors are temporary. In addition, stock price decimalization coincides
with other regulation events, such as Regulation Fair Disclosure (Reg. FD). Thus, these settings
may not be ideal for studying risk-taking.
2. As we discuss next, high pay-for-performance sensitivity (PPS) may also reduce managerial
risking.
3. Because detailed data on option-based compensation plans in China were not available, we
cannot directly test the effect of liquidity on delta as in the U.S. analysis. However, Jayaraman
and Milbourn (2012) show that while PPS increases with stock liquidity, the sensitivity of cash-
based compensation to firm performance decreases with liquidity. We confirm a negative rela-
tion between liquidity and the sensitivity of cash-based compensation to firm performance. This
finding is consistent with the conclusion that higher liquidity leads to greater PPS, and we rely
Appendix (continued)
Variable Definition
Leverage The ratio of total debt to total assets
Growth The annual growth rate of sales.
ROA EBIT divided by total assets.
Tobin’s Q Tobin’s Q, defined as the sum of market value of tradable shares, book value of
nontradable shares, and liabilities, scaled by book value of total assets.
Ln_age The natural log of (1 + the number of years since IPO).
Index National Economic Research Institute (NERI) Index of Marketization of China’s
Provinces, which is a comprehensive marketization index that serves as a proxy
for the institutional development of a province in China (Fan Wang, 2012).
Note. EBITDA = earnings before interest, tax, depreciation, and amortization; IPO = initial public offerings.
Hsu et al. 25

on evidence presented in prior studies that higher PPS leads to greater risk-taking (e.g.,
Armstrong, Larcker, Ormazabal, Taylor, 2013; Coles, Daniel, Naveen, 2006; Efendi,
Srivastava, Swanson, 2007).
4. We do not further explore the potential effect of improved corporate governance related to split
share structure reform (SSSR), as there is not clear prediction whether or not improved corporate
governance would lead to more risk taking.
5. We note that in Chinese markets, hostile takeovers are rare and investors are primarily individual
investors. Thus, the effect of takeover threats is less relevant in China.
6. According to Li, Wang, Cheung, and Jiang (2011) and Liao, Liu, and Wang (2014), 1,260 listed
firms that completed the reform by December 31, 2007, representing almost 85% of Chinese A-
share market capitalization at the end of 2007.
7. Note that we drop firms that completed the reform in year 2006 so that we can construct prere-
form and postreform periods for the treatment and control groups.
8. Because the reform could take place in the middle of 2005, we drop year 2005 to remove the
partial effect of the SSSR on the treatment group for that year.
9. In column 3 and column 6, we also include managerial ownership (M_ownership) and an indica-
tor variable (Incentive) that is equal to one if the firm granted stock-based incentive compensa-
tion plans to managers in the reform zero otherwise to control for managerial incentives, which
we address later.
10. As shown in Table 4, our control group is much smaller than the treatment group within the
SSSR sample, making the matching less efficient for the propensity-score matching (PSM)
analysis.
11. Note that prior studies also use RD investment to measure risk-taking (e.g., Li, Wang, Cheung,
Jiang, 2011). Because RD data are not publicly available before 2007 in China and the dis-
closure of RD data is not mandatory, we do not use RD investment as a proxy for corporate
risk-taking in our study.
12. In untabulated univariate tests of the cost of capital, the difference-in-differences (DID) estimate
of the cost of capital is –0.011, which is significant at the 1% level.
13. As stated in China Securities Regulatory Commission’s (CSRC) notice to listed firms on March
9, 2007, ‘‘to encourage listed firms to seriously cooperate with this activity, the listed firms were
not allowed to apply for managerial stock incentive schemes until they had completed all three
stages of this activity’’ (CSRC, 2007).
14. We also use accounting-based performance (ROA) instead of Return in Equation 3. Our results
(untabulated) are similar.
15. The objective of this analysis is to examine whether the association between firm performance
and managerial cash compensation is lower when stock liquidity is high versus when stock
liquidity is low. We use indicator variable (HIGHLIQ) rather than continuous raw variable
(TOVER or LR) as liquidity measure in Equation 3 to facilitate the interpretation of our results.
Our untabulated results are similar if we use the continuous variable (TOVER or LR) and its
interaction term in our analysis.
16. We do not conduct the test using total sample because privatization is an effect of SSSR. To test
the effect of privatization, it is meaningful to use the DID design with SSSR as the treatment.
Our untabulated results are similar if we use the PSM sample in Table 7 for this test.
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