Does Stock Liquidity Affect Corporate Debt Maturity Structure?
Joseph Marks
Bentley University
[email protected]
Chenguang Shang
Bentley University
[email protected]
First Draft: March 2017
This Draft: April 2017
Abstract
In this study, we empirically examine the relation between corporate debt maturity and stock liquidity. We
find strong evidence that firms’ use of short-term debt is negatively associated with stock liquidity. Our
results are robust to a battery of control variables, different fixed effects, various alternative measures of
debt maturity and stock liquidity, an instrumental variable approach, as well as a difference-in-difference
approach that uses decimalization as a quasi-natural experiment. We also document that the impact of stock
liquidity on corporate debt maturity structure is more pronounced for firms that are subject to severe agency
problems, and the relation is attenuated for innovative firms for which monitoring is less necessary. Overall,
the findings in this paper suggest that the use of short-term debt and stock market liquidity are substitute
monitoring mechanisms.
JEL Classification: G12; G14; G32
Keywords: Debt maturity structure; stock liquidity; agency problems

Corresponding author. Department of Finance, Bentley University, 175 Forest St. Waltham, MA 02452, USA.
Email address: [email protected]. Tel: 781-891-3416.
1. Introduction
Previous studies show that higher stock liquidity facilitates easier and cheaper entry and exit for
blockholders (Maug, 1998; Admati and Pfleiderer, 2009; Edmans, 2009; Edmans and Manso, 2011),
increases takeover threats (Kyle and Vila, 1991), and enhances information production and informed
trading (Holmstrom and Tirole, 1993). The evidence provided by these studies suggests that stock market
liquidity may have real effects on the management and corporate decisions. For instance, as the prices of
liquid stocks are more efficient and more accurately reflect the fundamental value of the firm, a liquid stock
market can potentially mitigate the information asymmetry problems between firms’ insiders and outside
investors. It is also possible that the price pressure generated by the exit of large blockholders may
significantly affect managers’ personal wealth, especially when their compensation packages consist of a
substantial amount of equity based compensation. Additionally, as a liquid stock market makes it easier for
corporate buyers to form toeholds in target firms, the increased likelihood of hostile takeovers may exert
pressure on the manager to take actions to avoid becoming a potential target. There is a growing line of
research that connects stock market liquidity with various strands of corporate finance literature (Banerjee,
Gatchev, and Spindt, 2007; Fang, Noe, and Tice, 2009; Lipson and Mortal, 2009; Jayaraman and Milbourn,
2011; Bharath, Jayaraman, and Nagar, 2013; Cao and Wan, 2014; Fang, Tian, and Tice, 2014; Nyborg and
Wang, 2014; Dass, Huang, Maharjan, and Nanda, 2016; Chang, Chen, and Zolotoy, 2016; Jiang, Ma, and
Shi, 2017). This link is critical because it demonstrates that trading activity in the stock market is a
significant factor that impacts firm value and shapes corporate policies.
Fang, Noe, and Tice (2009) document a robust positive association between stock liquidity and
firm value. The authors provide evidence that the positive impact of stock liquidity on firm value is due to
improved information content of market prices and an increase in managerial performance-compensation
sensitivity. Jayaraman and Milbourn (2011) find that stock liquidity has significant impact on executive
compensation; specifically, stock liquidity is associated with higher equity based compensation and higher
pay-for-performance sensitivity. Cao and Wan (2014) provide evidence that stock market liquidity reduces
firms’ tax-avoiding activity. While stock liquidity allows prices to be more informative, it is possible that
the consequences of high stock liquidity, such as potential hostile takeover threats, may exert enough
pressure for the manager to behave in a myopic fashion. Fang, Tian, and Tice (2014), for example, show
that an increase in stock liquidity reduces future innovation productivity. Chang, Chen, and Zolotoy (2016)
examine the relation between stock liquidity and stock price crash risk and indicate that stock liquidity
induces managers to withhold bad news, which results in higher stock price crash risk. Collectively, prior
research establishes that stock liquidity may have significant influence on the management and serve as a
managerial disciplinary device. To the extent that stock liquidity disciplines the management and alters the
manager’s behavior, it is reasonable to expect the level of stock market liquidity to influence certain
corporate contracting decisions that are commonly made in the presence of agency conflicts, such as the
debt maturity structure decision. In this study, we examine the empirical relation between stock market
liquidity and the debt maturity structure decision.
Debt maturity structure is a crucial aspect of the firm’s capital structure and financing decisions.
Over the last several decades, a large volume of research has focused on understanding the determinants
and implications of debt maturity. Several seminal works suggest that short-term debt can be used to
mitigate agency cost of debt, such as the underinvestment problem, and find a negative association between
firms’ growth opportunities and debt maturity (Myers, 1977; Barclay and Smith, 1995; Johnson, 2003).
Another line of research argues that debt maturity structure can be used as a signaling device. For example,
Flannery (1986) presents a model where firms with high information asymmetry may use short-term debt
to signal their quality. Several papers suggest that firms trade off the preference for short-term debt due to
private good news for liquidity risk and document that while high quality and low quality firms use more
short-term debt, medium quality firms use more long-term debt (Diamond, 1991; Guedes and Opler, 1996;
Stohs and Mauer, 1996). The alignment of interest between managers and stakeholder has also been shown
to be an important determinant of debt maturity. Datta, Iskandar-Datta, and Raman (2005) and Harford, Li,
and Zhao (2008) indicate that firms with higher managerial ownership and stronger boards use more shortterm debt, while Brockman, Martin, and Unlu (2010) find a positive association between risk-taking
preferences in CEO compensation portfolios and the use of short-term debt. Additionally, recent studies
show that corporate debt maturity structure is related to important financial decisions and the maturity of
debt in the US has been declining over time (Custodio, Ferreira, and Laureano, 2013; Harford, Klasa, and
Maxwell, 2014; Fu and Tang, 2015).
Our study is most closely related to the strand of research that focuses on the disciplinary function
of the use of short-term debt. An entrenched manager may have the incentive to choose long-term debt over
short-term debt in order to avoid frequent monitoring (Datta, Iskandar-Datta, and Raman, 2005). Because
short-term debt needs to be renewed frequently, it can be used as a powerful device by the lenders to monitor
the borrowers at the time of renegotiation. Previous studies, such as Eastbrook (1984), Rajan and Winton
(1995), and Stulz (1990), suggest that while long-term lenders can only rely on covenant terms to collect
verifiable information, short-term debt gives lenders the flexibility to effectively and frequently monitor
managers. Therefore, compared to long-term lenders, short-term lenders are better able to protect their
lending by reviewing the firm’s financial conditions and demanding the borrower’s disclosure of private
information when negotiating the renewal of short-term debt.
Researchers have documented a large amount of evidence supporting the monitoring role played
by the use of short-term debt. For example, Billett, King, and Mauer (2007) find that short-term debt can
substitute for restrictive covenants in controlling the asymmetric information problem. Graham, Li, and Qiu
(2008) offer evidence that banks significantly shorten the maturities of loans provided to firms previously
involved in misreporting. Gul and Goodwin (2010) find that the use of short-term debt is negatively related
to audit fees. Dang, Lee, Liu, and Zeng (2016) argue that short-term debt can play a monitoring role over
managers and constrain bad news hoarding behavior. The authors document a negative relation between
the proportion of short-term debt and stock price crash risk.
To the extent that stock market liquidity may discipline managers through facilitating information
discovery, forming blockholdings, enhancing informed trading, and increasing hostile takeover possibility,
lenders from the debt market may find it less necessary to enforce strict and frequent monitoring on the
management of the borrowing firm, leading to less use of short-term debt. As a result, we expect an inverse
relation between stock market liquidity and the use of short-term debt. In other words, the use of short-term
debt and stock liquidity may be substitute monitoring mechanisms. This is the main hypothesis that we
propose and empirically test in this study.
We find strong and robust evidence that stock liquidity is significantly and negatively associated
with the firm’s use of short-term debt. In other words, ceteris paribus, firms with more liquid (illiquid)
stocks use a higher proportion of long (short) term debt. This inverse relation between stock market liquidity
and corporate short-term debt is consistent with our hypothesis that the disciplinary role played by stock
liquidity allows firms to reduce the amount of monitoring imposed by the debt market, and hence less shortterm debt in the debt maturity structure. Put another way, stock market liquidity and the use of short-term
debt can be viewed as alternative disciplinary monitoring mechanisms. Our results are robust to controlling
for a battery of variables that are commonly considered in the literature as determinants of debt maturity
structure as well as industry and firm fixed effects. Our findings still hold when we use alternative debt
maturity and stock liquidity measures. To address potential endogeneity concerns, we employ a 2SLS
approach and a difference-in-difference approach. The results of these additional analyses indicate that the
documented negative relation between stock liquidity and the use of short-term debt appears to be causal.
Furthermore, we provide additional evidence indicating that the trade-off between the monitoring
from stock market liquidity and the monitoring from the use of short-term debt depends on firms’ need for
monitoring. Specifically, we find that the effect of stock liquidity on debt maturity is more pronounced for
firms that are subject to severe agency problems, namely, diversified firms, firms that actively engage in
acquisitions, non-dividend payers, and firms operating in low competition environments. In addition, we
show that the negative association between stock liquidity and the use of short-term debt is significantly
weaker for innovative firms for which intense monitoring may be unnecessary.
We make three important contributions to the literature. First, we extend the literature that studies
the determinants and effects of debt maturity structure (Myers, 1977; Barclay and Smith, 1995; Rajan and
Winton, 1995; Johnson, 2003; Datta et al., 2005; Brockman et al., 2010; among others) by showing that
stock market liquidity has significant influence on the use of short-term debt. Secondly, we add to the young
but quickly growing line of research that connects the microstructure literature with traditional corporate
finance literature (Banerjee, Gatchev, and Spindt, 2007; Fang, Noe, and Tice, 2009; Lipson and Mortal,
2009; Bharath, Jayaraman, and Nagar, 2013; Fang, Tian, and Tice, 2014; Chang, Chen, and Zolotoy, 2016).
This strand of research focuses on how stock market liquidity may affect financial outcomes and corporate
decisions. We identify debt maturity structure as another corporate decision that firms may adjust based on
their stock liquidity. Thirdly, we join a group of recent literature that examines the substitution between
different forms of corporate governance. For instance, Ferreira, Ferreira, and Raposo (2011) report that
stock price informativeness and board monitoring are mutual substitutes. Guo, Lach, and Mobbs (2015)
find evidence supporting the notion that firms treat internal and external governance as substitutes. Our
empirical results are consistent with this literature and suggest that stock market liquidity and short-term
debt maturity can be used as alternative monitoring mechanisms.
The rest of our paper proceeds as follows. We describe our sample selection and summarize the
descriptive statistics in Section 2. In section 3, we discuss the research design and report our main empirical
results. We address endogeneity concerns in Section 4. Section 5 examines scenarios where the relation
between debt maturity and stock liquidity is more or less relevant. Section 6 concludes.
2. Sample Selection and Descriptive Statistics
To construct our sample, we identify firms covered by both Compustat and CRSP from 1985 to
2013. The sample period starts in 1985 because it is the first year credit rating data, one of our control
variables, became available. We exclude the following observations from the sample: financial firms (SIC
from 6000 to 6999), firms with non-positive total assets or sales, firms that are not traded on NYSE, AMEX,
or NASDAQ, firms with share codes other than 10 and 11, firms with fewer than 50 daily stock price
records during a fiscal year, and firms without sufficient data to calculate the control variables described
below.1 Additionally, following the convention in the debt maturity structure literature (Johnson, 2003;
Datta et al., 2005; Brockman, Martin, and Unlu, 2010), we omit firms with short-term debt to total debt
1
Further restricting the sample to the industrial sector only (SIC from 2000 to 5999) yields similar results.
ratios that are less than 0% or greater than 100%. 2 Our final sample consists of 71,626 firm-year
observations with 10,766 unique firms.
2.1. Liquidity measures
We use the Amihud (2002) illiquidity measure as the main liquidity measure in this study. This
measure is widely employed in the literature and has been demonstrated to be an appropriate proxy for cost
of trading. For example, Goyenko, Holden, and Trzcinka (2009) document that among twelve proxies that
use daily data, the Amihud illiquidity measure most accurately captures price impact. Hasbrouck (2009)
shows that, compared to other daily proxies, the Amihud illiquidity measure is the most strongly correlated
with the TAQ-based price impact coefficient. Fong, Holden, and Trzcinka (2017) suggests that the Amihud
illiquidity measure is the best daily cost-per-dollar-volume proxy. We calculate the Amihud illiquidity
measure as the daily ratio of the absolute value of stock returns to dollar volume, averaged over firm i’s
fiscal year t:
𝐷
𝐴𝑚𝑖ℎ𝑢𝑑 𝐼𝑙𝑙𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑖,𝑡
1
|𝑅𝑒𝑡𝑖,𝑑 |
=
×∑
, (1)
𝐷𝑖,𝑡
𝐷𝑜𝑙𝑙𝑎𝑟 𝑉𝑜𝑙𝑢𝑚𝑒𝑖,𝑑
𝑑=1
Where 𝑅𝑒𝑡𝑖,𝑑 and 𝐷𝑜𝑙𝑙𝑎𝑟 𝑉𝑜𝑙𝑢𝑚𝑒𝑖,𝑑 are the return and dollar volume of firm i on day d, respectively, and
𝐷𝑖,𝑡 is the total number of trading days during firm i’s fiscal year t.
Since the distribution of the Amihud Illiquidity measure is highly skewed, we follow Edmans, Fang,
and Zur (2013)’s approach to modify the Amihud Illiquidity measure by taking the natural logarithm of
(Amihud Illiquidity plus one). We refer to this modified measure as Illiq in the rest of the paper. To examine
the robustness of the relationship between stock liquidity and corporate debt maturity structure, we employ
alternative liquidity measures including the lagged value of Illiquidity, the cost-per-price measure
developed by Fong, Holden, and Trzcinka (2017) (FHT hereafter), the Gibbs measure developed by
Hasbrouck (2009), and share turnover. We describe these alternative liquidity measures in detail in Section
3.2.
2
We get very similar results if we simply winsorize these observations instead of excluding them.
2.2. Debt maturity measures
Following prior literature (Barclay and Smith, 1995; Johnson, 2003; Datta et al., 2005; Brockman
et al., 2010; Dang and Phan, 2016), we use the fraction of debt due within three years (ST3) as our main
proxy for short-term debt. This variable is calculated as the ratio of debt in current liabilities plus debt
maturing in two and three years to total debt. We also employ a number of alternative measures of debt
structure maturity, namely, the fraction of debt due within one year (STNP, ST1), two years (ST2), four
years (ST4), and five years (ST5), to check the robustness of our main results and to mitigate the concern
that the use of ST3 may be based on an arbitrary cutoff point.3
2.3. Control variables
We include the following control variables that are commonly used in the debt structure literature
(Johnson, 2003; Datta et al., 2005; Brockman et al., 2010; Dang and Phan, 2016): firm size (Size), firm size
squared (Size Squared), leverage (Leverage), market-to-book (MB), abnormal earnings (AbEarn), asset
maturity (ATM), asset volatility (ATV), research and development expenses (R&D), missing R&D dummy
(Miss R&D), bond rating dummy (Rated), and term structure of interest rate (Term Structure). Detailed
variable definitions are included in the Appendix. All continuous variables are winsorized at the 1st and 99th
percentile to alleviate the impact of outliers.
2.4. Summary statistics
Table 1 reports the summary statistics of the variables used in this study. The mean of our main
short-term debt measure, ST3, is 52.1%, similar to the mean value of 55% reported in Johnson (2003). The
mean and median of our primary explanatory variable, Illiq, are 0.686 and 0.087 respectively. While the
Illiq variable is used as the liquidity measure, the higher value of this variable indicates that the stock of the
firm is more illiquid. The summary statistics of the rest of the variables reported in Table 1 are comparable
to those documented in previous research.
<Table 1 about here>
3
STNP is the fraction of debt maturing in one year relative to total debt, net of the current portion of long-term debt.
As a result, this measure is not affected by maturing long-term debt.
We present the correlation between stock liquidity and the main short-term debt measure, ST3, as
well as alternative shot-term debt measures, STNP, ST1, ST2, ST4, and ST5, in Table 2. The Illiq variable
is strongly and positively correlated with all the short-term debt variables (Panel A), suggesting that firms
with more illiquid stocks use more short-term debt in their debt maturity structure. This finding is consistent
with our prediction of a negative relation between stock liquidity and the use of short-term debt. We split
the sample into quartiles based on the Illiq measure, with the 1st Illiq quartile containing the most liquid
stocks while the 4th Illiq quartile containing the least liquid stocks (Panel B). It is clear that the proportion
of short-term debt monotonically decreases in stock liquidity. The mean (median) of our main short-term
variable, ST3, is 69.79% (79.37%) in the least liquid stock quartile and 34.55% (26.64%) in the most liquid
stock quartile. A visual demonstration of this pattern is presented in Figure 1.
<Table 2 about here>
<Figure 1 about here>
3. Research Design and Empirical Results
3.1. Baseline estimation
In this section, we investigate the empirical relationship between stock liquidity and corporate debt
maturity structure in a multivariate setting. The following specification is our baseline estimation:
𝑆𝑇3𝑖,𝑡 = 𝛼 + 𝛽𝐼𝑙𝑙𝑖𝑞𝑖,𝑡 + 𝛾𝑋𝑖,𝑡 + 𝜀𝑖,𝑡 , (2)
Where the dependent variable, 𝑆𝑇3𝑖,𝑡 , is the short-term debt measure, 𝐼𝑙𝑙𝑖𝑞𝑖,𝑡 is the primary variable of
interest, the stock liquidity measure, and 𝑋𝑖,𝑡 includes all the control variables described in Section 2.3.
Table 3 reports the baseline estimation results. Column 1 of Table 3 shows that controlling for a
full set of control variables without year and industry fixed effects, the Illiq variable is significantly and
positively associated with the amount of short-term debt on the firm’s balance sheet. Alternatively, firms
with more liquid stocks use less short-term debt. In addition to including all the control variables, Columns
2 – 4 control for Year, Industry, and Year and Industry fixed effects, respectively. Industry classification is
defined by 2-digit SIC codes. The positive correlation between Illiq and ST3 remains significant after
controlling for these fixed effects. Finally, to further control for omitted firm specific characteristics not
captured by the control variables, we include year and firm fixed effect in Column 5. The coefficient on
Illiq in Model 5 is still positive and significant at the 1% level. These baseline estimation results are
consistent with our main hypothesis that stock market liquidity reduces the use of short-term debt.
<Table 3 about here>
The coefficients on the control variables in Table 3 are intuitive and are generally consistent with
those reported in previous studies. Firm size (Size) is negatively associated with the use of short-term debt.
Given that firm size may be used as a proxy for credit quality (Diamond, 1991; Johnson, 2003), it may be
easier for larger firms with strong credit quality to obtain longer term debt. The coefficient on firm size
squared (Size Squared) is significant and positive, suggesting a potentially non-monotonic relation between
firm size and debt maturity (Diamond, 1991; Stohs and Mauer, 1996). Leverage is strongly and negatively
related to the use of short-term debt, as highly levered firms may be more concerned about refinancing risk.
Consistent with Myers (1977), firms with higher growth opportunities (MB) are more likely to use shortterm debt to mitigate the underinvestment problem. The coefficient on abnormal earnings (AbEarn) is
significant and negative. While theory suggests a positive coefficient on this variable as firms with more
asymmetric information may issue more short-term debt to signal their quality (Flannery, 1986), recent
studies such as Dang and Phan (2016) find results similar to ours. We find a negative relation between asset
maturity and short-term debt, consistent with the prediction that firms match debt maturity with asset
maturity (Stohs and Mauer, 1996). Theoretically, the relation between asset volatility (ATV) and debt
maturity can be ambiguous. On the one hand, firms with high asset volatility may tend to issue more shortterm debt in order to rebalance their capital structure when necessary (Ju and Ou-Yang, 2006). On the other
hand, these firms may prefer to use longer-term debt to avoid refinancing and liquidity risk (Kane, Marcus,
and McDonald, 1985). The coefficient on the asset volatility variable in our study appears to be sensitive
to the types of fixed effects included in the regressions. Similar to Dang and Phan (2016), we document a
positive association between R&D expenditure (R&D) and the use of short-term debt, as firms with high
R&D intensity are subject to a higher level of information asymmetry. The missing R&D dummy variable
(Miss R&D) is included to account for the fact that many firms do not explicitly report R&D expenses. This
variable generally carries a positive sign, indicating firms that do not disclose their R&D activity may bear
additional information asymmetry and therefore use more short-term debt. As expected, firms with long
term credit rating (Rated) use significantly more long-term debt (Datta et al., 2005). Firms operating in the
regulated industries (Reg Dummy) tend to use more long-term debt (Barclay and Smith, 1995). The
coefficient on term structure (Term Structure) is generally not significant once year and industry fixed
effects are included in the regressions.
3.2. Robustness
While the results in Table 3 confirm our hypothesis that firms with high stock liquidity use less
short-term debt, in this section, we conduct a number of additional analyses to test the robustness of this
finding. First, in Table 4, we rerun the baseline estimation using alternative short-term debt measures
(STNP, ST1, ST2, ST4, and ST5) as the dependent variables with Illiq being the primary independent
variable. The regressions reported in Table 4 include all control variables as well as year and industry fixed
effects. We find that the negative association between liquidity and the use of short-term debt is not sensitive
to the use of alternative debt maturity measures; the positive coefficient on Illiq remains significant at 1%
across all specifications.
<Table 4 about here>
Secondly, we address the concern that the use of Illiq based on Amihud (2002) is an arbitrary choice
by using several different stock liquidity measures that have been accepted in the microstructure literature.
Specifically, in Table 5, we use the lagged value of Illiq, FHT, the Gibbs estimator, and share turnover as
alternative stock liquidity measures.
FHT is developed by Fong, Holden, and Trzcinka (2017) and is defined as
1+𝑧
𝐹𝐻𝑇𝑖,𝑡 = 2𝜎𝑁 −1 (
) , (3)
2
Where 𝜎 is the standard deviation of non-zero returns for firm i over year t, 𝑁 −1 ( ) is the inverse function
of the cumulative normal distribution, and z is the proportion of zero return days relative to the number of
total trading days for firm i over year t. The FHT measure captures return volatility and proportion of zero
returns, two important aspects of transaction cost. Fong, Holden, and Trzcinka (2017) show that this
measure is highly correlated to cost-per-price benchmarks calculated using intraday data. The FHT is used
as the liquidity measure in recent studies such as Marshall, Nguyen, and Visaltanachoti (2012), Edmans,
Fang, and Zur (2013), Karnaukh, Ranaldo and Soderlind (2015), and Schestag, Schuster and UhrigHomburg (2016).
We also use the Gibbs sampler estimation measure proposed in Hasbrouck (2009). Gibbs is an
updated estimate of Roll (1984) trading cost measure calculated using daily data.4 Hasbrouck (2009) shows
that Gibbs is a good proxy for effective cost. Since the Gibbs measure is calculated based on calendar year,
we use the lagged Gibbs measure in our analysis to ensure that the debt maturity structure decisions do not
lead the stock liquidity measure. The share turnover measure is calculated as the ratio of daily volume to
shares outstanding over firm i’s fiscal year t. Unlike the other stock liquidity measures used in this study
that essentially measure stock illiquidity, a higher value of the share turnover measure is associated with
higher liquidity. Lipson and Mortel (2009) use Gibbs and share turnover as alternative liquidity measures
to the Amihud Illiquidity measure to show that firms with more liquid equity tend to have lower leverage.
Table 5 Panel A and Panel B displays the summary statistics of the alternative liquidity measures
and their correlation with the main liquidity measure, Illiq, used in this paper. The number of observations
for each measure varies depending on data availability. The mean and median of the alternative measures
reported in Panel A are qualitatively similar to those documented in prior studies (Edmans, Fang, and Zur,
2013; Hasbrouck, 2009; Lipson and Mortel, 2009). Panel B shows that these alternative measures are
significantly, but not perfectly, correlated with our main explanatory variable, Illiq. The imperfect
correlation among the various liquidity measures make them meaningful alternative measures for the
purpose of checking the robustness of our results.
4
We thank Joel Hasbrouck for making the Gibbs measure available at
http://people.stern.nyu.edu/jhasbrou/Research/GibbsEstimates2006/Liquidity%20estimates%202006.htm. Since this
data is available until 2005, our sample also stops in 2005 when using the Gibbs measure as the alternative liquidity
measure.
<Table 5 about here>
Table 6 reports the baseline regression results using the alternative liquidity measures as the main
independent variables. We find that, after including all the control variables as well as year and industry
fixed effects, the negative association between stock liquidity and the use of short-term debt persists, and
the coefficients on the alternative stock liquidity variables are all significant at 1%. Overall, the baseline
results reported in Table 3 are robust to using alternative debt maturity measures and other stock liquidity
proxies.
<Table 6 about here>
4. Endogeneity
There are two endogeneity issues that need to be addressed. First, firms’ debt maturity and capital
structure may be simultaneously determined (Barclay, Marx, and Smith, 2003). The coefficient estimates
of the Illiq variable in the OLS regressions may be biased without addressing the potential simultaneity
relation between debt maturity and leverage. The second endogeneity issue is related to the empirical
relation between stock liquidity and debt structure. It is possible that there is an unobserved variable not
captured by our specification that drives both stock liquidity and firms’ debt structure decision. Reverse
causality between stock liquidity and debt maturity is another concern: it may be that market participants
have a preference to trade stocks of firms with shorter debt maturity, which may result in higher liquidity.
These endogeneity issues are addressed using a two stage least square (2SLS) approach in Section 4.1. and
a difference-in-difference approach in Section 4.2.
4.1. 2SLS
To address the simultaneity relationship between debt maturity and leverage, we follow the
previous literature and adopt a 2SLS approach (Barclay, Marx, and Smith, 2003; Brockman et al., 2010;
Dang and Phan, 2016; Datta et al., 2005; Johnson, 2003). We use fixed asset ratio (ratio of PPE to total
assets) and profitability (ROA) as instrumental variables (IVs) for the endogenous variable, leverage, in the
first stage regression, and then insert the predicted value of leverage into the second stage regression with
ST3 as the dependent variable. Previous studies, such as Barclay, Marx, and Smith (2003) and Johnson
(2003), show that fixed asset ratio and profitability are important determinants of firms’ leverage. However,
there is little theoretical link between these IVs and debt maturity. Therefore, these IVs satisfy both the
relevance and exclusion conditions to be valid instruments. The first and second stage regressions (Equation
4 and Equation 5, respectively) are formally expressed as follows:
𝑃𝑟𝑒𝑑. 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 = 𝛼1 + 𝛽1 𝐼𝑙𝑙𝑖𝑞𝑖,𝑡 + 𝛾1 𝑍𝑖,𝑡 + 𝜃1 𝐼𝑛𝑠𝑡𝑟𝑢𝑚𝑒𝑛𝑡𝑠𝑖,𝑡 + 𝜀1 𝑖,𝑡 , (4)
𝑆𝑇3𝑖,𝑡 = 𝛼2 + 𝛽2 𝐼𝑙𝑙𝑖𝑞𝑖,𝑡 + 𝛾2 𝑃𝑟𝑒𝑑. 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 + 𝜃2 𝑍𝑖,𝑡 + 𝜀2 𝑖,𝑡 , (5)
Where the vector Z includes the full set of control variables mentioned above, except for the leverage
variable, and Instruments represents the two instrumental variables, PPE and ROA.
Table 7 Column 1 (first stage) and Column 2 (second stage) report the 2SLS results addressing the
simultaneity relation between leverage and debt maturity. We find that consistent with prior studies, the
IVs, namely fixed asset ratio and profitability, are significant determinants of leverage in the first stage
regression. Using the predicted value of leverage obtained from the first stage regression as one of the
control variables, we regress ST3 on our liquidity measure in the second stage analysis. We find that the
significant negative relation between stock liquidity and the use of short-term debt still holds after
addressing the simultaneity issue (Column 2).
<Table 7 about here>
We address the endogenous relation between stock liquidity and debt maturity structure in Table 7
Column 3 and Column 4 by following an approach similar to that adopted by Fang, Noe, and Tice (2009)
and Jayaraman and Milbourn (2011). Specifically, we treat stock liquidity, Illiq, as endogenous and use two
IVs to predict stock liquidity in the first stage regression (Column 3). The two IVs employed in the analysis
are the lagged value of the Illiq variable and the industry average of the Illiq variable excluding the firm in
a given year. These two IVs are expected to be significantly correlated with a firm’s stock liquidity, but
there is no obvious reason to believe that they have direct impact on the underlying firm’s debt maturity
through channels other than the firm’s stock liquidity.5 The first and second stage regressions (Equation 6
and Equation 7, respectively) are formally expressed as follows:
𝑃𝑟𝑒𝑑. 𝐼𝑙𝑙𝑖𝑞𝑖,𝑡 = 𝛼1 + 𝛽1 𝑋𝑖,𝑡 + 𝛾1 𝐼𝑛𝑠𝑡𝑟𝑢𝑚𝑒𝑛𝑡𝑠𝑖,𝑡 + 𝜀1 𝑖,𝑡 , (6)
𝑆𝑇3𝑖,𝑡 = 𝛼2 + 𝛽2 𝑃𝑟𝑒𝑑. 𝐼𝑙𝑙𝑖𝑞𝑖,𝑡 + 𝛾2 𝑋𝑖,𝑡 + 𝜀2 𝑖,𝑡 , (7)
Where the vector X contains the full set of control variables as in Equation (2), and Instruments represents
the two instrumental variables, lagged value of Illiq and the industry average Illiq excluding the firm.
As reported in Table 7 Column 3, in the first stage regression, both IVs are positively associated
with the endogenous Illiq variable and the coefficients on the IVs are significant at 1% level. In the second
stage regression with debt maturity as the dependent variable (Column 4), the coefficient on the predicted
value of Illiq is positive and significant at 1%, which confirms our baseline estimation result of a negative
association between stock liquidity and the use of short-term debt. We obtain very similar results using
FHT, Gibbs, and share turnover as alternative stock liquidity measures in the 2SLS setting.
4.2. Difference-in-Difference
As discussed previously, the relation between stock liquidity and debt maturity may be subject to
reverse causality concerns. To further establish causality, we adopt a Difference-in-Difference (DiD)
approach in this section. Similar to prior studies (Fang, Noe, and Tice, 2009; Fang, Tian, and Tice, 2014;
Chang, Chen, and Zolotoy, 2016), we identify the exogenous tick-size decimalization event in 2001 as a
quasi-natural experiment that alters stocks’ liquidity. The New York Stock Exchange (NYSE) and
American Stock Exchange (AMEX) changed the minimal tick size from 1/16th of a dollar to 1 cent on
January 29th, 2001, while NASDAQ converted all stocks to decimal price form on April 9 th, 2001. 6
Decimalization has been shown to increase stock liquidity as the bid-ask spreads are on average
significantly narrower following the event, which substantially reduces trading cost and encourages trading
activity (Bessembinder, 2003; Furfine 2003). This quasi-natural experiment provides an ideal setting to
5
We note that we get similar results if we use either of the two IVs separately in the 2SLS regression.
A small number of stocks were part of a pilot program prior to the conversion of all stocks to decimal prices. For
instance, decimalization was introduced to 158 out of 3,525 stocks during the period between August 2000 and January
2001 (Fang, Noe, and Tice, 2009).
6
address the potential reverse causality issue because it is an event that exogenously affects stock market
liquidity but is highly unlikely to have been caused by firms’ debt maturity structure decisions. Furthermore,
previous literature points out that decimalization does not equally impact all stocks: Bessembinder (2003)
and Furfine (2003) find evidence that while decimalization increases stock liquidity for the most actively
traded stocks, there is little change in the liquidity for the infrequently traded stocks following
decimalization.
Based on the evidence and methodology presented in this literature, we focus on the fiscal year
before (t-1) and the fiscal year after (t+1) decimalization (the year of decimalization is excluded) in the DiD
approach. Our goal is to examine the impact of decimalization on the change in the use of short-term debt.
Given that liquidity increases more for the most actively traded stocks (Bessembinder, 2003; Furfine 2003)
following decimalization, we rank stocks in the year prior to decimalization into terciles based on the Illiq
measure. The stocks in the bottom tercile have the lowest Illiq values, meaning that they are the most
actively traded stocks before decimalization and therefore should be most affected by the event. We refer
to this group of stocks the “Treatment” group. The stocks in the top tercile are the least frequently traded
stocks and, according to previous research, should have little change in liquidity following decimalization.
Therefore, we label the stocks in the top tercile the “Control” group. The middle tercile is excluded from
the analysis. Our empirical specification is presented below:
∆𝑆𝑇3𝑖 = 𝛼 + 𝛽𝑇𝑟𝑒𝑎𝑡 + 𝛾∆𝑋𝑖 + 𝜀𝑖 , (4)
Where ∆𝑆𝑇3𝑖 is the change in the fraction of debt due in three years for firm i before and after
decimalization, Treat is a dummy variable that is equal to one if the firm is actively traded before
decimalization (in the bottom tercile) and zero otherwise, and ∆𝑋𝑖 is the change in the full set of control
variables used in Table 3. If greater stock liquidity indeed reduces firms’ use of short-term debt, then we
would expect a significant negative coefficient on the Treat variable as the treatment group experiences the
largest increase in liquidity following decimalization.
In Table 8 Panel A, we report the DiD regression results using ∆𝑆𝑇3 as the dependent variable. In
Column 1, we use the Treat variable as the only regressor to predict ∆𝑆𝑇3, and we find that the coefficient
on Treat is negative and significant at 1%, suggesting that an increase in liquidity following decimalization,
particularly for actively traded firms, leads to a decrease in the fraction of short-term debt relative to the
firm’s total debt. In Column 2 and 3, we add in the changes in all the control variables used in the baseline
estimation regression as well as industry fixed effect. The coefficient on the Treat variable remains negative
and is significant at the 5% level.
In Table 8 Panel B, we re-run the DiD regression using the changes in alternative short-term debt
measures as the dependent variables with Treat as the primary independent variable. The coefficient on the
Treat variable remains negative in all specifications. Notably, the negative effect of Treat is most
pronounced for very short-term debt, namely short-term debt excluding the current portion of long-term
debt, and debt due in one, and two years (Column 1, 2, and 3). This finding is intuitive since debt of
relatively longer maturity is stickier and less adjustable compared to debt in current liabilities. Collectively,
the results reported in Sections 4.1. and 4.2. suggest that the inverse relation between stock liquidity and
the use of short-term debt documented in this study is robust to the use of a 2SLS approach and a quasinatural experiment approach.
5. Additional Evidence of the Disciplinary Role of Stock Liquidity
Our analyses thus far document an inverse relation between short-term debt and stock liquidity.
The interpretation is that stock liquidity can serve as an alternative form of monitoring that influences
managers’ behavior and mitigates agency problems, which, in turn, reduces firms’ need for the use of shortterm debt as a monitoring device. This argument is consistent with recent studies that suggest a substitution
effect between different forms of monitoring (Ferreira, Ferreira, Raposo, 2011; Guo, Lach, and Mobbs,
2015). In the following section, we conduct additional empirical analyses to test how the relation between
stock liquidity and debt maturity may depend on firms’ specific need for monitoring. Intuitively, monitoring
is most relevant for firms that are subject to severe agency problems. Therefore, we expect the effect of
stock liquidity on debt maturity to be stronger for firms that have particularly high agency cost. On the
contrary, for firms where disciplinary actions may be less necessary, the effect of stock liquidity on debt
maturity should be weaker.
5.1. Firms subject to severe agency cost
We examine whether the effect of stock liquidity on debt maturity is amplified for firms subject to
higher levels of agency cost. If higher stock liquidity indeed plays a disciplinary role on the management,
it may allow these firms to reduce the monitoring effort from other sources. We identify four types of firms
that are suitable for this empirical test. First, we use the level of diversification as an indicator of agency
cost. Previous studies show that diversified firms are associated with higher agency problems and CEOs of
diversified firms allocate resources inefficiently (Denis, Denis, and Sarin, 1997; Scharfstein and Stein,
2000). Secondly, we consider firms that are actively engaged in acquisitions to be associated with higher
levels of agency conflict. A large volume of literature documents that CEOs have the incentive to grow the
size of the firm, and the empire building behavior destroys firm value (Loughran and Vijh, 1997; Moeller,
Schlingemann, and Stulz, 2004; Harford and Li, 2007). Thirdly, as suggested by the corporate payout policy
literature, dividend payout may be used to reduce free cash flows and limit the manager’s ability to extract
firm value for private benefits (Easterbrook, 1984; Jensen, 1986). Therefore, we consider non-dividend
payers to have higher agency costs compared to dividend payers. Lastly, since competition in the product
market has been shown to have the ability to mitigate managerial slack (Giroud and Mueller, 2010), we
consider firms operating in low (high) competition industries to have more (less) severe agency problems.
In Table 9, we empirically test our hypothesis by interacting the Illiq measure with proxies for agency cost.
The specification is formally expressed as follows:
𝑆𝑇3𝑖,𝑡 = 𝛼 + 𝛽𝐼𝑙𝑙𝑖𝑞𝑖,𝑡 + 𝛾𝐴𝑔𝑒𝑛𝑐𝑦 𝐶𝑜𝑠𝑡𝑖,𝑡 + 𝜃𝐼𝑙𝑙𝑖𝑞𝑖,𝑡 ∗ 𝐴𝑔𝑒𝑛𝑐𝑦 𝐶𝑜𝑠𝑡𝑖,𝑡 + 𝛿𝑋𝑖,𝑡 + 𝜀𝑖,𝑡 , (5)
Where 𝐴𝑔𝑒𝑛𝑐𝑦 𝐶𝑜𝑠𝑡𝑖,𝑡 represents one of the four proxies for agency problems, namely the degree of
diversification (Seg Number), acquisition intensity (Acquisition), dividend payer (Dividend), and product
market competition (HHI). Seg Number is the number of business segments reported by the firm in a given
year. Acquisition is measured as acquisition expenses divided by total assets. Dividend is a dummy variable
that equals one if the firm pays dividends in a given year or zero otherwise. Product market competition is
proxied by the sales-based Herfindahl Index (HHI) measured at the three digit-SIC level. We expect higher
agency cost in firms that are more diversified, that are more active in acquisitions, that do not pay dividends,
and that operate in high HHI industries. 𝐼𝑙𝑙𝑖𝑞𝑖,𝑡 is the same liquidity measure used in the previous analyses.
The key variable in this specification is the interaction term between Illiq and the agency cost proxies. 𝑋𝑖,𝑡
includes the full set of control variables used in the baseline estimation.
<Table 9 about here>
Table 9 reports the empirical results. We find that the coefficients of the interaction terms in all
models are statistically significant and have signs that support our hypothesis: the negative relation between
stock liquidity and the use of short-term debt is more pronounced when the firm is diversified, when the
firm has high acquisition intensity, when the firm does not make dividend payments, and when the firm
operates in a less competitive environment. These findings lend further support to our hypothesis that stock
liquidity plays a disciplinary role, and this monitoring function of stock liquidity is especially valuable for
firms that are subject to high levels of agency conflict.
5.2. Innovative firms
We conduct another analysis to further explore our hypothesis that stock market liquidity affects
debt maturity through firms’ need for monitoring. We focus on innovation driven firms where monitoring
is less necessary and can even be costly (Faleye, Hoitash, and Hoitash, 2011; He and Tian, 2013; Fang,
Tian, and Tice, 2014). Prior studies show that intense monitoring may exert an excessive amount of pressure
on the management, induce managerial myopia problems, and lead to lower innovation productivity. Given
that the success of innovative firms relies less on monitoring, we expect the trade-off between stock market
liquidity and the use of short-term debt to be less relevant for R&D intensive firms. We use a modified
version of Equation (5) where we replace Agency Cost with proxies for innovation to conduct this test:
𝑆𝑇3𝑖,𝑡 = 𝛼 + 𝛽𝐼𝑙𝑙𝑖𝑞𝑖,𝑡 + 𝛾𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 + 𝜃𝐼𝑙𝑙𝑖𝑞𝑖,𝑡 ∗ 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 + 𝛿𝑋𝑖,𝑡 + 𝜀𝑖,𝑡 , (6)
Where Innovation is one of the following four proxies for innovation activity: R&D intensity (R&D),
whether the firm is from a high-tech industry (HiTech), the number of patent filings (Patent), and average
citations generated by each patent (Cite/Pat). R&D intensity is measured as R&D expenses scaled by total
assets. HiTech is a dummy variable equal to one if the firm is from a high tech industry and zero otherwise.
The high tech industry classification is based on the definitions in Cliff and Denis (2004). Patent and citation
data is from the NBER patent database which contains patent and citation data up to 2006. Therefore, when
patent and citation are used as proxies for innovation, our sample period is truncated in year 2006, resulting
in a smaller sample size. Following the convention in the innovation literature, Patent is calculated as the
natural log of the number of patents filed in a given year plus one, measuring the quantity of innovation,
while Cite/Pat is calculated as the natural log of the average number of citations received by each patent in
a given year plus one, measuring the quality of innovation. The primary variable of interest in this analysis
is the interaction term between Illiq and Innovation. The empirical results of this test are reported in Table
10.
<Table 10 about here>
In Table 10, the interaction terms between Illiq and the four proxies for innovation all have a
coefficient that is negative and significant. These findings suggest that for firms that are more R&D
intensive, that operate in high-tech industries, and that produce more and higher quality patents, i.e., firms
that require less monitoring, the association between stock market liquidity and debt maturity is attenuated.
Overall, the evidence documented in Table 10 further demonstrates that the monitoring substitution effect
between stock liquidity and debt maturity depends on the firm’s need for monitoring.
6. Conclusions
In this study, we empirically examine the relation between stock liquidity and corporate debt
maturity structure. We find strong evidence that firms’ use of short-term debt is negatively associated with
stock liquidity. The interpretation is that firms trade off the two alternative monitoring mechanisms: the use
of short-term debt vs. stock market liquidity. This finding is robust to a battery of control variables, different
fixed effects, various alternative measures of debt maturity and stock liquidity, an instrumental variable
approach, as well as a difference-in-difference approach that uses tick-size decimalization as a quasi-natural
experiment that alters stock liquidity. We also document that the relation between debt maturity and stock
liquidity depends on firms’ need for monitoring. Specifically, the effect of stock liquidity on corporate debt
maturity structure is more pronounced for firms that are subject to severe agency problems (when
monitoring is more important), and the relation is attenuated for innovative firms for which intense
monitoring may be unnecessary. Overall, the findings in this paper are consistent with the hypothesis that
the use of short-term debt and stock market liquidity are substitute monitoring mechanisms.
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Appendix. Variable Definitions
Variable
Debt Maturity Measures
STNP
ST1
ST2
ST3
ST4
ST5
Stock Liquidity Measures
Illiq
FHT
Gibbs
Share turnover
Control Variables
Size
Leverage
MB
AbEarn
ATM
ATV
R&D
Miss R&D
Rated
Reg Dummy
Term Structure
Definition
The ratio of debt in current liabilities without the current proportion of longterm debt (np) to total debt (dlc + dltt).
The ratio of debt in current liabilities (dlc) to total debt (dlc + dltt).
The ratio of debt in current liabilities (dlc) plus debt maturing in two years
(dd2) to total debt (dlc + dltt).
The ratio of debt in current liabilities(dlc) plus debt maturing in two and
three years (dd2 + dd3) to total debt (dlc + dltt).
The ratio of debt in current liabilities (dlc) plus debt maturing in two, three,
and four years (dd2 + dd3 + dd4) to total debt (dlc + dltt).
The ratio of debt in current liabilities (dlc) plus debt maturing in two, three,
four, and five years (dd2 + dd3 + dd4 + dd5) to total debt (dlc + dltt).
The natural logarithm of Amihud illiquidity plus one, where Amihud
illiquidity is calculated as the daily ratio of the absolute value of stock
returns to dollar volume, averaged over firm i’s fiscal year t.
The calculation of FHT follows Fong, Holden, and Trzcinka (2017).
The calculation of Gibbs follows Hasbrouck (2009). Data is downloaded
from Prof. Hasbrouck’s website.
The ratio of daily volume to shares outstanding over firm i’s fiscal year t.
The natural logarithm of book value of total assets (at).
The ratio of total debt (dlc + dltt) to book value of total assets (at).
The ratio of the market value of assets (csho∗prcc_f+at–ceq) to the book
value of total assets (at).
The ratio of the difference between the income before extraordinary items,
adjusted for common equivalents (ibadj) in year t and t − 1, to the market
value of equity (prcc_f∗cshpri)
Property, plant, and equipment over depreciation (ppegt/dp) times the
proportion of property, plant, and equipment in total assets (ppegt/at), plus
the ratio of current assets to the cost of goods sold (act/cogs) times the
proportion of current assets in total assets (act/at)
The standard deviation of the stock return (during the fiscal year) times the
market value of equity (csho∗prcc_f), divided by the market value of assets
(csho∗prcc+at–ceq)
The ratio of research and development expense (xrd) to total assets (at).
R&D is assigned a value of zero if xrd is missing.
Dummy variable equal to one if R&D expenses are missing and zero
otherwise.
Dummy variable equal to one if the firm has an S&P long-term credit rating
(splticrm) and zero otherwise.
Dummy variable equal to one if the firm’s SIC code is between 4900 and
4939.
The difference between the yield on 10-year government bonds and the
yield on 6-month government bonds.
Instrumental Variables
PPE
ROA
Other Variables
Treat
Seg Number
Acquisition
Dividend
HHI
HiTech
Patent
Cite/Pat
The ratio of net property, plant, and equipment (ppent) to the book value of
total assets (at).
The ratio of operating income before depreciation (oibdp) to the book value
of total assets (at).
Dummy variable equal to one if the firm’s stock belongs to the bottom
tercile of the Illiq variable (actively traded) in the fiscal year prior to
decimalization and zero otherwise.
The number of business segments reported by the firm in a given year.
The ratio of acquisition expenses (aqc) to he book value of total assets (at).
Dummy variable equal to one if the firm pays dividend in a given year and
zero otherwise.
The sales-based Herfindahl index based on three-digit SIC codes.
Dummy variable equal to one if the firm belongs to the high tech industries
defined by Cliff and Denis (2004).
The natural logarithm of one plus the number of patents filed by the firm in
a given year.
The natural logarithm of one plus the average number of citations received
by each patent filed by the firm in a given year.
Table 1: Summary Statistics
This table reports the summary statistics of the variables used in the main analyses. Detailed variable definitions are
provided in the Appendix.
Illiq
STNP
ST1
ST2
ST3
ST4
ST5
Size
Size Squared
Leverage
MB
AbEarn
ATM
ATV
R&D
Miss R&D
Rated
Reg Dummy
Term Structure
N
Mean
Median
71,626
71,626
71,626
71,626
71,626
71,626
71,626
71,626
71,626
71,626
71,626
71,626
71,626
71,626
71,626
71,626
71,626
71,626
71,626
0.6856
0.1543
0.2930
0.4177
0.5213
0.6078
0.6903
5.4172
34.2486
0.2794
1.7672
-0.0322
10.4772
0.0211
0.0338
0.4565
0.2991
0.0486
1.5199
0.0874
0.0000
0.1459
0.3031
0.4699
0.6267
0.7821
5.3527
28.6514
0.2574
1.3519
0.0054
6.8857
0.0165
0.0000
0.0000
0.0000
0.0000
1.5111
25th
Percentile
0.0052
0.0000
0.0377
0.1074
0.2007
0.3075
0.4310
3.7944
14.3975
0.1195
1.0735
-0.0377
3.3263
0.0100
0.0000
0.0000
0.0000
0.0000
0.6735
75th
Percentile
0.9197
0.1564
0.4475
0.7376
0.9209
0.9912
1.0000
7.0002
49.0023
0.3951
1.9334
0.0299
13.5568
0.0274
0.0304
1.0000
1.0000
0.0000
2.4290
STD
1.1108
0.2880
0.3313
0.3551
0.3552
0.3419
0.3176
2.2141
25.2183
0.2032
1.2790
0.3571
10.6644
0.0157
0.0759
0.4981
0.4579
0.2150
0.9957
Table 2: Correlation between Short-Term Debt and Stock Liquidity
Panel A reports the correlation between the primary independent variable, Illiq, and different short-term debt measures.
Panel B reports the mean (median) of different short-term debt measures based on stock liquidity quartiles. Detailed
variable definitions are provided in the Appendix. *, **, and *** indicate significance at the 10%, 5%, and 1% levels,
respectively.
Panel A: Correlation
Illiq
Illiq
1.0000
STNP
0.1982***
ST1
0.2612***
ST2
0.3064***
ST3
0.3112***
ST4
0.2952***
ST5
0.2636***
STNP
ST1
ST2
ST3
ST4
ST5
1.0000
0.7321***
0.5697***
0.4708***
0.4012***
0.3378***
1.0000
0.8460***
0.7282***
0.6360***
0.5443***
1.0000
0.8822***
0.7830***
0.6778***
1.0000
0.9000***
0.7872***
1.0000
0.8840***
1.0000
Panel B: Short-Term Debt by Illiquidity Quartiles
STNP
ST1
st
1 Illiq Quartile
Mean
0.0946
0.1780
(Median)
(0.0000)
(0.0795)
2nd Illiq Quartile
Mean
0.1134
0.2411
(Median)
(0.0000)
(0.1027)
3rd Illiq Quartile
Mean
0.1648
0.3250
(Median)
(0.0000)
(0.1753)
4th Illiq Quartile
Mean
0.2444
0.4278
(Median)
(0.0000)
(0.3162)
ST2
ST3
ST4
ST5
0.2578
(0.1654)
0.3455
(0.2664)
0.4400
(0.3813)
0.5481
(0.5151)
0.3535
(0.2192)
0.4580
(0.3658)
0.5522
(0.5203)
0.6462
(0.7029)
0.4689
(0.3787)
0.5837
(0.5828)
0.6691
(0.7441)
0.7416
(0.8713)
0.5906
(0.6029)
0.6979
(0.7937)
0.7698
(0.9044)
0.8252
(0.9724)
Table 3: Baseline Estimation
This table reports the estimates of OLS regressions of the main short-term debt measure (ST3) on stock liquidity and
the control variables. Different fixed effects are included in Model 1 – Model 5. Industry fixed effects are based on
two-digit SIC codes. Standard errors are clustered at the firm level. T-statistics are displayed in the parentheses.
Detailed variable definitions are provided in the Appendix. *, **, and *** indicate significance at the 10%, 5%, and
1% levels, respectively.
Illiq
Size
Size Squared
Leverage
MB
AbEarn
ATM
ATV
R&D
Miss R&D
Rated
Reg Dummy
Term Structure
Constant
Year FE
Industry FE
Firm FE
No. of Obs.
Adj. R-Squared
(1)
ST3
0.0307***
(13.39)
-0.0793***
(-17.83)
0.0043***
(11.73)
-0.3722***
(-35.05)
0.0077***
(4.38)
-0.0494***
(-15.31)
-0.0034***
(-15.13)
1.0449***
(7.27)
0.2520***
(9.03)
0.0138***
(2.97)
-0.1552***
(-27.29)
0.0057
(0.59)
0.0028**
(2.30)
0.9133***
(56.91)
No
No
No
71,626
0.342
(2)
ST3
0.0262***
(11.37)
-0.1020***
(-22.19)
0.0053***
(14.17)
-0.3867***
(-36.34)
0.0093***
(5.19)
-0.0442***
(-13.79)
-0.0033***
(-14.67)
-0.1814
(-1.17)
0.2243***
(8.02)
0.0134***
(2.88)
-0.1485***
(-26.03)
0.0108
(1.10)
0.0003
(0.06)
0.9467***
(47.78)
Yes
No
No
71,626
0.354
(3)
ST3
0.0293***
(12.91)
-0.0815***
(-18.34)
0.0045***
(12.33)
-0.3581***
(-33.37)
0.0075***
(4.30)
-0.0492***
(-15.31)
-0.0027***
(-10.75)
1.0043***
(6.98)
0.2281***
(7.84)
0.0144***
(2.73)
-0.1537***
(-27.33)
0.0341*
(1.84)
0.0030**
(2.50)
0.9278***
(21.77)
No
Yes
No
71,626
0.350
(4)
ST3
0.0248***
(10.86)
-0.1035***
(-22.51)
0.0055***
(14.64)
-0.3716***
(-34.47)
0.0092***
(5.19)
-0.0440***
(-13.81)
-0.0026***
(-10.63)
-0.1891
(-1.22)
0.2003***
(6.90)
0.0144***
(2.74)
-0.1478***
(-26.18)
0.0410**
(2.14)
0.0005
(0.11)
0.9584***
(21.26)
Yes
Yes
No
71,626
0.361
(5)
ST3
0.0244***
(9.55)
-0.0679***
(-8.02)
0.0011
(1.57)
-0.3414***
(-22.77)
0.0055***
(2.71)
-0.0314***
(-9.21)
-0.0009***
(-2.84)
-1.0263***
(-6.02)
0.1046**
(2.09)
-0.0127
(-1.40)
-0.1230***
(-16.03)
-0.0872
(-0.82)
0.0026
(0.50)
0.8800***
(28.16)
Yes
No
Yes
71,626
0.549
Table 4: Alternative Measures of Short-Term Debt
This table reports the estimates of OLS regressions of alternative short-term debt measures on stock liquidity and the
control variables. Different fixed effects are included in Model 1 – Model 5. Industry fixed effects are based on twodigit SIC codes. Standard errors are clustered at the firm level. T-statistics are displayed in the parentheses. Detailed
variable definitions are provided in the Appendix. *, **, and *** indicate significance at the 10%, 5%, and 1% levels,
respectively.
Illiq
Size
Size Squared
Leverage
MB
AbEarn
ATM
ATV
R&D
Miss R&D
Rated
Reg Dummy
Term Structure
Constant
Year FE
Industry FE
No. of Obs.
Adj. R-Squared
(1)
STNP
0.0172***
(6.10)
-0.0799***
(-15.36)
0.0057***
(15.00)
-0.1120***
(-10.30)
0.0085***
(4.16)
-0.0373***
(-10.81)
-0.0014***
(-4.95)
-0.4164**
(-2.20)
-0.0612*
(-1.76)
-0.0053
(-0.95)
-0.0372***
(-8.10)
0.0650***
(5.21)
-0.0003
(-0.07)
0.4987***
(10.73)
Yes
Yes
71,626
0.119
(2)
ST1
0.0223***
(8.34)
-0.1044***
(-21.15)
0.0063***
(17.23)
-0.3344***
(-30.09)
0.0129***
(6.60)
-0.0682***
(-18.15)
-0.0020***
(-7.84)
0.5476***
(3.09)
0.2536***
(7.55)
-0.0013
(-0.23)
-0.0566***
(-11.60)
0.0791***
(6.21)
-0.0043
(-0.89)
0.6958***
(14.50)
Yes
Yes
71,626
0.276
(3)
ST2
0.0274***
(11.20)
-0.1114***
(-23.31)
0.0062***
(16.71)
-0.3875***
(-35.53)
0.0117***
(6.26)
-0.0602***
(-17.16)
-0.0023***
(-9.48)
0.2891*
(1.76)
0.2505***
(8.13)
0.0088
(1.63)
-0.1046***
(-19.88)
0.0583***
(3.72)
-0.0037
(-0.74)
0.8761***
(19.15)
Yes
Yes
71,626
0.344
(4)
ST4
0.0195***
(9.23)
-0.0872***
(-20.13)
0.0044***
(12.24)
-0.3168***
(-30.14)
0.0059***
(3.49)
-0.0345***
(-12.03)
-0.0028***
(-11.31)
-0.2622*
(-1.82)
0.1359***
(4.97)
0.0169***
(3.37)
-0.1718***
(-29.03)
0.0236
(1.09)
-0.0019
(-0.42)
0.9928***
(22.52)
Yes
Yes
71,626
0.351
(5)
ST5
0.0141***
(7.16)
-0.0642***
(-15.59)
0.0031***
(8.93)
-0.2303***
(-22.46)
0.0050***
(3.15)
-0.0273***
(-10.87)
-0.0030***
(-11.83)
-0.1045
(-0.77)
0.0569**
(2.19)
0.0171***
(3.49)
-0.1835***
(-29.53)
0.0008
(0.03)
0.0019
(0.46)
0.9527***
(21.34)
Yes
Yes
71,626
0.329
Table 5: Alternative Liquidity Measures
This table reports the summary statistics of alternative stock liquidity measures (Panel A) and the correlation between
the main liquidity measure used in this study, Illiq, and the alternative liquidity measures (Panel B). Detailed variable
definitions are provided in the Appendix. *, **, and *** indicate significance at the 10%, 5%, and 1% levels,
respectively.
Panel A: Summary Statistics
N
Lag Illiq
FHT
Lag Gibbs
Turnover
68,115
71,611
46,173
71,626
Mean
Median
0.6420
0.0289
0.0130
0.0054
0.0890
0.0102
0.0072
0.0034
Panel B: Correlation among Liquidity Measures
Illiq
Lag Illiq
Illiq
1.0000
Lag Illiq
0.8819***
1.0000
***
FHT
0.7695
0.6639***
***
Lag Gibbs
0.8142
0.8661***
Turnover
-0.2904***
-0.2297***
25th
Percentile
0.0057
0.0024
0.0037
0.0016
75th
Percentile
0.8492
0.0300
0.0169
0.0070
STD
1.0434
0.0543
0.0143
0.0058
FHT
Lag Gibbs
Turnover
1.0000
0.7507***
-0.1884***
1.0000
-0.1457***
1.0000
Table 6: Alternative Measures of Liquidity
This table reports the estimates of OLS regressions of the main short-term debt measure (ST3) on alternative stock
liquidity measures and the control variables. Year and Industry fixed effects are included in all regressions. Industry
fixed effects are based on two-digit SIC codes. Standard errors are clustered at the firm level. T-statistics are displayed
in the parentheses. Detailed variable definitions are provided in the Appendix. *, **, and *** indicate significance at
the 10%, 5%, and 1% levels, respectively.
Lag Illiq
(1)
ST3
0.0146***
(6.33)
FHT
(2)
ST3
(3)
ST3
0.2543***
(5.99)
Lag Gibbs
1.3120***
(6.47)
Turnover
Size
Size Squared
Leverage
MB
AbEarn
ATM
ATV
R&D
Miss R&D
Rated
Reg Dummy
Term Structure
Constant
Year FE
Industry FE
No. of Obs.
Adj. R-Squared
(4)
ST3
-0.1175***
(-25.39)
0.0064***
(16.95)
-0.3531***
(-31.88)
0.0058***
(3.17)
-0.0462***
(-14.17)
-0.0027***
(-10.31)
-0.2536
(-1.54)
0.2055***
(6.74)
0.0160***
(2.95)
-0.1486***
(-25.72)
0.0446**
(2.27)
0.0010
(0.20)
1.0043***
(21.01)
Yes
Yes
68,115
0.353
-0.1154***
(-24.47)
0.0061***
(15.61)
-0.3598***
(-33.63)
0.0050***
(2.95)
-0.0443***
(-13.83)
-0.0025***
(-10.18)
-0.0735
(-0.48)
0.1794***
(6.21)
0.0146***
(2.78)
-0.1494***
(-26.33)
0.0411**
(2.12)
0.0007
(0.15)
1.0079***
(22.28)
Yes
Yes
71,611
0.360
-0.1339***
(-20.62)
0.0084***
(16.51)
-0.3567***
(-26.31)
0.0070***
(3.04)
-0.0497***
(-12.00)
-0.0030***
(-9.70)
-0.5978***
(-2.74)
0.1873***
(5.07)
0.0138**
(2.20)
-0.1511***
(-23.55)
0.0313
(1.61)
0.0000
(0.00)
1.0813***
(17.39)
Yes
Yes
46,173
0.348
-1.5933***
(-4.34)
-0.1249***
(-30.76)
0.0068***
(19.49)
-0.3474***
(-33.04)
0.0041**
(2.42)
-0.0457***
(-14.15)
-0.0026***
(-10.45)
0.2089
(1.30)
0.1749***
(6.07)
0.0145***
(2.76)
-0.1489***
(-26.26)
0.0388**
(2.01)
0.0006
(0.12)
1.0419***
(23.46)
Yes
Yes
71,626
0.359
Table 7: Addressing Endogeneity
This table reports the estimates of 2SLS regressions. Leverage is treated as the endogenous variable in Model (1) (first
stage) and Model (2) (second stage), while the main stock liquidity measure, Illiq, is treated as the endogenous variable
in Model (3) (first stage) and Model (4) (second stage). PPE and ROA are used as instrumental variables to predict
leverage in Model (1). The lagged value of Illiq and the industry average Illiq (excluding the firm) are used as
instrumental variables to predict Illiq in Model (3). Year and Industry fixed effects are included in all regressions.
Industry fixed effects are based on two-digit SIC codes. Standard errors are clustered at the firm level. T-statistics are
displayed in the parentheses. Detailed variable definitions are provided in the Appendix. *, **, and *** indicate
significance at the 10%, 5%, and 1% levels, respectively.
Illiq
Size
Size Squared
(1)
Leverage
First Stage
0.0513***
(31.95)
0.0422***
(12.82)
-0.0044***
(-15.73)
Leverage
MB
AbEarn
ATM
ATV
R&D
Miss R&D
Rated
Reg Dummy
Term Structure
IVs
PPE
ROA
0.0202***
(14.46)
-0.0115***
(-4.77)
-0.0022***
(-11.02)
-5.6459***
(-51.75)
-0.1303***
(-5.44)
0.0226***
(5.96)
0.1166***
(24.97)
-0.0197
(-1.40)
0.0041
(1.57)
(2)
ST3
Second Stage
0.0325***
(10.36)
-0.1004***
(-21.51)
0.0050***
(12.90)
-0.5264***
(-12.01)
0.0121***
(6.12)
-0.0493***
(-13.66)
-0.0026***
(-10.54)
-1.0397***
(-3.68)
0.2191***
(7.41)
0.0191***
(3.50)
-0.1289***
(-16.71)
0.0390**
(1.96)
0.0013
(0.27)
-0.4178***
(-52.20)
0.0267***
(47.12)
0.3868***
(24.59)
-0.1202***
(-35.89)
-0.0763***
(-7.99)
0.0002
(0.77)
2.3198***
(6.99)
-0.1114**
(-2.53)
0.0015
(0.24)
-0.0203***
(-3.93)
-0.0390**
(-2.12)
-0.0155
(-1.34)
(4)
ST3
Second Stage
0.0214***
(6.45)
-0.1083***
(-20.31)
0.0058***
(14.19)
-0.3612***
(-31.90)
0.0085***
(4.32)
-0.0444***
(-13.76)
-0.0027***
(-10.30)
-0.2935*
(-1.78)
0.2078***
(6.80)
0.0160***
(2.97)
-0.1485***
(-25.72)
0.0454**
(2.31)
0.0016
(0.31)
0.9970***
(21.24)
Yes
Yes
71,625
0.356
0.6909***
(122.85)
0.0238***
(6.14)
1.6602***
(27.27)
Yes
Yes
68,043
0.797
0.9659***
(19.73)
Yes
Yes
68,043
0.355
0.1880***
(17.14)
-0.2333***
(-25.59)
Lag Illiq
Ind Illiq
Constant
Year FE
Industry FE
No. of Obs.
Adj. R-Squared
(3)
Illiq
First Stage
0.1575***
(5.55)
Yes
Yes
71,625
0.335
Table 8: Difference in Difference
This table reports the estimates of OLS regressions in a difference-in-difference approach. The changes in
variables are measured from the fiscal year before decimalization to the year after decimalization. Panel A
reports the result using the change in the main dependent variable (ST3) as the dependent variable. Panel
B reports the results using changes in alternative short-term debt measures as the dependent variables.
Industry fixed effects are based on two-digit SIC codes. Standard errors are clustered at the firm level. Tstatistics are displayed in the parentheses. Detailed variable definitions are provided in the Appendix. *, **,
and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Difference in Difference - ST3
(1)
(2)
(3)
Chg. ST3
Chg. ST3
Chg. ST3
Treat
-0.0585***
-0.0524**
-0.0562**
(-2.80)
(-2.49)
(-2.39)
Chg. Size
-0.1258**
-0.1186**
(-2.37)
(-2.13)
Chg. Size Squared
0.0053
0.0047
(1.28)
(1.05)
Chg. Leverage
-0.1074
-0.1190
(-1.33)
(-1.33)
Chg. MB
-0.0119
-0.0107
(-1.44)
(-1.25)
Chg. AbEarn
-0.0002
-0.0001
(-0.91)
(-0.46)
Chg. ATM
-0.0000
0.0001
(-0.03)
(0.09)
Chg. ATV
-0.2833
0.0094
(-0.41)
(0.01)
Chg. R&D
0.1391
0.1364
(0.49)
(0.48)
Chg. Miss R&D
0.0490
0.0141
(0.64)
(0.21)
Chg. Rated
-0.2763***
-0.2837***
(-3.51)
(-3.53)
Chg. Reg Dummy
0.0000
0.0000
(.)
(.)
Chg. Term Structure
-0.0230
-0.0209
(-1.19)
(-0.87)
Constant
0.0119
0.0729
0.3517*
(0.79)
(1.37)
(1.68)
Industry FE
No
No
Yes
No. of Obs.
960
960
960
Adj. R-Squared
0.007
0.035
0.036
Panel B: Difference in Difference - Alternative Short-Term Debt Measures
(1)
(2)
(3)
(4)
Chg. STNP
Chg. ST1
Chg. ST2
Chg. ST4
Treat
-0.0676***
-0.0756***
-0.1040***
-0.0277
(-3.93)
(-3.74)
(-4.63)
(-1.25)
Chg. Size
-0.0835
-0.1820**
-0.1862***
-0.0357
(-1.43)
(-2.05)
(-2.91)
(-0.79)
Chg. Size Squared
0.0052
0.0074
0.0083*
-0.0018
(1.44)
(1.24)
(1.86)
(-0.39)
Chg. Leverage
0.1041
0.0208
-0.1210
-0.0267
(1.46)
(0.22)
(-1.42)
(-0.31)
Chg. MB
0.0097
0.0043
-0.0019
-0.0122
(1.34)
(0.49)
(-0.24)
(-1.37)
Chg. AbEarn
-0.0000
0.0003
0.0001
-0.0002
(-0.26)
(1.14)
(0.37)
(-0.81)
Chg. ATM
0.0016
0.0004
0.0004
-0.0002
(1.32)
(0.55)
(0.65)
(-0.34)
Chg. ATV
-1.1055*
-2.2879**
-1.7400
0.2620
(-1.67)
(-2.52)
(-1.64)
(0.38)
Chg. R&D
-0.4812*
0.1921
-0.0907
0.1982
(-1.85)
(0.51)
(-0.35)
(0.71)
Chg. Miss R&D
0.0285
-0.0287
-0.0054
0.0439
(0.54)
(-0.41)
(-0.07)
(0.60)
Chg. Rated
-0.1016
-0.1861***
-0.3084***
-0.2351***
(-1.53)
(-3.05)
(-4.16)
(-2.66)
Chg. Reg Dummy
0.0000
0.0000
0.0000
0.0000
(.)
(.)
(.)
(.)
Chg. Term Structure
-0.0130
-0.0357*
-0.0505**
0.0011
(-0.65)
(-1.75)
(-2.20)
(0.05)
Constant
0.0525
0.1715**
0.1962
0.2578
(0.88)
(2.11)
(1.39)
(1.14)
Industry FE
Yes
Yes
Yes
Yes
No. of Obs.
960
960
960
960
Adj. R-Squared
0.046
0.095
0.077
0.024
(5)
Chg. ST5
-0.0349*
(-1.70)
-0.0308
(-0.78)
-0.0019
(-0.45)
-0.0550
(-0.70)
-0.0200***
(-2.81)
-0.0004*
(-1.93)
0.0000
(0.05)
0.0297
(0.04)
0.1469
(0.59)
0.0255
(0.46)
-0.2430***
(-3.05)
0.0000
(.)
0.0062
(0.31)
0.1519
(0.57)
Yes
960
0.062
Table 9: Firms with Agency Cost
This table examines the effect of stock liquidity conditional on the level of agency cost the firm is subject to. The
focus in this table is the interaction terms between the illiquidity measure, Illiq, and various agency costs measures.
Year and Industry fixed effects are included in all regressions. Industry fixed effects are based on two-digit SIC codes.
Standard errors are clustered at the firm level. T-statistics are displayed in the parentheses. Detailed variable
definitions are provided in the Appendix. *, **, and *** indicate significance at the 10%, 5%, and 1% levels,
respectively.
(1)
(2)
(3)
(4)
ST3
ST3
ST3
ST3
Illiq
0.0197***
0.0226***
0.0264***
0.0177***
(6.29)
(9.78)
(11.50)
(5.95)
Seg Number
0.0004
(0.25)
Illiq*Seg Number
0.0034**
(2.42)
Acquisition
-0.2401***
(-10.39)
Illiq*Acquisition
0.0702***
(3.26)
Dividend
-0.0062
(-1.12)
Illiq*Dividend
-0.0226***
(-4.17)
HHI
-0.0222
(-1.12)
Illiq*HHI
0.0394***
(3.87)
Size
-0.1020***
-0.1013***
-0.1018***
-0.1040***
(-21.96)
(-21.74)
(-22.08)
(-22.54)
Size Squared
0.0053***
0.0053***
0.0054***
0.0055***
(13.97)
(14.16)
(14.25)
(14.70)
Leverage
-0.3696***
-0.3624***
-0.3786***
-0.3719***
(-33.93)
(-33.19)
(-34.64)
(-34.50)
MB
0.0094***
0.0079***
0.0093***
0.0090***
(5.24)
(4.53)
(5.26)
(5.06)
AbEarn
-0.0449***
-0.0435***
-0.0438***
-0.0440***
(-13.86)
(-13.47)
(-13.73)
(-13.80)
ATM
-0.0027***
-0.0028***
-0.0025***
-0.0026***
(-10.62)
(-11.16)
(-10.23)
(-10.65)
ATV
-0.1279
-0.1524
-0.2865*
-0.1788
(-0.82)
(-0.97)
(-1.87)
(-1.15)
R&D
0.2090***
0.1991***
0.1953***
0.1999***
(7.18)
(6.81)
(6.72)
(6.87)
Miss R&D
0.0144***
0.0152***
0.0146***
0.0141***
(2.72)
(2.87)
(2.78)
(2.68)
Rated
-0.1464***
-0.1507***
-0.1485***
-0.1474***
(-25.65)
(-26.08)
(-26.24)
(-26.09)
Reg Dummy
0.0383*
0.0381*
0.0418**
0.0380*
(1.91)
(1.93)
(2.21)
(1.96)
Term Structure
0.0007
0.0017
0.0005
0.0005
(0.14)
(0.34)
(0.10)
(0.09)
Constant
0.9496***
0.9573***
0.9634***
0.9627***
(20.95)
(20.48)
(21.22)
(21.28)
Year FE
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
No. of Obs.
69,238
68,290
71,551
71,626
Adj. R-Squared
0.360
0.360
0.362
0.362
Table 10: Innovative Firms
This table examines the effect of stock liquidity conditional on the level of R&D intensity. The focus in this table is
the interaction terms between the illiquidity measure, Illiq, and various R&D intensity measures. Year and Industry
fixed effects are included in all regressions. Industry fixed effects are based on two-digit SIC codes. Standard errors
are clustered at the firm level. T-statistics are displayed in the parentheses. Detailed variable definitions are provided
in the Appendix. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
(1)
(2)
(3)
(4)
ST3
ST3
ST3
ST3
Illiq
0.0281***
0.0270***
0.0257***
0.0267***
(11.88)
(11.14)
(10.65)
(11.02)
Illiq*R&D
-0.1108***
(-6.23)
HiTech
0.0175**
(2.21)
Illiq*HiTech
-0.0127***
(-3.19)
Patent
0.0139***
(5.09)
Illiq*Patent
-0.0110**
(-2.50)
Cite/Pat
0.0087***
(3.80)
Illiq*Cite/Pat
-0.0067***
(-3.28)
R&D
0.2933***
0.1888***
0.1840***
0.1868***
(8.46)
(6.30)
(5.91)
(5.99)
Size
-0.1056***
-0.1050***
-0.1076***
-0.1091***
(-23.07)
(-22.88)
(-22.38)
(-22.60)
Size Squared
0.0057***
0.0056***
0.0061***
0.0064***
(15.21)
(14.97)
(15.30)
(16.05)
Leverage
-0.3715***
-0.3715***
-0.3561***
-0.3581***
(-34.42)
(-34.39)
(-30.57)
(-30.77)
MB
0.0086***
0.0088***
0.0092***
0.0094***
(4.82)
(4.92)
(4.97)
(5.06)
AbEarn
-0.0436***
-0.0436***
-0.0456***
-0.0458***
(-13.71)
(-13.69)
(-13.00)
(-13.04)
ATM
-0.0026***
-0.0026***
-0.0031***
-0.0031***
(-10.57)
(-10.56)
(-11.57)
(-11.57)
ATV
-0.1691
-0.2010
-0.1208
-0.1209
(-1.09)
(-1.29)
(-0.73)
(-0.73)
Miss R&D
0.0142***
0.0149***
0.0199***
0.0180***
(2.70)
(2.82)
(3.57)
(3.23)
Rated
-0.1465***
-0.1466***
-0.1510***
-0.1508***
(-25.96)
(-25.92)
(-25.36)
(-25.26)
Reg Dummy
0.0414**
0.0418**
0.0317*
0.0268
(2.16)
(2.17)
(1.68)
(1.43)
Term Structure
0.0002
0.0004
-0.0007
-0.0005
(0.03)
(0.09)
(-0.13)
(-0.10)
Constant
0.9610***
0.9619***
0.9790***
0.9802***
(21.42)
(21.35)
(19.96)
(19.79)
Year FE
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
No. of Obs.
71,626
71,626
59,298
59,298
Adj. R-Squared
0.362
0.362
0.364
0.364
Figure 1. Short-Term Debt and Illiquidity Quartiles
This graph plots the mean (in blue) and median (in red) of the proportion of debt maturing in 3 years by
illiquidity quartiles.