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Does Expected Bond Liquidity Affect Financial Contracts?*
December 1, 2016
Yaxuan Qi
Department of Economics and Finance
College of Business
City University of Hong Kong
Hong Kong SAR
Yuan Wang
Department of Finance
John Molson School of Business
Concordia University
Canada
* We thank Efraim Benmelech, Gennaro Bernile, Matthew Billett, Zhi Da, Zhiguo He, Jean Helwege, Kose John, John
Wald, Junbo Wang, Xueping Wu, the participants at the 2015 China International Conference in Finance and the 2015
Midwest Finance Association meetings, and the seminar participants at the City University of Hong Kong and the
University of Chinese Academy of Sciences for their helpful suggestions.
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Does Expected Bond Liquidity Affect Financial Contracts?
Abstract
This paper shows that bond market liquidity plays an important role in determining corporate debt
contracts. We find that bonds with better expected market liquidity are issued with fewer restrictive
covenants, longer maturities, and lower offering yield spreads. These results are robust to a quasi-
natural experiment using the implementation of TRACE as an exogenous shock to bond market
liquidity, and an instrumental variable regression controlling for the endogeneity of bond liquidity.
Further investigation shows that the effect of liquidity is more pronounced in firms subject to more
credit supply frictions, firms with poorer credit ratings and more rollover risk, and firms relying
more on debt financing.
Key words: bond liquidity, debt covenants, debt maturity, cost of debt
JEL code: G32, G14, G18
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1. Introduction
The liquidity of bond markets has captured a great deal of attention from researchers,
practitioners and policy makers. While the literature mainly focuses on the effect of liquidity on
bond pricing in secondary markets (e.g., Longstaff, Mithal, and Neis (2005), Chen, Lesmond and
Wei (2007), Bao, Pan and Wang (2011), and Lin, Wang, and Wu (2011)), this paper examines the
effect of bond liquidity on newly issued bonds in primary markets. Specifically, we examine
whether and how the expected liquidity of newly issued bonds affect their contractual terms;
namely, the use of restrictive covenants, debt maturity, and offering yield spread. To our
knowledge, this is the first comprehensive study on the relation between bond market liquidity and
financial contracts.
Why would expected bond liquidity affect bond contracts? This can be motivated for at least
three reasons. First, liquidity describes the degree to which a security can be bought or sold in the
market without causing a significant movement in the price. High liquidity indicates good
tradability allowing investors to sell unfavorable securities at relatively low transaction costs. This
liquid resale option serves as an ad hoc protection for bondholders and makes holding corporate
bonds more attractive to a broad group of potential investors. Thus, better expected bond liquidity
enhances credit capital supply and consequently endows bond issuers with stronger bargaining
power. All else being equal, firms with better expected bond liquidity can issue bonds with more
favorable contractual terms, such as fewer covenants, longer maturities, and lower costs of debt.
This notion is consistent with the growing empirical evidence that the credit supply is important
in determining capital structure and debt contracts (Faulkender and Petersen (2006), Sufi (2009),
Lemmon and Roberts (2010), Murfin (2012), Massa, Yasuda, and Zhang (2013), and Saretto, and
Tookes (2013)).
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Second, theoretical research has shown that bond liquidity affects default risk.1 For instance,
Ericsson and Renault (2006) show that a negative liquidity shock increases firms’ credit risks
because firms with poorer bond liquidity face higher renegotiation costs in financial distress. He
and Xiong (2012) demonstrate that negative liquidity shocks increase default risk because the
deterioration of bond market liquidity raises the transaction cost of debt rollover. Because default
risk is a fundamental determinant of financial contracts, we hypothesize that the expected bond
liquidity of new bonds may shape debt contractual terms by affecting the ex-ante credit risk.
Third, research on stock market liquidity argues that stock liquidity affects investor monitoring
(Coffee (1991), Bhide (1993), and Maug (1998), Admati and Pfleiderer (2009), Edmans (2009),
and Edmans, Fang and Zur (2013)). Although these studies focus on stock market liquidity, the
general principle regarding how liquidity affects investors’ monitoring may apply to bond markets.
Creditors’ monitoring incentives are an important determinant of debt contracts. Therefore, we
conjecture that the expected bond liquidity of new bonds may shape debt contracts by affecting
the monitoring incentive of creditors.
The above three mechanisms indicate that the contracts of newly issued bonds may be
determined by the expectation of those bonds’ liquidity in the secondary markets. However, the
expected liquidity of newly issued bonds is unobservable at the time of issuance. We therefore use
the liquidity of a firm’s existing bonds to construct proxies of the expected liquidity of the firm’s
new bonds. Good proxies for the expected liquidity of a bond should be able to predict the realized
liquidity of the bond after issuance. We test the validity of our expected bond liquidity measures
and show that these measures significantly predict the realized liquidity of the firm’s newly issued
1 We use “default risk” and “credit risk” interchangeably.
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bonds. We then use these measures to study the effect of expected bond liquidity on the bonds’
contractual terms, specifically, the use of covenants, maturities and offering yield spreads.
Our baseline analysis is based on a system of simultaneous equations that control for the
interactions among covenants, maturities, and offering yield spreads. This is because bond
contractual terms affect one another and are jointly determined at issuance. For instance, bonds
with more restrictive covenants and shorter-term maturities are usually associated with lower
offering yield spreads. We follow the prior literature (Johnson (2003), Billett, King, and Mauer
(2007), and Saretto and Tookes (2013)) and estimate a system of simultaneous equations in which
the offering yield spread, debt maturity, and a covenant index are endogenous dependent variables
and the expected bond liquidity is the key explanatory variable. The estimation results show that
firms with better bond liquidity tend to issue bonds with fewer restrictive covenants, longer
maturities, and lower offering yield spreads. The estimated coefficients on bond liquidity are
statistically significant at the 1% level in all three equations. The economic magnitude of the bond
liquidity effect is also substantial. For instance, the deterioration of bond liquidity by one standard
deviation from the sample average decreases the index of covenants by 0.53 (13% of the average),
lengthens debt maturity by 1.03 years (9% of the average), and increases the offering yield spread
by 18 basis points (8% of the average). The results are robust to alternative measures of bond
liquidity.
One potential concern with our baseline model is the endogeneity of bond liquidity because
omitted variables may affect liquidity and debt contracts simultaneously. We conduct three sets of
empirical tests to alleviate the endogeneity concern. First, we use firm fixed-effect models, which
allows us to control for unobservable firm-level factors, and examine the within-firm changes in
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offering yield spreads, maturities, and covenants over the changes of bond liquidity. We obtain
similar results to those from our baseline model.
Second, the literature has shown that the introduction of TRACE increases the liquidity of the
corporate bond market (Bessembinder, Maxwell, and Venkataraman (2006), Edwards, Harris, and
Piwowar (2007), and Goldstein, Hotchkiss, and Sirri (2007)). We, therefore, conduct a quasi-
natural experimental tests using the introduction of TRACE as an exogenous liquidity shock to the
bond market. Specifically, we create a TRACE dummy variable that equals one if the bond’s
expected market liquidity is affected by TRACE implementation and zero otherwise. We classify
a bond as TRACE-affected if the transactions of the firm’s prior bonds were disseminated in
TRACE. Since TRACE was implemented in multiple phases from 2002 to 2006, we use a sample
of bonds issued from 2001 to 2007 to estimate the simultaneous equations model, in which the
bond contractual terms are endogenous dependent variables and the TRACE dummy is the key
explanatory variable. We find that the TRACE dummy indeed decreases the use of covenants,
increases debt maturities, and reduces offering yield spreads.
Furthermore, we conduct a difference-in-differences test by comparing the contractual terms
of bonds issued by the same firm before and after the introduction of TRACE. We classify a firm
into the treatment group if the transactions of the firm’s bonds were disseminated in TRACE,
otherwise into the control group. Our results show that bonds issued after TRACE are associated
with fewer covenants, longer maturities, and lower offering spread than bonds issued before
TRACE. More importantly, the changes in bond terms due to the introduction of TRACE are more
pronounced for firms in the treatment group than for firms in the control group.
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Finally, we use the value-weighted average of the bond age of a firm’s existing bonds as an
instrumental variable to control for the potential endogeneity of bond liquidity, and we confirm
our findings in the baseline model using two-stage least squares regressions.
Having established the causal relation between bond liquidity and debt contracts, we turn to
identifying the potential mechanisms through which bond liquidity might affect debt contracts.
First, we use whether a firm has traded CDS contracts to proxy for credit supply friction because
Saretto and Tookes (2013) show that the existence of CDS facilitates risk hedging and alleviates
credit supply frictions. We find that the effect of bond liquidity on bond contracts is more
pronounced for firms without CDS contracts, that is, firms subject to more credit supply frictions.
This result is consistent with the notion that bond liquidity affects debt contracts by influencing
the credit capital supply. Second, we find that the effect of liquidity on bond contracts is more
pronounced for bonds with non-investment grade ratings, and firms with more short-term debt.
This result supports the idea that liquidity shapes debt contracts by affecting credit and rollover
risks. However, we do not find evidence that the effect of bond liquidity on bond contracts is
related to the degree of creditor monitoring.
We further examine the effect of bond liquidity on various types of bond covenants. Similar to
our baseline model findings, good liquidity reduces the use of anti-takeover covenants, default-
related covenants, and borrowing-related covenants. However, the relation reverses for the use of
stock issuance covenants. This is because firms with poor bond liquidity often have limited access
to debt financing, and may rely more on equity financing. Such firms are thus reluctant to impose
restrictive covenants of stock issuance.
This paper contributes to the existing literature in several ways. First, it contributes to the
research on optimal financial contracts. The design of optimal financial contracts is one of the most
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important topics in corporate finance. The existing literature has shown that debt contracts are
determined by a firm’s fundamentals, such as financial distress risk and the degree of agency
conflicts, and by the contractual environment which includes legal creditor protections and the
monitoring incentive of creditors (e.g., Smith and Warner (1979), Billett et al (2007), Qian and
Strahan (2007)). The liquidity of debt securities has received little attention in this literature. This
paper suggests that the expected liquidity of bonds has a significant impact on the choice of
contractual bond terms.
Second, this study extends the line of research on bond liquidity. While prior studies typically
focus on a single dimension of corporate bond pricing (Chen, Lesmond, and Wei (2007), Bao, Pan
and Wang (2011), Dick-Nielsen, Feldhütter and Lando (2012), Lin, Wang and Wu (2011), and
Helwege, Huang and Wang (2014)), we focus on both the pricing term and the two non-pricing
terms of newly issued bonds: covenants and maturity. This multidimensional empirical framework
paints a more complete picture of how bond liquidity shapes firms’ external debt financing.
Third, this paper is related to the literature on how TRACE implementation affects bond
markets. Early studies show that the introduction of TRACE contributed to market transparency
and reduced transaction costs (Bessembinder, Maxwell, and Venkataraman (2006), Edwards,
Harris, and Piwowar (2007), and Goldstein, Hotchkiss, and Sirri (2007)). Asquith, Covert, and
Pathak (2013) suggest that TRACE implementation reduces both price dispersion and trading
volume in some corporate bonds. In this paper, we show that the TRACE implementation helps
improve bond market liquidity and has a positive impact on firms’ external debt financing.
The rest of the paper is organized as follows. Section 2 summarizes the related literature and
develops testing hypotheses. Section 3 details the data collection and sample construction. Section
4 discusses the empirical results, and Section 5 concludes.
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2. Related Literature
In this section, we first discuss the literature on the determinants of debt contracts. We then
review the literature that provides potential explanations for why expected bond liquidity may
shape debt contracts.
2.1 Literature on the determinants of bond contracts
A bond contract consists of many terms such as the borrowing cost of debt, maturity, collateral,
embedded options, and a set of complex covenants. While we cannot explore every aspect of a
bond contract, we focus on three important dimensions of a bond contract: the use of restrictive
convents, maturities, and offering yield spreads.
First, debt covenants have been long recognized as an effective method mitigating agency
conflicts between shareholders and bondholders. Jensen and Meckling (1976) and Myers (1977)
provide insightful discussions on the agency conflict between shareholders and debtholders. Smith
and Warner (1979) develop the costly contracting hypothesis, suggesting that debt covenants help
mitigate the agency cost of debt by preventing managers from exploiting bondholders. Subsequent
studies further demonstrate how debt covenants can serve as an effective method monitoring firms
and mitigating the agency cost of debt (see, e.g., Berlin and Loeys (1988), and Rajan and Winton
(1995)). Often, the degree of the agency conflict between shareholders and debtholders is more
severe when firms are close to insolvency. Collectively, the use of debt covenants is determined
by the credit risk of issuers and the agency conflict between debt holders and shareholders.
Second, a handful of studies examine the determinants of debt maturity. Barnea, Haugen, and
Senbet (1980, 1985) suggest that shortening maturity of debt is an effective means of resolving the
agency problems of debt associated with informational asymmetry, risk incentives, and
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underinvestment. Barclay and Smith (1995) find that firms with larger information asymmetries
issue more short-term debt. Diamond (1991) suggests that the optimal debt maturity is determined
by the tradeoff between the debt rollover risk and potential gains due to future improved firm
fundamentals. Stohs and Mauer (1996) find that large, less-risky firms with long asset maturity
have longer-term debt. A recent study by Saretto and Tookes (2013) show that firms with traded
CDS contract are able to borrow with longer debt maturity and they argue this is because CDS
contracts facilitate the hedging of credit risk and encourage credit supply. Overall, debt maturity
is affected by a number of factors including credit risk, agency risk, rollover risk, and information
asymmetry related to the fundamental value of firms.
Finally, the literature argues that offering yield spreads are mainly determined by default risk.
A growing body of empirical research suggests that liquidity is also priced in bond pricing
(Longstaff, Mithal, and Neis (2005), Chen, Lesmond and Wei (2007), Bao, Pan, and Wang (2011),
Lin, Wang, and Wu (2011), and Huang and Huang (2012)).2 Moreover, the debt contract terms
play important roles in affecting offering yield spreads. For example, the use of covenants can help
reduce the agency cost of debt and leads to lower offering yield spreads. Short-term bonds are
often issued with lower offering yield spreads than long-term bonds.
Taken together, none of these prior studies address the potential effect of bond market liquidity
on the contractual terms of corporate bonds. In addition, prior works on bond yield spreads mainly
2 Longstaff, Mithal, and Neis (2005) show that the majority of corporate yield spreads are due to default risk, but that
the non-default component of yield spreads is strongly related to bond liquidity. Chen, Lesmond, and Wei (2007)
show that bond liquidity is priced in corporate bonds and that more illiquid bond earn higher yield spreads. Bao, Pan
and Wang (2011) find that both market-level and bond-level illiquidity explain substantial part of yield spreads. Lin,
Wang, and Wu (2011) show that liquidity risk is priced in the cross-section of bond returns.
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focus on the secondary market, and few have explored the offering yield spreads of newly issued
bonds in the primary market.
2.2 How expected bond liquidity might shape debt contracts
2.2.1 Expected bond liquidity and credit supply
A growing body of empirical evidence advocates the importance of the credit capital supply in
determining the capital structure and debt structure. Faulkender and Petersen (2006) show that
firms with access to the public bond market can borrow substantially more debt. Sufi (2009) studies
the introduction of syndicated bank loan ratings, and shows that firms with bank loan ratings are
able to borrow more debt. Lemmon and Roberts (2010) study exogenous shocks to the supply of
credit and show that credit supply substantially affects firms’ debt financing and investment.
Murfin (2012) shows that the negative shocks to the bank credit supply leads to more restrictive
loan covenants. Massa, Yasuda, and Zhang (2013) show that bond capital supply uncertainty has
a negative effect on a firm’s financial leverage and debt maturity. Saretto and Tookes (2013) show
that the existence of the CDS markets allows creditor to hedge risk, hence, firms with traded CDS
can borrow more debt and use more long-term debt. Overall, these studies document that the credit
supply is crucial in determining debt contract terms.
In the same vein, we believe that better expected bond liquidity in the secondary markets may
shape the contracts of newly issued bonds through enhancing the credit capital supply in the
primary debt market. The corporate bond market is notoriously known as an illiquid market. By
definition, liquidity refers to the ability to convert an asset into cash on short notice and at a
minimal discount. Better bond liquidity allows investors to sell bonds at relatively lower
transaction cost. Conventional wisdom suggests that investors prefer assets that are easier to sell
afterward. High expected liquidity indicates a liquid resale option and makes holding corporate
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bonds more attractive. In addition, better expected liquidity can also make holding corporate debt
more attractive to a broad group of potential investors. Thus, better expected liquidity alleviates
frictions on the credit supply and encourage creditors to purchase bonds at primary markets. This
increase capital supply endows debt issuers with stronger bargaining power so that they can issue
bonds with more favorable contractual terms.
As a result, all else being equal, this “easy-to-exit” option due to better expected liquidity
encourages credit supply and reduces the need to use protective contractual terms such as
restrictive covenants and short-term maturity. Thus, the protection to investors from good expected
liquidity somewhat substitutes for the protections from covenants and short maturity. Additionally,
the enhanced credit supply due to better expected liquidity also allows firms to issue bonds with
lower cost of debt.
2.2.2 Expected bond liquidity and credit risk
Theoretical studies provide insightful explanations on how liquidity risk might increase ex-
ante default risk. Ericsson and Renault (2006) show that negative liquidity shocks increase a firm’s
debt renegotiation costs and reduce the expected value of the firm’s bonds in financial distress.
Therefore, the deterioration of bond liquidity increases the ex-ante default risk of the firm. He and
Xiong (2012) suggest the rollover risk channel and show that the deterioration of bond market
liquidity causes firms to suffer losses in rolling over their maturing debts. Shareholders bear the
rollover losses while bondholders of mature debt are fully paid. This increased rollover cost due
to the deterioration of bond liquidity leads the firm to default at a higher fundamental threshold.
Hence, negative liquidity shocks in the secondary market increase firms’ default risk and
exacerbate the agency conflict between shareholders and bondholders.
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Because default risk is one of the most important factors affecting the design of debt contracts,
we believe that the expected bond liquidity of new bonds affects the debt contracts by influencing
the ex-ante default risk of issuers. Consequently, we argue that firms with better expected bond
liquidity are exposed to lower default risk and hence can issued bonds with lower offering spreads,
fewer covenants and longer maturities.
2.2.3 Bond liquidity and creditor monitoring
There are a large number of studies focusing on the relation between stock market liquidity
and shareholders’ monitoring. For instance, Maug (1998) argues that improved stock liquidity
helps the formation of active blockholders and makes corporate governance more effective.
Admati and Pfleiderer (2009) argue that good stock liquidity makes the “threat of exit” of large
shareholders more credible and hence enhances corporate governance. Recent works by Edmans
(2009), Bharath, Jayaraman, and Nagar (2013), and Edmans, Fang and Zur (2013) support the idea
that good liquidity enhances corporate governance. 3
Although these studies focus on stock market liquidity, the general principle about how
liquidity affects investors’ monitoring may apply to the bond market.4 Therefore, we conjecture
that expected bond liquidity may shape the debt contract by affecting the monitoring incentives of
bondholders. If better expected bond liquidity facilitates the formation of dominant bondholders
or makes bondholders’ “threat of exit” more credible, it implies stronger monitoring of creditors.
3 Earlier works argue that improved stock liquidity reduces investor monitoring incentives by facilitating the exit of
current blockholders who are potential monitors (Coffee (1991), and Bhide (1993)). A recent paper by Back, Li, and
Ljungqvist (2015) argues that high liquidity is harmful to corporate governance. 4 Institutional investors in the bond market, such as insurance companies and pension funds, are often categorized as
passive investors that do not actively monitor a firm’s operation. Recent empirical studies, however, advocate the
importance of debtholder’s control rights (Chava and Roberts (2008), Roberts and Sufi (2009), Garleanu and Zwiebel
(2009), Nini, Smith and Sufi (2012), and Feldhütter, Hotchkiss, and Karakaş (2016)).
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In this way, stronger expected bond liquidity should result in lower levels of agency conflict
between bondholders and shareholders and lead to lower yield spreads, fewer covenants, and
longer maturities.
3. Data and Empirical Specification
In this section, we describe the data sources, measures of bond liquidity, and sample
construction. The empirical specification and econometric issues are discussed at the end of this
section. Our bond data are from two major sources. The price and transaction data for corporate
bonds in the secondary market are from the Transaction Reporting and Compliance Engine
(TRACE). Data on the bond characteristics at the time of issuance are collected from the Fixed
Investment Securities Database (FISD). We collect the accounting information, stock return data,
and variables of market-wide economic conditions from COMPUSTAT, CRSP, and DataStream,
respectively. In Table 1, we provide the definitions and data sources of all of the variables.
[INSERT TABLE 1 HERE]
3.1 Bond Liquidity Measures
We use TRACE to construct several proxies of bond liquidity. In January 2001 the SEC had
approved rules that required members of the National Association of Securities Dealers (NASD)5
to report their over-the-counter corporate bond transactions through TRACE. On July 1, 2002,
TRACE began to report bond transactions, requiring that transaction information be disseminated
for investment grade securities with an initial issue size of $1 billion or greater. TRACE was
expanded in stages and was fully implemented in February 2005, covering essentially all publicly
5 In July 2007, the NASD was consolidated in to the Financial Industry Regulatory Authority (FINRA).
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traded bonds.6 There are a number of problematic trades during the early period of the database.
Consequently, we eliminate canceled, corrected, and commission trades from the data following
Dick-Nielsen (2009). Bond transactions under $100,000 are deleted to avoid the effects of retail
investors. We also remove bonds with time to maturity of less than one year because of high pricing
errors.
Using the high-frequency transaction data in TRACE, we construct four liquidity measures:
Amihud Ratio (Amihud), Price Dispersion (PD), Imputed Roundtrip Cost (IRC), and Inter-quartile
Range (IQR). In the Appendix, we describe how to construct these bond liquidity measures. It is
worth noting that a smaller (larger) magnitude of these measures indicates better (poorer) bond
liquidity. Therefore, our measures represent the degree of illiquidity of a bond in the secondary
market.
We first construct daily bond liquidity measures, and then take the median of the daily
liquidities in each month of each bond to build a monthly bond liquidity. This monthly bond-level
liquidity is used to construct quarterly measures by taking the moving average of every three
months’ liquidity. Finally, we construct the firm-level quarterly bond liquidity by calculating the
offering-amount weighted average of bond-level liquidity. We winsorize the sample so that
liquidity above the 99th percentile is set to the 99th percentile and liquidity below the first
percentile is set to the first percentile.
3.2 Sample construction
We obtain newly issued U.S. corporate bonds from 2002 to 2015 from FISD. FISD reports
detailed information about issue- and issuer-specific characteristics such as coupon rate, maturity,
6 We provide the time line of the multiple phases of TRACE implementation in the internet appendix
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issue amount, provisions, and credit ratings. We remove unit deals, Yankee bonds, convertible,
and medium-term notes, and issues without covenant information. This screening procedure
provides a sample of 10,987 bonds.
We merge TRACE and FISD to combine bond transactions and characteristics. We match for
each bond issued by firm i in month t with the aforementioned firm-level bond liquidity, which is
constructed using the liquidity of bonds issued by firm i in month t-1. Given the sparse trading of
bonds, a match may not always exist. The dataset is further merged with Compustat to obtain a
firm’s accounting information, and with CRSP to extract stock information. We remove
observations that have missing bond characteristics and accounting information. Since TRACE is
available starting in July 2002, and we need three months of data to construct the liquidity
measures, our sample starts from October 2002. The final dataset contains a sample of 2,631 bonds
issued by 601 firms during the period of October 2002 to December 2015.
[INSERT TABLE 3 HERE]
Table 3 provides the descriptive statistics for our main variables. In Panel A, we compare the
bond liquidity measures of our final sample with those of the entire TRACE sample. As a smaller
magnitude of these measures indicates better liquidity, our sample contains firms with better bond
liquidity. For example, the average Amihud of our final sample (0.008), is lower than the average
Amihud for the entire TRACE sample (0.011). Our sample also has lower standard deviations of
bond liquidity measures. For instance, the standard deviation in price dispersion (PD) for our final
sample is 0.129, while that in the TRACE sample is 0.338.
In Panel B of Table 2, we compare selected bond-level variables between our sample and the
entire FISD sample. Our sample includes bonds with slightly lower offering yield spreads (2.33%
vs. 2.72%), longer maturities (11.96 year vs. 10.46 year), more covenants (4.01 vs. 2.89); larger
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size deals (662.05 vs. 517.95); and slightly poorer bond ratings (13.82 vs. 12.64). In addition, our
sample has lower variation in bond characteristics than the FISD sample, as evidenced by the
smaller standard deviations of all of the selected variables.
Panel C of Table 2 provides the summary statistics for the firm-level and market-wide variables.
We compare our final sample with the entire CRSP/Compustat sample. As shown in Panel C, our
sample includes firms with large size, low Tobin’s Q, good profitability, high leverage, and more
tangibility. Our sample also includes more firms with credit rating. Overall, we conclude that our
sample comprises relatively large and mature firms.
In Panel D of Table 2, we show the correlations between bond liquidity and the three terms of
debt contracts. Our main measure of bond liquidity is the Amihud Ratio, and the three bond
contractual terms of interest are covenant index, bond maturity, and offering yield spread. We
take the natural log transformations of these variables because they are heavily skewed. As shown
in Panel D, Log(Amihud) is positively related to covenants with a coefficient of 0.022 but is not
statistically significant, and it is positively related to offering spread with a coefficient of 0.262 at
a 1% significance level. The correlation between Log(Amihud) and debt maturity is -0.032 at a 10%
significance level. These preliminary results suggest that poor bond liquidity is associated with an
increase in the use of covenants and offering yield spreads, but reduced maturities.
The three bond contractual terms are highly correlated with each other. Specifically, the
covenant index is negatively related to offering spreads with a coefficient of -0.284, which is
consistent with the notion that the use of covenants helps mitigate agency costs and reduce the cost
of debt. Bond maturity is positively related to the covenant index and offering spreads. This is
consistent with the argument that long-term debt is more likely to be subject to agency risk and
interest risk, and is hence associated with more restrictive covenants and a higher cost of debt.
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Overall, these results demonstrate the importance of using simultaneous equations to control for
the interactions among debt contractual terms.
3.3 Empirical specification
We study the effect of bond liquidity on both pricing and non-pricing contractual terms of
newly issued bonds. In a bond contract, terms such as the use of covenants, debt maturity, and
offering yield spread are jointly determined. Following the prior literature (Johnson (2001), Billett,
King and Mauer (2007), and Saretto and Tookes (2013)), we employ a system of simultaneous
equations with the covenant index, maturity, and offering spread as endogenous dependent
variables. The system is estimated using GMM. As a robustness check, we also conduct reduced-
form tests by estimating the equations of offering spread, covenant index, and maturity separately
using OLS. The estimates of the simultaneous equations are referred to as “Simultaneous
Equations” and the estimates of the reduced-form equations are referred to as “Individual
Equations” in all of the tables. Our baseline model is stated as follows:
, , 1, 1, 1 , 1 , , 1 , , 1 , ,
, , 2, 2, 2 , 2 , , 2
( ) ( )+ ( ) ( )
( ) ( ) ( ) (
i j t i t i t i j t i j t i j t
i j t i t i t i j t
Log Covenant Log Liqudity Log OfferingSpread Log Maturity Controls
Log Maturity Log Liqudity Log OfferingSpread Log Coven
, , 2 , ,
, , 3, 3, 3 , 3 , , 3 , , 3 , ,
)
( ) ( ) ( )+ ( )
i j t i j t
i j t i t i t i j t i j t i j t
ant Controls
Log OfferingSpread Log Liqudity Log Covenant Log Maturity Controls
(1)
where i, j, and t represent, respectively, the firm, bond, and time; i are a set of dummy variables
that represent industry (in simultaneous equations) or firm (in individual equations) fixed effects,
and t are dummy variables for the year fixed effects. Controls include bond-level, firm-level, and
market-wide variables, which are discussed later in this section. Log(Liquidity) is the key
independent variable. Our main measure of liquidity is the Amihud Ratio, and we use another three
liquidity measures for robustness checks.
17
Note that in the regressions of the individual equations, the three dependent variables are not
considered to be simultaneously determined. Thus, covenant, maturity, and offering spread are not
included in the right-hand side of equation (1) in the regressions of the individual equations model.
3.3.1 Dependent variables
The key dependent variables are defined as follows. Offering spread is the difference between
the bond offering yield and the yield of the maturity-matched Treasury bonds. Based on the
maturity of each newly issued bond, we obtain the maturity-matched risk-free yield using the
constant maturity benchmark yields, which are from DataStream and which are available for the
following yearly maturities: 1/12, 1/4, 1/2, 1, 2, 3, 5, 7, 10, 20, and 30 years. If there is no maturity-
equivalent Treasury security available to match the maturity of a corporate bond, we choose the
Treasury securities with the closest maturity to that of the corporate bond.
Covenant is an index constructed by adding 22 covenant dummy variables, which are
constructed using the covenant, issuer’s restriction, and subsidiary’s restriction variables reported
in the FISD (e.g., Qi, Roth, and Wald, (2011)).7 Each covenant dummy indicates whether a specific
type of activity is restricted. For example, a dividend payment dummy indicates the presence of
covenants limiting the issuer or its subsidiary to pay dividends. We further classify the 22 covenant
dummies into 8 major covenant categories: payment restriction, borrowing restriction, asset and
investment restriction, stock issue restriction, default-related covenants, antitakeover-related
covenants, profit maintenance, and rating decline triggers. We create the covenant sub-indices for
these eight categories by adding the covenant dummies within each category.
7 We provide the details concerning the construction of the 22 covenant indicators and covenant indices in the
internet appendix.
18
Maturity is simply the number of years remaining until a bond matures.
In our regression analyses, we take the natural logarithm of the offering spread, covenant index,
and maturity because these variables are highly skewed. Since the offering spread can be less than
one, to avoid negative values, we define the Log(OfferingSpread) as the natural logarithm of one
plus the offering spread. Similarly, because the covenant index can be zero, Log(Covenant) is
defined as the natural logarithm of one plus the covenant index. In addition, to be included in our
sample, a bond must have at least one year to maturity, hence Log(Maturity) is simply the natural
logarithm of years to maturity, where a zero value of Log(Maturity) indicates one year to maturity.
3.3.2 Explanatory variables
We use the variables from the credit risk literature for the regression of the bond spreads, and
follow Johnson (2003) and Billett, King and Mauer (2007) for the covenant and maturity equations.
We first introduce the common explanatory variables that are included in all of the regressions. At
the market level, we control for two variables that proxy for general market-level liquidity shocks:
the change of the spreads between Baa-rated corporate bonds and 10-year Treasury bonds
(CRSPRD) (Longstaff, Mithal, and Neis (2005), Dick-Nielsen, Feldhütter, and Lando (2012),
Boyson, Stahel, and Stulz (2010)), and the change in the Treasury-Eurodollar spread (TEDSPRD)
(Gupta and Subrahmanyam (2000), Campbell and Taksler (2003), Taylor and Williams (2009),
and Boyson, Stahel, and Stulz (2010)). At the firm level, we control for leverage, firm size, a
dummy of whether a firm is regulated, a dummy of whether a firm has a credit rating, and stock
return volatility because these firm characteristics are fundamental determinants of the issuer’s
credit risk (see Johnson (2003) and Billett et al (2007)). In addition, we control for a dummy for
whether or not a bond is callable.
19
In order to identify the simultaneous equations model, we add exclusive explanatory variables
in each equation. In the regression of the offering spread, we control for the coupon rate (Elton,
Gruber, Agrawal, and Mann, 2001; Chen, Lesmond, and Wei, 2007), PPE ratio, profitability, and
bond ratings.8 In the covenant regression, we control for the average of the covenant index used in
the firm’s prior bonds. In the regression of maturity, we control for the asset maturity, term spread
(yield spread between 10-year and 6-month treasury bonds), investment tax credit, and abnormal
earnings (Billett, King and Mauer (2007). For the simultaneous equations model, we only include
bond ratings in the regression the offering spread and do not control for bond ratings in regressions
of covenant and maturity because we include the offering yield spread in these regressions. For
the individual equations model, we include bond ratings in all of the regressions. In addition, we
control for Tobin’s Q as a proxy of growth opportunities in covenant and debt maturity regressions
(Billett, King and Mauer (2007) and Saretto and Tookes (2013)).
3.3.3 Endogeneity of bond liquidity
One potential concern with our baseline model is the endogeneity of bond liquidity because
unobserved omitted variables may simultaneously affect liquidity and debt contracts. To alleviate
this concern, we conduct a quasi-natural experiment by using the implementation of TRACE. Prior
research has shown that bond liquidity improved after the introduction of TRACE (e.g.,
Bessembinder, Maxwell, and Venkataraman (2006), Edwards, Harris, and Piwowar (2007), and
Goldstein, Hotchkiss, and Sirri (2007)). We therefore use a firm’s incidence in TRACE as an
exogenous shock to the firm’s bond liquidity. Specifically, we create a dummy variable
8 Elton et al (2001) argue that the coupon rate captures the tax effect and hence affects the yield spread of bonds.
Bond ratings are not controlled for in the other two regressions because bond spreads are instead included as
endogenous variables in the simultaneous equations model.
20
,_ i tD TRACE that equals one if firm i’s bonds were included in TRACE before time t and zero
otherwise. The regression specification is as follows:
, , 1, 1, 1 , 1 , , 1 , , 1 , ,
, , 2, 2, 2 , 2 , , 2 , , 2
( ) _ + ( ) ( )
( ) _ ( ) ( )
i j t i t i t i j t i j t i j t
i j t i t i t i j t i j t
Log Covenant D TRACE Log OfferingSpread Log Maturity Controls
Log Maturity D TRACE Log OfferingSpread Log Covenant
, ,
, , 3, 3, 3 , 3 , , 3 , , 3 , ,( ) _ ( )+ ( )
i j t
i j t i t i t i j t i j t i j t
Controls
Log OfferingSpread D TRACE Log Covenant Log Maturity Controls
(2)
where i are a set of fixed effect dummies of the industry (in the simultaneous equation) or
firm (in the individual equation), and t are dummies of the four phases of TRACE
implementation.9 All other variables are defined the same as those in equation (1). The key
independent variable in this model is the liquidity shock by TRACE, ,_ i tD TRACE . To avoid
sample selection bias, only firms that have bond issuances both before and after TRACE are
included in the sample.
TRACE was initiated in July 2002 and fully implemented in February 2005. We select the time
interval for the test starting from January 1, 2001, one and a half years before the introduction of
TRACE, and ending in December 31, 2007, to avoid the impact of the subprime mortgage financial
crisis.
4. Empirical Results
In this section, we present our baseline empirical tests of whether firm-level bond liquidity
affects the use of covenants, maturity, and offering yield spread in Subsection 4.1. Subsection 4.2
examines how the implementation of TRACE affects debt contractual terms. Subsection 4.3
presents the two-stage least squares test using bond age as the instrument. In subsection 4.4, we
investigate whether the firm-level bond liquidity of existing bonds is a good proxy of the expected
9 We provide detailed information about the four phases of TRACE implementation in the internet appendix.
21
liquidity of newly issued bonds in the future. We explore the mechanisms through which bond
liquidity may affect bond contracts in Subsection 4.5. Subsection 4.6 studies the effects of bond
liquidity on various types of bond covenants. Additional robustness checks are discussed in
Subsection 4.7.
4.1 Bond liquidity and debt contracts
Table 3 reports the estimates of the effects of bond liquidity on debt contracts. Columns (1)-
(3) present the estimation results of the simultaneous equations using GMM. We find that
Log(Amihud) is positively related to Log(Covenant) with a coefficient of 12.52, negatively related
to Log(Maturity) with a coefficient of 11.30, and positively related to Log(OfferingSpread) with a
coefficient of 6.67. These coefficients are statistically significant at the 1% level for all three
equations. Because Amihud measures illiquidity, these results suggest that better bond liquidity
decreases offering yield spread, reduces the use of restrictive covenants, and lengthens debt
maturity. Thus, firms with better bond liquidity can issue bond with more favorable terms.
[INSERT TABLE 3 HERE]
The economic impact of bond liquidity on debt contracts is substantial.10 A one standard
deviation increase of Amihud from the sample average leads to an increase of offering spread of
18 basis points from the average, that is, an increase of 7.78 % (=0.18/2.33, where 2.33 is the
average of the bond spreads).11 Similarly, we find that one standard deviation increase of Amihud
10 Because we define Log(OfferingSpread)=ln(1+OfferingSpread) and Log(Amihud)=ln(1+Amihud); the point
estimate of Log(Amihud) in the regression of offering spread is actually / (1 )
/ (1 )
OfferingSpread OfferingSpread
Amihud Amihud
.
11 Given that the average of Amihud is 0.008 and one standard deviation of Amihud is 0.008 (as reported in Panel A
of Table 2), a one standard deviation increase of Amihud from the sample average leads to an increase of
Log(Amihud) by 0.008 (=ln(1+0.008+0.008)-ln(1+0.008)). Given that the coefficient of Log(Amihud) in the
regression of Log(OfferingSpread) is 6.67 (as reported in column (3) of Table 3), an increased Log(Amihud) leads
to an increase of Log(OfferingSpread) by 0.053( 6.67 0.008 ). Given that the average of the offering spreads is
22
causes an increase of covenants index by 0.53 from the average,12 which is equivalent to an
increase of 13.17% (=0.53/4.01, where 4.01 is the average covenant index). And one standard
deviation increase of Amihud leads to the decrease of debt maturity by 1.03 year from the
average,13 which is equivalent to a decrease of 8.64% (=1.03/11.91, where 11.91 is the average
maturity in years).
In the simultaneous equations model reported in columns (1) - (3) of Table 3, we observe the
relationship among the three endogenous terms of bond contracts: covenants, maturity, and
offering spreads. For instance, the use of bond covenants reduces offering spreads while longer
maturity increases offering spreads. Bonds with longer maturity are more likely to include
restrictive covenants; offering spreads and covenants are both positively related to maturity. These
results are consistent with the literature.
In columns (4) - (6) of Table 3, we report the results of the individual equation estimates using
OLS. Since we control for firm fixed effects, variables that are not time-varying, such as the
Regulated dummy and the rated-firm dummy, are dropped. These firm fixed-effect models serve
as robustness tests and allow us to control for some unobserved firm characteristics that may affect
bond liquidity and debt contract simultaneously. The results confirm our main findings in the
simultaneous equation setup. The deterioration of bond liquidity increases offering yield spread
and the use of covenants, and shortens debt maturity. The estimated coefficients of Log(Amihud)
in the individual equations are smaller than those reported in simultaneous equations. Since the
2.33% (as reported in Panel B of Table 2), the average of Log(OfferingSpread) is equal to 1.203 (=ln(1+2.33)).
Taken together, the increase of Log(OfferingSpread) by 0.053 means that an increase of offering spread by 18 basis
points ( 1.203 0.053 1.203e e ).
12 The change of covenants is estimated as 0.008 12.52(4.01 1) ( 1)e =0.53 13 The change of debt maturity is estimated as 11.30 0.00811.91 ( 1)e =1.03
23
individual equations are the reduced-form of the simultaneous equations model, the estimates do
not represent the real effect of bond liquidity on debt contracts (see, Greene (2008)).
In Table 3, the estimates of the other variables in both the simultaneous and individual
equations mostly have the expected signs. Highly leveraged or small-size firms issue bonds with
higher yield spreads, more covenants and shorter maturities. Profitability is significantly
negatively related to offering spreads. Firms with more volatile stock returns and firms in regulated
industry issue bonds with higher yield spreads, more covenants, and shorter maturities. Callable
bonds are related to higher offering spreads and greater use of covenants. The impact of callable
bonds on maturity is mixed, as shown in columns (2) and (5). The coupon rate is significantly
positively related to offering spreads. Prior covenant is significantly positively related to the use
of covenants in the simultaneous equations model as shown in column (1), but significantly
negatively related to the use of covenants in the individual equations model as shown in column
(4). This may be because the individual equations model does not control for the terms of debt
contracts simultaneously. As expected, term spreads are negatively related to debt maturity,
suggesting that firms are more likely to use long-term debt when the term structure is flat.
4.2 Using TRACE implementation as an exogenous shock to bond liquidity
To alleviate endogeneity concerns about bond liquidity, we conduct a quasi-natural experiment
using the implementation of TRACE as an exogenous shock to bond liquidity. The prior literature
documents that the introduction of TRACE increases the liquidity of the corporate bond market
(e.g., Bessembinder et al. (2006), Edwards et al. (2007), and Goldstein et al. (2007)). A recent
study by Asquith, Covert, and Pathak (2013) argues that the implementation of TRACE reduces
trading activity and price dispersion in some corporate bonds. In this paper, we check the change
24
of a firm’s bond liquidity before and after the firm’s first entrance into TRACE. Using the four
liquidity measures and the enhanced TRACE database,14 we confirm that all four of the bond
liquidity measures decline after the introduction of TRACE, suggesting that bond liquidity
improved after TRACE.15
In this TRACE test, we replace the liquidity measure in the regressions with a dummy variable,
D_TRACE, which equals one if the firm’s prior bonds were included in TRACE (i.e., the firm
affected by TRACE), and zero otherwise. Helwege, Huang and Wang (2014) show that the
liquidity of a corporate bonds is highly related to the liquidity of other bonds in the same firm, and
that firm-level liquidity is one of the most important determinants of the liquidity of individual
corporate bonds. By the same logic, after a firm’s first bond entered TRACE, investors would be
able to know more about the bond transaction information of the firm, therefore, the bond liquidity
of the firm is improved. Consequently, the TRACE dummy, D_TRACE, can proxy for the
improvement of the firm-level liquidity of bonds.
The results of this TRACE test are reported in Table 4. Columns (1)-(3) provide the GMM
estimates of simultaneous equations, in which industry and TRACE’s phase fixed effects are
controlled for. Columns (4)-(6) report the estimates of OLS regressions for individual equations,
in which firm and TRACE’s phase fixed effects are controlled for.
[INSERT TABLE 4 HERE]
In the simultaneous equations model, the coefficients of D_TRACE in the regressions of
covenant, maturity, and offering spread are, respectively, -1.17 at a 1% significance level, 0.57 at
14 FINRA began to report transactions of disseminated bonds from July 2002. Simultaneously, FINRA also collected
non-disseminated trade data. In March 2010, FINRA released the enhanced TRACE dataset, which includes both
disseminated and non-disseminated transaction records. We therefore use the enhanced TRACE to compare bond
liquidity before and after TRACE implementation. 15 We do not report this result in the paper, but it is available in the internet appendix.
25
a 5% significance level, and -0.44 at a 10% significance level. In the individual equation model,
the coefficients of D_TRACE are -1.91 with a 1% significance level in the regression of covenants,
-0.19 but not statistically significant in the regression of maturity, and -0.19 at a 5% significance
level in the regression of offering spread. Overall, Table 5 confirms our expectation that the
introduction of TRACE improves bond liquidity and leads to lower offering spread, less use of
covenants, and longer maturity of newly issued bonds.
We conduct a difference-in-differences test to further explore the impact of TRACE on debt
contracts. A typical difference-in-differences test should estimate the change in variables of
interest before and after the event, then compare the changes between the treatment and control
groups. Our sample of newly issued bonds does not have a panel structure at bond level because
each bond is a new issue. We therefore conduct a difference-in-differences test by examining the
change in contractual terms of bonds issued by the same firm before and after TRACE. Because
firms usually do not issue bonds frequently, we need to use a relatively long time interval to obtain
multiple bond issues of one firm. The implementation of TRACE was conducted in multiple phases,
and the time interval between two phases is often not long enough to obtain multiple bond issues
of one firm. We therefore pool the four phases of TRACE implementation together, and use the
date of TRACE introduction, i.e., July 1 2002, as the event date.
We construct the treatment and control groups as follows. First, we classify a firm as TRACE-
affected if its bonds were included in TRACE, otherwise we classify the firm as TRACE-non-
affected. Next, bonds issued by the TRACE-affected firms either before or after TRACE are
classified into the treatment group, and bonds issued by the TRACE-non-affected firms are in the
control group.
26
Additionally, we use the NAIC insurance database to collect bond transactions before and after
TRACE. We then use the method discussed in the data section to estimate the firm-level bond
liquidity to proxy for the expected bond liquidity of newly issued bonds before and after TRACE.
[INSERT TABLE 5 HERE]
Table 5 reports the results of the difference-in-differences test. Panel A presents the average
Amihud Ratio, covenant index, maturity and offering spreads of bonds issued before and after
TRACE for the control and treatment groups. We first examine the change of expected bond
liquidity. The Amihud Ratio for the control group declines from 0.0069 in the pre-TRACE period
to 0.0066 in the post-TRACE period, and this change is not statistically significant. In contrast,
the Amihud Ratio of treatment group drops from 0.0063 in the pre-TRACE period to 0.0049 in the
post-TRACE period, and the change is significantly at the 1% level. Because Amihud Ratio
measures the degree of illiquidity, the result here suggests that bond liquidity in the treatment
group was significantly improved after the introduction of TRACE while that in the control group
was not changed. This evidence confirms the argument that TRACE implementation helps enhance
market liquidity.
Next, we examine the change of bond contractual terms. As shown in Column (1)-(3) in Panel
A, for the control group, bonds issued after TRACE have higher covenant index (0.38 but not
significant), longer maturity (0.31 but not significant), and lower offering spread (-0.45 at the 1%
level) than bonds issued before TRACE. As reported in Columns (4)-(6), for the treatment group,
bonds issued after TRACE experience a large decrease in covenant index (-0.93 at the 1% level),
a significant increase in maturity (1.79 at the 1% level), and a substantial reduce in spread (-0.77
at the 1% level). Columns (7) and (8) test the significance of the difference-in-differences and
27
show that the differences of the changes in three contract terms between the treatment and control
groups are all statistically significant.
In Panel B of Table 5, we redo the difference-in-differences test using a matching sample.
Specifically, for each bond in the treatment group, we pair it with a bond in the control group that
was issued in the same month and with closest offering amount as the bond in the treatment group.
Since there are more bonds in the treatment group than in the control group, we allow one bond in
the control group to match with multiple bonds in the treatment group. By matching bond issuance
time, we are able to control for the possible time trend that may affect debt contractual terms. We
further require that the matched bonds have similar offering amount by setting the ratio of
matching bonds’ offering amount to be between 0.5 and 2.
The empirical results reported in Panel B of Table 5 show that the differences of bond liquidity
and bond contractual terms between the control and treatment groups are significantly widened
after TRACE. For example, before TRACE, bonds in the treatment group are associated with 0.35
lower covenants than bonds in the control group. After TRACE, bonds in the treatment group are
associated with 1.12 lower covenants than bonds in the control group. The difference of the
differences is 0.78 and statistically significant at the 1% level (t-stat=3.37). Overall, our difference-
in-differences tests confirm our previous findings that TRACE implementation helps reduce the
use of covenant, decrease the offering spread and increase the maturity of newly issued bonds.
4.3 Instrumental variable estimation
To further alleviate the concern of endogenous liquidity, we consider instrumental variable
estimation using two-stage least squares (2SLS). In our case, a valid instrument should affect bond
liquidity (i.e., relevance restriction) but have no impact on the terms of bond contracts (i.e.,
28
exclusion restriction). Using a firm’s existing bonds, we estimate bond age of each bond (i.e., the
time of years from issuance date of this existing bond to the date when the newly bond are issued)
and then use the value-weighted average of bond age as the firm-level bond age. We use the firm-
level bond age as the instrumental variable for firm-level bond liquidity. Bond age is well
documented to be a determinant of bond liquidity in the literature, and there is no theory indicating
that averaged bond age of a firm’s exiting bonds may affect the contracts of a firm’s newly issued
bonds.16
Table 6 reports the results of the second-stage regression. We use the fitted Log(Amihud) from
the first-stage regression as the key independent variable, and redo our baseline model in Table 3
using GMM. The fitted Log(Amihud) is positively related to the use of covenants with a coefficient
of 12.95 at the 1% significance level, negatively related to maturity with a coefficient of -11.38 at
the 1% significance level, and positively related to offering spread with a coefficient of 7.34 at the
1% significance level. Overall, the instrumental variable regression confirms the key findings in
our baseline model.
[INSERT TABLE 6 HERE]
4.4 Is liquidity of the existing bonds a good proxy for the expected liquidity of newly issued bonds?
Our empirical identification strategy is based on the presumption that the liquidity of a firm’s
existing bonds proxies for the expected liquidity of the firm’s newly issued bonds. Because the
expected liquidity of newly issued bonds is not observable, we then check if the liquidity of a
firm’s existing bonds can predict the future realized liquidity of the firm’s newly issued bonds
when there are negative shocks to the bonds. Specifically, we collect bonds that were downgraded
16 The estimates of the first-state regression and the test of the exclusion restriction are provided in the internet
appendix. In the first-stage regression, we find strong results that bond age significantly contributes to the firm’s
bond liquidity. We regress debt contractual terms on bond age and confirm that bond age is not significantly related
to the bond contractual terms: offering yield spread, covenants, or maturity
29
to study whether the firm-level bond liquidity at the time of these bonds’ issuance has predictive
power for the selling pressure of these downgraded bonds.
[INSERT TABLE 7 HERE]
In Table 7, the dependent variable is the average Amihud Ratio in the month following the
bonds’ rating downgrade. A high value of the Amihud Ratio indicates poor liquidity and more
difficulty selling. For the independent variable, we rank the firm-level bond liquidity at the
issuance time of the downgraded bonds into quintiles, with zero indicating the best firm-level bond
liquidity and four representing the worst. This ranking is the key independent variable used in
Panel A. By the same logic, we also create a simpler version of the ranking with a dummy of
liquidity at issuance, which equals one if the firm-level bond liquidity at the issuance time of the
downgraded bond is above the median, and zero otherwise. Panel B of Table 7 reports the
regression results using the dummy. In all of the regressions, we control for bond and firm
characteristics as in Table 3. In column (2) we control for bond rating fixed effects, and in column
(3), we add firm and year fixed effects.
As shown in Panels A and B of Table 7, the coefficients of the ranking of liquidity are positive
and statistically significant at the 5% level or above. The economic magnitude is also significant.
As shown in the column (3) of Panel B in Table 7, the selling pressure of the bond after
downgrading is 24% (=0.0019/0.008, where 0.0019 is the coefficient and 0.008 is the sample
average of the Amihud Ratio larger for bonds whose firm-level Amihud at its issuance is above the
sample median than for those whose Amihud is below the median.
Overall, the results in Table 7 show that the liquidity of a firm’s existing bonds indeed predicts
the tradability of the firm’s newly issued bonds. When firm-level bond liquidity is poor at the time
30
of the bond issuance, it is more costly to sell the bond when there is a negative shock to the bond
in the future, such as rating downgrade.
4.5 Mechanisms through which bond liquidity affects bond contracts
In this subsection, we explore the mechanisms behind the effect of bond liquidity on debt
contracts. Specifically, we add an interaction term between bond liquidity and the proxy of
potential mechanisms to the baseline model. Our empirical model is stated as follows:
, , 1, 1, 1 , 1 , 1 , ,
1 , , 1 , , 1 , ,
, , 2, 2, 2 , 2 ,
( ) ( ) ( )
+ ( ) ( )
( ) ( )
i j t i t i t i t i t i t
i j t i j t i j t
i j t i t i t i t
Log Covenant Log Liqudity Z Log Liqudity Z
Log OfferingSpread Log Maturity Controls
Log Maturity Log Liqudity Z
2 , ,
2 , , 2 , , 2 , ,
, , 3, 3, 3 , 3 , 3 , ,
3 , , 3
( )
( ) ( )
( ) ( ) ( )
( )+
i t i t
i j t i j t i j t
i j t i t i t i t i t i t
i j t
Log Liqudity Z
Log OfferingSpread Log Covenant Controls
Log OfferingSpread Log Liqudity Z Log Liqudity Z
Log Covenant Log
, , 3 , ,( )i j t i j tMaturity Controls
(3)
where ,i tZ is the proxy of the underlying channel. We are particularly interested in the coefficients of
. If and have the same sign, then the factor Z exaggerates the liquidity effect. In contrast, if
and have different signs, then the factor Z reduces the liquidity effect.
[INSERT TABLE 8 HERE]
One way bond liquidity might influence debt contracts is that good bond liquidity can enhance
credit capital supply in the primary market. Saretto and Tookes (2013) argue that the existence of
the CDS market allows creditors to hedge credit risk and makes holding bonds more attractive.
They show that firms with traded CDS contracts can maintain higher leverage and use more long-
term bonds because of an improved credit capital supply. Similarly, good bond liquidity may also
attract investors and increase the credit capital supply because it serves as an ad hoc protection to
creditors so that investors can sell bonds with a low “cost of exit”. The liquidity protection to
investors can substitute for CDS protection, thus we expect that the effects of bond liquidity are
31
more pronounced in firms without CDS contracts. Panel A of Table 8 confirms this conjecture.
Specifically, as shown in columns (1) and (2), respectively, Log(Amihud) x D_CDS has a negative
effect on covenants with a coefficient of -11.77, and a positive effect on maturity with a coefficient
of 9.57. This suggests that the effect of Log(Amihud) on covenants and maturity is mitigated by
the CDS dummy. In addition, as shown in column (3), D_CDS has no effect on offering spreads.
This is consistent with Aschcraft and Santos (2009) who find that the introduction of CDS has no
impact on the price of debt financing.
In Panel B of Table 8, we study the interaction between bond liquidity and Tobin’s Q. Firms
with a high Tobin’s Q usually have a high stock market valuation, and therefore depend less on
debt financing. As a result, the liquidity effect on bond contracts should be weaker for firms with
a high Tobin’s Q and be more pronounced for firms with a low Tobin’s Q. The results in Panel C
confirm this hypothesis.
In Panel C of Table 8, we study the interaction between Log(Amihud) and a high yield dummy
that equals one if the bond is rated as non-investment grade or not rated. Ericsson and Renault
(2006) show that the deterioration of bond liquidity increases firms’ credit risk. If bond liquidity
affects debt contracts by influencing the credit risk, we expect that the effect of bond liquidity
should be more pronounced for high-yield bonds. The results presented in Panel C confirm this
hypothesis.
Panel D of Table 8 studies the rollover risk channel proposed by He and Xiong (2012), who
argue that bond liquidity shocks increase the rollover cost and hence raise the default threshold.
We study the interaction between bond liquidity and the short-term debt ratio, which is defined as
the ratio of short-term debt to long-term debt. Short-term debt is defined as debt with three year of
maturity or less while long-term debt is debt with more than three years of maturity. Firms with
32
more short-term debt are subject to higher rollover risk. The results in Panel D show that the
liquidity effect on bond contracts is more pronounced in firms that use more short-term debt.
We do not find evidence to support monitoring arguments. In unreported tests, we examine the
interactions between liquidity and firm size. It is commonly believed that small size firms are
subject to more agency conflicts. However, regressions show insignificant interaction terms.
4.6 The effects of bond liquidity on various types of covenants
In Table 9, we examine the effects of bond liquidity on various types of covenants. Covenants
provide effective bondholder protections; however, different types of covenants protect investors
in different manners. For example, payment restriction covenants are designed to protect
bondholders by restricting dividend payments, borrowing restriction covenants are used to limit
firms’ leverage, and anti-takeover covenants and default-related covenants are designed to provide
protection to bondholders when events such as mergers and acquisitions and defaults happen.
We argue that good bond liquidity serves as an ad hoc protection for creditors. Thus, we expect
that liquidity protection should substitute for covenant protection. By examining the effect of bond
liquidity on various types of covenants, we gain further insight into how bond liquidity helps
protect creditors.
In Table 9, we report the effect of bond liquidity on four types of covenants: restriction on
borrowing activities, restriction on stock issuance, restriction on mergers and acquisitions, and
default-related covenants. The effect of bond liquidity on the other four types of covenants:
payment restriction, asset-investment restriction, profit maintenance and rating decline triggers,
are insignificant and therefore not reported.
[INSERT TABLE 9 HERE]
33
As shown in Panel A of Table 9, bond liquidity has strong effects on covenants related to
external financing. Similar to the results of the baseline model, the deterioration of bond liquidity
increases the use of restrictive covenants on debt financing. In contrast, illiquidity decreases the
use of covenants on equity financing. This result is consistent with the friction of capital supply
argument that the deterioration of bond liquidity increase the difficulties of debt financing. These
firms are therefore more likely to rely on equity financing and to have less incentive to include
covenants that restrict equity financing.
In Panel B of Table 9, we study the effects of bond liquidity on two event-related covenants.
The coefficient of Log(Amihud) is 3.47 in the regression of anti-takeover restrictive covenants as
shown in column (7), and 4.69 in the regression of default related covenants as shown in column
(10). These results suggest that good bond liquidity helps reduce the use of event-related covenants,
which is consistent with our conjecture that good liquidity serves as creditor protection because
creditors can sell bonds with low cost when an unexpected negative shock occurs.
4.7 Robustness Checks using alternative liquidity measures
In Table 10, we consider alternative measures of bond liquidity and these tests yield similar
results to our baseline model. In all of the panels, we find that the deterioration of bond liquidity
significantly increases offering spreads and the use of covenants, but shortens bond maturity.
[INSERT TABLE 10 HERE]
5. Conclusions
Despite the large number of theoretical papers that predict the relationship between liquidity
and bond pricing in the secondary market, researchers have not yet explored the potential impact
of liquidity on bond contracts. Our paper aims to fill this gap in the literature. Specifically, we
34
examine whether and how bond market liquidity affects the use of restrictive covenants, debt
maturity, and the borrowing costs of firms’ newly issued bonds.
This paper provides unequivocal evidence that bond market liquidity is crucial in determining
debt contractual terms. We find that the improvement of bond liquidity reduces the borrowing cost
of debt, decreases the use of restrictive covenants, and lengthens bond maturity. These results are
robust to an instrumental variable test that controls for the endogeneity of bond liquidity, and a
quasi-natural experiment, in which TRACE implementation is used as an exogenous shock to bond
market liquidity. In addition, we find that the effects of bond liquidity on debt contracts are more
pronounced in firms subject to more credit market friction, firms depending more on debt financing,
firms with higher credit risk, and firms with more short-term debt. Finally, by examining the
impact of bond liquidity on various types of bond covenants, we find that bond liquidity has strong
impacts on event-related covenants and external financing-related covenants.
Overall, this paper shows that bond market liquidity can have a substantial impact on firms’
external debt financing through affecting debt contracts.
35
Appendix: Measures of bond market liquidity
In this appendix, we describe the construction of four measures of bond liquidity.
Amihud Ratio (Amihud)
The Amihud Ratio (Amihud (2002)) measure is calculated using high-frequency transaction
data from TRACE and is defined as the daily average of the absolute returns of consecutive
transactions divided by the trade size (in million $):
, 1, 1,
1 ,
| | /1 tNj t j t j t
t
jt j t
P P PAmihud
N Q
(4)
where is the number of returns on day t. jP and
1jP are the prices of two consecutive trades.
and jQ is the trading volume of trade j. At least two transactions are required on a given day to
calculate the measure.
Price Dispersion (PD)
Jankowitsch, Nashikkar, and Subrahmanyam (2008) and Friewald, Jankowitsch, and
Subrahmanyam (2012) model price dispersion effects in over-the-counter (OTC) markets to show
that in the presence of inventory risk for dealers and search costs for investors, traded prices may
deviate from the expected market valuation of an asset. They interpret this deviation as a liquidity
effect and develop a new liquidity measure quantifying the price dispersion in the context of the
US corporate bond market. The price dispersion is estimated as follows:
2
, ,
1,1
1Price Dispersion
t
t
N
t j t t j tNjj tj
P m QQ
(5)
where ,j tP is the trading price of trade j on day t for a bond, is the average price of the bond on
day t, ,j tQ is the trading volume of trade j on day t, and is the number of returns on day t.
Imputed Roundtrip Cost (IRC)
tN
m t
tN
36
Feldhütter (2010) and Dick-Nielsen, Feldhütter, and Lando (2012) measure bid-ask spreads
using Imputed Roundtrip Trades (IRT). Most of the data do not contain information about the buy
and sell side of trades. IRTs are based on finding two trades that are close in time and that are
likely to be a buy and a sell. These two trades are regarded as one IRT. If a number of trades with
the same trade size take place on one day, and there are no other trades with the same size on that
day, they define these trades as one IRT. For each IRT, we can calculate the bid-ask spread. Then
we can calculate the daily averaged bid-ask spread of all IRTs to obtain the imputed roundtrip cost
(IRC)
max, , min, ,
1 max, ,
1 tNj t j t
t
jt j t
P PIRC
N P
(6)
where tN is the number of IRTs on day t, max, ,j tP is the largest price in the jth IRT on day t, and
in, ,m j tP is the lowest price in the jth IRT on day t.
Inter-quartile Range (IQR)
Finally, the inter-quartile range (IQR) is a liquidity measures used by Han and Zhou (2008)
and Hewlege, Huang and Wang (2013). IQR is defined as the difference between the 75th
percentile and the 25th percentile of prices for one day normalized by the average price on that
day. That is,
75 25
100th th
t tt
t
P PIRQ
P
(7)
where 75th
tP is the 75th percentile of prices on day t, 25th
tP is the 25th percentile of prices on day t,
and P is the average price on day t.
37
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43
Table 1: Variable Description
This table describes the definitions of variables and data sources. Panel A provides the definitions for bond liquidity. Panel B provides the definitions for the bond-
level variables. Panels C and D describe the firm-level and market-wide control variables, respectively.
Variable Definition Data Source
Panel A. Proxy of bond liquidity
Amihud (Amihud) The daily average of the ratio of the absolute returns of two consecutive
transactions to trading volume (in million $).
TRACE
Price dispersion (PD) The daily trading volume weighted average of the bond price dispersion. TRACE
Imputed roundtrip cost(IRC) The daily average of the bid-ask spreads of all imputed roundtrip trades. TRACE
Inter quartile range(IQR) The difference between the 75th percentile and the 25th percentile of prices for one
day normalized by the average price on the day.
TRACE
Log(Amihud) The natural log transformation of one plus Amihud TRACE
D_TRACE A dummy variable that equals one if the transactions of a firm’s prior bond(s) were
disseminated in TRACE and zero otherwise.
FISD, TRACE
B. Bond Characteristics
Log (Offering spread) The natural log transformation of one plus the difference between the offering yield
of a corporate bond and the yield to maturity on its maturity-equivalent Treasury
bond.
FISD, DataStream
Log (Maturity) The natural log transformation of a bond’s maturity in years. FISD
Log (Covenant) The natural log transformation of one plus the covenant index, which is the sum of
the firm’s 22 covenant indicator variables.
FISD
Coupon rate The coupon rate of a corporate bond in percentage. FISD
Callable A dummy variable that equals one if a bond is callable, and zero otherwise.
High yield dummy A dummy variable that equals one if a bond is rated as below BBB- or not rated,
and zero otherwise.
Bond ratings S&P credit rating of bonds. FISD
C. Firm Characteristics
Tobin's Q The ratio of the market value of total assets to the book value of assets, where the
market value of assets is estimated as the book value of assets minus the book value
of equity plus the market value of equity.
Compustat
44
Leverage The book value of total debt (long-term debt plus debt in current liabilities) divided
by the market value of assets, where the market value of assets is estimated as the
book value of assets minus the book value of equity plus the market value of
equity.
Compustat
Firm size A firm’s book value of total assets. Compustat
Stock return volatility The volatility of one year of daily equity returns. CRSP
PPE The ratio of net property, plant, and equipment to the book value of total assets Compustat
Profitability The ratio of earnings before interest, taxes, depreciation, and amortization
(EBITDA) to the book value of total assets.
Compustat
Investment tax credit Dummy Dummy that equals one if a firm has an investment tax credit in the fiscal year, and
zero otherwise.
Compustat
Abnormal earning The difference between earnings per share in year t + 1 (excluding extraordinary
items and discontinued operations and adjusted for any changes in shares
outstanding) minus earnings per share in year t, divided by the share price in year t.
Compustat
Rated-firm dummy Dummy that equals one if a firm has an S&P rating on Compustat and zero
otherwise.
Compustat
Regulated dummy Dummy that equals one if a firm is in a regulated industry (SIC codes between
4900 and 4939) and zero otherwise.
CRSP
Asset maturity The book value-weighted maturity of long-term assets and current assets, where the
maturity of long-term assets is computed as the gross property, plant, and
equipment divided by the depreciation expense and the maturity of current assets is
computed as current assets divided by the cost of goods sold.
Compustat
Short-term debt The ratio of short-term debt to long-term debt. Short-term debt is defined as debt
maturing within three years; and long-term debt is defined as debt maturing after
three years.
Compustat
D. Market-wide Controls
CRSPRD The change of the yield spread between Baa-rated corporate bonds and the 10-year
Constant Maturity Treasury bonds.
DataStream
TEDSPRD The change in the Treasury Eurodollar spread. DataStream
Term spread The yield spread between10-year and 6-month Treasury bonds. DataStream
45
Table 2: Descriptive Statistics
This table presents the descriptive statistics of bond liquidity proxies and other main variables. Panel A presents the summary statistics of the liquidity proxies of
our sample and compares them with the entire TRACE sample. Panel B reports the statistics of the bond features and compares our sample with the entire FISD
sample. Panel C reports the statistics of firm characteristics and market-wide variables, and compares our sample with the merged sample of CRSP and Compustat.
Panel D presents the correlations between Amihud, covenant index, offering spread, and maturity. ***, **, and * denote statistical significance at the 1%, 5%, and
10% levels, respectively
Panel A: Descriptive statistics of liquidity proxies
Final sample TRACE sample
Mean Std. Dev. P25th P50th P75th Mean Std. Dev. P25th P50th P75th
Amihud 0.008 0.008 0.004 0.006 0.009 0.011 0.018 0.002 0.005 0.012
Roundtrip (IRC) 0.006 0.005 0.003 0.005 0.007 0.006 0.009 0.002 0.004 0.007
Inter quartile range 0.371 0.260 0.214 0.303 0.434 0.452 0.937 0.176 0.307 0.522
Price dispersion 0.204 0.129 0.120 0.174 0.249 0.223 0.338 0.094 0.168 0.276
Panel B: Descriptive statistics of bond variables
Final sample FISD sample
Mean Std. Dev. P25th P50th P75th Mean Std. Dev. P25th P50th P75th
Offering spread 2.33 1.89 1.04 1.72 3.08 2.72 3.04 0.92 1.77 3.58
Maturity 11.91 8.89 5.58 10.00 10.17 10.46 9.14 5.00 8.00 10.08
Covenant index 4.01 2.56 2.00 4.00 6.00 2.89 3.05 0.00 2.00 5.00
Offering amount 662.05 615.98 325.00 500.00 800.00 517.95 2404.19 150.00 300.00 600.00
Bond rating 13.36 4.03 10.00 13.00 16.00 12.64 4.36 9.00 13.00 16.00
46
Panel C: Descriptive statistics of firm-level and market-wide variables
Final sample CRSP/Compustat sample
Mean Std. Dev. P25th P50th P75th Mean Std. Dev. P25th P50th P75th
Firm size 37.07 55.60 5.87 16.29 38.17 9.96 40.36 0.17 0.75 3.09
Tobin’s Q 1.69 0.79 1.15 1.47 2.02 2.01 1.70 1.06 1.44 2.23
Profitability 0.15 0.07 0.10 0.14 0.19 0.04 0.22 0.02 0.08 0.14
Leverage 0.27 0.15 0.16 0.23 0.34 0.17 0.19 0.01 0.09 0.26
PPE 0.38 0.27 0.13 0.31 0.60 0.19 0.24 0.02 0.08 0.28
Stock return volatility 0.02 0.01 0.01 0.02 0.02 0.03 0.02 0.02 0.02 0.04
Abnormal earning -0.03 0.65 -0.03 0.00 0.03 0.91 0.49 -0.00 0.01 0.20
Asset maturity 7.46 6.33 3.43 5.35 8.87 5.97 8.19 1.89 3.30 6.34
Proportion of firm-years with
Rated-firm dummy 0.90 n/a n/a n/a n/a 0.74 n/a n/a n/a n/a
Investment tax credit 0.02 n/a n/a n/a n/a 0.01 n/a n/a n/a n/a
Proportion of regulated firm-years 0.15 n/a n/a n/a n/a 0.11 n/a n/a n/a n/a
TEDSPRD -0.01 0.23 -0.02 0.00 0.05
CRSPRD 0.00 0.24 -0.10 0.00 0.09
Term Spread 1.93 1.10 1.49 2.11 2.73
Panel D: Correlations between Amihud, covenant index, offering spread, and time to maturity
Log(Amihud) Log(Covenant index) Log(Offering spread) Log(Maturity)
Log(Amihud) 1
Log(Covenant index) 0.022 1
Log(Offering spread) 0.262*** -0.284*** 1
Log(Maturity) -0.032* 0.064** 0.047** 1
47
Table 3: Estimation of the Liquidity Effect on Bond Contracts
This table reports the estimates of the effects of bond liquidity on debt contracts. Columns (1)-(3) present estimates of the simultaneous equations using the GMM.
Bond rating fixed effect is not controlled for in column (1) and (2) because offering spread is included in the regressions. Columns (4)-(6) report the results of the
regression of the individual equations using OLS. The dependent variables are Log(Offering Spread), Log(Covenant) and Log(Maturity), respectively. The bond
liquidity is proxied by Log(Amihud). All of the variables are defined in Table 1. T-statistics, reported in parentheses, are calculated based on standard errors
clustered by firms. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Simultaneous Equations Individual Equations
Log(Covenant
Index) Log(Maturity)
Log(Offering
Spread)
Log(Covenant
Index) Log(Maturity)
Log(Offering
Spread)
(1) (2) (3) (4) (5) (6)
Log(Amihud) 12.52*** -11.30*** 6.67*** 6.14*** -3.70* 3.29*** (5.51) (-4.62) (5.37) (3.09) (-1.71) (6.24)
Log(Offering spread) -1.31*** 1.36***
(-15.84) (17.73)
Log (Covenant) 0.81*** -0.37***
(12.48) (-11.36)
Log (Maturity) 0.79*** 0.49***
(18.04) (8.8) Leverage 0.28** -0.63*** 0.30*** 0.22 0.13 0.06*** (2.00) (-4.73) (4.28) (1.15) (0.63) (1.12)
Firm size -0.10*** 0.13*** -0.07*** 0.10* 0.01 -0.02 (-5.97) (8.08) (-8.08) (1.92) (0.12) (-1.49)
Tobin’s Q 0.02 0.01 0.01 0.07* (1.16) (0.90) (0.16) (1.67)
Stock return volatility 19.39*** -21.92*** 12.20*** -0.27 -2.53 6.62*** (7.26) (-8.89) (8.93) (-0.16) (-1.28) (14.49)
PPE -0.005
-0.001
(-0.27) (-0.01)
Asset maturity -0.002** 0.01
(-2.14) -1.23
Profitability -0.26**
-0.21**
(-3.44) (-2.51)
Abnormal earning -0.75 0.49
(-1.13) -0.25
Regulated dummy 0.39*** -0.49*** 0.25*** dropped dropped dropped
48
(7.23) (-8.05) (7.09)
Rated-firm dummy 0.02 -0.08 0.04 dropped dropped dropped (0.31) (-1.52) (1.45)
Callable 0.36*** -0.24*** 0.12*** 0.29*** 0.86*** 0.03* (4.86) (-3.27) (3.12) (6.64) (17.81) (1.91)
Investment tax credit -0.01 0.07
(-1.17) (1.20)
Prior covenant 0.22*** -0.73*** (5.97) (-8.41)
Coupon rate 0.05***
0.15***
(3.80) (66.36)
Term spread -0.08*** -0.04
(-5.20) (-1.04)
CRSPRD 0.31*** -0.30*** -0.14* -0.02 0.11* 0.20*** (3.7) (-3.31) (-1.88) (-0.33) (1.6) -13.2
TEDSPRD -0.1 0.19 0.21*** 0.05 -0.05 -0.14*** (-0.74) (1.41) (4.29) (0.64) (-0.57) (-7.43)
Constant 0.43* -0.39 0.37** 1.48** 1.47** 0.45***
(1.77) (-1.50) (2.85) (2.28) (2.14) (2.63)
Industry fixed effect Yes Yes Yes No No No
Year fixed effect Yes Yes Yes Yes Yes Yes
Firm fixed effect No No No Yes Yes Yes
Bond rating fixed effect No No Yes Yes Yes Yes
Observations 2,631 2,631 2,631 2,631 2,631 2,631
49
Table 4: Liquidity Effect on Bond Contracts Using TRACE Implementation as an Exogenous Shock
This table reports the effect of TRACE implementation (i.e. an exogenous shock to bond liquidity) on covenant index, maturity and offering spread. A dummy
variable D_TRACE equals one if a firm’s bonds were included in TRACE and zero otherwise. Columns (1)-(3) present the GMM estimates of the simultaneous
equations. Bond rating fixed effect is not controlled for in column (1) and (2) because offering spread is included in the regressions. Columns (4) – (6) report the
OLS estimates of individual equations. All variables are defined in Table 1. T-statistics, which are reported in parentheses, are calculated based on standard errors
clustered by firms. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Simultaneous Equations Individual Equations
Log(Covenant
Index) Log(Maturity)
Log(Offering
Spread)
Log(Covenant
Index) Log(Maturity)
Log(Offering
Spread)
(1) (2) (3) (4) (5) (6)
D_TRACE -1.17*** 0.57** -0.44* -1.91*** -0.19 -0.19** (-4.28) (1.97) (-1.67) (-5.17) (-0.36) (-2.41)
Log(Offering spread) -1.02*** 1.17*** (-5.30) (5.83)
Log (Covenant) 0.56*** -0.28 (5.09) (-1.56)
Log (Maturity) 0.48*** 0.14
(3.79) (1.17) Leverage 0.18 -0.79** 0.24* 0.08 0.19 0.11 (0.52) (-2.43) (1.90) (0.12) (0.13) (1.12)
Firm size 0.04 0.04 -0.02 0.22 -0.53* -0.01 (0.89) (0.91) (-0.99) (1.59) (-1.66) (-0.42)
Tobin’s Q 0.03 0.12* -0.02 -0.31 (0.39) (1.80) (-0.11) (-1.03)
Stock return volatility 0.2 -0.7 -0.39 1.69 2.33 -0.02 (0.20) (-0.60) (-0.62) (0.93) (0.83) (-0.06)
PPE 0.14* -0.39*** (1.76) (-2.84)
Asset maturity 0.02 0.10*** (1.59) (3.11)
Profitability -0.65** 0.06 (-2.54) (0.24)
Abnormal earning 0.001 0.001 (1.24) (0.39)
50
Regulated dummy 0.21 -0.44* 0.31***
(1.46) (-1.66) (3.30)
Rated-firm dummy 0.56* 0.05 -0.1
(1.87) (0.15) (-0.54)
Callable 0.1 0.52** -0.03 0.14 0.93*** -0.09*** (0.45) (2.07) (-0.25) (1.48) (4.22) (-4.72)
Investment tax credit 0.08 0.32 (1.47) (1.35)
Prior covenant 0.04 -1.55*** (0.45) (-4.09)
Coupon rate 0.15*** 0.18*** (2.80) (31.55)
Term spread -0.11* -0.18 (-1.86) (-1.59)
CRSPRD 0.87*** -0.86*** 0.07 -0.29 -0.13 0.25*** (2.80) (-2.71) (1.20) (-1.07) (-0.28) (5.08)
TEDSPRD 0.16* -0.13 0.42** 0.21** -0.06 -0.03** (1.89) (-1.44) (3.00) (2.56) (-0.44) (-2.13)
Constant 0.24 -0.22 0.37 1.1 6.06** 0.04
(0.29) (-0.28) (1.04) (0.77) (2.17) (0.18)
Phase dummies Yes Yes Yes Yes Yes Yes
Industry fixed effect Yes Yes Yes No No No
Firm fixed effect No No No Yes Yes Yes
Bond rating fixed effect No No Yes Yes Yes Yes
Observations 1,500 1,500 1,500 1,500 1,500 1,500
51
Table 5: The Difference-in-Differences Test using TRACE Implementation as an Exogenous Shock to Bond Liquidity
This table reports the results of the difference-in-differences test. Since TRACE was introduced on July 1st, 2002, we classify bonds issued after July 1st, 2002 as
post-event, and bonds issued before July 1st, 2002 as pre-event. Our sample period is January 2001 to December 2007.We classify a firm as TRACE-affected if the
firm’s bonds had been included in TRACE, otherwise the firm is not TRACE-affected. Bonds issued by the TRACE-affected firms form the treatment group, and
bond issued by not TRACE-affected firms form the control group. Amihud ratio, the bond liquidity measure, is estimated using bond transactions from the NAIC
insurance database. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively
Panel A: Changes of bond terms before and after TRACE for the control and treatment groups
Control group Treatment group Diff-in-Diff
Pre-
TRACE
Post-
TRACE
Diff
(Post - Pre)
Pre-
TRACE
Post-
TRACE
Diff
(Post - Pre)
(6)-(3) T-stat
(1) (2) (3) (4) (5) (6) (7) (8)
Amihud ratio 0.0069 0.0066 -0.0003 0.0063 0.0049 -0.0014*** -0.0011 (-1.56)
Covenant index 4.02 4.40 0.38 3.86 3.32 -0.55*** -0.93*** (-3.52)
Maturity 9.06 9.37 0.31 9.10 10.89 1.79*** 1.49** (2.23)
Offering spread 3.12 2.67 -0.45*** 2.41 1.64 -0.77*** -0.32* (-1.71)
Observations 442 353 973 1,733
Panel B: Difference in bond terms between the control and treatment group before and after TRACE
Pre-TRACE Post-TRACE Diff-in-Diff
Control
group
Treat
group
Diff
(Tre – Col)
Control
group
Treat
group
Diff
(Tre – Col) (6)-(3) T-stat
(1) (2) (3) (4) (5) (6) (7) (8)
Amihud ratio 0.0070 0.0063 -0.0007 0.0070 0.0054 -0.0015 -0.0008 (1.39)
Covenant index 4.26 3.91 -0.35*** 4.36 3.24 -1.12*** -0.78*** (-3.37)
Maturity 9.33 9.28 -0.05 9.09 10.59 1.50*** 1.55*** (3.23)
Offering spread 2.87 2.47 -0.40*** 2.53 1.61 -0.92*** -0.52*** (4.15)
Observations 869 869 624 624
52
Table 6: Liquidity Effects on Bond Contracts Using Instrumental Variables
This table reports the second-stage regression of a two-stage least squares test on the estimation of the liquidity effect on the offering spread, covenant index, and
debt maturity of newly issued bonds. The first-stage estimation of firm-level bond liquidity is as follows:
1 1 , 3 , 4 ,,
, , ,
( )
+
i t i t i ti t
i t i t i t
Log Amihud Bond Age Bond Volatlity Log Bond Trading Volume
Firm Controls Market Conditions Year dummy Industry dummy
where the instrumental variable is the firm-level offering-amount weighted average of bond age. Firm controls includes the Log(firm size), book leverage, equity
volatility, capital expenditure, asset maturity, abnormal earnings, and firm credit rating dummies; market conditions include the term slope of treasury bonds,
shocks in market-level credit risk, and shocks in the Euro-dollar and treasury spread. The second-stage is a GMM estimation of the simultaneous equations of
offering yield spread, covenant, and maturity. The estimation results of the second stage are reported below. In all regressions, we control for industry and year
fixed effects. In the regression of offering yield spread, we further control for dummies of bond ratings. T-statistics, reported in parentheses, are calculated based
on standard errors clustered by firms. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Log(Covenant Index) Log(Maturity) Log(Offering Spread)
(1) (2) (3)
Fitted Log(Amihud) 12.95*** -11.38*** 7.34***
(3.20) (-2.75) (3.37)
Log(Offering spread) -1.36*** 1.39***
(-15.78) (18.21)
Log (Maturity) 0.83*** 0.52***
(18.25) (9.76)
Log (Covenant) 0.79*** -0.37***
(12.48) (-11.53)
Leverage 0.30** -0.56*** 0.28***
(2.07) (-4.22) (3.96)
Firm size -0.12*** 0.15*** -0.08***
(-6.67) (8.90) (-8.95)
Tobin’s Q 0.01 0.01
(0.83) (0.93)
Stock return volatility 21.02*** -24.10*** 13.57
(7.43) (-9.61) (9.26)
PPE 0.02E-1
(0.13)
Asset maturity -0.02E-1**
(2.01)
Profitability -0.25***
(-3.41)
53
Abnormal earning -0.32
(-0.52)
Regulated dummy 0.40*** -0.53*** 0.28***
(7.24) (-8.59) (7.46)
Rated-firm dummy 0.01 -0.06 0.03
(0.18) (-1.02) (1.03)
Callable 0.45*** -0.36*** 0.18***
(6.41) (-5.75) (5.20)
Investment tax credit -0.01*
(-1.72)
Prior covenant 0.24***
(5.82)
Coupon rate 0.04***
(3.28)
Term spread -0.07***
(-4.61)
CRSPRD 0.33*** -0.38*** -0.22***
(3.86) (-4.39) (-2.71)
TEDSPRD -0.28* 0.33** 0.25***
(-1.95) (2.20) (5.30)
Constant 0.45* -0.33 0.32**
(1.77) (-1.31) (2.41)
Industry fixed effect Yes Yes Yes
Year fixed effect Yes Yes Yes
Bond rating fixed effect No No Yes
Observations 2,524 2,524 2,524
54
Table 7: Relation between Firm-level Bond Liquidity at Bonds’ Issuance and Realized Bond Liquidity after the Bonds’
Downgrading
We collect bonds that are issued and also downgraded in our sample time period. This table examines whether firm-level bond liquidity at the issuance of a new
bond has predictive power for selling pressure when the bonds are downgraded in the future. The dependent variable is daily average Amihud Ratio in the month
following the bond rating downgrade, which proxies the degree of difficulty in trading the downgraded bonds. In Panel A, the main explanatory variable is the rank
of firm-level bond liquidity at the time of bond issuance. We divide the sample into quintiles based on the rank of the firm-level Amihud Ratio at the time of the
bond issuance. The quintiles are labeled from zero to four, with zero indicating the best firm-level bond liquidity and four representing the worst firm-level bond
liquidity. In panel B, we create a dummy variable that equals one if the firm-level bond liquidity at the issuance time of the downgraded bond is above the median
and zero otherwise. T-statistics, which are reported in parentheses, are calculated based on standard errors clustered by firms. ***, **, and * denote statistical
significance at the 1%, 5%, and 10% levels, respectively.
Panel A. Ranking the sample into quintiles by firm-level bond liquidity at new bonds’ issuance
(1) (2) (3)
Quintile rank of liquidity at issuance 0.0007*** 0.0006** 0.0007**
(2.66) (2.28) (2.12)
Firm controls Yes Yes Yes
Bond characteristics Yes Yes Yes
Bond rating fixed effect No Yes Yes
Year fixed effect No No Yes
Firm fixed effect No No Yes
Observations 1,564 1,564 1,564
Panel B: Ranking the sample into high and low groups by firm-level bond liquidity at new bonds’ issuance
(1) (2) (3)
Dummy of liquidity at issuance 0.0017** 0.0022** 0.0019**
(2.25) (2.28) (2.04)
Firm controls Yes Yes Yes
Bond characteristics Yes Yes Yes
Bond rating fixed effect No Yes Yes
Year fixed effect No No Yes
Firm fixed effect No No Yes
Observations 1,564 1,564 1,564
55
Table 8: Mechanisms behind the Effects of Bond Liquidity on Debt Contracts
This table reports the GMM estimates of the simultaneous equations for the liquidity effect on debt contracts. The
dependent variables are the natural logarithms of offering spread, debt maturity and covenant index. Bond liquidity is
proxied by Log(Amihud). Panel A studies the interaction between bond liquidity and the D_CDS dummy, which equals
one if there were traded CDS contracts of the firm before the time of bond issuance and zero otherwise. Panel D
examines the interaction between bond liquidity and firm value proxied by Tobin’s Q. Panel C examines the
interaction between bond liquidity and the high-yield dummy, which equals one if the bond is rated as a high-yield
bond and zero otherwise. Panel C studies the interaction between liquidity and the ratio of short-term debt to long-
term debt, where short-term debt (long-term debt) is defined as debt maturing within (above) three years. We control
for firm characteristics and industry and year fixed effects in all regressions. In the regression of offering yield spread,
we further control for dummies of bond ratings. T-statistics, which are reported in parentheses, are calculated based
on standard errors clustered by firms. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,
respectively.
Panel A. The effects of CDS on the effect of bond liquidity
Log(Covenant Index) Log(Maturity) Log (Offering Spread)
(1) (2) (3)
Log(Amihud) 20.15*** -19.39*** 10.32***
(4.21) (-3.78) (3.58)
Log(Amihud) x D_CDS -11.77** 9.57* -3.71
(-2.30) (1.74) (-1.20)
D_CDS 0.12** -0.04 0.01E-1
(1.99) (-0.68) (0.02)
Log (Offering spread) -1.02*** 1.26***
(-14.81) (17.02)
Log (Maturity) 0.57*** 0.56***
(15.96) (7.36)
Log (Covenant) 0.81*** -0.42***
(12.23) (-10.65)
Other controls Yes Yes Yes
Industry fixed effect Yes Yes Yes
Year fixed effect Yes Yes Yes
Bond rating fixed effect No No Yes
Observations 2,632 2,632 2,632
Panel B. The effects of firm value on the effect of bond liquidity
Log(Covenant Index) Log(Maturity) Log (Offering Spread)
(1) (2) (3)
Log(Amihud) 37.96*** -31.65*** 17.52***
(5.58) (-4.83) (4.29)
Log(Amihud) x Tobin’s Q -15.36*** 13.49*** -7.11***
(-3.69) (3.42) (-2.88)
Tobin’s Q 0.03 0.04 -0.04*
(0.84) (1.32) (-1.91)
Log (Offering spread) -1.51*** 1.37***
(-16.47) (19.30)
Log (Maturity) 0.92*** 0.63***
(18.56) (9.38)
Log (Covenant) 0.65*** -0.32***
(11.79) (-9.12)
Other controls Yes Yes Yes
56
Industry fixed effect Yes Yes Yes
Year fixed effect Yes Yes Yes
Bond rating fixed effect No No Yes
Observations 2,631 2,631 2,631
Panel C: The effects of credit rating on the effect of bond liquidity
Log(Covenant Index) Log(Maturity) Log (Offering Spread)
(1) (2) (3)
Log(Amihud) 13.95*** -15.50*** 6.91***
(2.94) (-3.27) (3.83)
Log(Amihud) x High yield dummy 21.24** -15.68* 2.40
(2.34) (-1.75) (0.70)
High Yield 0.23** -0.39*** 0.49***
(2.18) (-3.94) (5.52)
Log (Offering spread) -1.90*** 1.98***
(-15.19) (17.36)
Log (Maturity) 0.90*** 0.31***
(19.79) (8.50)
Log (Covenant) 0.85*** -0.31***
(14.65) (-13.40)
Other controls Yes Yes Yes
Industry fixed effect Yes Yes Yes
Year fixed effect Yes Yes Yes
Bond rating fixed effect No No Yes
Observations 1,781 1,781 1,781
Panel D. The effects of the proportion of short-term debt on the effect of bond liquidity
Log(Covenant Index) Log(Maturity) Log (Offering Spread)
(1) (2) (3)
Log(Amihud) 10.49** -9.15** 4.19**
(2.52) (-2.45) (2.30)
Log(Amihud) x Short-term debt 21.09** -16.01* 8.90*
(2.00) (-1.70) (1.92)
Short-term debt 0.01 -0.03 0.01
(0.13) (-0.31) (0.20)
Log (Offering spread) -1.54*** 1.38***
(-18.36) (19.10)
Log (Maturity) 0.98*** 0.45
(21.80) (7.89)
Log (Covenant) 0.76*** -0.32**
(14.30) (-12.01)
Other controls Yes Yes Yes
Industry fixed effect Yes Yes Yes
Year fixed effect Yes Yes Yes
Bond rating fixed effect No No Yes
Observations 2,375 2,375 2,375
57
Table 9: Liquidity Effect on the Use of Various Types of Covenants
This table reports the GMM estimation of the liquidity effect on the terms of debt contracts. We decompose the overall covenants index into several sub-indices.
The dependent variables are the log of offering spread, the log of debt maturity, and the log of sub-covenant indices. In all of the regressions, we control for firm
characteristics and industry and year fixed effects. In the regression of offering yield spread, we further control for the dummies of bond ratings. T-statistics, which
are reported in parentheses, are calculated based on standard errors clustered by firms. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,
respectively.
Panel A: Effects of bond liquidity on external financing-related covenants
Borrowing Restrictive Covenants Stock Issue Restrictive Covenants
Log(Sub-covenant
Index) Log(Maturity)
Log(Offering
Spread) Log(Sub-covenant
Index) Log(Maturity)
Log(Offering
Spread)
(1) (2) (3) (4) (5) (6)
Log(Amihud) 9.43*** -9.47*** 5.59*** -1.81*** -7.77*** 3.76***
(5.72) (-4.27) (5.92) (-4.46) (-3.69) (5.88)
Log(Offering spread) -0.80*** 1.11*** 0.21*** 1.22***
(-12.54) (18.00) (12.68) (18.17)
Log (Maturity) 0.55*** 0.32*** -0.16*** 0.15***
(15.03) (8.23) (-16.80) (3.90)
Log (Covenant) 0.76*** -0.30*** -3.67*** 0.08
(12.69) (-10.78) (-12.20) (0.38)
Other controls Yes Yes Yes Yes Yes Yes
Industry fixed effect Yes Yes Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes Yes Yes
Bond rating fixed effect No No Yes No No Yes
Observations 2,631 2,631 2,631 2,631 2,631 2,631
58
Panel B: Effects of bond liquidity on event-related covenants
Antitakeover Restrictive Covenants Default Related Covenants
Log(Sub-covenant
Index) Log(Maturity)
Log(Offering
Spread) Log(Sub-covenant
index Log(Maturity)
Log(Offering
Spread)
(7) (8) (9) (10) (11) (12)
Log(Amihud) 3.47*** -9.54*** 5.04*** 4.69*** -12.83*** 5.78***
(3.85) (-3.54) (4.69) (4.62) (-4.10) (5.05)
Log(Offering spread) -0.50*** 1.48*** -0.61*** 1.83***
(-16.88) (-17.80) (-21.51) (18.32)
Log (Maturity) 0.30*** 0.33*** 0.32*** 0.30***
(27.69) (8.54) (28.94) (8.42)
Log (Covenant) 2.70*** -1.00*** 2.96*** -1.08***
(17.59) (-12.67) (15.50) (-15.30)
Other controls Yes Yes Yes Yes Yes Yes
Industry fixed effect Yes Yes Yes Yes Yes Yes
Year fixed effect Yes Yes Yes Yes Yes Yes
Bond rating fixed effect Yes No No Yes No No
Observations 2,631 2,631 2,631 2,631 2,631 2,631
59
Table 10: Robustness Check Using Alternative Measures of Bond Illiquidity
This table reports the GMM estimates of the liquidity effect on the terms of debt contracts. The dependent variables
are covenant index, maturity, and offering spread. The illiquidity is proxied by price dispersion (PD) in Panel A,
imputed roundtrip cost (IRC) in Panel B, and inter quartile range (IQR) in Panel C. All of the variables are defined
in Table 1. In all of the regressions, we control for firm characteristics and industry and year fixed effects. In the
regression of the offering spread, we further control for the dummies of bond ratings. ***, **, and * denote statistical
significance at the 1%, 5%, and 10% levels, respectively.
Panel A: Price dispersion (PD) Log(Covenant Index) Log(Maturity) Log(Offering Spread)
(1) (2) (3)
PD 0.52*** -0.46*** 0.30*** (3.30) (-2.89) (3.64)
Log(Offering spread) -1.33*** 1.35***
(-15.76) (17.57)
Log (Maturity) 0.82*** 0.50*** (18.31) (8.90)
Log (Covenant) 0.79*** -0.36***
(12.45) (-11.15)
Panel B: Imputed roundtrip cost (IRC) Log(Covenant Index) Log(Maturity) Log(Offering Spread)
(1) (2) (3)
IRC 18.64*** -19.70*** 11.94*** (3.52) (-3.77) (4.47)
Log(Offering spread) -1.35*** 1.37***
(-15.67) (17.55)
Log (Maturity) 0.82*** 0.49*** (18.32) (8.93)
Log (Covenant) 0.79*** -0.35***
(12.45) (-11.08)
Panel C: Inter quartile range (IQR) Log(Covenant Index) Log(Maturity) Log(Offering Spread)
(1) (2) (3)
IQR 0.35*** -0.29*** 0.18*** (4.50) (-3.67) (4.25)
Log(Offering spread) -1.31*** 1.35***
(-15.75) (17.65)
Log (Maturity) 0.80*** 0.50*** (17.82) (8.86)
Log (Covenant) 0.80*** -0.37***
(12.27_ (-11.07)
Other controls Yes Yes Yes
Industry fixed effect Yes Yes Yes
Year fixed effect Yes Yes Yes
Bond rating fixed effect Yes No No
Observations 2,631 2,631 2,631
60
Internet Appendix for “Does Expected Bond Liquidity Affect Financial Contracts?”
Yaxuan Qi and Yuan Wang
This internet appendix provides additional information about the data, supplemental analyses and
robustness test to accompany the main article.
Table IA1: Multiple Phases of TRACE Implementation
Table IA2: Construction of the Covenant Indices
Table IA3: Change of Bond Liquidity before and after TRACE Implementation
Table IA4: First-stage Regression of the Instrumental Variable Tests
Panel A: Estimates of the First-stage Regression
Panel B: Relation between the Instrumental Variable and Debt Contract Terms
61
Table IA1: Multiple Phases of TRACE Implementation
This table presents the effective dates and announcement dates of TRACE implementation. The effective dates and descriptions of each implementation are
collected using FINRA’s TRACE FactBook (http://www.finra.org/Industry/Compliance/MarketTransparency/TRACE/FactBook/). The announcement dates are
collected using the FINRA’s regulation notice ( http://www.finra.org/Industry/Regulation/Notices/2013/index.htm)
Phase
Effective
Date Event description
Announcement
Date FINRA Notice
I Jul 1, 2002
During Phase I, public transaction information was
disseminated: (1) Investment-Grade debt securities
having an initial issue of $1 billion or greater; and (2)
50 Non-Investment-Grade (High-Yield) securities
disseminated under FIPS2 that were transferred to
TRACE. 75-minute transaction reporting requirement
Jan 23, 2001
01-18 SEC Approves Rules to Require Fixed Income
Transaction Reporting and Dissemination; posted on
03/21/2001
II
Mar 3, 2003
to
Apr 14, 2004
Phase II, fully effective on April 14, 2003, expanded
public dissemination to include transactions in smaller
Investment-Grade issues: (1) all Investment-Grade
TRACE-eligible securities of at least $100 million par
value (original issue size) or greater rated A3/A- or
higher; and (2) a group of 120 Investment-Grade
TRACE-eligible securities rated Baa/BBB and 50 Non-
Investment-Grade bonds.
Jan 31, 2003
03-12 SEC Approves Amendments to TRACE Rule 6250
and Other TRACE Rules: Transaction Information to be
disseminated on More Than 4,000 Corporate Debt
Securities, posted on 02/14/2003
III
Oct 1, 2004
to
Feb 7, 2005
In Phase III, fully effective on February 7, 2005,
approximately 99 percent of all public transactions and
95 percent of par value in the TRACE-eligible
securities market were disseminated immediately upon
receipt by the TRACE System. However, transactions
over $1 million in certain infrequently traded-
Investment-Grade securities were subject to
dissemination delays, as were certain transactions
immediately following the offering of TRACE-eligible
securities rated BBB or below.
30-minute transaction reporting requirement effective
on Oct 1, 2004
Apr 2, 2004
04-39 SEC 04/02/2004 Approves Amendments to Clarify
the Term "TRACE-Eligible Security" and to Expand the
Scope of an Exemption from TRACE Reporting
Requirements; Posted on 05/19/2003, Effective Date
06/17/2004; 05-02 Stage Two of the Expansion of
Dissemination of TRACE Transaction Data to Begin on
February 7, 2005 Instead of February 1, 2005 , announced
on January 04,2005; posted on 01/04/2005
IV Jan 9, 2006 Immediate dissemination of all public TRACE-
reportable transactions
Dec 28, 2005
06-01 SEC Approves Immediate Dissemination of
Information on TRACE Transactions;
Posted on 01/03/2006; Effective Date: 01/09/2006
62
Table IA2: Construction of the Covenant Indices
This table presents the details on how to construct the covenant index and sub-covenant indices. “FISD covenants”
are the original covenant variables provided by the FISD. We group these variables in to 22 indicators that equals one
if a specific covenant is included in the debt contract and zero otherwise. We further classify these 22 indictors into 8
groups based on the type of protection provided by the specific covenant. We create the sub-covenants indices by
adding the indicators of covenant within each group. Finally, we aggregate the 22 indictors to create a covenant index.
A high value of covenant index indicates greater restriction of covenants.
Group(8) Indicators(22) FISD covenants FISD definition of covenants
Payment
Dividend
payment
Dividends related
payments
Flag indicating that payments made to shareholders or other entities
may be limited to a certain percentage of net income or some other
ratio
Subsidiary dividend
related payments
Limits the subsidiaries’ payment of dividends to a certain percentage
of net income or some other ratio. For captive finance subsidiaries,
this provision limits the amount of dividends that can be paid to the
parent. This provision protects the debtholder against a parent from
draining assets from its subsidiaries.
Other payment Restricted payments Restricts issuer’s freedom to make payment (other than dividend
related payments) to shareholders and others
Asset
Transaction Transaction affiliates Issuer is restricted in certain business dealings with its subsidiaries
Investment
Investments Restricts issuer's investment policy to prevent risky investments
Subsidiary
investments Restricts subsidiaries’ investment
Asset sales
Asset sale clause Covenant requiring the issuer to use net proceeds from the sale of
certain assets to redeem the bonds at par of at a premium. This
covenant does not limit the issuers right to sell assets
Sale assets
Restriction on the ability of an issuer to sell assets or restrictions on
the issuer's use of the proceeds from the sale of assets. Such
restrictions may require the issuer to apply some or all of the sales
proceeds to the repurchase of debt through a tender offer or call.
Asset transfer Subsidiary sale assets
unrestricted
Issuer must use proceeds from sale of subsidiaries' assets (either
certain asset sales or all asset sales over some threshold) to reduce
debt.
Borrowing
Funded debt
Subsidiary funded
debt Restricts issuer's subsidiaries from issuing additional funded debt
(debt with an initial maturity of longer than one year)
Funded debt Restricts issuer from issuing additional funded debt. Funded debt is
an debt with an initial maturity of one year or longer
Subordinated
debt
Subordinated debt
issuance Restricts issuance of junior or subordinated debt
Senior debt Senior debt issuance Restricts issuer to the amount of senior debt is may issuer in the
future
Secured debt Negative pledge
covenant The issuer cannot issue secured debt unless it secures the current
issue on a pari passu basis
Indebtness
Indebteness Restricts user from incurring additional debt with limits on absolute
dollar amount of debt outstanding or percentage total capital
Subsidiary
indebteness Restricts the total indebtedness of the subsidiaries
Leverage test Restricts total-indebtedness of the issuer
Subsidiary leverage
test Limits subsidiaries' leverage
63
Leaseback
Sales leaseback
Restricts issuer to the type or amount of property used in a sale
leaseback transaction and may restrict its use of the proceeds of the
sale. A sale leaseback transaction is a method of raising capital in
which an organization sells some specific assets to an entity that
simultaneously leases the asset back to the organization for a fixed
term and agreed upon rate.
Subsidiary sales
leaseback
Restricts subsidiaries from selling then leasing back assets that
provide security for the debtholder. This provision usually requires
that assets or cash equal to the property sold and leased back be
applied to the retirement of the debt in question or used to acquire
another property to increase the debtholders' security
Liens Liens In the case of default, the bondholders have the legal right to sell
mortgaged property to satisfy their unpaid obligations
Subsidiary liens Restricts subsidiaries from acquiring liens on their property
Guarantee Subsidiary guarantee Subsidiary is restricted from issuing guarantees for the payment of
interest and/or principal of certain debt obligations
Stock
Common
stock
Stock issuance Restricts issuer from issuing additional common stocks
Subsidiary stock
issuance
Restricts issuer from issuing additional common stock in restricted
subsidiaries. Restricted subsidiaries are those which are considered
to be consolidated for financial test purposes.
Preferred
stock
Subsidiary preferred
stock issuance Restricts subsidiaries' ability to issue preferred stock
Other stock Stock transfer sale Restricts the issuer from transferring, selling, or disposing of it's own
common or the common stock of a subsidiary
Default Default
Cross acceleration A bondholder protective covenant that allows the holder to
accelerate their debt, if any other debt of the organization has be
accelerated due to an event of default
Cross default A bondholder protective covenant that will activate an event of
default in their issue, if an event of default has occurred under any
other debt of the company
Anti-
takeover Anti-takeover
Change control put
provisions
Upon a change of control in the issuer, bondholders have the option
of selling the issue back to the issuer (poison put). Other conditions
may limit the bondholder's ability to exercise the put option. Poison
puts are often used when a company fears an unwanted takeover by
ensuring that a successful hostile takeover bid will trigger an event
that substantially reduce the value of the company
Consolidation merger Indicates that a consolidation or merger of the issuer with another
entity is restricted
Profit
Earnings
Fixed charge coverage Issuer is required to have a ratio of earnings available for fixed
charges, of at least a minimum specified level.
Subsidiary fixed
charge coverage Subsidiaries are required to maintain a minimum ratio of net income
to fixed charges
Net earnings test
issuance
To issue additional debt the issuer must have achieved or maintained
certain profitability levels. This test is a variations of the (more
common) fixed coverage tests
Net worth
Maintenance net
worth Issuer must maintain a minimum specified net worth
Declining net worth If issuer's net worth (as defined) falls below minimum level, certain
bond provisions are triggered
Rating
decline Rating decline
Rating decline trigger
put A decline in the credit rating of the issuer (or issue) triggers a bond
holder put provision
64
Table IA3: Change of Bond Liquidity before and after TRACE Implementation
This table reports the difference of bond liquidity before and after the bond was included in TRACE. The
sample period is from July 2002 to December 2007. We use the enhanced TRACE to calculate bond liquidity
measures because the enhanced TRACE database includes both non-disseminated and disseminated bond
transactions. We identify the date of a bond disseminated through TRACE based on the first date the bond
appeared in the TRACE database. The liquidity measures are annual measures. That is, the liquidity of bonds
in the year before (after) it entered the TRACE. A large value of liquidity proxy indicates poor liquidity. The
negative change in the bond liquidity measure indicates the liquidity in the year after TRACE is smaller than
that in the year before TRACE. It means that bond liquidity indeed was improved after TRACE.
Amihud PD IRC IQR
Liquidity change -0.00027 -0.04844 -0.00163 -0.11180
T-statistics (-4.76) (-13.21) (-12.52) (-11.54)
Observations 3,243 3,243 3,243 3,243
65
Table IA4: First-Stage Regression of Instrumental Variables Tests
Panel A of this table presents the first-stage regression of our instrumental variable regression. The dependent
variable is the firm-level Log(Amihud). Panel B of this table examines the exclusive condition of our instrumental
variable by regressing debt contract terms over the instrumental variable bond age.
Panel A: Estimation of the first-stage regression
Firm bond age 0.0002***
(6.07)
Log(Trading volume) -0.0004***
(-5.93)
Bond volatility 0.0379***
(10.62)
Log of asset 0.0006***
(5.54)
Book leverage 0.0031***
(4.47)
Equity volatility 0.0415***
(4.81)
Capital expenditure -0.0010
(-0.54)
Asset maturity -0.00002
-1.49
Abnormal Earnings -0.0092
(-0.96)
TEDSPRD -0.0032***
(-6.93)
CRSPRD 0.0009**
(2.35)
Treasury slope 0.0002
(1.17)
Intercept -0.0010***
(-0.73)
Credit rating dummy Yes
Industry dummy Yes
Yearly dummy Yes
Panel B: Relation between the instrumental variable and debt contract terms
IV= bond age Log(Offering Spread) Log(Covenant Index) Log(Maturity)
(1) (2) (3)
Firm bond age 0.0004 0.0051 -0.0006 (0.12) (0.70) (-0.07)
Log(Offering spread) -1.36*** 1.44***
(-15.83) (18.24)
Log (Maturity) 0.34*** 0.82***
(9.40) (19.37)
Log (Covenant) -0.33*** 0.85***
(-12.51) (13.05)
The other controls are the same as those in Table 3.