Download Report

Credit Default Swaps, Debt Financing and
Corporate Liquidity Management∗
Marti G. Subrahmanyam
Stern School of Business, New York University
E-mail : [email protected]
Dragon Yongjun Tang
Faculty of Business and Economics, University of Hong Kong
E-mail : [email protected]
Sarah Qian Wang
Warwick Business School, University of Warwick
E-mail : [email protected]
May 19, 2015
ABSTRACT
Creditors monitor their borrowers less vigilantly and become tougher bargaining
parties in debt renegotiations when they can hedge their exposure using credit default
swaps (CDS). Thus, the inception of CDS trading on the debt of a ﬁrm can aﬀect its
ﬁnancing abilities and risk management policies. We ﬁnd that CDS ﬁrms hold more
cash after CDS trading is introduced – cash that might be partly ﬁnanced by new debt
issues. These increased cash holdings are more pronounced for CDS ﬁrms that do not
pay dividends, own more intangible assets, and have higher marginal value of liquidity.
For CDS ﬁrms with higher cash ﬂow volatility, these increased cash holdings do not
entail higher leverage. Overall, although trading in CDS facilitates debt ﬁnancing, it
also induces CDS-referenced ﬁrms to adopt more conservative liquidity policies.
∗
For helpful comments on previous drafts of this paper, we thank an anonymous referee, Lauren Cohen,
Miguel Ferreira, Andrea Gamba, Robin Greenwood, Jarrad Harford, Victoria Ivashina, Andrew Karolyi, Beni
Lauterbach, Kai Li, Chen Lin, Tse-chun Lin, Ron Masulis, Florian Nagler, Joshua Pollet, Henri Servaes, Laura
Starks, Ren´e Stulz, Neng Wang, Toni Whited, Ashraf Al Zaman, and seminar and conference participants at
the University of Hong Kong, the University of Warwick, the University of M¨
unster, University of Reading,
University of Manchester, the 2012 NTU International Conference on Finance, the 2012 SFM Conference at
the National Sun Yat-sen University, the 2013 European Finance Association Meetings, the 2014 Jerusalem
Finance Conference, the 2014 Annual Global Finance Conference, the 4th International Conference of F.E.B.S.
at the University of Surrey, the 2014 Risk Management Institute Annual Conference at National University
of Singapore, and the 2014 Northern Finance Association Annual Meetings.
Credit Default Swaps, Debt Financing and Corporate
Liquidity Management
ABSTRACT
Creditors monitor their borrowers less vigilantly and become tougher bargaining
parties in debt renegotiations when they can hedge their exposure using credit default
swaps (CDS). Thus, the inception of CDS trading on the debt of a ﬁrm can aﬀect its
ﬁnancing abilities and risk management policies. We ﬁnd that CDS ﬁrms hold more
cash after CDS trading is introduced – cash that might be partly ﬁnanced by new debt
issues. These increased cash holdings are more pronounced for CDS ﬁrms that do not
pay dividends, own more intangible assets, and have higher marginal value of liquidity.
For CDS ﬁrms with higher cash ﬂow volatility, these increased cash holdings do not
entail higher leverage. Overall, although trading in CDS facilitates debt ﬁnancing, it
also induces CDS-referenced ﬁrms to adopt more conservative liquidity policies.
1.
Introduction
Credit default swaps (CDS) are one of the major ﬁnancial innovations in recent decades and
are the main construct of the multi-trillion dollar credit derivatives market. CDS allow creditors to hedge credit risk without formal borrower approval. As a result, CDS can aﬀect
the creditor-borrower relationship and have implications for corporate ﬁnancial management.
Because corporate risk and liquidity management policies are better considered in an integrated framework, the inception of CDS trading on a ﬁrm’s debt oﬀers an opportunity for
researchers to observe the eﬀects of some of these joint considerations. There is anecdotal
evidence that corporate ﬁnance executives, such as CFOs and treasurers, take CDS market
positions into account in practice. “Like it or not, CFOs will increasingly be forced to deal
with the default-swap gamblers.”1 In this paper, we empirically examine how the introduction of CDS trading on individual ﬁrms’ debt aﬀects corporate liquidity – particularly when
it also aﬀects corporate leverage.
The theoretical foundation for our empirical analysis is developed in by Bolton and
Oehmke (2011) and Bolton, Chen, and Wang (2011). In this formulation, there is a tension between the beneﬁts and costs of CDS: On the one hand, CDS help increase the current
credit supply, because creditors are able to transfer part of their credit risk into the CDS market; on the other hand, the existence of CDS may change the relationship between creditors
and borrowers and may impose future ﬁnancing constraints – or costs – on borrowers. Bolton
and Oehmke (2011) show that lenders can use CDS to gain bargaining power with borrowers
in renegotiations and become tougher creditors particularly when engaged with borrowers
facing ﬁnancial distress. As a consequence, borrowers may attempt to avoid such renegotiations under those circumstances. Bolton, Chen, and Wang (2011) present a framework in
which ﬁrms consider liquidity and risk management jointly, and note that the marginal value
of liquidity is a major determinant of corporate ﬁnancial policies. The key state variable for
corporate ﬁnancial policies in their model is the cash-capital ratio of the ﬁrm. Moreover, cash
can be a more eﬀective risk management tool when other types of hedging are more costly
because of margin requirements and other frictions. If CDS transform lenders into tough
bargaining parties, the marginal value of liquidity will be higher because of the need to avoid
the contingency of renegotiation, and we therefore expect that corporate cash holdings will
be higher following the initiation of CDS trading on a ﬁrm’s debt.
1
“Too Big to Ignore: Debt derivatives markets are encroaching on corporate ﬁnance decisions.”
CFO Magazine, September 26, 2007, available online at http://www.cfo.com/printable/article.cfm/9821507?origin=archive (retrieved on May 11, 2014).
1
Nevertheless, creditor monitoring may be less stringent after the introduction of CDS
trading on a ﬁrms debt (Parlour and Winton (2013)). In this case, the ﬁrm may also engage
in risk-shifting and hold less cash to maximize the value of equity. Moreover, conventional risk
management analysis suggests that when lenders can hedge their risk eﬃciently, borrowers
may not have to undertake costly hedging strategies, which means that they can hold less
cash. In addition, considering the relaxed credit supply constraint after CDS trading begins
on a ﬁrm, its precautionary demand for holding cash may decrease. The ultimate eﬀect
of CDS will reﬂect this tension between these conﬂicting eﬀects, and the eﬀect of CDS on
corporate cash holdings is, thus, best determined empirically.
We construct a comprehensive dataset of the introduction of CDS trading on ﬁrm debt
to study the eﬀects of CDS on both cash and leverage. We rely on multiple data sources to
pin down the date of CDS introduction for particular ﬁrms. Over the 1997-2009 period, we
identify 901 CDS introductions for U.S. corporations with data from CRSP and Compustat.
Our ﬁrst main ﬁnding is that introducing CDS trading on a ﬁrm leads to an increase in that
ﬁrm’s cash holdings, after controlling for the existing determinants of corporate cash holdings.
The eﬀect is also quantitatively important: The level of cash holdings as a proportion of total
assets is 2.6% higher following the introduction of CDS trading on a ﬁrm, while the average
level of cash holdings for those ﬁrms before CDS introduction is approximately 9.5%.
Our ﬁnding for cash holdings is robust to variations in model speciﬁcations and to the
selection of ﬁrms for CDS trading. We employ both propensity score matching and instrumental variable (IV) methods to address the endogeneity concern that ﬁrms facing a potential
increase in cash holdings may be the very ones selected for CDS trading. The CDS eﬀect on
cash holdings remains statistically signiﬁcant even after matching ﬁrms based on CDS trading
propensity and following such instrumentation. The ﬁndings are consistent with the “tough
creditor” argument because the CDS eﬀect is more pronounced for ﬁrms closer to ﬁnancial
distress or with stringent ﬁnancial constraints, as measured by credit market access. However,
the CDS eﬀect on cash holdings is similar for well-monitored and unmonitored ﬁrms when
we use analyst coverage as a proxy for monitoring, which suggests that monitoring does not
negate the tough creditor problem. Moreover, the CDS eﬀect is more pronounced when there
are more CDS contracts outstanding and also when the ﬁrm’s directors and oﬃcers have
greater ﬁnancial expertise, which suggests that both the magnitude of the empty creditor
problem and the sophistication of ﬁrm management play an important role.
If cash is simply regarded as negative debt, then the increase in cash holdings may imply
a decrease in leverage. However, Saretto and Tookes (2013) and Subrahmanyam, Tang, and
Wang (2014) ﬁnd that ﬁrm leverage and default risk are both higher after the introduction of
2
CDS trading. Indeed, we ﬁnd that the high-cash phenomenon coexists with the high-leverage
phenomenon after CDS trading is introduced. We reconcile these seemingly contradictory
ﬁndings regarding cash holdings and leverage using the theoretical framework of Bolton,
Chen, and Wang (2014) to further illuminate the joint eﬀects of CDS on cash and leverage.2
Bolton, Chen, and Wang (2014) show that ﬁrms may simultaneously issue additional debt and
hold the proceeds as cash to weather potential ﬁnancial stress. Moreover, ﬁrms may simply
raise capital when market conditions are favorable, even without an immediate ﬁnancial need,
as shown by Bolton, Chen, and Wang (2013). Their framework provides implication for the
impact of ﬁnancial constraints and cash ﬂow volatility on corporate policies. When a ﬁrm’s
credit risk increases, a high debt–high cash holdings strategy is more favorable from the
shareholder’s perspective than a low debt–low cash holdings strategy, even for the same level
of net debt, based on the increase in the marginal value of cash holdings that is associated
with increases in leverage in this model. Therefore, CDS trading may lead to higher cash
holdings and higher leverage.
We ﬁnd evidence that ﬁrms raise debt and hold some of the proceeds as cash. However,
the parallel increase in leverage and cash holdings that is indicated by the correlation between
these two changes is less than perfect (although it is substantial), which suggests that they
do not always occur together. Firms do not always hold all the proceeds from debt issuance
as cash; however, they also do not always source additional cash through debt issuance.
Indeed, there are situations in which ﬁrms do not increase leverage but nonetheless increase
their cash holdings. For example, when ﬁrms have high cash ﬂow volatility, they increase
their cash holdings but not their leverage after CDS trading. Alternatively, when a ﬁrm has
substantial cash ﬂows, pays dividends, and is far from ﬁnancial distress, the threat from tough
creditors is minimal. Indeed, we ﬁnd that dividend-paying ﬁrms (i.e., ﬁrms that are generally
healthier than other ﬁrms) do not increase their cash holdings after CDS trading, although
they typically do increase their leverage. When a large amount of ﬁrm debt is coming due
and when the ﬁrm faces reﬁnancing risk or “rollover risk”, it holds more cash because the
marginal value of liquidity is high in such situations.
The Bolton, Chen, and Wang framework also considers other factors, including derivatives
hedging, lines of credit, and asset sales. A holistic examination of their model yields predictions that are beyond the scope of our study. However, we ﬁnd additional evidence consistent
with their predictions. For example, because tangible assets are more liquid and saleable than
intangible assets, ﬁrms may substitute asset liquidity for ﬁnancial liquidity. When a ﬁrm has
2
As discussed above, in the Bolton, Chen, and Wang (2011) framework, the marginal value of cash holdings
is a function of leverage, among other variables. Therefore, if leverage changes following the inception of CDS
trading, we must jointly consider cash holdings and leverage.
3
more intangible assets, an asset sale is less feasible; thus, for those ﬁrms, we ﬁnd a larger cash
increase after CDS trading is introduced.
Our study helps illuminate the dynamics of corporate liquidity, in general, and cash holdings in particular. Bates, Kahle, and Stulz (2009) document a dramatic increase in corporate
cash holdings in recent years. We conjecture that the advent of the credit derivatives market
may have contributed to this increase because CDS pose a potential threat to corporate decision makers. The creditor concern can also increase reﬁnancing risk, which has been shown
by Harford, Klasa, and Maxwell (2014) to be a determinant of corporate cash holdings.
Overall, our empirical ﬁndings are consistent with the predictions of Bolton, Chen, and
Wang (2011, 2013, and 2014). Importantly, we demonstrate the interactions between cash
and leverage policies that arise from their model, although cash holdings and outstanding
debt are both aﬀected by CDS. If we focus on only one aspect at a time, we may ﬁnd that
the two eﬀects seem to conﬂict with one another. However, under a uniﬁed framework with
external ﬁnancing costs, the combination of high leverage and high cash holdings is a natural
outcome of prudent corporate risk management.
Our paper provides new insights into the real eﬀects of credit derivatives on corporate
ﬁnancial decisions. Whereas CDS have been labeled “ﬁnancial weapons of mass destruction,”
they remain robust and eﬀective ﬁnancial tools for hedging credit risk and for undertaking
additional exposure and are widely utilized by ﬁnancial institutions as a result.3 Indeed,
banks’ use of CDS has even expanded since the 2007-2009 ﬁnancial crisis as a result of the
gradual implementation of the Basel III benchmarks and the capital relief that CDS provide
to banks under the new regulations. Thus, increases in cash holdings and leverage remain a
side-eﬀect of the active participation of lenders such as banks in the CDS market.
The paper proceeds as follows. Section 2 presents the related literature and the development of our hypotheses. In Section 3, we describe our sample and empirical methods. Section
4 presents our main empirical results regarding the eﬀect of CDS on cash holdings. The joint
eﬀects of CDS trading on cash and leverage are presented in Section 5. Section 6 concludes.
3
Berkshire Hathaway annual report for 2002: http://www.berkshirehathaway.com/2002ar/2002ar.pdf
4
2.
The Theoretical Framework and Its Testable
Predictions
We motivate our empirical analysis by sketching out the framework underlying the hypotheses
that follow. Although we do not present a formal model in this study, we draw upon prior work
in the ﬁeld to present the economic intuition underlying the model. The following corporate
ﬁnancing and debt renegotiation scenario involving the contingency of ﬁnancial distress sets
the stage for our subsequent analysis: An entrepreneur must ﬁnance an investment project
with a choice between debt and outside equity. The ﬁrm must pay creditors a pre-speciﬁed
amount as part of the loan contract on an intermediate date. There is the possibility of
renegotiation between the two counterparties if the reported cash ﬂow on the intermediate
date is insuﬃcient to meet the obligations to the debt holders. This situation can result
either when the cash ﬂow is actually low and reported by the entrepreneur to be such or
when the entrepreneur declares an artiﬁcially low cash ﬂow although it is, in fact, suﬃcient to
make the payment to the debt holders. In the latter instance, the borrower may strategically
report an artiﬁcially low cash ﬂow to divert part of the cash ﬂow to himself. In either event,
renegotiation of the debt ensues, and the ﬁrm could be liquidated if it fails or persist following
a renegotiated agreement between the entrepreneur and the debt holders. In anticipation of
such ﬁnancial distress and the consequent uncertain renegotiation process, the ﬁrm can hoard
enough cash to secure the intermediate payment and avoid renegotiation when the realized
cash ﬂow is low. This is the key insight developed by Hart and Moore (1998) and employed in
the context of CDS by Bolton and Oehmke (2011) in a discrete-time setting.4 Bolton, Chen,
and Wang (2011, 2013, and 2014) present a continuous-time variation of this setting in which
the information and incentive problems are modeled in a reduced-form fashion and give rise
to external ﬁnancing costs.
The focus of this study is to examine how ﬁrms’ corporate ﬁnancial policies are related to
the presence of CDS trading. In the classic sense pioneered by JPMorgan, banks buy CDS
to hedge their credit exposures, freeing up their balance sheets to fund additional corporate
loans.5 Bolton and Oehmke (2011) use this central insight to propose the ﬁrst theory of
corporate ﬁnance in the academic literature that considers the presence of CDS contracts,
4
Hart and Moore (1998) derive suﬃcient conditions for the debt contract to be optimal in this context,
whereas other models focus on equity, debt or both. This same study was also the ﬁrst to show that it is
optimal for the borrower to simultaneously hold cash and take on leverage when renegotiation is costly.
5
The introduction of CDS contracts in the early 1990s was, to a large extent, motivated by corporate
ﬁnancing needs in the context of constrained bank balance sheets (see Tett (2009)). Some of the determinants
of CDS trading are also discussed by Oehmke and Zawadowski (2014).
5
arguing that CDS raise the creditor’s bargaining power and simultaneously act as a device
for borrowers to pay out more of their cash ﬂow to debt holders. The former argument arises
from the reduced credit exposure of creditors, who are thus able to extract more from these
debtors in their renegotiations. Simultaneously, debtors are less incentivized to strategically
negotiate down their debt commitments. Nevertheless, there is a greater likelihood of default
in the context of recalcitrant creditors which can even result in bankruptcy rather than an
eﬃcient recapitalization – according to the latter argument. Employing similar reasoning,
Arping (2014) argues that CDS may even discourage the use of debt in anticipation of such
an eventuality. The increase in credit supply associated with CDS trading is empirically
corroborated by Saretto and Tookes (2013). When creditors are tougher in their debt renegotiations, they may force more bankruptcies than are necessary from a welfare perspective;
this is particularly true when creditors buy more CDS protection than their risk exposures
necessitate purely for hedging purposes, leading them to become so-called “empty creditors.”
This increase in bankruptcy risk following the initiation of CDS trading is documented by
Subrahmanyam, Tang, and Wang (2014) and is shown to be robust to a variety of controls.6
It should be emphasized that the literature has, thus far, focused on the leverage dimension
and the attendant consequences for bankruptcy risk, with each aspect being examined separately. By contrast, the consequences of CDS for corporate liquidity management have not yet
received suﬃcient attention. In this paper, we remedy this research lacuna by jointly considering the liquidity, leverage and risk management decisions of the ﬁrm after CDS referencing
the ﬁrms debt have been introduced.7
We conceptually superimpose the Bolton and Oehmke (2011) “empty creditor” model on
the uniﬁed framework of Bolton, Chen, and Wang (2011) to motivate our empirical analysis.
A central notion in this model is the marginal value of liquidity, which is determined endogenously. Bolton, Chen, and Wang (2011) study a ﬁrm’s investment and ﬁnancing problems in
a continuous-time model with adjustment costs and external ﬁnancing costs.8 Intended as a
model for mature ﬁrms, their baseline model only considers equity for external ﬁnancing (with
a credit line being added in an extended model and with the possibility of debt ﬁnancing later
being included in Bolton, Chen, and Wang (2014)).
6
Augustin, Subrahmanyam, Tang, and Wang (2014) provide an overview of the literature on CDS relating
to corporate ﬁnance, placing this issue in context. Bolton and Oehmke (2013) also discuss the strategic
conduct of CDS market participants in this setting.
7
Almeida, Campello, Cunha, and Weisbach (2014) survey the literature on liquidity management and call
for further examination to distinguish the dramatic increase in cash holdings in recent years from the time
series patterns of other forms of liquidity management, such as (bank) lines of credit.
8
They consider time-invariant ﬁnancing opportunities. Bolton, Chen, and Wang (2013) extend this analysis
to time-varying ﬁnancing opportunities and also consider the market timing of the ﬁrms equity issuance.
6
The key state variable for decision makers in these various models is the cash-capital
ratio of the ﬁrm. Their formulation shows that the ﬁrm may be in one of the three regions,
depending on the state of its intermediate cash ﬂow: payout, internal ﬁnancing, and external
ﬁnancing or liquidation. The marginal value of liquidity is low in the payout region and high in
the external ﬁnancing region because of external ﬁnancing frictions. The introduction of CDS
may increase future external ﬁnancing costs, particularly for high credit risk ﬁrms (Ashcraft
and Santos (2009)). Therefore, CDS trading may shift the boundaries between the three
regions such that the ﬁrm is more likely to be in the external ﬁnancing or liquidation region
because of the tougher creditors. Therefore, on average, the marginal value of liquidity will
be higher following the introduction of CDS trading. Moreover, as ﬁrms become riskier after
CDS trading has been initiated (Subrahmanyam, Tang, and Wang (2014)), they accumulate
higher cash reserves due to their precautionary motives and rely on cash more than lines of
credit for liquidity management (as argued by Acharya, Devydenko, and Strebulaev (2012),
and Acharya, Almeida, and Campello (2013)). Therefore, this line of analysis predicts that
ﬁrms will increase their cash holdings following the introduction of CDS trading when the
marginal value of liquidity is high.9
Hypothesis 1 (CDS, Tough Creditors and Cash Holdings) The cash holdings of ﬁrms
increase after the inception of CDS trading on their debt.
In addition to the tougher creditor theory, other theories may also have implications for
the relationship between CDS trading and cash holdings. A reasonable concern is that banks
may reduce debtor monitoring when they can buy CDS on the debt (as in Morrison (2005),
Parlour and Winton (2013)). In such a case, the borrower may engage in risk shifting (see,
e.g., Campello and Matta (2013), Karolyi (2013)). Such moral hazard may result in less cash
holding by the ﬁrm, as argued by the agency theories of cash, which are discussed by, among
others, Harford, Mansi, and Maxwell (2008), particularly as a ﬁrm nears ﬁnancial distress
or bankruptcy. Therefore, the liquidity and monitoring arguments can yield contradictory
predictions, particularly for ﬁrms close to ﬁnancial distress.
The central theme highlighted by Bolton, Chen, and Wang (2011) is that “cash management, ﬁnancial hedging, and asset sales are integral parts of dynamic risk management.”
9
Alternative mechanisms might also lead to the positive relationship between CDS trading and cash holding. For example, CDS may increase the bargaining power of bank lenders as CDS-protected banks are in
better negotiating positions. Pinkowitz and Williamson (2001) argue that bank power may aﬀect corporate
cash holdings. Strong banks persuade ﬁrms to hold large cash holdings as a cushion rather than to use such
cash to pay down their debt. In this vein, these authors ﬁnd that cash holdings are higher in Japan because
of the greater negotiating power of large banks.
7
Gamba and Triantis (2014) also emphasize the value created by a dynamically integrated risk
management strategy. Thus, the main objective of our study is to examine diﬀerent ways of
managing ﬁrm risk in a uniﬁed framework, particularly with respect to the joint eﬀects of
CDS trading on both cash and leverage.10 Cash is not simply regarded as negative debt when
ﬁrms face heightened risk, as argued by Acharya, Almeida, and Campello (2007). Firms may
raise external funds, e.g., issue new equity (Bolton, Chen, and Wang (2013)) or debt (Bolton,
Chen, and Wang (2014)), hoarding the proceeds as cash even when there is no immediate
use of the funds, particularly with benign market conditions at issuance. The notion that
ﬁrms may issue long-term debt and save the proceeds as cash was ﬁrst suggested by Hart and
Moore (1998) in a context in which there is a possible renegotiation stage in the interim.11
Bolton, Chen, and Wang (2014) present a dynamic model of optimal capital structure and
liquidity management. In their model, ﬁrms face external ﬁnancing frictions and need to use
liquidity reserves to service outstanding debt (i.e. debt servicing costs). The interactions of
the two factors exacerbate the precautionary demand for cash. Therefore, ﬁnancially constrained ﬁrms will exploit the increased credit supply to increase leverage, on the one hand,
but hold more cash as a precaution, on the other.
As discussed above, the Bolton, Chen, and Wang (2011, 2014) formulation shows that ﬁrms
may be in one of three diﬀerent regions in terms of their marginal value of cash holdings. In
the ﬁrst of these, following the introduction of CDS trading, the marginal value of cash is
low when ﬁrms are in the payout region. In this region, the threat from empty creditors
based on CDS is minimal, and ﬁrms are less likely to increase their cash holdings as a result.
However, the ﬁrm can still enjoy the beneﬁts of increased credit supply and their leverage will
nonetheless increase. Therefore, we predict that cash and leverage increases are more likely
for dividend nonpayers than payers following the introduction of CDS trading. For dividend
payers, such increases result in increased leverage but not cash holdings.
Hypothesis 2 (Dividend Payout) The eﬀect of CDS trading on cash holdings is more
pronounced for ﬁrms that do not pay dividends. In contrast, the eﬀect of CDS trading on
leverage is signiﬁcant for both dividend payers and non-payers.
One unique prediction of the Bolton, Chen, and Wang (2014) model is that ﬁrms increase
their leverage when cash ﬂow volatility increases (instead of decreasing leverage as other
10
We thank an anonymous referee for suggesting that we study the simultaneous eﬀects of CDS on cash
and leverage.
11
This insight is further explored by Acharya, Huang, Subrahmanyam and Sundaram (2006), and Anderson
and Carverhill (2012), who show that cash increases with the level of long-term debt. Eisfeldt and Muir (2014)
document a positive relationship between debt issuance and cash accumulation.
8
structural models would predict) and hold more cash because the high leverage–high cash
strategy is better from the equity holders’ perspective than the low leverage–low cash strategy
even for the same level of net debt. Bolton, Chen, and Wang (2014), Figure 3, Panel A and
Panel B, show that when cash ﬂow volatility is in the highest region, leverage decreases and
cash holding increases with cash ﬂow volatility. In this setting, the high marginal value of
cash increases the demand for cash. But the concern about debt servicing costs, i.e., that
debt payments drain the ﬁrms valuable liquidity reserves, decreases the demand for leverage.
These divergent relationships between cash and leverage oﬀer a good setting in which to test
these theoretical predictions.
Hypothesis 3 (Cash Flow Volatility) For ﬁrms with higher cash ﬂow volatility, CDS trading has a stronger eﬀect on cash holdings but a weaker eﬀect on leverage.
The uniﬁed framework of Bolton, Chen, and Wang (2011, 2013, and 2014) is so rich that
it is diﬃcult for a single empirical study to fully explore all its implications. Moreover, other
studies have provided complementary evidence. For example, Bolton, Schaller, and Wang
(2013) empirically examine the investment implications of the Bolton, Chen, and Wang (2011)
model and ﬁnd supporting evidence. In this spirit, we attempt a somewhat broader empirical
analysis of their framework and consider alternative risk management tools in this uniﬁed
framework, and one of these is raising cash through asset sales. Because tangible assets,
including cash, are more liquid than intangible assets, they can be used as collateral for risk
management or borrowing, as argued by Rampini and Viswanathan (2013) and Rampini, Suﬁ
and Viswanathan (2014). They are also more diﬃcult to be expropriated by managers for
their private beneﬁt, and ﬁrms with more tangible assets are, thus, likely to be more immune
to debt-equity conﬂicts.
Hypothesis 4 (Asset Tangibility) For ﬁrms with more intangible assets, CDS trading has
a stronger eﬀect on cash but a weaker eﬀect on leverage.
In the following sections on empirical analysis, we test the predictions made above, while
bearing in mind the concern that CDS trading is endogenous. In particular, we address
such concerns carefully by using IVs and following the prior literature, including Saretto and
Tookes (2013) and Subrahmanyam, Tang, and Wang (2014).
9
3.
Data and Empirical Specification
3.1. Data
We use CDS transaction data to identify a sample of ﬁrms with CDS contracts referencing
their debt. Our CDS transaction data are from CreditTrade and the GFI Group. In contrast
to the CDS quote data employed in some of the previous studies, our data contain actual
trading records with complete contractual information. Given the over-the-counter nature of
CDS contracts, we use the ﬁrst CDS trading date in our sample as the CDS introduction
date and compare the changes in corporate cash holdings and leverage following the onset of
CDS trading. We further cross-check this CDS sample against the Markit database, which
provides end-of-day valuations based on a survey of broker-dealers. In an auxiliary analysis, we
also utilize more detailed transaction information and construct continuous measures of CDS
exposures. The combined sample covers the period from June 1997 to April 2009 and includes
901 North American corporations that have CDS initiated on their debt at some time during
the sample period. The industry coverage of the ﬁrms on which CDS are traded (henceforth,
CDS ﬁrms) in our sample is quite diversiﬁed. Most are in the manufacturing, transportation,
communications, and utilities sectors.12 Our data on corporate cash holdings, leverage and
other ﬁrm characteristics are from the Compustat database. Following Bates, Kahle, and
Stulz (2009), we measure cash holdings by the ratio of cash and marketable securities to total
assets.13 We obtain credit ratings data from Compustat and FISD, ﬁnancial expertise data
from RiskMetrics, and analyst coverage data from I/B/E/S.
Panel A of Table 1 presents the year-wise summary of CDS trading and cash ratios for
all ﬁrms in the Compustat database during the 1997-2009 period: the number of Compustat
ﬁrms (column 2), the number of CDS ﬁrms (columns 3 and 4), and cash ratios for ﬁrms with
and without CDS trading (columns 5 and 6). As the fourth column of the table shows, CDS
trading was initiated on the largest number of new ﬁrms during the 2000-2003 period. As
shown in the ﬁfth and sixth columns, similar to the ﬁndings in Bates, Kahle, and Stulz (2009),
there is an increasing trend over time in the cash ratios for both non-CDS and CDS ﬁrms
in our sample, but the increase is relatively larger for CDS ﬁrms: The average cash ratio for
12
We use the entire sample, including ﬁnancial ﬁrms, for our main analysis and report the estimation
results. However, we have also conducted our analysis by excluding ﬁnancial ﬁrms. The estimation results in
the Internet Appendix Table A2 show that our ﬁndings are similar in all cases whether we include or exclude
ﬁnancial ﬁrms.
13
Although the ratio of cash and marketable securities to assets is the most conventional measure of cash
holdings, we also analyzed alternative measures of the cash ratio and found similar results. The CDS eﬀects
are robust to diﬀerent deﬁnitions of cash holdings.
10
non-CDS ﬁrms increases by 16% from 1997 to 2009, whereas the corresponding increase in
the cash ratio is 43% for CDS ﬁrms, which have lower cash ratios to begin with. As shown in
Subrahmanyam, Tang, and Wang (2014), CDS ﬁrms are relatively large ﬁrms compared with
their non-CDS counterparts. Large ﬁrms generally hold less cash due to their economies of
scale: They incur lower transaction costs per unit in converting ﬁxed assets into liquid assets.
In our sample, the average cash ratio for non-CDS ﬁrms (0.209) is more than twice that of
CDS ﬁrms (0.082).
Summary statistics for ﬁrm characteristics are provided in Table 1, Panel B. Most of our
analysis is for CDS ﬁrms and their matching ﬁrms (we will discuss matching methods in
Section 4.2 below). In the regression sample, the average cash ratio is 0.095 and the average
leverage ratio is 0.274. On average, 57.2% of ﬁrms in the matching sample pay dividends. The
Pearson correlation coeﬃcient between Cash and Leverage is −0.318. In addition to cash ﬂow
volatility, the cash ratio has a high correlation with future investment opportunity measures,
including the Market to Book and R&D/Sales ratios (0.311 and 0.509, respectively).
3.2. The Baseline Empirical Specification
We employ the regression model in Opler, Pinkowitz, Stulz and Williamson (1999) and Bates,
Kahle, and Stulz (2009) to investigate the eﬀect of CDS on corporate cash holdings.The
dependent variable is the ratio of cash and marketable securities to total assets, which is
regression on a set of determinants of cash holdings and other controls including ﬁrm ﬁxed
eﬀects. The determinants of cash holdings in our empirical speciﬁcation of cash holdings
models are motivated by the transaction and precautionary explanations for cash holdings.
The set of independent variables include the industry cash ﬂow risk (Industry Sigma), the ratio
of cash ﬂow to total assets (Cash Flow/Assets), a measure of the investment opportunities
(Market to Book ), the logarithm of total assets (Size), the working capital ratio (Net Working
Capital/Assets), capital expenditures (Capital Expenditure), the leverage (Leverage), the ratio
of research and development to sales (R&D/Sales), dividend payments (Dividend Dummy),
the ratio of acquisitions to total assets (Acquisition Activity), and the proportion of foreign
pretax income(Foreign Pretax Income). We explain the variable construction and data source
in the Appendix.
We use an indicator variable in the model speciﬁcation to estimate the impact of CDS
trading on corporate cash holdings and leverage, following Ashcraft and Santos (2009), Saretto
and Tookes (2013), and Subrahmanyam, Tang, and Wang (2014). Our key independent
variable, CDS Trading, is a dummy variable that equals one for a CDS ﬁrm after the inception
11
of the ﬁrm’s CDS trading and zero before that time. The regression analysis is conducted on
the sample that includes CDS ﬁrms and non-CDS ﬁrms. Given the unobservable diﬀerences
between ﬁrms, we control for ﬁrm ﬁxed eﬀects in our panel data analysis. Therefore, the
coeﬃcient of CDS Trading captures the impact of the inception of CDS trading on cash
holdings and leverage.
A challenge in establishing causal eﬀects of CDS trading on corporate cash holdings and
leverage is the potential endogeneity of CDS trading as ﬁrms are selected into CDS trading.
There is the possibility that a third factor is aﬀecting the introduction of CDS trading,
corporate cash holdings and leverage. In that case, the observed eﬀects on cash holdings and
leverage might not be caused by the CDS contracts but might instead be the result of the
impact of this third factor. We use multiple methods to address this endogeneity concern,
including propensity score matching analysis and an instrumental variable approach, which
are discussed below.
Firms may make their ﬁnancing and risk management decisions simultaneously. We further
investigate the CDS eﬀect in a uniﬁed framework of corporate policies by jointly estimating
debt and cash policies in a simultaneous equation system. Our analysis of leverage follows
Saretto and Tookes (2013) and Subrahmanyam, Tang, and Wang (2014) but incorporates the
liquidity decision into the analysis.
4.
CDS Trading and Cash Holdings
In this section, we establish the empirical relationship between CDS trading and corporate
cash holdings to pave the way for a joint analysis of cash and leverage in the next section.
(The eﬀect of CDS trading on leverage itself is previously established by Saretto and Tookes
(2013) and Subrahmanyam, Tang, and Wang (2014).) We consider the endogeneity of CDS
trading by using propensity score matching and IVs. We aim to understand the source of the
CDS eﬀect by scrutinizing the ex ante precautionary eﬀect and comparing it against the ex
post monitoring eﬀect of the empty creditor theory.
4.1. Changes in Corporate Cash Holdings around CDS Introduction
The summary statistics in Panel A of Table I suggest that there is an increase in the cash
ratio for both CDS and non-CDS ﬁrms. To demonstrate that CDS ﬁrms experience a more
signiﬁcant increase in this ratio, we focus on the changes in the cash ratio around the inception
of CDS trading (deﬁned as date 0). Figure 1 shows the changes in the cash ratios for CDS
12
and non-CDS ﬁrms, from one year before the inception of CDS trading to zero (-1,0), one
(-1,1), two (-1,2) or three (-1,3) years following its inception. Non-CDS-matching ﬁrms are
selected from a sample of ﬁrms that do not have CDS trading at any time during the entire
sample period. For each CDS ﬁrm, we ﬁnd a non-CDS matching ﬁrm that is in the same
industry (measured by the 4-digit SIC code) that has the closest size to the CDS ﬁrm (as
measured by total assets). It is evident that the average cash ratio increases for both CDS
and non-CDS ﬁrms. However, the increase is more pronounced for the CDS ﬁrms. We observe
a 0.6% increase in the cash ratio for both CDS ﬁrms and non-CDS matching ﬁrms from year
−1 to year 0. However, from year −1 to year +3, the increase in cash holdings for CDS ﬁrms
is 0.7% more than that of the non-CDS matching ﬁrms. Given the mean cash ratio of 8%
across the CDS ﬁrms and their non-CDS industry and size matched ﬁrms, the 0.7% additional
increase in the cash ratio for CDS ﬁrms is economically meaningful. Therefore, we obtain
a preliminary indication from this ﬁgure that the increase in the cash ratio over the years
is greater for CDS ﬁrms following the introduction of CDS trading than for their non-CDS
counterparts.
4.2. Impact of CDS Trading on Cash Holdings
4.2.1. Propensity Score Matching
The endogeneity of CDS trading complicates the interpretation of the impact of CDS trading
on cash holdings. It is possible that investors may anticipate a ﬁrm’s increase in cash holdings, and initiate CDS trading on it as a result. Of course, we control for ﬁrm ﬁxed eﬀects
in all model speciﬁcations, which accounts for the time-invariant diﬀerences in characteristics
between CDS and non-CDS ﬁrms, and may partially address this issue. However, it is still
necessary to address the endogeneity directly. To that end, we implement alternative econometric methodologies, suggested by Li and Prabhala (2007) and Roberts and Whited (2012),
to control for endogeneity. We use propensity score matching and the instrumental variable
approach, to estimate the CDS eﬀect, after controlling for the selection of ﬁrms into the CDS
sample.
To implement these approaches, we ﬁrst predict the presence of CDS trading for individual
ﬁrms. Following Ashcraft and Santos (2009), Saretto and Tookes (2013), and Subrahmanyam,
Tang, and Wang (2014), the prediction model for CDS trading is estimated utilizing a probit
speciﬁcation with a dependent variable that equals one after the introduction of CDS trading,
and zero otherwise. The CDS prediction models are reported in Internet Appendix Table
A1. Table A1 shows that CDS trading can be explained reasonably well by the explanatory
13
variables that have a pseudo-R2 of approximately 38.9%. We further construct a propensity
score matching sample based on the CDS prediction model: for each CDS ﬁrm, we ﬁnd one
non-CDS matching ﬁrm with a similar propensity score for CDS trading. Next, we run the
cash holdings analysis on this matched sample. In constructing our propensity score matching
sample, we use four diﬀerent propensity score matching criteria to choose matching ﬁrms: (1)
the one non-CDS ﬁrm nearest the CDS ﬁrm in terms of propensity score; (2) the one ﬁrm
with the propensity score nearest the CDS ﬁrm and within a diﬀerence of 1%; (3) the two
non-CDS ﬁrms with the propensity scores nearest the CDS ﬁrm; and (4) the two non-CDS
ﬁrms with the propensity scores nearest the CDS ﬁrm and within a diﬀerence of 1%.
Roberts and Whited (2012) discuss the “parallel trends” assumption that requires “any
trends in outcomes for the treatment and control groups prior to treatment to be the same.”
Given the central importance of this assumption for the diﬀerence-in-diﬀerences estimator,
we ﬁrst compare the trends in the cash ratio during the pre-treatment era. The results are
presented in Figure 2. We compare cash holdings for CDS ﬁrms and their propensity-scorematched ﬁrms from two years prior to the CDS treatment to two years following treatment.
We ﬁnd that CDS ﬁrms have slightly lower cash ratios than non-CDS ﬁrms before treatment.
Afterwards, CDS ﬁrms catch up with their matching ﬁrms, and exhibit a greater increase in
cash holdings. Importantly, there is no signiﬁcant diﬀerence between the time series trends in
the cash ratios during the pre-treatment era for CDS and non-CDS matched ﬁrms. Following
Roberts and Whited (2012), we further conducted a t-test of the diﬀerence in the average
growth rates of the cash ratio between CDS and control ﬁrms prior to the treatment. The
t-test results indicate that the cash growth rate diﬀerence is not statistically signiﬁcant (tstatistic=−1.288) before CDS introduction. Therefore, it seems that the propensity-scorematched sample satisﬁes the parallel trend assumption.
We then conduct the propensity score matching analysis. Unlike the case when using
all non-CDS ﬁrms in the Compustat sample as the control group, ﬁrms in the restricted
propensity score matching sample are more comparable with one another. Table 2 presents
the regression results.14 In all these speciﬁcations, the coeﬃcient estimates for CDS Trading
are signiﬁcantly positive, which indicates that corporate cash holdings increase after CDS
trading has been introduced. The economic magnitudes are also substantial: For example,
compared with the sample mean cash ratio of 9.5% for this restricted sample, the 2.6%
change in cash holdings following the introduction of CDS, in the results using “nearest one”
14
We use all four alternative propensity score matching criteria discussed above to assess the robustness
of our propensity score matching results. Propensity scores are calculated based on Model 3 in Internet
Appendix Table A1. We also use all three CDS prediction models as a robustness check.
14
matching, represents a 27.4% increase in the mean cash ratio.15
The coeﬃcients of the control variables in this propensity-score-matched sample are consistent with prior ﬁndings. As predicted, ﬁrms with high cash ﬂow risk, as measured by
Industry Sigma, hold more precautionary cash. The negative sign of the coeﬃcient of Size
relates to economies of scale involved in holding cash: large ﬁrms hold proportionately less
cash. The coeﬃcient of Capital Expenditure is negative and signiﬁcant because capital expenditures create assets that can be used as collateral for future borrowing, thus reducing
the precautionary demand for cash holdings. As in the ﬁndings in the previous literature,
the sign of the Leverage coeﬃcient is negative.16 R&D/Sales is a measure of future growth
opportunities. Firms with higher R&D expenditures incur greater costs of being ﬁnancially
constrained because they must plan for future investment opportunities and must hold more
cash as a result. The coeﬃcient of Acquisition Activity has the same sign as that of Capital Expenditure, which is expected because acquisitions and capital expenditures are likely
to be substitutes for one another. Multinational ﬁrms with foreign income (Foreign Pretax
Income) may seek to hold more cash due to taxes associated with repatriating foreign income,
as documented in Foley, Hartzell, Titman, and Twite (2007).
We note that the propensity score matching approach is only eﬀective in controlling for
the observable diﬀerences in ﬁrm characteristics between the treatment and control groups.
It is possible that there is an unobservable variable that drives both the introduction of CDS
trading and corporate cash holdings; if this supposition were true, then propensity score
matching would not eﬀectively address endogeneity in this setting. In the next section, we
also use the IV approach to address the endogeneity issue directly to mitigate this concern.
4.2.2. The Instrumental Variable Approach
To allow for the possibility of time-varying unobserved heterogeneity across ﬁrms, we estimate
a two-stage least squares (2SLS) model with IVs in which the indicator variable, CDS Trading,
is treated as endogenous. Speciﬁcally, the cash holdings and the CDS contract status of a
ﬁrm can be modeled as follows:
15
We have conducted a placebo test in the propensity-score-matched sample in Internet Appendix Table A2
Panel C. We use data from the 1980s when there was no CDS trading, and perform the cash holding analysis
using pseudo-CDS ﬁrms and their control groups. We ﬁnd no eﬀect for the artiﬁcial CDS introductions on
cash holdings.
16
Leverage and cash policies might be jointly determined. Firms may use cash to reduce leverage, and
leverage might be a source of cash. We address the simultaneous ﬁnancing and liquidity management decisions
in much greater detail in Section 5.
15
Cash = βX + γ1 CDS Trading + δY + ϵ,
(1)
∗
CDS Trading = λZ + ω,
CDS Trading = 1, if CDS Trading∗ > 0; CDS Trading = 0, otherwise.
The dependent variable in the above speciﬁcation is the cash ratio, which is measured by
the ratio of cash and marketable securities to total assets. X is a vector of determinants of
cash holdings, and Y is a vector of other controls, such as ﬁrm ﬁxed eﬀects. The coeﬃcient
of interest is γ1 , which captures the impact of CDS on corporate cash holdings. The instrumented variable CDS Trading∗ represents the latent propensity of a ﬁrm to have CDS trading
introduced on its debt. In the above speciﬁcation, CDS Trading is allowed to be endogenous
because corr(ϵ, ω) ̸= 0. For identiﬁcation, we include IVs that aﬀect a ﬁrm’s propensity for
CDS introduction but do not aﬀect its cash holdings directly – other than through the impact
of CDS introduction. Therefore, Z in equation (1) includes the IVs.
Our choice of IVs is motivated by both econometric and economic considerations. We
use both Lender FX Usage and Lender Tier 1 Capital as instruments (Saretto and Tookes
(2013) and Subrahmanyam, Tang, and Wang (2014) provide more details on the construction
of the IVs). Econometrically, the instruments must satisfy both the relevance and exclusion
restrictions. The relevance condition is met based on the results in Internet Appendix Table
A1 which show that CDS trading is signiﬁcantly associated with Lender FX Usage and the
Lender Tier 1 Capital ratio. The instruments we use are economically sound because they
are associated with the overall hedging interest of the lenders or credit suppliers. Speciﬁcally,
lenders with a larger hedging position are generally more likely to trade the CDS of their
borrowers. Moreover, banks with lower capital ratios have a greater need to hedge the credit
risk of their borrowers via CDS.17
The ﬁtted value of CDS Trading is included in the second-stage analysis of the determinants of cash holdings. Table 3 presents the estimation results. To show the robustness of
our results, we present the IV results for each IV separately and two IVs jointly. In Model 1
we only employ Lender FX Usage as the instrumental variable. In Model 2, Lender Tier 1
Capital is the instrumental variable. In Model 3, we use both Lender FX Usage and Lender
Tier 1 Capital as instruments. We ﬁnd that Instrumented CDS Trading have positive and
signiﬁcant coeﬃcient estimates for all model speciﬁcations, suggesting that the presence of
17
It is notable that the instruments we use are not weak: We ﬁnd that the Sargan F -test statistics are
above 10 for both IVs, thus strongly rejecting the hypothesis of weak instruments.
16
CDS contracts leads to higher cash ratios even after ensuring that the key independent variable is identiﬁed. Therefore, the evidence supports a causal interpretation of CDS trading on
corporate cash holdings.
4.3. Firms’ Precautionary Considerations
Empty creditors may have a more disciplinary eﬀect when the manager has more ﬁnancial
expertise and when the CDS market is more prevalent. We examine whether such auxiliary
predictions of the empty creditor theory hold in the data.
4.3.1. The Financial Expertise of Top Management
The background of senior corporate executives and board members can greatly inﬂuence a
ﬁrm’s ﬁnancial policies. For example, ﬁrms run by CEOs who have previously experienced
ﬁnancial diﬃculties save more cash (Dittmar and Duchin (2013)). Furthermore, banks take
more risk when independent board directors have more ﬁnancial expertise (Minton, Taillard,
and Williamson (2014)). In addition, ﬁrms with CEOs who are ﬁnancial experts may hold
less cash and more debt (see Custodio and Metzger (2014)). In this spirit, ﬁrms with greater
ﬁnancial expertise among their board members and senior managers may have a better understanding of the potential eﬀect of CDS on their ﬁrms’ creditor relationships. Therefore,
they are more likely to take preemptive actions to protect themselves from the CDS-induced
“tougher” creditors. If such tougher creditors are indeed a major concern for borrowers, we
expect that the increase in cash holdings exhibited by CDS ﬁrms will be more pronounced
for those ﬁrms with management that has more ﬁnancial expertise.
We measure the ﬁnancial expertise of ﬁrms based on information from RiskMetrics. The
Directors Data in Riskmetrics include a range of variables related to the individual board
directors and the oﬃcers of ﬁrms, including their names, ages, whether they have ﬁnancial
expertise, etc. In our regression analysis, we deﬁne Number of Financial Experts as the log of
the number of ﬁnancial experts on the board. The interaction term of CDS Trading×Number
of Financial Experts captures the eﬀect of CDS trading on the cash holdings of ﬁrms with
ﬁnancial expertise. We conduct the cash holdings analysis on the sample of CDS ﬁrms after
controlling for their ﬁnancial expertise. The results in Model 1 Table 4 indicate that ﬁrms
with more ﬁnancial expertise hold less cash, a ﬁnding that resembles that of Custodio and
Metzger (2014). Moreover, the positive and signiﬁcant coeﬃcient for the interaction variable
implies that CDS ﬁrms with ﬁnancial expertise hold more cash.
17
4.3.2. CDS Amount Outstanding
Instead of using the regime variable, CDS Trading, which equals one after CDS trading is
introduced, we utilize detailed information about the notional amount of CDS contracts outstanding to construct a continuous measure of CDS exposure. Continuous economic variables
also help further address the self-selection concern in analyzing the eﬀects of CDS trading. As
noted by Li and Prabhala (2007), the magnitude of the selection variable (for CDS trading)
both introduces an independent source of variation and aids the identiﬁcation of the treatment
eﬀect, while simultaneously ameliorating self-selection concerns. In addition, the continuous
CDS outstanding measure is a proxy for the severity of the CDS eﬀect: The larger is the
amount of CDS outstanding, the greater the beneﬁts to the CDS-protected creditors and,
as a result, the tougher the empty creditors are likely to be in the process of re-negotiation.
Moreover, the amount of CDS outstanding is a proxy for CDS market liquidity; therefore,
the CDS spread of a ﬁrm with more CDS contracts outstanding and with a more liquid CDS
market as a result will be more sensitive to new information, such as the ﬁrm’s credit and
liquidity status. Therefore, the feedback eﬀect from the CDS market to the bond market will
be more severe for ﬁrms with larger amounts of outstanding CDS. When corporate liquidity
declines, the CDS market responds by increasing the CDS spread particularly for ﬁrms with
a liquid CDS market. A sharp decline in cash holdings, which results in a spike in the CDS
spread, could undermine market conﬁdence in the ﬁrm and reinforce a negative view of it.
As a result, it may be judicious for a ﬁrm to retain more cash on hand after the introduction
of CDS trading on its debt, in particular. Thus, both the tougher creditor mechanism and
the CDS feedback eﬀect predict that ﬁrms will have a greater incentive to hold cash reserves
when there are proportionately more CDS contracts outstanding on their debt.
We measure the level of corporate CDS outstanding by the ratio of the notional dollar
amount of CDS contracts outstanding to the total dollar amount of debt outstanding at the
same time (CDS Outstanding/Total Debt). We use the maturity date of each contract in our
CDS data to identify the outstanding amount of CDS at any given time.18 We scale the CDS
position by total debt to relate the dollar amount of CDS outstanding to the potential total
demand of creditors. We conjecture that ﬁrms with greater relative proportions of outstanding
CDS are likely to be more vulnerable to the CDS eﬀect. Our estimation results are presented
in Model 2 in Table 4. The analysis is conducted in the CDS sample, and we again ﬁnd a
signiﬁcant and positive coeﬃcient. These ﬁndings suggest that greater CDS exposure leads
to higher corporate cash holdings.
18
The Depository Trust & Clearing Corporation (DTCC) uses the same method but covers the entire CDS
universe. However, the DTCC only discloses data for the largest ﬁrms to the public.
18
4.4. Risk-shifting and Monitoring
The results, thus far, suggest that ﬁrms adopt a more conservative liquidity management
policy because of their precautionary considerations following the introduction of CDS trading
on their debt, which is consistent with the prediction that CDS trading creates tougher
creditors. However, CDS can change creditors’ incentives in multiple ways. Ex ante, when the
ﬁrm is far from distress, empty creditors are more willing to lend based on the risk mitigation
eﬀect of CDS, which also weakens creditors’ monitoring incentive. Ex post, when the ﬁrm
is in distress, empty creditors tend to be tougher and are incentivized to push the ﬁrm into
bankruptcy. These diverse empty creditor incentives have diﬀerent eﬀects on ﬁrms’ behavior.
Whereas the tougher creditor eﬀect predicts more conservative liquidity policies, there are
other implications associated with reduced monitoring by creditors. The decreased monitoring
incentives of empty creditors may induce CDS ﬁrms to take on more risk in the form of a lower
liquidity cushion. In addition to the risk-taking eﬀect, governance from other parties may
oﬀset the eﬀect of the decreased monitoring by creditors. Moreover, internal governance may
be strengthened to compensate for the lack of monitoring by creditors. Furthermore, weakened
monitoring may further aﬀect the cost of debt (as suggested by Ashcraft and Santos (2009)
and Che and Sethi (2014)), which increases precautionary cash savings. In this section, we
investigate the eﬀects of monitoring on the tension in the relationship between cash holdings
and CDS trading.19
4.4.1. Financial Constraints
The marginal value of cash may depend on a ﬁrm’s ﬁnancial constraints. Faulkender and
Petersen (2006) use credit ratings to measure access to the credit markets and ﬁnancial constraints. The “tougher creditor” implication of the empty creditor theory is that more ﬁnancially constrained ﬁrms may be concerned more about the impact of CDS trading. Therefore,
the eﬀect of CDS trading on corporate cash holdings may vary with the credit rating of the
issuing ﬁrm. Almeida, Campello, and Weisbach (2004) ﬁnd that ﬁrms facing greater capital
market frictions, i.e., ﬁnancially constrained ﬁrms, are more likely to retain more cash from
their free cash ﬂows. Similarly, because ﬁnancially constrained ﬁrms have fewer alternative
external ﬁnancing options when their lenders become tougher CDS-protected creditors, they
19
The risk mitigation eﬀect also predicts a decrease in cash holdings because the relaxed borrowing constraint
may induce ﬁrms to rely more on external ﬁnancing to manage the liquidity risk. We ﬁnd evidence that is
consistent with this prediction. For example, in Section 5.5 below, we ﬁnd that CDS ﬁrms with more tangible
assets have a greater increase in leverage and a lesser increase in cash holdings. Therefore, these multiple
channels may co-exist, and the net eﬀect is an empirical question that depends on ﬁrms’ characteristics.
19
tend to build up greater cash holdings after CDS trading is introduced on their debt.20 However, the liquidity and monitoring arguments can yield contradictory predictions. Borrowers
may engage in risk shifting in the form of lower liquidity cushion following the decreased creditor monitoring, particularly for ﬁrms closer to ﬁnancial distress or with stringent ﬁnancial
constraints. Therefore, if the tougher creditor eﬀect dominates, we expect the cash holdings
of ﬁnancially constrained ﬁrms to be more positively aﬀected by CDS trading.
Table 5 examines the impact of CDS trading on cash holdings, conditional on ﬁrms’
ﬁnancial constraints. Following the previous literature, credit ratings are used as the measure
of the tightness of ﬁnancial constraints facing a ﬁrm. Firms are divided into rated and unrated
categories based on their rating status during the current quarter, and unrated ﬁrms are
expected to be more ﬁnancially constrained. Next, we conduct the cash holdings analysis and
compare ﬁrms with and without credit ratings. The variable of interest is CDS Trading, which
captures the CDS trading impact and is conditional on a ﬁrm’s rating status. The results
indicate that the impact of CDS trading on ﬁrms cash holdings is even more signiﬁcant for
ﬁrms without credit ratings (0.040 vs. 0.023), which is consistent with the “tougher creditor”
argument.
4.4.2. Analyst Coverage
Governance of a ﬁrm from other parties, such as ﬁnancial analysts, may oﬀset the eﬀects of
decreased creditor monitoring, because ﬁnancial analysts play an important role in corporate
governance (see, e.g., Chen, Harford, and Lin (2015)). Using analyst coverage as a proxy for
monitoring, we expect that CDS may induce risk taking and decrease cash holdings for unmonitored ﬁrms (CDS ﬁrms without analyst coverage). Furthermore, Shan, Tang and Winton
(2014) ﬁnd that the covenant loosening eﬀect (decreased creditor monitoring) associated with
CDS trading is most pronounced for ﬁrms with less serious information asymmetry problems (i.e., ﬁrms with analyst coverage). Table 6 provides the results of our test of whether
monitoring aﬀects the relationship between cash holdings and CDS trading. We separate the
sample by analyst coverage information from I/B/E/S. The ﬁrst model in this table presents
the results for ﬁrm-quarters with analyst coverage. The second model presents the results for
ﬁrm-quarters with no analyst coverage. The coeﬃcient on CDS Trading in the ﬁrst model is
similar in both magnitude and signiﬁcance level to that of the second model (0.025 vs. 0.023),
which suggests that monitoring is not the driving force for the CDS eﬀect on cash holdings.
20
In addition, the CDS spread provides valuable information about the credit quality of a ﬁrm without
credit ratings. Consequently, it is even more valuable for unrated ﬁrms to maintain liquidity for purposes
of sustaining a reasonable CDS spread. Thus, the CDS feedback eﬀect argument also predicts that unrated
ﬁrms will be more heavily aﬀected by CDS trading.
20
We have further explored the distinctive role of monitoring by adding a corporate governance control. Corporate governance is another factor that may restrict the risk-taking
behavior of borrowers. We expect CDS ﬁrms with better governance (i.e., a low E-index) to
be less aﬀected by relaxed bank monitoring after CDS introduction and to exhibit a higher
increase in cash holdings. We ﬁnd some evidence (see Internet Appendix Table A3) that ﬁrms
with poor corporate governance have smaller increases in cash holdings after the introduction
of CDS. Therefore, the evidence is consistent with the monitoring and governance theory, but
the overall eﬀect is dominated by the precautionary eﬀect.
5.
Cash and Debt in A Unified Framework
The ﬁnancing and liquidity management policies of ﬁrms are likely to be jointly determined.
Because we ﬁnd increased cash holdings – whereas Saretto and Tookes (2013) and Subrahmanyam, Tang, and Wang (2014) document an increase in leverage – following the inception
of CDS trading, we investigate the joint CDS eﬀects on cash and leverage using a simultaneous equations system in this section. We aim to shed light on the overall eﬀects of CDS on
corporate ﬁnance in a uniﬁed framework proposed by Bolton, Chen, and Wang (2011, 2013,
and 2014).
5.1. Linkage Between Cash and Leverage: Cash Is Not Negative Debt
When there are external ﬁnancing costs, corporate policies are intertwined (Bolton, Chen,
Wang (2011)). In this setting, cash is not simply regarded as negative debt, and corporate policies are characterized by the marginal value of liquidity. Firms may prefer to issue
additional debt and save the proceeds as cash holdings when their hedging needs are high
(Acharya, Almeida, and Campello (2007)). With the same level of net debt, a high debt–high
cash strategy helps ﬁrms to be in the lower credit risk region than a low debt–low cash strategy (as suggested by the Bolton, Chen, and Wang (2014) model). Moreover, under stochastic
ﬁnancing conditions, ﬁrms have both a precautionary savings motive and a market timing
motive. Firms may time the market and obtain external ﬁnancing during favorable market
conditions (in a period of low external ﬁnancing costs) even when there is no immediate need
for external funds (as Bolton, Chen, and Wang (2013) argue). Eisfeldt and Muir (2014) also
predict a positive relationship between issuance and accumulated liquidity. Firms raise external ﬁnancing and use it for liquidity accumulation when the cost of their external ﬁnancing
is low or their return to liquidity accumulation is high. Under this uniﬁed framework of
21
corporate policies, we expect CDS ﬁrms to time their ﬁnancing decisions by borrowing more
ex-ante during favorable borrowing conditions in the market and simultaneously to increase
their cash holdings due to their precautionary motives, i.e., they will be characterized by both
high leverage and high cash holdings after the introduction of CDS.
We ﬁrst investigate whether debt issuance is an important source of cash holdings for CDS
ﬁrms. In Table 7, we jointly estimate the marginal cash savings and debt issuance decisions
for CDS ﬁrms and non-CDS ﬁrms, respectively. ∆Debt is the ratio of the net long-term debt
issuances to the total book value of assets, and ∆Cash is the change in the holdings of cash
and other marketable securities divided by total assets. We follow the previous literature,
such as Acharya, Almeida, and Campello (2007), and use Cash Flow/Assets, Market to Book,
Size, and Lag Debt or Lag Cash (i.e., lagged levels of long-term debt and cash holdings
scaled by total assets) as controls. The results indicate that both cash ﬂow and long-term
debt issuances are important source of cash for CDS ﬁrms, as evidenced by the positive
and signiﬁcant coeﬃcients for Cash Flow/Assets and ∆Debt (0.163 and 0.093, respectively).
Increased cash ﬂow increases debt capacity, whereas long-term debt issuance signiﬁcantly
increases cash holdings. However, for non-CDS ﬁrms, the main source of cash is operating
cash ﬂow, as shown by the signiﬁcant and positive coeﬃcient for cash ﬂow (0.322). Moreover,
the coeﬃcient for ∆Debt is insigniﬁcant (0.005) in the change in the cash model for non-CDS
ﬁrms. Therefore, long-term debt issuance does not seem to signiﬁcantly increase cash for
non-CDS ﬁrms.
Our ﬁnding of an increase in cash holdings following the introduction of CDS trading
may imply a decrease in leverage because ﬁrms may wish to preserve their debt capacity for
future contingencies if cash is, in fact, negative debt. However, Saretto and Tookes (2013)
and Subrahmanyam, Tang, and Wang (2014) ﬁnd that ﬁrm leverage is higher after CDS
trading. To understand the joint eﬀect of CDS on leverage and cash, we further estimate
the leverage and cash equations simultaneously by two-stage least squares procedures. In the
leverage equation, we include Cash based on the idea that ﬁrms may use cash to pay down
leverage. In the cash equation, Leverage is included because leverage might be a source of
cash. In addition to the conventional determinants of leverage and cash holdings, we also
add industry variables for identiﬁcation in the simultaneous equation model. As discussed
in Saretto and Tookes (2013), the potential simultaneity of corporate policies is expected to
occur at the ﬁrm level. Industry Leverage is included in the leverage model but is excluded
from the cash model. Industry Cash is in the cash model but is excluded from the leverage
model. Industry leverage (cash) should not aﬀect individual ﬁrms’ cash holding (leverage)
decisions, after controlling for the ﬁrm level variables. Panel A of Table 8 reports the estimated
22
coeﬃcients of the leverage and cash models. In the interests of brevity, we show the secondstage estimates for both leverage and cash equation. We ﬁnd that cash and leverage policies
are indeed intertwined, which is evidenced by the signiﬁcant coeﬃcients for cash and leverage
in the simultaneous equation model. More importantly, both cash and leverage increase
following introduction of CDS trading in the joint estimation. The coeﬃcients of CDS Trading
are positive and statistically signiﬁcant for both the leverage and the cash models. These
results suggest that there are substitution eﬀects between leverage and cash holdings (i.e., less
conservative leverage but more conservative cash holdings). The evidence of less conservative
leverage and more conservative cash is consistent with the argument that cash is not negative
debt. Moreover, the magnitudes of the coeﬃcients are economically signiﬁcant, with a higher
increase in leverage than in the cash ratio (0.040 and 0.018, respectively).
In Panel B of Table 8, we further explore the short-term and long-term eﬀects of CDS on
both leverage and cash holdings. The simultaneous equation models for leverage and cash are
estimated for diﬀerent windows, i.e., from t + 1 to t + 5. For date t + i (i = 1, 2, ..., 5), we keep
observations from the beginning until year i after the CDS introduction date and investigate
the CDS eﬀect. We ﬁnd that CDS trading continues increasing the leverage, from 0.023 in
window t + 1 to 0.042 in window t + 5. The eﬀect of CDS trading on cash is quite stable,
with a jump in year t + 1 and remains at approximately 2%. These result suggests that there
are some unique aspects of the presence of CDS trading for both cash and leverage and that
the pure substitution of one for the other is not the entire story.
In sum, the results in this section indicate that CDS trading increases both leverage and
cash holdings, suggesting that cash is not equivalent to negative debt. Debt issuance is an
important source of cash for CDS ﬁrms. However, the correlation between leverage and the
increase in cash holdings is less than perfect. On average, the increase in leverage after CDS
trading is higher than that for cash holdings. In the following sections, we identify settings
that are based on the marginal value of liquidity and the demand for external ﬁnancing to
distinguish the CDS eﬀects on leverage and cash policies.21
5.2. Dividend Payout
A key result of the Bolton, Chen, and Wang framework is that a ﬁrm has three regions for
the future state of the world in terms of its cash-capital ratio. To more speciﬁcally tie our
21
Lines of credit are an alternative tool for liquidity and risk management. As a robustness check, we
estimate a three-equation model in which leverage, cash holdings, and lines of credit are jointly determined.
We ﬁnd that CDS trading increases both leverage and cash holdings. However, the impact on the lines of
credit is not signiﬁcant (Internet Appendix Table A4).
23
empirical analysis to the model speciﬁcation, we also investigate the CDS eﬀect for dividendpaying ﬁrms and their non-dividend-paying counterparts: When a ﬁrm is not paying out
dividends (a characteristic of ﬁnancially constrained ﬁrms), the existence of CDS positions
increases both its leverage and cash holdings. However, when it is making dividend payments,
the existence of CDS positions increases its leverage much more than its cash holdings.
According to the results in Bolton, Chen, and Wang (2011, 2014), a ﬁrm’s cash policy
involves a double-barrier policy characterized by the marginal value of liquidity, and continuous management in between the barriers. At the upper barrier (i.e., the payout region), the
marginal value of cash is low. The threat from the empty creditors is minimal because the
ﬁrm has adequate liquidity, and it is thus less optimal for the ﬁrm to accumulate even more
cash. However, even following the introduction of CDS, ﬁrms can still time their ﬁnancing
decision to better take advantage of favorable ﬁnancing conditions in the market. Therefore,
we expect CDS trading to increase leverage – but not cash holdings – for dividend payers.
For non-dividend payers, the marginal value of cash is higher. These ﬁrms may borrow more
and simultaneously increase their cash holdings due to their precautionary motives. Thus,
CDS trading increases both leverage and cash for such non-dividend payers.
Table 9 provides the estimation results, which are consistent with these predictions. Firms
are classiﬁed into Non-dividend Payers and Dividend Payers, based on dividend payment
information from Compustat during a particular quarter. The simultaneous equation models
for leverage and cash are then estimated for Non-dividend Payers and Dividend Payers.
The ﬁrst two models in this table report the results for quarters in which ﬁrms do not pay
dividends. Models 3 and 4 report the ﬁndings for quarters in which ﬁrms pay dividends. As
expected, the coeﬃcients on CDS Trading are signiﬁcantly positive in both the leverage and
cash models for non-dividend payers. For dividend payers, CDS trading signiﬁcantly increases
leverage but not cash holdings, which is consistent with the prediction that the value of cash
is low in the payout region.
5.3. Cash Flow Risk
CDS trading increases leverage due to increased credit supply (Saretto and Tookes (2013)).
However, the eﬀect of CDS trading on corporate policies also depends on ﬁrms’ demand for
leverage and precautionary cash savings. Cash ﬂow risk can be used as a proxy for a ﬁrm’s
demand for both leverage and cash.
A unique and surprising prediction from the Bolton, Chen, and Wang (2014) model is
the ﬁrm’s response to cash ﬂow volatility. Thus, conditional on debt ﬁnancing, ﬁnancially
24
constrained ﬁrms would raise their debt levels to increase their cash buﬀers in response to
an increase in their cash ﬂow volatility. For CDS ﬁrms with high cash ﬂow risk, the demand
for debt is lower. When cash ﬂow volatility reaches suﬃciently high levels, “debt ﬁnancing
becomes more costly than equity due to the toll of debt servicing costs on corporate liquidity”
(Bolton, Chen, and Wang (2014)). Therefore, an increase in cash ﬂow risk reduces the demand
for leverage. With high cash ﬂow risk, the marginal value of liquidity is that much greater.
Firms may choose to build a larger cash reserves to reduce the probability of liquidation.
Thus, an increase in cash ﬂow risk increases the demand for precautionary cash holdings, and
we therefore expect CDS ﬁrms with greater cash ﬂow risk to have a lower increase in leverage
but a higher increase in cash holdings.
To investigate the eﬀects of cash ﬂow volatility, we include Cash Flow Volatility and the
interaction term of CDS Trading×Cash Flow Volatility in the simultaneous equation model.
The results are presented in Table 10 where we measure Cash Flow Volatility for each ﬁrm.
We ﬁnd that CDS trading increases both leverage and cash. Moreover, the positive association
between CDS trading and cash holdings increases with the ﬁrms’ cash ﬂow risk. The coeﬃcient
for the interaction term CDS Trading×Cash Flow Volatility is positive and signiﬁcant for the
cash model, which is consistent with our expectations because the value of cash is higher for
ﬁrms with greater cash ﬂow risk (and, therefore, a greater precautionary demand for cash).
However, the eﬀect of CDS on leverage decreases with the cash ﬂow risk. The coeﬃcient of the
interaction term CDS Trading×Cash Flow Volatility is -0.055, which is signiﬁcantly negative
in the leverage model. These ﬁndings are consistent with the predictions from Bolton, Chen,
and Wang (2014) in that ﬁrms may choose the high leverage–high cash strategy. However,
when the cost of debt is high (high cash ﬂow risk), ﬁrms may increase their cash holdings but
decrease their leverage as a result of concerns over debt servicing costs.
5.4. The Marginal Value of Cash
Corporations often roll over their outstanding debt, i.e., issuing new debt to redeem existing
debt. Such reﬁnancing of existing debt may involve rollover risk, particularly with respect
to future states of the world in which ﬁrms’ credit situations change adversely, when new
debtholders are reluctant to lend (as in He and Xiong (2012) and Choi, Hackbarth, and Zechner (2014)). In this setting, the marginal value of liquidity is high. Firms may mitigate this
rollover risk by holding additional cash and reducing debt in anticipation of this contingency.
Harford, Klasa, and Maxwell (2014) provide empirical support for this conjecture by showing
that debt rollover risk is a key determinant of corporate cash holdings. Similarly, in our
setting, if CDS trading were to aﬀect ﬁrms’ ﬁnancial policy decisions, we would expect that
25
CDS ﬁrms with a higher rollover risk would exhibit correspondingly larger increases in their
respective cash holdings than those ﬁrms with no CDS trading on their debt.
We follow Gopalan, Song, and Yerramilli (2014) and measure rollover risk as the long-term
debt payable within a year, as deﬁned in the Compustat data base. Long-Term Debt Due in
One Year is the ratio of debt due in one year to total debt. Table 11 presents the results
after controlling for this rollover risk. As expected, we ﬁnd that the coeﬃcient of Long-Term
Debt Due in One Year is positive for cash holdings and negative for leverage, which implies
that a ﬁrm’s rollover risk has a positive (negative) eﬀect on its cash holdings (leverage).
To examine whether the CDS eﬀect on ﬁrms’ ﬁnancial policies is larger for ﬁrms with
a greater rollover risk, we further incorporate an interaction variable, CDS Trading×LongTerm Debt Due in One Year, into the regression models. We ﬁnd that the coeﬃcient of the
interaction term is signiﬁcantly positive for the cash model, indicating that CDS trading has
a more pronounced positive eﬀect on cash holdings for ﬁrms with greater reﬁnancing risk,
which is consistent with our prediction. Based on the model, CDS trading leads to an extra
1.7% increase in cash holdings. However, for ﬁrms with greater reﬁnancing risk, CDS trading
leads to a 3.4% increase in cash holdings, which is an economically signiﬁcant increase. By
contrast, there is no moderating eﬀects from rollover risk for the CDS-leverage relationship.
5.5. Asset Tangibility
Bolton, Chen, and Wang (2011) also discuss the role of asset sales in corporate risk management. Because tangible assets are more liquid and saleable, they provide real liquidity when
ﬁrms must consider asset sales to manage their liquidity. In the worst case scenario, ﬁrms
can sell part of their assets to weather ﬁnancial distress, but only if they are liquid. Edmans
and Mann (2015) solve a model to determine the conditions under which ﬁrms raise external
funds by selling assets rather than by selling equity. Arnold, Hackbarth, and Puhan (2015)
demonstrate that a large proportion of investments are ﬁnanced with asset sales. Therefore,
we examine asset tangibility to distinguish its diﬀerential eﬀects on leverage and cash policies
after CDS trading has been introduced.
Asset sales are a useful alternative tool for risk management because tangible assets can
be used as collateral for borrowing (Rampini and Viswanathan (2013) and Rampini, Suﬁ and
Viswanathan (2014)). Hence, more tangible assets imply greater debt capacity. However, the
value of cash might be lower for ﬁrms with more tangible assets because ﬁrms may rely on
asset sales as an alternative tool for risk management and may thus substitute asset liquidity
for ﬁnancial liquidity. In our setting, we expect a greater increase in leverage – and a lesser
26
increase in cash – for CDS ﬁrms with more tangible assets.
We investigate this prediction and present the estimation results in Table 12. We measure
Tangible Assets as the ratio of property, plant and equipment to total assets. We also include
the cross-term of CDS Trading and Tangible Assets in the simultaneous model for leverage
and cash. The positive and signiﬁcant coeﬃcient estimate for CDS Trading indicates that
CDS trading increases both leverage and cash holdings after asset tangibility is controlled for.
The magnitudes of the CDS eﬀects are quite similar for leverage and cash (0.029 versus 0.021).
Notably, the coeﬃcient for CDS Trading×Tangible Assets is signiﬁcantly positive (0.043) for
the leverage model but is signiﬁcantly negative (−0.015) for the cash model. The results
imply that CDS ﬁrms with more tangible assets exhibit higher increases in their leverage and
lower increases in their cash holdings. Such ﬁndings are consistent with the prediction that
ﬁrms also incorporate asset liquidity into their risk management framework.
6.
Conclusion
This paper investigates the impact of credit default swaps (CDS) on corporate risk and liquidity management. Using a comprehensive data set tracking the introduction of trading in
North American corporate CDS between 1997 and 2009, we ﬁnd evidence that the initiation
of CDS trading on ﬁrms’ debt increases their cash holdings. On average, the cash ratios for
ﬁrms increase by 2% following the introduction of CDS trading on their debt. Given a mean
cash ratio of 8.2% for CDS ﬁrms, this increase is economically signiﬁcant. This ﬁnding of increased cash holdings prevails even after controlling for the endogeneity of the introduction of
CDS trading using propensity score matching and instrumental variable estimation. The empirical results are consistent with the predictions of the model by Bolton and Oehmke (2011)
motivated by the idea of tougher CDS-protected creditors: Creditors tend to be excessively
“tough” negotiators after CDS trading has been introduced on a ﬁrm’s debt. Anticipating
the potential threat of these tougher creditors, ﬁrms hold more cash ex ante to be able to
manage their future liquidity needs.
Our ﬁnding is consistent with the insights from Bolton, Chen, and Wang (2011, 2013, and
2014) that cash holdings will be high when the marginal value of cash holdings is high. Their
uniﬁed framework provides guidance for a joint analysis of both cash and leverage. We ﬁnd
that part of the cash increase following the introduction of CDS trading can be attributed to
debt issuance. However, when ﬁrms are paying dividends, suggesting that they have abundant
liquidity, they increase their leverage but do not increase their cash holdings. By contrast,
when ﬁrms are characterized by high cash ﬂow volatility, the increase in cash holdings is more
27
pronounced than the increase in leverage after introduction of CDS trading.
Our research contributes to the ongoing debate regarding the real eﬀects of CDS. In
contrast to the redundant security argument that is the basis of derivatives pricing, growing
empirical evidence suggests that CDS increase the credit supply, corporate leverage, and
bankruptcy risk. However, we delve further into the ﬁrms’ responses to the increase in
credit risk than previous studies by showing that CDS trading aﬀects both corporate liquidity
policies and their risk management practices. We identify and contrast the ex ante and ex
post eﬀects of so-called empty creditors that result from the introduction of CDS trading.
These ﬁndings have implications for policy discussions regarding the welfare eﬀects of the
CDS markets. On the one hand, CDS trading can increase the credit supply and help increase
the CDS ﬁrms’ leverage. If the additional funding is used to ﬁnance valuable new investment
projects, beneﬁting shareholding interests, this increase might be welfare-enhancing. On
the other hand, ﬁrms might simply hold onto the new funds in the form of corporate cash
reserves based on precautionary motives. In that case, the increased borrowing capacity
might not necessarily translate into higher welfare beneﬁts for the economy.22 Future research
can add more evidence and provide an even more comprehensive picture of CDS eﬀects on
corporate ﬁnance to help market participants and regulators develop more eﬀective policies
and practices.
22
For example, in the current context of industrialized economies suﬀering anemic growth, strong motives to
hold additional cash might complicate (and even work against) government eﬀorts to stimulate the economy
by lowering corporate borrowing costs by means of ﬁscal and monetary measures. It is frequently argued
that ﬁrms tend to postpone valuable investments not because of the higher cost of borrowing but because of
precautionary motives that drive them to accumulate additional liquidity.
28
References
Acharya, Viral V., Heitor Almeida, and Murillo Campello, 2007, Is cash negative debt? A
hedging perspective on corporate ﬁnancial policies, Journal of Financial Intermediation
16, 515–554.
Acharya, Viral V., Heitor Almeida, and Murillo Campello, 2013, Aggregate risk and the
choice between cash and line of credit, Journal of Finance 68, 2059–2116.
Acharya, Viral V., Jing-zhi Huang, Marti G. Subrahmanyam, and Rangarajan K. Sundaram,
2006, When does strategic debt service matter? Economic Theory 29, 363–378.
Acharya, Viral V., Sergei A. Davydenko, and Ilya A. Strebulaev, 2012, Cash holdings and
credit risk, Review of Financial Studies 25, 3572–3609.
Almeida, Heitor, Murillo Campello, Igor Cunha, and Michael Weisbach, 2014, Corporate
liquidity management: A conceptual framework and survey, Annual Review of Financial
Economics 6, 135–162.
Almeida, Heitor, Murillo Campello, and Michael Weisbach, 2004, The cash ﬂow sensitivity
of cash, Journal of Finance 59, 1777–1804.
Anderson, Ronald W., and Andrew Carverhill, 2012, Corporate liquidity and capital structure, Review of Financial Studies 25, 797–837.
Arnold, Marc, Dirk Hackbarth, and Tatjana Xenia Puhan, 2015, Financing asset sales and
business cycles, Working paper, University of St. Gallen, Boston University, and University of Zurich.
Arping, Stefan, 2014, Credit protection and lending relationships, Journal of Financial Stability 10, 7–19.
Ashcraft, Adam B., and Jo˜ao A.C. Santos, 2009, Has the CDS market lowered the cost of
corporate debt? Journal of Monetary Economics 56, 514–523.
Augustin, Patrick, Marti G. Subrahmanyam, Dragon Yongjun Tang, and Sarah Qian Wang,
2014, Credit default swaps: A survey, Foundations and Trends in Finance 9, 1–196.
Bates, Thomas, Kathleen Kahle, and Rene Stulz, 2009, Why do US ﬁrms hold so much more
cash than they used to? Journal of Finance 64, 1985–2021.
29
Bolton, Patrick, Hui Chen, and Neng Wang, 2011, A uniﬁed theory of Tobin’s q, corporate
investment, ﬁnancing, and risk management, Journal of Finance 66, 1545–1578.
Bolton, Patrick, Hui Chen, and Neng Wang, 2013, Market timing, investment, and risk
management, Journal of Financial Economics 109, 40–62.
Bolton, Patrick, Hui Chen, and Neng Wang, 2014, Debt, taxes, and liquidity, Working paper,
Columbia University and MIT.
Bolton, Patrick, and Martin Oehmke, 2011, Credit default swaps and the empty creditor
problem, Review of Financial Studies 24, 2617–2655.
Bolton, Patrick, and Martin Oehmke, 2013, Strategic conduct in credit derivative markets,
International Journal of Industrial Organization 31, 652–658.
Bolton, Patrick, Huntley Schaller, and Neng Wang, 2013, The marginal value of cash and
corporate savings, Working paper, Columbia University.
Campello, Murillo, and Rafael A. Matta, 2013, Credit default swaps, ﬁrm ﬁnancing and the
economy, Working paper, University of Illinois.
Che, Yeon-Koo, and Rajiv Sethi, 2014, Credit market speculation and the cost of capital,
American Economic Journal: Microeconomics 6, 1–34.
Chen, Tao, Jarrad Harford, and Chen Lin, 2015, Do analysts matter for governance? Evidence from natural experiments, Journal of Financial Economics 115, 383–410.
Choi, Jaewon, Dirk Hackbarth, and Josef Zechner, 2014, Granularity of corporate debt,
Working paper, University of Illinois at Urbana-Champaign, Boston University, and
Vienna University of Economics and Business.
Cust´odio, Cl´audia, and Daniel Metzger, 2014, Financial expert CEOs: CEO’s work experience and ﬁrm’s ﬁnancial policies, Journal of Financial Economics 114, 125–154.
Dittmar, Amy, and Ran Duchin, 2013, Looking in the rear view window: The eﬀect of managers’ professional experience on corporate ﬁnancial policy, Working paper, University
of Michigan and University of Washington.
Edmans, Alex, and William Mann, 2015, Financing through asset sales, Working paper,
LBS, Wharton, and UCLA.
30
Eisfeldt, Andrea, and Tyler Muir, 2014, Aggregate issuance and savings waves, Working
paper, UCLA and Yale.
Faulkender, Michael, and Mitchell A. Petersen, 2006, Does the source of capital aﬀect capital
structure? Review of Financial Studies 19, 45–79.
Foley, Fritz, Jay Hartzell, Sheridan Titman, and Garry Twite, 2007, Why do ﬁrms hold so
much cash? A tax-based explanation, Journal of Financial Economics 86, 579–607.
Gamba, A., and A. J. Triantis, 2014, Corporate risk management: Integrating liquidity,
hedging, and operating policies, Management Science 60, 246–264.
Gopalan, Radhakrishnan, Fenghua Song, and Vijay Yerramilli, 2014, Debt maturity structure and credit quality, Journal of Financial and Quantitative Analysis 49, 817–842.
Harford, Jarrad, Sandy Klasa, and William Maxwell, 2014, Reﬁnancing risks and cash holdings, Journal of Finance 69, 975–1012.
Harford, Jarrad, Sattar Mansi, and William Maxwell, 2008, Corporate governance and ﬁrm
cash holdings in the US, Journal of Financial Economics 87, 535–555.
Hart, Oliver, and John Moore, 1998, Default and renegotiation: A dynamic model of debt,
Quarterly Journal of Economics 113, 1–41.
He, Zhiguo, and Wei Xiong, 2012, Dynamic debt runs, Review of Financial Studies 25,
1799–1843.
Karolyi, Stephen, 2013, Borrower risk-taking, CDS trading, and the empty creditor problem,
Working paper, Yale University.
Li, Kai, and Nagpurnanand R. Prabhala, 2007, Self-selection models in corporate ﬁnance,
in B. Espen Eckbo ed., Handbook of Corporate Finance: Empirical Corporate Finance
I, Elsevier, Chapter 2, 37–86.
Minton, Bernadette A., Jerome A. Taillard, and Rohan Williamson, 2014, Financial expertise
of the board, risk taking and performance: Evidence from bank holding companies,
Journal of Financial and Quantitative Analysis 49, 351–380.
Morrison, Alan D., 2005, Credit derivatives, disintermediation, and investment decisions,
Journal of Business 78, 621–47.
31
Oehmke, Martin, and Adam Zawadowski, 2014, The anatomy of the CDS market, Working
paper, Columbia Business School and Boston University.
Opler, Tim, Lee Pinkowitz, Rene M. Stulz, and Rohan Williamson, 1999, The determinants
and implications of corporate holdings of liquid assets, Journal of Financial Economics
52, 3–46.
Parlour, Christine A., and Andrew Winton, 2013, Laying oﬀ credit risk: Loan sales versus
credit default swaps, Journal of Financial Economics 107, 25–45.
Pinkowitz, Lee, and Rohan Williamson, 2001, Bank power and cash holdings: Evidence from
Japan, Review of Financial Studies 14, 1059–1082.
Rampini, Adriano A., Amir Suﬁ, and S. Viswanathan, 2014, Dynamic risk management,
Journal of Financial Economics 111, 271–296
Rampini, Adriano A., and S. Viswanathan, 2013, Collateral and capital structure, Journal
of Financial Economics 109, 466–492.
Roberts, Michael R., and Toni M. Whited, 2012, Endogeneity in empirical corporate ﬁnance, in George Constantinides, Milton Harris, and Rene Stulz, eds., Handbook of the
Economics of Finance 2, Elsevier, 493–572.
Saretto, Alessio, and Heather Tookes, 2013, Corporate leverage, debt maturity and credit
supply: The role of credit default swaps, Review of Financial Studies 26, 1190–1247.
Shan, Susan Chenyu, Dragon Yongjun Tang, and Andrew Winton, 2014, Do credit derivatives lower the value of creditor control rights? Evidence from debt covenants, Working
paper, SJTU, University of Hong Kong, and University of Minnesota.
Subrahmanyam, Marti G., Dragon Yongjun Tang, and Sarah Qian Wang, 2014, Does the
tail wag the dog? The eﬀect of credit default swaps on credit risk, Review of Financial
Studies 27, 2927–2960.
Tett, Gillian, 2009, Fool’s Gold, The Free Press, New York, NY.
32
Figure 1: Changes in Cash Ratios around the Introduction of Credit Default Swaps.
This ﬁgure plots the changes in cash ratios for ﬁrms with CDS and their corresponding matching
ﬁrms, from one year before the inception of CDS trading to zero, one, two or three years after the
inception of CDS trading. Matching ﬁrms are selected based on industry and size. The cash ratio
is measured as the ratio of cash and marketable securities to total assets. The CDS data come from
CreditTrade and the GFI Group. There are 901 ﬁrms in our sample that have CDS traded at some
point during the sample period of June 1997 to April 2009.
33
Figure 2: Cash Ratios for CDS Firms and Propensity-Score-Matched Non-CDS Firms.
This ﬁgure plots cash ratios for ﬁrms with CDS and their corresponding matching ﬁrms, from two
years before the inception of CDS trading to two years after the inception of CDS trading. For
each CDS ﬁrm, we select a non-CDS matching ﬁrm based on propensity scores that measure the
probability of CDS trading at the time of CDS introduction.
34
Table 1
Summary Statistics
This table provides summary statistics for our sample ﬁrms. Panel A reports the distribution of ﬁrms in our
sample, including those with CDS traded, and their average cash ratios, by year, between 1997 and 2009.
The overall sample of ﬁrms is taken from Compustat, and includes all companies in that database during
1997-2009. The CDS data are taken from CreditTrade and the GFI Group. There are 901 ﬁrms in the sample
that have CDS traded at some point during the sample period of June 1997 to April 2009. We measure the
cash ratio as cash and marketable securities divided by total assets. The ﬁrst column in the table is the
year. The second column shows the total number of U.S. companies included in the Compustat database.
The third column reports the number of ﬁrms for which CDS trading was initiated during that year. The
fourth column presents the number of ﬁrms with active CDS trading during each year. The last two columns
report average cash ratios for non-CDS and CDS ﬁrms respectively. Panel B provides summary statistics
of ﬁrm characteristics for the matching sample discussed in Section 4.2. Leverage is the book value of the
long-term debt plus debt in current liabilities, divided by total assets. Industry Cash is the industry mean
cash ratio across two-digit SIC codes. Industry Leverage is the industry mean leverage ratio across two-digit
SIC codes. Industry Sigma is the industry cash ﬂow risk, measured by the mean cash ﬂow volatility across
two-digit SIC codes. Cash Flow/Assets is the ratio of cash ﬂow to total assets, where cash ﬂow is deﬁned as
the earnings after interest and related expenses, income taxes, and dividends. Market to Book is the book
value of assets minus the book value of equity plus the market value of equity, all divided by the book value
of assets. Size is the logarithm of total assets. Capital Expenditure is the ratio of capital expenditure to total
assets. R&D/Sales is the ratio of R&D to sales. Net Working Capital/Assets is measured as net working
capital minus cash, divided by total assets. Dividend Dummy is a dummy variable that equals one if the ﬁrm
pays a common dividend. Acquisition Activity is the ratio of acquisitions to total assets, and Foreign Pretax
Income is the ratio of foreign pretax income to total assets.(† from June 1997, ‡ until April 2009)
(Continued)
35
(1)
Year
1997†
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009‡
Total/Average
Table 1 – Continued
Panel A: CDS Trading and Cash Ratios by Year
(2)
(3)
(4)
(5)
Total #
# of New
# of Active
Non-CDS Firm
of Firms
CDS Firms
CDS Firms
Cash Ratio
9366
22
22
0.187
9546
58
72
0.191
9545
55
106
0.202
9163
102
196
0.200
8601
172
334
0.201
8190
221
547
0.203
7876
93
582
0.221
7560
58
593
0.221
7318
73
629
0.224
6993
28
533
0.226
6651
9
418
0.225
6223
9
375
0.205
5686
1
234
0.216
901
0.209
Panel B: Summary Statistics for Propensity-Score-Matched Sample
Mean
StdDev
Q1
Median
Cash
0.095
0.114
0.017
0.048
Leverage
0.274
0.171
0.154
0.257
Industry Cash
0.171
0.098
0.091
0.138
Industry Leverage
0.259
0.080
0.197
0.243
Industry Sigma
0.083
0.046
0.046
0.072
Cash Flow/Assets
0.023
0.029
0.014
0.023
Market to Book
1.936
1.312
1.198
1.516
Size
8.391
1.334
7.554
8.300
Capital Expenditure
0.033
0.035
0.009
0.021
R&D/Sales
0.027
0.052
0.000
0.000
Net Working Capital/Assets
0.043
0.143
−0.031
0.031
Dividend Dummy
0.572
0.495
0.000
1.000
Acquisition Activity
0.022
0.037
0.000
0.002
Foreign Pretax Income
0.021
0.039
0.000
0.005
36
(6)
CDS Firm
Cash Ratio
0.072
0.070
0.068
0.064
0.072
0.081
0.090
0.095
0.092
0.089
0.084
0.088
0.103
0.082
Q3
0.134
0.370
0.243
0.310
0.109
0.035
2.131
9.273
0.044
0.028
0.123
1.000
0.025
0.034
Table 2
CDS Trading and Cash Holdings: Propensity Score Matching
This table presents the estimates of the eﬀect of CDS on corporate cash holdings in a sample including ﬁrms
with CDS and non-CDS propensity-score-matched ﬁrms. Propensity-score-matched ﬁrms are selected based
on propensity scores estimated from Model 3 of the probability of CDS trading presented in Internet Appendix
Table A1. We use four diﬀerent propensity score matching criteria to choose matching ﬁrms: (1) the one
non-CDS ﬁrm nearest the CDS ﬁrm in terms of propensity score; (2) the one ﬁrm with the propensity score
nearest the CDS ﬁrm and within a diﬀerence of 1%; (3) the two non-CDS ﬁrms with the propensity scores
nearest the CDS ﬁrm; and (4) the two non-CDS ﬁrms with the propensity scores nearest the CDS ﬁrm and
within a diﬀerence of 1%. To estimate the impact of CDS trading on the corporate cash holdings, we include
CDS variables in the model speciﬁcations. CDS Trading is a dummy variable that equals one if the ﬁrm has
CDS traded on its debt one year before time t. The coeﬃcient of interest is that of CDS Trading, which
captures the impact of the inception of CDS trading on cash holdings. The sample period is 1997-2009, based
on quarterly observations. (*** denotes signiﬁcance at the 1% level, ** signiﬁcance at the 5% level, and *
signiﬁcance at the 10% level. The numbers in parentheses are standard errors.)
Cash
Nearest One
Nearest One
Nearest Two
Nearest Two
Matching
PS Diﬀ<1%
Matching
PS Diﬀ<1%
CDS Trading
0.026∗∗∗
0.025∗∗∗
0.027∗∗∗
0.027∗∗∗
(0.006)
(0.006)
(0.006)
(0.006)
Industry Sigma
0.060
0.055
0.081
0.089∗∗
(0.044)
(0.038)
(0.050)
(0.043)
Cash Flow/Assets
−0.008
−0.043
−0.010
−0.059
(0.070)
(0.062)
(0.079)
(0.063)
Market to Book
−0.001
−0.001
−0.000
−0.000
(0.002)
(0.002)
(0.002)
(0.003)
Size
−0.025∗∗∗
−0.022∗∗∗
−0.027∗∗∗
−0.021∗∗∗
(0.007)
(0.006)
(0.007)
(0.006)
Net Working Capital/Assets
−0.058
−0.054
−0.044
−0.040
(0.055)
(0.049)
(0.060)
(0.048)
Capital Expenditure
−0.166∗∗∗
−0.170∗∗∗
−0.176∗∗∗
−0.185∗∗∗
(0.025)
(0.026)
(0.030)
(0.031)
Leverage
−0.048
−0.061∗
−0.055
−0.062
(0.039)
(0.036)
(0.048)
(0.041)
R&D/Sales
0.243∗∗
0.246∗∗
0.238∗
0.242∗
(0.111)
(0.110)
(0.132)
(0.136)
Dividend Dummy
−0.017
−0.017
−0.021∗
−0.017∗
(0.010)
(0.010)
(0.011)
(0.009)
Acquisition Activity
−0.181∗∗∗
−0.151∗∗∗
−0.188∗∗∗
−0.136∗∗
(0.058)
(0.055)
(0.067)
(0.061)
Foreign Pretax Income
0.223∗∗∗
0.213∗∗∗
0.257∗∗∗
0.248∗∗∗
(0.058)
(0.056)
(0.063)
(0.060)
Time Fixed Eﬀect
Yes
Yes
Yes
Yes
Firm Fixed Eﬀect
Yes
Yes
Yes
Yes
Clustered Standard Error
Yes
Yes
Yes
Yes
N
40668
36426
57684
48872
R2
75.25%
74.57%
73.53%
72.81%
37
Table 3
CDS Trading and Cash Holdings: An Instrumental Variable Approach
This table presents the second-stage estimation of the two-stage instrumental variable (IV) estimation results.
The second-stage analysis looks at the impact of CDS on corporate cash holdings in a sample including
ﬁrms with CDS and all non-CDS ﬁrms in Compustat. In Model 1, we employ Lender FX Usage as the
instrumental variable which is a measure of the FX hedging activities carried out by the ﬁrm’s lending banks
and underwriters. In Model 2, Lender Tier 1 Capital is the instrumental variable which measures the Tier 1
capital ratio of the bank lenders. In Model 3, we use both Lender FX Usage and Lender Tier 1 Capital as
instruments. The coeﬃcient of interest is that of Instrumented CDS Trading, which captures the impact of the
inception of CDS trading on cash holdings. The sample period is 1997-2009, based on quarterly observations.
(*** denotes signiﬁcance at the 1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at the 10% level.
The numbers in parentheses are standard errors.)
Cash
(1)
(2)
(3)
Instrumented CDS Trading
0.062∗∗∗
0.038∗∗
0.045∗∗
(0.023)
(0.016)
(0.019)
Industry Sigma
0.077∗∗∗
0.076∗∗∗
0.076∗∗∗
(0.016)
(0.016)
(0.016)
Cash Flow/Assets
0.065∗∗∗
0.068∗∗∗
0.068∗∗∗
(0.009)
(0.009)
(0.009)
Market to Book
0.008∗∗∗
0.008∗∗∗
0.008∗∗∗
(0.001)
(0.001)
(0.001)
Size
−0.009∗∗∗
−0.010∗∗∗
−0.010∗∗∗
(0.001)
(0.001)
(0.001)
Net Working Capital/Assets
−0.046∗∗∗
−0.046∗∗∗
−0.046∗∗∗
(0.004)
(0.004)
(0.004)
Capital Expenditure
−0.211∗∗∗
−0.207∗∗∗
−0.206∗∗∗
(0.012)
(0.012)
(0.012)
Leverage
−0.083∗∗∗
−0.083∗∗∗
−0.083∗∗∗
(0.005)
(0.005)
(0.005)
R&D/Sales
0.200∗∗∗
0.199∗∗∗
0.199∗∗∗
(0.014)
(0.014)
(0.014)
Dividend Dummy
0.006∗∗∗
0.006∗∗∗
0.006∗∗∗
(0.002)
(0.002)
(0.002)
Acquisition Activity
−0.198∗∗∗
−0.192∗∗∗
−0.190∗∗∗
(0.013)
(0.014)
(0.013)
Foreign Pretax Income
0.004∗
0.004∗
0.004∗
(0.002)
(0.002)
(0.002)
Time Fixed Eﬀect
Yes
Yes
Yes
Firm Fixed Eﬀect
Yes
Yes
Yes
Clustered Standard Error
Yes
Yes
Yes
N
307672
307672
307672
38
Table 4
CDS Trading and Cash Holdings: Financial Expertise and CDS Outstanding
This table presents the estimates of the eﬀect of CDS on corporate cash holdings after controlling for the
ﬁnancial expertise of board members and CDS outstanding. The analysis is conducted on the sample of
CDS ﬁrms. The measure of ﬁnancial expertise comes from Riskmetrics. Number of Financial Experts is the
number of ﬁnancial expert board members the ﬁrm has. CDS Outstanding/Total Debt is the ratio of total
notional CDS outstanding to the book value of the total debt. The coeﬃcients of interest are those of CDS
Trading, CDS Trading×Number of Financial Experts and CDS Outstanding/Total Debt, which capture the
impact of the inception of CDS trading on cash holdings. The sample period is 1997-2009, based on quarterly
observations. (*** denotes signiﬁcance at the 1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at
the 10% level. The numbers in parentheses are standard errors.)
Cash
(1)
(2)
∗∗∗
CDS Trading
0.019
(0.002)
Number of Financial Experts
−0.003∗∗∗
(0.001)
CDS Trading×Number of Financial Experts
0.004∗∗∗
(0.001)
CDS Outstanding/Total Debt
0.005∗∗
(0.003)
Industry Sigma
0.016∗∗∗
0.040
(0.006)
(0.029)
Cash Flow/Assets
−0.137∗∗∗
−0.039
(0.018)
(0.026)
Market to Book
0.013∗∗∗
0.000
(0.001)
(0.002)
Size
−0.010∗∗∗
−0.007∗∗∗
(0.000)
(0.002)
Net Working Capital/Assets
−0.128∗∗∗
−0.056∗∗∗
(0.004)
(0.012)
Capital Expenditure
−0.136∗∗∗
−0.116∗∗∗
(0.014)
(0.022)
Leverage
−0.145∗∗∗
−0.050∗∗∗
(0.003)
(0.013)
R&D/Sales
0.644∗∗∗
0.143∗∗
(0.011)
(0.063)
Dividend Dummy
−0.041∗∗∗
0.004
(0.001)
(0.005)
Acquisition Activity
−0.212∗∗∗
−0.082∗∗∗
(0.014)
(0.024)
Foreign Pretax Income
0.262∗∗∗
0.164∗∗∗
(0.013)
(0.037)
Time Fixed Eﬀect
Yes
Yes
Industry Fixed Eﬀect
Yes
No
Firm Fixed Eﬀect
No
Yes
Clustered Standard Error
Yes
Yes
R2
42.90%
71.87%
N
29120
29120
39
Table 5
CDS Trading and Cash Holdings: Financial Constraints
This table presents the estimates of the eﬀect of CDS on corporate cash holdings in a sample including
ﬁrms with CDS and non-CDS propensity-score-matched ﬁrms. Firms are separated into Rated and Unrated
categories based on their credit ratings. Unrated ﬁrms are expected to be more constrained. The coeﬃcient
of interest is that of CDS Trading, which captures the impact of the inception of CDS trading on cash
holdings. The sample period is 1997-2009, based on quarterly observations. (*** denotes signiﬁcance at the
1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at the 10% level. The numbers in parentheses are
standard errors.)
Cash
CDS Trading
Industry Sigma
Cash Flow/Assets
Market to Book
Size
Net Working Capital/Assets
Capital Expenditure
Leverage
R&D/Sales
Dividend Dummy
Acquisition Activity
Foreign Pretax Income
Time Fixed Eﬀect
Firm Fixed Eﬀect
Clustered Standard Error
R2
N
Rated
0.023∗∗∗
(0.006)
0.052
(0.045)
0.011
(0.078)
−0.000
(0.002)
−0.027∗∗∗
(0.009)
−0.036
(0.059)
−0.111∗∗∗
(0.022)
−0.022
(0.043)
0.197∗
(0.112)
−0.018
(0.011)
−0.168∗∗∗
(0.054)
0.272∗∗∗
(0.065)
Yes
Yes
Yes
71.67%
35169
40
Unrated
0.040∗∗
(0.016)
0.057
(0.090)
−0.152∗
(0.079)
−0.004
(0.006)
−0.017∗∗∗
(0.006)
−0.083∗
(0.047)
−0.241∗∗∗
(0.064)
−0.154∗∗∗
(0.034)
0.239
(0.244)
−0.013
(0.015)
−0.208∗∗
(0.091)
0.052
(0.165)
Yes
Yes
Yes
82.35%
5499
Table 6
CDS Trading and Cash Holdings: Analyst Coverage
This table presents the estimates of the eﬀect of CDS on corporate cash holdings in a sample including ﬁrms
with CDS and non-CDS propensity-score-matched ﬁrms. Firms are separated into With Analyst Coverage
and Without Analyst Coverage categories based on the analyst coverage information from I/B/E/S. The
coeﬃcient of interest is that of CDS Trading, which captures the impact of the inception of CDS trading on
cash holdings. The sample period is 1997-2009, based on quarterly observations. (*** denotes signiﬁcance at
the 1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at the 10% level. The numbers in parentheses
are standard errors.)
Cash
With Analyst Coverage
Without Analyst Coverage
CDS Trading
0.025∗∗∗
0.023∗∗∗
(0.006)
(0.007)
Industry Sigma
0.118∗∗
0.018
(0.046)
(0.066)
Cash Flow/Assets
−0.019
0.002
(0.091)
(0.087)
Market to Book
−0.001
−0.003
(0.002)
(0.005)
Size
−0.020∗∗∗
−0.031∗∗∗
(0.007)
(0.009)
Net Working Capital/Assets
−0.016
−0.132∗∗∗
(0.065)
(0.042)
Capital Expenditure
−0.179∗∗∗
−0.138∗∗∗
(0.029)
(0.045)
Leverage
−0.073
0.014
(0.048)
(0.048)
R&D/Sales
0.320∗∗
0.081
(0.130)
(0.160)
Dividend Dummy
−0.023∗
−0.005
(0.013)
(0.008)
Acquisition Activity
−0.208∗∗∗
−0.104∗
(0.062)
(0.060)
Foreign Pretax Income
0.274∗∗∗
0.191∗∗∗
(0.094)
(0.061)
Time Fixed Eﬀect
Yes
Yes
Firm Fixed Eﬀect
Yes
Yes
Clustered Standard Error
Yes
Yes
R2
73.74%
70.60%
N
29021
11647
41
Table 7
Source of Cash and Long-term Debt Issuance
In this table, we jointly estimate the marginal cash savings and debt issuance decisions, in the sample of CDS
ﬁrms and propensity-score-matched non-CDS ﬁrms respectively. ∆Debt is the ratio of the net long-term
debt issuances to total book value of assets, and ∆Cash is the change in the holdings of cash and other
marketable securities divided by total assets. Lag Debt and Lag Cash are lagged levels of long-term debt and
cash holdings scaled by total assets. The sample period is 1997-2009, based on annual observations. (***
denotes signiﬁcance at the 1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at the 10% level. The
numbers in parentheses are standard errors.)
Cash Flow/Assets
Market to Book
Size
∆Cash
Lag Debt
∆Debt
Lag Cash
R2
N
CDS Firms
∆Debt
∆Cash
0.205∗∗∗
0.163∗∗∗
(0.038)
(0.014)
0.007∗∗∗
0.001
(0.003)
(0.001)
−0.034∗∗∗
−0.006∗∗∗
(0.003)
(0.001)
0.159∗
(0.096)
0.320∗∗∗
(0.016)
0.093∗∗∗
(0.020)
−0.259∗∗∗
(0.010)
44.06%
22.74%
6281
6281
42
Non-CDS Firms
∆Debt
∆Cash
0.067
0.322∗∗∗
(0.047)
(0.015)
−0.001
−0.001
(0.002)
(0.001)
−0.031∗∗∗
−0.023∗∗∗
(0.004)
(0.002)
0.568∗∗∗
(0.102)
0.489∗∗∗
(0.018)
0.005
(0.017)
−0.239∗∗∗
(0.011)
44.8%
33.02%
5123
5123
Table 8
Simultaneous Eﬀect of CDS on Leverage and Cash
This table presents the estimates of the simultaneous eﬀect of CDS on corporate leverage and cash holdings
in a sample including ﬁrms with CDS and non-CDS propensity-score-matched ﬁrms. Leverage and Cash
equations are estimated simultaneously by two-stage least squares procedures. The coeﬃcient of interest
is that of CDS Trading, which captures the impact of the inception of CDS trading on cash holdings and
leverage. In Panel A, the sample period is 1997-2009, based on quarterly observations. In Panel B, the
simultaneous equation models for leverage and cash are estimated for diﬀerent windows, from t + 1 to
t + 5. For date t + i (i = 1, 2, ..., 5), i.e., we keep observations from the beginning until year i after the
CDS introduction date and investigate the CDS eﬀect. The coeﬃcients for CDS Trading are reported. (***
denotes signiﬁcance at the 1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at the 10% level. The
numbers in parentheses are standard errors.)
(Continued)
43
Table 8 – Continued
Panel A: Simultaneous Eﬀect on Leverage and Cash
Leverage
Cash
CDS Trading
0.040∗∗∗
0.018∗∗∗
(0.002)
(0.003)
Cash
−0.582∗∗∗
(0.069)
Industry Leverage
0.147∗∗∗
(0.018)
Leverage
0.173∗
(0.095)
Industry Cash
0.401∗∗∗
(0.030)
Industry Sigma
0.002
0.029∗∗∗
(0.013)
(0.009)
Cash Flow/Assets
−0.224∗∗∗
0.050∗
(0.021)
(0.026)
Market to Book
−0.014∗∗∗
0.002∗
(0.001)
(0.001)
Size
−0.028∗∗∗
−0.024∗∗∗
(0.002)
(0.001)
Net Working Capital/Assets
−0.050∗∗∗
−0.035∗∗∗
(0.007)
(0.005)
Capital Expenditure
−0.191∗∗∗
−0.125∗∗∗
(0.021)
(0.016)
R&D/Sales
−0.004
0.271∗∗∗
(0.030)
(0.022)
Dividend Dummy
−0.023∗∗∗
−0.012∗∗∗
(0.002)
(0.002)
Acquisition Activity
0.077∗∗∗
−0.209∗∗∗
(0.019)
(0.019)
Foreign Pretax Income
−0.050∗∗
0.232∗∗∗
(0.023)
(0.020)
Time Fixed Eﬀect
Yes
Yes
Firm Fixed Eﬀect
Yes
Yes
Clustered Standard Error
Yes
Yes
R2
71.46%
69.05%
N
40293
40293
Panel B: Short-term and Long-term Eﬀect on Leverage and Cash
Year after CDS Introduction
Leverage
Cash
1
0.023∗∗∗
0.023∗∗∗
(0.004)
(0.002)
2
0.030∗∗∗
0.026∗∗∗
(0.003)
(0.002)
3
0.037∗∗∗
0.028∗∗∗
(0.003)
(0.002)
4
0.039∗∗∗
0.024∗∗∗
(0.003)
(0.002)
5
0.042∗∗∗
0.019∗∗∗
(0.003)
(0.003)
44
Table 9
Dividend Payout and The CDS Eﬀects on Cash and Leverage
This table presents the estimates of the simultaneous eﬀect of CDS on corporate leverage and cash holdings
in a sample including ﬁrms with CDS and non-CDS propensity-score-matched ﬁrms. Leverage and Cash
equations are estimated simultaneously by two-stage least squares procedures. Firms are separated into
Non-dividend Payers and Dividend Payers based on dividend payment information from Compustat. The
coeﬃcient of interest is that of CDS Trading, which captures the impact of the inception of CDS trading on
cash holdings and leverage. The sample period is 1997-2009, based on quarterly observations.(*** denotes
signiﬁcance at the 1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at the 10% level. The numbers
in parentheses are standard errors.)
CDS Trading
Cash
Industry Leverage
Leverage
Industry Cash
Industry Sigma
Cash Flow/Assets
Market to Book
Size
Net Working Capital/Assets
Capital Expenditure
R&D/Sales
Acquisition Activity
Foreign Pretax Income
Time Fixed Eﬀect
Firm Fixed Eﬀect
Clustered Standard Error
R2
N
Non-dividend Payers
Leverage
Cash
(1)
(2)
0.064∗∗∗
0.050∗∗∗
(0.016)
(0.004)
−1.094∗∗∗
(0.390)
0.092
(0.066)
−0.478∗∗∗
(0.152)
0.075
(0.056)
−0.062∗
−0.011
(0.032)
(0.024)
−0.145∗∗∗
−0.054
(0.036)
(0.036)
−0.011∗∗∗
−0.006∗∗∗
(0.001)
(0.002)
−0.052∗∗∗
−0.042∗∗∗
(0.014)
(0.002)
−0.059∗∗∗
−0.040∗∗∗
(0.016)
(0.009)
−0.305∗∗∗
−0.236∗∗∗
(0.084)
(0.029)
0.128∗
0.126∗∗∗
(0.066)
(0.026)
−0.200∗∗
−0.200∗∗∗
(0.089)
(0.019)
0.084
0.132∗∗∗
(0.086)
(0.034)
Yes
Yes
Yes
Yes
Yes
Yes
66.16%
64.79%
17060
17060
45
Dividend Payers
Leverage
Cash
(3)
(4)
0.031∗∗∗
0.001
(0.002)
(0.003)
−0.475∗∗∗
(0.064)
0.149∗∗∗
(0.019)
0.218∗∗
(0.111)
0.444∗∗∗
(0.032)
0.125∗∗∗
−0.088∗∗∗
(0.014)
(0.019)
−0.382∗∗∗
−0.023
(0.030)
(0.044)
−0.013∗∗∗
−0.003∗∗
(0.001)
(0.001)
−0.009∗∗∗
−0.004∗∗∗
(0.001)
(0.001)
−0.056∗∗∗
−0.057∗∗∗
(0.009)
(0.007)
−0.140∗∗∗
−0.060∗∗∗
(0.021)
(0.019)
−0.038
0.232∗∗∗
(0.045)
(0.036)
0.249∗∗∗
−0.092∗∗∗
(0.016)
(0.031)
−0.143∗∗∗
0.234∗∗∗
(0.023)
(0.029)
Yes
Yes
Yes
Yes
Yes
Yes
72.08%
55.82%
23233
23233
Table 10
Cash Flow Volatility and the CDS Eﬀects on Cash and Leverage
This table presents the estimates of the simultaneous eﬀect of CDS on corporate leverage and cash holdings in
a sample including ﬁrms with CDS and non-CDS propensity-score-matched ﬁrms. Leverage and Cash equations are estimated simultaneously by two-stage least squares procedures. Cash Flow Volatility is a measure of
individual ﬁrm’s cash ﬂow risk. The coeﬃcient of interest are those of CDS Trading and CDS Trading×Cash
Flow Volatility, which capture the impact of the inception of CDS trading on cash holdings and leverage. The
sample period is 1997-2009, based on quarterly observations. (*** denotes signiﬁcance at the 1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at the 10% level. The numbers in parentheses are standard errors.)
CDS Trading
CDS Trading×Cash Flow Volatility
Cash Flow Volatility
Cash
Industry Leverage
Leverage
0.042∗∗∗
(0.002)
−0.055∗∗∗
(0.016)
0.029∗∗∗
(0.005)
−0.572∗∗∗
(0.066)
0.145∗∗∗
(0.018)
Leverage
Industry Cash
Cash Flow/Assets
Market to Book
Size
Net Working Capital/Assets
Capital Expenditure
R&D/Sales
Dividend Dummy
Acquisition Activity
Foreign Pretax Income
Time Fixed Eﬀect
Firm Fixed Eﬀect
Clustered Standard Error
R2
N
−0.224∗∗∗
(0.021)
−0.014∗∗∗
(0.001)
−0.029∗∗∗
(0.002)
−0.053∗∗∗
(0.007)
−0.192∗∗∗
(0.021)
−0.015
(0.030)
−0.023∗∗∗
(0.002)
0.079∗∗∗
(0.019)
−0.052∗∗
(0.023)
Yes
Yes
Yes
71.74%
40287
46
Cash
0.018∗∗∗
(0.003)
0.032∗∗
(0.013)
0.021∗∗∗
(0.004)
0.140
(0.093)
0.403∗∗∗
(0.030)
0.042
(0.026)
0.002
(0.001)
−0.024∗∗∗
(0.001)
−0.035∗∗∗
(0.005)
−0.127∗∗∗
(0.016)
0.266∗∗∗
(0.022)
−0.013∗∗∗
(0.002)
−0.208∗∗∗
(0.019)
0.229∗∗∗
(0.020)
Yes
Yes
Yes
69.93%
40287
Table 11
Rollover Risk and the CDS Eﬀects on Cash and Leverage
This table presents the estimates of the simultaneous eﬀect of CDS on corporate leverage and cash holdings
in a sample including ﬁrms with CDS and non-CDS propensity-score-matched ﬁrms. Leverage and Cash
equations are estimated simultaneously by two-stage least squares procedures. Long-Term Debt Due in
One Year is the ratio of long-term debt due in one year to total debt. The coeﬃcient of interest are those
of CDS Trading and CDS Trading×Long-Term Debt Due in One Year, which capture the impact of the
inception of CDS trading on cash holdings and leverage. The sample period is 1997-2009, based on quarterly
observations. (*** denotes signiﬁcance at the 1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at
the 10% level. The numbers in parentheses are standard errors.)
Leverage
0.035∗∗∗
(0.002)
0.005
(0.007)
−0.062∗∗∗
(0.004)
−0.552∗∗∗
(0.070)
0.154∗∗∗
(0.018)
CDS Trading
CDS Trading × Long-Term Debt Due in One Year
Long-Term Debt Due in One Year
Cash
Industry Leverage
Leverage
Industry Cash
−0.008
(0.013)
−0.239∗∗∗
(0.021)
−0.012∗∗∗
(0.001)
−0.025∗∗∗
(0.002)
−0.056∗∗∗
(0.007)
−0.184∗∗∗
(0.021)
−0.003
(0.030)
−0.023∗∗∗
(0.002)
0.081∗∗∗
(0.019)
−0.060∗∗∗
(0.023)
Yes
Yes
Yes
72.04%
40293
Industry Sigma
Cash Flow/Assets
Market to Book
Size
Net Working Capital/Assets
Capital Expenditure
R&D/Sales
Dividend Dummy
Acquisition Activity
Foreign Pretax Income
Time Fixed Eﬀect
Firm Fixed Eﬀect
Clustered Standard Error
R2
N
47
Cash
0.017∗∗∗
(0.002)
0.017∗∗∗
(0.006)
0.031∗∗∗
(0.007)
0.153∗
(0.088)
0.389∗∗∗
(0.028)
0.034∗∗∗
(0.009)
0.055∗∗
(0.026)
0.001
(0.001)
−0.025∗∗∗
(0.001)
−0.033∗∗∗
(0.005)
−0.127∗∗∗
(0.015)
0.262∗∗∗
(0.020)
−0.013∗∗∗
(0.002)
−0.203∗∗∗
(0.018)
0.232∗∗∗
(0.020)
Yes
Yes
Yes
69.81%
40293
Table 12
Asset Tangibility and the CDS Eﬀects on Cash and Leverage
This table presents the estimates of the simultaneous eﬀect of CDS on corporate leverage and cash holdings
in a sample including ﬁrms with CDS and non-CDS propensity-score-matched ﬁrms. Leverage and Cash
equations are estimated simultaneously by two-stage least squares procedures. Tangible Assets is the ratio of
property, plant and equipment to total assets. The coeﬃcient of interest are those of CDS Trading and CDS
Trading×Tangible Assets, which capture the impact of the inception of CDS trading on cash holdings and
leverage. The sample period is 1997-2009, based on quarterly observations.(*** denotes signiﬁcance at the
1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at the 10% level. The numbers in parentheses are
standard errors.)
CDS Trading
CDS Trading × Tangible Assets
Tangible Assets
Cash
Industry Leverage
Leverage
0.029∗∗∗
(0.004)
0.043∗∗∗
(0.006)
−0.341∗∗∗
(0.033)
−0.900∗∗∗
(0.111)
0.177∗∗∗
(0.019)
Leverage
Industry Cash
Industry Sigma
Cash Flow/Assets
Market to Book
Size
Net Working Capital/Assets
Capital Expenditure
R&D/Sales
Dividend Dummy
Acquisition Activity
Foreign Pretax Income
Time Fixed Eﬀect
Firm Fixed Eﬀect
Clustered Standard Error
R2
N
0.006
(0.015)
−0.195∗∗∗
(0.023)
−0.014∗∗∗
(0.001)
−0.040∗∗∗
(0.003)
−0.077∗∗∗
(0.009)
−0.115∗∗∗
(0.020)
0.121∗∗∗
(0.042)
−0.024∗∗∗
(0.003)
−0.020
(0.028)
−0.024
(0.027)
Yes
Yes
Yes
68.32%
40293
48
Cash
0.021∗∗∗
(0.002)
−0.015∗∗∗
(0.005)
−0.264∗∗∗
(0.009)
0.118
(0.077)
0.273∗∗∗
(0.027)
0.041∗∗∗
(0.008)
0.055∗∗
(0.022)
0.001
(0.001)
−0.028∗∗∗
(0.001)
−0.046∗∗∗
(0.005)
−0.036∗∗∗
(0.013)
0.302∗∗∗
(0.019)
−0.010∗∗∗
(0.002)
−0.232∗∗∗
(0.016)
0.190∗∗∗
(0.018)
Yes
Yes
Yes
71.91%
40293
Appendix: Variable Definitions
Variable
Cash
∆Cash
Lag Cash
Leverage
∆Debt
Lag Debt
CDS Trading
CDS Outstanding/Total Debt
Industry Cash
Industry Leverage
Industry Sigma
Cash Flow/Assets
Market to Book
Size
Net Working Capital/Assets
Capital Expenditure
R&D/Sales
Dividend Dummy
Acquisition Activity
Foreign Pretax Income
Lender FX Usage
Lender Tier 1 Capital
Number of Financial Experts
Unrated
Rated
With/Without Analyst Coverage
Long-Term Debt Due in One Year
Tangible Assets
Deﬁnition
The ratio of cash and marketable securities to total assets. Source:
Compustat
The change in the holdings of cash and marketable securities divided by
total assets. Source: Compustat
The ratio of cash and marketable securities to total assets in previous
year. Source: Compustat
The book value of the long-term debt plus debt in current liabilities,
divided by total assets. Source: Compustat
The ratio of the net long-term debt issuances to total assets. Source:
Compustat
The ratio of long-term debt to total assets in previous year. Source:
Compustat
A dummy variable that equals one if the ﬁrm has CDS traded on its
debt one year before current month. Source: CreditTrade, GFI, Markit
The ratio of the total notional dollar amount of all CDS contracts outstanding (before maturity) in our database to the total dollar amount
of debt outstanding. Source: CreditTrade, GFI, Compustat
The industry mean cash ratio across two-digit SIC codes. Source: Compustat
The industry mean leverage across two-digit SIC codes. Source: Compustat
The industry cash ﬂow risk, measured by the mean cash ﬂow volatility
across two-digit SIC codes. Source: Compustat
The ratio of cash ﬂow to total assets, where cash ﬂow is deﬁned as the
earnings after interest and related expenses, income taxes, and dividends. Source: Compustat
The book value of assets minus the book value of equity plus the market value of equity, all divided by the book value of assets. Source:
Compustat
The logarithm of total assets. Source: Compustat
Net working capital minus cash, divided by total assets. Source: Compustat
The ratio of capital expenditure to total assets. Source: Compustat
The ratio of R&D to sales. R&D is set to zero if missing. Source:
Compustat
A dummy variable that equals one if the ﬁrm pays a common dividend.
Source: Compustat
The ratio of acquisitions to total assets. Source: Compustat
The ratio of foreign pretax income to total assets. Source: Compustat
A measure of the average FX hedging activities carried out by the ﬁrm’s
lending banks and underwriters. Source: Dealscan, FISD, Call Report
A measure of the average Tier 1 capital ratio of the bank lenders. Source:
Dealscan, FISD, Compustat
The number of ﬁnancial expert board members of the ﬁrm. Source:
Riskmetrics
Indicator variable which equals to 1 if the ﬁrm has no bond rating.
Source: Compustat, FISD
Firms with a bond rating are classiﬁed as rated. Source: Compustat,
FISD
Dummy variables indicating whether ﬁrms are covered by equity analysts or not. Source: I/B/E/S
The ratio of long-term debt due in one year to total debt. Source:
Compustat
The ratio of property, plant and equipment to total assets. Source:
Compustat
49
Internet Appendix to
“Credit Default Swaps, Debt Financing and Corporate
Liquidity Management”
(not to be included for publication)
Table A1
Probability of Credit Default Swaps Trading
This table presents the estimates of the probability of credit default swaps (CDS) trading, obtained using a
probit model. Propensity scores are estimated based on the model parameters. ln(Assets) is the logarithm
of the ﬁrm’s total asset value. Leverage is deﬁned as the ratio of book debt to total assets. ROA is the
ﬁrm’s return on assets. rit−1 − rmt−1 is the ﬁrm’s excess return over the past year. Equity Volatility is
the ﬁrm’s annualized equity volatility. PPENT/Total Asset is the ratio of property, plant and equipment
to total assets. Sales/Total Asset is the ratio of sales to total assets. EBIT/Total Asset is the ratio of
earnings before interest and tax to total assets. WCAP/Total Asset is the ratio of working capital to total
assets. RE/Total Asset is the ratio of retained earnings to total assets. Cash/Total Asset is the ratio of cash
to total assets. CAPX/Total Asset is the ratio of capital expenditure to total assets. Rated is a dummy
variable that equals one if the ﬁrm is rated. Senior Unsecured Debt is the ratio of senior unsecured debt
to total debt. Lender Size is a measure of the size of the lending banks and underwriters. Lender Credit
Derivatives measures the credit derivative activities of the lenders. Lender FX Usage is a measure of the FX
hedging activities of the lending banks and underwriters, and Lender Tier 1 Capital is the Tier 1 capital
ratio of the lenders. The sample period is 1997-2009. (*** denotes signiﬁcance at the 1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at the 10% level. The numbers in parentheses are standard errors.)
50
Ln(Assets)
Leverage
ROA
rit−1 − rmt−1
Equity Volatility
PPENT/Total Asset
Sales/Total Asset
EBIT/Total Asset
WCAP/Total Asset
RE/Total Asset
Cash/Total Asset
CAPX/Total Asset
Rated
Senior Unsecured Debt
Lender Size
Lender Credit Derivatives
Lender FX Usage
CDS Prediction
Model 1
0.790∗∗∗
(0.006)
0.429∗∗∗
(0.025)
−0.001
(0.001)
−0.104∗∗∗
(0.011)
0.063∗∗∗
(0.017)
0.306∗∗∗
(0.031)
−0.026∗∗∗
(0.009)
0.315∗∗∗
(0.064)
0.142∗∗∗
(0.024)
0.022∗∗∗
(0.005)
0.290∗∗∗
(0.023)
−1.611∗∗∗
(0.122)
0.667∗∗∗
(0.203)
0.375∗∗∗
(0.014)
0.369∗∗∗
(0.011)
1.006∗∗∗
(0.024)
8.979∗∗∗
(0.788)
Probability of CDS Trading
CDS Prediction
Model 2
0.804∗∗∗
(0.006)
0.440∗∗∗
(0.025)
−0.001
(0.001)
−0.104∗∗∗
(0.011)
0.069∗∗∗
(0.017)
0.321∗∗∗
(0.031)
−0.027∗∗∗
(0.003)
0.375∗∗∗
(0.064)
0.145∗∗∗
(0.024)
0.023∗∗∗
(0.005)
0.302∗∗∗
(0.023)
−1.677∗∗∗
(0.122)
0.645∗∗∗
(0.205)
0.377∗∗∗
(0.014)
0.378∗∗∗
(0.011)
1.013∗∗∗
(0.024)
−3.865∗∗∗
(0.756)
26.13
0.000
Yes
Yes
Yes
Yes
38.79%
690111
Lender Tier 1 Capital
F-statistic (instruments)
p-value (F-statistic)
Credit Rating Controls
Time Fixed Eﬀect
Industry Fixed Eﬀect
Clustered Standard Error
Pseudo R2
N
129.89
0.000
Yes
Yes
Yes
Yes
38.96%
690111
51
CDS Prediction
Model 3
0.797∗∗∗
(0.006)
0.431∗∗∗
(0.026)
−0.001
(0.001)
−0.104∗∗∗
(0.011)
0.067∗∗∗
(0.017)
0.307∗∗∗
(0.031)
−0.026∗∗∗
(0.003)
0.338∗∗∗
(0.064)
0.143∗∗∗
(0.024)
0.024∗∗∗
(0.005)
0.294∗∗∗
(0.023)
−1.604∗∗∗
(0.122)
0.638∗∗∗
(0.205)
0.375∗∗∗
(0.014)
0.385∗∗∗
(0.011)
1.019∗∗∗
(0.025)
9.104∗∗∗
(0.789)
−4.000∗∗∗
(0.757)
159.74
0.000
Yes
Yes
Yes
Yes
38.99%
690111
Table A2
Eﬀect of CDS on Cash Holdings: Robustness Checks
This table presents robustness checks for the eﬀect of CDS trading on cash holdings. In Panel A, Model 1
is based on the sample of all Compustat ﬁrms. Model 2 is based on the sample of all Compustat ﬁrms, for
ﬁrm-years with at least 100 million in assets. Model 3 is the cash holding analysis conducted on the sample
of all Compustat ﬁrms, excluding ﬁnancial ﬁrms. Model 4 is the cash holding analysis conducted on the
sample of all Compustat ﬁrms with ﬁrm-years with at least 100 million in assets, excluding ﬁnancial ﬁrms.
Panel B investigates the CDS eﬀect in the propensity-score-matched sample, excluding ﬁnancial ﬁrms. Panel
C conducts a placebo test on the propensity-score-matched sample. We use data from the 1980s when there
was no CDS trading, and perform the cash holding analysis using pseudo-CDS ﬁrms and their control groups.
(*** denotes signiﬁcance at the 1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at the 10% level.
The numbers in parentheses are standard errors.)
Panel A: All Compustat Firms As Control Group
All Firms
Non-Financial Firms
(1)
(2)
(3)
(4)
CDS Trading
0.020∗∗∗
0.024∗∗∗
0.020∗∗∗
0.023∗∗∗
(0.003)
(0.003)
(0.003)
(0.003)
Industry Sigma
0.076∗∗∗
0.071∗∗∗
0.073∗∗∗
0.074∗∗∗
(0.016)
(0.017)
(0.016)
(0.017)
Cash Flow/Assets
0.066∗∗∗
0.016
0.062∗∗∗
0.013
(0.009)
(0.013)
(0.010)
(0.013)
Market to Book
0.008∗∗∗
0.008∗∗∗
0.008∗∗∗
0.007∗∗∗
(0.001)
(0.001)
(0.001)
(0.001)
Size
−0.009∗∗∗
−0.011∗∗∗
−0.009∗∗∗
−0.011∗∗∗
(0.001)
(0.001)
(0.001)
(0.001)
Net Working Capital/Assets
−0.046∗∗∗
−0.063∗∗∗
−0.046∗∗∗
−0.061∗∗∗
(0.004)
(0.006)
(0.004)
(0.006)
Capital Expenditure
−0.211∗∗∗
−0.146∗∗∗
−0.212∗∗∗
−0.148∗∗∗
(0.012)
(0.011)
(0.012)
(0.011)
Leverage
−0.084∗∗∗
−0.073∗∗∗
−0.084∗∗∗
−0.075∗∗∗
(0.005)
(0.006)
(0.005)
(0.006)
R&D/Sales
0.195∗∗∗
0.161∗∗∗
0.196∗∗∗
0.152∗∗∗
(0.015)
(0.032)
(0.015)
(0.033)
Dividend Dummy
0.007∗∗∗
0.001
0.008∗∗∗
0.001
(0.002)
(0.002)
(0.002)
(0.002)
Acquisition Activity
−0.197∗∗∗
−0.164∗∗∗
−0.191∗∗∗
−0.161∗∗∗
(0.013)
(0.012)
(0.013)
(0.012)
Foreign Pretax Income
0.004∗
0.108∗∗∗
0.004∗
0.101∗∗∗
(0.002)
(0.028)
(0.002)
(0.027)
Time Fixed Eﬀect
Yes
Yes
Yes
Yes
Firm Fixed Eﬀect
Yes
Yes
Yes
Yes
Clustered Standard Error
Yes
Yes
Yes
Yes
N
308510
167492
294893
160998
R2
74.94%
83.38%
75.05%
83.35%
52
Panel B: Propensity-score-matched Firms as Control Group, Excluding Financial Firms
CDS Trading
Leverage
Single Equation
(1)
Cash
0.025∗∗∗
(0.006)
−0.046
(0.039)
Simultaneous Equations
(2)
Leverage
Cash
0.039∗∗∗
0.017∗∗∗
(0.002)
(0.003)
0.195∗
(0.101)
0.405∗∗∗
(0.031)
−0.574∗∗∗
(0.071)
0.143∗∗∗
(0.018)
0.002
0.036∗∗∗
(0.013)
(0.009)
−0.222∗∗∗
0.052∗
(0.021)
(0.027)
−0.014∗∗∗
0.003∗
(0.001)
(0.001)
−0.028∗∗∗
−0.024∗∗∗
(0.002)
(0.001)
−0.051∗∗∗
−0.033∗∗∗
(0.007)
(0.005)
−0.190∗∗∗
−0.124∗∗∗
(0.021)
(0.016)
−0.005
0.275∗∗∗
(0.031)
(0.022)
−0.023∗∗∗
−0.012∗∗∗
(0.002)
(0.002)
0.075∗∗∗
−0.212∗∗∗
(0.019)
(0.020)
−0.045∗
0.236∗∗∗
(0.023)
(0.021)
Yes
Yes
Yes
Yes
Yes
Yes
39652
39652
71.47%
67.68%
Industry Cash
Cash
Industry Leverage
Industry Sigma
Cash Flow/Assets
Market to Book
Size
Net Working Capital/Assets
Capital Expenditure
R&D/Sales
Dividend Dummy
Acquisition Activity
Foreign Pretax Income
Time Fixed Eﬀect
Firm Fixed Eﬀect
Clustered Standard Error
N
R2
0.067
(0.044)
−0.008
(0.070)
−0.001
(0.002)
−0.026∗∗∗
(0.007)
−0.057
(0.056)
−0.167∗∗∗
(0.025)
0.245∗∗
(0.110)
−0.017∗
(0.010)
−0.181∗∗∗
(0.058)
0.223∗∗∗
(0.058)
Yes
Yes
Yes
40018
75.01%
53
Panel C: Placebo Test
Cash
0.007
(0.004)
−0.001
(0.001)
−0.085∗∗
(0.036)
0.027∗∗∗
(0.003)
−0.010∗∗∗
(0.002)
−0.229∗∗∗
(0.018)
−0.273∗∗∗
(0.030)
−0.201∗∗∗
(0.015)
0.031
(0.028)
−0.058∗∗∗
(0.007)
−0.104∗∗∗
(0.013)
Yes
Yes
Yes
10333
56.11%
Placebo CDS Trading
Industry Sigma
Cash Flow/Assets
Market to Book
Size
Net Working Capital/Assets
Capital Expenditure
Leverage
R&D/Sales
Dividend Dummy
Acquisition Activity
Time Fixed Eﬀect
Firm Fixed Eﬀect
Clustered Standard Error
N
R2
54
Table A3
Simultaneous Eﬀect of CDS on Corporate Finance: Corporate Governance Control
This table presents the estimates of the simultaneous eﬀect of CDS on corporate leverage and cash holdings
in a sample including ﬁrms with CDS and non-CDS propensity-score-matched ﬁrms. Leverage and Cash
equations are estimated simultaneously by two-stage least squares procedures. E-index is the entrenchment
index, which is a measure of the quality of ﬁrms’ governance provisions. The coeﬃcient of interest are those
of CDS Trading and CDS Trading×E-index, which capture the impact of the inception of CDS trading on
cash holdings and leverage. The sample period is 1997-2009, based on quarterly observations. (*** denotes
signiﬁcance at the 1% level, ** signiﬁcance at the 5% level, and * signiﬁcance at the 10% level. The numbers
in parentheses are standard errors.)
CDS Trading
CDS Trading×E-index
E-index
Cash
Industry Leverage
Leverage
0.064∗∗∗
(0.003)
−0.008∗∗∗
(0.001)
0.004∗∗∗
(0.001)
−0.582∗∗∗
(0.072)
0.164∗∗∗
(0.019)
Leverage
Industry Cash
Industry Sigma
Cash Flow/Assets
Market to Book
Size
Net Working Capital/Assets
Capital Expenditure
R&D/Sales
Dividend Dummy
Acquisition Activity
Time Fixed Eﬀect
Firm Fixed Eﬀect
Clustered Standard Error
R2
N
0.044∗∗∗
(0.015)
−0.345∗∗∗
(0.023)
−0.014∗∗∗
(0.001)
−0.031∗∗∗
(0.002)
−0.052∗∗∗
(0.007)
−0.200∗∗∗
(0.023)
−0.043
(0.031)
0.016∗∗∗
(0.005)
0.101∗∗∗
(0.019)
Yes
Yes
Yes
72.06%
34516
55
Cash
0.032∗∗∗
(0.004)
−0.004∗∗∗
(0.001)
0.002∗∗∗
(0.001)
0.000
(0.077)
0.376∗∗∗
(0.028)
0.059∗∗∗
(0.009)
0.005
(0.031)
0.002
(0.001)
−0.022∗∗∗
(0.002)
−0.010∗
(0.006)
−0.139∗∗∗
(0.016)
0.239∗∗∗
(0.021)
−0.035∗∗∗
(0.004)
−0.168∗∗∗
(0.018)
Yes
Yes
Yes
73.17%
34516
Table A4
Simultaneous Eﬀect of CDS on Leverage, Cash and Lines of Credit
This table presents the estimates of the simultaneous eﬀect of CDS on corporate leverage, cash holdings
and lines of credit in a sample including ﬁrms with CDS and non-CDS propensity-score-matched ﬁrms.
Lines of credit data are drawn from Dealscan. Leverage, cash, and lines of credit equations are estimated
simultaneously. (*** denotes signiﬁcance at the 1% level, ** signiﬁcance at the 5% level, and * signiﬁcance
at the 10% level. The numbers in parentheses are standard errors.)
Leverage
Cash
Lines of Credit
CDS Trading
0.040∗∗∗
0.019∗∗∗
0.127
(0.002)
(0.003)
(0.129)
Lines of Credit
0.009∗∗∗
−0.004∗∗∗
(0.001)
(0.001)
Industry Leverage
0.150∗∗∗
(0.018)
Cash
−0.501∗∗∗
−5.210∗∗
(0.069)
(2.319)
Industry Lines of Credit
0.931∗∗∗
(0.025)
Industry Cash
0.385∗∗∗
(0.026)
Leverage
0.119
−5.976∗∗
(0.087)
(2.794)
Industry Sigma
−0.022∗
0.046∗∗∗
0.388
(0.013)
(0.009)
(0.295)
Cash Flow/Assets
−0.214∗∗∗
0.044∗
−2.987∗∗∗
(0.021)
(0.024)
(0.788)
Market to Book
−0.014∗∗∗
0.002∗
−0.026
(0.001)
(0.001)
(0.040)
Size
−0.025∗∗∗
−0.025∗∗∗
−0.266∗∗∗
(0.002)
(0.001)
(0.092)
Net Working Capital/Assets
−0.036∗∗∗
−0.040∗∗∗
−1.324∗∗∗
(0.007)
(0.005)
(0.210)
Capital Expenditure
−0.160∗∗∗
−0.146∗∗∗
−3.125∗∗∗
(0.021)
(0.014)
(0.712)
R&D/Sales
−0.010
0.231∗∗∗
−0.763
(0.029)
(0.019)
(0.641)
Dividend Dummy
−0.022∗∗∗
−0.012∗∗∗
−0.152∗
(0.002)
(0.002)
(0.085)
Acquisition Activity
0.095∗∗∗
−0.200∗∗∗
−0.334
(0.019)
(0.018)
(0.444)
Time Fixed Eﬀect
Yes
Yes
Yes
Firm Fixed Eﬀect
Yes
Yes
Yes
Clustered Standard Error
Yes
Yes
Yes
R2
72.23%
70.34%
53.03%
N
40293
40293
40293
56