DBJ Discussion Paper Series, No. 1604

The Real Effect of Financial Innovation:
Evidence from Credit Default Swaps Trading and Corporate Innovation

Xin Chang(The University of Cambridge and Nanyang Technological University)
Yangyang Chen
(The Hong Kong Polytechnic University)
Sarah Qian Wang
(The University of Warwick)
Kuo Zhang(Chinese University of Hong Kong)
Wenrui Zhang(Chinese University of Hong Kong)

September 2016

Discussion Papers are a series of preliminary materials in their draft form. No quotations,reproductions or circulations should be made without the written consent of the authors in orderto protect the tentative characters of these papers. Any opinions, findings, conclusions or
recommendations expressed in these papers are those of the authors and do not reflect the viewsof the Institute.

The Real Effect of Financial Innovation:
Evidence from Credit Default Swaps Trading and Corporate Innovation∗

Xin Changa, Yangyang Chenb, Sarah Qian Wangc, Kuo Zhangd, Wenrui Zhangda

The University of Cambridge and Nanyang Technological Universityb
The Hong Kong Polytechnic Universityc
The University of WarwickdChinese University of Hong Kong
This version: July 2016

Abstract
We document that credit default swaps (CDS) trading on a firm’s debt positively influences its
technological innovation measured using patents and patent citations. The positive effect is more
pronounced for firms relying more on debt financing, using more bank debt, borrowing fromfewer lenders, having more restrictive debt covenants, or using more short-term debt prior to
CDS introduction. Further analysis shows that firms’ innovation strategies become more risky
and long-term oriented after the advent of CDS trading. These results suggest that CDS foster
borrowing firms’ innovation via enhancing lenders’ risk tolerance and borrowers’ risk taking in
the innovation process. Taken together, our findings reveal the real effects of financial
innovation (i.e., CDS) on companies’ investment and technological progress.
JEL Classification: G32, G34, O31
Keywords: Credit Default Swaps; Corporate Innovation; Risk Taking

Emails: x.chang@jbs.cam.ac.uk or changxin@ntu.edu.sg, yangyang.chen@polyu.edu.hk, qian.wang@wbs.ac.uk,
zhangkuo@baf.cuhk.edu.hk, and zhangwenrui@baf.cuhk.edu.hk, respectively. We are grateful for the valuablecomments and suggestions from Kenneth Ahern, Utpal Bhattacharya, Thomas Chemmanur, Henrik Cronqvist,
Andrea Gamba, Masaharu Hanazaki, Harrison Hong, Po-Hsuan (Paul) Hsu, Kentaro Iwatsubo, Bart Lambrecht,
Gustavo Manso, Martin Oehmke, Raghavendra Rau, Toyoharu Takahashi, Dragon Tang, Xuan Tian, Yanzhi Wang,
K.C. John Wei, James Weston, Andrew Winton, Noriyoshi Yanase, Ayako Yasuda, and seminar participants at
Central University of Finance and Economics, Chinese University of Hong Kong, Deakin University, Hitotsubashi
University, Hong Kong Institute for Monetary Research, Kobe University, Monash University, National Taiwan
University, the University of Hong Kong, the University of Melbourne, Queens University of Belfast, Renming
University of China, Sungkyunkwan University, Tsinghua University, and the University of Warwick, andparticipants of the 2015 Securities and Financial Markets Conference, 2016 Financial Management Writer'sWorkshop, and 2016 Asian Finance Association Conference. We are also grateful for Noah Stoffman and Dimitris
Papanikolaou for sharing the innovation data. A major part of research was done at the Research Institute of Capital
Formation of Development Bank of Japan when Chang visited as a Shimomura Fellow in June-July 2016. Wenrui
Zhang acknowledges financial support from the CUHK Business School (Project number: DAT1415-4057061). Allerrors are our own.
∗

"I wish somebody would give me some shred of evidence linking financial innovation with abenefit to the economy."
Paul Volcker (2010), former Chairman of the Federal Reserve
I. Introduction
Companies’ technological innovation is vital for their competitiveness and long-term growth, but
it is difficult to finance with debt. Unlike conventional investments such as capital expenditures
and acquisitions, corporate innovation produces intangible assets and involves a long-term andrisky process that has both a high likelihood of failure and some prospects for extraordinary
positive returns (e.g., Holmstrom, 1989). Thus, fostering innovation requires strong risk-taking
incentives, substantial tolerance for early failure, and rewards for long-term success (Manso,2011). Compared with shareholders, lenders are generally more risk averse, more short-termoriented, and less likely to benefit from firms’ innovation success. As such, debt has been
regarded by prior studies (e.g., Stiglitz, 1985; Hall and Lerner, 2010) as a disfavored source offinance for innovation relative to equity. Further, amongst various types of debt financing, bankdebt is viewed as less suited to financing innovation than public debt (Rajan and Zingales, 2003;
Atanassov, 2015), and short-term debt is shown to be less conducive to innovation than longterm debt (Hasan, O’Brien, and Ye, 2013).
As one of the most important financial innovations since the turn of the century, credit
default swaps (CDS hereafter) are credit derivative contracts, in which CDS sellers offer CDS
buyers protection against default or other credit events of underlying reference entities inexchange for a periodic premium payment from CDS buyers.1 If CDS are traded on a borrowing

1

A reference entity bears the credit risk of the CDS contract, and can be a corporation, government, or other legal
entity that issues debt of any kind. Credit events mainly include defaults on interest or principal payments and thebankruptcy filing of the debtor, and may also include debt restructuring and credit-rating downgrades in some CDS
contracts. If a credit event occurs, CDS sellers should make the payment equal to the face value of the debt due. In
return, CDS buyers should deliver either the current cash value of the debt or the actual bonds to CDS sellers,1firm’s debt, the lenders can buy CDS and thus hedge the credit risk associated with theirinvestment (such as a loan or a bond), while retaining legal ownership of the investment.2 Even if
lenders do not purchase CDS, the existence of CDS markets provides them with a valuable
option to hedge against credit risk (Saretto and Tookes, 2013).
Does the existence of such hedging products influence the compatibility between debt
financing and corporate innovation? In other words, does the availability of CDS trades tolenders affect borrowing firms’ technological innovation? Through addressing these questions,this paper aims to reveal the real effects of innovation in financial markets on companies’
investment and technological progress.
We develop our main hypothesis based on prior literature examining the effects of CDS
trading on payoffs, incentives, and behaviors of contractual parties (i.e., lenders and borrowers)
to existing debt. In particular, we posit that CDS positively influences innovation throughpromoting borrowing firms’ risk-taking behaviors. While risk taking is essential for innovation,lenders are generally averse to it because their payoff is a concave function of borrowing firms’
value (Jensen and Meckling, 1976). Specifically, as shown in Figure 1, lenders’ payoff is linear
and upward sloping in the region of default and fixed in the region of repayment. As such, forlenders, higher risk-taking of borrowing firms implies a higher probability of losses without thesame potential for gains that shareholders would capture. With CDS protection, lenders’ net
payoff increases in the region of default and slightly decreases in the regional of repayment after
deducting CDS premium, which increases with borrowing firms’ default risk. Essentially, CDS

depending on the terms agreed upon at the onset of the contract. According to the International Swaps andDerivatives Association (ISDA), the size of the CDS market has reached a peak of $62.2 trillion of notional
outstanding value at the end of 2007, making CDS one of the most important financial derivatives for managing
credit risk in the debt market.
2
CDS sellers have no control rights with respect to the underlying loan and typically have no direct contractual
involvement with borrowers.
2

protection weakens concavity of lenders’ payoff function, thus enhancing lenders’ tolerance
towards borrowing firms’ risk taking.
[Insert Figure 1 Here]
Further, a salient feature of the traditional lender-borrower relationship is that lenders,
especially banks, protect themselves against default risk by continuously monitoring borrowers’
investment choices even outside the payment default states (e.g., Fama, 1985; Nini, Smith, andSufi, 2012). Lenders’ continuous monitoring may involve hands-on evaluation of borrowers’
investment decisions, imposing stringent financial covenants to constrain borrowers' investment
and financing policies, and exerting influence on borrowers’ managerial turnover. However, it isparticularly costly to monitor borrowing firms’ innovation investment because of theuncertainties that innovative projects involve and the difficulty of negotiating and implementing
covenants. Recent studies, such as Morrison (2005) and Parlour and Winton (2013), show thatthe onset of CDS trading weakens lenders’ incentives to engage in costly monitoring andintervene in borrowers’ governance because their claims can be insured via CDS. Shan, Tang,and Yan (2015) find that covenants on a borrower's debt become less strict if there are CDS
contracts referencing the borrower’s debt at the time of loan initiation. In response to reduced
lender monitoring, borrowing firms can have more opportunities to direct their efforts andresources towards higher-quality innovative projects that are riskier by nature.3 Additionally, as
laxer debt covenants reduce the probability of covenant violations, borrowing firms can achieve
greater flexibility and tolerance to experimentation, which results in higher-quality innovations(Atanassov, 2015). Taken together, we expect CDS to foster borrowing firms’ innovation via

3

While borrowing firms do not necessarily observe their lenders’ purchase of CDS contracts, they can observe CDS
trade initiation on their debt (Martin and Roychowdhury, 2015). In addition, Arping (2014) argues that managers at
borrowing firms can typically detect any weakening of lenders’ monitoring intensity in general.
3enhancing lenders’ risk tolerance and allowing borrowing firms to take more risk in theinnovation process. We refer to this mechanism as the risk-taking channel.
Prior studies also suggest several other economic forces that could potentially discourageborrowing firms from taking risk in their innovation investment upon CDS trade initiation. For
example, in response to CDS-insured lenders’ reduced monitoring, uninsured lenders of aborrowing firm may increase their monitoring efforts to constrain the borrower’s risk taking.
Moreover, it is possible that CDS sellers can fully anticipate the incentives of CDS-insured
lenders and price it into the CDS premium. To lower protection prices or avoid reputation costs
arising from adverse credit events caused by reduced monitoring, CDS-insured lenders may
continue monitoring borrowers intensively in the post-CDS period.4 Finally, Hu and Black (2008)
point out that lenders can separate their cash flow rights from control rights through the purchaseof CDS protection, turning themselves into “empty creditors”. As a result, CDS-insured lenders
can be tougher in debt renegotiations, and might even be better off pushing financially distressedborrowers into inefficient bankruptcy or liquidation for the CDS settlement (e.g., Bolton andOehmke, 2011).5 Anticipating this, borrowing firms might have weaker ex ante incentives toundertake risky innovative projects in order to avoid defaults and covenant violations that trigger
debt renegotiations. In sum, all these factors (i.e., uninsured lenders’ monitoring efforts, CDSinsured lenders’ cost concerns, and CDS-insured lenders’ superior bargaining power in financial
distress) might limit borrowing firms’ incentives and opportunities to pursue risky innovative
4

In principle, CDS sellers, many of which are large insurance companies, can price-protect themselves by charging
a higher premium if they can infer reduced lender monitoring based on heightened defaults of borrowers after CDS
trade initiation. However, in practice, it is difficult to always attribute ex post borrower defaults to ex ante lenders’reduced monitoring. Thus, CDS sellers typically price-protect only on average across all CDS buyers. An easier-toimplement and more cost-efficient protection method is to diversify credit risk exposure through selling CDS
referenced to companies in different industries. By doing so, losses generated by one contract can be compensatedby premiums earned from other contracts (Martin and Roychowdhury, 2015).
5
Gopalan, Nanda, and Yerramilli (2011) show that relationship banks of severely distressed firms bear substantial
reputational costs. Thus, lenders’ reputation concerns may prevent them from being excessively tough in debt
renegotiations.
4projects, thereby weakening the risk-taking channel outlined above. Thus, the net effect of CDS
on innovation should reflect the tension among various forces, and should be best determined
empirically.
In this paper, we identify 782 U.S. listed firms on which CDS trading was introduced
between 1997 and 2008. We follow previous studies (e.g., Hirshleifer, Low, and Teoh, 2012;
Chang et al., 2015) and measure the quantity of borrowing firms’ innovation output using thenumber of patents granted by the U.S. Patent and Trademark Office (USPTO). In addition, we
use the number of patent citations to capture the quality of innovation.
Our CDS firms are not randomly assigned. It is plausible that some factors determining aborrowing firm’s innovation output may also drive the likelihood of the firm to be selected into
CDS trading. For example, a firm’s investment opportunity may affect both innovation output
and the onset of CDS trading. To address this selection concern, we follow prior studies (e.g.,
Ashcraft and Santos, 2009) and implement a matched-sample analysis using the propensity scorematching procedure. We include both treated (CDS) firms and control (matched non-CDS) firms
in the regression analyses. Our baseline specification is a difference-in-differences (DiD) modelwith firm and year fixed effects, which essentially compares the change in innovation output
around CDS trade initiation for CDS firms with that for non-CDS firms.
Our main results are that CDS firms, compared with the non-CDS firms, create
significantly more patents and their patents generate more citations after the introduction of CDS
trading on their debt. The positive impact of CDS trade initiation on innovation outcome is both
statistically and economically significant. Specifically, after CDS trading is introduced, an
average CDS firm generates 14.8% more patents and 20.2% more citations than their non-CDS

5counterparts. We perform various checks and confirm that our main findings are robust toalternative matching methods, model specifications, and variable definitions.
An important concern of our analysis is that the timing of CDS introduction is endogenous.
For instance, unobservable factors correlated with both corporate innovation and CDS trade
initiation could bias our results (i.e., the omitted variable concern). Further, in response to aborrowing firm’s increased risk taking in innovation, its lenders may initiate hedging contracts
and CDS markets emerge (i.e., the reverse causality concern). We perform a number of tests toalleviate endogeneity concerns. Among these tests, we follow Saretto and Tookes (2013) andemploy lenders’ hedging activities on foreign exchange as the instrumental variable for CDS
trading. The instrument choice is based on Minton, Stulz, and Williamson’s (2009) finding thatbanks using foreign exchange derivatives to hedge currency risk are more likely to use CDS tohedge credit risk. Further, lending banks’ decision to hedge currency risk should be exogenous toa borrowing firm’s innovation output. The main results still hold, suggesting a causal effect of
CDS trading on innovation.
Having established the sign of and causality for the CDS trading-innovation relation, we
further explore its potential mechanisms. To support the risk-taking channel through which CDS
trading positively affects innovation, we examine how firm specific characteristics interact with
CDS trading to affect corporate innovation by partitioning our sample in several ways. First, wefind that CDS trading has a stronger impact on firms’ innovation output when firms are more
dependent on debt financing, which confirms the role of CDS as a debt market instrument to help
improve the compatibility between debt financing and innovation. We also find that the effect of
CDS trading on firms’ patents and patent citations is more evident when firms are more likely touse bank debt versus public debt in debt financing and when firms have fewer bank lenders and6

greater debt covenants. These findings are consistent with our argument because banks are morerisk averse and exert more influence on firms’ risk-taking activities by imposing strict covenants,
which are more harmful to firms’ innovation (Rajan and Zingales, 2003; Atanassov, 2015). In
addition, we find that our results are more pronounced for firms with more short-term debt
versus long-term debt in debt financing prior to CDS introduction. Since short-term bondholders
are more myopic and are more likely to pursue safe investments, which have an adverse effect onfirms’ innovativeness (Hasan, O’Brien, and Ye, 2013), the findings provide additional supports
to our argument that CDS trading improves lenders’ tolerance towards risk taking.
To further substantiate the risk-taking channel, we present evidence on the effect of CDS
trading on firms’ innovation strategies, which play an essential role in maintaining corporate
technological advantages (Hall, 1993; Cockburn, Henderson, and Stern, 2000). Generally, we
find that firms’ innovation strategies become more risky and long-term after CDS trade initiation.
Specifically, we find that CDS trading has a positive effect on firms' exploratory innovation thatdeparts from existing knowledge and builds on new knowledge a negative effect on firms'
exploitative innovation that arises from existing knowledge with a short development cycle andless uncertainty. In addition, we find that firms create more production patents that usually leadto more creative products rather than process patents after CDS are introduced on these firms.
Finally, we find that firms apply for patents that cite other patents in a wide array of technology
classes. Overall, these findings lend further support to our argument that CDS trading promotes
corporate innovation through encouraging managers’ risk-taking incentives and motivating
managers’ to take long perspectives.
Although our findings are consistent with the view that CDS trading encourages firms’
innovation via the risk-taking channel, these findings are also consistent with the notion that7

CDS trading mitigates firms’ financing constraints through increasing borrowing firms’ debt
capacity (e.g., Saretto and Tookes, 2013) and allowing more innovative projects to be financed
by debt. To examine this alternative channel, we conduct three tests. First, we include additional
controls for the changes in debt capacity around CDS trading ignition and find the results are notaffected. Second, we investigate the effect of CDS trading on R&D expenditures and find that
CDS trading does not significantly increase firms’ spending on R&D. Third, we partition thesample according to various measures of financial constraints. The financing channel predicts
that financially constrained firms benefit more from the relaxed borrowing constraint and thus
exhibit greater increase in innovation output. However, we find that the effect of CDS trading on
firms’ innovative outcomes does not depend on their financial constraints. Collectively, theseresults suggest that our findings are not driven by CDS trading relaxing borrowers’ financial
constraints.
Last, we investigate how CDS trading affects firms' innovation efficiency and also theeconomic value from innovation. We find that firms have higher number of patents and citations
per R&D dollars after CDS trade initiation, suggesting that CDS firms are more efficient inconverting R&D investments into innovation outputs. The finding reconciles the results that
CDS trading increases innovation output but do not have significant impact of R&D input.
Further, we find that the market valuation of patents increases after CDS trade initiation,suggesting that in addition to scientific value, CDS firms are also better able to produce patents
with higher economic value.
Our study contributes to the extant literature in two ways. First, our paper adds to theliterature on the financing of innovation. While most prior studies (e.g., Hall, 2002; Brown,
Fazzari, and Petersen, 2009) suggest that innovation should primarily rely on internal funds and8equity financing, several recent studies (e.g., Nanda and Nicolas, 2014; Kerr and Nanda, 2014)
reveal the increasing importance of debt financing for innovation, particularly for large firms.
We extend this literature by showing that the risk-transferring function of CDS can improve thecompatibility between innovation and debt financing.6 This finding suggests a useful focus forpolicymakers who are interested in fostering innovation in the economy. Second, our study
contributes to the ongoing debate on the effects of financial innovation. On the one hand, CDS,
as a major form of financial innovation, have been shown by previous studies to facilitate credit
risk transfer, promote risk sharing, and relax capital supply constraints (e.g., Saretto and Tookes,
2013). On the other hand, Warren Buffett referred to CDS as "financial weapons of mass
destruction". Subrahmanyam, Tang, and Wang (2014) reveal a dark side of CDS in terms ofincreasing the probability of default and credit rating downgrade. We document that CDS
encourage borrowing firms to engage in risky experimentation, thereby spurring innovation. By
doing so, our analysis discovers a specific micro-level channel through which financial
innovation may drive economic growth, and hence complements the macro-level findings ofBeck et al. (2014) who show that financial innovation allows countries and industries to better
reap growth opportunities and achieve higher economic growth.
The reminder of the paper is organized as follows. Section 2 reviews related literature.
Section 3 describes the data, the sample, and the variable construction. Main empirical results arepresented in Section 4. Additional tests are reported in Section 5. Section 6 concludes.

2. Related Literature

6

In response to the global financial crisis of 2007–08, there was a wide-spread debate on the bright and dark sides offinancial innovation. In particular, the Economist organized an online debate in early 2010 between Ross Levine andJoseph E. Stiglitz regarding the costs and benefits of financial innovation.
9

Our paper builds on two strands of literature: recent works that focus on the effects of CDS
trading on various corporate policies; and research on the financing of corporate innovation.
2.1. CDS trading and corporate policies
CDS provide credit suppliers with an effective tool to hedge their downside risk. CDS
protected creditors tend to be more risk tolerant in their lending practices. For example, several
studies show that as creditors’ exposure to borrowers’ default risk is protected by CDS, these
creditors’ interest is more aligned with that of shareholders and hence their intervention incentive
is weakened. Specifically, Morrison (2005) and Parlour and Winton (2013) theoretically model
this incentive. Shan, Tang, and Winton (2014) provide empirical evidence that debt covenants
imposed by lenders become less restrictive after the initiation of CDS trading on reference firms’
debt. As a result, creditors’ enhanced risk tolerance encourages these firms’ risk-taking. Martin
and Roychowdhury (2015) show that a borrowing firm’s reporting conservatism declines after
CDS trading is introduced on this firm. Karolyi (2013) finds that borrowers increase operationalrisk taking after the introduction of CDS trading. Furthermore, CDS may affect reference firms’
financing policies. For example, Ashcraft and Santos (2009) find that CDS trading decreases thecost of debt for safe and transparent firms. Saretto and Tookes (2013) find that CDS firms areable to maintain higher leverage and longer debt maturity. Shan, Tang, and Yan (2014) find thatbanks that are active CDS users grant larger amounts of loans to CDS-referenced borrowers.
However, firms may also face “tougher negotiators” in the process of debt restructuring.
CDS create “empty creditors” who have control rights of the firm but different risk alignment
from other creditors without CDS protection (Hu and Black, 2008). Bolton and Oehmke (2011)
formally model the CDS-induced empty creditor problem. Consistent with their model
predictions, Subrahmanyam, Tang, and Wang (2014) find increased bankruptcy risk for the10reference entities after CDS trading. Subrahmanyam, Tang, and Wang (2015) further find that
CDS firms increase precautionary cash holdings in response to the potential threat from tougher“empty creditors”.
Different from previous literature that mainly focuses on the effect of CDS trading on firms’
financing activities or reporting practice, our study investigates the real effect of CDS trading on
the output of firms’ innovation investment. Given the huge economic value and strategic
importance of corporate innovation, we add to the literature by identifying a unique role of CDS
trading in affecting firm value.
2.2. Financing of corporate innovation
Given the importance of technological innovation for growth and competitiveness, anumber of papers focus on the determinants of corporate innovation, especially about its
financing. Previous studies mainly focus on the role of equity market in affecting corporate
innovation. For example, Brown, Martinsson, and Petersen (2013) find that firms in countries
with stronger shareholder protections and easier access to equity financing are associated withhigher rates of R&D investment. Hsu, Tian, and Xu (2014) show that firms in countries withmore developed equity markets are more innovative especially in industries that rely heavily on
external finance. Bushee (1998) documents that managers in firms with high institutional
ownership are less likely to cut R&D when there is a decline in earnings. However, high shortterm institutional ownership pressures managers to cut R&D to boost declining earnings in the
short run. Tian and Wang (2014) find that IPO firms backed by more failure tolerant venture
capitalists are more innovative after their IPOs. Aghion, Van Reenen, and Zingales (2013) showthat greater institutional ownership that alleviates managers’ career concern encourages
innovation. Fang, Tian, and Tice (2014) present evidence that stock liquidity impedes innovation11

by increasing the likelihood of hostile takeover as well as the holdings of short-term institutional
investors.
Several recent studies also investigate the role of debt financing and find mixed evidence.
On the one hand, the enhanced creditor power may impede corporate innovation due to creditors’
excessive focus on short-term performance (Acharya and Subramanian, 2009; Hsu, Tian, and Xu,
2014; Chava et al., 2013). On the other hand, different types of debt financing arrangements canhave different impacts on the incentives of firms to innovate. For example, Atanassov (2015)
finds that that firms that rely more on bank financing generate fewer patents and fewer citationsof the patents than those that use arm's length financing because of less flexibility and tolerance
to experimentation associated with bank financing. Hasen, O’Brien, and Ye (2013) provide
evidence that short-term bondholders, compared with long-term bondholders, discourage firms
from making innovations both in quantity and in quality. This is consistent with Manso (2011)
who proves that greater tolerance to experimentation is the key to innovation. Moreover, similar
to equity financing, debt financing may have a positive effect on innovation due to increased
credit supply. Amore, Schneider, and Zaldokas (2013) and Chava et al. (2013) find the positiveeffect of increased credit supply via bank deregulation on corporate innovation.
We contribute to the literature by documenting the bright side of CDS as an effective
instrument to encourage borrowing firms’ risk taking, and hence overcome the potential
detrimental effect of debt financing on corporate innovation.

3. Data, Variables, and Summary Statistics
3.1. Data and sample selection

12

We obtain our data from multiple sources. Accurate CDS initiation dates are difficult toobtain from a single data source because CDS are not traded on centralized exchanges. We thus
assemble CDS inception and transaction data by combining three data sources used in previous
studies: Markit, CreditTrade, and the GFI Group.7 Our sample starts from 1997, which is thebroad inception year of the CDS market for company names (Tett, 2009).
To measure firms’ innovation output, we rely on patents granted by the U.S. Patent andTrademark Office (USPTO) between 1976 and 2010.8 Patent citations are obtained from theHarvard Business School (HBS) Patent Network Dataverse.9 There is, on average, a two-year lag
between the date when inventors file for patents (the application date) and the date when patents
are granted. Since the latest year for our patent and citation data is 2010, patents applied for in2009 and 2010 may not be completely covered by the database as it only includes patents alreadygranted. As suggested by Hall, Jaffe, and Trajtenberg (2001), we end our sample period in 2008
to address this issue.10 We obtain firm financial information from the Compustat Industrial
Annual files. Data on stock prices and returns are retrieved from the Center for Research inSecurity Prices (CRSP) files. Analyst coverage data are obtained from the Institutional Brokers’
Estimate System (IBES) and bank debt and debt covenants data are obtained from S&P Capital
IQ and Loan Pricing Corporation’s (LPC) DealScan database, respectively.

7

For previous studies using different CDS data sources, see among others, Subrahmanyam, Tang, and Wang (2014),
Acharya and Johnson (2007), and Blanco, Brennan, and Marsh (2005).
8
We download the patent data from Noah Stoffman’s website (https://iu.app.box.com/patents). A detailed
discussion on how the data set is constructed can be found in Kogan et al. (2015) who collect the raw patent datafrom the USPTO, and identify the company (the assignee), to which each patent belongs. Then they identify
company names in the raw patent database and match each of them to firm names in the CRSP using an automatedname matching algorithm. In addition, they also validate the accuracy of data extraction and matching by comparing
the final database with the National Bureau of Economic Research (NBER) Patent and Citation database.
9
The database contains all citations of utility patents granted by the USPTO, and is constructed by Lai et al. (2012).
We download the data from http://thedata.harvard.edu/dvn/dv/patent.
10
We use the patent application year rather than the grant year to merge the patent and citation dataset and CDS data
since Hall, Jaffe, and Trajtenberg (2001) suggest that the application date is closer to the actual time of inventions
than the grant date.
13

Following common practice (e.g., Hirshleifer, Low, and Teoh, 2012), we exclude firms inany four-digit Standard Industrial Classification (SIC) industries that have no patents between
1976 and 2010 and firms in financial and utility industries (SIC codes 6000-6999 and 4900-4999,
respectively). Also excluded are observations with missing values for the variables employed in
the regressions. These restrictions result in a sample that consists of 782 firms that have CDS
trading initiated between 1997 and 2008.
3.2. Variables
To identify CDS trade initiation for the firms in our sample, we use the first CDS trading
date as the CDS initiation date. Panel A of Appendix A presents the distribution by year for the
782 firms on which CDS trading was introduced during the sample period.11 Panel B reports the
distribution by one-digit SIC industry. We find that our CDS firms are mainly from rubber, stone,
computer, food, petroleum refining, paper, and transportation industries. Following priorliterature (e.g., Saretto and Tookes, 2013), we construct a binary variable, CDS Trading, tocapture CDS trading activities between CDS buyers and sellers on the referenced borrowing firm.
Specifically, CDS Trading is equal to one in and after the inception year of CDS trading on areference firm, and zero prior to it. In addition, we construct several variables measuring theliquidity of CDS trading and the ease of access to the CDS market for investors: the average
number of daily CDS quotes during a year (Daily Quotes), the average number of distinct dealers
providing CDS quotes during a year (Distinct Dealers), and the average number of distinct
maturities of CDS contracts traded on a firm in a given year (Distinct Maturities).
We measure corporate innovation using patents and patent citations. Our first measure ofinnovation output, Patent, is the number of patents that were applied for during each firm-year
11

Consistent with Martin and Roychowdhury (2015), we also find that the number of CDS trade initiations
decreases drastically after 2004, reflecting the effects of the looming financial crisis.
14and were eventually granted. Patent counts offer a good indicator of the level of innovation
output as patenting is an important means by which firms protect their technological inventions.
Nevertheless, patent counts imperfectly capture innovation success because patents vary
drastically in their technological and economic significance (Hirshleifer, Low, and Teoh, 2012).
We therefore follow Hall, Jaffe, and Trajtenberg (2001, 2005) and use forward citations of apatent to measure its quality (importance).12 More significant and important patents are expected
to be cited more frequently by other patents. The raw citation counts are subject to the truncation
bias due to the finite length of the sample. Patents receive citations from other patents over along period of time, thus patents in the later years of the sample have less time to accumulate
citations. To correct for this bias, we follow prior studies (e.g., Hirshleifer, Low, and Teoh, 2012;
Chang et al., 2015) and adjust the raw citation counts using the fixed-effect approach, which
involves scaling the raw citation counts by the average citation counts of all patents applied forin the same year and in the same technology class. The fixed-effect approach accounts for thediffering propensity of patents in different years and in different technology classes to cite otherpatents. The sum of the adjusted citations in each firm-year (Citation) is used as our second
measure of innovation output.
To isolate the effect of CDS trading on innovation output, we control for an array of firm
characteristics that have been documented as important determinants of innovation by previous
studies (e.g., Hirshleifer, Low, and Teoh, 2012; Chang et al., 2015). The first control variable isR&D expenses scaled by total assets (R&D/Assets), which captures the observable quantitative
input to the innovation process (e.g., Aghion, Van Reenen, and Zingales, 2013). Firm-years with

12

We include self-citations since Hall, Jaffe, and Trajtenberg (2005) find that self-citations are more valuable than
external citations. They argue that self-citations, which come from subsequent patents, reflect strong competitive
advantages, less need of technology acquisitions, and lower risk of rapid entry. The robustness check in Section 4.2
shows that excluding self-citations has no material effects on our main results.
15

missing R&D information are assigned a zero R&D value (Hirshleifer, Low, and Teoh, 2012).
Hall and Ziedonis (2001) argue that large firms and capital-intensive firms generate more patents
and citations. We thus use the natural logarithm of the book value of assets (Ln(Assets)) tocontrol for firm size, and use the natural logarithm of the net property, plant, and equipment
scaled by the number of employees (Ln(PPE/Employees)) to account for capital intensity. We
employ the natural logarithm of firm age (Ln(Firm Age)) to capture the effect of a firm’s life
cycle on its innovation ability. Firm Age is the number of years elapsed since a firm enters the
CRSP database. Return on assets (ROA), which equals EBIT divided by the book value of assets,
is included to capture operating profitability. To control for growth opportunities, we included
the market-to-book ratio (MB) defined as the market value of assets divided by the book value of
assets, and Sales Growth, which is the logarithm of one plus the annual sales growth rate.
Leverage (i.e., the ratio of total debt over the book value of assets) and the cash-to-assets ratio(Cash/Assets) are added to account for the effects of capital structure and cash holdings oninnovation. Further, since Chan, Lakonishok, and Sougiannis (2001) document that investments
in innovation are positively related to stock return volatility, we include the standard deviation ofdaily stock returns over the fiscal year (Stock Volatility) as an additional control. Finally, toaccount for the inverted U-shaped relation between product market competition and innovation
documented by Aghion et al. (2005), we include the Herfindahl index (Herfindahl), which is
calculated as the sum of squared market shares in sales of a firm’s three-digit SIC industry, andits squared term (Herfindahl2).
3.3. Matched control firms
By no means are firms randomly assigned to be treated with or without CDS trading.
Instead, there are many factors determining the likelihood of a firm being selected into CDS

16

trading (e.g., Ashcraft and Santos, 2009). To the extent that these factors are also correlated withcorporate innovation, our estimated effect of CDS trading on innovation is subject to selection
biases. To mitigate this concern, we match each treated firm (CDS firm) with a control firm withno CDS traded on it (non-CDS firm) in the sample period, and use both treated and control firms
in the matched sample throughout our regression analyses.
To construct the matched sample, we first follow prior studies (e.g., Subrahmanyam, Tang,and Wang 2014; Martin and Roychowdhury, 2015) and model the firm-level probability of CDS
trade initiation in a given year as a function of borrowing firms’ characteristics. Specifically, we
estimate the following probit model using our CDS firms and all non-CDS firms that are in theCompustat database during the period 1997-2008 and have non-missing values for the variables
used in the model:
𝑃𝑟𝑜𝑏 𝐶𝐷𝑆 𝑇𝑟𝑎𝑑𝑖𝑛𝑔!,! = 1 = Φ 𝛼! + 𝛼! 𝑋!,!!! + 𝛼! 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦!,! + 𝛼! 𝑌𝑒𝑎𝑟! ,(1)

where Φ is the cumulative distribution function of the standard normal distribution and CDS
Tradingi,t is the binary variable for CDS firm i in year t. It equals zero for non-CDS firms in all
years. Because our focus here is on predicting the likelihood of CDS trade initiation, we follow
Subrahmanyam, Tang, and Wang (2014) and exclude from the estimation post-initiation years
for CDS firms. For the array of borrowing firms’ characteristics (X), we follow Martin andRoychowdhury (2015) and include four proxies for borrowing firms’ credit risk (i.e., Credit
Rating, Investment Grade, Leverage, and ROA), and three variables (i.e., Ln(Assets), Stock
Volatility, and MB) that account for the effects of information environment and growth
opportunities on the demand and supply of CDS contracts. Credit Rating is an indicator variable
that equals one if a borrowing firm has a debt rating assigned by Standard & Poor’s, and zerootherwise. Investment Grade is an indicator variable that equals one if a borrowing firm has a17credit rating assigned by Standard & Poor’s above BB+, and zero otherwise. To further alleviate
the concern that the determinants of innovation output may also affect the likelihood of CDS
trade initiation, we include in X all control variables that affect innovation output (defined in
Section 3.2).13 To ensure that no outcome variable is included as a regressor, all independent
variables are lagged by one year. Industry and Year represent two-digit SIC industry and year
fixed effects.
The probit regression results of estimating Eq. (1) are tabulated in Panel A of Table 1. The
pseudo-R2 of 0.465 indicates that the onset of CDS trading can be reasonably well predicted by
the explanatory variables. The coefficients of the explanatory variables are generally consistent
with those in previous studies (e.g., Martin and Roychowdhury, 2015). For example, we find thatlarger and more mature firms and those with higher leverage and better credit ratings are more
likely to have CDS trading initiated during the sample period.
[Insert Table 1 Here]We then calculate the predicted probability (p) of CDS trade initiation based on theestimation results of Eq. (1). The propensity score is computed as Φ-1(p). For each CDS firm in
the year prior to the initiation of CDS, we find a matching firm that has the closest propensity
score but has no CDS trading throughout our sample period. We utilize the above procedure to
identify matched non-CDS firms for those 782 CDS firms which as a result generates a total of
16,636 CDS and non-CDS firm-years between 1997 and 2008. We use this matched sample forour regression analyses in Section 4.
Panel B of Table 1 presents the comparison of characteristics between CDS firms andmatched non-CDS firms prior to CDS trade initiation. The results indicate that before the onset
13

In particular, we include R&D/Assets, Ln(PPE/Employees), Ln(Firm Age), Sales Growth, Cash/Assets, Herfindahl,
and Herfindahl2. Robustness checks (untabulated) show that our main results are unaffected if we exclude themfrom the matching procedure.
18

of CDS trading, CDS firms and the matched non-CDS firms are close in asset value, firm age,
R&D/Assets, MB, Leverage, Cash/Assets, stock volatility, and Herfindahl, suggesting that thesecharacteristics are unlikely to primarily drive the difference in innovation output after CDS trade
initiation. While CDS firms and non-CDS firms are still different in terms of profitability (ROA),
sales growth, and Ln(PPE/Employees), they have similar probabilities of CDS trade initiation, as
evidenced by the insignificant difference in propensity scores (0.003). At last, we compare
innovation output variables (Patent and Citation), which are not included in the computation of
the propensity scores, and find no statistically significant differences between CDS and thematched non-CDS firms before the advent of CDS trading.
3.4. Summary statistics
Table 2 presents the summary statistics of our final sample, which consists of 16,636 firmyear observations of CDS firms and matched non-CDS firms. Dollar values of total assets areconverted into 2000 constant dollars using the Gross Domestic Product (GDP) deflator. Allvariables are winsorized at the 1% level at both tails of their distributions. Not surprisingly, thefirms in our sample are larger than an average firm in Compustat since larger firms are more
likely to be CDS referenced firms.
[Insert Table 2 Here]
Turing to corporate innovation measures, we find that an average firm in our sample
obtains 78.148 patents and receives 63.703 citations for its patents in a year. The distributions ofpatent and citation counts are highly skewed. Untabulated statistics reveal that approximately 54%(65%) of firms obtain no patents (receive no citations) in a given year, thus the median numberof patent (citation) counts is zero. To reduce the skewness of our innovation measures, we use
the natural logarithm of one plus these variables (i.e., Ln(1+Patent) and Ln(1+Citation)) in the19regression analyses. Further, 26.8% of the firm-years in our sample have CDS trading. Amongthe firm-years with CDS trading, there are on average 236 daily quotes, 6.732 distinct dealers
providing CDS quotes, and 7.467 distinct maturities of CDS contracts traded.

4. Main Results
4.1. Univariate analysis
To investigate the relation between CDS trading and corporate innovation, we start with a
univariate analysis on the changes in innovation output around CDS trade initiation for CDS
firms (treatment group) benchmarked to the matched non-CDS firms (control group).
Specifically, we define the year of CDS trade initiation as event year 0, and compute the change
in innovation output from one year before CDS trade initiation (i.e., event year -1) to t years (t =1, 2, and 3) after CDS trade initiation. The event years of non-CDS firms are defined according
to their CDS counterparts.
Panel A of Figure 2 plots the changes in the number of patents for the event windows (-1,1), (-1, 2), and (-1, 3), respectively. On average CDS firms experience an increase in the numberof patents by 13.671 from event year -1 to 1. In contrast, the average increase for non-CDS firms
is around 3.798. The difference in the increases between CDS and non-CDS firms is not only
economically significant but also statistically significant at the 1% level (p-value = 0.003). Thechanges in the number of patents for event windows (-1, 2) and (-1, 3) display larger gaps
between CDS and non-CDS firms, indicating that the positive effect of CDS trading on
innovation is persistent and increasing over time. This finding is also consistent with the fact thatmany innovative projects involve long gestation periods.
[Insert Figure 2 Here]

20

Panel B of Figure 2 presents the results for the number of patent citations. For CDS firms,
the number of citations on average increases by 14.510, 17.646, and 18.976 for event windows (1, 1), (-1, 2), and (-1, 3), respectively. The corresponding increases are only 3.796, 0.313, and
0.556 for non-CDS firms. The differences between CDS and non-CDS firms in the citation
increases are statistically significant (p-value = 0.002, 0.003, and 0.027 for t = 1, 2, and 3,
respectively). Overall, the patterns in Figure 2 suggest that compared with matched non-CDS
firms, CDS firms experience larger increases in both the quantity and quality of innovation
output after the CDS initiation year. Since these two groups of firms are ex ante similar in
fundamentals and the propensity of having CDS traded on their debt, the univariate results are
consistent with our conjecture that CDS trading stimulates corporate innovation. Collectively,
these findings provide preliminary evidence for the positive relation between CDS trading andcorporate innovation output. Although interesting, these unconditional relations require morerefined multivariate tests, which we turn to next.
4.2. The baseline model
We conduct multivariate regression analysis using a difference-in-differences (DiD)
approach. Our baseline regression specification is written as follows:14
𝐿𝑛 1 + 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛!,! = 𝛽! + 𝛽! 𝐶𝐷𝑆 𝑇𝑟𝑎𝑑𝑖𝑛𝑔!,!!! + 𝛾𝑌!,!!! + 𝛿𝐹𝑖𝑟𝑚! + 𝜃𝑌𝑒𝑎𝑟! + 𝜀!,! , (2)where Innovationi,t, represents our innovation output measures (Patent and Citation) of firm i inyear t. The key independent variable is CDS Tradingi,t-1, which equals one if firm i has CDS
trading on its debt during year t-1. β1 captures the difference-in-differences effect due to CDS
14

For our setting, a typical DiD approach estimates the following regression using the Ordinary Least Squares (OLS)
estimation: 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 = 𝛽! + 𝛽! ×𝐶𝐷𝑆 𝐹𝑖𝑟𝑚×𝑃𝑜𝑠𝑡 + 𝛽! 𝐶𝐷𝑆 𝐹𝑖𝑟𝑚 + 𝛽! 𝑃𝑜𝑠𝑡 + 𝛾𝑌 + 𝜀. CDS Firm is thetreatment variable that equals one if a firm has a traded CDS contract on its debt at any time during our sample
period, and zero otherwise. The post-treatment indicator (Post) equals one in the post-CDS period, and zerootherwise. When year and firm fixed effects are included, there is no need to include the non-interacted Post and
CDS Firm dummy variables. Then the DiD model is reduced to Eq. (2), because by construction, CDS Trading =CDS Firm × Post.
21

trade initiation. As we have logarithmically transformed the innovation measures to reduceskewness of the dependent variables, β1 yields the percentage of innovation differential that canbe attributed to CDS trading. Y represents the set of control variables described in Section 3.2.
Except for Stock Volatility, which are measured between year t-1 and t, all control variables aremeasured at t-1 in the regressions. We include firm fixed effects to control for the impact ofunobservable time-invariant firm characteristics. Year fixed effects are included to account for
the aggregate time variation in innovation output. The standard errors of the estimatedcoefficients allow for clustering of observations by firm but our conclusions are not affected if
we allow clustering by both firm and year.
The baseline regression results are presented in Table 3. Columns (1) and (2) present theresults on patent count and citation count, respectively. In both columns, the coefficients on CDS
Trading are positive and statistically significant (t-statistics = 3.3 in both columns), suggestingthat compared with non-CDS firms, CDS firms experience a greater increase in the number ofpatents and patent citations after CDS trade initiation. Economically, the coefficient of CDS
Trading in column (1) implies that after the initiation of CDS trading, on average the annual
increase in the number of patents for CDS firm is 9.883 more than that for non-CDS firms.15
This difference-in-differences effect is approximately 14.8% of the mean value of Patent(78.148). Column (2) indicates that after CDS trade initiation, on average, the annual increase in
the number of patent citations for CDS firms is 11.493 more than that for non-CDS firms. The
citation differential amounts to 20.2% of the average number of citations (63.703) across firms.

15

Specifically, because d[Ln(1+y)]/dx = [1/(1+y)] dy/dx, dy = d[Ln(1+y)]/dx×(1+y)dx. For example, whenquantifying the effect of the change in CDS Trading (dx) on the change in Patent (dy), we increase CDS Tradingfrom zero to one, so dx = 1. The change in Patent (dy) from its mean value (78.148) is then equal to0.147×(1+78.148)×1 = 11.63, which amounts to 14.8% of the mean value of Patent.
22

Untabulated statistics show that the mean Variance Inflation Factor (VIF) is below 2, suggestingthat multicollinearity is not a major issue in our setting.
[Insert Table 3 Here]The coefficients of control variables are largely consistent with prior literature (e.g.,
Hirshleifer, Low, and Teoh, 2012; Chang et al., 2015). For instance, larger and older firms andthose with higher capital intensity have more patents and citations. Further, firms investing more
in R&D generate greater innovation output. Firms with lower leverage, higher market-to-bookratio, more cash holdings, or greater stock volatility are more innovative.
We perform a number of additional tests to ensure that our baseline results are robust toalternative matching methods, model specifications, and variable definitions. For brevity, weonly tabulate the coefficients of CDS-related variables in Appendix B. In particular, none of thefollowing has a major effect on our results: (a) using an alternative matching method requiring a
CDS firm and the matched non-CDS firm to be in the same two-digit SIC industry to alleviate
the concern that our results are driven by the industry differences between the two groups offirms;16 (b) running negative binomial regressions (instead of OLS regressions) to address theissue that patent and citation counts are non-negative and discrete;17 (c) using innovation output
measures at t+2 (rather than at t) as the dependent variables to account for the possibility that it
may take more than one year for CDS trading to have effects on innovation;18 (d) excluding

16

In untabulated tests, we use another two matching criteria and obtain similar results. First, when matching each
CDS firm with a non-CDS firm, we require the difference in the propensity score between the two firms to be lessthan 0.01. This reduces the sample to 11,420 firm-year observations since some CDS firms do not have very close
matches. Second, we match each CDS firm with two non-CDS firms that have the closest propensity scores with the
CDS firm. The inclusion of additional matching firms increases our sample size to 33,272 firm-year observations.
17
For this test, the dependent variables are the numbers of patents and adjusted citations, rather than their log values.
To control for the time-invariant firm characteristics that can be correlated with innovation output, we use a meanscaling approach (Aghion, Van Reenen, and Zingales, 2013), which involves including the pre-sample average ofpatents and citations between 1976 and 1997 in the negative binomial model.
18
The results remain qualitatively the same if we use innovation output measured at t+1 or t+3 as dependent
variables.
23

firm-years with zero patents and citations; (e) excluding self-citations when defining Citation toaddress the concern that the number of citations can be inflated by firms continuously citing theirown patents; (f) using as dependent variable, the average citations per patent (rather than total
citations) that measure the average importance or quality of patents; (g) excluding firms
engaging in mergers and acquisitions (identified using the Securities Data Company Mergers &Acquisitions database) in the previous two years, to address the concern that firms may acquire
patents and citations through takeovers rather than via in-house innovation activities incentivizedby CDS trading; (h) excluding the period of the technology boom (1998–2000) to address theconcern that the presence of highly risky new-economy firms drives both innovation output and
CDS trade initiation.

4.3. Tests on endogeneity
While we have documented a robust positive relation between CDS trading and corporate
innovation, its causal interpretation remains hypothetical. Apart from the selection issue
discussed in Section 3.3, our main results are potentially subject to two types of endogeneity.
The first type is omitted variable bias. Although in Eq. (2) we have controlled for a standard setof variables that have been shown by previous studies to affect corporate innovation, the CDSinnovation relation may be spurious if our model omits any variables affecting both innovationand the presence of CDS on a firm’s debt. The other plausible endogeneity issue is reverse
causality running from corporate innovation to CDS trade initiation. For instance, if lenders
observe borrowing firms’ increasing levels of risky investment in innovation, they may initiate
CDS trading to hedge their exposure to these borrowers. To alleviate these endogeneity concerns(i.e., selection bias, omitted variables, and reverse causality), our first strategy is to explicitly24describe the issues that we can think of, and design specific tests to address them. In our second
strategy, we use the instrumental variable approach to mitigate any remaining endogeneity
concerns. We tabulate the results of the endogeneity tests in Table 4. While all control variables
in Eq. (2) are still included in the new tests, we only report the coefficients of CDS Trading andthe newly added variables for brevity.
[Insert Table 4 Here]4.3.1. Tests on selection issues
Our matched-sample analysis is designed to address the selection concern that CDS firms
are different from non-CDS firms in ways that are systematically related to innovation output. To
further alleviate this concern, we conduct two additional analyses. First, we follow
Subrahmanyam, Tang, and Wang (2014) and add to our baseline model a binary variable (CDS
Firm) that equals one if a reference firm has CDS trading on its debt at any time during the entire
sample period, and zero otherwise. CDS Firm is used to capture the unobservable differencesthat may drive the differences in innovation output between CDS and matched non-CDS firms.
Because CDS Firm is a time-invariant variable, we replace firm fixed effects with two-digit SIC
industry fixed effects. The regression results reported in Panel A of Table 4 show that our main
results still hold.
Second, we avoid selection concerns by conditioning the sample on firm-years that have
CDS trading (i.e., firm-years for which CDS Trading = 1). Using this sub-sample, we relate
corporate innovation to the three CDS liquidity measures defined in Section 3.2 (i.e., Daily
Quotes, Distinct Dealers, and Distinct Maturities). Saretto and Tookes (2013) argue that more
liquid CDS contracts are easier and less costly to trade, thereby increasing the likelihood oflenders using CDS contracts as hedging instruments. We thus expect the CDS effect on25

innovation to be stronger when the CDS market referencing borrowers' debt is more liquid.
Panels B1-B3 of Table 4 report the regression results obtained by replacing CDS Trading in Eq.
(1) with our three CDS liquidity measures, respectively. Despite that the sample is reduced to
4,330-4,579 firm-years, the coefficients of the three CDS liquidity measures are positive andstatistically significant with t-statistics ranging from 2.1 to 3.9. In terms of economic significance,
for example, a one-standard-deviation rise in Daily Quotes increases Patent (Citation) by 6.1%
(12.1%) from its mean value.19 These results not only reveal the positive effect of CDS contract
liquidity on corporate innovation, but also suggest that our main finding is robust to thisalternative procedure of controlling for selection bias.

4.3.2. Tests on omitted variables
We conduct several tests to tackle the omitted variables problem. First, we augment thebaseline model by replacing year fixed effects with two pairs of fixed effects, i.e., the location
state-by-year and industry-by-year fixed effects. We include state-by-year fixed effects toaccount for unobserved, time-varying state-level factors, such as political economy or local
business cycles. For instance, prior studies (e.g., Amore, Schneider, and Zaldokas, 2013;
Cornaggia et al., 2015) document that the staggered banking deregulation across U.S. statesaffects corporate innovation output through enhancing state-level credit supply. As such,deregulation of state-level banking and branching may drive both corporate innovation and thedemand/supply of CDS for credit protection. We determine a firm’s location state based on thelocation of its headquarter, which is usually where its major operations and plants are located(Gormley and Matsa, 2016). We include industry-by-year fixed effects to control for potential

19

The standard deviation of Daily Quotes is 59.742, thus a one-standard-deviation rise in Daily Quotes is associatedwith a 0.001 × (1+78.148) ×59.742 = 9.45 increase in Patent, which is equivalent to 6.1% of its mean (78.148).
26

differential trends in patenting activities and CDS trading across industries over time. Panel C of
Table 4 suggests that our main results continue to hold after including both state-by-year andindustry-by-year fixed effects.
Second, the key variable of our interest, CDS Trading, is constructed using the actual CDS
trade initiation dates, which exhibit a clustered pattern (Panel A of Appendix A). Thus, it ispossible that the concentration of CDS trade initiations around particular time periods could giverise to spurious results. We employ the methodology of Bekaert, Harvey, and Lundblad (2005) toaddress this concern. Specifically, we draw 782 uniform random numbers and randomly assign
one of the actual CDS initiation dates to each of the 782 CDS firms in our sample. We then reestimate Eq. (2) using CDS and matched non-CDS firms with randomly assigned initiation dates,
and repeat the simulation procedure one thousand times. Because the distribution of actual CDS
initiation dates is preserved in simulated data, if our main results are driven by event clustering,
many of the replications should yield coefficients close to those obtained using the actual
initiation dates. The results reported in Panel D of Table 4 indicate that this is not the case. Boththe mean and the median of the coefficients for CDS Trading, which is constructed using
randomized CDS initiation dates, are very close to zero. The coefficients reported in Table 3(0.147 and 0.199) are very far out in the right tail of the distribution (i.e., higher than the 99th
percentiles), implying that assigning the initiation date to the right firm really matters and that
our results are not merely a statistical artifact reflecting certain time trends or event clustering.
In Panel E we augment Eq. (2) by including a set of additional control variables that proxyfor corporate governance, which may affect both CDS trade initiation and corporate innovation.
For instance, on the one hand, prior studies (e.g., Bhojraj and Sengupta, 2003) document thatpoor governance mechanisms can increase default risk by aggravating agency costs and27

information asymmetry between a firm and its lenders, thereby increasing the likelihood oflenders using CDS for hedging. On the other hand, Chemmanur and Tian (2011) show that firms
shielded with a larger number of anti-takeover provisions generate better innovative outcomes
because anti-takeover provisions alleviate the short-term pressure on managers from thecorporate control market. To ensure our findings are not driven by corporate governance, we
include as additional controls the governance index (G-index) compiled by Gompers, Ishii, andMetrick (2003), board size, and institutional ownership. Because of missing values for these
additional controls, we perform our analysis with a much smaller sample of 5,317 firm-year
observations. Our main results, however, are unaffected.20

4.3.3. Tests on reverse causality
Next, we conduct two tests to alleviate the reverse causality concern. First, we examine thedynamics of innovation differentials between CDS and non-CDS firms over the years
surrounding CDS trade initiation. If reverse causality drives our results, we should observe
increases in innovation output prior to CDS trade initiation. To detect this possibility, we use themethod of Bertrand and Mullainathan (2003) and replace CDS Trading in Eq. (2) with five year
indicators, namely, Year-2, Year-1, Year0, Year+1, and Year≥+2. Yearj equals one in the jth year
relative to the year of CDS trade initiation, and zero otherwise. Year≥+2 captures the CDS effects
from the second year after CDS trade initiation onwards. The results presented in Panel F of
Table 4 show that the coefficients of Year-2, Year-1, and Year0 are statistically insignificant,suggesting that compared with the matched non-CDS firms, CDS firms do not have greater

20

In untabulated tests, we also find that results are robust to controlling for alternative proxies for corporate
governance (e.g., the percentage of independent directors), an proxy for management quality (Milbourn, 2003),
which is abnormal stock returns (CAPM adjusted) accumulated over the period [t-3, t-1], or the measures offinancial constraints (e.g., Kaplan and Zingales’ (1997) and Whited and Wu’s (2006) indices).
28

innovation output before and during the year of CDS trade initiation. This finding to a large
extent ameliorates the reverse causality concern. Further, the coefficients of Year+1 and Year≥+2
are positive and significant (t-statistics ranging from 2.2 to 3.2), indicating that the innovationdifferentials appear only after the advent of CDS trading. Interestingly, the coefficients ofYear≥+2 (0.204 and 0.15) are larger than those of Year+1 (0.106 and 0.099), indicating that while
the CDS effect on innovation manifests immediately (i.e., one year after CDS trade initiation), it
becomes much stronger subsequently. This result is consistent with the patterns in Figure 2.
Second, we incorporate into Eq. (2) several additional variables to explicitly account forreverse causality that a borrowing firm’ innovative activities increase its risk profile and thusincrease its lenders’ needs for CDS trading. Specially, to control for the level of innovation
investment, we measure past innovation investment (past innovation success) as the rolling
average R&D/Assets (number of patents or citations) during the previous five years, i.e., fromyear t-2 to t-6.21 Moreover, we use forward looking implied volatility to capture borrowing firms’
perceived risk by CDS market participants. Hilscher, Pollet, and Wilson (2015) show that
information in equity markets leads information in CDS markets, thus forward looking implied
volatility, which is estimated using stock and option prices, should help gauge investors’ risk
perception in CDS markets. The results, reported in Panel G of Table 4, reveal that thecoefficients of CDS Trading remain positive and significant, substantiating forward causality
running from CDS trading to innovation.

4.3.4. The instrumental variable approach

21

Alternatively, in untabulated tests, we measure past innovation investment (past innovation success) as the presample five-year average R&D/Assets during 1991–1996, and obtain similar results.
29

To further address endogeneity concerns, especially those not particularly identified
previously, we employ an instrumental variable approach similar to that of Subrahmanyam, Tang,and Wang (2014) and Saretto and Tookes (2013). Specifically, we employ lenders’ hedging
activities on foreign exchange (Lender FX hedging) as the instrumental variable for CDS trading.
Minton, Stulz, and Williamson (2009) find that banks that hedge tend to hedge more than one
component of their portfolios. In particular, they document that banks hedging their currency risk
using foreign exchange derivatives tend to hedge their credit risk using CDS as well. Thus, froma relevance perspective, lending banks’ foreign exchange hedging activities should be positively
correlated with their hedging demand using CDS and the likelihood of CDS contracts being
initiated on their borrowers. This instrument is likely to meet exclusion criteria because lenders’foreign exchange derivatives position is a macro hedge rather than a firm-level hedge. Lenders’foreign exchange hedging should not directly drive borrowing firms’ innovation output. We
define Lender FX hedging as the average notional volume of foreign exchange derivatives used
for hedging (not trading) purposes relative to the bank’s total assets, across all the banks thathave served either as lenders or bond underwriters to the firm over the previous five years.22
We then follow Saretto and Tookes (2013) and employ Wooldridge’s (2002) three-stepprocedure, which involves using the fitted probability from a probit model for CDS Trading as
the instrumental variable. In the first step, we predict CDS Trading as in Table 1 using Lender
FX hedging as the instrument. In steps 2 and 3, we use a standard two-stage least squares (2SLS)
approach, with the predicted value obtained from step 1 as the instrument.23 The first-step22

We identity firms’ lenders using the Dealscan syndicated loan database. Bond underwriters are identified using
the Mergent Fixed Income Securities Database (FISD). We then use Federal Reserve call report data to obtain theforeign exchange derivative positions of the lenders and bond underwriters.
23
Wooldridge (2002) shows that, under fairly general conditions, this procedure provides efficient estimations. In
addition, as we use the fitted value of CDS Trading as an instrument, the effect of misspecification in the first-step
probit model is mitigated.
30

regression (untabulated) shows that Lender FX hedging indeed positively and significantly
predicts CDS Trading (t-statistic =3.7), suggesting that the instrument meets the relevancecriteria econometrically. The weak instrument test (untabulated) generates a p-value less than0.01, thus rejecting the weak instrument hypothesis. We then replace the actual CDS trading
indicator in Eq. (2) with the fitted value of CDS trading from the first-stage regression. The
results of the second-stage regressions are presented in Panel H of Table 4. The panel shows thatthe coefficients of fitted CDS Trading are positive and statistically significant at the 5% level inboth regression models with patent and citation counts as the dependent variables. This evidence
is consistent with a causal interpretation of the CDS effect on corporate innovation.
To summarize, while endogeneity is a perennial issue that no empirical test can entirely
rule out, we conduct a battery of tests to alleviate these endogeneity concerns, and find that our
main conclusion holds. Although each test can be subject to criticism, the balance of evidence
points to a causal relation going from CDS trading to corporate innovation.

5. Additional Analysis
Our baseline results imply a positive and causal relation between CDS trading andcorporate innovation. In this section, we conduct a number of tests to pin down the channel
through which CDS trading enhances borrowing firms’ innovation output. If the risk-taking
channel plays an important role in shaping the positive CDS trading-innovation relation, theeffect of CDS trading on innovation should be more pronounced for firms where debt financing
is less compatible with borrowing firms’ risk taking in innovation. Furthermore, after the adventof CDS trading, borrowing firms’ innovation efforts should be directed towards more risky
innovation projects. Also, we use several tests to examine the possibility that CDS trading31

promotes innovation through alleviating borrowing firms’ financing constraints and allowing
more innovative projects to be financed by new debt issuance. Finally, we examine the effect of
CDS trading on firm innovation efficiency to reconcile our findings that CDS trading improves
innovation output but do not have significant impact on R&D input. We further investigate
whether patents generated after CDS trading have higher economic values, instead of simply
scientific values in terms of citations.
5.1. Cross-section differences in results
Our regression results so far show that CDS trading enhances corporate innovation. In thissection, we conduct a number of cross-section tests to identify the channels through which CDS
trading impacts corporate innovation. Following the practice of the difference-in-differences
approach (e.g., Low, 2009), we use variables one year prior to the initiation of CDS trading topartition the sample and examine the cross-sectional difference in our regression results. Again,
we only report the coefficient on CDS trading, the key variable of interest for brevity.
We first examine the effect of firms’ debt financing dependence on the results. Since CDS
is a debt market financial derivative, we expect that the effect of CDS trading on innovation isstronger when firms are more dependent on debt financing. To test this prediction, we divide oursample firms into two groups according to the sample median of industry debt financing
dependence, where firm level debt financing dependence is calculated as sum of net amount ofdebt issued over the past decade divided by the sum of capital expenditures over the past decadeaccording to Rajan and Zingales (1998) and Duchin, Ozbas, and Sensoy (2010), and perform theregression in Eq. (2) on the two subsamples of firms with high and low values separately. The
results are presented in Panel A of Table 5. The results show that the coefficient of CDS trading
is positive and significant only for the subsample of firms in industries with higher dependence32

on debt financing, suggesting that CDS trading mainly affects innovative output of firms inindustries that are more dependent on debt. This finding is consistent with the view that CDS
trading mitigates the agency problem in debt market resulting in more risk-tolerant creditors, andthus is beneficial for firms’ innovative output.
Second, we examine the impact of bank debt on the relation between CDS trading andinnovative output. Compared with public bondholders, banks are usually more conservative (e.g.,
impose more restrictions through covenants) and are more likely to exert active intervention on
firms’ operation, and thus are likely to be more harmful to corporate innovation (Rajan andZingales, 2003; Atanassov, 2015). Hence we expect that the positive effect of CDS trading on
innovative output is stronger for firms with bank debt. To test this prediction, we partition thesample into two groups of firms with and without bank debt. We then perform the regression in
Eq. (2) for the two groups, respectively. The results are presented in Panel B of Table 5.
Consistent with our expectation, the results indicate that the positive relations between CDS
trading and both patent and citation counts are mostly driven by the subsample of firms withbank debt.
[Insert Table 5 Here]Third, we examine how the number of bank lenders affects the relation between CDS
trading and corporate innovativeness. The firm-lender relationship tends to be stronger if a firmhas fewer bank lenders (Carvalho, Ferreira, and Matos, 2013). Relationship lenders are more
likely to constrain their borrowers due to their information advantage and strong bargaining
power (Rajan, 1992; Petersen, 1999; Pinkowitz and Williamson, 2001). Hence we expect theeffect of CDS trading on corporate innovation to be stronger for firms with fewer bank lenders.
To test the prediction, we partition a subsample of firms with bank debt into two groups with low

33and high number of lending banks according to the sample median. Then, we perform theregression in Eq. (2) for the two groups and present the results in Panel C of Table 5. The results
show that the positive relation between CDS trading and patent or citation counts is stronger forfirms with a smaller number of lending banks.
Fourth, we examine how the relation between CDS trading and corporate innovation varies
depending on debt covenant intensity. To protect their own interest, banks lenders usually
include debt covenants in the loan agreement to limit firms’ ability to engage in risky investment
projects, which are detrimental to innovation. Hence we would expect the effect of CDS tradingon corporate innovation to be stronger for firms with debt covenants.24 To test the prediction, we
partition firms with bank debt into two groups according to covenant intensity. Specifically, highcovenant intensity group includes firms with at least one of the above two types of covenants,
while low covenant intensity group includes firms neither of the two types of covenants. We
estimate the regression in Eq. (2) on the two groups separately and present the results in Panel Dof Table 5. The results show that the effect of CDS trading on corporate innovation is positiveand significant for firms with high covenant intensity but insignificant for firms with low
covenant intensity.
Fifth, we examine how the effect of CDS trading on corporate innovation differs according
to debt maturity. Hasan, O’Brien, and Ye (2013) argue that short-term bondholders are more
myopic and are more likely to pursue safe investments, which may have an adverse effect on
corporate innovative success. Thus we expect the effect of CDS trading on firms’ innovative
outcome to be more pronounced in firms that have more short-term debt. To examine this24

We focus on two types of loan covenants that directly affect firm investments, namely, secured debt and financial
ratio covenants. Secured debt covenant requires that the borrowing firm protect the loan with a collateral asset that is
at least of the same value as the face value of the loan. Financial ratio covenant is based on various common
accounting ratios that the firm must maintain while the debt is outstanding.
34

prediction, we split the sample firms into two groups of firms with shorter and longer maturity
debt according to the sample median, where debt maturity is defined as the proportion of debts
that will mature within 3 years.25 We then estimate the regression in Eq. (2) for the two groups
separately and present the results in Panel E of Table 5. The results show that the coefficient of
CDS trading is positive and significant for the subsample of firms with higher proportion ofshort-term debt but statistically insignificant for the subsample of firms with low proportion ofshort-term debt. Collectively, the findings in Panels A to E provide further support to ourarguments that CDS trading enhances creditors’ risk-taking incentive and debtholders’ myopia
by alleviating their concern on borrowing firms’ default risk.
Last, we examine the role of financial distress in the relation between CDS trading andcorporate innovation. Since financial distress increases the likelihood of firms’ debt
renegotiations, CDS-insured creditors might become tougher in firms’ debt renegotiations
because they have more bargaining power and require better conditions in order to agree on arenegotiation (Bolton and Oehmke, 2011).26 If CDS-insured creditors become tougher in case offinancial distress, firms that anticipate this incentive may choose to adopt conservative corporate
policies ex ante at the expense of shareholders, which offsets the positive role of CDS trading inaligning the interests of creditors and shareholders. Alternatively, CDS-insured creditors may
restrict firms’ risk taking ex ante to avoid higher premiums charged by CDS sellers on risky
reference entities (Shan, Tang, and Winton, 2015). Either way, we would expect that the positiveeffect of CDS trading on innovation is weaker for financially distressed firms.

25

We obtain similar results if we define short-term debt as the proportion of debt that will mature within 1 year.
As supporting evidence, Subrahmanyam, Tang, and Wang (2014) and Danis (2015) document an increased
bankruptcy risk after CDS introduction.
26

35

To test this prediction, we partition firms into financially undistressed firms and distressedfirms according to Altman’s Z-score.27 Specifically, financially undistressed firms are those with
Z-score in the top quartile for two consecutive years prior to CDS introduction, while financiallydistressed firms are those with Z-score in the bottom quartile for two consecutive years prior to
CDS introduction. We then perform the regression in Eq. (2) for the two groups separately andpresent the results in Panel F of Table 5. The results show that the coefficient of CDS trading is
positive and significant for the subsample of financially undistressed firms but negative for the
subsample of financially distressed firms despite an insignificant sign. The findings are
consistent with the notion that financially distressed firms are indeed concerned about CDSinsured creditors becoming tougher in the debt renegotiation. However, it is less likely to be adominant force for our results.
5.2. Innovation strategies
In this subsection, we explore the effect of CDS trading on firms’ innovation strategies.
Corporate innovation strategies are highly heterogeneous and play an essential role inmaintaining corporate competitive advantages (Hall, 1993; Cockburn, Henderson, and Stern,2000). We focus on three types of innovation strategies, i.e., exploratory versus exploitative
innovation, production innovation versus process innovation as well as patent originality andgenerality.
First, we examine the effect of CDS trading on firms’ strategic choice between exploratory
innovation and exploitative innovation.28 Exploratory innovation involves a high level of radical

27

The results are similar if we use Ohlson’s O-score as an alternative measure of financial distress.
Prior literature has identified two innovation strategies with distinct impact on firm performance. See Levinthaland March (1993), McGrath (2001), Benner and Tushman (2002), and Smith and Tushman (2005) for detailed
description of the two strategies, and Katila and Ahuja (2002), He and Wong (2004), Uotila, Maula, Keil, and Zahra(2009) for a discussion of the distinct impact of the two strategies on firm performance.
28

36

innovation outside of the firm’s knowledge base and thus takes longer time to realize and is morerisky, while exploitative innovation is based on firms’ existing business activity and knowledge
and thus has a short development cycle and less uncertainty. We define exploratory andexploitative patents following Benner and Tushman (2002) and Gao, Hsu and Li (2014).
Specifically, a patent is categorized as exploratory if 60 percent or more of its citations are basedon new knowledge outside of a firm’s existing expertise (i.e., not citing the firm’s existing
patents or the citations made by those patents), while a patent is categorized as exploitative if 60
percent or more of its citations are based on a firm’s existing expertise (i.e., the firm’s existing
patents and the citations made by those patents). We then divide the number of patents falling
into each category by the total number of patents applied by each firm in each year. We performthe same regression specification as in Eq. (1), with the dependent variables being the proportionof exploitative and exploratory patents, respectively. The results presented in columns (1) and (2)of Table 6 show that CDS trading is positively and significantly related to the proportion of
exploratory patents, while negatively related to the proportion of exploitative patents.
[Insert Table 6 Here]
Second, we test how CDS trading affects firms’ production and process innovation output.
According to Chava et al. (2013), production innovation, compared with process innovation,
helps create new products, and thus is more risky but potentially more beneficial. We follow
Chava et al. (2013) and define patents that fall into the International Patent Classification (IPC)
category B01 as process patents and defined all other patents as product patents.29 We thenestimate the regression model in Eq. (1) with dependent variables being the logarithm of one plus
product patent counts and process patent counts, respectively. The results are reported incolumns (3) and (4) of Table 6. These findings indicate that CDS trading has a positive and29

IPC category B01 mainly focuses on Physical and Chemical Process.
37

significant association with product innovation but an insignificant association with process
innovation.
Third, we investigate the role of CDS trading in affecting the distribution of citations by
employing a patent-based measure, i.e., patent originality. Patent originality reflects how far apatent is away from the extant technology class, and thus indicates the impact of innovation.
Following Hall, Jaffe, and Trajtenberg (2001), we define originality as the mean of theoriginality scores of a firm’s patents, where the originality score of each patent is calculated as
one minus the Herfindahl index of the three-digit technology class distribution of all the patents
that this patent cites. We estimate the same regression specification as in Eq. (2) by replacing
patent and citation counts with patent originality. The results are reported in columns (5) of
Table 6. These findings show that CDS trading is positively and significantly related to bothpatent originality scores.
Taken together, the findings in Table 6 provide more supportive evidence to our argumentthat CDS trading promotes corporate innovation through encouraging managers’ risk-taking
incentives and motivating managers’ to take long perspectives.
5.3. Testing the financing channel
CDS trading may also affect corporate innovation through a financing channel: CDS
trading increases firms’ debt capacity (Saretto and Tookes, 2013), and thus enables firms to
make more innovation investment that is financed with additional debt issuance. We develop
three tests to examine this channel. First, we control for the possible increase in firms’ debt
financing capacity associated with the CDS introduction. Specifically we estimate the regressionin Eq. (2) with two additional controls, namely, the change in firms’ leverage ratio from year t-1
to year t+1 and firms’ total net debt issuance from year t-1 to year t+1. The results are presented38

in Panel A of Table 7. The panel shows that the coefficient on CDS trading remains positive andsignificant in the regression of both patent and citation counts at the 1% level, suggesting that thepositive effect of CDS trading on corporate innovation is not driven by the increase in firms’
debt financing.
[Insert Table 7 Here]
Second, we examine the effect of CDS trading on firms’ R&D investments, the input ofinnovative projects. If CDS trading fosters corporate innovation through improving the fundingof innovative projects, we would expect an increase in R&D investments after the CDS
introduction. We estimate the regression in Eq. (1) with the dependent variable being R&D
investments, which is defined as the ratio of firm’s R&D expenditure over book assets. The
results are presented in Panel B of Table 7, showing that the coefficient on CDS trading isstatistically insignificant with or without controlling for lagged R&D investments. The findings
suggest that CDS trading does not enhance the funding of innovative projects, but rather aligns
the interests of creditors and shareholders and thus fosters innovation.
Third, we examine the cross-sectional difference in results depending on firms’ financial
constraints by partitioning the sample according to Hadlock and Pierce’s (2010) financial
constraint index (HP index), firms’ tangibility ratio, and whether the firm has investment-gradecredit rating, respectively.30 Specifically, a firm is defined as financially constrained if its HP
index is above the sample median, if its tangibility is below the sample median, or if it does nothave the investment-grade bond rating, and a firm is defined financially unconstrained if its HP
index is below the sample median, if its tangibility is above the sample median, or if it has the30

Hadlock and Pierce’s (2010) index is defined as -0.737 × Ln (Assets) + 0.043 × Ln (Assets)2 - 0.04 × Firm age.
Hence a higher HP index indicates more financial constraint. Tangibility ratio is defined as the natural logarithm of
net property, plant, and equipment over the number of employees. In addition, firms with higher tangibility or
investment grade rating have better access to external finance and thus are less likely to be financially constrained.
39

investment-grade bond rating. Similar to the tests of cross-sectional difference in Section 5.1, we
use partitioning variables one year prior to the initiation of CDS trading to split the sample.
We estimate the regression in Eq. (2) for the financially constrained and unconstrained
subsamples separately. The regression results are presented in Panel C of Table 7. These results
show that the effect of CDS trading on innovative outcomes is not affected by firms’ financial
constraint status, suggesting that the argument that CDS trading relaxes financial constraint
through increasing firms’ debt financing capacity is less likely to be a dominant explanation to
our findings.
5.4. Innovation efficiency and value creation from innovation
Our baseline results in Table 3 show that firms' number of patents and citations increasesignificantly after CDS trading, suggesting that CDS trading enhances innovative output.
Nevertheless, the results in Panel B of Table 7 show that R&D investments which are innovative
inputs do not experience any significant change around CDS trading. To reconcile these findings,
we examine the effect of CDS trading on firms’ innovation efficiency, i.e., how efficiently firms
are able to utilize R&D investment and converting them into patents, Specifically, we follow
Hirshleifer, Hsu, and Li (2013) and construct innovation efficiency measures as the natural
logarithm of one plus the number of patents and citations over the average R&D expenditures in
the past five years. We then replace the dependent variables in Eq. (2) with the two innovation
efficiency measures and re-estimate the regressions. The results are reported in Columns (1) and(2) of Table 8, showing that the coefficients on CDS trading are positive and significant. Thesefindings suggest that CDS trading enhance innovative output through improving firms’
innovation output per dollar R&D expenditures, instead of increasing R&D expenditures itself.
[Insert Table 8 Here]40

Since firms direct innovation efforts to more risky innovation projects and are more
efficient in generating patents after CDS trading, we further examine, in addition to higher
scientific values in terms of citations, whether patents generated after CDS trading also havehigher economic values. We follow Kogan et al. (2015) and measure the economic value ofpatents as measured as the stock market reaction to the announcement of a patent being granted
to a public firm by the USPTO. Specifically, we estimate patent value as the increase in market
valuation of the firm in the three-day period of patent announcements after adjusting forbenchmark return, idiosyncratic stock return volatility, and various fixed-effects.31 For firms withmore than one patent in a fiscal year, we sum the value of all patents filed for that year. Then, we
take the natural logarithm of one plus the patent value to mitigate the skewness in patent value.
Last, we replace the dependent variables in Eq. (2) with the patent value measures and rerun the
regressions. The regression results are reported in Columns (3) and (4) of Table 8. The results
show that CDs trading is positively and significantly related to patent value, suggesting that CDS
trading enhances the value of the firm through increasing the value creation from innovation.

6. Conclusion
Does financial innovation impede or enhance technological innovation? We address thisquestion via investigating the impact of CDS trading on corporate innovative activities. Using asample of U.S. listed firms covered by the USPTO from 1997 to 2008, we find evidence that the
introduction of CDS trading on a firm’ debt increases the firm’s innovative output. Firms’
patents and patent citations, on average, increase by 17% and 23%, respectively. These findings
are robust to various model specifications and variable definitions, and are less likely to be
31

We download the patent value data from Dimitris Papanikolaou’s website(https://dl.dropboxusercontent.com/u/7796025/Website/default.html).
41

driven by endogeneity issues and a general upward time trend of firms’ patenting activities. We
further show that the positive effect of CDS trading on innovation is more pronounced for firmsthat are more dependent on debt financing and for firms with bank debt, fewer bank lenders or
greater debt covenant intensity, and for firms with more short-term debt prior to the CDS
introduction. Moreover, firms tend to focus on the risky exploratory innovation strategy rather
than the relatively safe exploitative innovation strategy, and create more product patents rather
than process patents after the inception of CDS trading. In the meanwhile, after CDS is
introduced on a firm both the originality and generality of its patents are enhanced. Thesefindings are consistent with the argument that CDS-protected creditors tend to be more tolerant
to firms’ risk taking and thus encourage firms’ innovative activities.
Our study provides new evidence on the real effect of financial innovation, particularly
CDS. Recent papers document that CDS relax firms’ borrowing constraint, but also create
tougher creditors and increase firms’ bankruptcy risk. Firms respond by changing their corporate
policies. For example, firms borrow more but increase precautionary cash holdings due to thetougher CDS-protected creditors, especially for financially constrained firms. In this paper, we
further find that firms, after the introduction of CDS trading on their debt, adjust their innovative
activities, which are important to maintain the future growth and competitiveness.
Our findings also contribute to the debate on the impact of debt market on corporate
innovation. While previous literature documents the negative effect of debt financing on firms’
innovative activities because of the concave payoff structure of creditors, our results suggest that
CDS may change creditors’ incentive and mitigate the problem. Given a less concave payoff
structure from their CDS position, CDS-protected creditors are more tolerant to firms’ risk taking

42and tend to be more long-term focused. As a result, CDS mitigates the negative effect of debt
financing on corporate innovation and enhances corporate innovation.

43

Appendix A: Distributions of CDS firms over time and by industry
The sample consists of firms jointly covered in the CreditTrade, the GFI Group and Markit CDS database,
CRSP, and the USPTO patent and citation database between 1997 and 2008. The CDS initiation year isdefined as the first year in which a firm has CDS trading on its debt. Panel A reports the distribution of
CDS firms by initiation year. Panel B reports the distribution of CDS firms by one-digit SIC industry.
Panel A: Distribution of CDS firms by initiation year
(1)
Year
Number of new CDS firms
1997
21
1998
41
1999
35
2000
77
2001
207
2002
124
2003
94
2004
80
2005
38
2006
31
2007
26
2008
8
Total
782

Panel B: Distribution of CDS firms by one-digit SIC industry(1)
Industry
Number of CDS firms
Mining and construction
82
Food, petroleum refining, and paper
208
Rubber, stone, and computer
213
Transportation
99
Retail and wholesale
78
Business and personal services
80
Public services
22
Total
782

44

(2)
Percentage of all CDS firms
2.7%
5.2%
4.5%
9.8%
26.5%
15.9%
12.0%
10.2%
4.9%
4.0%
3.3%
1.0%
100%

(2)
Percentage of all CDS firms
10.5%
26.6%
27.2%
12.7%
10.0%
10.2%
2.8%
100.0%

Appendix B: Robustness checks
The sample consists of CDS firms that initiated CDS trading between 1997 and 2008 and the matched non-CDS
firms. Citation is the average citation counts per patent. All regressions include the same control variables as thosein Table 3, but their coefficients are not tabulated. Detailed variable definitions are in the legend of Table 3. The tor z-statistics in parentheses are calculated from the Huber/White/Sandwich heteroskedastic-consistent errors, which
are also corrected for correlation across observations for a given firm. The symbols ***, **, and * denotesignificance at the 1%, 5%, and 10% levels, respectively.
Panel A: Matching within the same industry (N = 16,636)
Ln(1+Patent)t
CDS Trading
0.082**(2.5)

Ln(1+Citation)t
0.076***(2.9)

Panel B: Negative binomial regressions (N = 16,636)
CDS Trading# of Patentt
0.235**(2.0)

Panel C: Using corporate innovative output measured at t+3 (N = 13,543)
Ln(1+Patent)t+2
CDS Trading
0.233***(3.0)

Ln(1+Citation)t+2
0.204**(2.2)

Panel D: Excluding firm-years with zero patents or citations (Npatent = 7,501; Ncitation = 5,710)
Ln(1+Patent)t
Ln(1+Citation)t
CDS Trading
0.207**
0.244**(2.1)(2.5)
Panel E: Excluding self-citations
Ln(1+Citation)t0.174***(3.0)

CDS Trading

Panel F: Using average citations per patent as dependent variables (N = 16,636)
Ln(1+Citation)t
0.016*(1.8)

CDS Trading

Panel G: Excluding firms engaging in M&A transactions in the previous two years (N = 13,320)
Ln(1+Patent)t
Ln(1+Citation)t
CDS Trading
0.169***
0.249***
(3.3)
(3.7)
Panel H: Excluding the tech bubble period (1998-2000) (N = 12,647)
Ln(1+Patent)t
CDS Trading
0.171***(3.0)

45

Ln(1+Citation)t
0.237***(3.2)

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49

Figure 1: Lenders’ net payoff with and without CDS protection
The figure shows lenders’ net payoffs with and without CDS protection. The horizontal axis denotes thevalue of borrowing firms’ assets and the vertical axis denotes lenders’ net payoff. The dashed (solid) line
indicates lenders’ net payoffs with (without) the CDS protection. With CDS protection, lenders’ net
payoff is the face of debt minus CDS premiums, which increases with borrowing firms’ default risk.

Lenders’
net payoff

Repayment region
Default
regionValue of assets
Face valueof debt

50

Figure 2: Changes in innovation output around CDS trade initiation
The sample consists of CDS firms that initiated CDS trading between 1997 and 2008 and the matched
non-CDS firms jointly The matched non-CDS firms is selected based on the nearest one propensity scorematching method, which is estimated using the probit model reported in Panel A of Table 1. Patent is thenumber of patents applied for, during the current year, which are eventually granted. Citation is the total
number of citations arising from patents. The citations are adjusted using the time-technology class fixed
effect. Panel A (B) plots the change in the number of patents (citations) around CDS trade initiation for
CDS firms and the matched non-CDS firms separately. For CDS firms, the year of CDS trade initiation isdenoted as event year 0. The changes in innovation output are computed from one year before CDS trade
initiation (i.e., year -1) to t years (t = 1, 2, and 3) after CDS trade initiation. The event years of non-CDS
firms are defined according to their CDS counterparts.
Panel A: Changes in the number of patents around CDS trading initiation

Panel B: Changes in the number of citations around CDS trading initiation

51

Table 1: The probability of CDS trade initiation and the matched sample
The sample includes our CDS firms and all non-CDS firms that are in Compustat during the period 1997-2008 andhave non-missing values for the variables used in the model. Panel A reports the coefficient estimates obtained fromestimating a probit model predicting the probability of CDS trade initiation. The dependent variable, CDS Trading,equals one in and after the inception year of CDS trading on a reference firm, and zero prior to it. It equals zero for
all non-CDS firms. Post-initiation years of CDS firms are excluded from the analysis. Credit Rating is an indicatorvariable that equals one if a borrowing firm has a debt rating assigned by Standard & Poor’s, and zero otherwise.
Investment Grade is an indicator variable that equals one if a borrowing firm has a credit rating assigned byStandard & Poor’s above BB+, and zero otherwise. Detailed definitions of other variables are in the legend of Table
2. Constant terms, two-digit SIC industry and year fixed effects are included in the regression, but they are nottabulated. The z-statistics in parentheses are calculated from the Huber/White/Sandwich heteroskedastic-consistenterrors, which are also corrected for correlation across observations for a given firm. The symbols ***, **, and * denotesignificance at the 1%, 5%, and 10% levels, respectively. Panel B compares the firm characteristics of CDS firms
with those of matched non-CDS firms. T tests are conducted to test for differences in mean values between CDS andnon-CDS subsamples. The symbols ***, **, and * indicate that subsample means are significantly different fromeach other at the 1%, 5%, and 10% levels, respectively. Panel C reports the regression results from a probit model
predicting the probability of CDS trade initiation using firm characteristics of CDS firms and propensity-scorematched non-CDS firms prior to CDS trade initiation.

Panel A: Probit regression on the probability of CDS trade initiation
Dependent variables
Credit Rating
Investment Grade
Ln(Assets)
Ln(Firm Age)
Ln(PPE/Employees)
R&D/Assets
ROA
MB
Sales Growth
Leverage
Cash/Assets
Stock Volatility
Herfindahl
Herfindahl2
Year FE
Industry FE
Observations
Pseudo R-squared

CDS Trading
0.586***(9.3)
0.528***(7.8)
0.410***(15.6)
0.092***
(3.3)
0.011
(0.5)
0.982
(1.6)
-0.157(-0.6)
0.036**(2.1)
0.218***(4.7)
0.853***(6.8)-0.338(-1.6)-5.583***(-3.4)
1.040*(1.8)-1.014(-1.5)
X
X
50,0200.465

52

Panel B: Comparison of firm characteristics prior to CDS trade initiation
Characteristics
Credit Rating(1)
CDS firm
0.898(2)Matched non-CDS firm
0.890

(3)
Difference
0.008

Investment Grade

0.644

0.635

0.009

Ln(Assets)

8.615

8.701

-0.086*

Ln(Firm Age)

3.000

2.912

0.088*

Ln(PPE/Employees)

4.454

4.496

-0.042

R&D/Assets

0.021

0.019

0.001

ROA

0.032

0.018

0.014**

MB

1.967

1.906

0.061

Sales Growth

0.142

0.155

-0.014

Leverage

0.326

0.330

-0.004

Cash/Assets

0.084

0.089

-0.006

Stock Volatility

0.029

0.030

-0.001

Herfindahl

0.155

0.145

0.011

Herfindahl2

0.044

0.038

0.006

Patent66.97370.442-3.469Citation

50.71151.888-1.177

Propensity Score

0.241

0.238

0.003

53

Panel C: Post-matching Probit regression of CDS trade initiation
Dependent variables
Credit Rating
Investment Grade
Ln(Assets)
Ln(Firm Age)
Ln(PPE/Employees)
R&D/Assets
ROA
MB
Sales Growth
Leverage
Cash/Assets
Stock Volatility
Herfindahl
Herfindahl2
Year FE
Industry FE
Observations
Pseudo R-squared

CDS Trading
0.082
(0.5)
-0.168(-1.1)
0.013
(0.2)
0.059
(0.8)
0.028
(0.4)
1.379
(0.9)
1.054
(1.6)
0.036
(0.6)
0.023
(0.2)
0.170
(0.5)
-0.320(-0.6)-6.709(-1.5)-1.321(-0.9)
1.311
(0.8)
X
X
1,564
0.097

54

Table 2: Summary statistics
The sample consists of CDS firms that CDS trading initiated between 1997 and 2008 and the matched non-CDS firms
jointly. Patent is the number of patents applied for, during the current year, which are eventually granted. Citation is thetotal number of citations arising from patents. The citations are adjusted using the time-technology class fixed effect.
Ln(1+Patent) is the log of one plus Patent. Ln(1+Citation) is the log of one plus Citation. CDS Trading is a dummy
variable that equals one after the introduction of CDS trading on a reference firm, and zero otherwise. Daily Quotes isthe average number of CDS daily quotes on a firm in a given year. Distinct Dealers is the average number of distinct
dealers providing CDS quotes on a firm in a given year. Distinct Maturities is the average number of distinct maturitiesof CDS contract traded on a firm in a given year. Ln(Assets) is the log of a firm’s book value of total assets. Ln(Firm Age)is the log of the number of years since a firm enters the CRSP database. Ln(PPE/Employees) is the log of the ratio of net
property, plant, and equipment to the number of employees. R&D is the R&D expenses scaled by total assets, where
missing R&D expenses are treated as zeros. ROA is EBIT scaled by total assets. MB is the market value of total assets
scaled by the book value of total assets. Sales Growth is the log of one plus the change in net sales scaled by lagged net
sales. Leverage is the book value of debts scaled by total assets. Cash is the cash holdings scaled by total assets. Stock
Volatility is the standard deviation of daily stock returns over the fiscal year. Herfindahl is the sum of squared market
shares in sales of a firm’s three-digit SIC industry.
(1)

(2)

(4)

(5)

(6)

Mean

(3)
Standard
deviation

Observations

Q1

MedianQ3

Patent16,63678.148261.783

0.000

0.000

14.000Citation

16,63663.703240.254

0.000

0.000

7.651

Ln(1+Patent)

16,636

1.504

2.136

0.000

0.000

2.708

Ln(1+Citation)

16,636

1.251

2.075

0.000

0.000

2.158

CDS Trading

16,636

0.268

0.443

0.000

0.000

1.000

Daily Quotes

4,579

236

59.742

146

261

262

Distinct Dealers

4,330

6.732

4.588

3.027

5.192

9.828

Distinct Maturities

4,433

7.467

3.451

5.115

8.588

10.485

Variables
Innovation measures

CDS variables

Control variables in innovation regressions
Ln(Assets)

16,636

8.588

1.526

7.480

8.400

9.789

Ln(Firm Age)

16,636

3.061

0.818

2.485

3.178

3.714

Ln(PPE/Employees)

16,636

4.454

1.400

3.470

4.291

5.266

R&D/Assets

16,636

0.023

0.046

0.000

0.002

0.028

ROA

16,636

0.088

0.088

0.048

0.084

0.131

MB

16,636

1.863

1.374

1.158

1.465

2.019

Sales Growth

16,636

0.105

0.288

-0.001

0.077

0.168

Leverage

16,636

0.298

0.205

0.152

0.267

0.397

Cash/Assets

16,636

0.105

0.121

0.023

0.063

0.140

Stock Volatility

16,636

0.028

0.015

0.018

0.024

0.033

Herfindahl

16,636

0.159

0.144

0.068

0.108

0.208

16,636

0.046

0.108

0.005

0.012

0.043

Herfindahl

2

55

Table 3: Effect of CDS trading on innovation output
The sample consists of CDS firms that initiated CDS trading between 1997 and 2008 and the matched
non-CDS firms. Patent is the number of patents applied for, during the current year, which are eventually
granted. Citation is the total number of citations arising from patents, which is adjusted using the timetechnology class fixed effect. CDS Trading equals one if a firm has CDS trading on its debt during a year,
and zero otherwise. Ln(Assets) is the log of a firm’s book value of total assets. Ln(Firm Age) is the log of
the number of years since a firm enters the CRSP database. Ln(PPE/Employees) is the log of the ratio of
net property, plant, and equipment to the number of employees. R&D/Assets is R&D expenses scaled bytotal assets. ROA is EBIT scaled by total assets. MB is the market value of total assets divided by the bookvalue of total assets. Sales Growth is the log of one plus the change in net sales scaled by lagged net sales.
Leverage is the book value of debts scaled by total assets. Cash/Assets is the cash holdings scaled by total
assets. Stock Volatility is the standard deviation of daily stock returns over the fiscal year. Except for
Stock Volatility, which are measured between year t-1 and t, all explanatory variables are measured at t-1
in the regressions.Herfindahl is the sum of squared market shares in sales of a firm’s three-digit SIC
industry. The t-statistics in parentheses are calculated from the Huber/White/Sandwich
heteroskedastic-consistent errors, which are also corrected for correlation across observations for a given
firm. The symbols ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
Dependent variables
CDS Trading
Ln(Assets)
Ln(Firm Age)
Ln(PPE/Employees)
R&D/Assets
ROA
MB
Sales Growth
Leverage
Cash/Assets
Stock Volatility
Herfindahl
Herfindahl2
Firm and year fixed effects
N/Adjusted R-squared(1)
Ln(1+Patent)t
0.147***(3.3)
0.247***
(5.6)
0.153
(1.4)
0.286***
(5.1)
0.339
(0.6)
0.117
(1.0)
0.105***(5.3)-0.074(-1.3)-0.262*(-1.7)
0.723***(2.6)
4.823***(3.4)-0.461(-0.8)
0.609
(1.5)

(2)
Ln(1+Citation)t
0.199***(3.3)
0.243***(4.5)
0.297**(2.3)
0.341***
(4.9)
0.507
(0.7)
-0.078(-0.4)
0.156***(5.6)-0.112(-1.4)-0.512***(-2.8)
0.357(0.8)
8.357***
(3.5)
0.485
(0.6)
-0.247(-0.4)

Yes
16,636/0.895

Yes
16,636/0.797

56

Table 4: Tests on endogeneity
The sample consists of CDS firms that initiated CDS trading between 1997 and 2008 and the matched
non-CDS firms. All regressions include the same control variables as those used in Table 3, but theircoefficients are not tabulated. CDS Firm that equals one if a reference firm has CDS trading on its debt at any
time during the entire sample period, and zero otherwise. Detailed definitions of dependent variables andcontrol variables can be found in the legend of Table 2. Panel A presents the regression results using
alternative model specification. Panel B presents the regressions results on the effect of CDS liquidity on
innovation output. Panel C presents the regression results controlling for state-by-year and industry-by-year
fixed effects. Panel D presents the distribution of coefficient estimates of CDS trading and associated tstatistics from regressions by randomizing the years of CDS trading initiation among the sample firms 1000
times. Panel E presents the regression results controlling for other corporate governance measures. Panel F
presents the regression results on the test of reverse causality. Year-2 (Year-1) is a dummy variable that takes
the value of one if CDS trading initiates in two (one) years, and zero otherwise. Year0 is a dummy variable thattakes the value of one if CDS trading initiates this year, and zero otherwise. Year+1 is a dummy variable thattakes the value of one if CDS trading initiates one year ago, and zero otherwise. Year≥+2 is a dummy variable
that takes the value of one if CDS trading initiates two or more years ago, and zero otherwise. Panel G presents
the regression results controlling for pre-sample innovation investments and implied volatility. Panel H
presents the second-stage estimation of the two-stage instrumental variable regression results. In the first-stage
regression, we predict the probability of CDS trading using Lender FX Hedging as the instrument variable.
Lender FX Hedging is defined as the average of FX derivatives used for hedging purposes, relative to total
assets, across the banks that have served as either lenders or bond underwriters for the firm over the previous
five years. Detailed definitions of dependent variables and control variables can be found in the legend of
Table 2. The t-statistics in parentheses are calculated from the Huber/White/Sandwich heteroskedastic
consistent errors, which are clustered by firm. The symbols ***, **, and * denote significance at the 1%, 5%, and
10% levels, respectively.
Panel A: Using alternative model specification (N =16,636)
Ln(1+Patent)t
CDS Trading
0.175**(2.0)
CDS Firm
0.040
(0.3)

Ln(1+Citation)t
0.221**
(2.4)
0.087
(0.6)

Panel B: Effect of CDS liquidity on innovation output
Panel B.1: Number of daily quotes (N = 4,579)
Daily Quotes
Panel B.2: Number of distinct dealers (N = 4,330)
Distinct Dealers
Panel B.3: Number of distinct maturities (N = 4,433)
Distinct Maturities

Ln(1+Patent)t

Ln(1+Citation)t

0.001***(3.9)

0.002***(3.8)

0.012**(2.1)

0.029**(2.5)

0.039***(2.9)

0.052***(2.8)

Panel C: Control for state-by-year and industry-by-year fixed effects (N = 11,605)
Ln(1+Patent)t
CDS Trading
0.091**(2.4)

Ln(1+Citation)t
0.091*(1.8)

Panel D: Monte Carlo analysis of the effect of CDS trading
Ln(1+Patent)t
CDS Trading (Mean)
0.003(0.173)
CDS Trading (Median)
0.007(0.155)Ln(1+Citation)t
0.002(0.196)0.008(0.116)

57

CDS Trading (2.5%)
CDS Trading (10%)
CDS Trading (90%)
CDS Trading (97.5%)-0.098(-1.212)-0.065(-0.872)
0.064(1.197)0.095(1.897)

Panel E: Control for corporate governance measures (N = 5,317)
Ln(1+Patent)t
CDS Trading
0.104***(3.4)
G-index0.018(1.2)
Board size
0.026
(0.3)
Institutional ownership
0.323***(2.9)-0.162(-1.545)-0.084(-1.252)
0.093(1.367)0.125(1.993)
Ln(1+Citation)t
0.083*
(1.9)
0.020
(1.0)
0.100
(0.8)
0.614***(3.8)

Panel F: Test of reverse causality (N = 16,636)
Year-2Year-1Year0
Year+1Year≥+2

Ln(1+Patent)t-0.010(-0.3)
0.016(0.5)
0.066**(2.0)
0.106***(2.6)
0.204**(2.5)

Ln(1+Citation)t-0.052(-1.2)
0.011
(0.2)
0.063
(1.3)
0.099**(2.2)
0.150***(3.2)

Panel G: Control for pre-sample innovation investments and implied volatility (N = 11,040)
Ln(1+Patent)t
Ln(1+Citation)t
CDS Trading
0.155***
0.227***
(3.9)
(3.9)
R&D capital
1.266
1.378
(1.3)
(1.0)
Implied volatility
0.204
0.061
(1.0)
(0.3)
Panel H: Instrumental variable approach (N = 16,636)
Ln(1+Patent)t
Instrumented CDS Trading
0.258**(2.0)

Ln(1+Citation)t
0.401**(2.3)

58

Table 5: Cross-sectional differences in the effects of CDS trading on innovation
The sample consists of firms jointly covered in the CreditTrade, the GFI Group and Markit CDS database, CRSP,
and the USPTO patent and citation database between 1997 and 2008. In Panel A, the sample is split according to thesample median industry debt financing dependence, where debt financing dependence is defined as the industry
median of firms’ dependence on debt financing following Rajan and Zingales (1998) and Duchin, Ozbas, andSensoy (2010). In Panel B, firms’ bank debt information is collected from Capital IQ. In Panel C, the sample is split
according to the sample median number of banks, where the number of banks is the number of unique banks that
lend to the firm. In Panel D, covenant intensity is high (low) if the firm has either collateral requirement or financial
ratio covenant in the loan contract (if the firm has neither of the two covenants in the loan contract). In Panel E, thesample is split according to the sample median short-term debt, where short-term debt is defined as the proportion ofdebts that will mature within three years. In Panel F, financially distressed (undistressed) firms are those with Zscore in the bottom (top) quartile for two consecutive years prior to CDS initiation. The same set of control variables
as those used in Table 3 is included in all regressions but the coefficient estimates of these variables are nottabulated for brevity. Detailed definitions of other variables can be found in the legend of Table 2. The t-statistics inparentheses are calculated from the Huber/White/Sandwich heteroskedastic consistent errors, which are clustered byfirm. The symbols ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
(1)
(2)
(3)
(4)
Dependent variables
Ln(1+Patent)t
Ln(1+Citation)t
Panel A: Partitioning the sample according to industry debt financing dependence (Nhigh = 8,119; Nlow = 8,517)
High
Low
High
Low
CDS Trading
0.303***
0.079
0.342***
0.113
(3.0)
(0.8)
(3.1)
(0.9)
Panel B: Partitioning the sample according to whether a firm has bank debt (Nyes = 5,351; Nno = 7,541)
Yes
No
Yes
No
CDS Trading
0.158***-0.0050.174**
0.036(3.4)(-0.1)(2.6)(0.6)
Panel C: Partitioning the sample according to the number of banks (Nlow = 2,684; Nhigh = 2,667)
Low
High
Low
CDS Trading
0.227**
0.095
0.298**
(2.5)
(1.5)
(2.1)

High
0.115
(1.4)

Panel D: Partitioning the sample according to covenant intensity (Nhigh = 3,136; Nlow = 2,215)
High
Low
High
CDS Trading
0.168**
0.075
0.169*
(2.5)
(1.0)
(1.8)

Low
0.090
(0.8)

Panel E: Partitioning the sample according to a firm’s debt maturity (Nshort = 8,182; Nlong = 8,235)
Short
Long
Short
CDS Trading
0.183***-0.0130.265***(2.8)(-0.3)
(2.9)

Long
0.001
(0.0)

Panel F: Partitioning the sample according to a firm’s financial distress (Ndistressed = 3,112; Nundistressed = 2,520)
Distressed
Undistressed
Distressed
Undistressed
CDS Trading-0.0930.246**-0.0740.224*(-1.3)(2.4)(-1.0)(1.9)

59

Table 6: Effect of CDS trading on innovation strategy
The sample consists of firms jointly covered in the CreditTrade, the GFI Group and Markit CDS database,
CRSP, and the USPTO patent and citation database between 1997 and 2008. Exploratory innovation is
the proportion of patents that are exploratory. Exploitative innovation is the proportion of patents that are
exploitative. A patent is categorized as exploratory if 60 percent or more of its citations are based on new
knowledge outside of a firm’s existing expertise (i.e., not citing the firm’s existing patents or the citations
made by those patents), while a patent is categorized as exploitative if 60 percent or more of its citations
are based on a firm’s existing expertise (i.e., the firm’s existing patents and the citations made by thosepatents). Process (product) patent is the number of patents that fall (do not fall) into IPC 8.0 class B01.
Originality is defined as the mean of the originality scores of a firm’s patents. The originality score of apatent is calculated as one minus the Herfindahl index of the three-digit technology class distribution of
all the patents that this patent cites. Detailed definitions of other variables can be found in the legend of
Table 2. The t-statistics in parentheses are calculated from the Huber/White/Sandwich heteroskedastic
consistent errors, which are clustered by firm. The symbols ***, **, and * denote significance at the 1%, 5%,
and 10% levels, respectively.
Dependent variables
CDS Trading
Ln(Assets)
Ln(Firm Age)
Ln(PPE/Employees)
R&D/Assets
ROA
MB
Sales Growth
Leverage
Cash/Assets
Stock Volatility
Herfindahl
Herfindahl2
Year FE
Firm FE
Observations
Adjusted R-squared(1)
Exploratory
Innovation
0.025**
(2.2)
0.012
(1.1)
-0.182***(-8.9)
0.046***(3.4)-0.008(-0.1)-0.030(-0.6)
0.009***(3.7)-0.066***(-4.6)-0.066*(-1.8)-0.162***(-3.9)
0.513
(1.2)
0.149
(0.7)
-0.204(-1.1)(2)
Exploitative
Innovation-0.027***(-2.7)
0.001(0.1)
0.143***(8.0)-0.055***(-4.8)
0.092
(0.8)
0.070
(1.4)
-0.008***(-3.7)
0.047***(3.7)
0.062*(1.9)
0.185***(4.8)-0.289(-0.7)-0.145(-0.8)
0.184
(1.1)

(3)
Product
Innovation
0.149***(3.4)
0.246***
(5.6)
0.152
(1.4)
0.285***
(5.1)
0.320
(0.6)
0.118
(1.0)
0.105***(5.3)-0.072(-1.3)-0.274*(-1.7)
0.722***(2.6)
4.871***(3.4)-0.486(-0.9)
0.628
(1.5)

(4)
Process
Innovation
0.027
(0.8)
0.020
(1.2)
0.129**
(2.1)
0.013
(0.9)
-0.002(-0.0)
0.137
(1.6)
0.004
(0.6)
-0.004(-0.2)
0.096***
(2.6)
0.222
(1.1)
0.966
(1.0)
-0.071(-0.2)
0.126
(0.5)

(5)
Originality
Index
0.029**
(2.6)
0.005
(0.3)
-0.023(-0.9)
0.010
(0.5)
0.118
(1.4)
-0.011(-0.2)-0.002(-1.1)
0.013
(1.0)
0.029
(0.9)
0.039
(1.1)
-0.682(-1.4)-0.162(-0.8)
0.276
(1.6)

X
X
7,775
0.580

X
X
7,775
0.575

X
X
16,632
0.895

X
X
16,632
0.707

X
X
6,982
0.381

60

Table 7: Testing the financing channel as an alternative explanation
The sample consists of firms jointly covered in the CreditTrade, the GFI Group and Markit CDS database, CRSP, and the
USPTO patent and citation database between 1997 and 2008. In Panel A, ∆Leveraget-1 to t+1 is the change in leverage ratiosfrom yeart-1 to yeart+1. Net Debt Issuancet-1 to t+1 is the total net debt issued from yeart-1 to yeart+1. Panel B presents theregression results on the effect of CDS trading on R&D investments in yeart+1. In Panel C, we divide the sample into
financially unconstrained subsample (UC) and financially constrained subsample (C) based on the sample median Hadlock
and Pierce (2010) index, the sample median tangibility ratio, and whether the firm has investment-grade credit rating or
not. The same set of control variables as those used in Table 3 is included in all regressions but the coefficient estimates ofthese variables are not tabulated for brevity. Detailed definitions of other variables can be found in the legend of Table 2.
The t-statistics in parentheses are calculated from the Huber/White/Sandwich heteroskedastic consistent errors, which areclustered by firm. The symbols ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Controlling for changes in debt financing
Dependent variables
CDS Trading∆Leveraget-1 to t+1
Net Debt Issuancet-1 to t+1(1)
Ln(1+Patent)t
0.149***
(3.4)
0.010
(0.1)
0.059
(0.8)

(2)
Ln(1+Citation)t
0.201***(3.3)-0.036(-0.3)
0.119
(1.1)

X
X
X
16,6280.895

X
X
X
16,628
0.797

(1)
R&Dt
-0.000(-0.2)
0.544***
(9.3)

(2)
R&Dt
-0.002(-0.8)

X
X
X
16,6360.871

X
X
X
16,636
0.854

Other controls
Year FE
Firm FE
Observations
Adjusted R-squared
Panel B: Effect of CDS trading on R&D investments
Dependent variables
CDS Trading
R&Dt
Other controls
Year FE
Firm FE
Observations
Adjusted R-squared

Panel C: Partitioning the sample according to a firm’s financial constraint status

61

Table 8: Effect of CDS trading on value creation from innovation
The sample consists of firms jointly covered in the CreditTrade, the GFI Group and Markit CDS database,
CRSP, and the USPTO patent and citation database between 1997 and 2008. The dependent variable incolumn (1) is the natural logarithm of one plus the number of patents over the average R&D expendituresin the past five years. The dependent variable in column (2) is the natural logarithm of one plus thenumber of citations over the average R&D expenditures in the past five years. The dependent variable incolumn (3) is the log of one plus the sum of estimated patent value from Kogan, Papanikolaou, Seru, andStoffman (2015). Detailed definitions of other variables can be found in the legend of Table 2. The tstatistics in parentheses are calculated from the Huber/White/Sandwich heteroskedastic consistent errors,
which are clustered by firm. The symbols ***, **, and * denote significance at the 1%, 5%, and 10% levels,
respectively.
Dependent variables
CDS Trading
Ln(Assets)
Ln(Firm Age)
Ln(PPE/Employees)
R&D/Assets
ROA
MB
Sales Growth
Leverage
Cash/Assets
Stock Volatility
Herfindahl
Herfindahl2
Year FE
Firm FE
Observations
Adjusted R-squared

(1)

(2)

Ln(1+Patent/R&D)t
0.020**(2.0)-0.013**(-2.5)-0.001(-0.1)
0.018*(1.7)-0.510***(-3.2)-0.097(-1.3)
0.013***(2.9)
0.117***(4.1)-0.121***(-3.1)
0.073(1.3)-0.093(-0.2)
0.084(0.6)-0.080(-0.5)

Ln(1+Citation/R&D)t
0.026**(2.0)-0.019***(-3.1)
0.007
(0.7)
0.011
(0.7)
-0.534***(-2.9)-0.073(-0.8)
0.016***(2.8)
0.133***(4.0)-0.124***(-2.7)
0.068
(0.9)
0.485
(0.7)
-0.035(-0.2)
0.026
(0.2)

(3)
Ln(1+Patent Value)t
0.081**(2.4)
0.299***(10.1)
0.271***(3.9)
0.364***(9.6)
1.414**(2.5)
0.301*(1.8)
0.183***(15.5)-0.086***(-2.7)-0.255***(-2.6)
0.126(0.8)
8.007***(6.5)
2.022***(3.7)-1.440**(-2.5)

X
X
9,057
0.558

X
X
9,057
0.543

X
X
16,6360.864

62