Do Traded Credit Default Swaps Impact Lenders’ Monitoring Activities? Evidence from Private Loan Agreements

Do traded credit default swaps impact lenders’ monitoring activities? Evidence from private loan agreements

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Jennifer Lynn Sustersic

Graduate Program in Accounting and MIS

The Ohio State University

2012

Dissertation Committee:

Professor Anne Beatty, Advisor

Professor Darren Roulstone

Professor Andy Van Buskirk

Professor Tzachi Zach

Copyrighted by

Jennifer Lynn Sustersic

2012

Abstract

This paper investigates how a new, opaque market for credit derivatives impacts the use of accounting information in debt contracting. Because credit default swaps allow creditors to transfer credit risk, while retaining control rights, the literature suggests these instruments may introduce a moral hazard problem and lead to decreased monitoring activities by creditors. In addition, the extent to which a lender transfers its credit risk through credit default swaps is not visible to outside parties; therefore, a credit default swap market may introduce new information asymmetries within lending syndicates. I provide evidence on these issues by studying changes in syndicated loan agreements surrounding the onset of credit default swap trading for corporate borrowers.

I show that lending syndicates increase their reliance on accounting-based, financial covenants to monitor borrowers and mitigate lead lender moral hazard, when a credit default swap market for the borrower provides an opportunity for the lead lender to lay off a loan’s credit risk. My findings suggest an increased role of accounting information in mitigating new information asymmetries associated with the presence of a credit default swap market.

Acknowledgments

I am grateful to my dissertation committee: Anne Beatty (chair), Darren

Roulstone, Andy Van Buskirk and Tzachi Zach. I also thank Jennifer Altamuro, Mike

Anderson, Anil Arya, Zahn Bozanic, Mike Iselin, Rick Johnston, Jim Kinard, Brian

Mittendorf, Neelam Soundarajan, Yunyan Zhang, Helen Zhang and seminar participants at the Ohio State University, Purdue University, the University of Notre Dame, Miami

University, Southern Methodist University, and Drexel University for their helpful feedback. All errors are my own.

iii

Vita

1997...... Revere High School

2001...... B.S. Business, Miami University

2002...... Master of Accountancy, Miami University

2002-2004 ...... Senior Associate, Fraud, Investigative and

...... Dispute Services, Ernst & Young LLP

2004-2006 ...... Senior Associate, Dispute Analysis and

...... Forensics, Alvarez & Marsal LLC

2006-2007 ...... Associate, Gerson Lehrman Group

2007 - present ...... Graduate Research and Teaching Assistant,

Department of Accounting & MIS, The

Ohio State University

Fields of Study

Major Field: Accounting and MIS

Table of Contents

Abstract ...... ii

Acknowledgments...... iii

Vita ...... iv

List of Tables ...... vi

Chapter 1: Introduction ...... 1

Chapter 2: Literature Review ...... 9

Chapter 3: Hypotheses Development...... 17

Chapter 4: Research Design ...... 20

Chapter 5: Main Results...... 27

Chapter 6: Supplemental Analysis of Covenant Enforcement ...... 32

Chapter 7: Sensitivity Analyses ...... 36

Chapter 8: Conclusion...... 42

References ...... 44

Appendix A: Variable Definitions ...... 51

List of Tables

Table 1. CDS Trading Initiation by Year………………………………………………...57

Table 2. Covenant and Covenant Slack Analysis: Descriptive Statistics……………….58

Table 3. Propensity Score Matching: Determinants of CDS Trading Onset – Covariate

Balance…………...... 61

Table 4. Covenant and Covenant Slack Analysis: Descriptive Statistics for Matched

Sample………...………………………………………………………………………….62

Table 5. Impact of CDS Trading on the Number of Covenants…………………………64

Table 6. Impact of CDS Trading on the Number of Covenants: Matched Sample...... 66

Table 7. Impact of CDS Trading on Covenant Slack……………………………………68

Table 8. Impact of CDS Trading on Covenant Slack: Matched Sample…………….....71

Table 9. Renegotiation Analysis: Descriptive Statistics of New Covenant Violations

Leading to Renegotiation………………………………………………………………...74

Table 10. Renegotiation Analysis: Renegotiation of New Covenant Violations……....76

Table 11. Impact of CDS Trading on the Relationship Between Number of Covenants and Lead Lender Share………………………………………………………………...... 77

Table 12. Impact of CDS Trading on Required Lenders…………...……………………79

Table 13. Credit Rating Changes Surrounding CDS Availability……………………….81

Table 14. Impact of CDS Trading on Covenant Inclusion………………………………82 vi

Chapter 1: Introduction

Fitch’s Assessment of the Credit Derivatives Market:

…Nevertheless, Fitch believes there are risks that bear watching, including low financial transparency, ‘informational asymmetries’ (which create the potential for unanticipated, incorrectly priced and poorly managed concentrations of risk), and the possible promotion of new forms of moral hazard within the banking system as the linkage between origination and management of credit risk becomes more attenuated.

-Fitch Ratings, September 24, 20031

The credit default swap (CDS) market has grown from $600 billion in 2001 to a high of $62 trillion in 2007.2 Academic literature and the popular press question whether the use of CDSs to hedge lenders’ risk reduces lenders’ incentives to monitor the borrower (Jackson [2007], Partnoy and Skeel [2007], Whitehead [2009]). Specifically, because CDSs allow lenders to shift the credit risk of a loan to a third party while still maintaining the control and monitoring rights, moral hazard issues may arise. Hu and

Black [2008a, b] refer to this separation of economic risk from control rights and the resulting moral hazard issues that accompany such a separation as the empty creditor problem.

1 “Global Credit Derivatives: A Qualified Success,” Fitch Ratings, September 24, 2003. 2 “The CDS Big Bang: Understanding the Changes to the Global CDS Contract and North American Conventions.” Markit Group Limited. March 13, 2009. 1

While some studies speculate that CDSs may lead to a decrease in monitoring activities by empty creditors, other studies provide reason to question this hypothesis.

Jarrow [2010] notes that, in equilibrium, the price of CDSs should incorporate expected lender monitoring. Therefore, an appropriately priced CDS market would offset potential benefits for lenders looking to buy insurance (CDSs) and decrease monitoring.3 In addition, Gopalan, Nanda, and Yermilli [2009] suggest that banks with previous loan defaults or bankruptcies face reputational consequences, which would provide a disincentive to decrease monitoring. Finally, based on the literature that stresses the importance of monitoring in the theory of the firm, lender monitoring reduces the agency costs of debt, which consequently increases the value of the firm. As this is in the interest of both the lender and the borrower, the parties will write contracts to facilitate lender monitoring and minimize the agency costs of debt (Jensen & Meckling [1976]).

To address these differing views in the literature, this study examines whether

CDS availability affects private lenders’ monitoring activities by considering monitoring provisions included in contract design at loan origination. Using a sample of private loan agreements, I explore changes in loan contracting around the availability of CDS trading for referenced, corporate borrowers. Specifically, I consider covenants and covenant slack as contractual monitoring provisions, as covenants legally provide lenders with certain rights to monitor the borrower and protect lender interests under the terms of the contract.

3 This argument does not take into account that lead banks receive origination fees for initiating new loans, as well as obtain private information with regard to the loan prior to other CDS market participants. 2

CDSs present two potential influences on lender monitoring and loan contract design. The first is the moral hazard (empty creditor) problem debated in the literature.

Secondly and relatively overlooked in the literature, the CDS market is characterized by information asymmetry. Very little information exists on parties’ use of, and exposure to, the CDS market. Outside parties may see that a particular lender uses credit default swaps, in aggregate; however, lenders’ specific CDS holdings are not visible.4 Therefore, the opacity of the CDS market may exacerbate potential moral hazard problems or introduce new information asymmetry problems since the extent to which the lender transferred its credit risk on a specific loan is not visible (Morrison [2005]).

The potential moral hazard and information asymmetry problems associated with

CDSs may be especially relevant in the syndicated loan market. The syndicated loan market represents at least $1 trillion in new financing to non-financial businesses each year.5 In a syndicated loan, the lead lender negotiates a preliminary deal with the borrower and then finds other lenders to participate in the lending facility, each funding a piece of the deal. The lead lender holds responsibility for monitoring the borrower on behalf of the other syndicate participants. Because monitoring is unobservable, the lead lender faces a moral hazard problem and often credibly commits to monitor by retaining a portion of the loan (Holmstrom [1979], Holmstrom and Tirole [1997], Sufi [2007]).

However, CDSs provide an option for the lender to lay off the credit risk associated with

4 This precludes a study of loan contracts for which a lender actually purchases a CDS. Regardless, the availability of CDS information and the option to purchase a CDS may impact lenders’ incentives to monitor or the information asymmetry within the syndicate. 5 Gadanecz, B. “The syndicated loan market: structure, development and implications.” BIS Quarterly Review, December 2004. 3 this lead lender share, without the knowledge of the other syndicate members.6

Therefore, the new credit derivatives market may influence the lead lender’s monitoring incentives, as well as increase the information asymmetry within the syndicate as syndicate members do not know the extent of the lead lender’s CDS use.

Given the potential increased moral hazard and information asymmetry associated with CDSs, this paper provides empirical evidence on the impact of CDS availability on lenders’ monitoring, specifically with regard to loan contract design. As banks are delegated monitors (Diamond [1984], Beatty, Liao and Weber [2012]), private lenders should incorporate monitoring provisions to facilitate efficient contracting. While the literature often uses lead lender share as a proxy for a lead lender’s commitment to monitor (Sufi [2007]), CDSs may allow the lead lender to lay off the credit risk associated with this retained share. However, other loan provisions may also serve as a commitment to monitor and potentially substitute for lead lender share, specifically accounting-based financial covenants (Dass, Nanda, and Wang [2011]).7

Accounting-based, financial covenants facilitate monitoring because they serve as contractual tripwires, which, upon violation, allow the lenders to intervene and take precautionary measures to protect the lenders’ interests. Additional covenants increase the likelihood of a violation and therefore the lenders’ ability to monitor the borrower.

6 J.P. Morgan purchased credit default swaps to reduce its exposure to WorldCom during the syndication process for a new credit facility for which it was underwriting in 2001. J.P. Morgan specifically structured its transaction to conceal this reduction of its risk exposure. See In re WorldCom, Inc. Sec. Litig., 346 F. Supp. 2d 628, 651–52 (S.D.N.Y. 2004). 7 Because of data limitations (lead lender share is often missing in LPC Dealscan), my main analysis focuses only on covenant use as monitoring provisions included in loans. In a sensitivity analysis, I consider the relationship between lead lender share and covenant use for a smaller sample with available data. 4

By providing additional control rights upon violation, covenants not only provide borrowers with incentives to avoid a violation but also provide lenders incentives to monitor borrowers (Rajan and Winton [1995], Mora [2010]). In addition, upon covenant violation, the lead lender must receive approval from the syndicate as to the course of action. Thus, covenants also help mitigate conflicts of interest between the lead lender and other syndicate participants by giving syndicate participants more control over the loan (Dass, Nanda, and Wang [2011]).

For my analysis, I conduct an event study surrounding the onset of CDS trading for corporate borrowers. 8 My treatment sample consists of private loans two years prior to and two years following CDS trading onset. I utilize two control samples. I first compare the treatment sample to all loans on Dealscan without CDS trading from 1999-

2009. To address potential endogeneity associated with the onset of CDS trading, I then use a propensity score matched sample and apply a difference-in-difference design to capture changes in loan structure related to monitoring, from pre-CDS trading onset to post-CDS trading onset, compared to firms with no CDS trading.

While a covenant violation gives the lender the option to intervene, upon violation the lender may choose to do nothing (grant a waiver), renegotiate the loan, or call the loan. In a supplemental analysis, I explore whether CDS availability leads to changes in

8 This study focuses on the availability of a CDS market for a given borrower. An active and visible market for a borrower’s CDSs provides the lender with information on the potential cost of insurance, as well as the assurance of a counterparty. The visibility of a CDS market for a given borrower also allows non-lead syndicate participants to see the option to easily hedge a position; however, the lack of information available on CDSs does not permit syndicate participants to discern whether or not the lead lender utilizes that option, thus increasing information asymmetry within the syndicate itself. 5 enforcement of these loan provisions, in the form of subsequent renegotiations or loans called following a covenant violation. 9

Contrary to the empty creditor hypothesis, I find no evidence of a decline in contractual monitoring provisions after the availability of a CDS market for a given borrower. I actually observe an increased use of contractual monitoring provisions in loans, in the form of increased covenant use and decreased covenant slack, post CDS availability. I propose syndicate members demand increased monitoring provisions in the loan contract to enhance incentives for the lead lender to monitor, as well as strengthen the syndicates’ control rights, as CDSs increase information asymmetry within the syndicate and non-lead members may no longer rely on lead lender share as a credible commitment device. These results may suggest an efficient contracting solution, in which loan contracts adjust to facilitate valuable bank monitoring in the presence of increased information asymmetry within syndicates.

This study contributes to several strands of literature. This paper contributes to the literature studying the impact of the CDS market on debt contracting. While various theoretical models speculate how a CDS market would affect lender monitoring (Duffee and Zhou [2001], Arping [2004], Morrison [2005], Parlour and Winton [2009]), little empirical evidence exists. Ashcraft and Santos [2009] find that, contrary to expectation, the onset of CDS trading increases the cost of bank debt for risky and opaque firms and

Gong, Martin and Roychowdhury [2010] find a decrease in borrowers’ accounting

9 Banks rarely call loans; however, they renegotiate them frequently. Following Nini, Smith, and Sufi [2011], I look for new loans within the 6 months following a covenant violation. If the new loan is with the same lead lender, I consider it a renegotiation, and if the new loan is from a different lead lender, I assume the lender called the loan. Under either scenario, I consider the lender enforced the covenant, as opposed to granting a waiver. 6 conservatism post-CDS onset. While these two empirical studies attribute their results to a decline in lender monitoring, neither paper provides direct evidence of decreased monitoring on the part of lenders. My research addresses this gap in the literature by exploring a more direct setting to test lender monitoring: monitoring provisions included in private lenders’ contracts. I show no evidence of a decline in lender monitoring associated with a CDS market for the borrower.

Secondly, this study contributes to the literature exploring information asymmetry within the syndicate. The literature suggests that moral hazard in monitoring plays a more prominent role than adverse selection in syndicate structure and contract design

(Sufi [2007], Mora [2010]). Mora [2010] provides evidence that covenants facilitate monitoring in the presence of information asymmetry within the syndicate, and Dass, et al. [2011] show that covenants help mitigate conflicts of interest between the lead lender and syndicate participants. My research complements these studies by providing evidence that a lead lender’s ability to hedge (through CDSs) results in changes to loan contract design, in response to increased information asymmetry within the syndicate.

Finally, this paper contributes to the literature exploring the use of accounting in debt contracting. Financial statement information is important for reducing agency conflicts through contracting (Watts and Zimmerman [1986], Holthausen and Watts

[2001], Armstrong, Guay and Weber [2010]). My results suggest lenders increase reliance on accounting-based, financial covenants in the presence of increased information asymmetry within syndicates due to the CDS market.

The paper proceeds as follows. Section 2 provides an overview of related literature; Section 3 discusses my hypothesis development. In Section 4, I outline my research design and I provide the main results in Section 5. Section 6 presents the supplemental analysis of covenant enforcement, Section 7 conducts sensitivity analyses and Section 8 concludes.

Chapter 2: Literature Review

The Credit Default Swap Market

A credit default swap is a bilateral, over-the-counter (OTC) contract between a protection buyer and protection seller with regard to a reference entity. While these are privately negotiated contracts between a protection buyer and protection seller, the

International Swaps and Derivatives Association (ISDA) provides standardized contracting for these agreements, which facilitates comparability among contracts and allows for a liquid market trading these instruments. Similar to other credit risk transfer mechanisms, a CDS would allow creditors to trade their credit risk exposure to a specific borrower. However, unlike other popular credit risk transfer mechanisms, the creditor still maintains the control and monitoring rights.

While the parties to CDS transactions are not visible, in aggregate, global banks are net protection buyers of CDSs according to a Fitch 2003 survey.10 Insurers, reinsurers, and financial guarantors tend to be on the other side of CDS transactions as net protection sellers.11 In addition, a Fitch 2004 survey suggests that hedge funds may

10 “Global Credit Derivatives: A Qualified Success,” Fitch Ratings, September 24, 2003. I use the older Fitch surveys to provide insight into the CDS industry around the time period I study. Today, banks are still net buyers of credit derivatives, although less so. 11 “Global Credit Derivatives: A Qualified Success,” Fitch Ratings, September 24, 2003. 9 comprise 20-30% of counterparty volume. 12 CDSs exist for single-name corporate reference entities, sovereigns, asset-backed securities, as well as in the form of indices, although single-name corporate reference entities dominate the market.13 The single- name, corporate reference entities tend to be large, public firms with strong information environments (Longstaff, Mithal and Neis [2005]). While banks tend to be net protection buyers, Minton, Stulz and Williamson [2009] and Duffie [2007] call into question the extent to which banks use CDSs to hedge default exposure of their loan portfolios.

However, as compared to these large banks’ total loan portfolios, CDSs only exist on a small portion of potential borrowers (large, household name firms). Therefore, these findings do not preclude banks’ potential use of CDSs to offset default risk of a specific reference entity’s loan.

Why would lenders use CDSs? CDSs provide credit suppliers with a method to reduce credit risk exposure. While credit insurance was available prior to credit default swaps, the liquid market for credit default swaps provides an easier and less expensive way for lenders to manage their credit risk. This provides several benefits for lenders.

CDSs allow lenders to diversify their exposure to a single issuer, geographic area or industry. As a real-world example, banks used credit derivatives to reduce their credit exposure to Enron, and therefore, Enron’s fall resulted in much smaller losses for several large lenders than it would have otherwise (Partnoy and Steel [2007]). Following this example, CDSs also allow the banking industry to shift some of their credit exposure

12 “Global Credit Derivatives Survey: Single-Name CDS Fuel Growth.” Fitch Ratings, September 4, 2004. 13 Ibid. 10 outside of the industry to other institutions with a less important role in providing liquidity for the economy. In addition, insuring loan assets with CDSs from an insurer with a lower credit risk than that of the borrower allows banks to assign lower risk weighting for regulatory capital requirements.14 Lastly, credit default swaps may allow banks to manage their credit risk, while maintaining the relationship with the borrower.

This may be especially relevant to lenders active in the syndicated loan market, as syndicated lending generates more revenue than underwriting equity or public debt

(Weidner [2000]).

Because credit default swaps provide risk-sharing capabilities, the ability of lenders to lay off some credit risk may allow them to extend more credit. The literature provides some evidence of credit risk transfer enhancing loan levels. Using proprietary bank level data, Hirtle [2009] provides evidence that greater credit derivative use increases credit extended. Cebenoyan and Strahan [2004] find that banks managing their credit risk by buying and selling loans on the secondary market make more risky loans

(as a percentage of total assets) than other banks. Goderis, Marsh, Castello, and Wagner

[2006] find that banks utilizing collateralized loan obligations (CLOs) increase their target loan levels permanently by 50%.

Because credit derivatives as a form of credit risk transfer allow banks to diversify their credit risk and improve loan-market liquidity, the credit derivatives market should ultimately benefit borrowers through easier and cheaper access to credit. Saretto and Tookes [2011] provide evidence that traded credit default swaps allow borrowers to

14 Basel II, page 49, Article 141. 11 maintain higher leverage ratios and longer debt maturities. On the other hand, Boehmer,

Chava and Tookes [2010] find that firms with traded CDSs have less liquid equity and less efficient stock prices and suggest an increase in information asymmetry due to the

CDS market.

Credit Default Swaps and Monitoring

The theoretical literature presents mixed predictions on the impact of various forms of credit risk transfer on lenders’ monitoring activities. In addition, moral hazard problems may relate differently to various forms of credit risk transfer. For instance, in the case of loan sales, the purchaser of the loan acquires the credit risk and the ability to continue to monitor the loan, and therefore, moral hazard problems may be less severe than under credit default swaps, in which the economic risk is transferred but the lender retains the control rights and monitoring ability. In addition, in the case of credit default swaps, the extent to which the lender insured itself/transferred its credit risk is not visible to outside parties. Both of these factors may exacerbate the moral hazard problems with regard to credit default swaps (Parlour and Winton (2009), Morrison (2005)).

Duffee and Zhou [2001], Arping [2004], and Morrison [2005] specifically address

CDSs, as a method of credit risk transfer, and their impact on lender monitoring incentives. Duffee and Zhou [2001] and Arping [2004] show that using a credit default swap with a maturity shorter than that of the underlying loan would maintain incentives to monitor. However, Morrison [2005] proposes that, because holding a CDS is unobservable to outside parties, lenders are unable to pre-commit to retain credit risk

12 when it is ex-post incentive compatible to lay off the credit risk. Therefore, credit default swaps decrease lenders’ incentives to monitor in his model. Parlour and Winton [2009] consider loan sales versus credit default swaps and the resulting effect on bank monitoring. The authors conclude that, “Overall, our model suggests that activity in the

CDS markets will be more likely to undermine monitoring than activity in loan sales, especially for weaker credits (p.29).”

The empirical evidence on the impact of various forms of risk transfer on lender monitoring is also mixed. Bushman and Wittenberg-Moerman [2009] and Drucker and

Puri [2009] present no evidence of decreased lender monitoring associated with loan sales from reputable lead lenders. Both studies suggest ex-ante efficient loan contracting to facilitate the ex-post loan sales. Bord and Santos [2011] and Wang and Xia [2011] do find evidence of decreased lender monitoring when a bank uses collateralized loan obligations (CLOs) to securitize loans. Marsh [2008] provides some insight into expected lender monitoring when the lender historically utilizes securitization or credit derivatives. By considering the equity market response to new loan announcements, the author finds decreased expected lender monitoring when the lender historically utilizes securitization (collateralized loan obligations – CLOs) but not when the lender historically uses credit derivatives. 15 However, the author does not consider the ex-ante debt contract design, in the presence of either securitization or CDS activity.

15 Marsh [2008] explores variation in the bank certification effect (James [1987]), proxied by the equity market response to new loan announcements, when the lender historically utilizes securitization (collateralized loan obligations – CLOs) or credit derivatives as forms of credit risk transfer. 13

While several studies address the impact of loan sales and securitization on lenders’ incentives to monitor, no study to date empirically explores the impact of traded credit default swaps on lenders’ overall monitoring incentives or ex-ante contract design.

Ashcraft and Santos [2009] find that, contrary to expectation, the onset of CDS trading increases the cost of bank debt for risky and opaque firms. The authors suggest that their results may indicate that debt market participants require higher compensation for potential decreased lender monitoring for those borrowers for which monitoring is most important. However, the authors provide no direct evidence of decreased monitoring on the part of lenders. Along these lines, Gong, Martin and Roychowdhury [2010] find a decrease in borrowers’ accounting conservatism post-CDS onset. The authors conclude this finding suggests a decline in lender demand for conservatism, in association with decreased lender monitoring. Again, the authors provide no direct evidence of a change in lender monitoring activity.

Information Asymmetry and Syndicate Structure

Several studies explore the impact of information asymmetry on syndicate structure and contract design in the syndicated loan market. These studies consider information asymmetry with respect to the borrower, as well as information asymmetry within the syndicate. In a syndicated loan, the lead lender arranges a preliminary deal with the borrower, negotiates ex-ante design of contract terms with the borrower and the syndicate, as well as maintains responsibility for ex-post monitoring of the borrower on behalf of the other syndicate participants. Because lenders are not equally informed,

14 syndicated loans suffer from agency problems in the form of adverse selection and moral hazard; however, both Sufi [2007] and Mora [2010] conclude that moral hazard represents the more important consideration in syndicate structure and contract design.16

Studies explore the decision to syndicate, as well as the proportion to syndicate, because the literature suggests the lead lender may mitigate incentive problems by retaining a piece of the loan (Leland and Pyle [1977], Holstrom and Tirole [1997]).

Borrower transparency and lender reputation mitigate information asymmetries and allow the lead lender to hold a smaller share and form a less concentrated syndicate (Dennis and Mullineaux [2000], Lee and Mullineaux [2004], Sufi [2007], Lin and Paravisini

[2011]). Along similar lines, Ball, Bushman and Vasvari [2008] provide evidence that the quality of borrowers’ accounting information, specifically its debt-contracting value, reduces the amount of a loan that a lead lender holds.

The literature also considers the impact of information asymmetry and syndicate structure on other contractual features of loans, such as covenants. Dass, Nanda, and

Wang [2011] find that covenants are more likely to be present in syndicated loans than in sole lender loans. They also show that covenants help mitigate conflicts of interest within a syndicate and provide some evidence that covenants substitute for lead lender share. Mora [2010] finds that a larger lead lender share is associated with better borrower performance and that covenants positively influence the relationship. Therefore, the

16 In addition to screening and monitoring borrowers, the lead lender maintains communications with the borrower on behalf of the syndicate, and therefore receives new information about the loan earlier than other syndicate participants. 15 author suggests that covenants serve to induce the lead lender to monitor and concludes that covenants and lead lender share are not necessarily substitutes.

Information asymmetry with respect to the borrower, as well as information asymmetry within the syndicate, may prove costly for the borrower. Poorer accounting quality firms face a higher cost of private debt and more stringent loan terms (Bharath,

Sunder and Sunder [2009]). Ivashina [2009] finds that asymmetric information between the lead lender and syndicate participants increases loan spreads, resulting in higher costs for borrowers. Moreover, Ashcraft and Santos [2009]’s findings suggest that a lead lender’s ability to hedge (through CDS availability) potentially increases information asymmetry within the syndicate and increases the cost of bank debt for firms that require more intense monitoring.

Chapter 3: Hypotheses Development

The theoretical and empirical evidence, to date, presents mixed results on the effect of CDSs on lenders’ incentives to monitor. Studies argue the empty creditor problem will lead to a decrease in lender monitoring. In summary, after a credit risk transfer to another party, the lender has less incentive to monitor a loan (and the borrower’s default risk) than if the bank had retained the credit risk (Gorton and Pennachi

[1995], Duffie [2007]). Moreover, because CDS use is not visible, moral hazard problems may escalate (Morrison[2005]). Other studies suggest CDSs will not lead to a decrease in monitoring due to CDS maturity (Duffee and Zhou [2001], Arping [2004]), equilibrium CDS pricing (Jarrow [2010]), bank reputation considerations (Gopalan,

Nanda, and Yermilli [2009]), or the importance of lender monitoring in reducing the agency costs of debt (Jensen & Meckling [1976]).

Lenders’ monitoring is interesting because banks are superior monitors (Diamond

[1984]), and therefore banks acting as delegated monitors should result in an efficient contracting outcome. Lenders will balance the costs and benefits of credit risk transfer, including a consideration of the costs of monitoring with the cost of CDSs. These include actual costs, as well as reputational costs, as Gopalan, Nanda, and Yermilli

[2009] suggest that banks with previous loan defaults/bankruptcy face reputational consequences in the syndicated loan market. I attempt to provide some additional insight 17 into the role of loan contracting and lender monitoring in the presence of an active CDS market.

Private lenders should incorporate monitoring provisions to facilitate efficient contracting. Accounting-based financial covenants are maintenance-based covenants, which transfer control to lenders and allow intervention upon borrower deterioration.17

Therefore, covenants help mitigate borrower moral hazard by minimizing manager/shareholder incentives to engage in value reducing risk shifting (Jensen and

Meckling [1976], Smith and Warner [1979]). In addition, covenants serve to mitigate lead lender moral hazard by providing lender incentives to monitor the borrower (Rajan and Winton [1995], Mora [2010]), as well as helping to manage conflicts of interest between the lead lender and syndicate participants (Dass, Nanda and Wang [2011]).

If the availability of a CDS market for a given borrower decreases the lenders’ incentives to monitor, consistent with the empty creditor hypothesis, I expect the lender to include fewer monitoring provisions, in the form of covenants, in the loan contract after a CDS market for the borrower emerges. On the other hand, if syndicate members recognize the potential impact of CDS availability on the lead lender’s incentives to monitor, the syndicate members may demand more monitoring provisions in the loan contract, ex-ante. I assume an additional covenant increases the likelihood of a covenant violation and therefore the opportunity for lender intervention. More covenants would not only provide the lead lender incentives to monitor, but also to give syndicate

17 As I am concerned with lender monitoring, I chose to focus on maintenance based covenants which require compliance on a regular basis or face the risk of lender intervention, as opposed to incurrence based covenants which only require compliance with certain metrics prior to specific borrower action (i.e. new debt issuance). 18 participants more control over the loan as a lead lender must receive approval from the syndicate for action taken following covenant violation. Therefore, I state the hypothesis in the null.

H1: CDS trading on a borrowing firm does not affect monitoring provisions included in private lending agreements, in the form of financial covenants.

Secondly, covenant slack provides insight into expected monitoring of the borrower because even modest deteriorations in performance allow the lender to intervene. Loan covenants tend to be fairly sticky for a given borrower (which will bias against finding any change associated with CDS trading), but covenant thresholds and tightness vary more over time (Demiroglu and James [2010]). If lenders decrease monitoring, I would expect an increase in covenant slack following CDS availability.

Alternatively, if syndicates demand increased control over the loan to mitigate the costs of information asymmetry within the syndicate, I would expect a decrease in covenant slack associated with CDS availability.

H2: CDS trading on a borrowing firm does not affect monitoring provisions included in private lending agreements, in the form of covenant slack.

Chapter 4: Research Design

Sample Selection

I begin with the CDS data from Credit Market Analysis (CMA). I retrieve the first day on which a CDS trades for all single-name, US corporate reference entities with

US dollar denominated CDSs. As the CDS market is an OTC market with no central clearing house during this time period, the first day a CDS is covered by a database is only a rough proxy for the onset of CDS trading; however, it does represent the availability and visibility of the price of credit insurance.18 In order to help ensure that I capture the earliest date possible, I verify the first CDS trading dates with Bloomberg and use the earlier of the two dates.

For my treatment sample, I match all non-financial, CDS referenced entities to

Compustat for annual accounting variables and then retrieve all US syndicated, dollar denominated private loan agreements from LPC Dealscan for the five years surrounding

CDS market onset.19,20 I require at least one Dealscan loan package in the two years prior

18 More recently, loan-only credit default swaps (LCDS), an evolution of the “plain vanilla” CDS, began trading when the ISDA first published standardized LCDS documentation in 2006. This introduction is well after the single-name CDS market had been established. Evidence suggests that banks used single- name, US corporate CDSs to hedge loan portfolio exposures prior to time period in which LCDS became traded (Partnoy and Steel [2007]). Therefore, I consider the introduction of a CDS market for a corporate borrower to be the appropriate event, especially as it relates to syndicate lenders’ knowledge of CDS availability. 19 I use the term loan package and deal interchangeably. 20 I would like to thank Chava and Roberts [2008] for their link file of Dealscan facilities to Compustat gvkeys. 20 to, and one in the two years following, the onset of CDS trading for a given referenced entity, as well as the availability of financial information and loan information. I exclude loans in the year of CDS onset. This results in 2,085 loan packages from 1999-2009 for

311 reference entities with CDS onsets between 2001 and 2007 (see Table 1). As covenants are included at the loan package (deal) level, I conduct all my empirical analyses at the loan package level.

I utilize two control samples. For the full control sample, I pull all US syndicated, dollar denominated private loan packages to non-financial firms from Dealscan for the period 1999-2009, which match to Compustat and do not have any CDS trading over the sample period. This sample selection returns 8,035 loans from 2,979 unique firms. For the matched sample, I use a propensity score matching method to match each treatment firm (with CDS market onset) to a control firm with no CDS trading over the 5-year window surrounding the year of CDS onset for the treatment firm.21 I am able to match

181 treatment firms with 181 control firms, resulting in 741 loan packages from treatment firms and 663 loan packages from control firms over the relevant 5-year periods.

The impact of traded CDSs on lender monitoring provisions included in loan contracts

For my first analysis, I examine changes in monitoring provisions around the onset of CDS trading by comparing loan contracting before and after the onset of CDS trading for a given reference entity/borrower, with a full sample of firms that do not have a CDS market. Because CDS availability may be correlated with other risk factors that

21 I match without replacement. 21 may also cause changes in loan contracting and monitoring provisions, I conduct an additional analysis to address this endogeneity. I apply a difference-in-difference design to a propensity score matched sample of firms without CDS trading over the 5-year window.

I select the number of financial covenants and net worth covenant slack as my proxies for lender monitoring provisions included in loan contracts. I base my model of the number of covenants on that provided in Costello and Wittenberg-Moerman [2010], which considers the impact of borrowers’ accounting quality on the number of financial covenants used in loan contracting. Following Ashcraft and Santos [2009], I include a dummy variable (CDS Firm) for any firm which exhibits CDS trading at any time over my sample period, in order to control for fundamental differences between CDS firms and non-CDS firms. Then, I include a dummy variable for any firm-year for which I observe actual CDS trading; therefore, CDS Trading is the variable of interest.22 Loan specific controls include Institutional Investor, Revolver, Interest Rate, Secured, Loan

Size, Maturity and Performance Pricing.

Because new lead lender-borrower relationships contain additional information asymmetry and therefore may require more monitoring [Sufi 2007], I include a dummy variable New Relationship. In addition, I include the number of lenders (# Lenders), as information asymmetry within the syndicate and coordination problems in monitoring may increase in the number of lenders, as well as Syndicate Relationship, which proxies for the past experience of syndicate participants in deals with the lead lender. To proxy

22 Mentioned previously, I exclude the loans in the year of CDS availability from my sample. 22 for lender reputational costs which may impact monitoring, I include a variable Lender

Reputation (Bushman and Wittenberg-Moerman [2011], Lin and Paravisini [2011], Ross

[2010]). I also include firm specific controls likely to impact the number of covenants.

Small, less profitable and higher leveraged firms experience higher agency costs of debt, which may impact covenants. Lastly, Costello and Wittenberg-Moerman [2010] show that accounting quality positively impacts the number of financial covenants. I include loan-year fixed effects and cluster by firm, and estimate the following model:

[1] #Covenants = α +β1CDS Firm + β2CDS Trading + β3Institutional Investor +

β4Revolver + β5Interest Rate + β6Secured + β7Loan Size + β8Maturity +

β9Performance Pricing + β10#Lenders + β11New Relationship + β12Syndicate

Relationship + β13Lead Lender Reputation + β14Firm Size + β15Profitability +

β16Leverage + β17Credit Rating + β18Accounting Quality + ε

My second proxy for ex-ante, monitoring provisions is net worth covenant slack.23 The net worth covenant represents a fairly straight forward debt covenant calculation, which permits ease of comparison across firms. In addition, the net worth covenant more frequently leads to violations than other financial covenants (Beneish and

Press [1993], Chen and Wei [1993], Sweeney [1994]).

As number of covenants and covenant slack are both proxies for lender monitoring provisions, I expect their determinants to be similar, and therefore I apply a modified version of the above model for the covenant slack analysis. I add several

23 I would have also conducted an analysis of covenant slack using the current ratio; however, a small sample size precluded this additional analysis. 23 controls specific to the net worth covenant. I include a dummy variable to distinguish tangible net worth covenants from net worth covenants, one to account for income escalators, which adjust the covenant threshold over the life of the loan by a percentage of net income each year (Beatty, Weber, and Yu [2008]), and one for a stock issuance buildup, as these provisions are likely correlated with covenant slack. The OLS model includes loan-year fixed effects and clusters by firm.

[2] Net Worth Covenant Slack = α +β1CDS Firm + β2CDS Trading + β3Tangible Net

Worth + β4% Net Income Escalator + β5Stock Issuance Buildup + β6Covenant

Count + β7Institutional Investor + β8Revolver + β9Interest Rate + β10Secured +

β11Loan Size + β12Maturity + β13Performance Pricing + β14#Lenders + β15New

Relationship + β16Syndicate Relationship + β17Lead Lender Reputation + β18Firm

Size + β19Profitability + β20Leverage + β21Credit Rating + β22Accounting Quality

+ ε

To address endogeneity, I conduct a difference-in-difference analysis of treatment observations (CDS initiation) compared to a matched sample of control observations - firms without any CDS trading over the same 5 year period (2 years prior and 2 years post CDS onset). As the availability of CDS Trading is the treatment of interest, I build my propensity score model of the conditional probability of receiving treatment (CDS

Trading on a major database), given observable firm characteristics. I match each treatment firm to one control firm, without replacement. I model treatment (CDS introduction) following Saretto and Tookes [2011]; however, I substitute Bond Lehman

24 for Bond Turnover, in order to increase the size of my sample.24 The authors argue CDS determinants relate to trading demand from banks and speculators, an activity distinct from hedging and material in nature.25 I measure all explanatory variables in the year prior to CDS introduction.

[3] CDS Trading Onset = α +β1Bond Lehman + β2Rated + β3 Leverage + β4Debt

Maturity + β5Firm Size + β6Investment Grade + β7Earnings Volatility +

β8Abnormal Earnings + β9Book-to-Market + ε

Bond Lehman represents a firm’s eligibility for inclusion in the Lehman Brothers

Corporate Bond Index, based on the amount of bonds outstanding, and should positively relate to the likelihood of CDS introduction.26 Along these lines, the availability of an

S&P Long-term Issuer Rating (Rated) suggests significant debt exposure in the public markets and therefore increased likelihood of CDS trading. Saretto and Tookes (2011) show that leverage and maturity increase in CDS trading; therefore, I expect positive coefficients on Leverage and Debt Maturity. Due to potential adverse selection problems, CDSs tend to exist on larger, investment-grade firms (Firm Size and

Investment Grade). In addition, I expect Earnings Volatility and Abnormal Earnings may increase trading demand for CDSs. Lastly, growth opportunities may impact CDS

24 Bond Turnover requires bond trading data from TRACE, which only becomes reliable beginning in 2004. Therefore, I use Lehman Bond indices eligibility which Saretto and Tookes applied in an earlier version of their paper. 25 British Bankers Association [2006] estimates approximately 40% of CDS protection purchases occur as a result of banks’ trading activities. 26 I match my sample to Mergent FISD in order to determine the dollar amount of bonds outstanding. 25 availability, but similar to Saretto and Tookes [2011], I do not predict a direction of impact.

For the difference-in-difference analysis, I apply a modified version of equations

1 and 2 to the treatment and control groups, separately. Specifically, I replace CDS Firm and CDS Trading with a Post Period dummy variable. For each treatment and matched control firm, I set the Post Period dummy variable to one for the loans in the two years following CDS onset. I test the differences in all coefficients with a t-test.

Chapter 5: Main Results

The descriptive statistics for the full sample, as well as for non-CDS firms, CDS firms and CDS trading loan observations are presented in Table 2 Panels A and B, respectively. As to be expected, CDS firms are larger and take out larger loans with larger syndicates and at better rates. CDS firms borrow from more reputable lead lenders and are less likely to have a new relationship loan. In addition, on average, loan agreements to CDS firms contain fewer covenants and more net worth slack. The CDS trading observations represent a subsample of the CDS firm observations. Compared to all CDS firm observations prior to CDS trading, CDS trading observations appear to indicate CDS trading is associated with larger loans, longer maturities (consistent with

Saretto and Tookes [2011]) and stronger borrower-lead lender relationships (the firm borrows even more often from more reputable lenders and is less likely to initiate a new lead lender relationship). In addition, the loans are more often secured and more often contain performance pricing than prior to CDS trading.

Table 3 presents the covariate balance between the treatment and control firms resulting from the propensity score match. After requiring common support and truncating observations with propensity scores larger than .9 and smaller than.1, my sample consists of 181 treatment and 181 matched control firms. I obtain covariate balance for all determinants of CDS trading onset, aside from firm size. Therefore, I 27 include firm size in all my main regressions (following Ho, Imai, King, and Stuart

[2007]). Table 4 offers descriptive statistics for my treatment and matched control firms’ loans (741 and 663 observations, respectively). The treatment and control loans exhibit characteristics very similar to those for CDS and non-CDS firm loans presented in Table

2. Treatment loans tend to be larger, with less covenants and better rates, from larger syndicates with more reputable lead lenders. Treatment firms are larger and more profitable, as well as less likely to take out a loan with a new lead lender.

Table 5 presents the results of the main regression exploring the number of covenants for the full sample, multi-lender agreements (syndicates) and sole lender loans.

The variable of interest, CDS Trading, is positive and significant for the full sample.

Therefore, while CDS firms on average are less likely have an additional covenant, CDS trading on a firm increases covenant use. All control variables appear to be in the predicted direction, aside from New Relationship and Profitability. The coefficient on

New Relationship is negative and significant, indicating a loan with a new lender has less covenants, and the coefficient on Profitability is positive and significant suggesting more profitable firms (or potentially higher growth firms) have more covenants. Leverage and maturity are not significant; however, Costello and Wittenberg-Moerman [2010] report similar findings for those variables. I also confirm Costello and Wittenberg-Moerman

[2010]’s finding that worse accounting quality (proxied by higher discretionary accruals in my study) relates to less covenants.

As I find an increase in covenants post-CDS trading, I need to investigate whether the increase in covenant use is consistent with increased information asymmetry within

28 the syndicate. If the lead lender increases covenants to signal to other syndicate members a continued commitment to monitor in the presence of CDS availability or if syndicate members demand more covenants to increase their control over the loan, I would expect my results to be stronger for syndicated loans. Moreover, I would expect the result to disappear in the case of sole lender loans. Table 5 also presents these results for the syndicate and sole lender subsamples. Consistent with an information asymmetry story, the coefficient on CDS Trading is larger and significant at the .01 level for the syndicate subsample. CDS Trading becomes negative and insignificant for the sole lender subsample; however the difference in coefficients between the subsamples is not significant.

Table 6 reports the results of the matched sample analysis. Treatment firms experience a positive and significant increase in the post period, while control firms display a negative, but insignificant, decline in covenants in the post period. Moreover, this difference is statically significant; therefore, treatment firms display significantly larger increase in the number of covenants in the post-CDS trading period than do the matched control firms. The control variables report results similar to those in Table 5.

These results corroborate that CDS availability increases the use of covenants.

Moving to the second proxy, covenant slack, the analysis in Table 7 presents a coefficient on CDS Trading that is negative and significant. Therefore, CDS trading results in tighter covenants. In addition, tangible net worth covenants tend to have more slack, while the existence of a share issuance buildup results in less slack. Deals with more slack tend to contain less covenants, more institutional investors, and smaller loan

29 sizes. In addition, stronger past experience between syndicate members and the lead lender (therefore less information asymmetry within the syndicate) results in more slack.

Larger firms, with higher ratings and profitability, exhibit more slack. Opposite my prediction, leverage is negatively associated with covenant slack. Worse accounting quality (higher discretionary accruals) is associated with less slack.27 When I consider the syndicate and sole lender subsamples, I find results similar to the covenant analysis -

CDS Trading becomes slightly larger and maintains significance for the syndicate subsample; however the difference in coefficients between the syndicate and sole lender loans is not significant.

The matched sample analysis in Table 8 presents similar results. The difference- in-difference test suggests treatment firms experience a larger decrease in covenant slack post-CDS trading as compared to a matched sample of control firms with no CDS trading. In addition, several loan terms impact covenant slack differently for treatment and control loans. For treatment loans, tangible net worth covenants and syndicate size more positively relate to covenant slack, while previous syndicate relationships and accounting quality more negatively relate to covenant slack.

Considering these two results together, CDS trading increases the use of covenants and decreases covenant slack. In addition, these results appear stronger in the syndicate subsample. These findings suggest that CDS trading increases monitoring

27 This may seem to contradict the results in Table 5 that worse accounting quality results in less covenant use, consistent with Costello and Wittenberg-Moerman [2010]. The authors of that study argue that, if accounting numbers are less reliable (as proxied by an internal control weakness), lenders are less likely to use them in covenants. The same would be true in my study; however, once the covenant is included, worse accounting quality may result in less slack, to the extent that lenders believe high discretionary accruals represent noisy numbers (already include “slack”) and therefore grant the borrower less covenant slack. 30 provisions included in loan contracts due to increased information asymmetry within syndicates.

Chapter 6: Supplemental Analysis of Covenant Enforcement

While covenants, specifically covenant violations, give creditors the opportunity to intervene and influence borrower policies/and or loan terms, the extent of creditor action upon violation may vary. On one end of the continuum of potential lender action following covenant violation, lenders may choose to do nothing - a waiver is closer to this end of the spectrum. Dichev and Skinner [2002] show that lenders waive a majority of covenant violations, suggesting lenders may not always use covenant violations to intervene. Closer to the other end of the spectrum, lenders may use covenant violations to take action and exert influence over the borrower through both formal (contractual) and informal (i.e. management changes) means. Nini, Smith, and Sufi [2009] show that loan renegotiations due to covenant violations include more stringent financial and non- financial terms, in order to facilitate additional monitoring by lenders. Roberts and Sufi

[2009] show decreased debt issuance activity following covenant violations. In a related study, Nini, Smith, and Sufi [2011] provide evidence of informal influence on borrower’s corporate governance following covenant violations in the form of changes in investment and financing activities and CEO turnover.

To assess whether the availability of a CDS market for a borrower impacts lenders’ enforcement of monitoring provisions, I consider lender actions following covenant violations, specifically the likelihood of loan renegotiation in the spirit of Nini, 32 et al. [2009] who show renegotiated loans contain more stringent terms. Lenders could also enforce the covenant by calling the loan upon a covenant violation instead of renegotiating it. Calling a loan occurs very infrequently, and because I consider calling or renegotiating a loan as lender action upon covenant violation, I do not differentiate between the two for my analyses and refer to both as a renegotiation.28

For my covenant violation sample, I begin with the sample of all covenant violations from 1999-2008 per Nini, Smith, and Sufi [2011] and only keep quarterly observations with a new covenant violation. 29,30 I match these new covenant violations to my full covenant sample used in the previous analyses to add CDS, loan (Dealscan) and financial (Compustat) information. I define a renegotiation (or called loan) as a violation for which I observe a new loan initiation within 6 months, where the maturity of the original loan is after the initiation of the renegotiated loan and the firm was not in violation at the time the original loan was made.

Table 9 provides details of my sample of loan renegotiations following a covenant violation. The sample includes 1,611 covenant violations from 1,209 unique firms. Of those violations, I find 362 subsequent renegotiations (approximately 22% of all violations) from 341 unique firms. CDS firms, on average, experience a 45% rate of renegotiation following covenant violations; whereas non-CDS firms only experience a

21% renegotiation rate (the difference is significant at the .01 level). However, for CDS

28I should see a new loan following a covenant violation in both cases; however, in the case of a renegotiation, I expect to see a new loan with the same lender, and for a called loan, I expect to see a new loan with a different lender. 29 2009 is not included in their sample and therefore is not yet available on Amir Sufi’s website. 30 Please see the appendix of Nini, Smith, and Sufi [2011] for a description of the covenant violation data. Nini, et al. [2011] define a new covenant violation as a violation without a violation in the previous four quarters. 33 firms, the difference between renegotiation rates prior to CDS trading and after CDS trading begins is not significantly different. I also consider violations followed by renegotiations for my matched covenant sample. Unfortunately, I only have a sample of

28 violations between my treatment (15 violations) and control firms (13 violations) when using the matched sample from my previous covenant analyses. I present the descriptive statistics in Table 9, Panel B, but do not conduct a multivariate analysis.

For my full sample of covenant violations, I then model the likelihood of loan renegotiation. I apply the logit model from Roberts and Sufi [2009], supplemented with the CDS Firm and CDS Trading dummy variables. Roberts and Sufi [2009] find that, given a violation, creditors are less likely to take action for firms with low leverage ratios, a S&P credit rating, high cash balances, and high cash flow. The authors explain that creditors are less likely to take unfavorable action if the firm has additional debt capacity or alternate sources of financing. Therefore, in modeling renegotiation, I expect a positive coefficient on Leverage and negative coefficients on Rated, Cash Balance and

Cash Flow.

[4] Renegotiation = α +β1CDS Firm + β2CDS Trading + β3Leverage + β4Rated +

β5Cash Balance + β6Cash Flow + ε

I present the results of the renegotiation analysis in Table 10. CDS Firm is positive and significant, indicating that creditors are more likely to take action after covenant violations and renegotiate loans for these firms. However, CDS Trading is not significant. Rated is positive and significant, opposite of my prediction, but cash balance is negative and significant. These results suggest no change in covenant enforcement, or

34 the likelihood of a renegotiation following covenant violation, associated with CDS trading.

Chapter 7: Sensitivity Analyses

Impact of CDS Trading on the Relationship Between Number of Covenants and Lead Lender

The literature suggests lead lenders can commit to monitoring borrowers by holding a share of the loan (Gorton and Pennacchi [1995], Holmstrom and Tirole [1997],

Sufi [2007]). Other mechanisms, such as covenants, may also induce a lender to monitor

[Rajan and Winton [1995]), although the relationship between lead lender share and covenants is not quite clear. Dass, Nanda and Wang [2011] provide evidence that covenants potentially substitute for lead lender share, while Mora [2010] suggests that lead lender share and covenants serve as complements. As CDSs may allow the lead lender to lay off the credit risk associated with this lead lender share, this signal may no longer serve as a credible commitment device to syndicate participants. Lenders may use covenants as a substitute to credibly commit to monitor in the presence of a CDS market for the borrower, as well as allow syndicate participants to monitor the lead lender.

Alternatively, if CDSs increase the information asymmetry within the syndicate, the lender may face difficulty finding syndicate participants and have to hold a larger piece of the loan; however, the lender may still include covenants to help mitigate this additional information asymmetry. Therefore, in this latter case, lead lender share and covenants may appear as complements.

As lead lender share and covenants both potentially play an important role in lenders’ monitoring and information asymmetry within the syndicate, Table 11 explores the relationship between lead lender share and covenants, in the presence of a CDS market for the borrower. LPC Dealscan often reports lead lender share missing; therefore, this analysis includes a much smaller sample than my main analyses.

Panel A reports Equation [1] (Table 5) with an additional control for lead lender share, as well as interactions between Lead Lender Share and CDS Firm, as well as between Lead Lender Share and CDS Trading. Lead Lender Share is negative and significant for the full sample, suggesting that lead lender share and covenants serve as substitutes for non-CDS firms. Both interaction terms are positive, indicating this relationship may be positive for CDS firms and even more positive after CDSs begin trading; however neither term is significant. For the syndicate subsample, Lead Lender

Share is negative but now insignificant, and the interaction with CDS Firm is positive and significant. Therefore, for syndicated loans of CDS firms, lead lender share and covenants appear as complements; however, this relationship does not seem to change in the presence of CDS trading.

Panel B shows results of the matched sample analysis (Table 6) with Lead Lender

Share, Lead Lender Share*CDS Firm, and Lead Lender Share*CDS Trading. Treatment firms show a positive relationship between lead lender share and covenants while control firms report a negative relationship between lead lender share and covenants, somewhat consistent with Panel A, although neither coefficient is significant. Moreover, the post-

CDS trading period does not significantly impact this relationship in either case.

Panels A and B report mixed, and relatively weak findings. Therefore, I am hesitant to draw any inferences regarding the relationship between lead lender share and covenants and how this varies in the presence of CDS trading.

Impact of CDS Trading on Required Lenders

My previous analyses suggest syndicate participants increase covenants in order to mitigate potential lead lend moral hazard when a CDS market provides an opportunity to hedge a loan’s credit risk. Accounting-based, financial covenants serve as tripwires, which transfer control to the lenders upon violation. Additional covenants (through an increased likelihood of violation) give more control to the lending syndicate, because upon violation, the lead lender must receive approval from the syndicate as to the plan of action. While covenants increase the lead lender’s responsibilities to the syndicate, a related contractual provision is the Required Lenders percentage. The Required Lenders percentage is the percentage of lenders required (in dollar amount outstanding, not number of lenders) to approve changes to a loan, including granting waivers.31 If syndicates desire further control over monitoring, lenders should increase the percentage required to approve waivers. The most stringent would be 100%. In this case, every lender would have a say and could veto proposed lead lender action.

Table 12 presents the results of the analysis examining the impact of CDS trading on the Required Lenders percentage. I could not find a model of Required Lenders in previous literature. Therefore, for this robustness check, I apply the model from Table 5,

31 Loan agreements contain another voting percentage - the percentage of lenders required to approve major changes to the loan agreement (rate, maturity, amount). This clause is almost always 100%, and for my full sample, I see no variation in this term (Term Changes always 100%). 38 as I expect the determinants of covenants as a tool for increased syndicate control would also apply to the Required Lenders percentage.32 I conduct this analysis for the full sample and matched sample with available data. I also require Lead Lender Share, as a

Required Lenders percentage less than the Lead Lender Share would result in no syndicate vote and therefore these observations are discarded from the sample.

I find an increased Required Lenders percentage following CDS trading onset for both the full and matched samples. Some of the control variables report consistent signs with Table 5 predictions, while a few other control variables exhibit directions opposite to those in Table 5. This may be due to the Required Lenders percentage relating to coordination problems within syndicates, which would not be surprising and warrants further study. Overall, these results support the main result of the paper - that CDS availability increases information asymmetry within the syndicate and lenders respond by increasing loan provisions to enhance syndicate participants’ monitoring ability, specifically more covenants, less covenants slack and increased syndicate voting rights.

Credit rating downgrades following CDS availability

I conduct difference-in-difference analyses using a propensity score matched sample to address the endogeneity that CDS trading may be associated with changes in credit risk, which in turn, may impact loan structure or lender monitoring. However, I conduct an additional robustness check to addresses this concern. I collect credit rating changes in the year preceding, the year of, and the year following CDS trading onset, for

32 Required Lenders deserves greater investigation in the literature. 39 the full and matched samples.33 Table 13 presents the results of the logit analysis predicting downgrades/upgrades. For the full sample, CDS firms are more likely to experience downgrades and upgrades, but CDS trading actually decreases the likelihood of a downgrade and does not significantly impact the likelihood of an upgrade. Similarly, for my matched sample, treatment firms are more likely to receive a downgrade than are control firms; in addition, they are also more likely to receive an upgrade. The magnitude of the positive coefficient is larger for upgrades than downgrades. As my treatment firms are large, household name firms, I interpret these results as suggesting that these firms receive more attention from ratings agencies and therefore experience more ratings updates or changes than the control firms. Based on this robustness check, I believe these results suggest that CDS onset is not associated with a decline in borrower credit quality.

Impact of CDS availability on covenant inclusion

I argue that each additional covenant increases the likelihood of a covenant violation, which by increasing lender intervention, increases potential lender monitoring.

However, some argue that the presence of the first covenant represents an increase in monitoring and each additional covenant is inconsequential. Therefore, if lenders increase covenants to mitigate new information asymmetries within the syndicate, we should see more loans with covenants. I conduct a robustness check to see if my results hold under a probit model for covenants versus no covenants. In Table 14, I do find that

33 The table presented represents the results for the three years surrounding CDS onset; however, the results were similar for three windows separately (year prior to, year of, year following CDS availability). 40 lenders are more likely to include covenants in loans post-CDS trading, consistent with my main results.

Chapter 8: Conclusion

The empty creditor hypothesis alleges lenders will have decreased incentives to monitor the borrower when lenders are able to transfer credit risk through CDSs but retain the control rights associated with a loan. I study whether CDS availability impacts private lenders’ incentives to monitor corporate borrowers, when an active CDS market exists for the borrower. I conduct an event study around the onset of CDS availability for corporate borrowers and examine changes their private loan agreements. Specifically, I examine changes in financial covenants and covenant slack, which are ex-ante provisions included in debt contracts designed to facilitate lender monitoring.

Based on my results, I find no evidence of decreased lender monitoring, as alleged by the empty creditor hypothesis. However, I do observe an increased use of ex- ante monitoring provisions in loan contracts in the form of increased covenants and decreased covenants slack, when CDSs trade for a given borrower. Moreover, I find an increase in the syndicate percentage required to approve covenant waivers associated with CDS availability. These results suggest syndicate participants demand increased monitoring provisions in loan contracts to mitigate lead lender moral hazard in monitoring and increase the monitoring ability of syndicate participants, as CDS trading potentially increases information asymmetry within the syndicate.

While my results suggest changes in loan contracting around CDS availability to provide more lender incentives to monitor and a greater ability of syndicates to monitor, future research should study the outcome of these contracting changes by considering additional lender monitoring action, as well as future borrower performance, and how

CDS availability may impact these interactions between contracting and outcomes. For instance, covenants included in debt contracts may assist lenders in monitoring by either inducing the borrower to take action prior to a covenant violation or by allowing lenders to take action following a violation. Through the supplemental analysis of covenant enforcement, I begin to address whether lender action following a covenant violation changes with CDS availability, but the bigger picture question deserves a more thorough analysis.

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Appendix A: Variable Definitions

Variable Definition

CDS Variables

CDS Firm An indicator variable equal to one if the firm displays CDS

trading at any time during the sample period, zero otherwise.

CDS Trading An indicator variable equal to one for all years in the sample

period following the first year in which the firm displays CDS

trading, zero otherwise.

Dependent Variables

Covenant Count The number of financial covenants for the Dealscan deal

(package).

Net Worth Slack For a net worth covenant, the ratio of total stockholders’ equity

in the year prior to entering into a loan contract minus the

stated covenant amount, divided by total assets.

For a tangible net worth covenant, the ratio of total

stockholders’ equity less goodwill and intangibles in the year

prior to entering into a loan contract minus the stated covenant

amount, divided by total assets.

Renegotiation An indicator variable equal to one if the borrowing firm

displays another loan within 1 year of a covenant violation

with a new maturity greater than the previous maturity, zero

otherwise.

Deal Controls

Institutional Investor The weighted average of the presence of an institutional

investor over all facilities in a deal. Institutional investor is an

indicator variable equaling one if the facility type is term loan

B, C, or D (institutional term loans), zero otherwise.

Revolver The weighted average of the presence of a revolver in a deal.

Revolver is equal to one if the facility type is a revolver, zero

otherwise.

Interest Rate The weighted average of the interest rate of all facilities in a

deal.

Secured The weighted average of the security of all facilities in a deal.

Security is an indicator variable taking the value of one if the

facility is backed by collateral, zero otherwise.

Loan Size A logarithm of the loan’s total deal amount.

Maturity The weighted average of the maturity of all facilities in a deal.

The maturity of a facility is calculated as the number of

months between the issue date and the date when the facility

matures.

Performance Pricing The weighted average of the performance pricing provisions

included in a deal. Performance Pricing is an indicator

variable taking the value of one if the loan contract

incorporates a performance pricing provision, zero otherwise.

Syndicate Controls

# Lenders The number of participants in the deal.

New Relationship The weighted average of the lead lenders’ prior interaction

with the borrowing firm. New Relationship is an indicator

variable equaling one if the facility represents the first loan

between a lead lender and a borrowing firm, zero otherwise.

Syndicate A measure of information asymmetry within the syndicate, Relationship quantifying the past experience between the lead lender and

syndicate participants. The number of syndicate participants

that funded a deal with the main lead lender in the past 5 years,

deflated by the total number of syndicate participants. Lower

values indicate less experience and more information

asymmetry. For sole lender loans, the measure is set to 1 (no

information asymmetry).

Lead Lender The weighted average of the lead lenders’ reputation included Reputation in a deal. Lead Lender Reputation is an indicator variable

taking the value of one if the lead lender holding the largest

share is JP Morgan Chase, Bank of America or Citigroup (see

Ross 2010), zero otherwise.

Borrower Controls

Firm Size A logarithm of the borrower’s total assets (AT) in the year

prior to entering into the loan deal.

Profitability The ratio of earnings before extraordinary items to total assets

(IB/AT) in the year prior to entering into a loan contract.

Leverage The ratio of total liabilities to total assets (LT/AT) in the year

prior to entering into a loan contract. 34

Credit Rating S&P’s Long-term Issuer Credit Rating coded from 1 to 22 with

1 being the most creditworthy (AAA). For firms without an

SandP Long-term Issuer Credit Rating, I included a separate

category with the highest value (22).

Accounting Quality Absolute value of abnormal accruals in the year prior to

entering into a loan contract.

Net Worth Controls

Tangible Net Worth An indicator variable taking the value of one if the deal

includes a Tangible Net Worth covenant, zero otherwise.

34 I also used the ratio of long-term debt to total assets (lltq/atq) as an alternative measure of leverage. Because this calculation resulted in a greater number of missing observations, I chose to use the definition listed in the table. The choice of leverage measure does not materially affect my results. 54

% Net Income An indicator variable taking the value of one if the deal

Escalator includes a percentage of net income escalator in the net worth

covenant, zero otherwise.

Stock Issuance An indicator variable taking the value of one if the deal

Buildup includes a stock issuance buildup in the net worth covenant,

zero otherwise.

Propensity Score Model – Firm-level explanatory variables measured at t-1

Bond Lehman An indicator variable if the firm meets the minimum dollar

amount of bonds outstanding for inclusion in the Lehman

Brothers Corporate Bond Index, zero otherwise. Prior to July

1, 1999, this amount was $100 million. From July 1, 1999 to

October 1, 2003, this amount was $150 million, and from

October 1, 2003 to July 1, 2004, this amount was $200 million.

After July 1, 2004, this amount was $250 million.

Rated An indicator variable taking the value of one if S&P provides a

Long-term Issuer Credit Rating for the firm, zero otherwise.

Leverage The ratio of total liabilities to total assets (LT/AT).

Debt Maturity The weighted average maturity of long-term debt outstanding.

Book-to-Market The ratio of book equity to market equity

(TEQ/(CSHO*PRCC_F)).

Earnings Volatility The standard deviation of the first differences in EBITDA over

the four years preceding CDS onset.

Abnormal Earnings Change in operating earnings per share, divided by previous

year’s share price.

Investment Grade An indicator variable taking the value of one if S&P’s Long-

term Issuer Credit Rating for the firm is BBB- or better, zero

otherwise.

Sensitivity Analyses

Lead Lender Share The lead arranger’s allocation in a deal. In the case of multiple

lead lenders, the sum of all lead lenders’ allocations in a deal.

For sole lender deals, the lead lender share is set to 100%.

Required Lenders The percentage of the syndicate (in amount) required to

approve action upon a covenant violation from Dealscan.

CDS Trading Unique

Initiation Firms

2001 5

2002 81

2003 83

2004 98

2005 27

2006 11

2007 6

Total CDS Firms 311

Total Non-CDS Firms 2979

Table 1. CDS Trading Initiation by Year

Panel A–Full Sample Deal N Mean Min 50thPctl Max

CDS Firm 10120 0.2060 0.0000 0.0000 1.0000

CDS Trading 10120 0.1053 0.0000 0.0000 1.0000

Covenant Count 10120 1.7649 0.0000 2.0000 5.0000

Institutional Investor 10120 0.0856 0.0000 0.0000 1.0000

Revolver 10120 0.6196 0.0000 0.9113 1.0000

Interest Rate 10120 189.2604 17.5000 162.5000 705.0000

Secured 10120 0.5002 0.0000 0.5904 1.0000

Loan Size 10120 5.0702 0.6931 5.2845 8.3664

Maturity 10120 40.6449 4.0000 36.0000 92.1220

Performance Pricing 10120 0.4983 0.0000 0.5000 1.0000

# Lenders 10120 7.7739 1.0000 5.0000 41.0000

New Relationship 10120 0.5230 0.0000 1.0000 1.0000

Syndicate Relationship 10120 0.8176 0.0000 1.0000 1.0000

Lead Lender Reputation 10120 0.4130 0.0000 0.0000 1.0000

Firm Size 10120 6.8633 2.2373 6.8260 10.9367

Rating 10120 16.0212 4.0000 16.0000 22.0000

Profitability 10120 0.0086 (0.8942) 0.0349 0.2435

Leverage 10120 0.6131 0.1151 0.5988 1.7532

AQ 10120 0.2037 0.0005 0.0299 2.2078

Net Worth Slack 2017 0.1062 (1.2443) 0.0931 0.6304

Tangible Net Worth 2017 0.4080 0.0000 0.0000 1.0000

% Net Income Buildup 2017 0.2241 0.0000 0.0000 1.0000

Stock Issuance Buildup 2017 0.5389 0.0000 1.0000 1.0000

Continued

Table 2. Covenant and Covenant Slack Analysis: Descriptive Statistics

Table 2 Continued

Panel B-By

Group Non-CDS Firms CDS Firms CDS Trading

Deal Deal Deal

N Mean Min Median Max N Mean Min Median Max N Mean Min Median Max

Covenant Count 8035 1.963 0.000 2.000 5.000 2085 1.001 0.000 1.000 5.000 1066 0.990 0.000 1.000 5.000

Institutional Investor 8035 0.091 0.000 0.000 1.000 2085 0.064 0.000 0.000 1.000 1066 0.068 0.000 0.000 1.000

Revolver 8035 0.659 0.000 1.000 1.000 2085 0.469 0.000 0.444 1.000 1066 0.631 0.000 1.000 1.000

Interest Rate 8035 208.98 17.500 192.500 705.00 2085 113.261 17.500 62.500 705.00 1066 114.643 17.500 62.500 705.000

Secured 8035 0.579 0.000 1.000 1.000 2085 0.195 0.000 0.000 1.000 1066 0.216 0.000 0.000 1.000

Loan Size 8035 4.688 0.693 4.868 8.366 2085 6.542 0.693 6.620 8.366 1066 6.743 0.693 6.908 8.366

Maturity 8035 41.568 4.000 37.000 92.122 2085 37.089 4.000 36.000 92.122 1066 45.272 4.000 60.000 92.122

Performance Pricing 8035 0.498 0.000 0.500 1.000 2085 0.498 0.000 0.500 1.000 1066 0.561 0.000 1.000 1.000

# Lenders 8035 6.301 1.000 4.000 41.000 2085 13.448 1.000 12.000 41.000 1066 13.816 1.000 12.000 41.000

Sole Lender 8035 0.249 0.000 0.000 1.000 2085 0.035 0.000 0.000 1.000 1066 0.035 0.000 0.000 1.000

New Relationship 8035 0.570 0.000 1.000 1.000 2085 0.343 0.000 0.000 1.000 1066 0.292 0.000 0.000 1.000

Syndicate Relation 8035 0.811 0.000 1.000 1.000 2085 0.842 0.000 0.950 1.000 1066 0.870 0.000 0.959 1.000

Lead Lender Rep 8035 0.349 0.000 0.000 1.000 2085 0.660 0.000 1.000 1.000 1066 0.700 0.000 1.000 1.000

Firm Size 8035 6.269 2.237 6.351 10.937 2085 9.152 5.310 9.233 10.937 1066 9.423 6.821 9.501 10.937 Continued

Table 2 continued

Rating 8035 17.817 4.000 22.000 22.000 2085 9.100 4.000 9.000 22.000 1066 9.341 4.000 9.000 22.000

Profitability 8035 0.000 (0.894) 0.033 0.243 2085 0.040 (0.613) 0.041 0.243 1066 0.039 -0.613 0.044 0.243

Leverage 8035 0.596 0.115 0.573 1.753 2085 0.680 0.179 0.670 1.739 1066 0.689 0.199 0.673 1.739

AQ 8035 0.218 0.000 0.035 2.208 2085 0.150 0.000 0.018 2.208 1066 0.176 0.000 0.017 2.208

Net Worth Slack 1843 0.104 (1.244) 0.091 0.630 174 0.129 (0.366) 0.119 0.630 53 0.151 -0.171 0.149 0.570

Tangible Net Worth 1843 0.433 0.000 0.000 1.000 174 0.144 0.000 0.000 1.000 53 0.170 0.000 0.000 1.000

% Net Income 1843 0.222 0.000 0.000 1.000 174 0.247 0.000 0.000 1.000 53 0.170 0.000 0.000 1.000

0 Buildup

Stock Issuance 1843 0.558 0.000 1.000 1.000 174 0.339 0.000 0.000 1.000 53 0.208 0.000 0.000 1.000

Buildup

CDS Trading Onset = Firm Size + Leverage + BTM + Average Debt Maturity + Earnings Volatility +

Abnormal Earnings + Lehman Bond Indices Eligibility + Rated + Investment Grade + ε

Treatment Control Difference

Determinants

Firm Size 8.8423 8.3725 0.4698 ***

Leverage 0.6734 0.6501 0.0233

BTM 0.3025 -9.0821 9.3846

Average Debt Maturity 7.3878 7.296 0.0918

Earnings Volatility 0.0336 0.0308 0.0028

Abnormal Earnings 11.8929 9.3967 2.4962

Lehman Bond Indices Eligibility 0.8343 0.779 0.0553

Investment Grade 0.7901 0.779 0.0111

Rated 0.9837 0.9805 0.0032

Number of Firm Observations 181 181

Table 3. Propensity Score Matching: Determinants of CDS Trading Onset – Covariate

Balance

Variable Treatment Firms Control Firms

DealN Mean Min Median Max DealN Mean Min Median Max

Covenant Count 741 1.0459 0.0000 1.0000 4.0000 633 1.1485 0.0000 1.0000 4.0000

Institutional Investor 741 0.0613 0.0000 0.0000 1.0000 633 0.0748 0.0000 0.0000 1.0000

Revolver 741 0.4684 0.0000 0.4857 1.0000 633 0.4519 0.0000 0.4000 1.0000

Interest Rate 741 104.3977 17.5000 62.5000 705.0000 633 118.51 17.500 75.0000 705.00

Secured 741 0.1940 0.0000 0.0000 1.0000 633 0.2150 0.0000 0.0000 1.0000

Loan Size 741 6.3842 0.6931 6.3969 8.3664 633 5.9452 0.6931 5.9269 8.3664

2 Maturity 741 35.5699 4.0000 36.0000 92.1220 633 35.420 4.0000 36.0000 92.1220

Performance Pricing 741 0.5198 0.0000 1.0000 1.0000 633 0.4923 0.0000 0.3846 1.0000

Lead Lender Share 302 27.3685 5.3333 18.0000 100.0000 238 33.465 5.3333 22.2222 100.0000

# Lenders 741 13.0918 1.0000 12.0000 41.0000 633 10.780 1.0000 9.0000 41.0000

Sole Lender 741 0.0324 0.0000 0.0000 1.0000 633 0.0379 0.0000 0.0000 1.0000

New Relationship 741 0.3339 0.0000 0.0000 1.0000 633 0.3946 0.0000 0.0000 1.0000

Syndicate Relationship 741 0.8289 0.0000 0.9412 1.0000 633 0.8221 0.0000 0.9231 1.0000

Continued

Table 4. Covenant and Covenant Slack Analysis: Descriptive Statistics for Matched Sample 62

Table 4 continued

Lead Lender Reputation 741 0.6426 0.0000 1.0000 1.0000 633 0.5539 0.0000 1.0000 1.0000

Firm Size 741 8.9216 6.7525 9.0210 10.9367 633 8.4270 6.1335 8.1626 10.9367

Rating 741 8.8711 4.0000 8.0000 22.0000 633 9.2670 4.0000 9.0000 22.0000

Profitability 741 0.0420 (0.4299) 0.0401 0.2435 633 0.0271 (0.8942) 0.0372 0.2435

Leverage 741 0.6691 0.1985 0.6546 1.7394 633 0.6522 0.2157 0.6481 1.7532

AQ 741 0.1463 0.0005 0.0190 2.2078 633 0.0549 0.0005 0.0146 2.2078

Net Worth Slack 70 0.1510 (0.2835) 0.1405 0.6304 73 0.1039 (0.3656) 0.1010 0.3301

Tangible Net Worth 70 0.1714 0.0000 0.0000 1.0000 73 0.1370 0.0000 0.0000 1.0000

3 % Net Income Buildup 70 0.2143 0.0000 0.0000 1.0000 73 0.1918 0.0000 0.0000 1.0000

Stock Issuance Buildup 70 0.3714 0.0000 0.0000 1.0000 73 0.5205 0.0000 1.0000 1.0000

#Covenants= α+ β1CDS Firm+ β2CDS Trading+ ζDeal Controls + κSyndicate Controls + λBorrower Controls +ε Full Syndicates Sole Lender

Intercept 0.890 *** 0.763 *** 0.640

(0.165) (0.196) (0.400)

CDS Variables CDS Firm -0.204 *** -0.213 *** -0.042

(0.047) (0.047) (0.227)

CDS Trading 0.160 *** 0.197 *** -0.033

(0.053) (0.055) (0.255)

Deal Controls Institutional Investor 0.637 *** 0.623 *** 0.219

(0.075) (0.080) (0.231)

Revolver 0.046 0.059 -0.025 (0.036) (0.040) (0.081)

Interest Rate 0.001 *** 0.001 *** 0.000

(0.000) (0.000) (0.000)

Secured 0.802 *** 0.810 *** 0.563 ***

(0.039) (0.044) (0.085)

Loan Size 0.041 ** 0.041 ** 0.103 *** (0.017) (0.018) (0.040)

Maturity 0.000 0.001 0.000 (0.001) (0.001) (0.002)

Performance Pricing 1.302 *** 1.404 *** 0.841 ***

(0.032) (0.035) (0.074)

Continued

Table 5. Impact of CDS Trading on the Number of Covenants

Table 5 continued

Syndicate Controls

# Lenders 0.015 *** 0.015 ***

(0.002) (0.002) New Relationship -0.068 *** -0.060 ** -0.138 *

(0.026) (0.026) (0.077)

Syndicate Relationship -0.137 *** -0.105 **

(0.049) (0.053) Lead Lender -0.092 *** -0.070 ** -0.165 *

Reputation

(0.030) (0.031) (0.100)

Borrower Controls Firm Size -0.161 *** -0.169 *** -0.146 *** (0.016) (0.018) (0.035)

Rating 0.018 *** 0.009 *** 0.062 ***

(0.003) (0.003) (0.011)

Profitability 1.044 *** 0.682 *** 1.178 ***

(0.110) (0.189) (0.137)

Leverage -0.092 -0.128 * -0.133

(0.062) (0.072) (0.108)

AQ -0.168 *** -0.157 *** -0.129 (0.029) (0.030) (0.082)

Loan Observations 10,120 8,048 2,072 Number of Firms 3,290 2,372 1,468 R2 44.42% 51.49% 20.93%

#Covenants = α +β1Post CDS Trading+ ζDeal Controls + κSyndicate Controls + λBorrower Controls + ε Treatment Control Difference

Intercept 1.464 ** -0.129 (0.574) (0.588)

CDS Variables Post CDS Trading 0.256 * -0.024 **

(0.147) (0.096)

Deal Controls Institutional Investor 0.281 0.846 *** (0.330) (0.249)

Revolver 0.027 0.177

(0.113) (0.142)

Interest Rate 0.001 0.000

(0.000) (0.001)

Secured 0.603 *** 0.692 *** (0.157) (0.213)

Loan Size 0.068 * 0.094 *

(0.036) (0.055)

Maturity -0.002 -0.001

(0.003) (0.003)

Performance Pricing 1.122 *** 1.361 ***

(0.088) (0.112)

Continued

Table 6. Impact of CDS Trading on Number of Covenants: Matched Sample

Table 6 continued

Syndicate Controls

# Lenders 0.012 ** 0.020 **

(0.005) (0.008)

New Relationship -0.060 0.064

(0.068) (0.082)

Syndicate Relationship 0.086 -0.018

(0.133) (0.178)

Lead Lender Reputation -0.102 -0.055

(0.071) (0.097)

Borrower Controls Firm Size -0.244 *** -0.188 ***

(0.044) (0.063)

Rating 0.022 * 0.026 (0.013) (0.017)

Profitability -1.311 * -0.686

(0.723) (0.846)

Leverage -0.114 -0.096

(0.241) (0.345)

AQ -0.136 ** -0.235 (0.059) (0.146)

Observations 741 633 1,374 R2 49.70% 62.88%

Net Worth Covenant Slack = α +β1CDS Firm + β2CDS Trading + γNet Worth Controls + ζDeal Controls + κSyndicate Controls

+ λBorrower Controls + ε

Full Syndicates Sole Lender

Intercept 0.195 *** 0.051 0.286 ** (0.058) (0.082) (0.118)

CDS Variables

CDS Firm -0.010 -0.026 0.020

(0.019) (0.020) (0.041)

CDS Trading -0.037 * -0.043 * -0.036

(0.022) (0.023) (0.049)

Net Worth Controls

Tangible Net Worth 0.066 *** 0.083 *** 0.028 *

(0.009) (0.012) (0.015)

% Net Income Buildup -0.007 -0.003 -0.009

(0.012) (0.015) (0.019)

Share Issuance Buildup -0.035 *** -0.033 ** -0.031 *

(0.011) (0.013) (0.016)

Deal Controls

Covcount -0.012 *** -0.004 -0.009

(0.004) (0.005) (0.006)

Institutional Investor 0.057 * 0.101 *** -0.041

(0.034) (0.039) (0.057)

Revolver 0.008 0.015 0.007

(0.013) (0.018) (0.021)

Interest Rate 0.000 0.000 0.000

(0.000) (0.000) (0.000)

Continued

Table 7. Impact of CDS Trading on Covenant Slack

Table 7 continued

Secured 0.014 0.016 0.012

(0.010) (0.012) (0.020)

Loan Size -0.031 *** -0.018 * -0.031 **

(0.008) (0.010) (0.013)

Maturity 0.000 0.000 0.001 *

(0.000) (0.000) (0.000)

Performance Pricing -0.003 0.007 -0.009

(0.010) (0.013) (0.016)

Syndicate Controls

# Lenders 0.001 -0.001

(0.001) (0.001)

New Relationship 0.000 -0.003

(0.008) (0.009) -0.007

Syndicate Relationship 0.030 ** 0.005 (0.016)

(0.014) (0.017)

Lead Lender Reputation -0.003 0.009 -0.031

(0.009) (0.009) (0.031)

Borrower Controls

Firm Size 0.045 *** 0.054 *** 0.033 ***

(0.007) (0.011) (0.010)

Rating -0.002 * 0.000 -0.003

(0.001) (0.001) (0.003)

Profitability 0.086 * 0.219 ** 0.090 *

(0.045) (0.107) (0.053)

Leverage -0.400 *** -0.352 *** -0.425 ***

(0.030) (0.035) (0.046)

Continued

Table 7 continued

AQ -0.041 ** -0.027 * -0.095 **

(0.016) (0.014) (0.043)

Observations 2,017 1,372 645

Number of Firms 1,225 781 526

R2 30.59% 30.55% 33.68%

Net Worth Covenant Slack = α +β1Post CDS Trading + γNet Worth Controls + ζDeal Controls + κSyndicate Controls +

λBorrower Controls + ε

Treatment Control Difference

Intercept 0.533 *** 0.159

(0.188) (0.210)

CDS Variables

Post CDS Trading -0.214 *** -0.002 **

(0.069) (0.011)

Net Worth Controls

Tangible Net Worth 0.211 *** 0.073 * **

(0.044) (0.042)

% Net Income Buildup 0.055 -0.004

(0.034) (0.030)

Share Issuance Buildup -0.017 -0.021

(0.027) (0.039)

Deal Controls

Covcount -0.035 -0.017

(0.020) (0.022)

Institutional Investor -0.195 0.037 *

(0.121) (0.106)

Revolver 0.073 -0.060

(0.053) (0.058)

Interest Rate 0.000 0.000

(0.000) (0.000)

Secured 0.063 0.001

(0.035) (0.033)

Continued

Table 8. Impact of CDS Trading on Covenant Slack: Matched Sample

Table 8 continued

Loan Size 0.005 0.018

(0.018) (0.019)

Maturity -0.002 0.000

(0.001) (0.001)

Performance Pricing -0.122 ** 0.029

(0.051) (0.019)

Syndicate Controls

# Lenders 0.001 -0.003 *** ***

(0.001) (0.001)

New Relationship 0.033 0.012

(0.027) (0.025)

Syndicate Relationship -0.021 0.024 *

(0.055) (0.038)

Lead Lender Reputation 0.081 *** 0.044 **

(0.025) (0.019)

Borrower Controls

Firm Size 0.027 0.031

(0.018) (0.020)

Rating -0.009 0.003

(0.009) (0.008)

Profitability 0.584 * 0.037

(0.330) (0.193)

Leverage -0.498 *** -0.454 ***

(0.149) (0.093)

AQ -0.128 -0.059 * **

(0.083) (0.031)

Continued

Table 8 continued

Loan Observations 70 73 143

R2 85.20% 76.89%

Panel A - Full Sample

Violations Renegotiation Total Percentage of Total (Unique Firms in 0 1 Violations Violations Renegotiated Parentheses)

Total Non-CDS Firm 1208 329 1537

Observations 21.41%

(1148)

Total CDS Firm 41 33

Observations 74 44.59%

(61)

Prior to CDS

Trading (0) 21 15 36 41.67%

(34)

CDS Trading (1) 20 18 38 47.37%

(36)

Total Violations 1249 362 1611 22.47%

(998) (341) (1209)

Continued

Table 9. Renegotiation Analysis: Descriptive Statistics of New Covenant Violations

Leading to Renegotiation

Table 9 continued

Panel B - Matched Sample

Violations Renegotiation Total Percentage of Total (Unique Firms in 0 1 Violations Violations Renegotiated Parentheses)

Control Firms - Violations 7 6 13 46.15%

(12)

Prior to CDS

Trading (0) 2 4 6

CDS Trading (1) 5 2 7

Treatment Firms - 7 8

Violations 15 53.33%

Prior to CDS

Trading (0) 4 4 8 50.00%

CDS Trading (1) 3 4 7 57.14%

Total Violations 14 14 28 50.00%

(14) (13) (27)

Renegotiation (1/0) = α + β1CDS Firm + β2CDS Trading + β3Leverage + β4Rated + β5Cash + β6Cash Flow

+ ε

Intercept -1.2445 ***

(0.206) CDS Firm 0.4157 **

(0.360) CDS Trading 0.3116 (0.460) Rated 0.6884 ***

(0.148) Leverage -0.0122 (0.198) Cash Balance -1.0772 *

(0.635) Cash Flow -0.1483 (1.472)

Covenant Violation Observations 1,611 Table 10. Renegotiation Analysis: Renegotiation of New Covenant Violations

Panel A

#Covenants = α +β1CDS Firm + β2CDS Trading + β3Lead Lender Share +β4Lead Lender Share*CDS Trading + ζDeal

Controls + κSyndicate Controls + λBorrower Controls + ε

Full Syndicates CDS Variables CDS Firm -0.359 *** -0.377 *** (0.093) (0.098) CDS Trading 0.105 0.188 (0.106) (0.119) Lead Lender Share Lead Lender Share -0.230 ** -0.110 (0.093) (0.143) Lead Lender Share*CDS Firm 0.361 0.659 **

(0.225) (0.296) Lead Lender Share*CDS Trading 0.040 0.202 (0.259) (0.383)

Controls Yes Yes Loan Observations 5,101 3,029 Number of Firms 2,617 1,496 R2 30.20% 41.78% Continued

Table 11. Impact of CDS Trading on the Relationship Between Number of Covenants and Lead Lender Share

Table 11 continued

Panel B

#Covenants = α +β1Post CDS Trading + β2Lead Lender Share +β3Lead Lender Share*CDS Trading + ζDeal Controls + κSyndicate Controls + λBorrower Controls + ε

Treatment Control Difference CDS Variables Post CDS Trading 0.250 -0.170 *

(0.252) (0.208)

Lead Lender Share Lead Lender Share -0.172 -0.261 (0.110) (0.250) Lead Lender Share*Post CDS Trading -0.239 -0.500 (0.392) (0.523)

Controls Yes Yes Loan Observations 302 238 Number of Firms 132 74 R2 34.65% 54.30%

Required Lenders % = Post CDS Trading + Deal Controls + Syndicate Controls + Borrower Controls + ε

Full Sample Matched Sample

Treatment Control Diff

Intercept 67.733 *** 52.670 *** 54.240 ***

(2.517) (8.488) (8.791)

CDS Variables

CDS Firm 1.381 **

(0.691)

Post CDS Trading 1.397 ** 0.347 -2.465 ** *

(0.635) (1.170) (1.210)

Deal Controls

Covenant Count 0.222 -0.491 -0.262

(0.179) 0.583 0.615

Institutional Investor -1.836 -5.351 0.557

(1.225) (3.848) (3.949)

Revolver 0.654 -1.266 2.416 *

(0.499) (1.358) (1.581)

Interest Rate 0.000 0.014 0.001

(0.002) (0.013) (0.007)

Secured 1.634 *** 2.073 0.145

(0.421) (1.628) (0.981)

Loan Size -0.723 ** -1.135 ** -1.397 **

(0.290) (0.555) (0.683)

Maturity -0.052 *** 0.039 -0.058 * **

(0.011) (0.037) (0.030)

Performance Pricing 0.173 1.078 1.707

(0.555) (1.977) (1.200)

Continued

Table 12. Impact of CDS Trading on Covenant Inclusion 79

Table 12 continued

Syndicate Controls

# Lenders -0.108 *** -0.045 0.153 *

(0.026) (0.072) (0.092)

New Relationship -0.992 *** 0.509 1.598

(0.294) (0.951) (0.992)

Syndicate -1.439 ** 0.752 6.282 **

Relationship

(0.592) (2.627) (3.103)

Lead Lender -0.042 -0.151 -1.573

Reputation

(0.314) (0.677) (1.145)

Borrower Controls

Firm Size -0.926 *** 0.192 -0.265

(0.232) (0.589) (0.817)

Rating 0.007 0.075 -0.372

(0.036) (0.155) (0.229)

Profitability -1.343 10.291 -6.541

(1.923) (8.752) (8.088)

Leverage -1.371 * -0.690 9.204 *** **

(0.800) (2.795) (3.155)

AQ 2.408 *** 1.281 1.043

(0.405) (1.351) (1.171)

Observations 2403 228 166 394 Number of Firms 1251 110 58 168 2 R 26.76% 15.31% 30.30%

Panel A – Full Sample

Downgrade/Upgrade = α +β1CDS Firm + β2CDS Window + ε

Downgrades Upgrades

Intercept -1.0374 *** -2.336

(0.042) (0.054)

CDS Variables

CDS Firm 0.851 *** 0.201 **

(0.071) (0.102)

CDS Window (year t-1 to year t+1) -0.225 ** 0.061

(0.096) (0.153)

Number of Firm-year Observations 9,023 9,023

Panel B - Matched Sample

Downgrade/Upgrade = α +β1Treatment Firm + ε

Downgrades Upgrades

Intercept -0.930 *** -2.7492 ***

(0.165) (0.249)

Treatment Firm 0.4334 ** 0.689 **

(0.198) (0.301)

Number of Firm-year Observations 909 909 Table 13. Credit Rating Changes Surrounding CDS Availability

Covenants (1/0) = α +β1CDS Firm + β2CDS Trading + ζDeal Controls + κSyndicate Controls +

λBorrower Controls + ε

Covenant (1/0)

Intercept -0.096 (0.195) CDS Variables CDS Firm -0.079 (0.074) CDS Trading 0.174 * (0.092) Deal Controls Institutional Investor 0.380 *** (0.089) Revolver 0.139 (0.050) Interest Rate 0.000 ** (0.000) Secured 0.937 *** (0.054) Loan Size 0.003 (0.024) Maturity -0.004 (0.001) Performance Pricing 2.012 *** (0.057) Continued

Table 14. Impact of CDS Trading on Covenant Inclusion

Table 14 continued

Borrower Controls

Firm Size -0.148 *** (0.022) Rating 0.011 ** (0.004) Profitability 0.514 *** (0.129) Leverage -0.048 (0.080) AQ -0.079 * (0.045)

Loan Observations 10,120 Number of Firms 3,290