Corporate loan securitization and the standardization of financial covenants*

Zahn BozanicThe Ohio State University

mailto:mbozanic.1@osu.edu

Maria LoumiotiUSC Leventhal School of Accounting

loumioti@marshall.usc.edu

Florin P. VasvariLondon Business School

fvasvari@london.edu

September 2014

Abstract

We apply textual analysis on a large sample of financial covenant definitions tomeasure covenant standardization and find that securitized corporate loans includemore standardized covenants. We document that financial covenantstandardization increases the liquidity of securitized loans in the primary andsecondary loan market. Consistent with a decrease in illiquidity premiums,covenant standardization decreases the cost of securitized loans without beingassociated with a lower likelihood of default. We also find that covenantstandardization is associated with less disagreement between credit ratingagencies, potentially contributing to the higher liquidity of securitized loans. Ourfindings suggest that financial covenant standardization is positively related tocorporate loan securitization and has a significant impact on loan liquidity.

Keywords: Securitization, Financial Covenants, Syndicated Loans,Standardization

JEL Classifications: G17, G21, G32, M41

* We are grateful to Panos Patatoukas and KR Subramanyam and workshop participants at London Business School,Stockholm School of Economics, University of Southern California and University of Oulu (Finland) for theirhelpful comments and suggestions. We thank Blake Sainz for his excellent research assistance. Loumiotiacknowledges financial support from Leventhal School of Accounting. Vasvari acknowledges funding from theLondon Business School RAMD Fund. All remaining errors are our own.

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1. Introduction

Although there is significant empirical evidence on the widespread use of financial

covenants in syndicated loan contracts, these covenants are typically written on a relatively small

set of accounting numbers. This is puzzling, given the large volume of accounting information in

borrowers’ financial statements and lenders’ sophistication (Skinner, 2011). In this paper, we

provide insights into this issue by exploring whether the securitization of syndicated loans

through collateralized loan obligations (“CLOs”) increases the standardization of accounting

information used in financial loan covenants. We define standardization as the process of

increasing the similarity and comparability of financial covenant definitions (e.g., De Franco,

Kothari and Verdi, 2011). In addition, we explore the real effects of financial covenant

standardization and investigate whether standardization affects the liquidity of securitized loans

by decreasing information processing costs.

Collateralized loan obligations (“CLOs”) are special purpose vehicles that are set up by an

investment bank (“CLO arranger”) and an investment management firm (“CLO manager”).1

CLOs’ investment strategy is to profit from the difference in the average interest rate on the

corporate loans they buy (“CLO collateral”) and the interest rate on the debt issued to finance the

acquisition of these loans (“CLO notes”). To achieve this interest rate differential, CLOs invest

in a large and highly diversified pool of corporate loans. Consequently, a CLO ends up holding

small tranches in more than 200 corporate loans from various borrowers covering 15 to 25

different industries. The large amount of accounting information that describes financial loan

covenants and determines creditors’ control rights associated with securitized loans can generate

1 CLOs grew to become the dominant institutional investor in the syndicated loan market reaching a 60 percentmarket share and securitizing syndicated loans with a total value of about $100 billion annually before the creditcrisis. Thereafter, by 2013, the level of annual investments in corporate loans by CLOs nearly reached pre-crisislevels (Standard and Poor’s, 2014).

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significant transaction costs.2 Such costs increase with the extent to which financial covenant

structures become more complex and borrower-specific since assessing these covenants involves

more extensive information collection, monitoring and enforcement efforts.

However, certain mechanisms specific to the CLO operating model constrain these costs.

First, the selection of leveraged corporate loans as eligible CLO collateral relies on specific and

predetermined diversification criteria on borrowers’ industry and geography as well as loans’

maturity and rating category. These restrictions are imposed at the CLO set-up stage by credit

rating agencies that rate CLO notes to diversify away the idiosyncratic credit risk of each

individual loan investment. Thus, covenant-based metrics are largely ignored in determining the

structure of the CLO pool.3 Second, CLO managers’ performance is monitored by specific

compliance tests such as over-collateralization criteria of the CLO notes and average loan rating

thresholds for the CLO collateral. These monitoring mechanisms exclude information related to

the covenant structure of the loans in the pool, since assessing the quality of so many covenants

and the accounting information used in covenant thresholds is costly and induces subjectivity.

Third, the set of loan characteristics disclosed to CLO investors does not include details about

financial covenants, consistent with the fact that investors place less weight on this information

to monitor CLO performance or face information processing costs themselves. Thus, CLO

investors receive information only on a narrow set of loan characteristics, such as loan

maturities, spreads, ratings and default rates which simplify disclosures about CLO portfolio

quality.

2 The transaction costs are potentially high given the typical size of the marginal investment that a CLO makes in anindividual loan. In our sample, the average size of an investment in a loan is $1.5 million, while the face value of theloan is $350 million.3 After 2010, about 50 percent of the CLOs issued included restrictions on the percentage of covenant-lite loans inthe CLO portfolio. Nevertheless, the average cap on the amount of covenant-lite loans has increased from 25-30percent in 2010-2011 to 50-60 percent in 2013 (Standard & Poor’s Rating Direct – Structured Finance, 2013).

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Because CLOs’ operating model has limited reliance on covenant information content or

covenant quality, we expect that CLOs will contribute to an increase in the standardization of

covenants. At the same time, loan underwriters are also likely to limit their use of customized

covenants. They often employ financial covenants and contractual choices from loan agreements

of prior borrowers to lower their contracting costs, further contributing to standardization across

covenant definitions (Simpson, 1973; Rajan and Winton, 1995; Choi and Triantis, 2014). When

underwriting banks prepare the documentation to launch a syndicated corporate loan, they

regularly start with their own preliminary term sheets for financial covenants. The covenant term

sheets are subsequently adjusted as underwriting banks negotiate with and receive feedback from

loan investors. Similarly, syndicated loans securitized right after their origination are more likely

to include standardized financial covenants, since loan underwriters will exert less effort to write

customized loan covenants with borrower-specific accounting information if these loans are

subsequently transferred to CLOs.

However, the rise of corporate loan securitization may not necessarily increase financial

covenant standardization primarily for two reasons. First, only a fraction of syndicated loan

tranches is securitized while the remaining tranches are sold to banks or other investors that do

not have similar incentives to CLOs. Second, syndicate members may negotiate complex and

borrower-specific financial loan covenants to obtain pecuniary benefits and/or ex post control

rights (Li, Vasvari and Wittenberg-Moerman, 2014). For example, when loans are renegotiated

due to covenant violations, lenders obtain significant benefits such us renegotiation fees, greater

interest rates or more control over the borrower’s investing and financing activities (e.g., Roberts

and Sufi, 2009; Roberts, 2013).

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We investigate the relationship between corporate loan securitization and financial

covenant standardization using a sample of US-based public companies which issued syndicated

loans in the period 2000–2009. We obtain data on securitized loans from Creditflux, a global

publication platform that covers detailed information on the origination and performance of

CLOs’ investment portfolios. We match these loans with LPC DealScan to obtain their

characteristics and Compustat to obtain borrowers’ financial information. For those loans with

complete Dealscan and Compustat data, we then retrieve the loan contracts from companies’

SEC filings in EDGAR. We are able to obtain a sample of 440 securitized and 703 non-

securitized institutional loan contracts. For both securitized and non-securitized loans, we hand

collect 3,303 financial loan covenant definitions. We focus our analysis on the complete

covenant definition rather than the covenant title since previous studies report that the definitions

of accounting terms vary substantially across financial covenants (e.g., Leftwich, 1983; Li,

2012).

To assess financial covenant standardization, we develop an empirical proxy by

measuring the similarity of the contracting language that is used to define individual covenants.

For each covenant, we calculate the number of words that overlap with the words in covenant

definitions of loans issued by other borrowers over the prior calendar year. Namely, we compute

the cosine textual similarity between covenant definitions using a vector space model similar to

models used in plagiarism software and search engine algorithms (e.g., Salton, Wong, and Yang,

1975).4 This approach has recently been introduced in the accounting and finance literatures

4 More specifically, cosine textual similarity is computed as follows: we take two complete definitions of similarfinancial covenant types from two loans of different borrowers. We identify and list all the words in thesedefinitions, excluding “stop-words” and keeping only the word stems. Then, we count how many times each word isused in each definition. This process creates two vectors with the number of times each word is mentioned in thetwo covenant definitions. The cosine of the angle between these vectors is our covenant similarity score. Moredetails on how the measure is computed are provided in Appendix B.

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(e.g., Brown and Tucker, 2011; Hoberg, Phillips and Prabhala, 2012; Bozanic and Thevenot,

2014). Since covenants are set at the loan level, we estimate the covenant standardization

measure for each loan by averaging the cosine similarities of its covenants with the same-type

covenants in all loans issued by other borrowers in the prior calendar year. The covenant

similarity score is a continuous variable with values ranging from zero (if two covenant

definitions share no common word) to one (if the definitions of two same-type covenants are

identical).5 Using a multivariate regression, we show that the covenant similarity score is higher

when borrower and loan characteristics are more similar and that these similar characteristics

explain a significant proportion of the variation in the covenant similarity score, thus validating

our empirical proxy.

We show that financial covenant standardization in loan contracts increased during 2000–

2007 and drastically dropped in 2008–2009, matching the evolution of the corporate loan

securitization volume over the 2000–2009 period. In multivariate analyses, we find that

corporate loan securitization is positively associated with covenant standardization, controlling

for borrower, loan and underwriter characteristics. More specifically, we find that securitization

increases our covenant similarity score by up to 20 percent of its standard deviation. These

results are robust to a propensity score matched analysis on borrower performance and loan

characteristics, as well as to tests which address the potential for reverse causality (i.e., whether

CLOs purchase loans with more standardized financial covenants).

Next, we investigate whether the standardization of financial covenants in securitized

loans affects loan liquidity in the primary and secondary syndicated loan market. Consistent with

standardization contributing to a decrease in screening costs and costly information disclosures

5 In addition, we use an alternative covenant standardization measure which is the average of the ratio of similarcovenants across the loans issued in the prior year. Our results are robust to this measure.

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(e.g., Amihud and Mendelson, 1988), we find that covenant standardization is negatively related

to the number of days a loan remains open after its launch date, our loan liquidity measure in the

primary loan market. An increase in the covenant similarity score by one standard deviation

decreases the time a loan remains open in the primary market by 3 trading days or 21 percent of

the standard deviation of the “time-on-the-market.” Moreover, we investigate the liquidity of

securitized loans in the secondary loan market and find strong evidence that securitized loans

with more standardized covenants trade more and are purchased by a greater number of CLOs.

This finding suggests that covenant standardization contributes to a decrease in CLOs’ and their

counterparties’ information processing costs when trading.

In complementary analyses, we investigate whether covenant standardization is

associated with a reduction in the illiquidity premiums reflected in the securitized loan’s spreads.

We document a negative relation between the covenant similarity score and the LIBOR-spreads

of securitized loans. A one standard deviation increase in covenant similarity decreases the

LIBOR-spread by 5 percent or 12 basis points. In addition, we do not find evidence that

borrowers are less likely to default on securitized loans with more standardized financial

covenants. This latter finding suggests that the lower spread is not due to securitized loans’ lower

propensity to default but could be due to a lower illiquidity premium as a result of the

expectation that these loans will be more liquid.

Finally, we investigate whether covenant standardization decreases the information

asymmetry between debt market intermediaries, thus facilitating loan trading. We find a positive

relation between covenant standardization and the agreement in securitized loans’ ratings issued

by Standard & Poor’s and Moody’s. Less disagreement in the credit assessments of these top two

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rating agencies likely reduces the uncertainty among CLO managers and their counterparties

when trading these loans.

We add to the debt literature in several ways. First, we provide novel evidence on how

developments in the credit market affect the standardization of covenants in loan contracts. We

show that corporate loan securitization, which relies on significant secondary loan market

trading, contributes to more similar financial covenant definitions across syndicated loans. As

such, our study is related to De Franco, Vasvari, Vyas and Wittenberg-Moerman (2013) who

find that bond covenant “stickiness” is partly driven by bond market intermediaries, such as lead

arrangers and legal advisors, who prefer standard covenant definitions. Also, by identifying a

loan market mechanism that amplifies financial covenant standardization, we add to the well-

established empirical literature on the factors that drive contractual terms in corporate loans. So

far, this literature has mainly investigated to role of agency based determinants (e.g., Beatty and

Weber, 2003; Asquith, Beatty and Weber, 2005; Bharath, Sunder and Sunder, 2008; Beatty,

Weber and Yu, 2009; Ball, Bushman and Vasvari, 2008).

Second, we provide first hand evidence on the consequences of debt contract

standardization, and in particular covenant standardization, with respect to loan liquidity in the

primary and secondary debt market. Thus, we add to the empirical literature on corporate loan

securitization (e.g., Ivashina and Sun, 2011; Nadauld and Weisbach, 2011) and secondary loan

trading (Wittenberg-Moerman, 2008) by highlighting an important determinant of loan liquidity

that affects information processing costs.

Third, we build on recent studies that investigate the important role of textual information

in corporate disclosures (e.g., Li, 2008; Hoberg and Phillips, 2010; Brown and Tucker, 2011;

Bozanic, Cheng and Zach, 2013). We assess the complexity of loan covenants’ specifications

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relative to covenants in the loan contracts of peer firms and its effect on the marketability of

syndicated loans. We also explore how a recently developed credit market mechanism, the

corporate loan securitization process, is shaping debt contracting language (e.g., Bozanic, Cheng

and Zach, 2013). Consequently, we show that contract standardization is not only initiated by

lawmakers (e.g., Smith, 2006), but also by market participants that have incentives to induce

contractual standardization.

2. Literature review and Hypothesis development

The role of accounting-based loan covenants in mitigating adverse selection and moral

hazard has been widely explored in the accounting and finance literatures (e.g., Smith and

Warner, 1979; Berlin and Mester, 1992; Rajan and Winton, 1995). Bank lenders often structure

loan covenants based on financial statement data and use accounting adjustments to better

capture borrower’s credit performance (e.g., Leftwich, 1983; Li, 2012). Thus, financial

covenants are a critical tool to monitor borrowers as they increase lenders’ control rights when

borrowers’ performance deteriorates. When they receive control rights, lenders are able to

provide cheaper and greater amounts of credit (Jensen and Meckling, 1976; Stiglitz and Weiss,

1981; Christensen and Nikolaev, 2010).

While economic theory suggests that the main objective of financial loan covenants is to

monitor borrowers by including variations and adjustments in accounting data to capture

borrowers’ heterogeneity, this argument may not always hold. As Rajan and Winton (1995)

emphasize, “…covenants are not always written in the fine detail such (economic) objectives

would suggest: many covenants are standard boiler-plate, fleshed out as much by lawyers as by

loan officers or treasurers.” This topic has received significant attention in the law literature.

Simpson (1973) suggests that lenders will not forego language that they are accustomed to and

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are likely to contract on covenants from loan agreements issued by other borrowers they have

dealt with in the past. Borrowers may accommodate lenders’ demands for comparability, since

they may not realize, ex ante, the future operational and financial restrictions related to covenant

standardization. Also, Choi and Triantis (2014) argue that debt underwriters prefer covenant

standardization to decrease contracting costs and because they might not want to take the risk to

depart from covenants that have been enforced by the courts in the past. While financial

covenant structure is argued to be significantly standardized (e.g., Skinner, 2011), empirical

studies in the accounting, finance and law literatures have not yet explored how innovations in

the syndicated loan market have potentially contributed to the standardization of financial

covenant structures in loan agreements.

Over the past few years, the most significant innovation in the syndicated loan market

was the advent of institutional investors, and more importantly CLOs. CLOs’ operating model

significantly differs from that of traditional lenders such as banks. CLOs invest in corporate

loans and issue notes backed by the cash flows generated from these loans. For this model to be

sustainable, the CLO collateral structure must be highly diversified with limited exposures across

loan maturities, ratings, borrowers and industries. Indeed, a CLO will typically acquire small

tranches of more than 200 loans issued by borrowers that span 15 to 25 industries. By these

means, the credit risk of the underlying portfolio is lower, and the CLO notes can be rated higher

than the average rating of the underlying collateral pool.

However, these diversification rules, which apply over the life of the CLO, can generate

high transaction and reading costs for CLO’s stakeholders (i.e., credit rating agencies, CLO

managers and investors) given the large number of covenants attached to the loans in the

collateral pool. For example, to effectively monitor the underlying loan quality, CLO managers

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would be required to assess the underlying control rights included in each individual loan,

monitor the quality of borrowers’ accounting information used in financial covenants, and

estimate subjective metrics of covenant quality. Because CLOs make marginal loan investments

relative to the face value of these syndicated loans, this process can dramatically increase

information processing costs in relative terms.6 In addition, credit rating agencies would also

incur higher processing costs if they were to analyze each individual financial covenant present

in the loan contracts represented in the CLO’s collateral pool. Similarly, to monitor the quality of

their investment, CLO investors would have to either rely on CLO managers’ due diligence and

assessment of financial covenants or demand a comprehensive list of the financial covenants in

the collateral pool to perform their own credit analysis. Such an analysis is often not feasible

given the large number of loans acquired by the typical CLO and that these investors commit

capital to multiple CLO pools.

To mitigate the information processing and transaction costs highlighted above, the CLO

stakeholders limit their reliance on financial loan covenants when assessing a CLO’s

performance. First, to ensure collateral diversification, CLOs mitigate idiosyncratic credit risks

by selecting corporate loans based on specific and predetermined diversification criteria

regarding borrowers’ industry and geography as well as loans’ maturity and rating category.

These restrictions are imposed upon the CLO at set-up stage by credit rating agencies that rate

the CLO’s notes. Thus, covenant-based metrics are largely ignored in determining the structure

of the CLO pool.7 Second, CLOs are monitored based on certain predetermined compliance tests

6 CLO managers also trade loans often (Lou, Loumioti and Vasvari, 2014). A detailed assessment of individualloans’ level of covenant protection at the time when a loan is purchased can increase significantly the transactioncosts.7 In fact, prior to 2010, most CLOs had no constraints with respect to the acquisition of covenant-lite loans, thusencouraging an extreme form of covenant standardization.

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that are not built on loan covenants, but on a closed set of loan characteristics (primarily on loan

ratings and maturity).8 Despite their important monitoring role, loan ratings only partially capture

the structure of financial loan covenants that facilitate lenders’ control rights due to the

complexity of the terms in syndicated loan contracts (Ayotte and Bolton, 2009). Since CLO

managers are not evaluated on covenant-based metrics, they are less likely to be interested in

loans with customized financial covenants that would allow them to monitor borrowers’

underlying business model and financial performance. Third, CLOs report to investors only a

few loan characteristics, such as maturities, ratings, and spreads, ignoring the structure, number,

or quality of the financial loan covenants.

Since financial covenants are not an important loan feature for CLOs’ business model, we

expect that CLOs will not demand customized loan covenants. As a result, CLOs will be less

likely to negotiate and provide feedback on financial loan covenants to loan underwriters. Also,

to the extent that an originating bank expects to securitize a loan immediately after its issuance

by transferring it to a CLO, it will not exert significant effort to customize the covenant terms.

On the other hand, financial covenants are set at the loan package level, and not all tranches in

the loan deal are securitized. Banks and other loan syndicates that keep these tranches on their

balance sheets may have incentives to demand more borrower-specific financial covenants that

meet strict internal risk management rules. Also, because bank lenders gain access to borrower-

specific private information via their relationships with borrowers and have low renegotiation

costs, they might favor more customized financial covenants that enhance their control rights and

limit their credit exposure to borrowers (e.g., Li, Vasvari and Wittenberg-Moerman, 2014).

8 Some of the compliance tests are the overcollateralization of senior and junior CLO securities, the averageweighted rating of the collateral pool, the percentage of loans in the risky CCC-bucket, and the percentage of loansfrom borrowers that defaulted on their payments.

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We further expect more standardized financial covenants to bring benefits to the CLO

managers who invest in loans with such covenants. Specifically, standardized covenants may

improve the marketability of syndicated loans in both primary and secondary loan markets. This

is because covenant standardization decreases trading counterparties’ contract reading costs and

information asymmetry, thus contributing to higher loan liquidity. As such, standardized

covenants reduce the need for costly information disclosures and additional accounting due

diligence (e.g., Amihud and Mendelson, 1988).

3. Sample selection

We obtain data on securitized corporate loans from the CLO-i database provided by

Creditflux. Creditflux is a global news platform covering structured investment issuance and

performance that has been tracking data on all CLO deals since January 2008. Creditflux

retrieves its data from monthly CLO trustee reports that disclose CLOs’ activities and securitized

loans’ performance to CLO investors. CLO-i includes complete data on CLO portfolio structure,

CLO compliance tests, and CLO transactions, including borrowers’ names, loan types, ratings,

balances, maturities and default events. We retrieve loan specific data from LPC DealScan which

provides information on loan terms, loan types, lenders in the syndicate as well as the period a

loan package is marketed in the primary loan market.

We match CLO-i data with LPC DealScan and Compustat databases, a process which

yields a sample of 1,075 unique securitized corporate loans issued by 605 unique public

borrowers during the period 2000–2009. Of those, we are able to retrieve the loan contracts for

440 securitized loans from borrowers’ SEC filings via EDGAR following the search procedure

outlined by Nini, Smith, and Sufi (2009). To ensure comparability with our sample of securitized

loans, we then match the securitized loans to a sample of institutional loans identified in LPC

DealScan. We focus on institutional loans to eliminate the effect of differences between the

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middle and highly leveraged loan market on contract design.9 In addition, unlike bank loans,

institutional loans are typically rated (Ivashina and Sun, 2011). Thus, we try to hold constant the

demand for homogenous loans from credit rating agencies that prefer these loans because they

facilitate comparisons of credit risk levels.

We classify a loan as institutional if it includes at least one term loan tranche B-H but

does not include a CLO in its primary syndication structure, as presented in LPC DealScan.10 To

improve the classification, we require that institutional tranches have LIBOR-spreads higher than

250 basis points since institutional investors typically buy into high-yielding loans. Based on

these filters, the total number of non-securitized institutional loans issued by public borrowers in

LPC Dealscan is 4,529 over the period 2000-2009. We then select institutional loans with

available data from a subsample of 1,951 loans where more than half of the tranches are

institutional; this requirement ensures that we do not select loans that are distributed mainly to

banks. From this sample, we are able to retrieve the actual loan contracts of 703 institutional

loans from the SEC filings in the EDGAR system. Our final sample therefore includes 1,143

unique loans (440 securitized and 703 non-securitized institutional loans) issued by 806 unique

borrowers.

Next, we hand collect the accounting-based covenants of the loan contracts in our

sample. Since lenders may use different language to describe a type of covenant, we categorize

covenants into twelve types based on the LPC DealScan classifications: “Max. Capex”, “Max.

Debt”, “Max. Debt-to-EBITDA”, “Max. Debt-to-Equity”, “Max. Debt-to-Net Worth”, “Max.

9 More specifically, middle market loans are generally issued by more financially healthy borrowers and are nottraded, while institutional loans are primarily issued by non-investment grade borrowers and are largely distributedto institutional investors that may subsequently trade these loans.10 It is likely that we misclassify some institutional loans as non-securitized. This is because institutional loans mighthave been sold after their origination to CLOs. The average CLO holding period is approximately 11 consecutivemonths, and approximately 2 years in total. To mitigate this concern, in a robustness test we rerun the analysis forloans originated after January 2005 and the results hold.

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Leverage”, “Min. Debt Service Coverage”. “Min. EBITDA”, “Min. Fixed Charge Coverage”,

“Min. Interest Coverage”, “Min. Liquidity”, and “Min. Net Worth.” We identify 3,303 unique

financial loan covenant definitions. We find that 156 loans (55 securitized and 101 non-

securitized) have no accounting-based covenants (i.e., they are “covenant-lite” loans). While two

loans may use the same financial covenant type, the definition of accounting terms across

contracts could vary significantly. Thus, we hand collect the definition of the accounting terms

used to define the financial covenants in our sample. For example, when the “Interest Coverage

Ratio” is defined as “EBITDA to Interest Expenses”, we collect the accounting definition for

EBITDA and interest expenses described in the contract, as well as the definitions of all

accounting terms used to define EBITDA and interest expenses (e.g., net income, leases, etc.).

Appendix A provides examples of financial covenant specifications.

Table 1 provides details on loan characteristics by year and covenant structure for the 440

securitized and 703 non-securitized loans in our sample. Table 1, Panel A reports the number of

loans (securitized and covenant-lite loans) and financial covenants by year, as well as the

average number of financial covenants by loan year. Consistent with the growth in securitized

loan issuance, the number of securitized loans and covenant-lite loans in our sample increases

during the period 2000–2007 and sharply drops afterwards. Moreover, the average number of

financial loan covenants steadily drops in the period 2000–2007 and increases in 2008–2009,

consistent with lenders’ lower monitoring incentives during the credit boom. Table 1, Panel B

reports the number of financial covenants by covenant type for the 3,303 covenants (1,355

securitized and 1,948 non-securitized) in our sample. Consistent with previous studies (e.g.,

Drucker and Puri, 2008), securitized loans include more financial covenants, and especially more

interest coverage, capital expenditures and leverage covenants.

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4. Research design and variable definition

4.1. Covenant similarity score

Developing a proxy for the similarity across financial covenants in different loans is

challenging, since lenders are likely to adjust accounting data in covenant definitions (Leftwich,

1983). For example, the way “minimum EBITDA” is defined in one contract may be completely

different from the EBITDA definition in another loan contract. Based on the underlying

assumption that standardized covenants will share more common words with other covenants in

the same covenant category, we proxy for accounting-based covenant standardization by

assessing the degree of overlap in the vector of unique words used to define covenants.

To do so, we first remove all stopwords (e.g., “and”, “a”, “the”, “of”) and pare the

remaining words down to their stems.11 Next, we calculate the pairwise cosine textual similarity

for all reduced-form financial covenant definitions based on a vector space model commonly

used in computational linguistics (e.g., Salton, Wong, and Yang, 1975), which has been recently

introduced in the accounting and finance literatures (e.g., Brown and Tucker, 2011; Hoberg,

Phillips and Prabhala, 2012; Bozanic and Thevenot, 2014). To perform the calculation, a

comparison is drawn between two N x 1 vectors, one vector representing the N unique words in a

given financial covenant definition and another vector for the same covenant type from a loan

issued by a different borrower in the prior year.12 The angle between these two vectors for each

pair of same-type covenants (e.g., minimum EBITDA compared to minimum EBITDA) is the

cosine textual similarity score.13

To compute a loan specific covenant standardization measure, we average the cosine

11 For example, “trusted” and “trusting” become “trust” for calculation purposes.12 The twelve covenant types are based on LPC Dealscan classifications. See Section 3 above.13 Appendix B provides additional detail on the computation of the measure.

16

similarities of the covenants in a loan with the same-type covenants in all other borrowers’ loans

that were issued in the prior year (Covenant Similarity Score). Thus, our proxy for covenant

similarity is a continuous variable with values ranging from zero (if two covenants share no

common word) to one (if the definitions of two same-type covenants are identical).14 By

definition, covenants classified in the category “others” will have a covenant similarity score of

zero. For covenant-lite loans, we code the covenant similarity score as one (i.e., the maximum

value). This is consistent with Ayotte and Bolton’s (2009) argument that covenant-lite loans are

perfectly comparable in terms of their covenant structure.15

Figure 1 shows the trend in covenant standardization over time. Consistent with the

growth in the securitized loan market, covenant similarity increases in the period 2000–2007.

This trend reverses in the period 2008–2009 when the securitization market froze. Figure 2

compares covenant standardization over time for institutional loans and securitized loans. While

covenant similarity for both institutional and securitized loans increases over time, the covenant

similarity score for securitized loans is consistently higher and reverses in the crisis years

tracking the trends in the securitization market. This pattern provides some preliminary evidence

with respect to the impact of securitization via CLOs on covenant standardization.

In addition, we compute an alternative covenant standardization measure which does not

rely on textual analysis. We compute the average of the ratio of similar covenants across the

loans issued in the prior year (Percentage of Same Covenants). This ratio is computed for each

loan pair as the number of common covenants between the current loan and the other loan

14 It is worth mentioning that the covenant similarity score reflects textual rather than semantic similarity. Forexample, if a net worth covenant is defined as assets minus liabilities in a loan contract and the definition of thesame-type covenant in another contract is book value of equity, these two covenants will have very low cosinesimilarity.15 In their model, lenders’ intention to completely standardize covenants in securitized loans leads to the exclusionof covenants from loan contracts. In robustness tests, we exclude covenant-lite loans and covenants classified as“others” from our tests and our results hold.

17

previously issued divided by the total number of financial covenants specified in the loan

contract.

4.2. Research design

4.2.1. Securitization and covenant standardization

Our first test explores the relation between corporate loan securitization and financial

covenant standardization at the loan level. We test our hypothesis using an OLS model, where

the dependent variable is the Covenant Similarity Score.

Covenant Similarity Score = α + β1*Securitized Loan + β2*Number of Covenants+ β3*LIBOR-spread+ β4*Loan Amount + β5*Loan Maturity + β6*Revolving tranche+ β7*Lending Relationship + β8*Syndicates + β9*Liquidity+ β10*ROA + β11*Leverage + β12*Cash Flow Volatility+ β13*Size + β14*Pct of Same Covenants

(Model 1)

The primary independent variable of interest is Securitized Loan, defined as one if the

loan is securitized and zero otherwise. We control for various loan characteristics, including: (i)

the number of financial loan covenants (Number of Covenants); (ii) the natural logarithm of all-

in-drawn LIBOR-spread of the loan term B tranche (LIBOR-spread); (iii) the natural logarithm

of loan size (Loan Amount); (iv) the natural logarithm of loan maturity (Loan Maturity); (v) the

average ratio of financial covenants that are the same relative to the other loans that are issued in

the prior year (Pct of Same Covenants); and (vi) whether the loan includes a revolving tranche

(Revolving Tranche). Also, we control for the strength of lending relationships, defined as the

ratio of the size of loans that a borrower raised from the lead lender in the past to the total size of

loans that the borrower issued in the syndicated loan market (Lending Relationships), and for the

number of loan co-syndicates (Syndicates).

18

We further control for borrowers’ financial performance in the year of a loan’s

origination. More specifically, we control for borrower: (i) liquidity, defined as current assets to

current liabilities (Liquidity); (ii) profitability, defined as operating income to total assets (ROA);

(iii) leverage, defined as total long-term debt to total assets (Leverage); (iv) business model

volatility, defined as the standard deviation of borrowers’ operating cash flows over the last five

years, divided by average total assets (Cash Flow Volatility); and (v) size, defined as the natural

logarithm of total assets (Size). We add year, industry (Fama and French 12 industry portfolios),

and loan purpose (“investing”, “financing”, “operating”, “default”, “other”) fixed effects to

capture differences over time, across industries, and by loan purpose. We also add lead lender

fixed effects to capture differences in lenders’ contracting language (52 unique lead lenders).

Appendix C provides descriptions of the variables.

4.2.2. Securitization, covenant standardization and loan liquidity

To the extent that securitization increases covenant similarity, we expect that securitized

loans will have lower reading costs and, thus, will be easier to trade. We first test for loan

liquidity in the primary loan market using an OLS model where the dependent variable is the

number of days the loan is traded in the primary loan market, defined as the difference between

loan completion date minus launch date (Time-on-Market). The greater the number of days a

loan remains outstanding in the primary market, the lower its liquidity.

Time-on-Market = α + β1*Covenant Similarity Score + β2*Securitized Loans+ β3*Covenant Similarity Score*Securitized Loans+ β4*Number of Covenants + β5* LIBOR-spread+ β6*Loan Amount + β7*Loan Maturity + β8*Revolving Tranche+ β9*Lending Relationships + β10*Syndicates + β11*Liquidity + β12*ROA+ β13*Leverage + β14*Cash Flow Volatility + β15*Size

(Model 2)

19

The primary coefficient of interest is β3, which we expect to be negative. Similar to the

previous models, we control for loan characteristics and borrower financial performance upon

loan origination. We add year, industry, and loan purpose fixed effects to capture differences

over time, across industries, and by loan purpose.

In our second test on the liquidity of securitized loans, we examine the secondary loan

market using an OLS model where the dependent variable is the number of annual loan trades

(loan sales and purchases less purchases where both transacting parties are CLOs) in the period

2008-2013 divided by the average trading activity of a securitized loan in the same period (Loan

Trades). Further, we use the annual change in number of CLOs that hold at least one tranche of a

loan to the average securitized loan distribution across all CLOs in the same period (Loan

Distribution).

Loan Trades or Distribution = α + β1*Covenant Similarity Score + β2*Number of Covenants+ β3*LIBOR-spread + β4*Loan Amount + β5*Loan Maturity+ β6*Revolving Tranche + β7*Syndicates +β8*Liquidity + β9*ROA+ β10*Leverage + β11*Cash Flow Volatility + β12*Size

(Model 3)

The primary coefficient of interest is β1, which we expect to be positive. Similar to the

previous models, we control for loan characteristics and borrower financial performance upon

loan origination and add year, industry, and loan purpose fixed effects. Appendix C provides a

description of the variables.

5. Summary statistics and validation tests

5.1. Summary statistics

Table 2 reports the summary statistics for covenant and loan characteristics, loan liquidity,

CLO and loan performance, and some borrower characteristics for our sample. The mean

covenant similarity score is 0.49. When we exclude covenant-lite loans, the mean covenant

similarity score is 0.38, suggesting that about 40 percent of the accounting terms and adjustments

20

in loan contracts are standardized. However, this finding shows that 60 percent of the accounting

covenant terms are not standardized, consistent with the fact that lenders use their access to

private borrower-specific information to determine the covenant structure. The average borrower

has a size of $2.5 billion, leverage ratio of 39 percent, a liquidity ratio of 1.58 and ROA is 6

percent. The mean loan size is $409 million with a mean maturity of 4.97 years. The mean

number of financial loan covenants is 2.6 and the mean LIBOR-spread is 246 basis points.

Further, most loans in our sample include a revolving tranche and the mean company has raised

about 20 percent of its syndicated loan issues from a relationship lender. The loans in our sample

remain outstanding in the primary loan market for 30 days on average, the mean (median)

number of trades is 1.06 (1.17) and the mean (median) loan distribution is 0.22 (0.10). The mean

default rate for the securitized loans in our sample is 1.2 percent and the average loan difference

between Standard and Poor’s and Moody’s loan ratings is less than one notch. Moreover, 45

percent of the covenants in a loan are the same to all other loans in our sample (when we exclude

covenant-lite loans the percentage drops to 41), suggesting that while a certain level of

standardization in loan covenants exists, the covenant mix used across different loans varies.

Panel A of Table 3 reports the univariate tests of differences in means of contract and

borrower characteristics for securitized and non-securitized loans. The results suggest that

securitized loans have higher covenant standardization than other institutional loans. Consistent

with prior studies (e.g., Drucker and Puri, 2008; Ivashina and Sun, 2011; Nadauld and Weisbach,

2011), we find that securitized loans have more financial covenants, lower spread, larger size,

higher liquidity, and longer maturity. Moreover, securitized borrowers are smaller, highly

leveraged companies and do not have strong prior lending relationships with their lenders. Panel

B of Table 3 reports the univariate tests of differences in the mean covenant similarity score by

21

type of financial covenant for securitized and non-securitized loans. We find that securitized

loans have greater covenant similarity to non-securitized loans across all financial covenants

except one (Minimum Debt Service Coverage). This univariate evidence indicates that

standardization is reflected in almost all financial covenants attached to securitized loan

contracts.

Untabulated univariate correlations show that our proxy for covenant standardization is

positively correlated to the probability of a loan being securitized (0.08), the LIBOR-spread

(0.13), the loan maturity (0.06) and the borrower financial leverage (0.11), and negatively related

to the number of financial loan covenants (-0.49), the loan amount (-0.05), the borrower’s ROA

(-0.03) and the strength of prior lending relationships (-0.15). Moreover, the probability of a loan

being securitized is positively correlated to the number of financial covenants (0.10), the loan

size (0.20) and the loan maturity (0.17), and negatively correlated to the borrower’s size (-0.12),

the LIBOR-spread (-0.08) and previous lending relationships (-0.09).

5.2. Validation Test

In Table 4, we validate our standardization proxy by investigating whether the similarity

between two covenants of the same type is related to borrower and loan characteristic similarity.

We find that two covenants of the same type share more similar definitions when issued by the

same lender. In addition, two covenants of the same type are similarly defined when the loans

have similar characteristics in terms of LIBOR-spread, maturity, number of covenants, or

number of co-syndicates. Further, two covenants of the same type are more similarly defined

when borrowers have comparable financial performance or are from the same industry. Overall,

the results from this test suggest that our proxy for covenant similarity captures similarities in

borrowers’ business models and in loan contract terms that are likely to drive covenant design

22

choices. Thus, our proxy for covenant similarity, although based on textual analysis, appears to

capture the underlying construct of covenant standardization.

6. Regression results

6.1. Securitization and covenant standardization

Panel A of Table 5 reports the results from the baseline OLS tests on the effect of loan

securitization on covenant standardization and Panel B reports several cross-sectional tests to

address competing explanations for the baseline results. In the first specification of Panel A, the

dependent variable is the percentage of same covenants. In all other specifications across the

panels, the dependent variable is the covenant similarity score. In specification (I), we find that

the coefficient on Securitized Loan is significantly positive, controlling for loan, borrower, and

lender characteristics. Thus, the covenant mix in securitized loans is more standardized. More

specifically, securitized loans have approximately a 4 percent higher similarity in their covenant

mix compared to other institutional loans. In specification (II), we find that the securitization of

loans increases their covenant similarity to other loans issued over the prior year by 0.05 or 20

percent of its standard deviation. Further, in specification (III), where we control for the extent to

which a loan is using covenants that are the same as the covenants used in previously issued

loans, we find that securitized loans have a covenant similarity which is higher by 0.02 or 10

percent of its standard deviation.

A natural question that arises is whether or not the above result is driven by an omitted

variable associated both with lenders’ decisions to securitize some tranches of a corporate loan

and with the covenant similarity. To address this concern, in specification (IV) of Panel B, we

test whether the effect of securitization on covenant standardization is stronger when more than

80 percent of the tranches within the same loan package are securitized (223 securitized loans).

We find that the result continues to be statistically significant and robust while the economic

23

magnitude of the effect is bigger: highly securitized loans have covenant similarity which is

higher by 0.06 or 28 percent of its standard deviation.

We further focus on the subsample of securitized loans and test whether highly

securitized loans have higher covenant similarity compared to other securitized loans (rather than

non-securitized loans, as in specification (IV)). We classify loans as highly securitized if more

than 80 percent of the loan tranches are purchased by CLOs. The advantage of this cross-

sectional test is that it mitigates concerns about selection issues that might drive the results in the

prior specification (i.e., concerns regarding observable or unobservable variables associated with

both the decision to securitize a loan and covenant similarity). In specification (V), we document

that covenant similarity is significantly increasing with the extent to which a loan package is

securitized. We find that highly securitized loans have a covenant similarity score which is

higher by 0.04 or 17 percent of its standard deviation.

Relatedly, another possible concern is that CLOs may choose to purchase loans that

include more standardized covenants from the secondary market. To alleviate this reverse

causality bias, we split our sample into loans that are securitized upon their origination and loans

that are sold to CLOs ex post (specifications (VI) and (VII), respectively).16 We find that when

loans are securitized upon their origination, i.e., when CLOs are expected to be more active in

setting covenant terms, the effect of securitization on covenant similarity is statistically and

economically stronger. More specifically, securitization of corporate loans at their origination

increases covenant similarity by 0.06 or 0.28 of its standard deviation. Although we also find

that CLOs buy more standardized loans in the secondary market, this effect is statistically

weaker. More specifically, the ex post securitization of corporate loans is associated with a

16 Ideally, we would use time to securitization as an instrumental variable, however, this is unobservable in our data.

24

covenant similarity which is higher by 0.05 or 0.21 of its standard deviation, relative to the

unsecuritized institutional loans. It is important to note that the time to securitization is an

important factor that affects this result. If a loan is sold to a CLO shortly after issuance, it is

likely that the bank originated the loan to securitize it and thus did not negotiate on borrower-

specific covenants. However, if a loan is sold to a CLO after a longer period following its

origination, then the relation between securitization and covenant standardization becomes

weaker as the originating bank is less likely to anticipate in advance the terms preferred by CLOs

when negotiating the loan contract at issuance. Our results in column (VII) cannot distinguish

between these alternatives.

In the last specification presented in Table 5, Panel B, we test whether the effect of

securitization on covenant standardization is driven by unobservable characteristics inherent in

companies that issue securitized loans. In specification (VIII), we identify a sample of companies

that issued both securitized and non-securitized loans in the period 2000–2009, which allows us

test whether securitization affects covenant design within the same borrower. We continue to

find that the securitized loans exhibit covenant similarity scores that are higher by 0.07 or 35

percent of the scores’ standard deviation.

Finally, we use a propensity score matching model to deal with the fact that the selection

to issue a securitized loan is non-random. We identify a set of control firms which we match to

the treatment firms using propensity scores based on both loan and borrower-specific

characteristics. Panel C of Table 5 reports the results of this propensity score matching model. It

reports the average treatment effect of securitization on covenant standardization for alternative

sets of matching characteristics. The one-to-one matching of treated loans is done in random

order and without replacement. Matching loans are within a distance (“caliper”) of 0.01 of the

25

propensity score of the loans in the treatment group. We find that our result is robust.

Securitization increases covenant standardization by 0.04 or 19 percent of its standard deviation

across all matching specifications.

6.2. Securitization, covenant standardization and loan liquidity

Table 6 reports the regression results for our tests that examine consequences of covenant

standardization with respect to loans’ liquidity. Liquidity is an important concern for CLO

managers that often trade the loans in their portfolios to enhance CLOs’ performance (Lou,

Loumioti and Vasvari, 2014). Panel A reports the results where the dependent variable is the

number of days that a loan remains outstanding in the primary market.17 Panel B reports the

results where the dependent variable is securitized loan trading or distribution in the secondary

market.

In Panel A, we find that securitized loans with higher levels of covenant standardization

“close”, i.e., are allocated to investors, more quickly. The time-on-market for these loans is 3

days shorter than that of institutional loans without standardized covenants, a decrease of 10%

relative to the average time-on-market, which is around 29 days. These results suggest that

covenant standardization is an important mechanism that enhances the liquidity of securitized

loans by decreasing information processing costs for CLOs. In Panel B, we find that covenant

standardization increases the number of trades of securitized loans, as well as their distribution

across different CLOs. An increase by one standard deviation in covenant standardization

increases securitized loan trading (distribution) in the secondary loan market by approximately

11 percent (6 percent).

17 In this panel, the number of observations drops to 343 loans due to data availability.

26

In sum, the results in this section indicate that covenant standardization is associated with

greater syndicated loan liquidity, consistent with the interpretation that more similar financial

covenants decrease trading parties’ due diligence costs and information asymmetry with respect

to the level of protection offered by the covenant structure (e.g., Amihud and Mendelson, 1988).

6.3. Further analysis

6.3.1. Covenant standardization and loan spread

We next investigate whether the greater liquidity associated with the standardization of

covenants in securitized loans is priced by loan syndicates via a lower liquidity premium in the

spreads of securitized loans. In Table 7, we explore whether financial covenant standardization

affects securitized loans’ spreads and find that covenant standardization decreases the cost of

securitized loans by 20 basis points (which is 5 percent of the average spread), controlling for

loan and borrower characteristics. While this result suggests that loans with more standardized

covenants have lower spreads, potentially due to a lower illiquidity premium, it is also possible

that these loans have lower expected default rates because they are less risky (and we fail to

control for this risk). We do not have information on loan expected default rates (i.e., spreads

from credit derivatives written on loans) available however we investigate whether the covenant

similarity measure predicts lower future loan default rates in column (II) of Table 7. We

document that our covenant similarity measure is not associated with a lower probability of loan

default ex post. Therefore, this analysis provides evidence that covenant standardization is

associated with a decrease in the cost of syndicated loans that are securitized and that this

decrease is likely due to a lower illiquidity premium.

6.3.2. Covenant standardization and loan ratings

To provide more evidence on the impact of covenant standardization on loan liquidity, in

our last set of tests, we explore a potential mechanism that may explain why covenant

27

standardization increases the marketability of securitized loans. Namely, we investigate whether

covenant standardization in securitized loans leads to less disagreement between credit rating

agencies which are critical information intermediaries in the debt market. Less disagreement in

the views of these institutions about the credit riskiness of an individual loan is likely to decrease

the information processing costs for all investors interested in transacting that loan (e.g., Morgan,

2002). To test for the effect of covenant standardization on Standard and Poor’s (S&P) and

Moody’s loan rating convergence, we use an OLS model where the dependent variables are (i)

the absolute value of the average notch difference between S&P and Moody’s loan ratings over

the period 2008–2013 (Loan Rating Difference) and (ii) the number of quarters S&P and

Moody’s agree on a loan rating, divided by the number of quarters the loan is held by CLOs

(Same Rating). Loan rating is a scale variable with values from 1 to 25, where 1=AAA, 2=AA+

(or Aa1)…, and 25=D. If financial covenant standardization is indeed reducing rating agencies

information asymmetry about the covenant structure of a loan, we expect the coefficient of the

covenant similarity score to be negative when the dependent variable is Loan Rating Difference

and positive when the dependent variable is Same Rating.

Table 8 reports the results. Consistent with our expectations, we find that the

standardization of financial covenants is associated with a greater convergence in the loan ratings

issued by different credit rating agencies, controlling for loan and borrower characteristics. An

increase by one standard deviation in the covenant similarity score decreases the difference in

S&P and Moody’s ratings by 0.2 notches, a significant effect given that the average notch

difference is 0.79. Similarly, an increase by one standard deviation in the covenant similarity

score increases the probability that S&P and Moody’s issue exactly the same quarterly rating on

a loan by approximately 10 percent. By comparison, the unconditional probability of both rating

28

agencies issuing the same loan rating is about 40 percent. Overall, the results suggest that

covenant standardization supports the standardization of credit risk evaluations by rating

agencies thus contributing to a lower information asymmetry in the loan market. In turn, this

lower information asymmetry should contribute to an increase in the likelihood that debt

investors trade a particular loan.

6.4. Robustness tests

We perform a series of sensitivity analyses to investigate the robustness of our results

regarding the effect of securitization on covenant standardization as well as the findings on the

consequences of covenant standardization on loan liquidity. First, we exclude covenant-lite loans

and covenants classified in the covenant category “other” and our results continue to hold.

Second, to alleviate the concern that we misclassify institutional loans as non-securitized when in

fact a CLO invested in this loan after its issuance, we restrict our sample to loans originated after

January 2005. If a CLO invested in these loans after their issuance, we would be able to pick up

this information from the CLO-i database whose coverage started in 2008. Therefore, any bias in

our results due to the misclassification of the control sample is more limited. We continue to find

results similar to those in our primary analyses. Third, we control for the number of words used

to describe a loan covenant as a proxy for covenant complexity and the findings across all tests

hold.

7. Conclusions

We explore whether corporate loan securitization increased the standardization of

accounting-based covenants in loan contracts, and whether covenant standardization has a real

effect on loan trading activity. Previous studies have documented that, despite the widespread

use of financial covenants in loan contracts, the design of loan covenants is based on a relatively

limited set of accounting data, which is puzzling given lenders’ sophistication (Skinner, 2011).

29

We hypothesize that the recent surge of CLOs in the syndicated loan market, whose business

model does not rely on obtaining creditor control rights, decreased the demand for customized,

borrower-specific financial covenants. To the extent that standardization decreases transaction

costs (i.e., information processing and contract reading costs), we further hypothesize that

covenant similarity of securitized loans will increase their liquidity.

To test our hypotheses, we hand collect the complete definitions of financial covenants

specified in securitized loans and non-securitized, institutional loans. Borrowing from the field of

computational linguistics, we apply a vector space model, which has been recently introduced in

the accounting and finance literatures, to proxy for financial covenant standardization. We

document that securitization leads to more standardized loan covenants, controlling for lender,

loan and borrower characteristics. We further find that covenant standardization in securitized

loans increases liquidity in the primary and secondary loan markets, suggesting that

standardization leads to lower information processing costs. In supplemental analyses, we find

that covenant standardization in securitized loans is associated with a reduction in the securitized

loans’ LIBOR-spreads without being associated with a lower default probability, potentially

suggesting that the spread reduction is related to a decrease in illiquidity premiums. In addition,

we document that financial covenant standardization in securitized loans leads to less credit

rating disagreement between the major credit rating agencies, consistent with the interpretation

that standardization leads to lower reading costs.

Our paper has certain limitations that present opportunities for future research. First,

since CLO managers have incentives to trade their loans to enhance CLOs’ performance (Lou,

Loumioti and Vasvari, 2014), we focus solely on loan liquidity as one of the main benefits

provided by covenant standardization. However, covenant standardization is likely to generate

30

other benefits for loan investors. For example, standardization is likely to decrease loan

renegotiation costs, which are important given the significant number of loans that are

renegotiated. Second, another interesting topic not investigated in this paper is the potential costs

of covenant standardization. It is possible that the use of less customized financial covenants

may lead to an inefficient allocation of control rights if borrowers are more likely to violate such

covenants suboptimally from the lenders’ perspective (e.g., a financially healthy firm might

violate a covenant because its specification is incomplete). Finally, as debt market information

intermediaries (e.g., rating agencies such as Moody’s or S&P) begin to provide more accessible

metrics that facilitate debt market participants’ understanding of covenant structures, CLO

managers and their investors might become more interested in using loan covenants to monitor

CLOs’ loan portfolios. We leave such avenues to future research.

31

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Appendix A

Examples of Financial Covenant Definitions

Example 1: Consolidated Interest Coverage Ratio

Consolidated Interest Coverage Ratio is defined as Consolidated EBITDA to Consolidated Interest Charges

“Consolidated EBITDA” means, for any period, for the Borrower and its Restricted Subsidiaries on a consolidatedbasis, an amount equal to Consolidated Net Income for such period plus (a) the following to the extent deducted incalculating such Consolidated Net Income: (i) Consolidated Interest Charges for such period, (ii) the provision forFederal, state, local and foreign income taxes payable by the Borrower and its Restricted Subsidiaries for suchperiod, (iii) depreciation and amortization expense, and (iv) other expenses of the Borrower and its RestrictedSubsidiaries reducing such Consolidated Net Income which do not represent a cash item in such period or anyfuture period and minus (b) the following to the extent included in calculating such Consolidated Net Income: (i)Federal, state, local and foreign income tax credits of the Borrower and its Restricted Subsidiaries for such period,and (ii) all non-cash items increasing Consolidated Net Income for such period; provided that for the purposes ofSection 7.20, if the Borrower or any Restricted Subsidiary shall acquire or dispose of any material property or aSubsidiary shall be redesignated as either an Unrestricted Subsidiary or a Restricted Subsidiary, in any case, duringthe period of four fiscal quarters ending on the last day of the fiscal quarter immediately preceding the date ofdetermination for which financial statements are available and up to and including the date of the consummation ofsuch acquisition, disposition or redesignation, then Consolidated EBITDA shall be calculated, in a mannersatisfactory to the Administrative Agent in its reasonable discretion, after giving pro forma effect to such acquisition(including the revenues of the properties acquired), merger, disposition or redesignation, as if such acquisition,merger, disposition or redesignation had occurred on the first day of such period.

“Consolidated Interest Charges” means, for any period, for the Borrower and its Restricted Subsidiaries on aconsolidated basis, the sum of (a) all interest, premium payments, debt discount, fees, charges and related expensesof the Borrower and its Restricted Subsidiaries in connection with borrowed money (including capitalized interest)or in connection with the deferred purchase price of assets, in each case to the extent treated as interest inaccordance with GAAP, excluding one-time charges in respect of loan origination or similar fees and non-cashamortized amounts with respect thereto, and (b) the portion of rent expense of the Borrower and its RestrictedSubsidiaries with respect to such period under capital leases that is treated as interest in accordance with GAAP.

“Consolidated Net Income” means, for any period, for the Borrower and its Restricted Subsidiaries’ gross revenuesfor such period, including any cash dividends or distributions actually received from any other Person during suchperiod, minus the Borrower’s and its Restricted Subsidiaries’ expenses and other proper charges against income(including taxes on income to the extent imposed), determined on a consolidated basis in accordance with GAAPconsistently applied after eliminating earnings or losses attributable to outstanding minority interests and excludingthe net earnings of any Person other than a Restricted Subsidiary in which the Borrower or any of its Subsidiarieshas an ownership interest. Consolidated Net Income shall not include (i) any gain or loss from the Disposition ofassets, (ii) any extraordinary gains or losses, or (iii) any non-cash gains or losses resulting from mark to marketactivity as a result of the implementation of Statement of Financial Accounting Standards 133, “Accounting forDerivative Instruments and Hedging Activities” (“SFAS 133”).

Example 2: Total Leverage Ratio

The ratio of Indebtedness to EBITDA

“Indebtedness” means of any Person (without duplication): (a) indebtedness created, issued or incurred by suchPerson for borrowed money (whether by loan or the issuance and sale of debt securities or the sale of property toanother Person subject to an understanding or agreement, contingent or otherwise, to repurchase such property fromsuch Person); (b) obligations of such Person to pay the deferred purchase or acquisition price of property or services,other than trade accounts payable (other than for borrowed money) arising, and accrued expenses incurred, in theordinary course of business so long as such trade accounts payable are payable within 90 days of the date the

35

respective goods are delivered or the respective services are rendered; (c) Indebtedness of others secured by a Lienon the property of such Person, whether or not the respective Indebtedness so secured has been assumed by suchPerson; (d) obligations of such Person in respect of letters of credit or similar instruments issued or accepted bybanks and other financial institutions for account of such Person; (e) Capital Lease Obligations of such Person; (f)Indebtedness of others guaranteed by such Person; (g) if the aggregate consideration payable by such Person toextend and exercise any option acquired in connection with any Acquisition (an “Extension and Exercise Price”)exceeds 20% of the aggregate consideration payable in connection with such Acquisition, such Extension andExercise Price; (h) any put obligations, but only to the extent that such Put Obligations (other than the PutObligations in existence on the Fourth Restatement Effective Date relating to WNAB-TV (Nashville, Tennessee)),whether arising under the same or different agreements, exceeding $25,000,000 in the aggregate shall not have beenapproved by the Administrative Agent (such approval not to be unreasonably withheld) prior to the incurrencethereof; and (i) obligations of such Person in respect of surety and appeals bonds or performance bonds or othersimilar obligations; provided that the term “Indebtedness” shall not include (i) obligations of such Person, (ii)obligations of such Person under any Program Services Agreement, Outsourcing Agreement or other similaragreement, (iii) any liability shown on such Person’s balance sheet in respect of the fair value of Interest RateProtection Agreements, (iv) any put obligations, and (v) any liability shown on the balance sheet of such Personsolely as a result of the application of FIN 46 and for which such Person is not primarily or contingently liable forpayment.

“Capital Lease Obligations” of any Person means the obligations of such Person to pay rent or other amountsunder any lease of (or other arrangement conveying the right to use) real or personal property, or a combinationthereof, which obligations are required to be classified and accounted for as capital leases on a balance sheet of suchPerson under GAAP, and the amount of such obligations shall be the capitalized amount thereof determined inaccordance with GAAP.

“EBITDA” means, for any period, the sum, for the Borrower and its Subsidiaries (determined on a consolidatedbasis without duplication in accordance with GAAP), of the following for such period (subject to Section 1.05(d)):(a) net income for such period; plus (b) the sum of, to the extent deducted in determining net income for such period,(i) provision for taxes, (ii) depreciation and amortization (including film amortization), (iii) Interest Expense, (iv)Permitted Termination Payments (or to the extent the same shall be included in determining corporate expensespursuant to clause (c)(ii) below for such period), (v) extraordinary losses (including non-cash losses on sales ofproperty outside the ordinary course of business of the Borrower and its Subsidiaries), (vi) all other non-cashcharges (including non-cash losses on derivative transactions and non-cash interest expenses), (vii) all transactioncosts paid or incurred by the Borrower in connection with the Fourth Restatement Effective Date Transactions andthe Tender Offer Transactions, and (viii) all amounts paid in cash by the Borrower and its Subsidiaries toCunningham and its Subsidiaries pursuant to the transactions contemplated by the Cunningham MOU that are inrespect of, or credited toward, the purchase price of any Stations to be acquired by the Borrower or any of itsSubsidiaries from Cunningham or are in respect of local marketing agreement fees, but not exceeding $11,000,000in the aggregate for any twelve month period; minus (c) the sum of, to the extent included in net income for suchperiod, (i) non-cash revenues, (ii) corporate expense (but only to the extent already not deducted in determining netincome for such period), (iii) interest and other income, (iv) extraordinary gains (including non-cash gains on salesof assets outside the ordinary course of business), (v) benefit from taxes, (vi) non-cash gains on derivativetransactions, and (vii) cash payments made during such period in respect of items under clause (b)(vi) abovesubsequent to the fiscal quarter in which the relevant non-cash charge was reflected as a charge in the statement ofnet income; minus (d) Film Cash Payments made or scheduled to be made during such period.

“Interest Expense” means, for any period, the sum, for the Borrower and its Subsidiaries (determined on aconsolidated basis without duplication in accordance with GAAP), of (a) all cash interest expense in respect ofIndebtedness during such period, (b) the net amounts payable (or minus the net amounts receivable) under InterestRate Protection Agreements accrued during such period (whether or not actually paid or received during suchperiod) and (c) restricted payments made during such period pursuant to Section 7.08(a) in respect of interestpayments on the Holding Company convertible debentures (including any such interest payments thereon madepursuant to Section 7.08 of the Existing Credit Agreement prior to the Fourth Restatement Effective Date during anyfiscal quarter that is included in such period). Any reference herein to calculating Interest Expense for any period ona “pro forma” basis means that, for purposes of the clause (a) above, (i) the Indebtedness on the basis of which

36

Interest Expense is so calculated shall mean Indebtedness outstanding as of the relevant date of calculation aftergiving effect to any repayments and any incurrence of Indebtedness on such date and (ii) such calculation shall bemade applying the respective rates of interest in effect for such Indebtedness on such date.

“Film Cash Payments” means, for any period, the sum (determined on a consolidated basis and withoutduplication) of all payments by the Borrower and its Subsidiaries made or scheduled to be made during such periodin respect of film obligations; provided that amounts applied to the prepayment of film obligations owing under anycontract evidencing a film obligation under which the amount owed by the Borrower or any of its Subsidiariesexceeds the remaining value of such contract to the Borrower or such Subsidiary, as reasonably determined by theBorrower, shall not be deemed to be Film Cash Payments.

Example 3: Limitation on Capital Expenditures

“Capital Expenditures” shall mean with respect to any Person for any period, the sum of (i) the aggregate of allexpenditures by such Person and its Subsidiaries during such period that in accordance with GAAP are or should beincluded in “property, plant and equipment” or in a similar fixed asset account on its balance sheet, whether suchexpenditures are paid in cash or financed, and (ii) to the extent not covered by clause (i) above, the aggregate of allexpenditures by such Person and its Subsidiaries during such period to acquire by purchase or otherwise the businessor fixed assets of, or the capital stock of, any other Person; provided that there shall be excluded from CapitalExpenditures the purchase price paid in any Permitted Acquisition; provided, further, that any rolling stock which isinitially accounted for as a Capital Expenditure at the time of acquisition thereof but which is transferred to a thirdparty and becomes subject to an operating lease within 60 days after the date of acquisition thereof which leasewould not be required to be treated as an addition to “property, plant and equipment” or in a similar fixed assetaccount on a consolidated balance sheet of Parent and its Subsidiaries prepared in accordance with GAAP, shall beexcluded from Capital Expenditures.

Example 4: Net Worth

“Net Worth” means, as of any date of determination, the total consolidated stockholders’ equity (determinedwithout duplication) of the Borrower and its Subsidiaries at such date.

37

Appendix B

Cosine Textual Similarity

We measure covenant standardization by assessing the degree of overlap in the vector of uniquewords used to define covenants. To do so, we first remove from the covenant definition allstopwords (e.g., “and”, “a”, “the”, “of”) and pare the remaining words down to their stems. Forexample, “trusted” and “trusting” become “trust” for calculation purposes.

Next, we estimate the extent to which two covenant definitions are similar by calculating thepairwise cosine textual similarity for all pairs of reduced-form financial covenant definitionsbased on a vector space model used in plagiarism software and search engine algorithms (seeSalton, Wong, and Yang, 1975) as follows:

We count how many times each word is used in each covenant definition. This processcreates two vectors with the number of times each word is mentioned in the two covenants.To illustrate, assume we have two covenant texts, T1 and T2, with three words (W1, W2, W3)each. W1 occurs in T1 2 times, W1 occurs in T2 3 times, and so forth:

T1 = (2W1, 3W2, 5W3)

T2 = (3W1, 7W2, W3)

The cosine similarity of the two vectors above is a mathematical measure of how similar thetwo vectors are on a scale of [0, 1] with 1 being the outcome if the vectors are either identicalor their values differ by a constant factor. For cosine similarities resulting in a value of 0, thecovenant definitions do not share any attributes (or words) because the angle between theword vectors is 90 degrees. The cosine similarity is computed as:

cos Ɵ = T1·T2 / ||T1||*||T2|| = 0.6758

where the vector product is T1·T2 = 2*3 + 3*7+ 5*1 and the normalized vectors are computedas ||T1|| = sqrt(22 + 32 + 52) and ||T2|| = sqrt(32 + 72 + 12).

To obtain a loan specific covenant standardization measure, we average the cosine similarities ofthe covenants in a loan with the same-type covenants in all other borrowers’ loans that wereissued in the prior year. Thus, the Covenant Similarity Score is a continuous variable with valuesranging from zero (if two covenants share no common word) to one (if the definitions of twosame-type covenants are identical).

38

Appendix C

Variable definitions

Variable Definition

Cash Flow VolatilityThe standard deviation of borrower’s operating cash flows over the last five years,deflated by total assets.

Covenant Similarity ScoreThe average similarity score at the loan level of covenant i in loan k with covenant jin loan m only if i and j are of the same covenant type. See Appendix B for furtherdetail.

Lending RelationshipThe ratio of total loan size a borrower took from the lead arranger in the past to totalsize of loans the borrower took in the past.

Leverage Total liabilities to total assets.

LIBOR-spread The natural logarithm of all-in-drawn LIBOR-spread of the Term B tranche.

Liquidity Current assets to current liabilities.Loan Amount The natural logarithm of the loan amount.

Loan DefaultBinary variable that equals one if the borrower defaulted on a securitized loan, andzero if the borrower did not default on a securitized loan.

Loan DistributionAnnual change in the number of CLOs holding at least one tranche of a securitizedloan, divided by the average number of CLOs holding a tranche of a securitized loanin the same year.

Loan TradesLoan sales and purchases less purchases where both transacting parties are CLOs fora securitized loan in a year, divided by the average trading activity of a securitizedloan in the same period.

Loan Rating DifferenceThe average difference between Moody's and Standard & Poor’s loan rating overour sample period.

Loan Maturity The natural logarithm of loan maturity (in months).

No covenantsBinary variable that equals one if a loan contract does not include financialcovenants, and zero otherwise.

Number of Covenants The number of financial loan covenants, including net worth covenants.

Pct. Same CovenantsAverage number of the same financial covenants with other loans originated in thelast year to the number of financial loan covenants.

Revolving TrancheBinary variable that equals one if the loan includes a revolving tranche, and zerootherwise.

ROA Operating income to total assets.

Same Loan RatingThe number of quarters a securitized loan's S&P and Moody's rating are the same,divided by the number of quarters the loan is held by CLOs. S&P (Moody's) loanrating is a scale variable, where 1=AAA,…, 25=D.

Securitized LoanBinary variable that equals one if the loan includes at least one securitized trancheand zero otherwise.

Size The natural logarithm of total assets.

Syndicates The natural logarithm of the number of co-syndicates in the loan.

Time-on-MarketThe number of days the loan remains outstanding in the primary loan market (Closedate- Launch date).

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Figure 1: Covenant similarity score

Figure 1 reports the average covenant similarity score for our sample of 703 institutional non-securitized and 440 securitized loans in2000–2009 (primary axis), including covenant-lite loans (covenant similarity=1). The pattern looks similar for the sub-sample of 608institutional non-securitized and 385 securitized loans in 2000–2009, excluding covenant-lite loans (primary axis). Using our sampleof 1,143 corporate loans, the percentage of securitized loans is estimated as total number of securitized loans issued in a year dividedby annual total loan issuance (secondary axis). The percentage of same covenants is the ratio of the same covenants a loan shares to allother loans in the sample of 1,143 loans, divided by the number of covenants in the loan.

Figure 2: Covenant similarity score, securitized vs. institutional loans

Figure 2 reports the average covenant similarity score for our sample of 703 institutional non-securitized and 440 securitized loans in2000–2009 (primary axis), including covenant-lite loans (covenant similarity=1).

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.35

0.40

0.45

0.50

0.55

0.60

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

%

Covenant similarityscore

Covenantsimilarity+Covenant-liteloansPercentage ofsecuritized loans

Percentage of samecovenants

0.40

0.42

0.44

0.46

0.48

0.50

0.52

0.54

0.56

0.58

0.60

Non-securitized loans

Securitized loans

40

Table 1

Descriptive Statistics on financial covenants

Panel A: Loans and financial covenants by year

Year Number ofloans

Pct. ofsecuritized

loans

Pct. ofcovenant-lite

loans

Number offinancialcovenants

Average number ofcovenants per

contract

2000 93 0.15 0.12 292 3.56

2001 135 0.08 0.13 386 3.30

2002 90 0.20 0.13 274 3.51

2003 109 0.31 0.09 365 3.69

2004 120 0.42 0.15 372 3.68

2005 110 0.49 0.15 334 3.59

2006 139 0.53 0.17 390 3.36

2007 194 0.68 0.17 481 2.90

2008 96 0.23 0.10 266 3.09

2009 57 0.47 0.08 143 3.18

Total 1,143 0.38 0.13 3,303 3.39

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Panel B: Financial covenant types

Covenant Type (Restated) N Securitized loans Non-securitized loans

MaxCapex 387 232 155

MaxDebt 50 8 42

MaxDebtEbitda 212 74 138

MaxDebtEquity 99 12 87

MaxDebtNW 69 15 54

MaxLeverage 910 449 461

MinDebtServiceCoverage 51 12 39

MinEBITDA 137 56 81

MinFixedChargeCoverage 413 161 252

MinInterestCoverage 612 259 353

MinLiquidity 87 20 67

MinNetWorth 271 57 214

Other 5 0 5

Total 3,303 1,355 1,948

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Table 2

Summary statistics

Variable N Mean S.D. Min 0.25 Mdn 0.75 Max

Covenant Similarity Score 1,143 0.49 0.22 0.17 0.37 0.44 0.51 1.00

Pct of Same Covenants 1,143 0.45 0.27 0.09 0.25 0.42 0.57 1.00

Securitized Loan 1,143 0.38 0.49 0.00 0.00 0.00 1.00 1.00

Number of Covenants 1,143 2.58 1.60 0.00 2.00 3.00 4.00 6.00

LIBOR-spread 1,143 5.42 0.45 3.82 5.21 5.52 5.58 6.48

Loan Amount 1,143 19.83 1.11 16.52 19.11 19.76 20.53 24.12

Loan Maturity 1,143 4.02 0.38 3.40 3.74 4.07 4.33 5.77

Revolving Tranche 1,143 0.57 0.49 0.00 0.00 1.00 1.00 1.00

Lending Relationship 1,143 0.23 0.37 0.00 0.00 0.00 0.44 1.00

Syndicates 1,143 1.81 0.91 0.00 1.10 1.95 2.48 3.18

Time-on-Market 377 29.47 14.43 0.00 20.00 30.84 31.00 85.00

Liquidity 1,143 1.58 0.64 0.53 1.14 1.66 1.69 3.42

ROA 1,143 0.06 0.05 -0.07 0.04 0.06 0.09 0.19

Leverage 1,143 0.39 0.21 0.01 0.24 0.40 0.49 0.92

Cash Flow Volatility 1,143 0.03 0.02 0.00 0.01 0.03 0.03 0.11

Size 1,143 7.79 1.11 5.63 7.05 7.80 8.39 10.29

Number of Trades 1,250 1.06 0.82 0.00 0.20 1.17 1.46 5.75

Loan Distribution 1,019 0.22 0.64 -1.42 0.00 0.10 0.40 2.96

Loan Default 440 1.22 3.65 0.00 0.00 0.00 1.00 35.00

Same Loan Rating 440 0.37 0.30 0.00 0.09 0.35 0.50 1.00

Loan Rating Difference 440 0.76 0.56 0.00 0.50 0.76 0.80 6.00

Variables are defined in Appendix C. The values of the continuous variables are winsorized at 1% and 99%.

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Table 3

Loan, borrower and covenant characteristics: securitized versus non-securitized loans

Panel A: Borrower and loan characteristics

Securitized Non-Securitized t-stat.Loans Loans

Covenant Similarity Score 0.51 0.48 -2.03 ***

(0.20) (0.23)Number of Covenants 2.76 2.47 -2.90 ***

(1.70) (1.53)LIBOR-spread 5.37 5.45 2.87 ***

(0.47) (0.44)Loan Amount 20.10 19.65 -6.74 ***

(1.15) (1.06)Loan Maturity 4.10 3.97 -5.66 ***

(0.26) (0.42)Revolving Tranche 0.70 0.49 -7.27 ***

(0.46) (0.50)Lending Relationship 0.19 0.25 2.92 ***

(0.35) (0.37)Syndicates 1.91 1.75 -2.98 ***

(0.79) (0.97)Time-on-Market 26.70 32.89 4.24 ***

(14.66) (13.43)Liquidity 1.59 1.58 -0.39

(0.63) (0.64)ROA 0.07 0.06 -1.29

(0.05) (0.05)Leverage 0.45 0.34 -8.87 ***

(0.22) (0.19)Cash Flow Volatility 0.03 0.03 -0.09

(0.02) (0.02)Size 7.62 7.90 4.05 ***

(1.14) (1.08)

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Panel B: Covenant similarity score by covenant type

Covenant Similarity Score

Covenants in Covenants in t-stat.securitized loans non-securitized loans

MaxCapex 0.35 0.33 -6.75 ***

(0.12) (0.13)

MaxDebt 0.41 0.31 -4.84 ***

(0.12) (0.11)

MaxDebtEbitda 0.41 0.40 -50.87 ***

(0.11) (0.12)

MaxDebtEquity 0.45 0.41 -2.48 ***

(0.14) (0.14)

MaxDebtNW 0.23 0.29 1.74 ***

(0.12) (0.14)

MaxLeverage 0.47 0.43 -37.77 ***

(0.13) (0.14)

MinDebtServiceCoverage 0.30 0.27 -0.30(0.12) (0.15)

MinEBITDA 0.41 0.38 -8.79 ***

(0.12) (0.14)

MinFixedChargeCoverage 0.47 0.46 -12.47 ***

(0.12) (0.12)

MinInterestCoverage 0.49 0.46 -23.32 ***

(0.12) (0.12)

MinLiquidity 0.55 0.44 -1.87 *

(0.06) (0.11)

MinNetWorth 0.26 0.23 -9.50 ***

(0.14) (0.11)Total 0.45 0.39 -51.36 ***

(0.14) (0.15)

Variables are described in Appendix C. Standard deviations reported in parentheses. All values of the continuous variables arewinsorized at 1% and 99% level. ***Significant at 1%, ** 5% and * 10% level.

45

Table 4

Covenant Similarity Score – Validation test

Covenant Similarity Score

Variable Coeff. t-stat.

D(Number of Covenants) -0.003 *** -4.22

D(LIBOR-spread) -0.002 *** -5.61

D(Loan Amount) -0.001 -1.42

D(Maturity) -0.023 *** -29.95

Same Lender 0.003 *** 2.15

D(Lending Relationship) 0.001 0.86

Same Loan Purpose 0.001 1.02

D(Syndicates) -0.009 *** -14.04

Same Industry 0.006 *** 2.90

D(Liquidity) -0.002 ** -1.96

D(ROA) -0.132 *** -11.68

D(Leverage) -0.011 *** -4.20

D(Cash Flow Volatility) -0.092 *** -4.22

D(Size) 0.000 -0.62

Constant 0.351 *** 12.81

N= 79,134R2= 0.28

The dependent variable is the Covenant Similarity Score defined as the average similarity score at the loan level of covenant i in loan kwith covenant j in loan m only if i and j are of the same covenant type. Same Lender equals one if the loans are issued by the same leadlender, and zero otherwise. Same Loan Purpose equals one if the loans have the same purpose, and zero otherwise. Same Industry equalsone if borrowers are from the same industry (12-industry FF), and zero otherwise. All other independent variables are the absolute valuesof the differences in loan and borrower characteristics where the financial covenants refer to. Variables are defined in Appendix C.Covenant type, lead lender, industry (12 industry portfolios), year of loan origination and loan purpose fixed effects included. The valuesof the continuous variables are winsorized at 1% and 99%. Standard errors are corrected for heteroskedasticity; cluster is at the loan level.*** Significant at 1%, ** 5% and * 10% level, two-tailed tests.

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Table 5

Securitization and Covenant Standardization

Panel A: Securitization and Covenant Standardization

All loansPct. of Same Covenants Covenant Similarity Score

I II IIIVariable Coeff. t-stat. Coeff. t-stat. Coeff. t-stat.

Securitized Loan 0.036 ** 2.18 0.045 *** 2.96 0.021 *** 2.41

Number of Covenants -0.083 *** -19.30 -0.051 *** -11.56 0.007 *** 2.68

LIBOR-spread 0.045 *** 2.55 0.049 *** 2.86 0.014 * 1.68

Loan Amount -0.002 -0.22 0.005 0.61 0.009 ** 1.62

Loan Maturity 0.092 *** 4.29 0.075 *** 3.84 0.020 1.58

Revolving Tranche -0.045 *** -2.50 -0.035 ** -2.10 -0.002 -0.24

Syndicates -0.022 ** -2.26 -0.034 *** -3.77 -0.019 *** -3.78

Lending Relationship -0.038 ** -2.15 -0.040 *** -2.62 -0.013 -1.25

Liquidity 0.006 0.61 -0.004 0.16 -0.007 -1.17

ROA -0.142 -0.93 -0.016 -0.12 0.055 0.73

Leverage 0.123 *** 3.00 0.082 ** 2.08 0.007 0.27

Cash Flow Volatility 0.216 0.69 0.144 0.52 0.010 0.06

Size -0.008 -0.84 0.004 0.40 0.005 0.81

Pct of Same Covenants 0.687 *** 32.27Constant 0.227 0.72 0.114 0.67 0.392 * 1.60

N= 1,143 N= 1,143 N= 1,143R2= 0.55 R2= 0.57 R2= 0.80

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Panel B: Securitization and Covenant Standardization: Cross-Sectional Tests

Highly securitized andnon-securitized loans

Highly securitizedloans

Securitized loans atorigination and non-

securitized loans

Securitized loans afterorigination and non-

securitized loans

Companies withsecuritized and non-

securitized loans

Covenant Similarity Score(IV) (V) (VI) (VII) (VIII)

Variable Coeff. t-stat. Coeff. t-stat. Coeff. t-stat. Coeff. t-stat. Coeff. t-stat.

Securitized Loan 0.061 *** 2.87 0.037 *** 2.09 0.061 *** 2.90 0.047 *** 2.68 0.077 ** 1.99

Number of Covenants -0.052 *** -10.58 -0.039 *** -7.99 -0.061 *** -10.51 -0.050 *** -10.85 -0.059 *** -5.29

LIBOR-spread 0.039 ** 2.07 0.082 *** 3.52 0.029 1.48 0.047 *** 2.55 0.109 *** 2.71

Loan Amount 0.005 0.47 0.005 0.42 0.015 1.43 0.004 0.39 -0.028 -1.21

Loan Maturity 0.073 *** 3.36 0.050 1.48 0.073 *** 3.23 0.078 *** 3.80 0.126 *** 2.76

Revolving Tranche -0.032 * -1.74 -0.033 -1.34 -0.033 * -1.69 -0.035 * -1.91 -0.014 -0.37

Syndicates -0.029 *** -2.92 -0.008 -0.62 -0.034 *** -3.26 -0.033 *** -3.39 -0.024 -1.01

Lending Relationship -0.032 * -1.78 -0.102 *** -5.50 -0.010 -0.54 -0.036 ** -2.17 -0.023 -0.45

Liquidity 0.005 0.44 -0.007 -0.66 0.007 0.57 0.000 0.04 0.011 0.45

ROA -0.147 -0.92 -0.031 -0.16 -0.008 -0.05 -0.118 -0.78 0.841 ** 2.11

Leverage 0.107 ** 2.27 0.076 1.58 0.085 * 1.85 0.066 1.50 0.183 ** 2.06

Cash Flow Volatility 0.214 0.65 -0.373 -1.04 0.345 1.03 0.019 0.61 -0.001 0.50

Size -0.003 -0.03 0.016 1.25 -0.013 -1.20 0.005 0.52 0.017 0.68

Constant 0.252 1.06 0.169 0.53 0.175 0.67 0.147 0.50 0.229 0.65

N= 926 N= 440 N= 828 N= 1,018 N= 171R2= 0.56 R2= 0.46 R2= 0.61 R2= 0.49 R2= 0.72

48

Panel C: Treatment Model

Securitized = 1Variable Coeff. z-stat. Coeff. z-stat. Coeff. z-stat.

Number of Covenants 0.072 * 1.78 0.027 0.63

LIBOR-spread 0.413 *** 2.69 0.167 0.98

Loan Amount 0.320 *** 4.86 0.638 *** 6.84

Loan Maturity 0.579 *** 3.16 0.113 0.57

Revolving Tranche 0.853 *** 5.37 0.526 *** 2.94Syndicates -0.055 -0.62 -0.003 -0.04Lending Relationship -0.339 ** -1.94 -0.450 *** -2.33

Liquidity 1.205 1.25 0.039 0.36

ROA 0.007 0.07 0.664 0.61

Leverage 1.816 *** 2.55 1.943 *** 5.21Cash Flow Volatility -1.562 -0.57 -0.835 -0.28Size -0.210 *** -3.56 -0.512 *** -5.49

Constant -11.698 *** -6.75 0.889 1.58 -11.493 *** -6.15

N= 1,143 N= 1,143 N= 1,143Pseudo- R2= 0.09 Pseudo- R2= 0.03 Pseudo- R2= 0.14

49

Treatment loans Securitized Loans

Matched loans Matched on loancharacteristics

Matched onborrower

characteristics

Matched onborrower and loan

characteristicsNumber of treatment loans 440 440 440

Number of matched pairs 394 389 327

Difference in CovenantSimilarityMean (treatment-match) 0.04 0.04 0.04

t-statistic 2.55 2.29 2.44

Balance summary statistics:p > chi2 – Raw 0.00 0.00 0.00p > chi2 – Matched 0.98 0.83 0.99

Mean bias – Raw 21.90 11.35 18.95

Mean bias – Matched 3.60 3.10 3.30

The table reports the tests for the relation between loan securitization and financial covenant standardization. Panel A reports the baseline OLS regression results. Panel B reportscross-sectional tests. The dependent variable in the first column is the Pct of Same Covenants, defined as the ratio of same covenants a loan has compared to all other loans in thesample originated in the last year to the total number of covenants in the loan. The dependent variable in all other specifications is the Covenant Similarity Score, defined as theaverage textual cosine similarity of the financial covenants in a loan compared to covenants in loans to different borrowers originated in the last year. In specification (IV), thesample includes non-securitized loans and securitized loans with more than 80 percent of their size being securitized. In the next two specifications, the sample includes non-securitized loans and loans securitized upon origination (V) or sold subsequently to CLOs (VI). In specification (VIII), we eliminate our sample to companies that issued bothsecuritized and non-securitized loans in our sample period. Panel C presents the diagnostic results for the propensity score matching tests. The treatment is whether a loan issecuritized, and the outcome variable is the Covenant Similarity Score, defined as the average textual cosine similarity of the financial covenants in a loan compared to covenantsin loans to different borrowers originated in the last year. The one-to-one matching of treated loans is done in random order and without replacement. Matching loans are within adistance (“caliper”) of 0.01 of the propensity score of the loans in the treatment group. The average treatment effect, t-statistic and balance statistics for the matching procedure arereported. All variables are defined in Appendix C. Lead lender, industry (12 industry portfolios), year of loan origination and loan purpose fixed effects included. The values of thecontinuous variables are winsorized at 1% and 99%. Standard errors are corrected for heteroskedasticity; cluster is at the borrower level (except specification (IV)). *** Significantat 1%, ** 5% and * 10% level, two-tailed tests.

50

Table 6

Covenant Standardization and Loan liquidity

Panel A: Covenant standardization and liquidity in the primary market

Time-on-Market

Variable Coeff. t-stat.

Covenant Similarity Score -4.189 -0.75

Securitized Loan -6.031 *** -2.32

Covenant Similarity Score *Securitized Loan -12.293 ** -2.15

Number of Covenants 0.473 0.78

Loan Amount -0.300 -0.27

Loan Maturity 1.570 0.89

Revolving Tranche 2.546 * 1.26

Lending Relationship -3.026 -1.05

Syndicates 0.350 0.32

Liquidity -1.698 -1.14

ROA -25.455 * -1.64

Leverage -11.782 *** -3.26

Cash Flow Volatility -24.740 -0.74

Size -2.359 *** -2.53

Constant 72.557 *** 3.52

N= 343

R2= 0.38

The table reports the tests for the relation between financial covenant standardization and loan liquidity in the primary loanmarket. The dependent variable is the number of days a loan remains open in the primary market (Time-on-Market). The sampleincludes 343 loans issued in 2000-2007. All variables are defined in Appendix C. Industry (12 industry portfolios), year of loanorigination and loan purpose fixed effects included. The values of the continuous variables are winsorized at 1% and 99%.Standard errors are corrected for heteroskedasticity; cluster is at the borrower level. ***Significant at 1%, ** 5% and * 10%level, two-tailed tests.

51

Panel B: Covenant standardization and liquidity in the secondary loan market

The table reports the tests for the relation between financial covenant standardization and loan liquidity in the secondary loan market. All variables are defined in Appendix C.Industry (12 industry portfolios), year of loan origination and loan purpose fixed effects included. The values of the continuous variables are winsorized at 1% and 99%. Standarderrors are corrected for heteroskedasticity; cluster is at the borrower level. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

Loan trades Loan Distribution

Variable Coeff. t-stat. Coeff. t-stat.

Covenant Similarity Score 0.492 *** 2.29 0.285 *** 2.57

Number of Covenants 0.003 0.20 0.020 1.57

LIBOR-spread 0.081 0.87 0.095 1.51

Loan Amount 0.318 *** 8.72 0.046 * 1.80

Loan Maturity 0.382 *** 2.71 0.008 0.07

Revolving Tranche 0.403 *** 4.41 0.029 0.52

Syndicates 0.051 * 1.93 0.037 1.04

Liquidity 0.017 0.26 0.087 *** 2.46

ROA 1.678 ** 2.11 1.170 *** 2.38

Leverage 0.820 *** 4.31 -0.167 -1.10

Cash Flow Volatility 1.290 0.82 1.310 1.07

Size 0.466 *** 11.49 0.042 ** 1.98

Constant 0.688 0.62 -1.588 -1.59

N= 1,250 N= 1,019

R2= 0.39 R2= 0.08

52

Table 7

Covenant Standardization and Loan Spread

LIBOR-spread Securitized Loan Default= 1

Variable Coeff. t-stat. DF/dx z-stat.

Covenant Similarity Score -0.206***

-2.32 0.117 0.21

LIBOR-spread 0.052 * 1.63

Number of Covenants 0.009 1.25 0.009 1.30

Loan Amount -0.046**

-2.13 0.030 *** 3.57

Loan Maturity -0.047 -0.74 0.086 *** 2.62

Revolving Tranche -0.122***

-2.94 0.047 0.92

Lending Relationship -0.029 -1.25 0.058 -0.59

Syndicates -0.015 -0.42 0.030 -0.16

Liquidity -0.006 -0.29 0.031 -1.41

ROA -1.073***

-3.59 0.440 -0.27

Leverage 0.163*

1.76 0.110 -0.74

Cash Flow Volatility 1.185*

1.80 0.964 0.43

Size 0.005 0.23 0.031 * -1.89

Constant 7.110 *** 19.40

N= 440 N= 415

R2= 0.36 Pseudo-R2= 0.22

The table reports the tests for the relation between financial covenant standardization, LIBOR-spread and the probability of aborrower’s defaulting on a securitized loan. LIBOR-spread is the all-in-drawn LIBOR-spread of the term B loan tranche. Defaultequals one if a borrower defaulted on a securitized loan in the period 2008-2013, and zero otherwise. In the second column, weuse a probit model, and marginal effects are reported. All variables are defined in Appendix C. Lead lender (only in specificationI), industry (12 industry portfolios), year of loan origination and loan purpose fixed effects included. The values of the continuousvariables are winsorized at 1% and 99%. Standard errors are corrected for heteroskedasticity; cluster is at the borrower level.***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

53

Table 8

Covenant Standardization and Loan ratings

Same Rating Loan Rating Difference

Variable Coeff. t-stat. Coeff. t-stat.

Covenant Similarity Score 0.363 *** 4.18 -0.800 *** -4.36

Number of Covenants 0.023 *** 2.83 -0.028 * -1.79

LIBOR-spread 0.009 0.21 0.078 1.11

Loan Amount 0.024 1.18 0.018 0.43

Loan Maturity -0.053 -0.78 0.170 1.33

Revolving tranche -0.080 ** -2.07 0.061 0.93

Liquidity 0.006 0.23 -0.102 ** -2.01

ROA 0.487 * 1.74 -1.663 *** -3.41

Leverage -0.004 -0.06 0.111 0.69

Cash Flow Volatility -0.181 -0.27 1.446 1.01

Size -0.005 -0.25 -0.067 * -1.73

Constant -0.392 -0.84 1.837 * 1.89

N= 440 N= 440R2= 0.11 R2= 0.15

The table reports the tests for the relation between financial covenant standardization and S&P and Moody’s loan ratingagreement. The dependent variable in specification I is the number of quarters that S&P and Moody’s issued the same loan ratingfor a securitized loan divided by total number of quarters the loan was held by CLOs (Same Rating). The dependent variable inspecification II is the absolute value of the average difference in quarterly loan ratings issued by S&P and Moody’s in 2008-2011(Loan Rating Difference). All variables are defined in Appendix C. Industry (12 industry portfolios), year of loan origination andloan purpose fixed effects included. The values of the continuous variables are winsorized at 1% and 99%. Standard errors arecorrected for heteroskedasticity; cluster is at the borrower level. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.