Measuring Financial Covenant Strictness in Private Debt Contracts

Transcription

1 Measuring Financial Covenant Strictness in Private Debt Contracts Peter R. Demerjian Foster School of Business, University of Washington Edward L. Owens Simon School of Business, University of Rochester January 2014 Abstract We examine the measurement of financial covenant strictness in private debt contracts using Dealscan data. Based on analysis of detailed covenant definitions from the Tearsheets dataset, for each Dealscan covenant type we specify a "standard" covenant definition that can be computed using Compustat. For most covenants, the average error induced by using our standard definition rather using the precise covenant definitions is insignificant. Applying these findings, we compute a single Dealscan-based comprehensive measure of financial covenant strictness that utilizes information about slack, volatility of underlying covenant parameters, and their covariance across the entire set of financial covenants included in a contract. We provide evidence that this measure is superior to alternative measures of covenant strictness used in prior literature. Although measurement error undoubtedly exists, our evidence endorses a comprehensive approach to measuring covenant strictness using the full breadth of covenant slack data available in Dealscan. * We appreciate the helpful comments of Dan Amiram, Anna Costello, Ilia Dichev, Valeri Nikolaev, Regina Wittenberg-Moerman, Jerry Zimmerman, and participants at the AAA FARS 2014 Midyear meeting. Demerjian gratefully acknowledges the financial support of the Goizueta Business School and the Foster School of Business. Owens gratefully acknowledges the financial support of the Simon School of Business. Corresponding author. Phone: ; pdemerj@uw.edu.

2 1. Introduction Recent years have seen a renewed interest in accounting research on debt contracting, and particularly on studies examining the inclusion and implications of accounting-based debt covenants. These questions are not new to the research; debt contracts and covenants play an important role in the development of positive accounting theory (Watts and Zimmerman 1978, 1986). The increase in research volume can be attributed, at least in part, to the introduction of LPC/Dealscan, a database of private loan agreements, in the late 2000s. Dealscan provides wide coverage of private loan issuance starting in the 1990s, and includes details such as lender and loan type, collateral requirements, and covenant inclusion. Prior to Dealscan, studies examining debt covenants required hand-collection from SEC filings, which generally resulted in small samples of less than 150 observations (e.g. El-Gazzar and Pastena 1991; Beneish and Press 1993, 1995; DeFond and Jiambalvo 1994; Sweeney 1994). Dealscan provides details on thousands of private loan contracts, and given the breadth of Dealscan s coverage of private loans, has allowed researchers to test hypotheses in large-sample, generalizable settings (e.g., Dichev and Skinner 2002). 1 Many studies on financial covenants require a measure of the strictness, or slack, of covenants. Covenant slack is defined as the difference between the required threshold value and the actual value of the covenant measure. For example, an interest coverage covenant may require a borrower to maintain earnings at a minimum of three times interest, indicating a threshold value of three. If the actual ratio of earnings to interest is six, this ratio can decline by three before the borrower is in default on the covenant. If the actual ratio is nine, the borrower 1 Chava and Roberts (2008) find that Dealscan covers 50%-75% of private loans in the early-1990s, and that their coverage rate increases in the mid-1990s. 1

3 has further space to decline that is, more slack before default. 2 Covenant slack holds a significant place in the positive theory of accounting: as initially described in Watts and Zimmerman (1986), the debt covenant hypothesis predicts that borrowers close to covenant thresholds will make income-increasing accounting choices to avoid costly technical default. More generally, covenant slack is considered an ex post proxy for borrower riskiness or the degree of agency conflicts. While Dealscan is an effective data source for research questions examining covenant inclusion, it is considerably less so for those exploring slack. This is because, while Dealscan provides information on what general type of covenant is used and its threshold value, it does not provide sufficient detail to calculate the actual value of the covenant measure. For example, interest coverage is generally defined as the ratio of earnings to interest expense. However, earnings can be net income, EBIT, EBITDA, or some other measure, and interest expense can be accrual- or cash-basis. Given Dealscan s fairly coarse set of categories it includes 15 classes of financial covenants in all and the presumed variation in covenant measures within each group, measuring covenant slack with Dealscan data has generally been avoided due to the potential for measurement error. A number of studies have mitigated this concern by focusing on one or two financial covenants where measurement error is assumed to be the lowest. 3 A significant drawback of this sort of analysis is that, by focusing on a subset of covenants and ignoring others, overall covenant strictness could be under- or overreported. Moreover, it ignores the customization of covenants that is common in private loans (Leftwich 1983). 2 There are a number of ways to calculate slack; we discuss ours in Section 4. 3 Dichev and Skinner (2002) and Frankel and Litov (2007) focus on current ratio and net worth. Kim (2010) and Kim et al. (2010) study net worth alone. Franz et al. (2012) examine only current ratio. Demiroglu and James (2012) study current ratio and debt-to-ebitda covenants. 2

4 In this study, we propose a way to use Dealscan to measure covenant strictness for all covenants, not just those with relatively homogeneous measurement. The analysis which we use to justify this approach proceeds in several steps: documenting the variation in covenant measures, determining standard covenant measures based on empirical observation, and quantifying the measurement error induced by using our standard measures. To document measurement variation, we start by collecting a sample of loans from Dealscan s Tearsheets database. Tearsheets, a database covering a subset of Dealscan loans, provides more detailed information, including specific covenant definitions that allow calculation of precise covenant slack. However, the data in Tearsheets is not machine-readable, making it difficult to use for large-sample studies. We hand-collect and code data on 5,278 financial covenants from 2,100 loans. Using the same 15 covenant definitions as Dealscan, we find considerable variation in the degree of homogeneity in covenant measurement. For example, Current Ratio covenants feature just ten different definitions across 283 covenants, including 270 (95.4%) measured as current assets over current liabilities. In contrast, we document 356 different definitions across 592 Fixed Charge Coverage covenants, suggesting this measure is extremely heterogeneous and likely customized to the features of the borrower. This descriptive evidence is consistent with prior evidence that there is significant variation in measurement across individual covenants (Leftwich 1983; El-Gazzar and Pastena 1990). Next, we quantify the measurement error induced by heterogeneity in covenant measurement. For each of the 15 financial covenant categories from Dealscan, we determine a standard definition which will be applied to Dealscan covenant information: this standard definition is the most common definition for the covenant used in Tearsheets. For example, of the 953 interest coverage covenants in Tearsheets, 725 (76.1%) are defined as EBITDA divided 3

5 by accrual-basis interest expense, making this definition the standard for interest coverage. 4 We proceed to measure slack for each covenant in Tearsheets using both our standard definition and the true definition as revealed by loan-specific detail in Tearsheets. Returning to interest coverage, we know that 76% of these covenants are actually measured with the standard measure meaning these covenants have no measurement error. For the remaining 24% of interest coverage covenants with actual definitions that deviate from the standard, we document the measurement error obtained by using the standard measure instead of the actual Tearsheets definition. Next, we analyze the error in measurement of initial slack, that is, the slack at contract inception. We tabulate the average differences between the Tearsheets-based slack and the standard definition-based slack. We find that for most covenants, the average error is insignificantly different from zero, suggesting that in most cases the standard measure serves as a reasonable proxy for measuring the initial slack of covenants when the precise definition is not known. This evidence suggests that, although measurement error undoubtedly exists, it is likely not as serious as the prior literature has generally presumed. Further, we believe the benefits of measuring covenant slack for all loans in Dealscan using the standard measures presented in our study outweighs the cost of measurement error. Finally, these results suggest that researchers can develop a comprehensive measure of covenant slack based on all the covenants in the loan package (rather than analyzing covenants individually, or measuring just a few). One such comprehensive approach has been offered by Murfin (2012), who uses Dealscan data to 4 We are able to adopt a standard definition for fourteen of the fifteen Dealscan covenant categories based solely on an ex ante analysis of Tearsheets. The exception is fixed charge coverage, which displays sufficient definitional heterogeneity that we cannot initially select a standard measure. We select a standard for fixed charge coverage that minimizes measurement error; we discuss this in Section 4. 4

6 compute an aggregate measure of covenant strictness incorporating the number of covenants, covenant slack, and the covariance of change in covenant values. We compute a similar measure of aggregate strictness, using Dealscan data and applying our standard covenant measures. 5 As a validation test, we examine whether our aggregate strictness measure is associated with financial covenant violations. Using a sample of actual technical defaults provided by Nini et al. (2012), we show that our Dealscan-based aggregate strictness measure is a significant predictor of technical default. Further, after controlling for aggregate strictness, neither the number of financial covenants nor the net worth covenant slack are associated with technical default over the life of the loan. Perhaps more strikingly, after controlling for aggregate strictness, the number of covenants attached to a loan is negatively associated with actual covenant violation during the first year of a loan. We acknowledge that the lack of precise covenant definitions is not the sole source of potential measurement error in using Dealscan to compute covenant slack. For instance, even if Dealscan provided exact, precise definitions for every contractual component, some financial statement data are not readily available to researchers (i.e. in Compustat) that would enable computation of the precise covenant definition. 6 Also, as is the case with any data source that requires data entry from source documents, it is possible that Dealscan may misclassify or omit covenants from actual loan contracts. Further, we use Tearsheets data as a proxy for actual loan contract data. To the extent that Tearsheets does not provide all contractual information (e.g. precise definitions of the components of covenant measures), the may also introduce measurement error. Finally, we analyze Tearsheets data to make inferences about the broader 5 Our formulation, while following Murfin (2012), differs in several important ways, including in the number of covenant categories and the measurement of slack; these are discussed in greater detail in Section 5. 6 Some covenant definitions feature elements not disclosed in Compustat, such as junior interest expense, investment fees, unamortized bond discount, and interest associated with capital leases. Additionally, covenants can sometimes be defined with vague parameters, such as non-cash items that cannot be accurately measured. 5

7 population of Dealscan loans. To the extent that the Tearsheets and Dealscan loan universes differ, evidence based on Tearsheets may not be generalizable to Dealscan loans. We explore these potential problems in Section 3.4, and conclude that they introduce minimal additional measurement error into our analysis. The paper proceeds as follows: Section 2 discusses the background and motivation for our study. Section 3 describes the data. Section 4 discusses our general research design and empirical findings. Section 5 presents a measure of aggregate covenant strictness based on the full breadth of covenant data in Dealscan, along with associated validity tests. Section 6 concludes. 2. Background Financial covenant slack the difference between the threshold value and the initial value of the financial covenant measure serves an important role in positive accounting theory. Watts and Zimmerman (1986) propose the debt covenant hypothesis, which predicts that firms close to covenant thresholds (i.e. with low slack) have incentives to make income-increasing accounting choices. Early empirical work in this area operationalized overall covenant strictness with borrower financial leverage; since contract terms were unobservable, these studies assumed firms with high leverage were closer to violating financial covenants (Duke and Hunt 1990). 7 The introduction of the Dealscan database in the late 1990s facilitated a new wave of research on private lending agreements, with a focus on financial covenant structure. A key early paper is Dichev and Skinner (2002), which examines the debt covenant hypothesis using Dealscan data. In their study, they discuss the advantages that Dealscan provides to researchers: the database has broad coverage of private loans, provides details on many contract provisions 7 Watts and Zimmerman (1986) originally describe the debt/equity hypothesis and use leverage as an empirical proxy for covenant strictness. 6

8 (such as loan amount, interest rate, collateral data, covenants), and most important for researchers is machine-readable. Recent studies examining the types of financial covenants in private loan contracts include Demerjian (2011), Zhang (2011), and Christensen and Nikolaev (2012). While Dealscan has allowed researchers to address a number of research questions related to debt and covenants, it is not without drawbacks. The most serious one, in terms of research involving financial covenants, is a lack of data on the exact definitions of covenants. For example, Dealscan may indicate a loan includes an interest coverage covenant with a threshold value of three. To calculate slack, the researcher must be able to measure the value of interest coverage and compare this to the required threshold. However, Dealscan does not provide the specific definition of interest coverage employed in the contract. So, while interest coverage is generally defined as the ratio of earnings to interest expense, earnings could take on many different definitions (e.g. net income, EBIT, EBITDA, etc.) and interest could be accrualor cash-basis. Given that covenants are frequently customized (Leftwich 1983; El Gazzar and Pastena 1990), not knowing the exact definition used in the contract introduces potential measurement error. Dichev and Skinner (2002) acknowledge this shortcoming of the data and adjust their research design accordingly. Specifically, they conduct their analysis using only current ratio and net worth covenants, which they consider most homogenously measured. A number of studies follow the same approach. Frankel and Litov (2007) also study current ratio and net worth covenants; Kim (2010) and Kim et al. (2010) examine net worth alone; Franz et al. (2012) study only the current ratio; and Demiroglu and James (2010) focus on current ratio and debt-to- EBITDA covenants. Other studies, such as Bradley and Roberts (2004) and Billett et al. (2007), 7

9 do not try to measure slack, but rather use the number of covenants as a proxy for overall financial covenant strictness. While it is possible that more covenants may mean more overall strictness, this is not necessarily the case. For example, a loan with one tightly set covenant could be more likely to enter technical default than a loan with three loosely set covenants. Murfin (2012) consolidates these two approaches into a single, aggregate measure of covenant strictness. Specifically, for each loan he uses the number of covenants, the estimated slack of these covenants, and the covariance between different covenant measures to compute the probability of default for any covenant in the loan package. To estimate slack, Murfin (2012) relies on one definition per covenant type potentially introducing the measurement error noted in Dichev and Skinner (2002). Murfin (2012) acknowledges this problem (p. 1573), but suggests that any measurement error will be absorbed in the model s error since he uses aggregate strictness as a dependent variable. This provides little comfort to researchers wanting to use aggregate strictness as an independent variable, as is often the case. 3. Data sources and descriptive analysis 3.1. Data sources Our main sources of loan data are Dealscan and Tearsheets. Dealscan is machinereadable and provides a variety of details on loans, including the use of specific financial covenants (but not sufficient detail to calculate precise slack). Our computation of aggregate covenant strictness and its validation tests use Dealscan data. Tearsheets covers a subset of Dealscan loans. This database provides more precise information about loans, including detail sufficient to calculate precise financial covenant slack. Tearsheets is not machine-readable, so we hand-code data from the records into a machine-readable format. Our descriptive evidence on financial covenant measurement, our assessment of standard covenant measures, and tests 8

10 quantifying measurement error using the standard measure all use the Tearsheets data. Additionally, we use Compustat quarterly data for accounting data (used to calculate slack and assess measurement errors), CRSP daily for stock return data (to calculate distance to default), and data on actual covenant violations provided by Nini et al. (2012) Tearsheets overview Tearsheets provides detailed loan information for a subset of deals from the Dealscan universe. According to LPC (the company that originally produced Dealscan and Tearsheets), a Tearsheets report is available for the more complex or uniquely structured deals in the market. 8 Beyond this brief description, there is little information indicating why a deal is selected for inclusion in Tearsheets. Dichev and Skinner (2002) report that Tearsheets includes bellwether loans of particular importance. Tearsheets provides many parameters of the loan contract, including the number and type of facilities, the maturities and interest spreads of these facilities, agency ratings for the borrower or loan, the number and identities of lenders (including their interest in the loan syndication), negative covenants, financial covenants, performance pricing, and collateral requirements. While data on many of these provisions is available from Dealscan, Tearsheets often provides a greater level of detail. Important for our purposes, Tearsheets provides sufficient detail on financial covenant definitions to calculate precise slack. For example, Dealscan may indicate that a loan features an interest coverage covenant, and give the initial threshold, but not say how the interest coverage ratio is defined. Tearsheets, in contrast, will indicate how both earnings and interest expense are defined. 8 This quote was taken from the LPC website in May LPC has since been sold to Thomson/Reuters, and there is no mention of Tearsheets on their webpage, though the data are still available to Dealscan subscribers. 9

11 Tearsheets includes records of 2,683 loan packages from 1,773 borrowers, which we match to Compustat to generate a sample of 2,100 loans. The loans in this Tearsheets-Compustat intersection sample are issued between 1987 and Tearsheets coverage is highest from 1992 through 2000, with at least 150 deals each year and 88% of aggregate deals issued during this period. Each loan package consists of individual loan facilities. The average package has 1.67 facilities, with a maximum of six. The most common facility is a revolving line of credit (in 91.7% of packages), which gives the borrower access to a line of credit with no required drawdowns or scheduled repayments. Term loans, which generally require immediate drawdown of funds and fixed repayment, are also common (32% of sample loan packages). Backup financing sources, such as letters of credit (55%) and swingline options (33%) are likewise common. Other facility types include revolvers that convert into term loans, bridge loans, and other types of private financing. None of these other types are included in more than five percent of loan packages. The most common stated purposes of sample loans are corporate purposes (76.3%) and debt (64.2%), while several other purposes are listed less frequently (e.g. working capital, takeover, acquisition ). Table 1, Panel A provides descriptive statistics on the Tearsheets loan sample. The average deal s principal amount (FACILITY) is $722M, with an average stated term to maturity (MATURITY) of about four and a half years (53 months). The facility-weighted average spread over LIBOR (SPREAD) is 121 basis points. Most of the loans are syndicated, where the average loan package has over 16 lenders (SYNDSIZE), with as few as one and as many as 149. For comparison, Table 1, Panel B presents corresponding statistics for the intersection of Dealcan and Compustat for the sample period. Consistent with their status as bellwether loans, 10

12 Tearsheets loans are larger than Dealscan on average; however, covenant use is similar across these two samples. Table 2, Panel A presents summary Compustat data on Tearsheets borrowers, where we match Tearsheets observations to the Compustat fiscal quarter-end most closely preceding loan initiation. For comparison, in Panel B we present corresponding statistics for all Dealscan borrowers over the sample period. The Tearsheets borrowers are on average large (total assets of $4,432M), have high sales ($3,104M), are profitable (ROA of 3.0%), and growing (asset growth of 17.0%). Relative to the full Dealscan population, these firms have high leverage (debt-toassets of 0.38 vs. 0.29) Dealscan financial covenant classifications and contract-level variation In the Dealscan database, financial covenant data is contained in the FinancialCovenant and NetWorthCovenant datasets. 9 These two datasets combined report 15 distinct classes of financial covenants (with general definitions): Min. Interest Coverage (earnings / interest expense) 2. Min. Cash Interest Coverage (earnings / interest paid) 3. Min. Fixed Charge Coverage (earnings / fixed charges) 4. Min. Debt Service Coverage (earnings / (interest expense + principal paid)) 5. Max. Debt to EBITDA (debt / EBITDA) 6. Max. Senior Debt to EBITDA (senior debt / EBITDA) 7. Max. Leverage Ratio (debt / assets) 8. Max. Senior Leverage (senior debt / assets) 9. Max. Debt to Tangible Net Worth (debt / (shareholders equity intangibles) 10. Max. Debt to Equity (debt / shareholders equity) 11. Min. Current Ratio (current assets / current liabilities) 12. Min. Quick Ratio ((cash + ST investments + A/R) / current liabilities) 13. Min. EBITDA (EBITDA) 9 These are the dataset names used in Dealscan downloaded via WRDS. 10 Dealscan also includes Max. Capex and Max. Loan to Value covenants in the 'financialcovenant' dataset. However, in our view, these are conceptually different from the notion of financial covenants that we analyze. Recent vintages of Dealscan also include the following covenants: Max. Long-term Investment to Net Worth, Max. Net Debt to Assets, Max. Total Debt (including Contingent Liabilities) to Tangible Net Worth, Min. Equity to Asset Ratio, Min. Net Worth to Total Asset, and Other Ratio. However, each of these covenants appears in an immaterial percent (i.e., < 0.05%) of Dealscan loan contracts, and did not exist as separate Dealscan categories during the Tearsheet coverage period; therefore, we omit them from our study. 11

13 14. Net Worth (shareholders equity) 15. Tangible Net Worth (shareholders equity intangibles) In order to document the variation in financial covenant measurement, we sort financial covenants from Tearsheets into the 15 Dealscan classes. This is to make our classification of Tearsheets covenants consistent, and to aid in quantifying measurement error. However, since Dealscan does not provide guidance on how financial covenants are classified (beyond the names of the categories given in their datasets), we must use judgment in classifying Tearsheets covenants into the 15 categories. In Appendix A we show our general rubric for classifying Tearsheets covenants, and we discuss the error this potentially introduces in Section 3.4. We provide detailed data on the measurement of covenants from each category in Appendix B. For the sake of parsimony, we summarize these data in Table 3, which presents the observations and frequency of covenants from each of the 15 classes. For example, interest coverage covenants (IC) are included 953 in the 2,100 Tearsheets deals (45.4%). The subsequent columns show the number of different measures used in defining these covenants. We separately document numbers used in the numerator and denominator, as many of the covenants are ratiobased. Further, we sort each of these into primary and secondary elements. We classify measures as primary when they are part of the fundamental definition of the covenant. For example, interest coverage is defined generally as the ratio of earnings to interest. As such, the primary element of the numerator is the selected measure of earnings (e.g. EBITDA, EBIT, net income, etc.) and the primary element of the denominator is the selected measure of interest (e.g. interest expense). Secondary elements are those outside of the general definition of the covenant. For interest coverage, certain items are added or subtracted from earnings in the numerator, such as taxes or capital expenditures; these are the secondary elements. It is important to note that some covenants do not have some elements by definition. Specifically, we consider all 12

14 denominator elements in coverage covenants (interest, cash interest, fixed charge, and debt service) as primary, and naturally there are no denominator elements for the non-ratio covenants (EBITDA, net worth, tangible net worth). The next column shows the number of different definitions used across all covenants in the class. For interest coverage, there are 34 definitions in the 953 instances this covenant is included in Tearsheets contracts. The final column shows the Variation Index, which we define as the observations of a covenant divided by the number of definitions. This index provides a means of quantifying and comparing the degree of heterogeneity in a covenant s measurement. A value of one indicates that each observation of a covenant has its own definition (i.e. complete heterogeneity), while higher values indicate less variation in measurement. The results in Table 3 provide a variety of descriptive facts about the measurement of financial covenants. First, even those covenants generally considered in the literature to be homogenously measured (e.g. current ratio, net worth, and tangible net worth) show some variation. Current ratio covenants feature 10 different definitions, while net worth and tangible net worth have 56 and 31 definitions respectively. 11,12 Second, as expected, some covenants feature a great deal of variation. Most striking is fixed charge coverage, with 356 definitions for just 592 covenants. No other covenant has even 100 different definitions; however, debt service coverage (48) and debt-to-equity (40) both feature a large number of definitions relative to their frequency of use. Third, some covenants that have generally been presumed to have high variation in measurement feature relatively few definitions. These include debt-to-ebitda (24 11 Much of the variation in definitions for net worth and tangible net worth can be attributed to the secondary elements, or escalators (Beatty et al. 2008). These provisions add values, such as net income or equity issuance proceeds, to the threshold of the covenant in the periods following loan inception. As such, if researchers are simply interested in measuring the initial slack in net worth or tangible net worth covenants, there are considerably fewer definitions used. 12 Dichev and Skinner (2002) identify this variation, and use Tearsheets data to supplement Dealscan in measuring net worth covenant slack. 13

15 definitions for 865 covenants), and interest coverage (34 definitions for 953 covenants). The Variation Index illustrates this homogeneity in measurement: both debt-to-ebitda (36.0) and interest coverage (28.0) have high measures, similar to current ratio (28.3). In contrast, the Index for fixed charge coverage is very low (1.7), suggesting each definition is used on average in less than two covenants. In total, we document that there is considerable variation in how financial covenants are measured, but the degree of heterogeneity differs across covenant classes. One upshot of documenting the various measurement rules for financial covenants is that we can observe whether certain definitions are used more frequently than others in defining financial covenants. Using the data in Appendix B, we identify the modal definition for each covenant class, which we term the standard definition of that covenant. This standard allows us to assess another dimension of the variation in financial covenant measurement. Specifically, even if a covenant class features a large number of definitions, if a large percentage of observations are concentrated in a single measure the risk of measurement error is low. We present the data on covenant standard definitions in Table 4. The table includes each covenant class, the standard definition, how the standard definition is implemented with Compustat variables, and the frequency with which the standard definition is used (conditional on the covenant being included in a loan). The results show that standard definitions are indeed used frequently for many of the covenants. Specifically, over the 15 covenant classes, 10 have the standard definition used over 75% of the time, and 12 have it used at least 50% of the time. 13 Debt-to-equity (47.6%) and debt service coverage (37.9%) both have modal definitions used in less than half the observations. However, the next most frequent definition for each is used less than three times as frequently (12.0% and 10.3% respectively). We consider the modal 13 The frequencies for net worth and tangible net worth are for definitions including and excluding escalators respectively. The second, larger number can be interpreted as the frequency with which a loan uses the standard for initial slack for that covenant. 14

16 definitions as the standard when assessing measurement error, even if it does not comprise the majority of observations. The only covenant that has no clear ex ante standard definition is fixed charge coverage. The most frequent definition is only used in 2.7% of covenants, and the majority of definitions (276 of 353) are used in a single covenant. As such, rather than assigning an ex ante standard definition, we attempt to determine ex post the best way to measure fixed charge coverage to minimize measurement error. We discuss this procedure in Section Additional measurement issues There are a number of measurement issues that potentially limit the inferences of our study. We describe these issues, and discuss their potential seriousness, below Data items not available on Compustat We rely on Compustat to compute covenant actual values and hence slack. However, as noted by Murfin (2012), some financial statement data are not readily available to researchers. For example, some covenants are written on junior interest expense, a variable not disclosed separately in Compustat. Hence, even if Dealscan provided precise covenant definitions, we would not be able to completely accurately calculate slack using Compustat. Fortunately for researchers, there are relatively few elements we cannot measure with Compustat, and such elements are used relatively infrequently. For example, fixed charge coverage (the covenant with the greatest number of such unmeasurables ) has seven of 36 denominator elements that we cannot measure, where these elements are included in just 14 loans. Due to the relative infrequency of this problem, we do not expect it to affect our results Consistency between Dealscan and Tearsheets 14 We consider four candidate definitions for fixed charge coverage; each has EBITDA in the numerator. 15

17 We use data on covenants in Tearsheets to make inferences on covenant measurement in Dealscan. As such, it is important that both databases report the same covenants and classify them the same way. This second point is particularly relevant because Dealscan provides no guidance on how covenants are classified; if Dealscan systematically classifies covenants differently than we do, this could introduce error into our analysis. 15 Using loans at the intersection of Dealscan and Tearsheets, we examine the consistency in covenant reporting and classification between the two databases. We report these results in Table 5. For each covenant class, we report three statistics: consistency (either in both databases or in neither), the frequency that a covenant is in Tearsheets but not Dealscan, and the frequency that a covenant is not in Tearsheets but in Dealscan. Using the minimum current ratio covenant as an illustration, the consistency of indicates that in 98.6% of loans current ratio covenants are either listed in both Dealscan and Tearsheets or not listed in either. For the remaining 1.4% of cases, either Tearsheets lists a current ratio covenant while Dealscan does not (1.0%), or Dealscan lists it while Tearsheets does not (0.4%). Overall consistency between the two databases ranges from (Interest coverage) to (Quick ratio), with consistency across all covenants at 94.9%. Errors most likely result from Dealscan misclassifying a covenant that appears in Tearsheets (e.g., coding a Debt-to- EBITDA covenant as Leverage). On average, the incidence of consistency errors is relatively minor. More important, they are split fairly evenly between the two types of errors (2.4% and 2.7%), suggesting that this error does not inject systematic bias into our analysis Tearsheets and actual contract detail A third source of potential measurement error stems from using Tearsheets to capture contract detail. Ideally, we would extract data from actual loan contracts in order to document 15 Drucker and Puri (2009) find that Dealscan sometimes incorrectly excludes covenants from their loan records. 16

18 the detailed covenant definitions. However, data on private loans is difficult to collect directly from contracts; only some contracts are disclosed publicly, and those that are publicly available are reported in a variety of SEC filings (10-k, 10-q, 8-k). Moreover, the contract detail in SEC filings does not have a standard format. We implicitly assume that Tearsheets data is equivalent to actual contract data. This assumption is true for some aspects of the contract (such as the elements included in the covenant definition), but not true for others. For instance, when Tearsheets indicates that EBITDA is used in a particular covenant, it does not provide detail on precisely how EBITDA is defined in the contract. While it is not clear how much variation there is (if any) in measurement of the elements of covenants, there is evidence that some definitions are commonly adjusted in contracts (Li 2010). To determine whether this assumption induces any further measurement error, we collect actual contract detail from 10-k, 10-q, and 8-k filings for a random sample of 100 the Tearsheets loans. We examine the contract-specific definitions of the most common elements of financial covenants EBITDA, interest expense, debt, senior debt, net worth, tangible net worth, current assets, and current liabilities and measure the difference (if any) between the actual definition and our assumed definition (from the standard definitions listed in Table 4). In most cases there is no error; that is, the element as defined in the actual contract is identical to how we define it based on Tearsheets. When there are errors, they are small, infrequent, and statistically insignificant. This suggests that using Tearsheets instead of actual contracts does not introduce any further measurement error into our analysis Generalizability of Tearsheets evidence to Dealscan data 17

19 Tables 1 and 2 illustrate the differences between Tearsheets and the broader Dealscan population. Tearsheets borrowers are larger, more profitable, have lower growth, are more highly levered, and have larger loans that are more widely syndicated. This raises the concern that evidence based on Tearsheets data may not be generalizable to the full Dealscan universe. Specifically, if covenant measurement differs systematically between the different deals, the inferences we draw from Tearsheets data cannot be generalized to Dealscan deals. One way to assess the differences between Tearsheets and non-tearsheets deals in Dealscan, in terms of covenant measurement, is to compare the frequency that the standard covenant measures are used in each. If the Tearsheets deals use standard covenant definitions less frequently, this suggests that the standards we have documented likely apply to all Dealscan deals. However, if the Tearsheets deals use the standard covenant definitions more frequently, using Tearsheets as a proxy for all Dealscan deals may not be appropriate: by using a less customized, more standardized subset of loans we could be missing detail from the broader Dealscan population. Since Dealscan does not provide sufficient detail to determine if the standard measure was used, we collect contract detail from 100 loan packages included in Dealscan that are not part of Tearsheets. We measure the frequencies of standard definitions for this set of loans and compare it with the frequencies from the Tearsheets sample. This data is presented in Table The second column shows the frequency of standard measurement for each covenant type (and in the bottom row, the aggregate frequency for all covenants) for our study s main sample of Tearsheets loans. The third column shows the frequency for the 100 non-tearsheets loans, while the fourth and fifth columns show the difference and t-statistic respectively. For individual 16 We do not include fixed charge coverage covenants in this analysis, as we cannot derive a standard measure as we do for other covenants. 18

20 covenants, there is only one statistically significant difference, where the Senior Debt-to- EBITDA covenant is less likely to be standardized in the Tearsheets deals. On an aggregate basis, 83.4% of covenants in the Tearsheets are measured with the standard measure, compared to 85.1% for the non-tearsheets sample, a statistically insignificant difference of 1.7%. Overall, this data suggests that our Tearsheets sample features standardization of covenants at a similar rate to the Dealscan population at large. This gives us confidence in using Tearsheets-based covenant definitions as proxies for the broader Dealscan population of loans, and particularly in drawing inferences on the most common measures of covenants. 4. Measurement and Analysis of Initial Slack In this section we examine the error in initial slack that arises from applying our standard covenant definitions rather than using the more precise actual definitions from Tearsheets, employing the full Tearsheets-Compustat intersection sample of 2,100 loans. Initial covenant slack measures the gap between the threshold level of the covenant financial measure and the borrower s actual value of the financial measure at loan initiation. The interpretation of a slack measure is slightly different based on whether the covenant imposes a minimum (e.g. current ratio) or maximum (e.g. leverage) threshold. We calculate slack in a form that allows an expression of the allowable movement of the underlying financial statement variable (before violation occurs) as a percentage of the threshold, as follows: = (1) Accordingly, SLACK for minimum (maximum) threshold covenants indicates how much the covenant financial measure can decrease (increase) before the threshold is reached. For example, consider a minimum interest coverage covenant with a minimum threshold of 3.0. A borrower 19

21 with actual interest coverage of 4.0 has SLACK = 1.33, i.e. slack of 33% above the minimum threshold. Next, consider a maximum leverage covenant with a threshold of 0.5. A borrower with actual leverage of 0.4 has SLACK = 0.80, i.e. slack of 20% under the maximum threshold. For covenants with minimum thresholds, SLACK of less than 1.0 indicates violation, while SLACK over 1.0 indicates violation for maximum threshold covenants. In order to quantify the error induced by using our standard covenant definition, we calculate two slack measures, one based on the detailed Tearsheets definition and one based on the standard definition shown in Table 4. This leads to two slack measures for each borrower i and covenant c, which we term SLACK i,c,ts and SLACK i,c,std respectively. Note that the threshold is the same regardless of the definition of the actual value of the covenant measure; in other words, the threshold is not subject to measurement error. We calculate measurement error induced by using our standard covenant definition as:, =,,,, (2) We measure the initial SLACK using the actual value of the ratios for the quarter-end most closely preceding loan inception. 17 We present descriptive statistics on initial slack and measurement error in Table 7. We divide covenants into those with minimum and those with maximum thresholds. Columns 1 and 2 show the number of observations for the specific covenant and the number of observations with errors, i.e. where the standard and Tearsheets slack measures differ. Columns 3 and 4 (5 and 6) present the mean and median initial slack for the standard (Tearsheets) measures. 17 We use the most recent quarter-end to capture the information that was available during contracting. Significant changes to the firm between the prior quarter and loan initiation, particularly for longer gaps, may introduce measurement error into our slack calculation. However, we expect any measurement error to affect standard slack and Tearsheets slack equally, hence not affecting our error computation. 20

22 Based on our analysis of the Tearsheets data, we are able to assign standard definitions for all covenant categories except fixed charge coverage; covenants in this class are so heterogeneously measured that no natural standard emerges from the data. Since our objective in developing standards is to provide useful definitions for researchers, we turn to the data to determine the best way to measure fixed charge coverage. We start by considering common numerator and denominator elements. The most common primary numerator element for this covenant is EBITDA, so we use this as the numerator; since no secondary elements are used in more than 25% of fixed charge coverage covenants, we do not include any secondary elements. For the denominator, we consider the four most commonly used elements: interest, principal payment, capital expenditures, and rent expense. We then measure alternate definitions of fixed charge coverage, including each element (and combinations of each), and compute the error as in Eq. (2). We acknowledge that this will lead to a relatively noisy standard definition, so we attempt to minimize the average error to at least remove any bias. We find that the ratio EBITDA / (interest + principal + rent) yields the lowest absolute error, with a mean and median value insignificantly different from zero. Therefore, we select this as our standard. 18 Focusing first on the minimum threshold covenants in Table 7, the mean initial Tearsheets slack ranges from a low of 37.1% above the threshold (Current Ratio) to a high of 1,296.9% above the threshold (Debt Service Coverage). However, the medians show a tighter range (from 14.6% for Fixed Charge Coverage to 70.5% for Cash Interest Coverage), suggesting the means are inflated due to outliers. 19 The distributions of initial standard slack are similar, with a wide range for means (37.4% for Quick Ratio to 1,476.8% for Debt Service Coverage) 18 There are likely further steps that can be taken to remove noise from this measure. For example, cross-sectional variation in different common charges (e.g. capex, rent, taxes, dividends) may allow for more precise measurement. We leave this sort of refinement for future research. 19 We neither truncate nor winsorize the slack measures in columns (3) and (5). 21

23 and a narrower one for medians (14.7% for Fixed Charge Coverage to 85.9% for Cash Interest Coverage). Turning to maximum threshold covenants, we find similarly skewed distributions of slack. Focusing on median Tearsheets SLACK reveals a range from 33.7% (Debt-to-EBITDA) to 51.3% (Senior Leverage) below the threshold. Median standard slack shows a similar range, from 34.1% (Senior Debt-to-EBITDA) to 48.8% (Leverage) below the threshold. Columns 7 and 8 present the mean ERROR c. Based on our calculations, ERROR represents measurement error induced by using the standard slack calculation rather than the more precise Tearsheets definition. For the minimum (maximum) threshold covenants, a positive error indicates that the standard measure overstates (understates) slack, while a negative error indicates understatement (overstatement). Among all minimum threshold covenants, there exists statistically significant measurement error in just two covenants, Interest Coverage and Cash Interest Coverage. Just one maximum threshold covenant, Debt-to-Equity, has a significant error. One concern in measuring the statistical significance of ERROR is the effect of outliers; specifically, since we do not truncate or winsorize the two slack measures, large differences between the two may be driven by outliers. 20 In column 8, we recompute ERROR after winsorizing both SLACK measures at 1% and 99% (results are substantively similar when we truncate rather than winsorize). ERROR for Interest Coverage and Cash Interest Coverage remain significant, while Debt-to-Equity shifts to be insignificant. Further, ERROR for Debt Service Coverage and Senior Debt-to-EBITDA become significant. 5. A Dealscan-based measure of overall financial covenant strictness The results of our analysis thus far suggest that a set of standard covenant definitions applied to the Dealscan universe can yield useful inferences for researchers studying financial 20 We do not test the difference in medians, as many of the covenants have a majority of the errors equal to zero. 22

24 covenants. As an application, we use the broad Dealscan population of loans and compute a loan-level measure of aggregate covenant strictness. Then, as a validation test, we examine the relation between this aggregate measure and actual technical defaults. We find this aggregate measure predicts future technical defaults, and does so more effectively than other strictness proxies that are common in extant literature (e.g., the number of financial covenants) Aggregate covenant strictness measure We adapt work by Murfin (2012) in calculating our aggregate strictness measure. This measure incorporates the four features that intuitively determine the overall strictness of the covenants attached to a loan: the number of covenants, their slack, their scale, and the variances and covariances of the underlying financial ratios. To fix intuition, consider a loan with a single minimum net worth covenant. The probability of covenant violation is a function of initial slack and the variance of net worth (i.e., similar to option pricing, greater volatility in the underlying measure of net worth results in a greater likelihood of technical default, ceteris paribus). 21 Generalizing to a loan with N covenants, the probability that at least one covenant enters technical default is a function of the slack of each individual covenant, the variance of each underlying financial measure, and the covariance between each of the N financial measures. As an extreme example, consider two financial measures whose changes are perfectly correlated. There is no benefit to including covenants written on both measures, as the second adds no incremental likelihood of technical default. However, adding covenants with less than perfectly correlated measures increase the likelihood a loan enters technical default; and this incremental effect is increasing inversely to the covariance of covenant measures. 21 Dichev and Skinner (2002) find that the variance of the underlying measure and the covenant slack are inversely related. The measure developed by Murfin (2012) accommodates this correlation. 23