The Information Content of Threshold Values in Earnings-Based Covenants*
Ningzhong Li London Business School
Regent’s Park, London, NW1 4SA, United Kingdom [email protected]
Florin P. Vasvari London Business School
Regent’s Park, London, NW1 4SA, United Kingdom [email protected]
Regina Wittenberg-Moerman
The University of Chicago Booth School of Business 5807 South Woodlawn Avenue, Chicago, IL, USA
[email protected] First Draft: April 2012
Early work and revision in progress
Please do not quote or circulate without permission
*We thank Phil Berger and Doug Skinner for helpful comments and discussions. We thank the Thomson Reuters Loan Pricing Corporation for providing the syndicated loan data. We also thank Ying Huang, Yun Lou, Yu Xie, and Sundipika Wahal for excellent research assistance. We gratefully acknowledge the financial support of the AXA Research Fund, the London Business School RAMD Fund and the University of Chicago Booth School Of Business. Regina Wittenberg-Moerman also gratefully acknowledges the financial support of the Neubauer Family Fellowship.
The Information Content of Threshold Values in Earnings-Based Covenants
Abstract
We investigate whether the presence of earnings-based financial covenants with increasingly restrictive threshold values over the duration of a syndicated loan contract reflects relevant private information about the borrower’s future financial performance. We find that loan contracts are more likely to include earnings-based covenants with a tight threshold trend when borrowers are credit risky. We also show that the tight threshold trend predicts lower profitability and interest coverage ratios and higher leverage for up to two years following the loan’s origination date. In addition, we examine the steepness of the tight threshold trend and find that while it predicts lower future profitability, it also predicts lower future leverage, suggesting that covenants with a steeper trend force borrowers to deleverage. Finally, we find that a tight covenant threshold trend predicts the downward revisions of equity analysts’
earnings forecasts. Our findings suggest that the structure of earnings-based financial covenants in loan contracts is informative about a borrower’s future financial performance.
Keywords: syndicated loans, financial covenants, covenant thresholds, private information
JEL Classifications: G17, G21, G32, M41
1. Introduction
Covenants in private loan agreements mitigate managers’ and shareholders’
incentives to engage in asset substitution and lender wealth expropriation (e.g., Jensen and Meckling, 1976, Myers, 1977, Smith and Warner, 1979) and protect against information asymmetries between borrowers and lenders (Garleanu and Zwiebel, 2009). Financial covenants serve as important “trip wires” that allow a transfer of control rights to lenders when a borrower’s performance falls below a predefined accounting threshold (e.g., Aghion and Bolton, 1992, Berlin and Mester, 1992, Dewatripont and Tirole, 1994, Rajan and Winton, 1995). While prior studies have examined the determinants of specific covenants and covenant intensity1 (e.g., Begley and Feltham, 1999, Bradley and Roberts, 2004, Christensen and Nikolaev, 2011) as well as the consequences of financial covenant violations (Beneish and Press, 1993, Dichev and Skinner, 2002, Chava and Roberts, 2008, Roberts and Sufi, 2009, and Nini, Smith, and Sufi, 2009, 2012), limited empirical work exists on the informational content of financial covenants’ terms. In this paper, we examine whether the choice of dynamic threshold values in the financial covenants present in syndicated loan contracts reveals private information about the borrower’s future performance.
Our analysis focuses on earnings-based financial covenants, such as Interest Coverage ratios, Debt to EBITDA ratios or minimum EBITDA levels. Typically, the syndicated loan contract specifies a grid that designates how these covenants’
thresholds change over the life of the loan.2 We identify four types of threshold trends.
The tight threshold trend sets stricter threshold values over the contract’s duration, relative to the threshold at loan initiation. For example, a contract may require an
1 Covenant intensity is typically measured by the number of financial or total covenants in the loan contract.
2 In Appendix A, we provide an example of how covenant thresholds are specified in a typical loan agreement.
interest coverage ratio of 1.5 during the first quarter after a loan’s initiation, 1.75 during the following two quarters and 2.5 thereafter. We find that the tight threshold trend is present in 20 to 40 percent of the earnings-based covenants we examine.
Financial covenants can also be subject to a looser trend, which relaxes the initial threshold value, or to a fluctuating trend, which includes both increasing and decreasing thresholds over the duration of the loan. Looser and fluctuating trends comprise a very small proportion of our sample covenants. The constant threshold trend requires the same covenant threshold value from the loan’s initiation date until maturity. When threshold values change over the duration of the loan, the contract specifies the exact date when the new threshold starts to apply, as well as its value.
We hypothesize that covenant threshold values reveal relevant information about a borrower’s future financial performance. First, through rigorous pre-deal screening, lenders typically obtain access to material private information about the borrower (e.g., Diamond, 1984, Ramakrishnan and Thakor, 1984, Fama, 1985). In the syndicated loan market in particular, loan origination depends crucially on borrowers providing lenders with confidential information (e.g., Dennis and Mullineaux, 2000, Sufi, 2007, Bushman et al., 2010). This confidential information includes timely financial disclosures, managers’ financial projections and plans for acquisitions or dispositions (Standard and Poor’s, 2007). The extensive knowledge of a borrower’s operations and the existence of well developed channels of communication with the borrower’s managers should assist lenders in forecasting future financial performance.
Second, a number of theoretical papers suggest that borrowers may use debt contract terms, including covenant threshold values, to credibly convey to lenders their private information about expected performance (e.g., Chan and Kanatas, 1985, Besanko and Thakor, 1987, Garleanu and Zwiebel, 2009). To the extent that this private information
is reflected in financial covenant threshold values, we expect these values to be informative about a borrower’s future performance.
The prediction with respect to the relation between the tightness of a covenant trend and future performance is, however, more ambiguous. On the one hand, covenants with increasingly restrictive thresholds may convey lenders’ concerns about borrowers’ future performance. Increasingly tighter covenants are likely to be more frequently violated, transferring the control rights to the lenders and thus allowing them to intervene timely into a borrower’s operations and/or require further protection.
Consistent with this prediction, the model of Rajan and Winton (1995) suggests the lenders are incentivised to monitor borrowers more intensively if that gives them the control rights in bad states of nature. Bradley and Roberts (2004) find that covenant intensity is higher for more risky borrowers, as reflected by their high leverage, small size and high growth options. Therefore, a tight threshold trend may be associated with future deterioration of a borrower’s financial performance.
On the other hand, borrowers may use a tight threshold trend to signal their
“good” type (Garleanu and Zwiebel, 2009) to lenders in the syndicated loan market.
By committing to covenants that become increasingly stricter, borrowers may signal to the lenders that they possess favourable private information regarding their expected financial performance. In addition, when lenders expect an improvement in a borrower’s performance, they are more likely to require a tight threshold trend so that covenants continue to act as "tripwires". Tight threshold values can also force borrowers to improve their performance to avoid covenant violations. These arguments suggest that a tight threshold trend may be associated with an improvement in a borrower’s future financial performance.
Our tests rely on a hand-collected sample of earnings-based financial covenants present in around 7,000 syndicated loan packages issued over the period from 1996 to 2009. We employ four measures that capture the trend in threshold values: (1) the existence of a tight threshold trend in at least one of the contract’s covenants, (2) the ratio of the number of covenants with the tight threshold trend to the total number of earnings-based covenants, (3) the slope of the tight threshold trend and (4) the number of threshold values specified in the tight threshold trend.
Estimating the covenant tightness by the threshold trend measures rather than by the slack at the loan inception, as performed by prior studies (e.g., Billett et al., 2007, Dyreng, 2009, Drucker and Puri, 2009, Demiroglu and James, 2010, Murfin, 2011), offers important advantages. First, covenant threshold trend measures potentially capture relevant private information regarding borrowers’ future performance, which is less likely to be reflected in the covenant slack measured at the loan origination.
Second, the covenant threshold trend measures allow us to avoid the inherent measurement errors when comparing threshold values at loan initiation to the ratios computed from financial statements, as required for the slack estimation. These errors occur because lenders often make substantial adjustments to GAAP numbers when defining covenant thresholds and because there is considerable variation in these adjustments across different loan agreements (Leftwich, 1983, Dichev and Skinner, 2002, Beatty et al., 2008, and Li, 2012). Further, these adjustments can also vary across different covenants in the same contract. For instance, earnings may be adjusted to include or exclude depreciation and amortization expenses, capital expenditures, cash taxes and/or cash distributions in one of the contract’s earnings-based covenants, while earnings may not be subject to these adjustments in other financial covenants present in the same loan contract (Li, 2012). These numerous adjustments require
tracking the precise covenant definition from the loan contract to accurately measure the covenant slack. Moreover, even if these definitions are hand-collected from the contracts, adjustments are often very difficult to implement using publicly available financial statements. In contrast, the covenant trend features capture changes in covenant thresholds, while keeping the same threshold definition, thus allowing us to accurately code covenants that become either more strict or looser over time, regardless of the specification of the covenants’ definition in the loan contract.
In our first set of analyses, we find that the tight threshold trend is more likely to be included in the contracts of borrowers with low Ohlson (1990) bankruptcy scores (or credit ratings), high leverage, low profitability, small size and high book-to-market ratios. We also find that the proportion of financial covenants with a tight trend, the steepness of the tight threshold trend and the number of thresholds included in the tight trend are similarly associated with the above mentioned characteristics. We infer that it is unlikely that borrowers would commit to a tight covenant trend to signal their
“good” type, rather, it is that lenders impose stricter covenants on more risky borrowers. These findings are similar to those of Bradley and Roberts (2004) and Demiroglu and James (2010) who show, respectively, that loan contracts of more risky borrowers have higher covenant intensity and lower slack.3
In a second set of tests, we examine whether measures that capture a tight threshold trend are informative about a borrower’s future financial performance. We find that, controlling for a variety of firm and loan characteristics, the tight threshold trend predicts lower profitability and interest coverage and higher leverage one year
3 For the Interest Coverage and Debt to EBITDA covenants, we also estimate the covenant slack at loan inception. While covenant slack is estimated with substantial noise, the positive association between the trend’s tightness and the initial slack indicates that these monitoring mechanisms supplement, rather than substitute, each other. These inferences are not causal, as covenant package features are likely to be simultaneously determined and we cannot identify an exogenous variation in them.
following the loan issuance. For example, borrowers with loans that impose a tight covenant threshold trend in at least one of the earnings-based covenants experience future profitability that is 1.2 percentage points lower, relative to borrowers with loans that do not impose earnings-based covenants with a tight trend. These findings are consistent with the prediction that lenders impose covenants that become more restrictive over time when they are concerned about a borrower’s future performance and require a more timely transfer of control rights when the performance deteriorates.
We then examine whether, conditional on getting tighter, covenants that become restrictive to a greater extent provide additional information about future performance.
We find that the trend’s slope and the number of threshold values are associated with lower future profitability. Interestingly, while the number of threshold values is positively related to future leverage, the steepness of the slope predicts lower leverage.
These results suggest that covenants with a steeper slope force borrowers to deleverage, likely to avoid covenant violations.
We show that the predictive power of the covenant threshold trend features diminishes in the second year after the loan’s issuance and disappears in the following years. One explanation is that lenders are not able to predict borrowers’ performance over longer horizons. Alternatively, lenders may be less incentivised to predict borrowers’ long-term performance if they expect the loan to be renegotiated before its maturity and financial covenants’ threshold values to be consequently adjusted.
According to Roberts and Sufi (2009) and Roberts (2010), most of the loans are renegotiated before less than half of the original stated maturity. 4
Finally, we investigate whether a tight covenant threshold trend can predict revisions of equity analysts’ earnings forecasts. Since the presence of covenants with a
4 Because the average maturity of our sample loans is 3.8 years, the majority of these loans are expected to be renegotiated within the first two years after issuance.
tight trend can serve as an early indicator of poor future performance, we expect it to be associated with downward analyst forecast revisions. Prior research suggests that earnings forecast revisions are associated with fundamental signals such as earnings announcements (e.g., Abarbanell and Bernard, 1992, and Chan et al., 1996) and with secondary signals such as stock returns (e.g., Lys and Sohn, 1990, Baginski and Hassell, 1990, and Abarbanell, 1991). However, it is unclear whether analysts have the expertise and incentive to decipher the signals that debt contracts provide about future earnings. We find that analysts do indeed incorporate covenant trend information into their earnings estimates and revise downward one and two year ahead EPS forecasts in the month following the loan’s issuance when the a loan is subject to a tight threshold trend. However, tight threshold trend measures continue to predict forecast revisions over the period following the month after the loan issuance and until the next annual earnings announcement, suggesting that equity analysts do not fully incorporate the information embedded in the loan covenant structure.
Our paper contributes to the literature across several dimensions. First, we extend the analysis in Demiroglu and James (2010), who show that tight covenant slack at loan inception is associated with an improvement in borrower performance.
While the difference in our findings can be attributed to the inaccurate measurement of the slack,5 it is also possible that the slack at loan inception and the threshold trend serve different monitoring functions, leading to opposite relations with respect to future performance. More generally, by documenting that changes in covenant thresholds can inform market participants about future financial performance, we add to the empirical literature on the role and consequences of the lenders’ access to
5 Demiroglu and James (2010) and, to the best of our knowledge, all previous studies that examine the covenant slack rely on the DealScan database, which does not provide sufficient information to identify covenant definitions and which also often misreports the covenants in the loan agreements.
private information (e.g., Dennis and Mullineaux, 2000, Sufi, 2007, Dass and Massa, 2011, Bushman et al., 2010, Ivashina and Sun, 2011, and Massoud et al., 2010).
Our findings also complement the recent literature that investigates the role of covenants in debt contracting (e.g., Chava and Roberts, 2008, Roberts and Sufi, 2009, Nini, Smith, and Sufi, 2009, 2012, Demerjian, 2010, and Christensen and Nikolaev, 2011). We show that lenders often impose covenants with increasing restrictiveness over the life of the loan, likely leading to significant creditor intervention and a shift in control rights when a borrower’s performance deteriorates. Our findings improve our understanding of how financial covenants serve as important “trip-wires” in loan contracts and clarify the specific mechanism through which this is achieved. In addition, the significant relation between the presence of covenants with a tight threshold trend and the deterioration in a borrower’s future performance that we document likely explains the high frequency of financial covenant violations documented by prior work (e.g., Dichev and Skinner, 2002, Chava and Roberts, 2008).
Finally, our analyses add to the earnings forecasting literature (e.g., Dechow, 1994, Sloan, 1996, Abarbanell and Bushee, 1997, Minton et al., 2002, and Dichev and Tang, 2009). The overwhelming majority of previous studies have investigated the predictive ability of either fundamental accounting numbers and their properties or analyst and management forecasts. One of the key implications of our study is that lenders’ private information embedded in the covenant threshold values in debt contracts can improve earnings predictability.
The remainder of the paper is organized as follows. Section 2 discusses the measurement of the strictness of the financial covenant structures. Section 3 describes the sample and data and presents descriptive statistics, while Section 4 presents our main results. Section 5 concludes the paper.
2. The Strictness of Financial Covenant Structures
Debt contract contingencies, such as financial and non-financial covenants, are designed to mitigate the agency costs of debt and are extensively used in syndicated loan contracts (Dichev and Skinner, 2002, Asquith et al., 2005, Ball et al., 2008, Roberts and Sufi, 2009, Nini et al., 2009, and Costello and Wittenberg-Moerman, 2011). The strictness of the financial covenants package captures the ex-ante probability of covenant violation and subsequent renegotiation. In syndicated loan contracts, financial covenants’ strictness depends on three important dimensions. First, it is a function of covenant intensity, i.e., the number of financial covenants included in the package (e.g., Bradley and Roberts, 2004). Each financial covenant typically covers different aspects of a borrower’s financial performance; therefore contracts with a higher number of covenants are likely to provide lenders with contingent control in more states of the world.
Second, the financial covenant strictness depends on the initial covenant slack, i.e., the difference between covenant threshold values and a borrower’s accounting numbers at loan initiation (e.g., Demiroglu and James, 2010). Financial covenants that are set closer to a borrower’s current financial performance levels will be triggered more often, giving the lender an option to renegotiate more frequently.
Financial covenant slack is extremely difficult to measure empirically, given that lenders often depart from GAAP rules when computing the financial covenant’s threshold value (Leftwich, 1983, Dichev and Skinner, 2002, Beatty et al., 2008, and Li, 2012).6 As a result, prior research focused primarily on measuring the slack of financial covenants that are less likely to be subject to GAAP modifications – net worth and current ratio covenants (e.g. Dichev and Skinner, 2002, and Beatty et al.,
6 Leftwich (1983) argues that these negotiated measurement rules of the initial covenant thresholds potentially reduce the management's ability to circumvent restrictions in lending agreements.
2008). However, lenders impose numerous adjustments on the three most commonly used financial covenants in the syndicated loan contracts – interest coverage, fixed charges coverage and debt to cash flow covenants. Li (2012) documents that benchmark GAAP variables such as EBITDA and EBIT are adjusted in the vast majority of loan contracts by adding various items, such as depreciation and amortization expenses or other non-cash expenses, and by subtracting items, such as non-cash income, capital expenditures, taxes paid in cash and/or dividend, stock repurchase or other cash distributions.
Third, covenant strictness is affected by whether the covenant threshold values change over the life of the loan. Threshold values may remain constant, but they often change, becoming looser or more binding. When a covenant threshold is changing (i.e., it has a trend feature), the lenders and borrower agree at loan inception to a covenant threshold grid. In Appendix A, we present a threshold grid extracted from a loan agreement between Citadel Broadcasting and its syndicate lenders signed on February 10, 2000. The grid for the interest coverage covenant indicates that it becomes more binding over time. At loan inception, for the first four quarters, the threshold is 1.5. Starting from the fifth quarter, the threshold increases to 1.75 for three quarters. Subsequently, it increases to 2.00, then 2.25 and finally to 2.50 beyond the 11th quarter. Such covenants with a tight trend are more likely to be breached, triggering more frequent renegotiations between the borrower and the lenders. In the same loan contract, threshold values for another financial covenant, the fixed charge coverage, remain constant from the loan’s initiation date until maturity, while a third covenant, the debt to cash flow ratio, also becomes more restrictive over time.
Our work focuses on this third dimension of financial covenants’ restrictiveness.
We center our analysis on a novel hand-collected data set that includes the universe of
earnings-based financial covenants reported in firms’ SEC filings between 1996 and 2009. We manually code the covenant threshold values and the timing of their changes in about 7,000 loan packages. One of the major advantages of examining the covenant trend to evaluate the restrictiveness of financial covenants is that we avoid the inherent measurement errors associated with the computation of the covenant slack.
Although the trend in financial covenant thresholds is a common feature in syndicated loan agreements, it has received very limited attention from the extant empirical literature on debt contracting. The only exception is a concurrent paper by Fang (2011) that also examines, but does not find, a significant association between the trend feature and a borrower’s future performance.7
3. Sample, Variable Definitions and Descriptive Statistics 3.1. Data Sources and Sample Selection
We obtain a comprehensive sample of loan contracts with financial covenants by searching all 10-K, 10-Q and 8-K documents filed with the Securities and Exchange Commission (SEC) on the EDGAR online system over the period of 1996-2009.8 We use a text-search program to scan these filings for syndicated loan contracts using combinations of the following keywords: "event(s) of default", "credit agreement",
"loan agreement" and/or "credit facility". All loan contracts define events that trigger default in a dedicated "Events of Default" section and contain references to either loan or credit agreements/facilities. Once we identify filings which contain syndicated loan contracts, we isolate the loan contract in the filing and match it manually to the
7 In contrast to our carefully hand-collected data on the structure of covenant packages, Fang (2011) relies primarily on DealScan, which does not provide sufficient information to accurately estimate the threshold trend features. We believe this is one of the key factors contributing to the discrepancy in our findings.
8 We start with the year 1996 because prior to that year, electronic filings are not consistently available in EDGAR.
syndicated loans available in DealScan, a database provided by the Thomson Reuters Loan Pricing Corporation. To ensure accuracy in our manual matching, we make sure that the name of the borrower, the size of the loan package, the package date and the names of the lead arranger bank(s) stated in the loan agreement in the SEC filing are exactly the same as in DealScan.
For the period from 1996 to 2009, DealScan reports 15,519 loan packages outstanding to public non-financial U.S. firms.9 Matching these packages with the SEC filings results in 9,999 loan packages. We identify and code five earnings-based financial covenants in the sample contracts: interest coverage (IC covenant thereafter), debt service coverage (DSC covenant thereafter), fixed charge coverage (FCC covenant thereafter), debt to cash flows (DCF covenant thereafter), and minimum
EBITDA (Min. EBITDA covenant thereafter). In the section below, we provide detailed explanations on the computation of these covenants. Conditional on the availability of at least one earnings-based covenant, our final sample contains 6,826 packages from 3,182 firms. Our regression tests have smaller samples due to additional restrictions on the data available to calculate the variables in each specification. Table 1, Panel A summarizes this sample selection process. As reported in Panel B, the loan packages in the sample are fairly evenly distributed across years.
The highest concentrations of loan packages are in the Wholesale, Retail and Some Services (16.3%), Manufacturing (15.32%) and Business Equipment (12.80%) industries (Panel C).
3.2. The Estimation of the Covenant Trend Measures and Descriptive Statistics As stated above, we manually code the name of the earnings-based covenant, the initial threshold of the covenant, changes in the covenant threshold values and the
9 We manually match borrowers in DealScan with firms in Compustat based on the borrower's name, industry and location.
timing of these changes. The IC covenant is typically defined as a ratio of an earnings number (e.g., EBITDA, EBIT, operating income, etc.,) to interest expense. Similarly, the FCC covenant is computed as a ratio of earnings to fixed charges, which may include interest expenses, principal payments, lease payments, etc. The DSC covenant is measured as the ratio of earnings to debt service (interest and principal payments).
The DCF covenant is generally the ratio of a debt measure (e.g., funded debt, senior debt, etc.) to an earnings measure (e.g., EBITDA or EBIT). Finally, the Min. EBITDA covenant is a covenant that requires the firm to maintain a minimum level of EBITDA (in some cases the covenant requires a minimum operating income, EBIT or other performance measures).
Panel A of Table 2 reports the frequency of the covenants in our sample, as well as the distribution of different types of threshold trends across each of the five earnings-based covenants we examine. The IC and DCF covenants are the most commonly used: the IC covenant is present in 3,079 loan packages (45% of the sample), while the DCF covenant is present in 4,458 loan packages (65% of the sample). In terms of covenant threshold trends, the threshold becomes tighter in 30%, 27%, 16%, 41% and 42% of the packages for the IC, FCC, DSC, DCF and Min EBITDA covenants, respectively. With the exception of the Min EBITDA covenant, the majority of the contracts impose a constant threshold over the life of the loan. The frequency of looser threshold trends is generally below 3%, suggesting that it is not common for financial covenants to become less strict over the life of the loan.
Similarly, covenants with fluctuating threshold trends are quite rare. The Min.
EBITDA covenant is a notable exception; we find that about 18% of these covenants have a fluctuating trend.
For each earnings-based covenant, we estimate the steepness of the covenant trend by first computing the threshold percentage change per quarter as follows:
/ .
In the formula above, Value is the threshold value and Date is the starting date of a covenant threshold. We divide the threshold percentage change by the period (stated in quarters) over which the change applies. If the covenant has n changes in thresholds, we compute a weighted average slope measure at the financial covenant level based on the slopes of each threshold change, where the weights are the time periods corresponding to each slope:
1
∗
Appendix A provides a detailed explanation of how the Slope measure is estimated for an interest coverage covenant with four thresholds over the life of the loan package.
For a covenant with a constant trend, the slope is zero. Finally, we estimate the steepness of the threshold trend at the loan contract level by averaging the individual covenant slope variables across all earnings-based covenants (N) in the loan package:
∑ .
Table 2, Panel B presents descriptive statistics for the Slope variable, computed for each financial covenant. These statistics are presented conditional on the slope being positive. The mean slope value for the IC covenant is 4.7%, indicating that the strictness of this covenant increases on average by 4.7 percent per quarter. The strictness of the FCC, DSC and DCF10 covenants increases on average by 5.0%, 7.3%
and 3.7% per quarter, respectively. The mean Slope for the min EBITDA covenant is
10 Note that increasing threshold values for the DCF covenant indicate a looser covenant over time, therefore we multiply the slope of the DCF covenant by -1.
substantially larger; this is often due to the very low threshold values of this covenant following a loan’s origination or a threshold grid that changes from negative to positive values.11 To address this issue, in robustness tests we exclude this covenant from the slope analysis and find that the results are robust.
Finally, we estimate the number of threshold values specified in the financial covenant grid. Because the vast majority of covenant trends have monotonically increasing thresholds, the number of values also reflects the restrictiveness of the trend. We measure Number of Values at the contract level by averaging the number of thresholds across all covenants in a loan contract. We present the distribution of this variable for non-constant covenant trends in Table 2, Panel C. The average number of threshold values ranges from 3 for the DSC covenant to 7 for the Min. EBITDA covenant. We note the significant variation in this variable with standard deviations ranging from 1.33 (DSC covenant) to 5.7 (Min. EBITDA covenant).
Table 2, Panel D presents the descriptive statistics of the variables we use in the regression analyses. About 39% of the sample loan packages have at least one earnings-based covenant that becomes tighter over time (the Tight Dummy variable equals 1). On average, 30% of the package covenants become tighter over time (the Tight Ratio variable). Averaged across the five earnings-based financial covenants, the
mean Slope is 8.6% (conditional on positive slope) and the mean Number of Values is 3.9 (conditional on the Number of Values greater than 1).
We also present descriptive statistics on the firm characteristics (firm size, profitability measures, leverage, book-to-market, etc.,) used as controls in our tests (see Appendix B for detailed variable definitions). All firm specific variables are measured in the quarter prior to the loan issuance and are obtained from Compustat.
11 We scale by the absolute value of if is negative. The slope is not well defined if equals zero.
We note that the sample firms are relatively large, with a mean value of total assets of
$2,121M. The firms have an average ratio of earnings before extraordinary items to total assets (ROA) of 1.9% and an average ratio of EBITDA to total assets (Profitability) of 11.5%. Sample firms have a mean and median S&P senior debt rating of BB. Firms’ senior debt ratings are obtained from the Standard and Poor's (S&P) historical database. If the S&P historical database does not cover a particular firm, we retrieve the Moody’s, Fitch or Duff and Phelps senior debt rating from the Mergent Fixed Income Securities Database (FISD). For borrowers with missing ratings on S&P and FISD, we collect ratings from the Internet-based version of DealScan. The firms in the sample have an average Ohlson's bankruptcy score (O- score) of -4.71 and an average Leverage, measured as the ratio of long term debt to
total equity, of 1.1. On average, approximately 37% of the firms in our sample obtain loans from a lead arranger that has managed one of their prior syndicated loan issues.
In terms of the loan-specific variables, the loan packages have an average size of
$332.57M. Given that a loan package typically has a number of tranches (loans) with different maturities and spreads, we compute the loan’s amount-weighted contract terms. The average loan amount-weighted maturity is 3.7 years, while the loan amount-weighted interest-spread is 189 basis points. The average number of financial covenants for the sample loan packages is 2.4. About 70% of the packages have at least one loan with a performance pricing provision.
4. Results
4.1. The Determinants of the Threshold Trend in Financial Covenants
We start by investigating the determinants of the presence of the tight threshold trend in earnings-based covenants. On the one hand, if borrowers accept this feature to
signal their “good” type or positive private information about their future performance, we expect variables that indicate high borrower credit risk to be negatively associated with the presence of the tight trend. On the other hand, if lenders impose a tight threshold trend when they are concerned about potential deterioration in a borrower’s future performance, then variables that capture a borrower’s credit risk should be positively associated with the presence of the trend.
We estimate the decision to include financial covenants with a tight trend using the following model:
∝ ∝ ∝
∝ ∝ .
(1)
Trend Measure is one of our four empirical proxies for the tightness of the covenant
trend: (1) Tight Dummy, an indicator variable that takes the value 1 if at least one financial covenant in the loan package has a tight threshold trend; (2) Tight Ratio, estimated by the ratio of the number of financial covenants with the tight threshold trend to the total number of earnings-based financial covenants in the loan package;
(3) Slope, the mean covenant slope across all earnings-based financial covenants available in a loan package and (4) Number of Values, computed as the average number of threshold values across all earnings-based financial covenants in the package.
We use several firm-specific control variables to measure firm credit riskiness and other firm specific characteristics. We include: the Ohlson bankruptcy score (a borrower’s credit rating when available), the return on assets (computed as the ratio of income before extraordinary items to total assets for the four quarters prior to the loan
agreement date), the book-to-market (ratio of the book value of common equity to market capitalization), firm leverage (measured as the ratio of long-term debt to total equity), firm size (natural logarithm of total assets) and earnings volatility (standard deviation of annual ROA, calculated using rolling four-quarter observations over the five years before the loan agreement date). While higher earnings volatility indicates higher riskiness, lenders may be reluctant to impose covenants with a tight trend on borrowers with volatile earnings because it can cause covenant violations that are too frequent. We also include an indicator variable that takes the value 1 if the loan package is issued by a relationship lead arranger. Previous relations with the lead bank(s) alleviate information asymmetry regarding the borrower, suggesting that relationship lenders are less likely to impose the restrictive covenants.
We also control for loan size (Loan Amount) and maturity (Maturity). Larger loans and loans with longer maturity are usually associated with higher credit risk.
Finally, our regressions include industry and year fixed effects; we cluster the standard errors at the firm level in all the analyses.
The first three columns of Panel A in Table 3 present the results of the Probit estimation of the Tight Dummy variable. Across all specifications, we find that variables that proxy for higher borrower riskiness significantly increase the likelihood that a tight threshold trend is present. Borrowers with higher O-Score or Rating variables, lower profitability, higher book-to-market, higher leverage and a smaller size are more likely to have earnings-based covenants with thresholds that get tighter over time. Further, larger loan packages and packages with longer maturities are more likely to include covenants with the tight trend. In columns (3) – (6), we replicate the analysis of the determinants of the covenant trend with the OLS estimation of the Tight Ratio variable. The results are similar and the inferences are unchanged. In Table 3,
Panel B, we extend the determinants analysis and focus on the subsample of syndicated loan packages with the trend feature. We find that the steepness of the slope and the number of threshold values increase with the riskiness of the borrower.
Overall, our findings indicate that it is unlikely that borrowers commit to financial covenants that become more restrictive over time to distinguish themselves and signal their “good” type. Instead, the evidence we provide suggests that lenders demand covenants with a tight trend in contracts of more risky borrowers. Such covenants should serve as more effective "trip-wires" for these borrowers, since they are likely to trigger more timely covenant violations and the subsequent transfer of control rights to lenders if the borrower’s performance deteriorates.
As in any study that examines debt contractual terms, our findings are subject to the endogeneity concern, as the contractual terms are jointly determined. We note however, that our results are robust to performing the analysis without loan controls (columns 1 and 4 in Panels A and B of Table 3). The results are also unchanged when we add to the model additional loan terms, such as the number of financial covenants, the presence of performance pricing provisions and the interest rate spread (untabulated). For the IC and DCF covenants, we also estimate covenant slack at loan inception and show that it is positively related to the tight trend feature. While slack is estimated with substantial noise, the positive association between the trend’s tightness and the initial slack indicates that these monitoring mechanisms supplement, rather than substitute, each other. Because it is infeasible to concurrently endogenize the covenant strictness measures, we caution that these inferences are not causal.
4.2. Does the Tight Covenant Threshold Trend Predict Future Performance?
We conjecture that covenant threshold values provide relevant information about a borrower’s future financial performance. In the process of originating and pricing
loans, syndicated lenders get access to material private information. Monitoring other firms in the borrower’s industry and/or prior loans issued by the same borrower also yields additional private information. This information is likely to provide lenders with a significant informational advantage, especially with respect to forecasting a borrower’s future financial performance. Lenders should require financial covenants that get stricter over time if they foresee that a borrower might have financial difficulties, suggesting that the presence of a tight threshold trend is associated with lower future profitability and/or higher levels of leverage, increasing the risk to the lenders. Conversely, lenders may require increasingly stricter financial covenants if they possess favourable information about a borrower’s expected performance. When future financial performance improves, covenants with increasing tightness are more efficient in the sense that they continue to act as "tripwires," thus providing the lenders with the opportunity to renegotiate the loan contract in the future. Covenants with a tight trend may also “force” borrowers to improve their performance, to avoid covenant violation. These alternative hypotheses suggest that the presence of the tight threshold trend is associated with better future financial performance.
We examine whether our covenant trend measures predict a borrower’s future profitability (Profitability), measured as EBITDA scaled by total assets, firm leverage (Leverage), measured as total long term debt scaled by total equity, and interest coverage ratio (Interest Coverage), measured as EBITDA scaled by interest expense.
The changes in the interest coverage ratio capture the joint effect of changes in earnings and leverage, as increases in leverage lead to the higher interest expense. We run the following OLS model:
,
∝ ∝ ∝
∝ ∝ ∝
∝
(2)
Financial performance measures are either firm profitability, leverage or the interest coverage ratio in the first and second year following the loan issuance. Trend Measure is one of our four empirical proxies for the tightness of the covenant trend.
We first control for the value of the financial performance measure in the quarter prior to loan initiation. Firm controls include O-Score (or Rating), Leverage, BTM, Firm size and Earnings Vol; in the leverage and interest coverage models, we also control
for profitability. Loan controls include the loan package size and maturity, the number of financial covenants, the interest spread and an indicator variable equal to 1 if the loan package has a tranche with performance pricing provisions (Performance Pricing). As in the previous tests, the regressions include industry and year fixed
effects and standard errors are clustered at the firm level.
In Table 4, Panel A, we present the results from estimating equation (2) where the dependent variable is profitability one year following the loan issuance and the main variables of interest are Tight Dummy and Tight Ratio. Across all specifications, we find that these two measures of the trend’s tightness are negatively and significantly associated with one year ahead profitability. These associations are also economically significant. For example, the coefficient of -0.012 on Tight Dummy in column 2 indicates that the one year ahead profitability is 1.2 percentage points lower for borrowers with loans that impose covenants with a tight covenant threshold trend relative to borrowers with loans that do not impose covenants with a tight trend. Based on the specification in column 5, a standard deviation increase in Tight Ratio translates
into 0.5 percentage points lower future profitability. In Table 4, Panel B, we replicate the analysis in Panel A using the other two trend tightness variables, Slope and Number of Values. We find that the coefficients on both Slope and Number of Values
obtain significant and negative coefficients consistent with the interpretation that borrowers receiving a steeper trend or a trend with a higher number of threshold values also experience lower future profitability.
In terms of control variables, we find that past profitability is a strong predictor of future profitability, consistent with profitability being persistent. We also find that firms with higher credit ratings, higher book-to-market ratios, a larger size and high historical earnings volatility have lower profitability one year ahead. Future profitability is also negatively associated with loan interest spread and positively associated with the existence of a performance pricing provision and loan maturity.12 Given the significant coefficients on our variables of interest, after controlling for a variety of other loan package terms, we infer that the information about future profitability embedded in the tight covenant trend measures is not subsumed by other loan characteristics.
In Table 5, we follow the structure in Table 4 and present the results from estimating equation (3), where the dependent variable is leverage one year following the loan issuance. Panel A presents the results where the main variables of interest are Tight Dummy and Tight Ratio, while Panel B presents the results where the variables
of interest are Slope and Number of Values. With the exception of the Slope variable, all proxies for the tight threshold trend are associated with significant increases in leverage. For instance, firms that receive covenants with a tight threshold trend have,
12 Note that there is no mechanical relation between future profitability and the interest spread because the profitability measure is based on the EBITDA value (i.e., the future interest expense does not impact the profitability measure).
one year following loan issuance, leverage ratios that are higher by 15 percentage points relative to the firms that do not receive covenants with the tight trend (see column 2 of Panel A).
However, when we condition the analysis on the sub-sample of contracts with a positive threshold trend slope, we find that the steepness of the trend is associated with a decrease in one year ahead leverage. In other words, although firms that receive covenants with the tight trend feature increase their leverage following a loan’s issuance, the relatively steep trends are related to lower future leverage. One possible interpretation of this result is that when covenants become strict to a greater extent, the borrowers "deleverage" to avoid a covenant violation.
With respect to the control variables, firms with higher pre-loan leverage and a higher O-Score have higher leverage one year following the loan issuance date. Loan characteristics such as the Loan Amount, Maturity and Interest Spread are also associated with higher future leverage.
Finally, in Table 6 we present results from estimating equation (2), where the dependent variable is the interest coverage one year following the loan issuance. As in Tables 4 and 5, Table 6, Panel A presents the results where the variables of interest are Tight Dummy and Tight Ratio, while Panel B presents the results where the variables
of interest are Slope and Number of Values. With the exception of the Slope variable, we find that all proxies for the tight threshold trend are associated with a significant decrease in the future interest coverage ratio. For example, based on the specification in column 2 of Panel 1, firms with loans subject to covenants with a tight threshold trend have an interest coverage ratio one year ahead that is smaller by 2.9 relative to the ratio of firms whose loans are not subject to covenants with the tight trend. An insignificant relation between Slope and future interest coverage is consistent with the
results presented in Tables 4 and 5 – Slope predicts lower future profitability, but also lower future leverage.
Overall, our findings are consistent with the prediction that the earnings-based covenant threshold trend is informative with respect to borrowers’ future financial performance. Lenders impose covenants that become more restrictive over time when they are concerned about a borrower’s future performance and require a more timely transfer of control rights when the performance deteriorates. Conditional on the existence of the tight threshold trend, the strictness of the trend provides additional information about future performance. The trend’s slope predicts lower future profitability but also lower leverage; the higher number of threshold values predict a worse future performance with respect to both profitability and leverage.
In Table 7, we replicate the results in Tables 4, 5 and 6 by investigating whether tight threshold trend measures predict financial performance two years following a loan’s issuance. We omit Tight Ratio from the analysis because it provides results very similar to those based on the Tight Dummy variable. We find evidence that the presence of the tight trend feature predicts a worse financial performance two years ahead (lower profitability and interest coverage as well as higher leverage). The results are weaker for Slope and Number of Values. These measures are insignificantly related to future profitability, but continue to predict, to some extent, future leverage and interest coverage. We conclude that the prediction power of the covenant trend features diminishes after the first year following the loan issuance. In untabulated analysis, we examine the predictive ability of covenant trend features in the third and fourth years following a loan’s issuance; we do not find statistically significant results.
One potential explanation for this outcome is that lenders might not be able to forecast a borrower’s financial performance more than two years in advance. However, most of
the loans are renegotiated before less than half of the original stated maturity (Roberts and Sufi, 2009, and Roberts, 2010). Given that the average maturity of our sample loans is 3.8 years, the majority of them are expected to be renegotiated within the first two years after issuance. If lenders expect a loan to be renegotiated and its financial covenants’ threshold values to be consequently adjusted, they may have low incentives to invest sufficient effort in predicting borrowers’ long-term performance.
4.3. The Covenant Threshold Trend and Equity Analysts' Forecast Revisions Because we document the predictive ability of the tight threshold trend measures with respect to future profitability, our last set of analyses investigates whether equity analysts incorporate the information provided by covenant thresholds when forecasting future earnings performance. If analysts revise earnings forecasts based on the covenant threshold values, it further highlights the importance of private information about future performance embedded in the specification of the covenant threshold values trend. Previous studies show that equity analysts incorporate new information in their forecasts, revising them after earnings announcements (e.g., Abarbanell and Bernard, 1992, Chan et al., 1996) or significant changes in stock returns (e.g., Lys and Sohn, 1990, Baginski and Hassell, 1990, Abarbanell, 1991).
However, it is unclear whether analysts have the expertise and incentives to use the information signals about future earnings performance that are provided by syndicated loan contracts.
We test these conjectures in a series of multivariate tests where we investigate whether our measures of the tightness of earnings-based financial covenants are associated with revisions in one year ahead and two years ahead analyst earnings forecasts. We measure the revision of the forecasts in the period right after the loan package origination date (i.e., consensus earnings forecast in montht+1 relative to
montht-1, where montht is the month when the syndicated loan was originated). We also examine the revisions in earnings forecasts in the post loan origination period (i.e., the consensus earnings forecast in the month just prior to the announcement of the subsequent annual earnings relative to montht+1,where montht is the month when the syndicated loan was originated). If equity analysts do not fully incorporate the information in the covenant thresholds, then our measures that capture the tightness of the covenant threshold trend should predict the revisions of equity analysts’ earnings forecasts that occur in the post loan origination period.
Following prior research (e.g., Abarbanell and Bernard, 1992, Hughes, Liu, and Shu, 2008, and Clement, Hales, and Xue, 2011), we estimate the following OLS regressions to examine forecast revisions:
;
∝ ∝ ∝ ∝
∝ ∝
∝ ∝
∝ ∝ ∝ .
(4)
The consensus forecast revisions (Forecast Revision) are changes in the consensus median EPS forecasts scaled by the mean of the absolute values of the consensus forecasts. For the Trend Measure, we use three of our empirical proxies for the tightness of the covenant trend: Tight Dummy, Slope and Number of Values. We omit Tight Ratio from the analysis because it provides results very similar to those based on the Tight Dummy variable. Firm size and BTM are defined as in the previous analyses. Momentum is the average monthly equity return inclusive of dividends from month t-6 to month t-1 where t is the loan agreement month. As prior research has shown that analyst forecasts reflect expectations embedded in the stock price with a lag (e.g., Hughes, Liu, and Shu, 2008), we expect the coefficient of this variable to be
positive. Concurrent Return is the raw stock return computed over the same period as that covered by the analyst forecast revision. Horizon Change is the number of months in the period over which the analyst forecast revisions are calculated. This variable controls for the different lengths of the revision periods for different loan contracts.
Forecast Horizon is the number of months from the forecast date of the revised
forecast to the target forecast fiscal year end. Earnings Surprise is calculated as (actual EPS - median EPS forecast before the earnings announcement)/stock price at the end of the month before the earnings announcement at the fiscal year end before the loan date. Analysts may not completely incorporate the earnings news in their prior earnings announcement (e.g., Abarbanell and Bernard, 1992), thus the coefficient is expected to be positive. Finally, we include industry and year fixed effects and cluster the standard errors at the firm level in all regressions.
In Table 8, Panel A, we present the results from estimating equation (4), where the dependent variable is either the one year ahead consensus earnings forecast revision in the month after the loan origination date or the one year ahead consensus earnings forecast revision in the post loan origination period, before the subsequent annual earnings numbers are announced. We find that all of our trend variables are negatively and significantly associated with the one year ahead revision in the consensus EPS forecast right after a loan package is issued (columns 1-3). We interpret this result as consistent with the conjecture that equity analysts do use the information about future earnings that is embedded in the specification of the threshold values. Note that the significant relation between the trend measures and the forecast revision is unlikely to be attributed to other information released around the loan issuance because we control for the concurrent stock return, which should capture this information. Further, we find that Tight Dummy and Slope also predict revisions
in the one year ahead EPS forecasts in the period after the loan origination until the next earnings announcement (columns 4-6). This finding suggests that equity analysts do not fully incorporate the information about future earnings reflected in the threshold values of the earnings-based covenants. In Table 8, Panel B, we replicate the analysis in Panel A using revisions in two years ahead EPS forecasts and find similar results. The control variables used in Table 8 load consistently with the findings in the previous literature on analyst forecast revisions.
5. Conclusion
In this paper, we investigate whether covenants’ threshold values in the syndicated loan contracts provide relevant information about borrowers’ future financial performance. Syndicate banks have extensive access to material private information about borrowers and might reveal this information when specifying thresholds in earnings-based covenants.
Using a hand-collected syndicated loan sample with detailed covenant structure data, we find that earnings-based financial covenants often have threshold values that become tighter over the duration of the loan. These covenants with a tight threshold trend are included in the loan contracts of more risky borrowers. We also find that measures that capture the existence of the tight threshold trend or the steepness of the trend predict borrower future financial performance, such as profitability, interest coverage and leverage, up to two years after the origination of the loan. Finally, we document that equity analysts revise their earnings estimates downward following the issuance of a loan package with financial covenants that have a tight threshold trend.
However, the tight covenant threshold trend still predicts future downward forecast
revisions, suggesting that analysts do not fully incorporate the information about the trend when the loan package is originated.
Our findings indicate that private information embedded in earnings-based covenant threshold values helps to predict a borrower’s future performance. Our analyses also emphasize the importance of the trend in the threshold values of financial covenants – a feature of loan contracting that was overlooked by prior research. Finally, our results provide an insight into how financial covenants serve as important “trip-wires” in loan contracts, extending the recent literature on the implications of covenants for debt contracting.
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Appendix A: Covenant Examples
The loan agreement between Citadel Broadcasting Company and its creditor signed on February 10, 2000 contains three earnings-based financial covenants: an interest coverage ratio covenant, a fixed charge coverage ratio covenant, and a debt to cash flow ratio (called “leverage ratio” in this contract) covenant. We provide excerpts from the loan contract that discuss these covenants.
CONSOLIDATED INTEREST COVERAGE RATIO. Permit the Consolidated Interest Coverage Ratio for any period of four consecutive fiscal quarters, in each case taken as one accounting period, ended during any period set forth below to be less than the amount set forth opposite such period below:
Period Ratio January 1, 2000 through March 31, 2000 1.50
April 1, 2000 through June 30, 2000 1.50 July 1, 2000 through September 30, 2000 1.50 October 1, 2000 through December 31, 2000 1.50 January 1, 2001 through March 31, 2001 1.75 April 1, 2001 through June 30, 2001 1.75 July 1, 2001 through September 30, 2001 1.75 October 1, 2001 through December 31, 2001 2.00 January 1, 2002 through March 31, 2002 2.00 April 1, 2002 through June 30, 2002 2.25 July 1, 2002 through September 30, 2002 2.25 Thereafter 2.50
CONSOLIDATED FIXED CHARGE COVERAGE RATIO. Permit the Consolidated Fixed Charge Coverage Ratio for any period of four consecutive fiscal quarters, in each case taken as one accounting period, to be less than 1.25 to 1.00.
MAXIMUM CONSOLIDATED LEVERAGE RATIO. Permit the Consolidated Leverage Ratio at any time during a period set forth below to be greater than the ratio set forth opposite such period below:
Period Ratio January 1, 2000 through March 31, 2000 7.25
April 1, 2000 through June 30, 2000 7.00 July 1, 2000 through September 30, 2000 6.75 October 1, 2000 through December 31, 2000 6.50 January 1, 2001 through March 31, 2001 6.25 April 1, 2001 through June 30, 2001 6.00 July 1, 2001 through September 30, 2001 6.00 October 1, 2001 through December 31, 2001 5.75 January 1, 2002 through March 31, 2002 5.50 April 1, 2002 through June 30, 2002 5.25 July 1, 2002 through September 30, 2002 5.00 October 1, 2002 through December 31, 2002 4.50 Thereafter 4.00
Computation of tight covenant threshold measures:
In the case of Citadel Broadcasting Company's loan package, the interest coverage and debt to cash flow covenants become tighter over time while the fixed charge coverage covenant is flat. Therefore, the variable Tight Dummy is coded as 1 for this contract.
The interest coverage, fixed charge coverage and debt to cash flow covenants contain 5, 1, and 12 different threshold values, respectively. Therefore, the variable Number of Values is calculated as (5+1+12)/3=6.
To compute the slope of the interest coverage covenant, we first code the thresholds and their dates as follows:
Value1=1.5, Date1=March 31, 2000;
Value2=1.75, Date2=March 31, 2001;
Value3=2.00, Date3=March 31, 2002;
Value4=2.25, Date4=June 30, 2002;
Value5=2.50, Date5=December 31, 2002.
We first calculate the slope from Valuei to Valuei+1 as / . This variable captures the percentage increase in threshold values per quarter. We calculate this slope for each change in the threshold values. We then calculate a weighted average of to using the time period for each , , as the weight; that is, the slope of the interest coverage covenant is computed as:
∑
1.75 1.5 1.5
2 1.75 1.75
2.25 2 2
2.5 2.25 2.25
90 1005 0.049,
The slope of the debt to cash flow covenant can be calculated in a similar way (0.047).
Note that decreasing threshold values for the debt to cash flow covenant means a tighter covenant over time; thus we multiply its slope by -1. Since the fixed charge coverage covenant is flat, the slope is 0.
Therefore, the Slope for this contract is (0.049+0+0.047)/3=0.032, which means, on average, the covenant gets tighter by 3.2% per quarter.
