The Structuring of Financial Covenants When Lenders Acquire Soft Information

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1 The Structuring of Financial Covenants When Lenders Acquire Soft Information Robert Prilmeier The Ohio State University May 14, 2011 Abstract Financial covenants aid monitoring by allocating state-contingent control rights between borrowers and lenders based on hard information. Much of the finance literature suggests that lenders acquisition of soft information over the course of a lending relationship supports effective monitoring. This paper investigates how the presence of soft information affects the number and tightness of hard information-driven covenants included in the contract. Consistent with borrowers trading off monitoring benefits with covenant-created hold-up costs, I find that the effect of relationship intensity on the number of covenants included in the contract follows an inverted U shape. This effect is stronger for borrowers with easier access to public debt markets and for sole lender and single lead arranger loans. I further argue that a covenant need not be particularly tight to act as a monitoring incentive. Consistent with covenant tightness addressing information asymmetry concerns, I find that tightness is reduced over the course of a relationship, especially when borrowers are informationally opaque. PhD candidate at The Ohio State University; prilmeier 1@fisher.osu.edu 1

2 1 Introduction What determines the structuring of financial covenants in debt contracts? The theoretical and empirical finance literature suggests that covenants are used to reduce agency costs between debtholders and shareholders (Myers, 1977; Smith and Warner, 1979), to aid in monitoring the borrower (Chava and Roberts, 2008; Roberts and Sufi, 2009; Nini et al., 2009, 2010), and to give the lender an incentive to monitor in the first place (Rajan and Winton, 1995; Park, 2000). Consistent with banks special role as monitors (Diamond, 1984, 1991; Fama, 1985), bank loans contain more extensive sets of financial covenants than public debt, and covenant violations are almost always related to bank debt (Kahan and Tuckman, 1993; Rauh and Sufi, 2010). However, little is known about the structuring of financial covenants when financial institutions have varying degrees of knowledge about a borrower s soft information (information not verifiable by outsiders). Financial covenants are restrictions in debt contracts that are written on hard information, i.e. accounting quantities that are verifiable in court. If the restriction stipulated by a covenant is violated, the lender obtains the right to demand immediate repayment of the loan. However, firms may violate covenants even when their future prospects are good. Indeed, many covenant violations are waived (Beneish and Press, 1995; Chen and Wei, 1993; Dichev and Skinner, 2002; Roberts and Sufi, 2009). Thus, it is likely that lenders use soft information to determine the optimal response to a covenant violation. In addition, soft information creates information asymmetries between borrowers and lenders and between lenders that have a relationship with the borrower and those that do not. How, then, does lenders acquisition of soft information in a banking relationship affect the structuring of financial covenants when contracting a loan? Finance theory offers an array of predictions regarding this question. While the agency theory of covenants of Myers (1977) and Smith and Warner (1979) suggests that covenants create value by reducing agency costs, they increase the incidence of renegotiations between the borrower and the lender. To the extent that the acquisition of soft information in a banking relationship reduces renegotiation costs (Boot, 2000), bank relationships support the inclusion of a broader set of covenants which will lead to renegotiation more frequently. Rajan and Winton (1995) and Park (2000) develop models in which the lender s claim competes with the claims of other stakeholders, such as investors, trade creditors or employees who are able to free-ride on 2

3 the lenders monitoring effort. This reduces the lender s incentives to monitor and shut down bad projects that the manager cannot credibly commit to shutting down. Rajan and Winton (1995) show that in this case, covenants can be used as an incentive to monitor since they make the effective maturity of the loan contingent on monitoring. In addition, Park (2000) shows that it is optimal to assign the monitoring incentive to the lender with the lowest monitoring cost. Since the acquisition of soft information in a relationship is likely to reduce the cost of monitoring (Boot, 2000), loans from relationship lenders should include a more extensive set of covenants. However, the presence of information asymmetries between the borrower and the lender predicts the opposite. Such information asymmetries are largest when the two parties have no prior relationship. To the extent that covenants are used to protect the lender from borrower moral hazard, one would expect the need for covenants to decrease as the lender learns the true nature of the borrower (Boot, 2000). When a lending relationship is sufficiently exclusive, the lender may be able to develop an information monopoly about the borrower in order to extract rents when renewing the loan (Greenbaum et al., 1989; Sharpe, 1990; Rajan, 1992). Such rent extraction could include imposing an overly extensive set of financial covenants on the borrower. However, financial covenants are unique among loan contract terms in the sense that they themselves may create a hold-up problem. When a borrower violates a financial covenant, the relationship lender is likely to be better informed than potential outside lenders about the true prospects of the firm. If the efficient decision is to liquidate the borrower, this makes no difference, but if the borrower s prospects are good, the violation will allow the relationship lender to extract rents. Provided that borrowers have sufficient bargaining power when entering the loan agreement, they should seek to reduce the intensity of financial covenants attached to the loan. These theories offer conflicting predictions. It is important to note that they are not mutually exclusive and they may interact with each other. For example, a borrower who is being held up by his lender at the contracting point will likely be unable to negotiate a reduced covenant load in order to avoid a covenant-created hold-up problem. Moreover, the relationship effect may be nonlinear, although the direction of such nonlinearity is an empirical question. If borrowers determine the covenant load that maximizes firm value by trading off monitoring 3

4 incentives with covenant-created hold-up costs, the relationship effect should follow an inverted U-shape. However, if contracting choices are determined by a trade-off between the reduction of information asymmetries between the borrower and the relationship lender and an increase in information advantages of the relationship lender over non-relationship lenders that fosters hold-up at the point of contracting the loan, the effect is more likely to follow a U shape. I test these theories using a sample of syndicated loans to non-financial U.S. borrowers. I measure relationship intensity as the proportion of the firm s borrowings over the previous five years that it has borrowed from the current lead arranger. I define covenant intensity as the number of financial covenants attached to a loan. Consistent with the idea that reduced renegotiation costs favor the inclusion of more restrictive covenants, but are ultimately countervailed by covenant-created hold-up problems, I find that financial covenant intensity increases with relationship intensity for low levels of relationship intensity, but decreases as the relationship becomes more and more exclusive. These results are robust to alternative measures of financial covenant intensity and relationship intensity. Further, I investigate the effect of variation in the borrower s bargaining power and the lender s stake in the loan. Access to the public debt market and other capital markets should increase the borrower s ex ante bargaining power and thus enable them to negotiate away the covenant-created hold-up potential more effectively. The evidence supports this hypothesis. The decrease in financial covenant intensity in exclusive relationships is concentrated in borrowers with access to outside sources of capital. In addition, a loan s covenant intensity affects its effective maturity relative to other creditors claims, but does not prevent loan participants or multiple lead arrangers from free-riding on each other. Hence, if covenants are used as a monitoring incentive, they should perform this role more effectively for sole lender loans or loans with only one lead arranger. Indeed, I find that covenant use increases more strongly in relationship intensity for such loans. Ex post competition from participant lenders or other lead arrangers in the loan syndicate at the time of a covenant violation might alleviate the covenantcreated hold-up problem, provided that these lenders also gather some soft information. I find some evidence that the decrease in covenant intensity is concentrated in loans with one lead arranger. I do not find evidence that covenant intensity is driven by a reduction in information asymmetries between the borrower and the relationship lender. 4

5 The choice to borrow from a relationship lender is likely endogenous. To rule out that the results are driven by selection on observable or unobservable firm characteristics, I investigate the relationship effect on loan contract terms to which the monitoring incentive and renegotiation cost as well as the covenant-created hold-up theories do not apply. I also employ propensity score matching methods and instrumental variables estimation. The results from these methods suggest that the relationship effect on covenant intensity is causal. Loan contracts not only involve a choice of how many covenants are included in the loan, but also how tight they are. I define covenant tightness as the probability that the loan s most restrictive covenant will be violated. Predictions for covenant tightness are not necessarily the same as for covenant intensity. Covenant tightness may be less important than covenant intensity in incentivizing lenders to monitor. In the model of Rajan and Winton (1995), the lender monitors because the covenant allows her to shorten the effective maturity of the loan when the borrower does poorly and monitoring is a precondition to exercising this right. To implement this, the covenant needs to give control to the lender in bad states of nature, but it need not necessarily be particularly tight. In addition, the borrower s risk of being held up by the lender when violating a covenant increases in covenant intensity, but is unlikely to increase in tightness. Covenant tightness is hard information known to outside lenders. If a very tight covenant is violated, this is unlikely to be taken as a negative signal by outside lenders and hence creates little possibility for the inside lender to hold up the borrower. Demiroglu and James (2010) find that the violation of tight covenants has little impact on the borrower. Consequently, I find evidence that covenant tightness is driven by information asymmetries. After accounting for the endogeneity induced by the private information content of covenant tightness (Demiroglu and James, 2010), tightness decreases in relationship intensity, and more so in situations where a reduction in information asymmetry is likely to be important. My paper contributes to the literature on several dimensions. To the best of my knowledge, the finance literature provides little evidence on the effect of lending relationships and soft information acquisition on hard-information monitoring tools such as financial covenants. In particular, this study is the first to provide evidence of a hold-up problem created by financial covenants. 1 Secondly, in a recent paper, Schenone (2010) finds a hold-up effect of banking 1 A few recent studies touch on specific aspects of banking relationships and financial covenants. Murfin (2010) finds that a borrower s default on a loan causes the lender to tighten the covenants of loans to other borrowers, and more so if the other borrowers relationship with the bank is more exclusive. Ivashina and Kovner (2010) 5

6 relationships on yield spreads only among borrowers not listed on a stock exchange, but finds no such effect among publicly listed borrowers. The strong rights that a covenant violation confers to the firm s creditors enable me to show that hold-up considerations apply even to publicly listed and rated borrowers and that borrowers adapt their loan contracts to trade off this cost with monitoring benefits. Finally, I provide further evidence that covenant intensity and covenant tightness are used in different ways, consistent with Demiroglu and James (2010) who find that covenant tightness contains private information about the firm s prospects, but covenant intensity does not. The remainder of this paper is structured as follows. Section 2 describes the predictions from finance theory. Section 3 details the data collection process. Section 4 discusses the results for covenant intensity, and section 5 addresses endogeneity concerns. Section 6 presents the results for covenant tightness, section 7 performs additional robustness checks, and section 8 concludes. 2 Theory One of the primary functions of banks is the monitoring of borrowers (Diamond, 1984, 1991; Fama, 1985). Debt covenants enhance firm value by allowing control rights to shift from shareholders to debtholders when the firm is performing poorly, even outside of bankruptcy (Aghion and Bolton, 1992; Dewatripont and Tirole, 1994; Dichev and Skinner, 2002). Smith (1993) and Sridhar and Magee (1996) argue that financial covenants serve as tripwires that enable flexible monitoring, where creditors response can range from waivers to new restrictions. However, loan renegotiation following a covenant violation involves costs. Creditors need to assess the reasons for the covenant violation and negotiate a response with the borrower. The case may need to be negotiated in court if the borrower and the lender cannot come to an agreement. Boot (2000) argues that the soft information acquired in a banking relationship reduces such renegotiation costs and thus supports the use of covenants. While covenants can serve as monitoring tools, Rajan and Winton (1995) and Park (2000) develop models in which covenants provide the lender with an incentive to monitor in the first place. In these models, the lender s claim competes with the claims of other investors and creditors, who will be able to free-ride on the lender s monitoring function. This reduces the lender s show that private equity firms with stronger bank relationships enjoy a less restrictive maximum debt to EBITDA covenant when financing a leveraged buyout. 6

7 incentive to acquire information about the borrower. At the same time, the entrepreneur s inability to credibly commit to abandoning projects in bad states of nature reduces firm value. Covenants help to overcome both problems since they make the lender s payoff contingent on continuous monitoring. A covenant breach allows the lender to reassess the borrower s credit risk and to impose restrictions that increase firm value, 2 but only if she can prove that the covenant has indeed been violated. While imposing firm value enhancing restrictions benefits other claim holders as well, being able to renegotiate in bad states of nature increases the bank s payoff contingent on monitoring. This enhances the lender s incentives to monitor. In the model of Rajan and Winton (1995), the lender will monitor if the value she receives from gathering the information exceeds monitoring costs. Since soft information acquired in a lending relationship lowers the lender s monitoring costs (Boot, 2000), covenants are more likely to achieve monitoring in the case of a relationship lender. In Park s model, the optimal debt contract involves a two-tiered structure: Monitoring is delegated to a senior lender whose claim is large enough to be impaired in the case of liquidation, so that junior lenders receive nothing in case of a liquidation and thus have no incentive to monitor. Therefore, the senior lender is given all the covenants. It then becomes optimal to assign the monitoring task to the lender with the lowest monitoring cost. To the extent that relationships lower monitoring costs, the relationship lender is likely to be in that position. 3 While the above theories predict an increase in covenant use as a lending relationship progresses, the agency theory of covenants developed by Jensen and Meckling (1976), Myers (1977), and Smith and Warner (1979) predicts the opposite. According to this theory, shareholders can take a number of actions that hurt debtholders claims, such as risk-shifting, excessive dividend payouts, over- and underinvestment and so forth. This moral hazard necessitates the need to monitor, especially when information asymmetries are severe. Therefore, when a bank lends to a borrower it has never dealt with before, it might use a relatively extensive set of covenants to 2 For some of the measures taken by lenders after covenant violations, see Chava and Roberts (2008), Nini et al. (2009), and Nini et al. (2010). 3 A different way of implementing monitoring, of course, is the short-term debt contract. Bharath et al. (2009) find that for borrowers with low credit quality, debt maturity decreases in relationship intensity. They argue that lower monitoring costs allow lenders to shorten the maturity as a means of commitment to monitoring. However, a short maturity is not a perfect substitute for restrictive covenants. First, covenants typically are monitored at a higher rate than the frequency with which short-term debt is rolled over. Compliance reports for debt covenants are often filed on a quarterly basis (Chava and Roberts, 2008), whereas short-term bank debt is typically issued on a lower frequency. Second, Rajan and Winton (1995) show that there are situations where short-term debt without covenants cannot implement monitoring, but long-term debt with covenants can. 7

8 curb moral hazard. With repeated interaction, however, information asymmetries are reduced as the bank learns borrower-specific information (Boot, 2000), and it can reduce the number of covenants written into the contract. Compared to other monitoring tools, the unique feature of financial covenants is that they shorten the effective maturity of the loan conditional on a signal of poor performance. This has interesting implications for the hold-up problem in a lending relationship. With covenants, there are two different types of hold-up problems: hold-up can occur at the point of contracting the loan, or at the point at which the covenant is violated. Hold-up problems that occur when contracting a new loan or rolling over a matured loan have received a significant amount of attention both in the theoretical and the empirical literature, although to my knowledge they have not been studied specifically for covenants. The idea follows naturally from the above argument. As relationship lenders acquire borrower-specific information, the borrower-lender information asymmetry declines, but the informational advantage of the relationship lender over outside lenders intensifies. The relationship lender s information monopoly may thus cause the borrower to become informationally captured (Greenbaum et al., 1989; Sharpe, 1990; Rajan, 1992). This may enable the lender to extract rents by imposing unfavorable contract terms (such as a more extensive set of restrictive covenants) on the borrower when entering into subsequent loan agreements. 4 However, there is an interesting and previously unexplored twist to the hold-up problem when loan contracts contain covenants. When the firm violates a covenant, the lender gains the right to accelerate the loan and thus shorten the effective maturity. In the Rajan (1992) model, the hold-up problem arises from short-term bank debt. Since covenants shorten the maturity in some cases, but not others, one might think that their hold-up potential is somewhere in between short- and long-term bank debt. However, the nature of debt covenants suggests that their hold-up potential may be stronger. With short-term bank debt, the borrower s loan is rolled over without prejudice when it matures. If a well-performing borrower is informationally captured by its bank, uninformed lenders will pool that borrower with less well-performing 4 Empirical studies of contracting hold-up thus far have focused on the yield spread the borrower is required to pay as the main aspect of rent extraction. A number of studies find that small, unlisted borrowers pay higher interest rates as the banking relationship progresses (Degryse and Cayseele, 2000; Ioannidou and Ongena, 2010; Schenone, 2010), although some studies find no such effect (Petersen and Rajan, 1994; Berger and Udell, 1995). Borrowers who are listed on a stock exchange have been found to pay lower yield spreads to relationship lenders (Bharath et al., 2009; Schenone, 2010). 8

9 borrowers and thus the relationship lender can extract rents. When a covenant is violated, however, this is a signal that the borrower may be in trouble, although he need not be given that many covenant violations are waived. Due to ongoing monitoring, relationship lenders are better able to assess the information content of the covenant violation than uninformed lenders. Outside lenders will now pool the firm with the set of violators, who are worse performers on average than the non-violators as long as covenants are written on mildly informative accounting quantities. Consider a firm that happens to violate a covenant due to a random negative realization in its accounting ratio, but whose soft information suggests that its prospects are good. The violation will leave the relationship lender s willingness to lend unchanged, whereas it may cause outside lenders to perceive the firm to be riskier than it is. This creates a potential for rent extraction by the relationship lender, which would not exist had the covenant not been written into the contract. 5 If the borrower foresees this problem when he enters into the loan agreement, one would expect him to try to negotiate it away ex ante, especially if the relationship with the lender is exclusive and hence her soft information advantage over outside lenders is large. The above theories are by no means mutually exclusive and they may be interacted with each other. For example, the borrower s ability to avoid covenant-created hold-up problems by reducing the set of covenants included in the loan is likely to depend on his ex ante bargaining power. To the extent that a borrower is already being held up when entering the loan agreement, he will be unable to bargain for a reduction in financial covenant intensity. Thus, one should expect that the reduction in covenant intensity for exclusive relationships is contingent on the borrower s access to other sources of capital, such as the public bond market. 6 Moreover, hold-up problems are likely to be nonlinearly increasing in the exclusivity of the borrower s relationship with the lender. To see this, suppose that X% of a borrower s loans over the past five years were supplied by the lender with whom the current loan agreement is made. 7 This means that the remaining percentage of loans were provided by other lenders who 5 Consistent with this, Roberts and Sufi (2009) find that few borrowers switch lenders after a covenant violation, even though the violation leads to an increase in interest rates and a tightening of credit on average. However, whether these effects of the violation simply reflect efficient responses on the part of the lenders or whether there is a hold-up component to them remains an open question. 6 Note that access to the public bond market would be expected to improve the borrower s ex ante bargaining power but not necessarily his bargaining power at the point of a covenant violation since public bond market participants are likely to be even more uninformed about the borrower than outside banks. 7 Following Bharath et al. (2009), I assume that soft information acquisition stops if borrower and lender have 9

10 therefore have some degree of knowledge about the borrower s soft information. As Schenone (2010) argues, lenders are likely to become increasingly effective in processing firm-specific information as the relationship progresses. If relationship intensity with the current lender increases, say, from 0% to 20%, this is unlikely to create hold-up potential since it means that the current lender has just started to learn firm-specific information and since there are other lenders who have more experience with the borrower. On the other hand, if it increases from 80% to 100%, the percentage change is the same but the impact is likely to be larger since the firm moves from a situation where some other lender is present and is processing firm-specific information to a situation where no other inside lender exists. Consequently, it is conceivable that hold-up issues dominate when relationship intensity is relatively high, whereas for lower levels of relationship intensity the other theories may be more important. This leads to the possibility of a non-linear relationship effect. In particular, if borrowers maximize firm value through an optimal level of monitoring by trading off monitoring incentives with covenantcreated hold-up costs, we should expect the effect of relationship intensity on covenant use to follow an inverted U, at least for borrowers that have sufficient bargaining power to negotiate for fewer covenants even when the relationship is exclusive. Alternatively, covenant choice may be driven by information asymmetries between the borrower and his lender and information advantages of the current lender over outside lenders, respectively. In this case, the reduction in borrower-lender information asymmetries due to a relationship should reduce the incidence of covenants initially, but the increasing information advantage of the relationship lender over outside lenders should increase the relationship lender s ability to hold up the borrower when contractig a loan by including more restrictive covenants. This theory predicts a U shape. Schenone (2010) finds evidence in favor of this pattern for yield spreads, but only before a firm s IPO. This suggests that publicly listed firms are able to leave or credibly threaten to leave the relationship before burdensome terms are imposed on them when contracting a new loan or rolling over an old one. However, an important difference between yield spreads and covenants is that with covenants, hold-up is state-contingent. not had a lending contact within five years. This appears reasonable since 84% of all loans in the sample have a maturity of five years or less. 10

11 3 The Data I obtain data on syndicated and large sole lender loans from Loan Pricing Corporation s DealScan database. DealScan reports yield spreads, covenants, maturities and other characteristics for loans made by bank and non-bank lenders to both U.S. and foreign corporations and accounts for a large proportion of the U.S. private loan market. 8 According to DealScan officials, the vast majority of information on covenants is collected from loan documents filed with the SEC. Consequently, DealScan contains little information about covenants for loans contracted before 1995, when companies started filing SEC documents electronically. Therefore, the sample ranges from January 1995 to December The sample consists of U.S. currency denominated loans obtained by U.S. firms that are not a member of the financial, utility, or public administration sectors. I merge this dataset with the borrowers accounting data in Compustat for the fiscal year prior to loan inception using a link file kindly provided by Michael Roberts and Sudheer Chava. 9 Borrowers S&P long-term issuer ratings are taken at the month before loan inception to reflect the borrower s risk assessment at the time the loan is made. After applying all filters, the final sample for which the required information is available consists of 7,923 loans incurred by 3,169 borrowers. Loans are reported in DealScan as packages (or deals), which contain one or more facilities. Information such as yield spreads, loan amounts, and maturities are available at the facility level, whereas covenants are reported at the package level. To avoid artificially weighting covenant observations by the number of facilities in the package, I aggregate all data to the package level. 10 Numerous bank mergers and acquisitions occurred during the sample period. To account for the M&A activity, I matched the DealScan lenders to FDIC institution IDs (RSSD IDs) based on name, geographical location and time. I performed this match at the individual bank level rather than the bank holding company level on the theory that information acquisition primarily occurs through direct interaction. Using this match, the Federal Reserve s National 8 According to Carey and Hrycay (1999), DealScan covers between 50 and 75% of all commercial loans (by value) in the U.S. in the early 1990s and coverage further increases thereafter. 9 Details on the construction of this link file can be found in Chava and Roberts (2008). 10 One might wonder to what extent the results are influenced by firms incurring multiple loans within one year. It turns out that in the final sample, 91.6% of the firm-year combinations are unique, while for 7.7% of the firm years there are two loans in the sample for 0.7% of the firm years there are three or four loans in the sample. Consequently, aggregating observations to firm-years does not change results. 11

12 Information Center allows me to track bank mergers over time and attribute an acquired bank s relationships to the surviving entity. I measure both financial covenant and non-financial covenant intensity as count variables that add one for each financial and non-financial covenant, respectively, as recorded in DealScan. Table 1 details the various types of financial and non-financial covenants. Financial covenants are grouped into six categories: debt to balance sheet, coverage, debt to cash flow, liquidity, net worth, and EBITDA covenants. Debt to balance sheet and debt to cash flow covenants restrict the maximum indebtedness the borrower is allowed to incur relative to the various balance sheet and cash flow items detailed in table 1. Coverage, liquidity, net worth, and EBITDA covenants all require the maintenance of certain minimum coverage or liquidity ratios or of a minimum net worth or EBITDA. Among financial covenants, coverage covenants are the most frequent, with 79% of all loans containing at least one coverage covenant. Debt to cash flow covenants and net worth covenants are included in 60% and 43% of the loans, respectively. Non-financial covenants include sweep provisions, dividend restrictions and capital expenditure restrictions. Sweep provisions require the borrowing firm to repay part or all of the loan prematurely if it takes certain actions. For example, if a loan carries an asset sales sweep, the borrowing firm must use asset sale proceeds in excess of certain allowances to repay the loan. Close to 80% of all loans have a dividend restriction, while about one fifth of the loans have a capital expenditure restriction. Among the 38% of all loans that carry a sweep provision, the majority has more than one such provision. 11 The empirical predictions require measuring relationship intensity in a way that captures both the lender s prior experience with the borrower as well as the exclusiveness of the relationship. I define the lender as the loan s lead arranger since the lead arranger acts as an intermediary between the borrower and the participant lenders and thus is better informed (Ivashina, 2009; Guerin, 2007). I designate as lead arrangers any lender for which the field lead arranger credit is denoted as Yes in DealScan as well as the lenders of all sole lender loans. In addition, I search the field lender roles and define the following roles as lead arrangers: 11 Note that data on non-financial covenants in DealScan is sometimes missing, even when data on financial covenants is available. Since the vast majority of DealScan s covenant information comes from loan documents filed with the SEC, whenever there is information on financial covenants, information on all covenants included in the loan should be available to LPC. Thus, I set non-financial covenants to zero if the information is missing, but data on financial covenants is available. This method appears to be similar to practitioners approach (e.g. May and Verde (2006)). In any case, results for financial covenants are unaffected by this issue. 12

13 agent, administrative agent, arranger, lead bank. These definitions coincide with Bharath et al. (2009), who describe these roles in more detail. I then identify all instances in DealScan where the borrower obtained funding in the five years prior to the current loan (including loans that are not in the final sample due to missing information) and measure relationship intensity as follows: 12 Relation (Max Amt) = max k j Loan amount j I(k) j Loan amount, (1) j where I(k) indicates lead arranger k s participation in loan j. In words, I determine relationship intensity as the total amount of loans over the past five years for which the current lead arranger acted as a lead arranger divided by the total amount of all loans over the past five years. If there are no loans in the previous five years, the measure is undefined. Thus, I require at least one prior loan to be observable. If the current loan has more than one lead arranger, I take the maximum of that ratio across all lead arrangers since soft information-driven decisions are likely to be led by the lead arranger that has learned the most soft information. As an alternative, I consider a relationship intensity measure that gives equal credit to each lead arranger involved in the loan and calculates the sum of relationship intensities across lead arrangers: Relation (Sum Amt) = k j Loan amount j/n j I(k) j Loan amount, (2) j where N j indicates the number of lead arrangers participating in loan j. Relative to the Relation (Max Amt) measure, this measure has the advantage that it equals one if and only if there is no lender outside of the current syndicate who has ever acted as a lead arranger to the firm. The disadvantage is that it is not clear why a lender in a syndicate with two lead arrangers should learn only half the information that a lender in a syndicate with one lead arranger would learn. In any case, the two measures differ only if multiple lead arrangers are involved in any of the firm s loans. 13 Consequently, they have a correlation of and lead to very similar results. Table 2 shows the number of loans available per firm for the final sample and for the sample used to determine relationship intensity. The median firm has two loans in the final sample and four loans in the sample used to determine relationship status. Thus, using all available loans 12 This measure is also used by Bharath et al. (2009) and Schenone (2010). 13 Among the loans in the sample, 74.4% have one lead arranger, 22.1% have two lead arrangers and 3.5% have more than two lead arrangers. 13

14 in DealScan to calculate relationship intensity is important if one wants to be able to detect any nonlinear effect in relationship intensity. Section 7 will discuss the reasons why these loans are excluded from the final sample and perform robustness checks to ensure that this does not affect the results. Table 3 presents univariate tests of differences in firm and loan characteristics conditional on relationship intensity. Relationship intensity is categorized as low if Relation (Amt) is less than 30%, high if it is more than 70%, and medium if it is in between. The results show that financial covenant use increases by about 4% from the low to the medium category and decreases by about 7% from the medium to the high category. The changes are highly statistically significant. Non-financial covenant use essentially does not change from the low to the medium category and decreases by about 13% from the medium to the high category, again statistically significant. Yield spread and collateral requirements decrease in relationship intensity, whereas maturity is largely unchanged. However, table 3 also shows that firm characteristics are correlated with relationship intensity, with firms with high relationship intensities being larger, more likely to be a member of the S&P 500, more likely to be rated, having better ratings, and having a lower current ratio. Therefore, I now turn to multiple regressions Multiple Regressions for Covenant Intensity 4.1 Baseline Tests Since the number of financial covenants is a count variable, I test the effect of relationship intensity on financial covenant intensity by estimating Poisson regressions. As argued in section 2, this effect may be nonlinear. One way to test for nonlinearity is to assume a quadratic form: log(fincov i ) = α 1 + β 1 Relation i + γ 1 Relation 2 i + δ 1 Controls i + ɛ 1,i, (3) where FinCov is the number of financial covenants included in a deal and Relation is one of the two relationship intensity measures. Since the linear and squared terms of relationship intensity 14 In unreported regressions, I confirm the negative effect of relationship intensity on spreads, collateral requirements, and maturity found by Bharath et al. (2009) and obtain very similar coefficients. Although data availability on financial covenants is not as extensive as for the terms they investigate, this similarity in results lends support to the quality of my sample. 14

15 are highly correlated, one may be concerned that regression estimates are an artifact of this correlation. Therefore, I focus on presenting results using a dummy variable specification: log(fincov i ) = α 2 + β 2 LowRelation i + γ 2 HighRelation i + δ 2 Controls i + ɛ 2,i, (4) where Low Relation and High Relation are dummy variables that equal one if relationship intensity is below 30% and at least 70%, respectively. Consequently, loans with medium relationship intensities become the base group, which allows testing for the existence of any U-shape. Controls include various loan and firm characteristics as displayed in table 3 as well as industry fixed effects at the one-digit SIC industry level, year fixed effects, and loan purpose and loan type fixed effects. If one deal contains two different types of loans, e.g. a revolver and a term loan, then both these dummy variables equal one for that deal. 15 As is customary in studies using Compustat data, the top and bottom 1% of all financial ratios are winsorized to control for outliers. 16 Table 4 shows the results. The effect of relationship intensity on financial covenant intensity appears to follow an inverted U-shape. For the quadratic specifications, the linear term is significantly positive and the quadratic term significantly negative. For the dummy variable specifications, both the low relationship and high relationship dummies indicate a significantly lower covenant intensity compared to loans with a medium relationship intensity. Table 4 also shows that financial covenant use decreases in the size of the loan and in firm size. The coefficient of leverage is positive as expected, but not significant. Covenant use increases in the number of lenders participating in the loan, which mirrors the result in Drucker and Puri (2009) that loans sold on the secondary market contain more covenants than loans that banks keep on their balance sheet. Both the current ratio and the coverage ratio enter positively in the regression. Many covenants are written on ratios related to these two. Such covenants may be more informative if these ratios are above a certain threshold. 17 Firms with a worse credit 15 I exclude loan purpose and loan type dummies that have fewer than ten nonzero observations in the sample. 16 These winsorizations have an effect on the coefficients of some of these ratios, but they do not change the results regarding the relationship effect. 17 Current ratio covenants typically stipulate a minimum ratio of 1.0 or higher, while interest coverage ratio covenants typically stipulate a minimum of 1.25 or higher. If one excludes loans from borrowers whose ratios are below one of these thresholds, the current ratio and coverage ratio are no longer significant in the regressions. 15

16 rating or no credit rating at all are subject to more covenants, while loans to members of the S&P 500 carry fewer covenants. 18 The relationship effect is economically significant. Figure 1 plots the effect of relationship intensity on covenant intensity using the quadratic specification from regression (2) in table 4 and a stepwise dummy variable specification using the same controls (not reported in the table). Financial covenant intensity increases by about 8% from low to medium relationship intensity and decreases by about 4% from medium to high relationship intensity. These changes are equivalent to the effect of a change in the rating by two to three notches and by one to two notches, respectively. For further comparison, a one standard deviation increase in leverage leads to an increase in financial covenant intensity by about 1% (or 2% if one removes the rating variable from the regression) and a one standard deviation increase in the log of assets leads to a decrease by about 4% (or 7% if one removes the S&P 500 dummy from the regression). The relationship effect on covenants is also similar in size to the effect on other loan terms. For example, Bharath et al. (2009) find that a change in relationship intensity from 0% to 100% leads to a decrease in the all-in-spread drawn by 5% (evaluated at their sample average spread). The stepwise specification in figure 1 also shows that the relationship intensity thresholds of 30% and 70% used for the dummy variable specification in regression (3), while somewhat arbitrary, capture the curve quite well. A variety of alternative cutoffs exists that would yield similar or stronger results. The results in this section show that the effect of relationship intensity on financial covenant use follows an inverted U. I now turn to determining which of the theories described in section 2 contribute to this effect. 4.2 Bargaining Power and Syndicate Structure An important way of distinguishing hold-up problems from information asymmetry effects is to consider the borrower s bargaining power. If financial covenant violations provide an exclusive lender with an opportunity to hold up the borrower, borrowers would be expected to seek to avoid this hold-up potential by writing fewer covenants into their loans. However, to be able 18 A potential problem with Poisson models is over- or underdispersion of the data relative to the model. Calculating the deviance for the models in table 4 and dividing by the degrees of freedom gives a value of 0.324, which is smaller than one and hence suggests that underdispersion is present. Standard errors scaled by the square root of the deviance-based dispersion are slightly smaller than standard errors clustered at the firm level. 16

17 to do so, they are likely to need sufficient bargaining power at the time the loan agreement is entered into. Thus, the hold-up argument predicts that a borrower with better access to outside capital will be better able to adjust its loan contracts for the covenant violation holdup problem by writing fewer covenants when the relationship with the current lender is more exclusive. Because borrowers with access to outside capital markets tend to be more transparent, an information asymmetry story would predict the opposite: The decrease in covenant intensity for high relationship intensities should be more pronounced for opaque firms that do not have access to outside capital markets. I use the existence of an S&P long-term issuer rating as well as a firm s access to the commercial paper market (as evidenced by a short-term rating of A-2 or better (Murfin, 2010)) as proxies for the firm s access to public debt markets. 19 Columns 1 and 5 of table 5 show that the decrease in covenant intensity for firms in exclusive relationships is concentrated in rated firms. Among rated firms, covenant intensity is between 6% and 7% lower for borrowers in exclusive relationships as compared to borrowers in medium intensity relationships. 20 Figure 2 plots the difference in the relationship effect for rated vs. unrated firms using a quadratic specification (not reported in table 5). For rated firms, covenant intensity increases until a relationship intensity of about 53% and decreases strongly thereafter, while for unrated firms covenant intensity increases until a relationship intensity of about 60% and remains relatively constant for higher relationship intensities. 21 Columns 2 and 6 of table 5 show that the interaction between high relationship intensity and access to the commercial paper market has similar coefficients to the interaction with being rated, but is at best marginally significant statistically. Consequently, it appears that having any rating is more important than having a rating that indicates a particularly high credit quality. I next turn to the impact of syndicate structure on the relationship effect. This matters 19 One might wonder whether the hold-up problem itself is smaller for rated borrowers. However, note that investors on the public debt market are most likely even more uninformed than potential outside lenders on the market for bank debt. Thus, access to the public debt market provides the borrower with bargaining power ex ante, but not necessarily ex post after a covenant violation has occurred. In any case, this would hurt my identification strategy. 20 This effect does not appear to be driven by the fact that controlling for the level of the rating helps the regression model better measure credit quality for rated borrowers than for unrated borrowers. When dropping the ordinal rating variable and thus leveling the playing field, coefficients on the interaction terms remain qualitatively and quantitatively similar. 21 Ai and Norton (2003) point out that the interpretation of interaction terms can be difficult in nonlinear models. This problem does not apply here since the Poisson model is linear in the log of the covenant count, which makes analyzing percentage changes straightforward. Couching the discussion in terms of percentage changes appears reasonable since financial covenants are written on correlated accounting ratios. On average, adding a second covenant should increase restrictiveness more than adding a tenth covenant. 17

18 for two reasons. First, an increase in the number of participants limits the extent to which monitoring benefits accrue to the lead arranger. In the Rajan and Winton (1995) model, covenants incentivize the lender to monitor despite the presence of other claim holders because a covenant violation allows the lender to demand early repayment or adjustments to the interest rate, the benefits of which accrue solely to her since other claim holders do not hold these rights. In a borrowing syndicate with multiple loan participants or multiple lead arrangers, every lender is treated equally in the event of a covenant breach, which again allows other lenders to free-ride on the monitor. Consequently, if monitoring incentives motivate the inclusion of financial covenants in contracts with relationship lenders, the increase in covenant use from low to medium relationship intensities should be stronger for sole lender loans than for loans syndicated to other loan participants and it should be stronger for loans with one lead arranger than for loans with multiple lead arrangers. Second, to the extent that other loan participants, or especially, co-lead arrangers learn soft information about the borrower, the potential to hold up a borrower when he violates a covenant may be reduced. If a hold-up attempt occurs, an informed competitor within the syndicate could offer the borrower better terms and win his business. This incentive to deviate for other syndicate members would limit the syndicate s ability to hold up the borrower in the first place. If this is the case, the need to reduce covenant intensity for high relationship intensities would be reduced. Columns 3 and 7 of table 5 show the increase in covenant intensity with an increase from low to medium relationship intensity is stronger for sole lender loans, consistent with covenants providing monitoring incentives or sole lenders investing more strongly in the acquisition of soft information. According to columns 4 and 8, the inverted U curve is flatter for loans with multiple lead arrangers, although the difference is significant only for the relationship measure that gives 1/N credit to each of the N lead arrangers. This is consistent with both monitoring incentives and within-syndicate competition among lead arrangers One may wonder whether loans with multiple lead arrangers are purely transactional in nature, such that no soft information is acquired. In section 5.1, I show that yield spreads decrease in relationship intensity by the same amount for loans with single vs. multiple lead arrangers. Hence, soft information acquisition does seem to occur in both types of loans. 18

19 4.3 The Borrower s Information Opacity As described in section 2, a reduction in information asymmetries over the course of a banking relationship might result in a lower need for financial covenants. I now test whether this is the case and whether the reduction in covenant intensity for exclusive relationships is driven by such an effect. Proxies for information opacity include dummy variables indicating whether the borrower s total assets were below the sample median during the start year of the loan, whether the borrower s stock was a member of the S&P 500 index, whether the borrower is a high tech firm (following Loughran and Ritter (2004)), whether the number of analysts following the borrower s stock was below the sample median for that year, whether the dispersion of analyst forecasts for the borrower s earnings per share is above the median, and whether the borrower is listed on NASDAQ as opposed to NYSE or Amex. It should be noted that many of these proxies are negatively related to a firm s access to capital markets and, hence, its ex ante bargaining power. To the extent that bargaining power matters more than information asymmetries, one would expect results to mirror those of the previous section. Although opaque firms might be easier to hold up after a covenant violation, they are likely to lack the bargaining power to adapt the loan contract to this hold up potential in the first place. Table 6 presents results using the Relation (Max Amt) measure. Results using the Relation (Sum Amt) measure are qualitatively and quantitatively similar and are omitted for brevity. Table 6 shows that the evidence is inconsistent with a lower need for covenants due to a reduction of information asymmetries over the course of a relationship. The downward sloping part of the inverted U is stronger for large borrowers, borrowers with a large analyst following and borrowers whose stock is part of the S&P 500. The first two of these interactions are statistically significant at the 5% level, while the interaction with S&P 500 membership is marginally significant at the 10% level. There is no difference for high tech vs. other firms, NASDAQ vs. NYSE/Amex firms, or firms with high vs. low dispersion of analyst forecasts, proxies that arguably are less related to capital markets access and more related to pure information asymmetries. In addition, there is no significant difference in upward slopes for low relationship intensities across borrowers of different opacity. Taken together, the results presented thus far support the theory that soft information acquisition in a banking relationship enhances covenant use due to a reduction in renegotiation 19

20 and monitoring costs and that borrowers trade off this benefit with the hold-up potential arising from a covenant violation. The evidence does not support the theory that the initial upward slope in covenant intensity is driven by ex ante hold-up since the initial increase is also present (or stronger, if anything) for large, and rated firms which succeed in negotiating a lower financial covenant intensity even in exclusive relationships. Likewise, the evidence is inconsistent with covenant intensity being driven by a reduction in information asymmetry between the borrower and the relationship lender since I find that the reduction in covenant intensity in exclusive relationships is driven by large, rated firms rather than small, opaque firms. 5 Endogeneity The choice of forming, developing, and breaking a banking relationship is likely to be endogenous. Firms that do not form relationships might differ from firms that have relationships with several banks and firms that have an exclusive relationship in ways that explain the inverted U-effect documented thus far. Note that any such endogeneity story would also have to account for the finding that the reduction in covenant intensity is concentrated in relatively transparent firms that have higher ex ante bargaining power. While it seems difficult to construct such a story, this section employs three different ways to test whether results are driven by selection on observable or unobservable firm characteristics. The first strategy analyzes loan terms to which neither the monitoring incentive argument nor the covenant-created hold-up argument applies. There should not be an inverted U-effect for these other loan terms. The second strategy discusses relationship effects estimated by propensity score matching methods and the third uses an instrumental variables approach. 5.1 Relationship effects on yield spreads and non-financial covenants Yield spreads do not offer the state-contingent control feature embedded in financial covenants. For this reason, neither the monitoring incentive and renegotiation cost argument nor the argument that a covenant violation creates hold-up is applicable to yield spreads. The two other theories presented in section 2 do apply to yield spreads. If relationship lenders succeed in holding up their borrowers at the contracting point, yield spreads should increase in relationship intensity. If relationships mitigate information asymmetries between the borrower and the 20

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