The Credit Rating Transition Effect and Bank Loan. Contracting

Transcription

1 The Credit Rating Transition Effect and Bank Loan Contracting Margot Quijano McCoy College of Business Administration Texas State University San Marcos Ha Chin Yi McCoy College of Business Administration Texas State University San Marcos September 1, 2012 Margot Quijano can be reached at: Ha Chin Yi can be reached at:

2 ABSTRACT Bank loan literature find a significant portion of newly originated loans are renegotiated later, as the borrowing firm undergoes a change in credit quality during the life of the loan. Using annual data from 1986 to 2009 on U.S. corporate bank loans, we study if a firm s probability of a future change in its credit quality, proxied by the rating transition effect, affects today s cost of its bank debt. The rating transition effect serves as a good proxy for changes in credit quality since it is exogenous to loan origination and borrower characteristics. We find that the rating transition effect found in credit ratings increases the interest rate paid on bank loans significantly. Furthermore, we find that the rating transition effect also affects non-pricing term such as loan covenants, making the covenant structure in loans more intense and strict; this effect is mitigated if such loan is secured. Keywords: bank loan pricing, credit ratings migration, rating transition effect.

3 1 Introduction In this paper we investigate if a firm s probability of a future change in its credit quality affects its cost of capital today. Corporate credit quality tends to exhibit a volatile pattern over time, a seemingly financially strong company in the current year may fall into junk level next year. When investors provide capital to a firm, they are not only concerned with the current credit quality of the firm but also its evolution in the future. In this paper we study if a firm s probability of a future change in its credit quality affects today s cost of its bank debt. We call the evolution of the borrower s credit risk and its potential impact on the cost of the firm s debt the rating transition effect. We argue that the rating transition effect influences loan price and other loan characteristics. Two theoretical arguments are rendered to support the premise. First, Roberts and Sufi (2009) document that a majority of syndicated loan agreements are renegotiated before their maturity, and one of the reasons associated with this renegotiations are changes to the credit quality of the borrower after the loan is granted. Since renegotiation implies costly bargaining between lender and borrower, we can expect a rational lender to charge a premium to a loan agreement that is more likely to be renegotiated in the future. Second, Rajan (1992) and Diamond (1984) posit that a bank is an information specialist with a monitoring advantage relative to at arms length lenders and therefore can issue a cost-effective and informed loan. Since the expected amount of repayment is dependent upon the borrowing firm s future operating conditions, the likelihood of change in credit quality should be priced upon loan contracting. Banks are superior monitors because a close bank firm relationship allows the lender to access the firm specific information that is proprietary or not easily available to other lenders. Thus, with more intimate details of inside operation and future operating conditions about the borrowing firm, the bank is more likely to price the future credit quality transition, or the rating transition effect in loan contracting. In order to estimate the probability of a change in the credit quality of a borrower, we exploit the 1

4 rating transition effect embedded in credit ratings. The rating transition effect is the relation between the length of time that a firm has held its current rating and the probability it will change in a specific future period. This effect can be derived from the historical pattern of changes in credit ratings accumulated over an extensive time period. 1 To the extent that bond ratings reflect a firm s credit quality, the rating transition effect of bond ratings allows us to proxy for the probability that a firm s credit quality will change during the life of the loan. Credit ratings can serve as very accessible tools to analyze potential borrowers credit quality. Chava and Roberts (2008) state that since most firms with a credit rating have publicly traded debt, banks can learn from the information impounded in bond prices, consistent with a feedback effect. In addition, the rating transition effect is unconditional of market and accounting variables that typically serve as control variables in loan spread regressions, thus allowing for a clean interpretation of coefficients and alleviating concerns about endogeneity. For a large sample of U.S. syndicated loans from 1986 to 2009 we estimate the ratings transition effect by the semi parametric hazard model of Carty and Fons (1993). We find strong evidence that the ratings transition effect impacts the terms of loan contracts, both in the price of the loan as well as the inclusion and tightness of covenants included in it. The rating transition effect in bond ratings is obtained by estimating the probability that a firm s credit rating changes after the loan has been granted but before it matures. We use the hazard model of Carty and Fons (1993), who model the life patterns of credit ratings as a Weibull distribution with different parameters for each rating. At the start of each loan, we estimate the hazard probability that the firm s credit rating will change before the loan matures. This probability depends on the firm s initial bond rating, the time it has maintained that rating up until the loan is signed, the loan maturity, and the Weibull parameters estimated by Carty and Fons (1993). Credit rating agencies claim that ratings reflect firms internal forecasts of profitability and 1 This rating transition effect has been widely studied in the credit risk literature, for an overview see Duffie and Singleton (2003). 2

5 cash flows, as well as capital budgeting plans within the company. Long (1974), and Kaplan and Urwitz (1979)) analyze the determinants of bond ratings, and find that firm-specific information has significant explanatory power on bond ratings. However, a bond rating is not only determined by firm-specific information. Carty and Fons (1993) show that credit ratings are partially determined by their rating transition effect. The rating transition or migration effect is the relation between the length of time that a bond issue has held its current rating and the transition probabilities of credit ratings. Lando and Skodeberg (2002), and Kavvathas (2001) both find a rating transition effect on credit ratings. Therefore, changes in bond ratings are not only determined by firm-specific information, but also by a rating transition effect. The latter, being independent to firm performance, serves to proxy changes in credit quality while being exogenous to loan origination. While there may be an endogenous feedback between the firm-specific information used to partially determine bond ratings and the firm-specific information used to set loans prices, there is no endogenous channel between rating transition effect and loan prices since the rating transition effect is independent of firm-specific characteristics. In our first hypothesis, we sustain that the rating transition effect will have a positive effect on bank loan prices. If the lender anticipates a high probability of a change in the borrower s credit quality, the lender may charge a premium anticipating the costs of renegotiating the loan. Our second hypothesis is that the rating transition effect is positively related to the tightness and intensity of the covenant s structure in debt contracts. If the lender foresees the possibility of renegotiating the loan, covenants are a good way to acquire bargaining power in the states of nature where the loan is renegotiated (Roberts and Sufi, 2009). We conduct our study on a large sample of U.S. syndicated loans from 1986 to This market has become the largest source of corporate financing worldwide over the past two decades (Ivashina, 2009), and it accounts for almost half of new debt financing to U.S. corporations while generating more underwriting fees than either the equity or bond market 3

6 (Sufi, 2007). Nini, Smith and Sufi (2009) report that roughly 80% of all public firms in the U.S. have a private debt agreement. As well, the syndicated loan market is a good laboratory for testing our hypotheses that address informational frictions between the borrower and the lender, as these informational frictions can be severe in syndicated loan markets where multiple banks, rather than a single lender, deal with the borrower(ivashina, 2009). The average maturity for a loan in our sample is slightly less than 4 years, nevertheless, we show in Panel A of Table 2, it is very common for firm s to change their credit rating before the loan matures. For instance, over half of BBB rated firms experienced their rating changed before loan matures. This example highlights the economic importance of accounting for possible changes in credit quality when determining the terms in loan contracts. Our findings confirm that the rating transition effect in credit ratings has a positive and statistically significant effect on loan spreads. Our baseline estimates suggest that for the median firm in our sample, a one standard deviation increase in the probability of a rating change results in a 10.52% increase in the interest rate paid to banks. In cross-sectional tests we find that pledging collateral reduces this effect. We also study the impact of the rating transition effect on the non-pricing term such as the tightness of covenants attached to loan contracts by estimating a probit regression similar to Demiroglu and James (2010), and find a positive and statistically significant effect. Furthermore, we estimate the impact of the rating transition effect on the intensity of covenants using a Poisson model, and again find a positive and significant effect. As a whole, our results suggest that lenders are sensitive to the possibility ex ante that borrowers credit quality might change after a loan contract is granted but before it matures, and lenders impound this information into debt contracts at the time the loan is signed. The remainder of the article is structured as follows. Section 2 reviews previous literature and develops our main hypotheses, Section 3 describes the data and sample selection process, and Section 4 reports the major findings. The last section provides concluding remarks. 4

7 2 Literature Review and Hypothesis Development The ex-post credit risk always concerns banks when issuing loans. We posit that the potential for change in credit quality of a corporation after a loan has been granted can be captured by the rating transition effect in credit ratings. The likelihood that a credit rating may change represents a major source of credit risk. This has several implications on financial markets. In this section, we summarize bank loan pricing in general and document the results and findings of several research papers related to our study that focus on the relation between credit ratings and bank loans, and on the determinants of credit ratings. Later, we include collateral and loan covenants in our conceptual framework to address the attenuating effect on moral hazard and adverse selection displayed by the borrower. 2.1 Information asymmetry and monitoring cost in syndicated loan market The existence of collateral and covenants in loan contracts serve as evidence of banks concern over future uncertainties and their need for ex post monitoring (Bradley and Roberts, 2004; Chava and Roberts, 2008; Demiroglu and James, 2010; Murfin, 2011). Banks are considered information specialists because of their ability to extract private information through the lending process and their efficient ex post monitoring role that mitigates information asymmetries (Diamond, 1984; Diamond, 1991; James, 1987; Fama, 1985; Besanko and Kantas, 1993). Diamond (1984) stresses that due to the potential moral hazard behavior of borrowers, banks will follow them closely and act as delegated monitors with respect to their loan portfolios. As more private information is extracted by the bank, a stronger and more intimate relationship may be established with the borrowing firm, this distinguishes bank capital from arm s length capital in which public debt is diffusely owned by numerous investors (Rajan, 1992). However, information acquired by monitoring through relationship with the borrower is not perfect and, upon the arrival of new information, renegotiations of 5

8 loan terms might be triggered during the life of the contract. Roberts and Sufi (2009) find that 90% of loan contracts are renegotiated before their maturity. In addition, monitoring costs are often prohibitive, preventing banks from accessing perfect information on the true riskiness of the borrower. Given that banks are not insiders, information gaps almost always appear even if long-term relationships exist with borrowers; nevertheless, other terms in the loan contracts, such as covenants and collateral, seem to keep strengthening the incentives for monitoring (Rajan and Winton, 1995). Enforcing a covenant requires the lender to seek for more information, thus the effective use of covenants forces the lender to do some monitoring, possibly getting more insight into the borrower s condition at little additional cost. A deterioration of pledged collateral is correlated with the potential for financial distress, requiring more monitoring post-loan issuance. Since information acquisition and monitoring are costly, banks can supplement their future monitoring with other information sources such as credit ratings. In next section, we discuss why ratings from a third-party agency can help banks supplement their monitoring efforts becoming an important factor in setting loan prices. 2.2 Relation between credit ratings and the market Bond ratings and changes in bond ratings are of great importance to many market participants. Several papers have reported that there is a relation between bond ratings and prices of securities. Research papers like Griffin and Sanvicente (1982), Holthausen and Leftwich (1986), Hand, Holthausen and Leftwich (1992), Goh and Ederington (1993), and Goh and Ederington (1999) document that changes in bond ratings affect stock and bond prices. A theoretical study by Millon and Thakor (1985) provides an analytical basis for informationsharing services by rating agencies that banks use to complement their imperfect monitoring efforts. Literature has also found that credit ratings affect firms capital structure. Graham and Harvey (2001) find that credit ratings are the second highest concern for CFOs when 6

9 determining their capital structure, while Kisgen (2007) stated that firms near a credit rating upgrade or downgrade issue less debt relative to equity than firms not near a change in rating. Yi and Mullineaux (2006) argue that ratings provide information not reflected in financial information. Ratings may capture idiosyncratic information about recovery rates or information about default prospects not available to the market. The evidence above has strongly tied the changes of bond ratings to firms capital structure, stock markets, and debt markets, thus a change in bond ratings might affect firms current cost of capital and consequently their future credit quality. If banks are concerned with changes in firms performance and credit quality after a loan is granted and if renegotiations are likely as Roberts and Sufi (2009) state, then banks have to take upon themselves the costly task of monitor and screen borrowers. Moreover, it is plausible to assume that banks will consider bond ratings as an additional source of firm s information to ease up on monitoring expenses. After all, bond ratings are a cheap source of information to banks, and it is assumed that banks do their due diligence and study the potential borrowers closely before granting a loan. Chava and Roberts (2008) stated that since most firms with a credit rating have publicly traded debt, banks can learn from the information impounded in bond prices, consistent with a feedback effect. Furthermore, the level of credit rating at the time of the loan origination only reflects the default prospect at that moment in time, rather than predicting the future change in firm s credit quality. However, evidence has shown that the probability of rating transitions can partially determine future firm s credit quality (Lando and Skodeberg, 2002; Kavvathas, 2001); thus we posit that the current rating alone does not capture future prospects, however a rating migration risk at the time of loan origination might be priced into the contract. 2.3 Rating transition effect of credit ratings As mentioned before, renegotiations are highly likely after the loan is granted, thus a bank will be very interested in any type of information that can signal an ex post change in credit 7

10 quality. A change in credit quality can be implied by a change in credit ratings. There is vast literature dedicated to understand the transitions or migration of credit ratings. Credit rating agencies claim that ratings reflect both private and public information. They have the firms internal forecasts of profitability and cash flows, as well as capital budgeting plans within the company. Long (1974), and Kaplan and Urwitz (1979)) analyze the determinants of bond ratings, and find that earnings, firm size, leverage, return on investments, and debt coverage ratios have significant explanatory power on bond ratings. However, inside information on companies is not the only factor impounded in credit ratings. Carty and Fons (1993) show that there is a dependence of transition probabilities of credit ratings on duration in a rating category or age, sometimes called duration effect. In other words, the duration effect or in our terms, rating transition effect, is the relation between the length of time that an issue has held its current rating and the transition probabilities of credit ratings. Lando and Skodeberg (2002), and Kavvathas (2001) both confirm duration effects on credit ratings and find that the prior credit rating is a significant determinant of the probability of a downgrade versus an upgrade for firms with a fixed rating and over a specific period of time. A potential for rating change ex post lending decision increase the credit risk and the monitoring costs faced by lenders. Therefore, our first hypothesis is as follows: Hypothesis 1: Holding everything else constant, the probability of an ex post change in borrowers credit quality, as measured by the rating transition effect in credit ratings, is positively related to loan spreads. In this paper, we only obtan the probability of a credit rating changing after loan origination, we do not disentangle upgrades from downgrades; however, if we do obtain a positive and significant relation between ratings transition effect and loan spreads, this would indicate that if downgrades were isolated the effect would likely be stronger. 8

11 2.4 Observable factors correlated with risk migration Collateral Banks employ several measures and suggest them in loan contract terms to manage migration risk ex post to loan origination. For example the presence of collateral pledged by the borrower attenuates financial frictions stemming from the informational problems such as moral hazard and adverse selection, faced by banks when lending (Aghion and Bolton, 1992; Johnson and Stulz, 1985; Hart and Moore, 1998; Stiglitz and Weiss, 1981; Besanko and Thakor, 1987; Chan and Thakor, 1987). A tangible collateral is a mechanism to recover loan and therefore reduce the expected losses upon default when the borrower fails to repay a promised interest and principal. A loan recovery is relatively high. Hinh, Mullineaux, and Yi (2012) find that loan recoveries are typically about 84%. Alleviation of moral hazard and adverse selection problem produce the beneficial effect on the borrower s financing. With data from the airline industry, Benmelech and Bergman (2009) find that the liquid collateral reduces the cost of debt. Lower risk borrowers are willing to pledge more and better collateral, given that their lower risk means they are less likely to lose the pledged collateral. Thus, collateral acts as a signal enabling the bank to mitigate or eliminate the adverse selection problem caused by the existence of information asymmetries between the bank and the borrower at the time of loan decision. In this sense, pledging collateral plays an instrumental role in reducing loan rates. Even if the bank knows the credit quality of the borrower and therefore, information asymmetry is not a major threat to the lender, the collateral helps to alleviate moral hazard problems once the loan has been granted. The collateral pledged helps align the interests of both lenders and borrowers, avoiding a situation in which the borrower shirk from ensuring the success of the project by making suboptimal decisions. Thus, collateral limits the problem of the moral hazard faced by the lender after granting a loan. Collateral can therefore be 9

12 seen as an instrument ensuring good behavior on the part of borrowers (Aghion and Bolton, 1992). Reciprocally, Berger, Espinosa-Vega, Frame and Miller (2005) find that the reduced private information gaps lower the need of collateral on small business loans. We argue in this study that the existence of collateral helps reduce future uncertainty or credit risk. Future uncertainty is proxied by the probability of a rating changing within the life of the loan. Gorton and Khan (2000) argue that a bank loan contract is virtually an embedded call option giving the right to a bank who can later liquidate the loan by exercising the right to seize collateral. If a borrower suffers a change in credit quality, collateral serves the lender as a bargaining tool and it also represents a lower bound on potential recoveries. We expect for lenders to discount the future effect of rating migration on loan spreads with the existence of collateral Loan Covenants Another measure to mitigate moral hazard ex post to loan contracts is through the placement of loan covenants. Dichev and Skinner (2002) show that covenant violations occur in approximately 30% of loans, while Roberts and Sufi (2009) find that 90% of loan contracts are renegotiated before their maturity. Thus the existence of loan covenants is evidence of bank s concern over an ex post change in credit quality; in other words, banks are worried about borrowers risk changing during the life of a loan. Bradley and Roberts (2004) find the presence of debt covenants to negatively impact the yields on corporate debt. Upon a violation of a covenant, creditors can intervene by exercising their control rights specified in debt contract, and can modify terms to choose their most preferred action or extract concession from the borrower. Typically, increased interest rate is followed after a breaching of covenants, but also lenders use their bargaining power to influence the firm s investment (Chava and Roberts, 2008; Nini, Smith and Sufi, 2009; Roberts and Sufi, 2009). In this sense, Rajan and Winton (1995) even states that covenants make a loan s effective matu- 10

13 rity. We posit that banks employ stricter and more covenants with the increasing degree of information asymmetry and moral hazard in order to exercise the right to intervene on the borrower s operations and investment in the case of a violation of covenants during the life of loan. Thus, we model covenant tightness and intensity as a function of the ex post probability of rating change. We hypothesize that if the rating transition effect in credit ratings is an important determinant in loan pricing, then it may also be an important factor in deciding how strict or intensive the covenants in a loan should be. Our intuition is that the greater the likelihood of a borrower s credit quality to change during the life of a loan, the more ex ante protection, through stricter or more intensive covenants, banks will want to have. Our third hypotheses are as follows: Hypothesis 2A: Holding everything else constant, the strictness of covenants is positively related to the probability of an ex post change in borrowers credit quality. Hypothesis 2B: After controlling for firm s and loan s characteristics, if the probability of a change in borrowers credit quality increases during the life of a loan, then more covenants will be put in place in such loan contract. 2.5 Putting it all together Just as credit rating agencies have access to inside information on firms accounting and finances, a bank also uses an internal screening process and will have access to privileged financial historical information on companies. Moreover, banks will also have access to a history of rating transitions. Banks are subject to moral hazard conflicts after they lend funds to borrowers. When a bank is lending funds to a borrower, it is concerned with a change in credit quality of the firm for the duration of the loan. In this study, we argue that banks will do their due diligence and consider any source of information that possibly signals a 11

14 future change in firms credit quality when determining the cost of the loan at the origination date; that is, aside from analyzing historical financial and accounting information, banks will also pay close attention to other determinants of credit ratings, such as the duration effect of ratings which has been empirically proven to partially affect future changes in credit ratings. 3 Data and Methodology 3.1 Carty and Fons (1993) In this section, we will present a simple and intuitive model that captures the rating transition effect on credit ratings for a particular firm and a particular loan; this model draws heavily from the empirical methodology of Carty and Fons (1993). We measure the rating transition effect on credit ratings by applying a conditional hazard model. In specific, we are interested in estimating the rating transition effect in credit ratings within the life of the loan, that is, the probability of a rating changing from the moment the loan is approved and granted until the expiration of such loan. Carty and Fons (1993) plot the distributions of historical rating lifespan, and found that a Weibull distribution most closely models the lifespan characteristics of credit ratings. The Weibull distribution is simply a generalization of the exponential distribution and is commonly used for modeling lifetime data. A random variable has a Weibull distribution if it has a cumulative distribution function of the form: F (t; a, b) = 1 e ( t a )b, where t represents time, a is a scale parameter, and b is a shape parameter. Based on the assumption that the Weibull distribution can reflect the lifetime pattern of ratings and using maximum likelihood techniques, Carty and Fons (1993) estimate the distribution s parameters of scale and shape for long-term bond ratings; since these parameters are estimated using a very large proprietary database that we do not have access to, we use these already estimated parameters and fit them directly into a hazard model to estimate the probability of a rating changing within the duration of a bank loan. 12

15 We can express the hazard function in terms of the Weibull cumulative distribution function as follows: P (t X < t + m X t) = P (t X < t + m)/p (X > t) = F (t + m) F (t) 1 F (t) (1) where m is the maturity of the loan, t is the start date of the loan, and X corresponds to the number of periods before a credit rating changes since it was last granted, that is the length of time that an issue has held its current rating. After constructing our main variable of interest, we perform fixed effects regressions that estimate the relation between the probability of a change in companies credit ratings within the duration of a loan and the cost of such loan. We later estimate the relation between our main variable and the strictness and tightness of covenants in a loan. We include several control variables described further ahead. The main model we estimate is as follows: Loan spread = f(probability of rating transition, Borrower characteristics, Loan characteristics, Macroeconomic factors, industry and time effects) 3.2 Dealscan and COMPUSTAT data The data is mainly obtained from 3 sources. The Weibull distribution parameters (which are used to estimate the probability of a change in ratings) come from Carty and Fons (1993) study. The bank loan spreads are obtained from Dealscan, and the accounting data and credit ratings of the borrowing companies are obtained from Compustat. The data spans from 1986 to 2009 and we use companies that are non-financial and non-utilities. Each deal or package consists of multiple tranches or in LPC Dealscan terms, facilities. For example, one deal consists of three facilities: one revolver loan, or a line of credits 13

16 that the borrower can draw as necessary and two term loans. Each facility is matched with accounting and credit rating from Compustat. The majority of companies in the LPC database are medium to large, public firms, and the majority of loans are syndicated rather than issued by a sole lender. Table 1 reports summary statistics of the variables used in this study. All variables are roughly similar to those found in previous studies (Chava, Livdan and Purnanandam, 2009; Yi and Mullineaux, 2006; Ivashina, 2009). 3.3 Control variables Our control variables are motivated by existing literature on bank loan pricing. We use firm-specific and loan-specific variables to control for their effect on bank loan spreads Firm characteristics Return on assets (ROA) is defined as net income divided by total assets. A more profitable firm (supported by a higher ROA) gets better terms and prices on their bank loans. We also include the borrower s asset size (Log of Assets) and the growth of sales (Sales Growth), since a larger firm with a higher growth in sales is likely to have a better access to external financing, and also have a more established reputation that reduces information asymmetry and adverse selection problems to lenders. A firm size (Log of Assets) is a proxy for information variable in that a larger firm is less information problematic, warranting a discount in loan rate. Therefore, the negative sign on Log of Assets is expected. Market to Book ratio (Market to Book) is measured as book value of debt plus market value of equity to book value of assets. Market to book ratio is a measure of the borrower s growth options and is often used to proxy for moral hazard problems such as underinvestment and debt overhang (Myers, 1977). We hypothesize that market to book ratio is positively related to the loan risk premium. We include firms leverage (Leverage) since firms with greater leverage have a greater likelihood to default and thus should expect a higher cost of debt, everything else held constant. 14

17 Leverage is defined as the ratio of long-term debt plus short-term debt to total assets. We also construct an accounting measure of the probability to default (Modified Z score) following Graham, Lemmon and Schallheim (1998). A low Z-score represents a shorter distance to default, thus in turn will materialize into a higher cost of debt financing. Furthermore, an investment rating dummy (Investment Rating Dummy) was included to control for the bond market s assessment for credit quality. If information on investmentgrade borrower is more transparent than that associated with speculative-grade borrowers, the lender will perceive an acceptable default risk, implying a negative sign on the coefficient of this dummy. Interest coverage is included where interest coverage is defined as to EBITDA divided by interest expense.the negative sign is expexcted on interest coverage variable Loan characteristics We also include variables to control for loan characteristics, such as loan size (Size of Loan) and maturity (Loan Maturity) of the loan. Everything else held constant, a larger and longer maturity loan may enhance banks exposure to moral hazard problems. However, self selection might also arise in these cases where larger and long-lasting loans are granted to more reliable borrowers which in turn will have a negative impact on loan spread. The loan maturity is to be discussed further here because literatures provides inconclusive results on debt maturity, and the impact of maturity on credit spread likewise is uncertain. One line of argument is that long-term debt is more likely to be used by larger, less risky firms with relatively poor growth opportunities (Stohs and Mauer, 1996), and bad firms are screened out of the long-term debt market because of the prospect of moral hazard problem like risky asset substitution (Diamond, 1984; Diamond, 1991). This implies that there will be a negative relationship between credit spreads and maturity. An alternative argument is that bad firms do not issue short-term debt to avoid inefficient liquidation (Guedes and Opler, 1996). Risky firms will not be able to rollover debt in case of deteriorating financial conditions, so they desire to lengthen the maturity of their debt. This implies that there will 15

18 be a positive relationship between credit spreads and maturity. Flannery (1986) supports this argument and posits that good firms will consider their long-term debt to be relatively underpriced and thus will issue short-term debt, while bad firms will sell overpriced long debt. Using bank loan data, Berger, Espinosa-Vega, Frame and Miller (2005) find that low-risk firms tend to have significantly shorter maturities, although they do not find the significant difference in maturities between high-risk firms and intermediate-risk firms. We are therefore uncertain about the sign of Loan Maturity in determining credit spread. Loan syndicate structure proxied by (Size of Syndicate) is related to loan risk premium because in existence of multiple lenders, lender composition and structure convey information frictions between lenders and borrowers. Contracting syndicated loan involves information asymmetry problems not only between the lenders and the borrower but also among lenders. If the private information collected by the lead arranger through due diligence or previous lending relationships cannot be credibly communicated to the participants, an adverse selection problem arises-that is, the lead arrangers are inclined to syndicate loans for unreliable, risky borrowers at the expense of participating banks (Petersen and Wilhelm, 2001). After having closed contract, if the participants delegate monitoring to a lead arranger whose efforts are unobservable, moral hazard problem arises-that is, lead banks may shirk from due diligence of monitoring, as described by Holmstrom (1979). Literature investigate how to resolve these agency problems through the structuring of syndicate and designing of the loan contract terms and conditions. For example, the borrower with less information asymmetry is more likely served with a group of syndicate lenders rather than the sole lender (Dennis and Mullineaux, 2000). Bolton and Scharfstein (1996) argue that it becomes harder for multiple lenders to reach a collective decision when a borrower is in financial distress, suggesting that a risky borrower ex ante is served with the concentrated syndicate. This is consistent with the finding of Sufi (2007) that the lead lender holds a larger portion of loans and forms a smaller number of participating banks for more information-problematic loans. Petersen and Wilhelm (2001) argue that the syndi- 16

19 cate structure is an organizational response to agency problem between the lead arranger and participating banks. The lead lender having information monopoly over the borrower is perceived risky by participating banks which consequently requires a higher rates or demand more diffused syndicate structure to share risk among other multiple lenders, in the hope that multiple lenders can jointly make effort to evaluate true risk of the borrower. Since the diffused syndicate is negatively correlated with the degree of moral hazard and adverse selection, we expect a negative sign on the coefficient of the size of syndicate (Size of Syndicate). In addition, as the syndicate is diffusely structure for a given loan, rating migration may not play a significant role in setting loan price because syndicate structure may dominate the effect of rating transition. Several collateral studies find a positive relation between loan rates and the existence of collateral in credit contracts, consistent moral hazard issue (Berger and Udell, 1990; Jimenez, Salas and Saurina, 2006; John, Lynch and Puri, 2003), while Besanko and Thakor (1987) argue that when borrowers have informational advantages about their default probabilities, the lowest risk borrowers will pledge collateral. Angbazo, Mei, and Saunders (1998) empirically shows that loans secured by a pledge of assets have higher yield. This confirms the view of Battacharya and Thankor (1993) that collateral backing signals more risky borrower. Because of the mixed results from literature, we are uncertain on the effect of collateral on loan risk premium. As well, the number of loans in the loan package (Quantity of Loans) called, traching, is also an critical component of loan pricing process (Maskara, 2010). A revolving loan or credit line requires more intensive monitoring because the borrower tends to draw credit line more actively when it is under financial crunch (Berger and Udell, 1990). Thus, we include the number of tranches (Quantity of Loans) in a loan package in our regression. 17

20 3.3.3 Other control variables Angbazo, Mei, and Saunders (1998) show that loan risk premiums increase with capital market risk premiums, but fail to discern between the credit risk and term structure influences. In this study, we include additional variables that help control for macroeconomic conditions that might affect bank loan pricing. We include a proxy for the market s estimate of credit risk premium (Credit Spread), the spread between Baa-rated and Aaa-rated bonds. We hypothesize that loan risk premiums increase with market credit risk premiums. As well, to determine whether shifts in the slope of the yield curve affect bank loan pricing,we include a term structure variable (Term Spread) which corresponds to the difference between the 10 and 1 year Treasury rates. Finally, regressions include year, industry, loan purpose, and loan type fixed effects. Loan type makes a distinction between a loan being a revolver loan or term loan, while loan purpose controls for a loan being granted for corporate purposes, debt repayment, working capital, takeover, or other. 3.4 Descriptive statistics Table 1 shows the summary statistics. A sample of more than 14,000 facilities shows that LPC Dealscan loans are relatively large with a mean value of loan size being approximately $223 million (=e(19.221)), serving medium-to-large firms with a mean value of $2.3 billion (=e(7.760)). The average firm leverage is 42% of the asset and average sales growth is 20%. The average loan maturity is approximately 4 years (or 46 months) with more than one loan facility per each loan deal or package (Quantity of Loans is 1.8). A typical loan package consists of revolvers or line of credits and multiple term loans. Approximately, 66% of loans in our sample are secured. The average facility spread is 164 basis point over LIBOR. A typical deal involves an average of 11 syndicate banks. Table 2 shows the summary statistics of the borrowing firms shown by credit rating category. Panel A shows that a majority of loans in Dealscan database is from the medium-to-high risky borrowers. More than 11,000 loans out of 14,000 loans are either BBB-rated or lower upon 18

21 loan origination. The frequency of rating change after a loan has been granted does not vary much across initial ratings, but initial ratings of speculative grade loans are less stable than that of investment grade loans. For example, the consecutive months for AAA is 154 months, while that of B is 20.4 months. months to change of those speculative borrowers, which are a majority in our sample, are shorter than average loan maturity (46 months). This means that a typical borrower is prone to deviate, within a relatively short period, from its rating initially obtained upon loan origination year. This should concern loan syndicate lenders or more likely the lead lender bounded by due diligence of monitoring ex post lending. Ratings on speculative loans are more transitory than non-speculative loans, as shown in probability rating change column. Panel B shows more descriptive statistics at loan level by rating. One can also observe that the better rating is, the lower spread is, the less secured is, and the shorter maturity is. Panel C shows that the better the rating of the borrowing firm is, the greater the assets, return on assets (ROA) and Market-to-book ratio are, as well the greater distance to default, and on average the lower leverage as a percentage of total assets. Finally, Table 3 shows that, through time, several changes to credit ratings occur. This further motivates our hypotheses; a bank, being an expert in screening and monitoring borrowers, will try to be aware of any potential ex post changes in borrowers credit quality and incorporate this information in loan contracts at the time of loan origination.* 4 Results 4.1 Regression results on rating transition effect in credit ratings Table 4 shows the results for our main explanatory variable, (Prob Rating), referred to us as the possible ex post change in borrowers credit quality measured as the rating transition effect in credit ratings. The first two columns correspond to regressions that use loan spreads of all loans (secured and unsecured), while the last two columns correspond to regressions that only use loan spreads of unsecured loans. We hypothesize that, at the time of loan 19

22 origination, a possible change in credit quality within the duration of the loan will positively affect the price of the loan. The first row shows the resulting coefficients for our variable of interest; they are all positive and are statistically significant, even after controlling for firmand loan-specific characteristics. The greater the chance that a rating will change within the life of a loan, the greater the cost of the loan. Our results supports the argument that the rating transition effect found in credit ratings convey important information to banks, which in turn affect bank loan prices. In general, firm and loan characteristics significantly affect loan price. Table 4 shows a profitable firm (ROA) with a higher growth potential Sales Growth get a loan rate discount. The coefficient of Interest Coverage is negative as expected with statistical significance across different model specifications. The large borrowers get discount in loan rates. The larger or more diffusely structured syndicate implies a low degree of information asymmetry (Sufi, 2009), as shown by the negative sign on the Size of Syndicate. Multi-tranched loans are priced higher. 4.2 Collateral and loan Pricing Berger and Udell (1990) argue that the riskier borrowers are required to pledge collateral, while Besanko and Thakor (1987) argue that when borrowers have informational advantages about their default probabilities, the lowest risk borrowers will pledge collateral. Thus, collateral creates a self-selection issue and we are unsure about the final effect. Columns (3) and (4) of Table 4 re-run the original regression model on unsecured loan sample. Even in non-secured sample, the probability of rating change is still significant. Market-to-Book ratio is negative in Non-Secure Sample. The large loan from large firms get discount in loan spread, while a highly leveraged firm get a higher loan spread. The Size of Syndicate is negatively related, confirming that loan syndicated is formed diffusely to share information problems with the larger number of participating banks (Sufi, 2009). A negative sign on on maturity variable implies that a short-term debt is viewed as an effective 20

23 tool to discipline risky borrowers by forcing them to expose to refunding risk in a way that a risky borrower may not be able to rollover short-term debts as their short-term debt expire more frequently than longer-term debts. Frequent renewals of short-term debt requires more monitoring by external screeners like credit rating agencies and financial market participants upon new debt issuance. As a result, the small firm uses a short-term debt to avoid inefficient liquidation (Guedes and Opler, 1996). Macro-economic factors such as Term Spread and Credit Spread are generally significant as well as other control variables such as loan type, loan purposes, year and industry dummies. 4.3 Covenant relationship with a probability of a change in credit ratings. Demiroglu and James (2010) study the determinants of financial covenant strictness in bank loans. They explore if the strictness in covenants depend on future changes of borrowers and loans characteristics, as well as on private information regarding future changes in the borrowers covenant variables and the expectations concerning the likelihood and impact of a covenant violation. In this paper, we model covenant strictness as a function of the probability of a change in borrowers credit quality after the loan is granted. We hypothesize that if the rating transition effect in credit ratings is a good proxy for future changes in credit quality, then it must be an important determinant in loan contracting as well; moreover, it might also be an important factor in deciding how strict the covenants in a loan can be. Our intuition is that the greater the chance that a borrower s credit rating might change during the life of a loan, the more protection, through stricter covenants, banks will want to implement ex ante. Following Demiroglu and James (2010) we measure covenant strictness as the distance between the level of the covenant variable at the origination of the loan agreement and the minimum (or maximum) covenant threshold permitted by the loan contract. The greater 21

24 the distance, the less restrictive the covenant. As well, as an alternative, we measure the intensity of covenants using the covenant intensity index proposed by Bradley and Roberts (2004). The covenant intensity index is defined as the sum of six covenant indicators (collateral, dividend restriction, more than two financial covenants, asset sales sweep, equity issuance sweep, and debt issuance sweep). The index is set to missing if one of the indicators is missing. Table 5 reports our results for this section. A Probit and Poisson regression were estimated to analyze the determinants of covenant strictness, and covenant intensity, respectively. Robust standard errors clustered by borrower were estimated. In the covenant strictness regressions, the dependent variable is an indicator variable that equals 1 if the borrower chooses a tight debt/ebitda covenant, and 0 otherwise. In the covenant intensity regressions, the dependent variable is the covenant intensity index proposed by Bradley and Roberts (2004). Following Demiroglu and James (2010) we control for borrowers and loan characteristics using several explanatory variables. The borrowers characteristics include an investment rating dummy, log of assets, leverage, Ebitda/Sales, Market-to-Book, log of firm age, cash flow volatility, and current ratio; and on the other hand, the loan characteristics include the number of deals between the lender and the borrower, the size of the syndicate, the size of the loan, the maturity of the loan, and a dummy for the performance pricing grid. We also include year, loan type, loan purpose and industry (1-digit SIC codes) fixed effects. When estimating the tightness regressions, loan characteristics also include collateral and financial covenant (such as current ratio, CapEx, debt-to-balance sheet, cash flow, debt-to-cash-flow, coverage, etc) fixed effects. Results for regressions (1) and (2) agree with a signaling effect for investment rated firms, where they are willing to accept a tighter covenant in order to signal their quality and confidence in future cash flows; while on the other hand, results for regressions (3) and (4) show that investment rated firms have a lower amount of covenants. The effect of leverage is as expected, we see a positive sign in both: covenant tightness and intensity regressions, 22

25 suggesting more levered borrowers have tighter and more covenants. As well, concerning cash flow volatility, our findings imply that if a firm has highly unstable cash flows, the loans granted to such firms will tend to have more covenants. Our variable of interest, probability of a change in rating or credit quality loads up positively and statistically significant in the probit equations (1) and (2). These results suggest that banks tend to grant loans with a tighter covenant structure, if the borrower (of such specific loan) has a greater chance for its rating to change within the duration of such specific loan. As well, the Poisson equations (3) and (4) suggests that an increase in the probability of a change in rating is also associated with a higher covenant intensity, meaning that firms more at risk of a credit change, also have more covenants restricting their possible future actions. 4.4 Cross-section over loan characteristics on loan spread Table 6 reports results for the interactions between our main variable and the top quartiles of syndicate number (Prob Rating x Syndicate), strength of borrower-lender relationship (Prob Rating x Relationship), and loans with collateral (Prob Rating x Secured). Booth (1992)) state that relationship banking can be a value-enhancing intermediation activity. Banks collect private information about borrowers through their lending relationships through repeated deals, which would otherwise not available through public debt markets. This informational advantage is known to have an impact on the borrowing costs. Our results in column (2) are in line with Bharath, Dahiya, Saunders and Srinivasan (2007); repeated borrowing deals from the same lender, or the relationship lender tends to lower load spread. A bank relation attenuate moral hazard and adverse selection. However, the stronger the relationship between bank and borrower, together with a higher likelihood of a ratings change has a positive effect on loans spreads. Moreover, column (3) shows that the effect of an increase in the likelihood of a rating change after the loan is granted, is mitigated if such loan is secured, this makes sense since the 23