Loan Rates and Collateral

Transcription

1 The Financial Review 42 (2007) Loan Rates and Collateral Aron A. Gottesman Lubin School of Business, Pace University Gordon S. Roberts Schulich School of Business, York University Abstract We investigate the relation between corporate loan spreads and collateralization. We use propensity scoring to create a matched sample of pairs of loan facilities from the Dealscan database. We find that noncollateralized loans are associated with lower spreads even after controlling for risk. Keywords: bank, borrower, loan, contract terms, collateral JEL Classification: G21 1. Introduction How does the presence of collateral affect the rate that borrowers pay? This is an important issue as over 70% of business loans in the United States are collateralized (Berger and Udell, 1990), while for large syndicated loans the percentage exceeds 80% (Gottesman and Roberts, 2004). Prior studies establish two empirical regularities. First, collateralized debt carries a higher yield than noncollateralized debt as shown Corresponding author: CIBC Professor of Financial Services, Schulich School of Business, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3; Phone: (416) , ext ; Fax: (416) ; groberts@schulich.yorku.ca Financial support for this project came from the Social Sciences and Humanities Research Council of Canada and the CIBC Professorship in Financial Services at the Schulich School of Business. The authors thank three referees for this journal as well as Linda Allen, Allen Goss, and Edward Yuan for useful comments. C 2007, The Eastern Finance Association 401

2 402 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) by Berger and Udell (1990, 1995) and Dennis, Nandy, and Sharpe (2000) for bank loans and by John, Lynch, and Puri (2003) for U.S. corporate bonds. Second, lending officers at financial institutions require collateral from riskier borrowers, as noted by Orgler (1970), Hester (1979), Berger and Udell (1990, 1995), Carey, Post, and Sharpe (1998), Harhoff and Korting (1998), and Roberts and Siddiqi (2004). Several theoretical papers predict a positive association between borrower risk and the presence of collateral (Boot, Thakor, and Udell, 1991; Bester, 1994; Coco, 1999; Pozzolo, 2002). Some researchers argue that collateral characterizes the borrowings of less risky companies (Bester, 1985, 1987, 1994; Chan and Kanatas, 1985; and Besanko and Thakor, 1987). There is also evidence of a positive association between the presence of collateral and information asymmetries between borrowers and lenders (Gonas, Highfield, and Mullineaux, 2004). Two competing interpretations of these empirical regularities differ in their view of how the addition of collateral affects agency problems in lending contracts. Under the bonding hypothesis, collateralized debt reduces agency problems of assetsubstitution and lowers monitoring costs (Smith and Warner, 1979; Stulz and Johnson, 1985). The higher yields associated with collateralized borrowings arise because collateral guarantees only partly offset the greater risk of the borrowing firm (Berger and Udell, 1990; Pozzolo, 2002; Jiminez and Saurina, 2004). Along the same lines, Booth and Booth (2006) argue that a straightforward yield comparison is misleading because the choice to collateralize a loan is made jointly with setting its yield. Their study contrasts with Dennis, Nandy, and Sharpe (2000) in reporting that collateral is associated with lower spreads. Alternatively, in the model developed by John, Lynch, and Puri (2003), agency problems inherent in collateralized debt increase its risk, accounting for the higher yield. Their management-consumption hypothesis holds that managers engage in increased perquisite consumption when debt is collateralized. The predictions of both approaches are consistent with empirical findings of higher ex ante yields on large U.S. public debt issues (John, Lynch, and Puri, 2003), as well as on far smaller U.S. commercial and industrial loans (Berger and Udell, 1990). The predictions are also consistent with Pozzolo s (2002) study of loans in Italy. The U.S. syndicated loan market is comparable in depth and characterized by average loan sizes falling between U.S. public issues and commercial and industrial loans. Armstrong (2003) estimates that loan balances drawn under U.S. syndicated facilities were 54% of commercial and industrial loans for the United States in He also shows that global gross issuance for syndicated loans exceeds that of bonds each year from 1995 to In addition, the role of banks as expert monitors could potentially substitute for collateral or prevent management consumption causing the pricing of collateral in syndicated loans to deviate from prior findings. Despite the importance of this market, the pricing implications of collateral have largely gone untested. Booth and Booth (2006) employ a two-stage model to explore collateral pricing using a limited sample of 972 loans originated between 1987 and To address this gap, we conduct an empirical study of syndicated loans and introduce a different method. The loans come from Dealscan, a Reuters Loan Pricing

3 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) Corporation database that consists of large, mainly syndicated, loans. In contrast with prior studies that use regression analysis as the principal analytical tool, we exploit a matched pair method to hold characteristics, such as credit ratings and default probabilities constant, and to allow us to focus on the influence of collateral. This approach has antecedents in research on how collateral and maturity affect corporate bond yields and bank loans. Roberts and Viscione (1984) use a paired sample of collateralized and noncollateralized bonds issued by utilities to demonstrate that, at the level of the firm, collateral reduces bond yield. Helwege and Turner (1999) study pairs of speculative-grade bonds with a common issuer and find that yields increase with maturity. Gottesman and Roberts (2004) use matching to test for the effect of maturity on syndicated loan rates. All three studies challenge the findings of antecedents that use pooled regressions. Bharath (2002) analyzes matched pairs of bank loans and bonds to measure debt agency costs. Matching techniques are commonly utilized in medical research, labor studies, and other areas of applied statistics, where one member of a pair is termed the treatment and the other the control. In our application, we can think of the noncollateralized loan as the treatment and the collateralized loan as the control. To isolate the effect of collateral from the impact of borrower quality and other loan characteristics, we employ matching to create a set of paired observations of interest rates on two loans, in which all observable characteristics are similar, differing primarily in collateral status. In particular, we employ the propensity-scoring matching technique detailed in Parsons (2000, 2001) to create matched pairs. We then perform mean of difference tests and regression tests, and find that the presence of collateral is associated with higher ex ante yields, as predicted by the bonding and management-consumption hypotheses, even in the presence of banks monitoring expertise. 2. Data and matching method We use the Dealscan database to identify loans initiated between 1988 and Typically, a loan deal consists of a number of dissimilarly designed loans, called facilities, made to a specific borrower on a specific date. Similar to other studies (e.g., François and Missonier-Piera, 2007), we conduct our analysis at the facility level. We employ two measures of riskiness: credit ratings reported by Dealscan, and our estimate of the implied probability of default. We include ratings to ensure that we incorporate the information associated with this widely used measure of credit risk. Our measures of credit rating are A, B, and C rated, and not rated, which are indicator variables that are based on the Moody s senior debt rating, equal to unity if the debt rating is between A3 and Aaa, B3 and Baa1, C and Caa, or not rated, respectively. However, credit ratings measure long-run average credit risk and do not reflect changes in shorter-term default risk. 1 Since lending and spread decisions are 1 See Helwege and Turner (1999), John, Lynch, and Puri (2003), and Standard and Poor s (2004).

4 404 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) related to the lenders assessment of the most recent evaluation of the borrower s default risk, measuring risk solely on the basis of ratings is insufficient. We therefore include the implied probability of default as an additional measure of risk Implied probability of default We estimate the implied probability of default by implementing the optionstheoretic approach detailed in Allen and Peristiani (2004) and Saunders and Allen (2002, Chapter 4). 2 Using Newton s nonlinear approximation technique, we solve the following system of two nonlinear equations for borrower i s asset value and asset volatility at time t, V Ait, and σ Ait : V Eit = V Ait N ( DD it ) e r t T L it N ( DD it σ Ait T ) (1) σ Eit = V Ait N ( ) DD it σait. (2) V Eit In the above equations, DD it = [ln(v Ait /L it ) + T (r t + 0.5σAit 2 )]/σ Ait T ; VEit is the market value of borrower i s equity at time t; L it is the borrower s debt; r t is the risk-free rate; σ Eit is borrower i s equity volatility at time t; T is the one-year estimation period; and N() is the normal distribution. After solving for asset value and asset volatility, we estimate the implied default probability as N( DD it ). The market value we use is that of the borrower s equity, extracted from CRSP on the date of loan initiation. The borrower s debt is the value of long-term debt as reported by Standard and Poor s Compustat for the fiscal year that ends in the same year as loan initiation. The risk-free rate is the constant maturity, one-year T-bill rate from the Federal Reserve. Volatility is the standard deviation of the borrower s daily returns, extracted from CRSP, for the 100 trading days before the date of the loan initiation. If daily returns are unavailable for all 100 trading days, then the standard deviation is calculated using all daily returns available during the 100 trading day period, as long as a minimum of 30 daily return observations are available. We perform our tests separately for term and revolver loans. The Dealscan database specifies loans that are revolver loans, and we treat other loans as term loans. As noted in Coleman, Esho, and Sharpe (2004), differences in borrower and lender characteristics between term and revolver loans suggest that empirical tests should be performed separately for the two. Table 1 provides a breakdown of the facilities and pairs in the database and tests. We eliminate any facility for which the implied probability of default, noncollateralized/collateralized, or all-in-spread 2 The implied probability of default measure we use corresponds to the Moody s/kmv Expected Default Frequency (EDF) measure, though Moody s/kmv determine the distance-to-default using a large proprietary database and we follow Allen and Peristiani (2004) in assuming normality. As Allen and Peristiani (2004) note, the resulting implied default probability does not correspond to the actual probability due to the normality assumption. However, they argue that this measure is time consistent and reflects variations in the probability of default.

5 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) Table 1 Development of the full and paired samples of syndicated term and revolver loan facilities, Facilities (1) All facilities in the Dealscan database for ,936 (2) All facilities in (1) excluding deals associated with non-u.s. companies 50,881 (3) All facilities in (2) for which borrower data are identified on CRSP and 16,068 Compustat (4) Full term sample: All term facilities in (3) excluding facilities for which the 3,203 implied probability of default, all-in-spread drawn, or noncollateralized variable is missing (5) Full revolver sample: All revolver facilities in (3) excluding facilities for 4,442 which the probability of default, all-in-spread drawn, or noncollateralized variable is missing (6) All facilities in (4) that are noncollateralized 536 (7) All noncollateralized facilities in (6) for which all independent variable data 529 required for propensity scoring are non-missing (8) Paired term sample: Noncollateralized/collateralized pairs formulated with noncollateralized facilities in (7) (9) All facilities in (5) that are noncollateralized 1,133 (10) All noncollateralized facilities in (9) for which all independent variable data 1,116 required for propensity scoring are non-missing (11) Paired revolver sample: Noncollateralized/collateralized pairs formulated with noncollateralized facilities in (10) Pairs drawn variables are missing, or for which the borrower is not based in the United States. The full sample of facilities that meet the above data requirements includes 3,203 term loans, of which 536 are noncollateralized, and 4,442 revolver loans of which 1,133 are noncollateralized Propensity-matching method We use matching to determine whether the spread associated with collateralized debt remains higher than the spread associated with noncollateralized debt when characteristics such as the default probability and credit rating are held constant. Matching entails creating a set of paired observations of two loan deals, differing in collateralization status. Since propensity matching is not commonly used in finance research, we provide a brief overview. 4 3 Because loan facilities are individual loans within a broader loan deal, we perform two robustness tests to see if spread differences are influenced by other facilities in the loan deals. First, we include the loan deal size as an explanatory variable and find very similar results. Due to the high correlation between deal size and facility size (approximately 94%), we exclude deal size from our analysis. Second, we identify 1,099 terms and 2,679 revolver loan facilities associated with loan deals that consist of a single facility, eliminating any effect of multiple facilities in a loan deal. We repeat all the tests in this paper using only single facility deals and find similar results (available from the authors). 4 We base our brief discussion here on points raised by a referee and draw on Dehejia and Wahba (2002).

6 406 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) In the present context, we wish to match a set of treated observations (noncollateralized loans) with an untreated set (collateralized loans) so that both observations in any pair have an equal probability of being treated based on observable ex ante data. If, after this matching, the difference in the pair s outcomes is statistically significant, we can argue that the treatment has an effect. Interpretation is complicated because, unlike a blind trial of a new drug, the subjects self select whether to receive treatment. If the treatment is beneficial (harmful) to all subjects, then all (none) of the subjects would seek to be treated. Given that treatment is obtained by only part of the sample, we must ask why not everyone acted in an optimal manner. One possibility is that the nonoptimal group made an error. However, this is unlikely in a corporate finance context where firms engage in repeated activities from which they can learn. A second alternative is that the observations differ in some way that is unobservable: under the bonding hypothesis riskier firms seek collateral to reduce lenders risk but could succeed only in part, resulting in firms with collateral remaining riskier than those without. A third possibility is that not all subjects agree whether the treatment is beneficial, as John, Lynch, and Puri (2003) hypothesize. Some managers value their consumption of perks above the cost to the firm s shareholders and have sufficient power to implement this choice. Thus, some managers select collateralized debt even though it carries a higher interest rate. With these considerations as background, our purpose in employing matching is to measure more precisely the impact of collateralization on the cost of corporate loans. We find that collateralized loans cost more and this could be consistent with either our second or third explanation. In the final section of the paper, we conduct some preliminary tests to distinguish between these two. Rosenbaum and Rubin (1983, 1985a, 1985b) suggest matching using the propensity score, which is the conditional probability of receiving a treatment given a set of observable independent variables (covariates). 5 Also used in Heckman, Ichimura, and Todd (1997, 1998) and Bharath (2002), propensity scoring allows researchers to create pairs based on a rigorous model that replaces ad hoc constraints on control variables. Prior studies often limit matches to bonds or loans with a common issuer and issue date (e.g., Roberts and Viscione, 1984; Gottesman and Roberts, 2004.) We do not use this approach here for two reasons. First, through matching observations with the same issuer and the same day we could control for borrower credit quality but not other loan characteristics, for example, maturity, loan size, and seniority, which can be jointly determined. Second, matching pairs of facilities that differ in collateral status but begin on the same day with the same borrower would significantly restrict the 5 An alternative to the propensity score method is to match on the basis of individual characteristics. As Rosenbaum (1989) notes, in a setting where matches are based on multiple characteristics, genuinely close matched pairs will be rare. The propensity score method is motivated by the objective of covariate balance and results in superior matched samples when there are multiple characteristics.

7 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) sample size. Therefore, we use matching where pairs of facilities made on differing days, to nonidentical borrowers, are permitted. We follow the procedure of Parsons (2000, 2001). First, propensity scores are estimated separately for the term and revolver loan samples using logistic regression. We estimate e(ˆα+ ˆβ X) ˆp = 1 + e (ˆα+ ˆβ, (3) X) where ˆp is the predicted probability of the dependent variable, noncollateralized, being equal to 1, ˆα is the intercept parameter estimate, ˆβ is the vector of slope parameter estimates, and X is the vector of explanatory variables: X = [Maturity, Probability of default, A rated, B rated, C rated, Not rated, Facility size, Year, Accounting-based performance pricing covenant (PPC), Debt -rating-based PPC, No PPC]. (4) We control for maturity, risk, and facility size following other studies that find that these variables strongly influence spreads (Helwege and Turner, 1999; Dennis, Nandy, and Sharpe, 2000; Bharath, 2002; Coleman, Esho, and Sharpe, 2004; Gottesman and Roberts, 2004; Bali and Skinner, 2006; and others). We include the year as an explanatory variable to control for time effects. Prior studies suggest that performance pricing covenants lower spreads on initiation because they offer compensation to lenders if the borrower s credit quality deteriorates (Panyagometh, Roberts, and Gottesman, 2004; Asquith, Beatty, and Webber, 2005), hence we control for performance pricing covenants. 6 We match collateralized facilities to noncollateralized facilities. Because the vast majority of facilities are collateralized, the algorithm attempts to find a match for each noncollateralized facility from the larger set of collateralized facilities. 7 Matches are identified using the five- to one-digit greedy matching algorithm detailed in Parsons (2001). 8 Initially, best matches are identified through rounding the propensity 6 The tests measure the effect of security on loan spreads at initiation. We do not track spreads over the life of the loan which can change due to performance pricing provisions. The results are robust to the inclusion of seniority as an independent variable as well. We exclude seniority because more than 99% of the facilities in our sample are senior. 7 The matching algorithm achieves the desired effect of insulating the existence of collateralization from other loan characteristics. However, because the algorithm attempts to find a match for each noncollateralized facility from the larger set of collateralized facilities, it effectively ignores most collateralized loans with largely different covariate values that do not conform to the specifications of noncollateralized loans on aggregate. Hence, our propensity score matching creates a paired sample, where mean difference tests of parameter values test statistical significance only in those cases where overall sample distribution survived the matching procedure. The regressions in Section 4.2 use the full sample, including those with parameters values that differ greatly from the noncollateralized loans. We thank an anonymous referee for this point. 8 As Parsons (2001) notes, for greedy algorithms a match is not reconsidered once it is made, in contrast to

8 408 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) score to five digits. Noncollateralized facilities are then ordered on the basis of the rounded propensity score, and successively matched to the collateralized facility with an identical rounded propensity score. Next-best matches are then performed in a hierarchical sequence, through rounding to incrementally fewer digits until no further matches can be identified. If multiple collateralized facilities are candidates for a single match with a noncollateralized facility, the collateralized facility is chosen at random. The matching method produces 378 term and 792 revolver loan pairs. There are three possible reasons for a noncollateralized facility not to be matched to a collateralized facility. First, when independent variable data are missing for a given facility, a propensity score cannot be estimated, as the logistic regression requires the existence of data for all independent variables. Without a propensity score, the facility is excluded from the matching algorithm. Of the 536 noncollateralized term and 1,133 revolver facilities, the independent variables used to estimate propensity scores are available for 529 and 1,116 facilities, respectively. Second, when the propensity scores for the noncollateralized and collateralized facilities are disjoint, matching is not universally possible. For the sample of term loans, the propensity score ranges for the noncollateralized and collateralized facilities are [0.018, 0.996] and [0.034, 0.999], respectively. For the sample of revolver loans, the propensity score ranges for the noncollateralized and collateralized facilities are [0.013, 0.999] and [0.035, 0.999], respectively. Third, for every sequence between five and one digits, matches are formed on the basis of the propensity scores, rounded to the number of digits associated with the sequence. If no previously matched collateralized facility shares the same rounded propensity score as the noncollateralized facility in the sequence, then no match is identified for the noncollateralized facility. For example, 21 noncollateralized term facilities and 14 collateralized term facilities have single digit rounded propensity scores equal to 0.4; at best, therefore, matches will be identified for only 14 out of 21 cases. 9 Matches are identified for approximately 71.5% of both the term and revolver loan noncollateralized facilities. To compare the spreads of the noncollateralized and collateralized elements of the pairs, we test variables that measure rates and fees. The most comprehensive and commonly used measure is all-in-spread drawn, the basis point coupon spread over LIBOR plus the annual fee and upfront fee, spread over the life of the loan. We also test alternative measures of spread, prime spread, LIBOR spread, and all-in-spread undrawn, and measures of fees, upfront fee, commitment fee, and LC fee. Table 2 lists the variables and number of observations. optimal matching algorithms, where previous matches are reconsidered before the current match. To permit us to follow the SAS method of Parsons (2000, 2001), we use a greedy matching algorithm. According to Hansen (2004 p. 612), citing Rosenbaum and Rubin (1985a), For pair matching with a large reservoir of controls, greedy algorithms often do nearly as well as optimal algorithms. 9 The ratio of 21 noncollateralized to 14 collateralized loans in this example is not representative of the ratio of all noncollateralized to collateralized term facilities, which is approximately 16%. The purpose of the nonrepresentative example is to demonstrate why not every sequence of the algorithm necessarily leads to a match.

9 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) Table 2 Variables related to loan rates and fees Noncollateralized is a dummy variable equal to 1 if the loan is noncollateralized. All-in-spread drawn is the basis point coupon spread over LIBOR plus the annual fee and upfront fee, spread over the life of the loan. A given loan facility can contain spreads quoted over both prime and LIBOR in which case the LIBOR spread is used. All-in-spread undrawn is the sum of the commitment fee and annual fee. Upfront fees are one-time, typically collected at the close of the deal. Commitment fees are charged on the unused portion of the credit. LC fee is the annual fee charged for the issuance of letters of credit. Maturity is the term to maturity of the loan facility measured in years. Facility size is the natural logarithm of the loan facility size. Year is the calendar year of the loan initiation. Accounting-based PPC, Debt-rating-based PPC, and No PPC are indicator variables equal to 1 if the loan facility is associated with an accounting-based performance-pricing covenant, debt-rating-based performance-pricing covenant, or no performance-pricing covenant. Performance pricing covenants require adjustments to the loan spread should there be a change in the borrower s performance after the loan is initiated. Examples of accounting-based performance measures are the Debt-to-EBITDA ratio or the ratio of total debt to cash flow. Debt-ratings-based performance measures include the Moody or S&P credit ratings for the borrower. The full samples contain 3,203 term facilities and 4,442 revolver facilities initiated in The paired samples are subsets of the full samples created using a propensity-scoring matching technique. Each pair consists of one collateralized and one noncollateralized term loan facility. There are 378 term pairs and 792 revolver pairs. Full samples Paired samples Number of facilities Number of facilities Number of pairs for which for which the variable for which the variable the variable is nonmissing is nonmissing is nonmissing for both elements Variable Term Revolver Term Revolver Term Revolver Variable Variable summary type sample sample sample sample sample sample Noncollateralized Equal to 1 if noncollateralized Dummy 3,203 4, , Maturity Term facility maturity Years 3,092 4, , Probability of default Implied probability of default Percentage 3,203 4, , A rated Equal to 1 if the borrower is rated A Dummy 3,203 4, , B rated Equal to 1 if the borrower is rated B Dummy 3,203 4, , C rated Equal to 1 if the borrower is rated C Dummy 3,203 4, , Not rated Equal to 1 if the borrower is not rated Dummy 3,203 4, , Facility size ln (amount facility size) ln (Dollar) 3,203 4, , Year Year of observation Calendar year 3,203 4, , Accounting-based PPC Equal to 1 if an accounting-based Dummy 3,203 4, , performance pricing covenant (PPC) Debt-rating-based PPC Equal to 1 if a debt-rating-based PPC Dummy 3,203 4, , (continued)

10 410 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) Table 2 (continued) Variables related to loan rates and fees Full samples Paired samples Number of facilities Number of facilities Number of pairs for which for which the variable for which the variable the variable is nonmissing is nonmissing is nonmissing for both elements Variable Term Revolver Term Revolver Term Revolver Variable Variable summary type sample sample sample sample sample sample No PPC Equal to 1 if no PPC Dummy 3,203 4, , All-in-spread drawn Rates all-in-spread drawn Basis point 3,203 4, , All-in-spread undrawn Rates all-in-spread undrawn Basis point 752 3, , Prime spread Rates prime spread Basis point 2,800 4, , LIBOR spread Rates LIBOR spread Basis point 2,208 3, , Upfront fee Upfront fee Basis point 1,275 1, Commitment fee Commitment fee Basis point 621 2, , LC fee LC fee Basis point 186 1,

11 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) Table 3 Descriptive statistics of variables related to loan rates and fees The full samples contain 3,203 term facilities and 4,442 revolver facilities initiated in The paired samples are subsets of the full samples created using a propensity-scoring matching technique. Each pair consists of one collateralized and one noncollateralized term loan facility. There are 378 term pairs and 792 revolver pairs. Full samples Paired samples Difference of means test between samples Term facilities Revolver facilities Term facilities Revolver facilities Term facilities Revolver facilities Mean Standard deviation Mean Standard deviation Mean Standard deviation Mean Standard deviation t-statistic t-statistic Noncollateralized Maturity Probability of default A rated B rated C rated Not rated ln (Facility size) Year Accounting-based PPC Debt-rating-based PPC No PPC All-in-spread drawn All-in-spread undrawn Prime spread LIBOR spread Upfront fee Commitment fee LC fee ,, indicate statistical significance at the 0.01, 0.05 and 0.10 level, respectively.

12 412 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) Table 4 Correlations between variables associated with loan rates and fees Correlations in the lower left half of the table are associated with the full revolver loan sample, while correlations in the upper right half of the table are associated with the full term loan sample. Variable [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] Noncollateralized [1] Maturity [2] Probability of default [3] A rated [4] B rated [5] C rated [6] Facility size [7] Year [8] Accounting-based PPC [9] Debt-rating-based PPC [10] All-in-spread drawn [11]

13 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) Table 5 Paired difference tests using term loan facilities The paired sample is derived from the full sample of 3,203 syndicated term loan facilities initiated in through a propensity-scoring matching technique. Each of the 378 pairs consists of one collateralized and one noncollateralized term loan facility. Number of Mean difference nonmissing (noncollateralized Wilcoxon Mean, Mean, pairs collateralized) t Z noncollateralized collateralized Noncollateralized Maturity Probability of default A rated B rated C rated Not rated Facility size Year Accounting-based PPC Debt-rating-based PPC No PPC All-in-spread drawn All-in-spread undrawn Prime spread LIBOR spread Upfront fee Commitment fee LC fee Indicates statistical significance at the 0.01 level.

14 414 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) Table 6 Paired difference tests using revolver loan facilities The paired sample is derived from the full sample of 4,442 syndicated revolver loan facilities initiated in through a propensity-scoring matching technique. Each of the 792 pairs consists of one collateralized and one noncollateralized revolver loan facility. Number of Mean difference nonmissing (noncollateralized Wilcoxon Mean, Mean, pairs collateralized) t Z noncollateralized collateralized Noncollateralized Maturity Probability of default A rated B rated C rated Not rated Facility size Year Accounting-based PPC Debt-rating-based PPC No PPC All-in-spread drawn All-in-spread undrawn Prime spread LIBOR spread Upfront fee Commitment fee LC fee and indicate statistical significance at the 0.01 and 0.05 level, respectively.

15 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) Table 3 reports descriptive statistics and difference of means tests between the full and paired samples. We find significant differences between them. For example, the mean probability of default is % for the full term sample and % for the paired term sample. This difference is attributable to the matching procedure, which chooses collateralized elements with characteristics similar to the corresponding noncollateralized elements. The noncollateralized firms typically are lower risk, and the matched collateralized firms tend to share this characteristic. Focusing on the paired revolver sample, the typical loan is approximately $77 million, has a maturity of 3.47 years, is not rated, and has a spread over LIBOR including fees (all-in-spreaddrawn) of basis points. Comparing these features of our typical loan with comparable features in Berger and Udell s (1990) sample of U.S. commercial and industrial loans and John, Lynch, and Puri s (2003) data on public debt issues supports our contention that our sample falls between the extremes. 10 The loans studied by Berger and Udell (1990) are considerably smaller ($253,000 for collateralized loans and $982,000 for noncollateralized) and shorter in maturity (3.9 to 5 months) than the syndicated loans in our sample. On the other hand, the typical public debt issue tested by John, Lynch, and Puri (2003) is $152.2 million, considerably larger than our syndicated loans. Their public issues are also much longer in maturity, averaging between five and 15 years. Table 4 reports the correlations among all variables that we use in the analysis in Section 4.2. Correlations reported are for the full revolver and term samples. 3. Paired difference tests We perform difference tests on the pairs formed from the term and revolver paired samples. For each variable, we calculate the mean difference for all observations, for all pairs for which the variable is available. We use Student s t and the standard normal approximation to the Wilcoxon signed rank statistic to test whether noncollateralized loans in the matched pairs display a positive or negative yield spread over the collateralized loans. The results are in Tables 5 (term sample) and Table 6 (revolver sample). By construction, the noncollateralized and collateralized samples differ in the values of the noncollateralized dummy variable, with the noncollateralized elements of the pairs valued at 1 and the collateralized elements valued at zero. The absence of statistically significant differences in almost all of the values of the 11 explanatory variables for estimating the propensity scores validates our matching method. The only exception is a significant difference in the probability of default. The average probability of default is lower for the noncollateralized facilities in the revolver sample, which tends to bias the tests toward finding lower rates for noncollateralized revolvers. There is strong evidence that the noncollateralized facilities are associated with lower rates for the term sample (Table 5). All-in-spread drawn, all-in-spread 10 The tables in Pozzolo (2002) are expressed in percentages and do not permit size and maturity comparisons.

16 416 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) undrawn, prime spread, and LIBOR spreads are, respectively, approximately , 9.10, 73.47, and basis points lower for the noncollateralized facilities than the collateralized facilities. The difference in rates associated with the revolver sample (Table 6) is similarly strong. In this case, all-in-spread drawn, all-in-spread undrawn, prime spread, and LIBOR spreads are, respectively, approximately 81.86, 10.90, 38.44, and basis points lower for the noncollateralized facilities than the collateralized facilities. These differences are significant at the 1% level. There also are significant differences in fees between the noncollateralized and collateralized elements, for both the term and revolver samples. For the term sample (Table 5), upfront fees and commitment fees are approximately and basis points lower, respectively, for the noncollateralized facilities than the collateralized facilities at the 1% level. There is no significant difference in letter of credit fees. This is because there is only a single pair for which such fees are nonmissing for the term sample, for both elements. For the revolver sample (Table 6), upfront fees, commitment fees, and letter of credit fees are, respectively, approximately 30.69, 8.83, and basis points lower for the noncollateralized facilities than the collateralized facilities at the 1% level. The paired tests in Tables 5 and 6 reinforce the results of prior studies and are consistent with both the bonding hypothesis and the management-consumption hypothesis. For both the term and revolver facilities, we find strongly significant evidence that noncollateralized loans are associated with lower rates and fees than collateralized loans. These paired tests are convincing, as risk (both rating and the implied probability of default), maturity, seniority, facility size, year, and the existence of performance pricing are controlled through the matching method. 4. Cross-sectional analysis Section 3 reports that noncollateralized facilities are associated with lower spreads. However, several outstanding issues remain. First, if the spread difference between the noncollateralized and collateralized facilities is attributable to the divergence in collateralization status, is this difference in spread invariant to the riskiness, maturity, and size of the borrowers? We address this question by conducting additional paired difference tests on subsamples that control these characteristics. Second, the tests in Table 4 identify significant differences between the full and paired samples. We run regressions to see if the results for the paired sample apply to the full sample as well Paired difference tests by risk, maturity, and loan size Table 7 reports difference tests conditioned on four measures: credit rating, probability of default, maturity, and facility size. For both the term and revolver paired subsets, we calculate the difference of each variable between the noncollateralized and collateralized facilities for each pair. Pairs are placed into each category based

17 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) Table 7 Paired difference tests by rating, probability of default, maturity, and facility size The paired samples are derived, using a propensity-scoring matching technique, from the full samples of 3,203 term facilities and 4,442 revolver facilities initiated in Each pair consists of one collateralized and one noncollateralized term loan facility. There are 378 term pairs and 792 revolver pairs. Term Revolver Mean Mean Number of difference Mean Mean Number of difference Mean Mean nonmissing (noncollateralized all-in-spread all-in-spread nonmissing (noncollateralized all-in-spread all-in-spread Category pairs collateralized) drawn, noncollateralized drawn, collateralized pairs collateralized) drawn, noncollateralized drawn, collateralized Credit rating A rated B rated C rated Not rated Probability of default Probability < 0.01% % < probability < 0.02% % < probability < 0.14% % < probability < 1.20% % < probability < 5.96% % < probability < 24.36% Probability > 24.36% 0 0 Maturity Maturity < 1 year year < maturity < 3 years years < maturity < 6 years Maturity > 6 years Facility size of noncollateralized element Size < $20 mill $20 mill. < size <$100 mill $100 mill. < size <$200 mill Size > $200 mill ,, indicate statistical significance at the 0.01, 0.05 and 0.10 level, respectively.

18 418 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) on the value of the noncollateralized element of the pair and the mean difference is reported for each category. First, categories are formed based on the credit rating levels A, B, and C rated, and not rated. Second, we design categories based on seven ranges of probability of default: less than 0.01%, between 0.01% and 0.02%, between 0.02% and 0.14%, between 0.14% and 1.2%, between 1.2% and 5.96%, between 5.96% and 24.36%, and greater than 24.36%. 11 Third, categories are created based on four ranges of maturity: less than one year, between one and three years, three to six years, and greater than six years. Fourth, categories are formed based on four ranges of facility size: less than $20 million, between $20 million and $100 million, between $100 million and $200 million, and greater than $200 million. For both the revolver and term subsets, the groupings on the basis of ratings demonstrate that noncollateralized loans are consistently associated with lower spreads, regardless of rating. For the C-rated category, there are only one and zero pairs for the revolver and term subsamples, respectively. As for ratings, mean of difference tests for different levels of probability of default demonstrate that spreads are lower for the noncollateralized borrower. This result ranges from significance at the 1% level to insignificant. The lower levels of significance are typically associated with groupings for which there are few pairs. The vast majority of borrowers are in the lowest probability of default (highest credit quality) category, for both the term (80.95%) and revolver (84.72%) samples. Finally, for both the revolver and term samples, the groupings on the basis of the maturity and facility size variables once again demonstrate that noncollateralized loans are associated with lower spreads, regardless of maturity or facility size levels Regressions This section reports regressions on the paired and full samples, relating loan spreads, and collateral while controlling for other loan contract characteristics. To test the core spread-collateral relation, all the regressions use all-in-spread-drawn as the dependent variable and the noncollateralized dummy as an independent variable. We include other independent variables in the model to control for facility maturity, riskiness (implied probability of default and credit rating) facility size, year, and performance pricing covenants. We run the regressions using the entire paired and full samples, for both term and revolver facilities. We repeat the regressions for subsamples, when data are available, of A-rated, B-rated, C-rated, and not rated facilities to provide a robustness check for imprecision in risk controls. 11 The assignments correspond to the mean historical one-year default rates associated with Moody s ratings, as reported in Moody s Investors Service (2001, Exhibit 38), corresponding to Moody s ratings ranging between Aaa through C. Moody reports that the mean historical one-year default rates are higher for Aa firms (0.01%) than for A firms (0.02%).

19 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) Table 8 Regressions of all-in-spread drawn The dependent variable is all-in-spread drawn, the basis point coupon spread over LIBOR plus the annual fee and upfront fee, spread over the life of the loan. The full samples contain 3,203 term facilities and 4,442 revolver facilities initiated in The paired samples are subsets of the full samples created using a propensity-scoring matching technique. Each pair consists of one collateralized and one noncollateralized term loan facility. There are 378 term pairs and 792 revolver pairs. Paired Full Parameter Term Revolver Term Revolver Intercept 3, , , Noncollateralized Maturity Probability of default A rated B rated C rated Facility size Year Accounting-based PPC Debt-rating-based PPC Adjusted R N ,092 4,351,, indicate statistical significance at the 0.01, 0.05 and 0.10 level, respectively Main results We perform the estimation for both the term and the revolver samples. For each type of loan, we run the regressions for all nonmissing facilities for both the full and paired samples. The regression results are in Table 8. Overall, the regressions validate the finding that noncollateralized debt is associated with lower spreads. For all regressions, the coefficients associated with the noncollateralized variable are significant and negative for all models at the 1% level, valued at and for the paired and full term samples, respectively, and valued at and for the paired and full revolver samples, respectively. These results provide further confirmation that noncollateralized facilities are associated with much lower spreads. The coefficients of maturity are negative and significant at the 5% level for the paired revolver sample and at the 1% and 5% level for the term and revolver full samples, respectively. With the exception of a single coefficient that is significant at the 5% level, the coefficients on probability of default are positive and significant at the 1% level. Hence, as expected, spreads are positively related to riskiness, as measured by probability of default. With the exception of a single coefficient that is significant at the 5% level, the coefficients associated with A-rated loans are negative and significant at the 1% level,

20 420 A. A. Gottesman and G. S. Roberts/The Financial Review 42 (2007) for all samples. The coefficients for B-rated facilities are insignificant. For the full samples, the coefficients associated with C-rated facilities are positive and significant, at the 1% level. The coefficient for C-rated facilities for the revolver paired sample is significant at the 10% level. The coefficients associated with A-rated and B-rated facilities are consistent with lower-risk borrowers receiving lower spreads. However, the insignificance of many of the B-rated and one of the C-rated facilities coefficients suggests that credit rating is not fully reflective of actual riskiness, validating our inclusion of probability of default as a control variable. The coefficients associated with facility size are negative in all models and significant at the 1% level, which suggests that larger loans are associated with lower spreads. The coefficient on the year variable attains negative significance only for the full revolver sample. This suggests that spreads decreased over the sample period. The coefficients for both measures of performance pricing covenants are negative. These results are significant at the 1% level and indicate that the inclusion of performance pricing covenants reduces spreads Rating-specific results This section reports regressions using subsamples by credit rating to ascertain whether the results in Table 8 are robust to an alternative specification of credit risk controls. There are no C-rated facilities in the paired term sample and only two in the paired revolver sample; hence we do not report C regressions for the paired sample. Results for the paired sample are in Table 9 and those for the full sample are in Table For all rating categories, for both the paired and full samples, the coefficient on the noncollateralized dummy remains negative and significant at the 1% level, with the exception of the coefficients associated with A rated for the paired revolver sample and C rated for the full samples (both term and revolver), which are insignificant. The insignificant coefficients could be attributable to the smaller sample sizes associated with these cases. For the term sample, both paired and full, the coefficient associated with A rated is much larger than the coefficient associated with either B rated or not rated. The opposite is true for the revolver sample. Turning to the control variables, the only significant coefficients associated with maturity are for the not rated category for the paired revolver sample, the A-rated category for the paired term sample and the not rated category in both full samples. We continue to identify a positive coefficient associated with probability of default, but not for the A- and B-rated paired term sample, the A- and C-rated full term sample, nor the A-rated full revolver sample. The negative coefficient associated with facility size is strong for both samples. The coefficients associated with year are no longer exclusively negative. Instead, we 12 The number of observations in each rating category in Table 9 differ slightly from the number of pairs (multiplied by two) reported in Table 7 because the groupings in Table 7 are based on the rating of the noncollateralized element of the pair, while the groupings in Table 9 are based on the pooling of all elements.