When Are Commercial Loans Secured?

Transcription

1 The Financial Review 39 (2004) When Are Commercial Loans Secured? John S. Gonas Belmont University Michael J. Highfield Louisiana Tech University Donald J. Mullineaux University of Kentucky Abstract We analyze the factors that influence the decision to secure a commercial loan. We find evidence that variables reflecting adverse selection, moral hazard, and the prospects for default all affect the likelihood a loan will be collateralized. We find no evidence in favor of the predictions of certain theoretical models that high-quality firms signal by providing collateral. Our results also show that lenders with less risk protection in the form of equity capital are more likely to require collateral, but that banks themselves are less likely to secure loans than nonbanks. Certain loan characteristics also influence the collateralization decision. Keywords: secured loans, collateral, credit risk, information asymmetry, moral hazard JEL Classifications: G20, G21, G28 Corresponding author: Department of Economics and Finance, College of Administration and Business, Louisiana Tech University, P.O. Box 10318, Ruston, LA 71272; Phone: (318) ; Fax: (318) ; mikehigh@cab.latech.edu. The authors thank an anonymous referee, Brent Ambrose, Dan Bradley, Mark Carey, Marcia Cornett, Steve Dennis, Larry Wall, and seminar participants at Louisiana Tech University s Research and Policy Forum, the 2002 Midwest Finance Association annual meeting, the 2002 Financial Management Association annual meeting, and the Symposium on Financial Institutions at the 2003 Eastern Finance Association annual meeting for helpful comments on earlier drafts of the paper. All errors remain ours. A previous version of this paper was presented under the title The Determinants of Secured Loans. 79

2 80 J. S. Gonas et al./the Financial Review 39 (2004) Introduction While there is a significant amount of research addressing the effect of collateral on credit risk premiums on bonds and bank loans, there is little empirical work on the factors that affect decisions to secure loans. Theoreticians have argued that collateral can play a multitude of roles, such as facilitating signaling, controlling information asymmetry problems, mitigating moral hazard problems, and providing respite against default and bankruptcy loss. Our objective in this paper is to examine whether one of these rationales is dominant in the collateralization decision or if each plays an independent role. We employ a large sample of transaction-specific loans from the Loan Pricing Corporation s DealScan database over the period December 1988 to January 2001 in the empirical analysis. We find evidence that collateral is more likely to be pledged in the presence of significant information asymmetries between borrowers and lenders. We use data that reflect whether the borrower is rated or exchange-listed as proxy variables for the quantity and quality of information about the borrower. We also take account of whether a borrower is domiciled outside the United States and argue that such firms are more information-problematic to lenders based in countries other than the borrower s. Moral hazard is a relatively more difficult phenomenon to investigate. We suggest that loan maturity can serve as a rough proxy for moral hazard and we observe that longer term loans are more likely to be collateralized. When we limit our sample to rated borrowers we can evaluate the impact of default risk on collateralization by using the borrower s senior debt rating as a measure of credit risk. The evidence indicates that riskier loans are much more likely to be secured. Indeed, loans to high-risk, non-investment grade borrowers are almost always secured. We also examine whether banks are more likely to require collateral than nonbank lenders like finance companies, investment banks, and insurance companies. We find that nonbank lenders are more likely to require collateral than banks. This could reflect the fact that nonbanks assume riskier credits, on average, than banks. We also examine whether lenders that are less well protected against risk themselves are more likely to require borrowers to pledge assets in a loan agreement. We find that banks with lower equity capital ratios are indeed more likely to seek collateral to secure their loans. This is consistent with evidence in the literature showing that lower-quality banks charge higher rates on loans. Throughout the empirical analysis, we control for certain loan characteristics, such as loan purpose, industry effects, and time-related influences. 2. Theories and evidence The literature provides three primary rationalizations for why some bank loans are secured: (1) information asymmetry and adverse selection problems, (2) moral hazard problems, and (3) borrower credit risk. Since credit risk could be higher in the presence of information asymmetry and/or moral hazard, the explanations are not

3 J. S. Gonas et al./the Financial Review 39 (2004) mutually exclusive. Leeth and Scott (1989) identify other factors that can affect the collateralization decision such as the effect of security on the lender s monitoring and administrative costs, the costs associated with restricting borrower asset usage, and the prospects for limiting the dilution of legal claims in bankruptcy Adverse selection and information asymmetry In an asymmetric information setting, collateral can convey valuable information to the lender. Besanko and Thakor (1987) and Chan and Thakor (1987) develop models demonstrating that, within a class of borrowers that appear equally risky, a borrower s willingness to provide collateral will be inversely related to the default risk on the loan. Consequently, banks can induce borrowers to reveal their characteristics by offering two loan contracts. The first involves a lower interest rate, but requires collateral. The second does not require security, but involves a higher borrowing rate. This leads to a separating equilibrium in which less risky borrowers will choose the contract requiring collateral, since offering security is relatively less onerous, while riskier borrowers will prefer the unsecured loan. In this signaling context, high quality firms are more likely to pledge collateral than low-quality firms. Similarly, Chan and Kanatas (1985) argue that securing debt enables high quality firms to signal their creditworthiness, and the theoretical models of Townsend (1975) and Bester (1985) also predict that collateral will be associated with higher-quality borrowers. In a more general setting, Boot, Thakor, and Udell (1991) emphasize the relevance of precontract, private information in loan contracting. In this case the lender is unaware of some exogenous parameter that influences the borrower s payoff distribution. The results reveal that private information unambiguously increases the use of collateral in loan contracts, but has uncertain effects on the relation between collateral and borrower risk under moral hazard. While the authors carry out some empirical tests, none of the variables in the model capture information-related factors. Furthermore, Dennis, Nandy, and Sharpe (2000) also find evidence that collateral is more likely in the presence of information asymmetries Moral hazard Moral hazard occurs when borrowers face incentives to take large risks during the life of the loan or when they have bargained in bad faith. Finance theory predicts that securing a loan reduces the probability that borrowers will engage in underinvestment, asset substitution, or provide an inadequate supply of effort. As noted above, Boot, Thakor, and Udell (1991) demonstrate that collateral serves to mitigate moral hazard in loan contracting, but the extent of the relation varies with the extent of private information. Myers (1977) demonstrates how the use of collateral eliminates underinvestment in profitable projects and reduces the probability of bankruptcy. Igawa and Kanatas (1990) examine a Myers-type model that shows how pledging collateral allows a high quality firm to optimize the net benefits gained from over-collateralizing (the value of pledged collateral exceeds the value of the loan), while simultaneously

4 82 J. S. Gonas et al./the Financial Review 39 (2004) underinvesting in the maintenance of such collateral. Similarly, Stulz and Johnson (1985) show that secured debt enhances firm value because it reduces the incentive to underinvest that results when a firm relies on equity or unsecured debt. In a study focused on moral hazard associated with asset usage and managerial effort, Smith and Warner (1979) predict that collateral prevents a borrower from consuming a loan or engaging in costly asset substitution Credit risk Collateral protects the lender against loss by granting title to specific assets in the event of default. Scott (1977) asserts that because secured claims have priority, collateralized debt can limit the degree of loss in the event of bankruptcy. He also demonstrates how issuing secured debt can increase the value of the firm. Critical to Scott s results is the fact that certain claimants (e.g., litigants and tax authorities) are disadvantaged in bankruptcy by the use of collateral, but are unable to extract compensation for such in ex ante contracting. There are a number of theoretical studies demonstrating that credit riskier firms are more likely to pledge collateral (Swary and Udell, 1988; Boot, Thakor, and Udell, 1991; Black and demeza, 1992). Empirical research by Morsman (1986) and Hempel, Coleman, and Simsonson (1986) shows that the due diligence efforts of banks often require observably more risky borrowers to pledge collateral. Orgler (1970) compiles individual loan data categorizing borrowers as either good or bad, based on the opinions of bank examiners. He finds a significant, positive relation between the presence of collateral and loans that were categorized bad. Hester (1979) regresses a secured/unsecured dummy variable on six accounting variables that are proxies for firm risk. He likewise finds that riskier firms are more likely to pledge collateral. Similarly, Leeth and Scott (1989) find that more collateral is pledged with loans to riskier, small businesses, and Berger and Udell (1990) find that riskier firms are more likely to borrow on a secured basis and that the average secured loan in their sample is riskier than the average unsecured loan. 3. Research design Building on the previous theoretical and empirical research on the determinants of secured loans, in this section we develop testable hypotheses regarding information asymmetry, moral hazard, and credit risk problems. We then introduce our sample and sketch the empirical models we use to test these hypotheses Hypotheses Collateralization is more likely in the presence of information asymmetry problems There are several ways to proxy information asymmetry problems. First, if the borrower has a senior credit rating, we argue there is less information asymmetry

5 J. S. Gonas et al./the Financial Review 39 (2004) between borrowers and lenders. Firms must submit significant documentation and undergo a detailed evaluation process to obtain a rating. Rating agencies have access to inside information, including internal forecasts of earnings and cash flows. Consequently, rated firms are more easily monitored and pose fewer adverse selection problems to lenders, implying that rated firms are less likely to secure loans than their nonrated counterparts. Signaling by higher-quality firms, on the other hand, would imply that listed firms are more likely to secure their loans. Given the listing requirements of stock exchanges and the SEC s associated reporting requirements, traded firms present more transparent information to lenders and investors than nontraded firms. Therefore, we expect that exchange-listed firms will involve relatively fewer adverse selection problems. We hypothesize that publicly traded firms will be less likely to pledge collateral if adverse selection motivates lenders to seek collateral. Likewise, larger firms pose relatively fewer information asymmetry problems to lenders than their smaller counterparts. Larger companies generally enjoy increased product and brand recognition. In addition, a larger firm is likely to be better known given its relatively large workforce, enhanced line of products, and increased community presence. Thus, we assume that a firm s revenues can proxy for its size and hypothesize that adverse selection problems decline as firms grow. Adverse selection would imply an inverse relation between borrower size and collateralization, while signaling could suggest an opposite relation. We also argue that lenders find it more difficult to gather information and monitor firms headquartered outside the United States. Such loans should involve higher prospects for information asymmetry. In addition, foreign borrowers are exposed to idiosyncratic forms of country risk, involving unpredictable changes in economic and political conditions, along with exchange rate risk. We hypothesize that banks making loans to borrowers outside the United States will be more likely to be secured. Finally, prior relationships can attenuate information asymmetry problems. In fact, Berger and Udell (1995) find evidence that the character of borrower-lender relationships influences loan contract terms. Consequently, we suggest that repeat borrowers pose fewer information asymmetry problems and should be less likely to secure loans Collateralization is more likely in the presence of moral hazard problems Although many theoretical models emphasize the relevance of moral hazard in debt contracting, empirical tests are difficult to implement. In this paper, we assume that moral hazard problems like underinvestment and asset substitution can be proxied by loan maturity. 1 We contend that asset substitution and underinvestment do not 1 Maturity might also be related to both credit risk and the presence of information asymmetries; however, we argue that loan maturity provides an effective proxy for moral hazard after controlling for other forms of information asymmetry and credit risk.

6 84 J. S. Gonas et al./the Financial Review 39 (2004) occur overnight, so agency problems are less likely to surface with a six-month than a six-year loan. If moral hazard is more prevalent over longer contracting periods, longer-term loans should be more likely to be secured than short-term loans. 2 Empirically, Dennis, Nandy, and Sharpe (2000) find a significantly positive relationship between the duration of a revolving credit agreement and its secured status. Boot, Thakor, and Udell (1991) find the opposite result Collateralization is more likely in the presence of credit risk We also examine a sample that includes only firms with credit ratings and hypothesize that firms with low default risk are less likely to secure loans than high-risk borrowers. John, Lynch, and Puri (2002) found such a result for public bond issues. This suggests that higher quality firms should find that the costs of securing loans, particularly in the form of loss of asset control, outweigh the benefits. Collateral is especially valuable in the event of bankruptcy since secured lenders hold a priority claim. For example, Moody s (1998) reports a recovery rate of 87% on senior secured bank loans in bankruptcy over the period from 1986 through 1997, versus 79% on senior unsecured loans over the same period. We hypothesize that the prospect a loan will be secured increases with credit risk and that loans to investment grade firms will be collateralized less often than loans to non-investment grade borrowers Research methods Sample selection We collect a sample of 7,619 commercial loans that closed between December 1988 and January The sample was obtained from the Loan Pricing Corporation (LPC) DealScan database, and we restrict the sample to loans with complete and confirmed information. DealScan contains information on individual loan transactions, including borrower information (name, credit rating, location, and annual sales), lender information (name, location, and role), and loan contract information (secured status, loan size, maturity, loan purpose, and rate). We also obtain the lender s asset and equity information from the Federal Reserve Bank of Chicago Commercial Bank 2 While our model specification assumes that the explanatory variables are exogenous, we recognize that loan contract terms, such as maturity and collateralization, could be determined simultaneously. Thus, due to this endogeneity, the estimate of the maturity coefficient in our model could be biased and/or inconsistent. However, assuming joint distribution conditional probability modeling, our results suggest that loan collateralization and maturity are associated, but we cannot draw any inferences about the direction of causality. If we wanted to say that there is a directional relationship we must acknowledge three possible directions. First, a longer maturity can cause collateralization. Second, collateralization can cause a longer maturity. Third, there is an omitted variable that underlies both collateralization and maturity such that an increase in the error term due to an increase in this omitted variable causes both collateralization and maturity to increase.

7 J. S. Gonas et al./the Financial Review 39 (2004) and Holding Company Database for the years 1988 through Lender capital ratios are then matched to the LPC sample by lender name, lender location, and year of loan origination. In Table 1, we present summary statistics for the explanatory variables. Just over 73% of the loans are secured. About 26% of the loans went to borrowers with a senior S&P rating, and a little over 12% of the borrowers had an investment grade S&P rating. About 21% of the loans went to borrowers with a Moody s rating, and approximately 10% of these borrowers had an investment grade rating. About 69% of the loans involve borrowers listed on a U.S. stock exchange. While the average borrower reports yearly sales of approximately $8.64 billion, the median is $257.9 million. Almost 4% of the borrowers are based outside the United States and obtained a loan from a lender located outside their home country, and some 38% of the loans in the sample were made to repeat borrowers. The average loan maturity is approximately 48 months. About 89% of the sample loans were made by banks, and of the 3,177 cases where lender information could be matched to the loans, banks maintained an average equity capital ratio of 9.3%. About 21% of the loans were used to refinance debt (PREF), while 26% were used for effecting changes in corporate control (PCC), such as acquisitions, leveraged buyouts, or employee stock option plans. Only 6% of the loans were made to finance fixed asset purchases, and 19% were used for general corporate purposes (PGCP). 4 The other 28% of the loans in the sample were listed as other purpose or they did not fall into one of the previous four categories The full sample logit model To test the hypotheses outlined above, we first estimate a model with the entire sample. This model is as follows: SECURED = β 0 + β 1 RATED + β 2 EXCHANGE + β 3 LNSALES + β 4 FOREIGN + β 5 REPEAT + β 6 LNMATURITY + β 7 (BANK or BANKCAR) + β PURPOSE CONTROLS + β INDUSTRY CONTROLS + β YEAR CONTROLS + ε (1) SECURED is a binary variable equal to one for secured loan agreements and zero for an unsecured loan agreement. We use RATED, a variable representing either an S&P rating (SPRATE) or a Moody s rating (MRATE), to test the hypothesis that 3 The Federal Reserve Bank of Chicago Commercial Bank and Holding Company Database is publicly available at For syndicated loans, we take the lead lender s capital ratio. 4 General corporate purposes (PGCP) includes loans with general corporate purposes as their stated purpose as well as loans for working capital and trade finance.

8 86 J. S. Gonas et al./the Financial Review 39 (2004) Table 1 Descriptive statistics The sample contains 7,619 commercial loan arrangements closed between December 1988 and January The descriptive statistics for selected variables are presented below. SECURED is a binary variable representing secured loans, SPRATED (MRATED) is a binary variable representing firms with an S&P (Moody s) rating available, SPINVEST (MINVEST) is a binary variable representing firms with a investment grade S&P (Moody s) rating, SPHIGHYLD (MHIGHYLD) is a binary variable for firms with a high-yield grade S&P (Moody s) rating, SPNR (MNR) is a binary variable representing a firm without an S&P (Moody s) rating, and SPORDERRATE (MORDERRATE) is a variable representing S&P (Moody s) ratings ranked from one (D, D) to ten (AAA, Aaa). EXCHANGE is a binary variable representing firms listed on an stock exchange at loan origination, SALES is the annual sales amount of the borrower in $billions, LNSALES is the natural log of sales size (SALES). FOREIGN is a binary variable for borrowers located outside the United States, and REPEAT is a binary variable for a repeat loan matching borrower to lender on a previous date in the sample. MATURITY is the term of the loan in months, and LNMATURITY is the natural log of the loan s maturity in months. BANK is binary variable identifying the lending institution as a bank, and BANKCAR is the capital asset ratio, defined as the ratio of bank equity to bank assets, of the lending institution. Also included are a set of binary variables for the purpose of the loan including refinancing, corporate control, fixed asset backing, general corporate purposes, or other purposes not listed above (PREF, PCC, PFAB, PGCP, POTH, respectively), a set of a set of binary variables for the industry of the borrower based on SIC codes (SIC0 SIC9, not presented), and a set of binary variables for the year of issue (YR1988 YR2001, not presented). Variable N Mean Std. dev. SECURED SPRATED SPINVEST SPHIGHYLD SPNR SPORDERRATE MRATED MINVEST MHIGHYLD MNR MORDERRATE EXCHANGE SALES LNSALES FOREIGN REPEAT MATURITY LNMATURITY BANK BANKCARATIO PREF PCC PFAB PGCP POTH

9 J. S. Gonas et al./the Financial Review 39 (2004) rated borrowers present fewer information asymmetry and adverse selection problems to lenders. The coefficient of the rating binary variables should be negative in our estimations if adverse selection drives the collateralization decision, but positive if firms signal their quality by pledging assets. We also use listed status and firm size as alternative measures of information asymmetry problems. EXCHANGE is a binary variable equal to one if the borrower is a listed firm and zero otherwise. Again, listed firms involve more transparent information, so the coefficient of this variable should be negative. We use the log of the borrower s annual sales (LNSALES) as a proxy for firm size, and we assume it is less costly to acquire information about large firms relative to small firms. A borrower with larger sales figures should be less likely to secure a loan, other things equal, implying a negative relation between borrower sales and the probability that a loan is secured. Again, signaling by high quality firms could imply an opposite sign. Foreign borrowers are likely to be more information problematic to lenders. Therefore, we expect a positive relation between the variable FOREIGN, a binary variable for firms located outside of the United States, and the probability that a loan is secured. 5 We also include REPEAT, a binary variable reflecting cases in which the borrower uses the same lender more than once during the sample period, as a proxy for the extent of the relationship between the contracting parties. Repeat borrowers should pose fewer information asymmetry problems to the lender, other things equal, so we hypothesize a negative sign for this coefficient. We include LNMATURITY, the natural logarithm of term to maturity of the loan, as a proxy for moral hazard problems. Maturity captures the length of the contractual relationship, and we argue that borrowers are more likely to engage in exploitative behavior in longer-term relationships. The coefficient of this variable should be positively signed, since banks are more likely to seek collateral in the presence of moral hazard. Although most of the loans in our sample are made by banks, our sample also includes nonbank lenders such as finance companies, insurance companies, and investment banks. Thus, we include the binary variable BANK, a binary variable equal to one if the lender is a bank and zero otherwise. Since the quality of bank loans is evaluated by bank examiners based on collectibility, we might anticipate that banks are more likely to secure loans than nonbanks. However, Staten, Gilley, and Umbeck (1990) suggests that nonbanks are willing to deal with relatively riskier borrowers, since they do not rely on insured deposits and hence are not subject to examination. This could imply that nonbanks would be more likely to seek collateral than banks giving a negative coefficient on this variable. 5 While most of the lenders are U.S. banks, when the lender is domiciled in a country different from the borrower (e.g., a loan by a Canadian bank to a Mexican firm), we code the variable as one, since the same arguments apply in such cases.

10 88 J. S. Gonas et al./the Financial Review 39 (2004) Hubbard, Kuttner, and Palia (2002) show that lower quality banks tend to charge higher prices on loans than high quality banks. We accordingly control for bank quality in our estimations by incorporating the lender s ratio of equity capital to assets in place of the variable BANK. We expect that a loan will be more likely to be secured as the lending bank s capital position deteriorates, so we posit a negative coefficient on BANKCAR. The loan s purpose could influence the bank s decision to require collateral since certain projects are inherently riskier than others. Loans to finance leveraged buyouts, for example, are quite risky and hence more likely to be secured. Conversely, if a loan s purpose is to purchase highly marketable fixed assets, we hypothesize the opposite effect. We also suggest that loans for refinancings carry more repayment risk and are more likely to warrant collateral. Like Kleimeier and Megginson (2000), we organize the various loan purposes into five broad categories and we include four of these purposes as binary variables in the model: bank refinancing (PREF), corporate control (PCC), fixed asset backing (PFAB), and general corporate purposes (POTH). All other loan purposes represent the excluded category (POTH). We also include a set of industry dummies based on one-digit SIC codes (SIC0 SIC9) since industries differ in their susceptibility to macroeconomic shocks. Some industries, like electric utilities, are highly regulated, possibly affecting their credit risk and the attendant use of collateral. Financial services firms offer another example, since they engage extensively in off-balance-sheet activities, and these types of contingent assets or liabilities pose more asymmetric information and moral hazard problems to lenders. Lastly, the loan market cycles over a period as long as a decade. A loan that might not be secured in 1990 might well be secured in 1997 simply because of changing market conditions. We control for such differences using dummy variables for the year of the loan commitment (YR1988 YR2001) The logit models for rated firms To test the hypothesis that collateralization is more likely in the presence of credit risk, we isolate the sample borrowers with an S&P rating. We then separate this subsample into investment grade (AAA, AA, A, and BBB) and high-yield (BB, B, CCC, CC, C, and D) borrowers. We do the same for firms rated by Moody s, and in both cases the control group is high-yield borrowers. Under a credit risk argument we should find that the investment grade dummies have negative coefficients, indicating that such firms are less likely to offer collateral than their high-yield counterparts. Under the signaling hypothesis, however, the high quality, investment grade firms should collateralize more often, giving a positive coefficient on the 6 The industry and year control variables are omitted from the tables to conserve space.

11 J. S. Gonas et al./the Financial Review 39 (2004) investment grade variable. The remainder of the model remains unaltered and is as follows: SECURED = β 0 + β 1 INVEST + β 2 EXCHANGE + β 3 LNSALES + β 4 FOREIGN + β 5 REPEAT + β 6 LNMATURITY + β 7 (BANK or BANKCAR) + β PURPOSE CONTROLS + β INSDUSTRY CONTROLS + β YEAR CONTROLS + ε (2) Lastly, we also run a regression using the credit ratings ranked ordinally from one through ten as S&P (Moody s) credit ratings increase from D (D) to AAA (Aaa). Again, as credit risk increases, the likelihood of collateral should rise and we should observe a negative coefficient on the ordinal variable. Signaling again implies a positive coefficient. Since this specification involves a linearity constraint on the impact of credit risk on collateral, we also include a squared term in the model to evaluate whether the true relation is nonlinear. The remainder of the model remains unaltered and is as follows: SECURED = β 0 + β 1 ORDERRATE + β 2 ORDERRATE 2 + β 3 EXCHANGE + β 4 LNSALES + β 5 FOREIGN + β 6 REPEAT + β 7 LNMATURITY + β 8 (BANK or BANKCAR) + β PURPOSE CONTROLS + β INSDUSTRY CONTROLS + β YEAR CONTROLS + ε (3) 4. Results 4.1. Basic data analysis In Table 2, we examine the default risk characteristics of the sample loans. Panel A shows the distribution of loan agreements by secured status across S&P senior debt ratings. When the borrower is rated AAA by S&P, only 11% are secured. Similar results are found for AA and A rated loans. A little more than a fifth of BBB rated loans are secured, but over 66% of the loans made to BB-rated borrowers and 95% of loans to B-rated borrowers were secured. All the loans made to CCC-, CC-, and D-rated borrowers were secured. These results imply lenders engage in credit rationing based on perceived default risk and the willingness/capacity of borrowers to provide collateral. Investment grade borrowers obtain the bulk of unsecured credit, while higher risk, non-investment grade borrowers must always provide collateral to qualify for loans. Looking at the Moody s ratings in Panel B of Table 2 we find very similar results. None of the loans to Aaa-rated borrowers were secured, and only 21% of the loans to Baa-rated borrowers were secured. Turning to high-yield bonds, the similarities

12 90 J. S. Gonas et al./the Financial Review 39 (2004) Table 2 Distribution of commercial loans by secured status across senior debt ratings and borrower s geographical location The sample contains 7,619 commercial loan arrangements closed between December 1988 and January Panel A of this table shows the distribution of the loans by S&P senior debt rating and collateral classification, and Panel B shows the distribution of loans by Moody s senior debt rating and collateral classification. Panel A: Standard and Poor s senior debt rating Classification S&P senior debt rating Secured (Percent) Unsecured (Percent) Total AAA % % 9 AA A BBB BB B CCC CC C D NR 4, , ,661 Total 5, , ,619 Panel B: Moody s senior debt rating Classification Moody s senior debt rating Secured (Percent) Unsecured (Percent) Total Aaa 0 0% % 4 Aa A Baa Ba B Caa Ca C D NR 4, , ,975 Total 5, , ,619 continue, with over 66% of the loans to Ba-rated borrowers and 95% of loans to B-rated borrowers being collateralized. Table 3 provides the correlation coefficients for selected independent variables. None of the independent variables are highly correlated and there appears to be little multicollinearity in the explanatory variable set. As an additional test for

13 J. S. Gonas et al./the Financial Review 39 (2004) Table 3 Pearson correlation coefficients The sample contains 7,619 commercial loan arrangements closed between December 1988, and January Introduction of bank capital asset ratios reduces the sample size to 3,177 observations. This table provides the Pearson correlation coefficients and the probability that absolute value of the correlation coefficient is greater than zero for selected variables. EXCHANGE is a binary variable representing firms listed on a stock exchange at loan origination. LNSALES is the natural log of sales size. FOREIGN is a binary variable for borrowers located outside the United States, and REPEAT is a binary variable for a repeat loan matching borrower to lender on a previous date in the sample. MATURITY is the term of the loan in months, and LNMATURITY is the natural log of the loan s maturity in months. BANK is a binary variable identifying the lending institution as a bank, and BANKCAR is the capital asset ratio, defined as the ratio of bank equity to bank assets, of the lending institution. EXCHANGE LNSALES FOREIGN REPEAT LNMATURITY BANK BANKCAR EXCHANGE LNSALES (0.0001) FOREIGN (0.0001) (0.0001) REPEAT (0.2475) (0.0009) (0.0974) LNMATURITY (0.0001) (0.0375) (0.6223) (0.0001) BANK (0.0001) (0.0008) (0.7635) (0.0017) (0.0001) BANKCAR (0.5952) (0.0001) (0.6252) (0.4931) (0.0929) (0.8709)

14 92 J. S. Gonas et al./the Financial Review 39 (2004) multicollinearity and ill-conditioned data, however, the matrix condition number is provided for each logit model estimated Results: The full sample logit model We initially use the entire sample of rated and nonrated borrowers to estimate logit model (1), predicting the probability a loan will be secured. The sample contains over 7,600 loans, but the subsample of loans in which we can observe the lender s capital-asset ratio consists of 3,177 observations. These results are presented in Table 4. With respect to information transparency, we find that borrowers with either S&P or Moody s ratings are less likely to secure loans. Consistent with the adverse selection hypothesis, banks appear to require collateral when information asymmetries are more severe. The evidence does not support the theoretical argument that high-quality firms signal using collateral. Likewise, firms listed on a U.S. stock exchange are less likely to secure loans than unlisted firms. Larger firms likewise are less likely to secure loans, providing additional evidence of the role of information asymmetries and adverse selection. We also hypothesize that repeat borrowings would enhance the quality of borrowerlender relationships and reduce the prospect of collateralization. While the coefficient is negative, the result is not significant. Nor is the result for the foreign borrower variable significant in the full sample. In general, these findings provide evidence that potential adverse selection problems lead lenders to require collateral. We argued above that longer-term loans allow borrowers more opportunities to engage in underinvestment and asset substitution, and, as shown in Table 4, we find that the probability of securing a loan increases significantly with loan maturity. This supports the hypothesis that collateralization increases in the presence of moral hazard problems. Somewhat surprisingly, we find that loans by banks are less likely to be collateralized than loans by nonbanks. Loans by finance companies and investment banks tend to have higher credit risk than bank loans, however, and this could account for our result. Finally, we find that the purpose of the loan affects the decision to secure a loan. Loans involving refinancings, changes in corporate control, and the purchase of fixed assets are more likely to be secured. The latter result is not surprising, since the fixed asset acquired can readily serve as collateral. The second column in each panel of Table 4 presents the results for the subsample that includes only bank lenders where we could observe the equity capital ratio. We 7 The condition index of the normalized data matrix is a vector composed of the ratio of the individual singular values to the minimum element. This index summarizes the ill-conditioning of the matrix. The square-root of the largest element in the condition index, the condition number, measures the illconditioning for the variable with the smallest contribution of independent information. Therefore, the condition number proves a convenient scalar measure of multicollinearity. Condition numbers greater than 30 imply the potential of disruptive ill-conditioning, while values over 50 indicate serious problems. For a detailed discussion of this point, see Chapter 3 in Belsley, Kuh, and Welsh (1980).

15 J. S. Gonas et al./the Financial Review 39 (2004) Table 4 The full sample logit model The sample contains 7,619 commercial loan arrangements closed between December 1988 and January Introduction of bank capital asset ratios reduces the sample size to 3,177 observations. A binary variable representing a secured loan (SECURED) is regressed on a binary variable for firms rated at the close of the loan by S&P (SPRATED) in Panel A, or Moody s (MRATED) in Panel B, a binary variable for firms listed on a U.S. stock exchange (EXCHANGE), the natural logarithm of the annual sales of the borrower (LNSALES), a binary variable for foreign firms (FOREIGN), a binary variable for a repeat loan matching borrower to lender on a previous date in the sample (REPEAT), the natural logarithm for the maturity of the loan in months (LNMATURITY), a binary variable for loans underwritten by a bank (BANK), the lending bank s capital asset ratio (BANKCAR), a set of binary variables for the purpose of the loan including refinancing, corporate control, fixed asset backing, general corporate purposes (PREF, PCC, PFAB, PGCP), a set of binary variables for the industry of the borrower based on SIC codes (not presented), and a set of binary variables for the year of issue (not presented). Please note that the control variable for rated firms (SPRATED or MRATED) is nonrated firms (SPNR or MNR, respectively); the control variable for the purpose binary variables is other purposes (POTH). The expected sign, based on our hypotheses in Section 2 of the paper, is provided for each coefficient. The standard errors (S.E.) of the estimates are reported in the parentheses immediately to the right of the estimate and Wald chi-square statistical significance is displayed by the use of one (10%), two (5%), or three (1%) stars. The number of observations, likelihood ratios, Wald statistics, Hosmer and Lemeshow goodness-of-fit statistics, and the matrix condition number are reported at the bottom of the table. Panel A: S&P ratings Regression Variable Expected sign Estimate S.E. Estimate S.E. INTERCEPT (1.940) (0.898) SPRATED (0.070) (0.116) EXCHANGE (0.070) (0.109) LNSALES (0.019) (0.030) FOREIGN (0.169) (0.495) REPEAT (0.095) (0.143) LNMATURITY (0.041) (0.065) BANK (0.126) BANKCAR (1.432) PREF (0.100) (0.168) PCC (0.077) (0.130) PFAB (0.137) (0.209) PGCP (0.081) (0.119) OBSERVATIONS 7, , LIKELIHOOD RATIO 1, WALD STATISTIC 1, HOSMER AND LEMESHOW MATRIX CONDITION NUMBER (continued ) find that the financial position of the bank lender affects loan structure, and banks with higher equity capital ratios are less likely to require collateral, other things equal. These results are consistent with those presented by Hubbard, Kuttner, and Palia (2002), who find that lower-quality banks tend to charge higher loan rates, suggesting

16 94 J. S. Gonas et al./the Financial Review 39 (2004) Table 4 (continued ) The full sample logit model Panel B: Moody s ratings Regression Variable Expected sign Estimate S.E. Estimate S.E. INTERCEPT (1.972) (0.895) SPRATED (0.074) (0.126) EXCHANGE (0.070) (0.109) LNSALES (0.018) (0.029) FOREIGN (0.169) (0.496) REPEAT (0.095) (0.143) LNMATURITY (0.041) (0.065) BANK (0.126) BANKCAR (1.432) PREF (0.100) (0.168) PCC (0.077) (0.129) PFAB (0.137) (0.209) PGCP (0.081) (0.118) OBSERVATIONS 7, , LIKELIHOOD RATIO 1, WALD STATISTIC 1, HOSMER AND LEMESHOW MATRIX CONDITION NUMBER Indicates statistical difference from zero at the 0.01 level. Indicates statistical difference from zero at the 0.05 level. Indicates statistical difference from zero at the 0.10 level. that such banks make riskier loans. The rest of the coefficients are quite similar to those in the full sample model, save for the coefficient on the listed/unlisted firm dummy, which declines in significance for the S&P results in Panel A but becomes insignificant for the Moody s results in Panel B Results: The logit models for rated firms We next estimate a logit model for a sample that takes into account the borrower s credit risk as reflected in the firm s senior debt rating. We first isolate the firms with S&P ratings; this reduces the sample size to 1,958 observations. If we additionally match this group to cases in which we can observe the lender s capital-asset ratio, the sample size declines to 652. Sampling borrowers with Moody s ratings, the sample size declines to 1,644 and 519 borrowers, respectively. The results of the estimation are presented in Table 5.

17 J. S. Gonas et al./the Financial Review 39 (2004) Table 5 The logit models for rated firms The sample contains 7,619 commercial loan arrangements closed between December 1988 and January After eliminating observations without an S&P (Moody s) rating, the testable sample size is reduced to 1,958 (1,644) observations. A binary variable representing a secured loan (SECURED), is regressed on a binary variable for firms rated as investment grade at the close of the loan by S&P (SPINVEST), in Panel A, or Moody s (MINVEST) in Panel B, or a ranked variable and its quadratic for S&P ratings (SPORDERRATE) in Panel C, or a ranked variable and its quadratic for Moody s ratings (MORDERRATE) in Panel D, a binary variable for firms listed on a U.S. stock exchange (EXCHANGE), the natural logarithm of the annual sales of the borrower (LNSALES), a binary variable for foreign firms (FOREIGN), a binary variable for a repeat loan matching borrower to lender on a previous date in the sample (REPEAT), the natural logarithm for the maturity of the loan in months (LNMATURITY), a binary variable for loans underwritten by a bank (BANK), the lending bank s capital asset ratio (BANKCAR), a set of binary variables for the purpose of the loan including refinancing, corporate control, fixed asset backing, general corporate purposes (PREF, PCC, PFAB, PGCP), a set of binary variables for the industry of the borrower based on SIC codes (not presented), and a set of binary variables for the year of issue (not presented). Please note that the control variable for rated firms (SPRATED or MRATED) is nonrated firms (SPNR or MNR, respectively); the control variable for the purpose binary variables is other purposes (POTH). The expected sign, based on our hypotheses in Section 2 of the paper, is provided for each coefficient. The standard errors (S.E.) of the estimates are reported in the parentheses immediately to the right of the estimate and Wald chi-square statistical significance is displayed by the use of one (10%), two (5%), or three (1%) stars. The number of observations, likelihood ratios, Wald statistics, Hosmer and Lemeshow goodness-of-fit statistics, and the matrix condition number are reported at the bottom of the table. Panel A: Categorized S&P ratings Regression Variable Expected sign Estimate S.E. Estimate S.E. INTERCEPT (1.288) (1.743) SPINVEST (0.151) (0.265) EXCHANGE (0.177) (0.282) LNSALES (0.041) (0.063) FOREIGN (0.427) (1.348) REPEAT (0.211) (0.343) LNMATURITY (0.098) (0.169) BANK (0.269) BANKCAR (3.471) PREF (0.216) (0.386) PCC (0.171) (0.299) PFAB (0.402) (0.607) PGCP (0.216) (0.350) OBSERVATIONS 1, LIKELIHOOD RATIO 1, WALD STATISTIC HOSMER AND LEMESHOW MATRIX CONDITION NUMBER (continued )

18 96 J. S. Gonas et al./the Financial Review 39 (2004) Table 5 (continued ) The logit models for rated firms Panel B: Categorized Moody s ratings Regression Variable Expected sign Estimate S.E. Estimate S.E. INTERCEPT (1.384) (2.235) MINVEST (0.167) (0.313) EXCHANGE (0.210) (0.392) LNSALES (0.043) (0.075) FOREIGN (0.419) (1.592) REPEAT (0.220) (0.373) LNMATURITY (0.106) (0.200) BANK (0.288) BANKCAR (4.257) PREF (0.220) (0.431) PCC (0.181) (0.350) PFAB (0.446) (0.865) PGCP (0.257) (0.438) OBSERVATIONS 1, LIKELIHOOD RATIO 1, WALD STATISTIC HOSMER AND LEMESHOW MATRIX CONDITION NUMBER (continued ) We analyze the role of credit risk in two ways. First, in Panel A we create a dummy variable equal to one for investment grade borrowers and zero otherwise for S&P ratings. Panel B repeats the analysis for Moody s investment grade borrowers. Second, in Panel C we create a variable that reflects the borrower s credit on an ordinal scale of one (D) to ten (AAA) for the S&P ratings, and the analysis of ordinal Moody s ratings is presented in Panel D. For both S&P and Moody s we also include the square of this ordinal term, since John, Lynch, and Puri (2002) provide evidence that there is a nonlinear relation between credit risk premiums and default risk prospects. In both cases, we find a strong relation between default prospects and loan collateralization. We also confirm that the relation is highly nonlinear. Thus, the results of the univariate analysis reported in Table 2 continue to hold in a multivariate setting. The probability a loan will be secured increases significantly as the borrower s credit rating deteriorates and borrowers migrate to non-investment grade states, other things equal. We also observe that the results relating to information proxies remain relatively robust in Table 5. Although EXCHANGE becomes insignificant when we consider credit risk, firm size continues to have the hypothesized effect. We also find limited evidence that international loans are more likely to be secured. Again, repeat customers are less likely to pledge collateral, but the result is not systematically significant.

19 J. S. Gonas et al./the Financial Review 39 (2004) Table 5 (continued ) The logit models for rated firms Panel C: Ordered S&P ratings Regression Variable Expected sign Estimate S.E. Estimate S.E. INTERCEPT (2.928) (4.257) SPORDERRATE (0.805) (1.169) SPORDERRATE (0.059) (0.084) EXCHANGE (0.198) (0.316) LNSALES (0.043) (0.065) FOREIGN (0.338) (1.512) REPEAT (0.213) (0.367) LNMATURITY (0.104) (0.183) BANK (0.305) BANKCAR (4.068) PREF (0.219) (0.404) PCC (0.178) (0.321) PFAB (0.382) (0.651) PGCP (0.234) (0.379) OBSERVATIONS 1, LIKELIHOOD RATIO 1, WALD STATISTIC HOSMER AND LEMESHOW MATRIX CONDITION NUMBER (continued ) Supporting the moral hazard hypothesis and suggesting that asymmetric information and moral hazard play roles independent of credit risk, longer term loans likewise continue to be associated with a higher probability of collateral. The remaining results are quite similar to the full sample findings, although the loan purpose dummies are generally less significant when we explicitly account for credit risk. This suggests that the purpose of the loan can proxy for default risk in the absence of credit rating information. In the second column of each panel we present the model including the lender s capacity to absorb risk. Again, we generally observe that banks with lower capital ratios are more likely to require collateral. The remaining coefficients are generally robust to the addition of this variable. 5. Summary and conclusion This paper examines the factors that determine when commercial loans will be secured. We examine 7,619 loans closed between December 1988 and January Overall, our findings suggest that information asymmetry, moral hazard problems, and credit risk all play significant roles, but we find no evidence in favor of the

20 98 J. S. Gonas et al./the Financial Review 39 (2004) Table 5 (continued ) The logit models for rated firms Panel D: Ordered Moody s ratings Regression Variable Expected sign Estimate S.E. Estimate S.E. INTERCEPT (3.708) (6.965) MORDERRATE (1.088) (1.928) MORDERRATE (0.083) (0.141) EXCHANGE (0.228) (0.442) LNSALES (0.043) (0.078) FOREIGN (0.441) (1.801) REPEAT (0.217) (0.396) LNMATURITY (0.109) (0.208) BANK (0.309) BANKCAR (2.961) PREF (0.226) (0.473) PCC (0.184) (0.368) PFAB (0.414) (0.798) PGCP (0.278) (0.493) OBSERVATIONS 1, LIKELIHOOD RATIO 1, WALD STATISTIC HOSMER AND LEMESHOW MATRIX CONDITION NUMBER Indicates statistical difference from zero at the 0.01 level. Indicates statistical difference from zero at the 0.05 level. Indicates statistical difference from zero at the 0.10 level. predictions of certain theoretical models that high-quality firms signal by providing collateral. Specifically, we find that firms with a credit rating are less likely to secure loans than unrated firms, implying that lenders are more likely to require collateral in the face of potential adverse selection problems. Likewise, the results indicate that a borrower s sales figure is negatively related to the probability a loan is collateralized. This supports the hypothesis that larger firms pose relatively fewer information asymmetry problems. Similarly, after controlling for default risk, we find some evidence that international loans are more likely to be collateralized than domestic loans. Overall, the results are again consistent with a role for information asymmetries. Supporting our hypothesis that collateral can address moral hazard problems like underinvestment and asset substitution, we find that longer-term loans are more likely to be collateralized. We also find that credit risk independently influences the decision to secure loans. Somewhat surprisingly, we find that loans by banks are less likely to be collateralized than loans by nonbanks. Lastly, lending banks that themselves have less protection against risk are more likely to require collateral.