developed market for Debtor-in-Possession (DIP) financing. The legal ... who propose that the specialization by banks and finance companies in private lending.
Information, Credit Risk, Lending Specialization, and Loan Pricing: Evidence from the DIP Financing Market
Kenneth N. Daniels Virginia Commonwealth University School of Business Richmond, Virginia 23284-4000 Gabriel G. Ramirez Kennesaw State University Michael J. Coles College of Business Kennesaw , GA 30144
Electronic copy available at: http://ssrn.com/abstract=1133522
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Information, Credit Risk, Lending Specialization, and Loan Pricing: Evidence from the DIP Financing Market Abstract We provide an empirical support for theories of lender specialization using the recently developed market for Debtor-in-Possession (DIP) financing. The legal environment in which DIP financing operates represents a natural laboratory for testing determinants of lending specialization (e.g. lender choice). We find that the choice of lender is not driven by credit risk, but by information considerations and that this lending specialization has loan pricing effects. In short, banks (non-bank lenders) lend to more (less) transparent firms and at lower (higher) loan spreads. Our results are consistent with the interpretation that banks provide important and useful services. JEL Classification Code: G21, G33 Keyword: Bank loans, financial intermediation, debtor in possession financing, loan contracting, lending specialization, loan pricing, information effects, credit risk, and chapter 11.
Electronic copy available at: http://ssrn.com/abstract=1133522
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Information, Credit Risk, Lending Specialization and Loan Pricing: Evidence from the DIP Financing Market
Despite an overwhelming amount of research, mostly theoretical but some empirical, indicating that borrower information asymmetry or differential screening capabilities among intermediaries induce lending specialization (the choice of lender), some recent research concludes that lending specialization in private debt markets is mainly determined by borrower’s credit risk and not by information asymmetry considerations. We further explore this issue by empirically testing theories of lender specialization. In particular, our paper contributes to this area in two ways. First, since credit risk or credit quality is highly correlated with information asymmetry or monitoring activities as documented by Blackwell and Kidwell (1988), Booth (1992), and Blackwell and Winters (1997), testing of the determinants of lending specialization must recognize the effects arising from credit risk and information asymmetry. We address this issue by studying lender specialization in an environment that leads to uniform credit quality, the DIP financing market. 1 This allows for a cleaner testing of whether lending specialization in bank loans is attributed to credit risk or information considerations. Secondly, while theoretical work by Hauswald and Marquez (2003) argues for informational considerations
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DIP financing has been extensively studied due to its important role in bankruptcy, but DIP loan pricing has not been given a lot of empirical scrutiny. Dahiya, John, Puri, and Ramírez (2003) demonstrate that DIP loan spreads should be lower in the presence of an existing relationship with a lead lender. In addition, Chatterjee, Dhillon, and Ramírez (2004) finds that DIP spreads are higher than loan spreads on non-DIP loans with similar features and attribute the greater cost of DIP loans to compensation for greater monitoring, higher risk of DIP loans or the market power of DIP lenders. Extant research on DIP financing concentrated on either presenting a general, legal description of the process and its merits and/or appropriateness [Rohman (1990), Triantis (1993), Kohn, Solow, and Taber (1995), Rosen, Gardner, Miller, and Basta (1998)], or understanding the role of DIP financing on the firm [John and Vasudevan (1995), and Chatterjee, Dhillon, and Ramírez (1997) and Dhillon, Noe, and Ramírez (2007)]. Most recently researchers focused on analyzin g the costs and benefits of DIP financing for the borrowers [Fayez and Thomas (2001), Dahiya et. al. (2003), Carapeto (2004), and Chatterjee et. al. (2004)] but no research has yet focused on financial intermediaries, their role, and the dynamics of the DIP lending market.
4 jointly determining the degree of lenders competition in credit markets and the pricing of financial instruments, extant work has investigated the lender choice and the loan pricing decision separately. In contrast, we jointly investigate the choice of lender and the determinants of loan pricing. This allows for the testing of the effects of the lender’s differential monitoring of information handling capabilities and lending specialization on loan pricing. We find that our measures of credit risk are not significant determinants of lending specialization. This view contrasts with the perspective of Carey, Post, and Sharpe (1998), who propose that the specialization by banks and finance companies in private lending markets is due to borrower’s ex-ante observable credit risk. Our study provides evidence that DIP lending specialization is due to information considerations. We find that in the DIP market, both non-bank lenders and banks are active participants but the lender’s differential monitoring or information handling capabilities leads to specialization which in turns is reflected in the loan pricing. Banks are more likely to have an advantage in monitoring (more information-savvy lenders), since they use their information acquisition investments and their capacity to compete in credit markets. This suggest that information considerations play an important role in lending specialization and loan pricing which is consistent with Gehirg (1998), Winton (1999), Jeong (2001), Almazan (2002), Hauswald and Marquez (2003, 2006) and Yasuda (2005) who argue that differential information or screening technologies among intermediaries induce specialization in lending. In short, we find that banks tend to lend to larger firms and charge lower DIP drawn all-in-spreads, while non-bank lenders tend to lend to smaller firms and charge higher DIP drawn all-inspreads. While our results provide evidence that is consistent with lender specialization,
5 these results can also arise if there are strong lender-borrower relationships which is not explored by us. The paper is organized as follows. Section II describes DIP financing environment and its potential effect on credit risk. Section III develops the hypotheses for lender specialization and loan pricing in the DIP market. The sample selection procedures are presented in Section IV. Section V presents descriptive statistics of the DIP lending market and describes time dynamics of loans made by banks and non-bank lenders. Methodology and empirical results for the choice of lender and the loan pricing models are presented in section VI. Section VII concludes the paper.
II. DIP financing The DIP financing market is for financially distressed firms restructuring under Chapter 11 that need funding for the continuation of their business. In the last decade, debtor-in-possession (DIP) financing has grown significantly in size and importance. In the year of inception, 1988, DIP loans amounted to $350 million. In 2004, the total amount of DIP loans grew to $15.4 billion with a peak of over $24 bil. in 2002 [As shown in Table I in this paper]. Also, about 12 % of the firms filing for Chapter 11 bankruptcy obtained DIP financing during the first two years of the market’s inception (1988-1989) while the ratio for the late 90’s is close to 50% [Carapeto (2004), Dahiya, John, Puri, and Ramírez (2003), and Chaterjee, Dhillon, and Ramírez (2004)]. Further, the availability of DIP financing for recent Chapter 11 filings of large publicly traded firms such as LTV Corp. (Dec 2000), AMF Bowling (July 2001), Bethlehem Steel Corp. (October 2001), WorldCom ($2 bil. in July 2002), United Airlines ($1.5 bil. in December 2002), and Delta
6 Airlines ($2 bil. in September 2005) provide an example of the vital role DIP financing plays in bankruptcy restructuring. DIP lending is possible mostly because of a complex set of re-contracting provisions of Section 364 of the Bankruptcy Reform Act of 1978 [See Triantis (1993), Kohn, Solow, and Taber (1995), Chatterjee, Dhillon, and Ramírez (1997), Rosen, Gardner, Miller, and Basta (1998), Dahiya et. al. (2003), Carapeto (2004), Dhillon, Noe, and Ramírez (2007) for a detail presentation of the DIP process]. 2 The DIP financing market represents an excellent opportunity to analyze lending specialization and loan pricing in revolving corporate credit markets. First, and probably most important, the “superpriority” nature of DIP loans given by the provisions in Section 364 of the Bankruptcy Reform Act of 1978, have the potential to significantly ameliorate borrower’s default risk. The super-priority status allows a DIP loan to have payment priority over unsecured prepetition debt and administrative expenses. DIP loan approval procedures appear to err on the conservative side providing a significant cushion over the distressed value of the borrowing base (See Chatterjee et. al. 2004). DIP loans must be repaid prior or within the reorganization plan. DIP loans also carry extensive covenants including negative pledges which implicitly represent a form of secured loan.3 Thus, the aspects of DIP financing create an environment where the credit risk of the borrowers can be considered uniform. One of the implications of this environment is the unique default and recovery risk faced by DIP loans. As of today, we only know of one DIP defaulted loan (fully paid with equity in the reorganization plan). Fitch has been rating DIP loans since the early 90s (though 2
In the normal course of business and without the Code, lenders will not provide financing in Chapter 11 because their claims represent quasi-equity. 3 In a negative pledge, offering of liens is prohibited or heavily restricted. The use of negative pledges with superpriority may provide the DIP lender with better protection than a simple collateralized loan (Chatterjee et. al. (2004)).
7 only a small portion of DIP loans are rated) starting with the first ever rated DIP loan to Hills Department Stores in 1991 which received an A rating. Fitch focuses in various quantitative, qualitative, and structural aspects of a DIP loan 4 and in the past, several DIP loans have received BBB to BBB+ ratings [See “Perspectives on Debtor-in-Possession Financing” Fitch report dated December 2002]. Loan ratings are more comprehensive and more insightful as they focus on both default risk and loan recovery rates. Thus, even though there is a probability of default there is also a high probability of recovery. This is so even in the event of liquidation of the DIP firm. Dahiya et. al. (2003) provide evidence that DIP lenders accelerate liquidation of DIP firms in order to preserve full payment of their loans. Indeed, an analysis (not reported here) of a small sample of 10 liquidated DIP firms, out of a total of 17 in the study period, shows that all priority claims which include DIP lenders were paid in full. 5 Second, DIP loans represent a very homogeneous sample of corporate revolving loans. As documented in Chatterjee et. al. (1997), DIP loans are mostly short term (1 to 2 years) revolving lines of credit for working capital purposes; must be paid before the reorganization plan is confirmed and implemented; and are mostly secured. This creates an econometric opportunity as the estimation of determinants of DIP loan pricing does not require the joint estimation of loan characteristics (e.g. maturity and collateral) as done in the literature (Dennis et. al. (2000), Hubbard, Kuttner, and Palia (2002), Shockley and Thakor (1997), among others).
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Fitch identifies as best practices associated with highly rated DIP loans the following: implementing reorganization plans prior to filing Chapter 11, appraisals from well-known professionals, monitoring of collateral, testing of performance benchmarks that are tied to the company's plan of reorganization, a strong covenant package and an excess availability requirement 5 Results are not reported in this paper due to space limitations and the smallness of the sample but are available upon request.
8 Third, the renegotiation effectiveness 6 issues discussed in Berlin and Loyes (1988) and Chemmanur and Fulghieri (1994) are not applicable to DIP loans since these are loan commitments by private lenders issued only under bankruptcy court supervision. Also, many of the agency problems alluded to in the contracting literature are not likely to exist in DIP loans as the loans are specifically authorized by the bankruptcy court for the operations of the firm (working capital), and many of the firm’s investment decisions are supervised by the court drastically reducing the misallocation of loan proceeds. That is, the underinvestment and overinvestment problems associated with the issuance of more senior (higher priority) debt are not present in DIP financing thus alleviating the need to use controls for agency problems and renegotiation features in the estimation of regressions. Finally, firms in Chapter 11 are highly levered firms, as documented in Hotchkiss (1995), Chatterjee, Dhillon, and Ramírez (1996), and Gilson (1997) among others. Since high levels of debt are associated with significant credit risk, this presents an excellent opportunity to test the robustness of our assertion that credit risk is not a determinant of the choice of lender.
III. Hypothesis development 3.1.
Lending specialization In this paper, lending specialization refers to the choice of lender and more
specifically banks or finance companies. Remola and Wulfekuhler (1992) argue that lending specialization between banks and finance companies results from credit segmentation. More recently, Carey et. al. (1998) provides evidence that this type of 6
It can also be argued based on Myers (1977) that agency costs are determinants of lending practices. Perhaps these dimensions can better be encompassed into the dimension of firm quality or reputation as modeled by Diamond (1991) and Rajan (1992).
9 lending specialization is a function of credit risk. The underlying notion of this type of market specialization can be traced at least to Boczar (1975, 1978), and Staten, Umbeck, and Gilley (1989) who study market risk segmentation in the consumer loan markets. However, there exists an extensive amount of literature which argues that lenders are likely to specialize based on their capabilities in both the production of information and the monitoring of the borrower [Gehrig (1998), Winton (1999), Jeong (2001), Almazan (2002) and Hauswald and Marquez (2006) and Yasuda (2005)]. The main implication is to highlight the importance of the information opacity of the borrower. We argue that information opacity is much more important in DIP financing since in the presence of bankruptcy there is significant information asymmetry caused by likely misevaluation of the DIP firm’s true value, along with conflicting incentives to represent or misrepresent the DIP firm’s intrinsic value.
Therefore, lending specialization based on borrower’s
information opacity and the lender’s ability to produce information about the borrower is a natural outcome of DIP lending. We seek to provide further insights into whether credit risk and/or information considerations represent the main determinant of the selection of a bank or a non-bank (e.g. finance companies, insurance companies, etc.) as provider of funds. We further suggest a link between this selection and the pricing of the loan. 3.1.1
Information induced lending specialization The vast amount of extant research on the role of financial intermediaries focuses
on the information-processing role of public and private lenders [See Leland and Pyle (1977), Campbell and Kracaw (1980), Diamond (1984), Ramakrishnan and Thakor (1984), Boyd and Prescott (1987), Diamond (1991), Rajan (1992), and Cehmmanur and Fulghieri (1994) among others]. A private lender in these studies is a generic lender, without
10 distinction between banks and non-banks (finance companies), that is hypothesized to have a monitoring advantage over public lenders. A significant portion of empirical research focuses on whether this informational advantage is unique to a certain type of private lender. Fama (1985), James (1987), Nakamura (1993), James and Smith (2000), Mester, Nakaruma, and Renault (2003), Altman, Gande, and Saunders (2004), and Dell’Ariccia and Marquez (2004) argue that banks, in particular, possess a unique ability in information access, processing, and monitoring resulting from their depositary role 7 . Further, capabilities to gather, process, and handle borrower’s information is likely to be different across private lenders in particular between finance companies and banks. As argued by Remolona and Wulfekuhler (1992), niche information is the source of finance companies’ advantage and large-scale data processing technologies provide banks with their own advantage. This view is clearly shown in Hauswald and Marquez (2006) model in which differential information or screening technologies among intermediaries induce specialization in lending. The implication from these studies is that finance companies and banks have different degrees of information processing and handling and thus, the following hypothesis is postulated, Hypothesis 1: The lending characteristics or profile of banks is significantly different than that of finance companies.
Berger, Miller, Petersen, Rajan, and Stein (2005) document that lending practices are in great part dictated by the type of information lenders have and are able to obtain about the borrower. An extension of their work is that banks are more likely to lend to
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Hubbard, Kuttner and Palia (2002) and Coleman, Esho and Sharpe (2006) empirically document the impact of lender characteristics on loan contract terms.
11 borrowers that have hard information, information that is easily codified in electronic form while finance companies will specialize on lending to borrowers with soft information. That is, the choice of lender is determined by the information opacity of the borrower. This relation is exactly what Burkart and Ellingsen (2004) model where banks provide lending to less opaque firms and finance firms specialize in lending to more opaque firms 8 . This relation is empirically supported by Hang (2004)’s study of informationally opaque small businesses where he uses distance as a proxy for information availability and finds that finance companies (banks) lend to distance (relatively close) borrowers. There is no consensus as to the proxies for borrowers’ information opacity. Firm size has been used to proxy for the information opacity of the firm (See Wittenberg-Moerman (2005) and references therein). Large firms are more likely to have well-developed reputations, more available information, and more stable cash flows (Strahan (1999)). Large borrowers are less opaque as they have more research coverage by analysts, more often access the capital markets, and in general there is more information disseminated about them, e.g. SEC filings, newswires, credit ratings, etc. The size and technological capability of the lender also plays a role in lending specialization. As shown by Marquez (2002), an increase in bank’s capacity lead to an increase in the banks’ informational advantage. Larger lenders are more likely to lead the front on technological progress, e.g. sophisticated credit scoring systems and data processing capabilities. There is also an extensive amount of papers arguing that large organizations favor the use of “hard” information resulting in a preference for lending to large firms. A full discussion of these issues is presented in Berger, Demsetz, and Strahan (1999) and Gorton and Winton (2002). The implication 8
It is also possible that opaque or information-problematic firms self-select into borrowing from finance companies since they face bank monitoring costs that outweigh its benefits as predicted by the models of Diamond (1991) and Rajan (1992) and argued by Berger, Klapper, and Udell (2001
12 from these studies is that due the interaction between the size of the lender and the borrower and its relation to information opacity, we would expect the following:
Hypothesis 2: Banks are more likely to lend to large borrowers and non-bank companies are more likely to lend to smaller borrowers.
3.1.2
Credit risk induced lending specialization A recent strand of empirical research argues that banks are not unique in serving
information sensitive borrowers rather such characterization applies to any financial intermediary. This line of research focuses on market risk segmentation in credit markets and, in particular, the specialization role of banks and finance companies in private debt markets. The idea of market risk segmentation is found, for example, in Simonson (1994) who argues that finance companies are thought of as high-risk institutions associated with high-interest loans to borrowers turned away by banks. 9 More specifically, Carey et. al. (1998) argue that both banks and financial companies are similar in terms of serving information-problematic borrowers and propose that the specialization by banks and finance companies in private lending markets is due to borrower’s ex-ante observable credit risk: Finance companies tend to lend to low credit quality firms and banks lend to firms with high credit quality. Further, Carey and Nin (2007) find that the most important factor in determining lending practices is credit risk. The notion that credit risk is an important determinant in credit markets is also found in the work of Denis and Mihov (2003). They investigate the choice between public debt, bank, and non-bank (mostly 9
There has been a significant number of studies of market risk segmentation in the consumer loan markets [Boczar (1975, 1978), Staten, Umbeck, and Gilley (1989), Remolona and Wulfekuhler (1992), and Barron and Chong (2003) among others].
13 SEC144) private debt and find that the most important determinant among these sources of debt is the borrower’s credit quality. However, the testing of credit risk induced specialization is made difficult by the fact that information and credit risk effects are correlated as documented by Blackwell and Kidwell (1988), Booth (1992), and Blackwell and Winters (1997). As described in the previous section, the institutional aspects of DIP financing afford us with an excellent opportunity to overcome this problem. Particularly, the super-priority nature of DIP loans, given by the provisions in Section 364 of the Bankruptcy Reform Act of 1978, has the potential to significantly reduce the impact of credit risk in lending specialization. More importantly, the legal and financial environment of DIP financing is likely to eliminate possible advantages that certain lenders may have to exploit credit risk segmentation and therefore credit risk should not be a predictive factor of lender specialization. This leads to the following hypothesis,
Hypothesis 3: Credit risk measures are not significant determinants of the choice of lender (e.g. bank or non-bank lender) to DIP borrowers.
Then, it follows that in the DIP financing market, information induced lending specialization plays a significant role in the competitiveness of the lender and therefore in the pricing of the loans. As postulated in the theoretical work of Hauswald and Marquez (2003), a natural outcome of lending specialization is the joint determination of the degree of lender’s competition and the pricing of financial instruments. Thus, a natural extension of our paper is to investigate the determinants of loan interest rates and consequently the impact of the lender choice on loan pricing.
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3.2. Determinants of loan interest rates In a frictionless competitive market, loan interest rates should be determined by the borrower’s credit risk and the lender’s cost of funds. Extant literature in this area has an extensive list of work on determinants of loan pricing in the presence of market imperfections. They include (1) credit risk, (2) borrower’s information opacity, (3) nonprice terms, (4) lending specialization e.g. type of lender, and (5) possibly market conditions affect the price and non-price terms of loans. Credit risk is a function of the nature of the borrower and its business, experience of the managers, firm’s track record, etc. (Strahan (1999)). Empirical research such as Ackert, Huang, and Ramírez (2007), Brick and Palia (2007), Moerman (2005), Hubbard, et. al. (2002), Dennis, Nandy, and Sharpe (2000), Strahan (1999), Blackwell and Winters (1997), among others find that the lower the quality of the borrower, the higher the credit risk, and the higher the cost of financing. For DIP financing, we argue that the legal environment impacts credit risk such that it is not a determinant of the lender choice, e.g. not much cross-sectional variation in credit risk. However, credit risk does exist and is priced. Therefore, we expect a positive significant relation between credit risk measures and DIP loan pricing. The information opacity of the borrower influences the level of monitoring the lender must exercise. Diamond (1984) suggests that the intermediary or lender needs cost effective monitoring of the borrowers because of ex-post information asymmetry. Wittenberg-Moerman (2005) documents that information asymmetry increases loan interests rates. Similarly, extant research such as Sufi (2007), Coleman, et. al. (2006), and
15 Dennis, et. al. (2000) documents a positive relationship between information opacity of the borrower and loan pricing. Non-price contract terms are also effective policy tools to determine loan pricing and control the risk of borrowers. Hubbard et. al. (2002), Dennis et. al. (2000) and Strahan (1999) argue that lenders limit their potential exposure by limiting the loan amounts; shortening the maturity of the loans; and reduce loan risk by requiring collateral (secured loans). Rajan and Winton (1995) argue that maturity is used by lenders as a way to handle information asymmetries - small and lesser known (more informationally opaque) firms use short-term loans and well known firms use long-term loans. Collateral is also an effective tool related to credit risk and to the level of information opacity of the firm. Rajan and Winton (1995) provide a theoretical model and Hubbard et. al. (2002), Carey et. al. (1998), and Berger and Udell (1990) present empirical evidence on the use of collateral to limit lender’s exposure (See Booth and Booth (2006) for an extensive description of this idea). Manove and Padilla (1999, 2001) and Jimenez and Saurina (2004) argue that lenders may ask for collateral as a condition to provide a loan as an alternative to screen and evaluate the borrower. Thus, lenders with a lower level of expertise or scarce resources to evaluate the borrower will have more incentives to use collateral as a substitute for costly effective monitoring of the firm. Fama (1985), James (1987), Nakamura (1993), James and Smith (2000), Mester, Nakaruma, and Renault (2003), Altman, Gande, and Saunders (2004), and Dell’Ariccia and Marquez (2004), argue that banks possess a unique ability in information access, processing, and monitoring resulting from their depositary role, and thus we would expect banks to have lower information production costs than non-bank lenders. The cost of this
16 monitoring is a function of the lender’s information processing technology, the lender’s available pricing options, and the level of information opacity of the firm. The cost of effective monitoring can manifest itself in different ways and alternative monitoring schemes have been investigated to reduce information opacity between the borrower and lender. Gorton and Winton (2002) argue that efficient information production is one way the lender can perform this cost effective monitoring. Indeed, the models in Gehrig (1998), Winton (1999), Jeong (2001), Almazan (2002) and Hauswald and Marquez (2006) document how differential information or screening technologies among intermediaries induce specialization in lending and effects loan pricing. Better technologies impacts loan pricing by allowing lenders to better screen and to be better informed about borrowers. It is expected that in a competitive environment, banks will pass some of these savings to borrowers in the form of lower drawn AIS. Hence, the following hypothesis:
Hypothesis 4: DIP loans made by banks carry lower fees than DIP loans made by non-banks.
IV.
Sample and data collection Data on DIP loans is obtained from DealScan, a database of loans to large firms
constructed by the Reuters Loan Pricing Corporation (LPC). 10 Dealscan contains information for mostly large publicly traded firms but as reported in Carey et. al. (1998), it covers a significant fraction of the dollar amount of outstanding consumer and industrial
10
LPC collects information from its institutional clients mostly from SEC filings and for large loan syndicators and staff reporters and thus the sample used is biased toward large borrowers.
17 loans. DealScan is often used because it contains an extensive amount of information regarding loans and lender characteristics. We found 970 DIP transactions for the period January 1988 to 2004 which includes multiple DIP loans by the same company. 11 In many instances a single DIP loan or deal is comprised of several facilities. We only use the first time a firm receives a DIP loan and we require the DIP loan to have a “drawn all-in-spread” (AIS) to be included in our sample, which reduces the sample to 650 transactions. 12 Drawn all-in-spread is a measure of the overall cost of a DIP loan as done in Strahan (1999), Dennis et. al. (2000), Hubbard et. al. (2002), and Hellman, Lindsey, and Puri (2008) among others. AIS is defined in DealScan as the sum of coupon spread, annual fees, and upfront fees expressed as a markup over LIBOR. 13 After requiring that transactions have a complete set of data for the regression analysis, the final sample is comprised of 551 DIP loans. Appendix A gives a full description of all variables used in the study. As a robustness check, we compared the omitted sample of 419 transactions with our final sample in terms of several key variables such as loan amount, and maturity and found no significant differences between the two samples. Thus, it is unlikely that the results provided in this paper are affected by the sample filters.
11
The first DIP loan recorded in the database is in 1988. Other papers by Chatterjee et. al. (2004) and Dahiya et. al. (2003), for example also have 1988 as the starting time of DIP loans. However, Carapeto (2004) period starts in 1986 with 3 DIP firms in 1986 and 2 in 1987 but these are not firms listed on DealScan database. 12 In the LPC dataset for DIP loans, about 320 transactions do not have a drawn all-in-spread. 13 If a LIBOR is not quoted, DealScan uses a minimum spread and then applies a LIBOR differential based on the spread used [LPC (94), page 19]. See also Hubbard et. al. (2002) footnote 7 for this procedure.
18 V. The DIP Lending Market 5.1. Sample description In this section, we present a brief description of the sample used in this study. Table I presents the distribution of DIP loans over time and across industries. Table I about here As shown in panel A, the market for DIP has significantly grown from 5 loans totaling $320 million in 1988 to 66 loans totaling $15,319 million in 2004 with the peak in 2002 totaling $23,130 million. Just the last four years account for about 46 percent of the DIP loans in the sample. A similar trend has been documented in Dahiya et. al (2003) and others. There does not appear to be a systematic upward trend in the mean loan amount over time except that since the late 1990s loans are about double the size of the early years. Panel B of Table I presents the distribution of DIP loans by industry and shows that DIP loans are concentrated in the manufacturing and retail industry with 38.66% and 27.40% of the sample respectively. 5.2. Borrower size effects in DIP lending Carey et. al. (1998) and to some extent Billett, Flannery, and Garfinkel (1995) find that different types of lenders tend to deal with different borrower classes. Similarly, Houston and James (1996) find differences in lending practices based on firm size. Thus, in this paper, we investigate whether there is a borrower size effect in DIP lending. Borrowers are partitioned into three equally ranked groups based on sales: small, medium
19 and large size firms. 14 Table II presents an analysis of several contract and borrower characteristics for each of these three groups. Table II about here In terms of contract characteristics, there appears to be some significant differences across size groups. The mean size of DIP loans for small borrowers is $56.5 million, which is significantly smaller than the mean loan size for medium and large borrowers ($114.56 and $367.14 million respectively). Another contrasting component of contract terms is the spreads (cost) of DIP loans across firm size. The mean AIS are 420 basis points (bps) for small size borrowers, 386 bps for medium borrowers, and 350 bps for large size borrowers. This hierarchy of AIS is similar to the empirical findings of Hubbard et. al. (2002), Strahan (1999), and Blackwell and Winters (1997). While the mean value for maturity is relatively the same across borrower classes, the short maturity of DIP loans for small firms is reflected in the fact that 45% of the loans are revolvers greater than one year while 68% of the DIP loans to medium and 60% of the DIP to large firms are revolvers greater than one year. Also, about 69% of the DIP loans to small firms are syndicated in contrast to 84% and 91% syndication rate for loans to medium and large firms, respectively. Thus, we will use dummy variables in our regression analysis to capture these differences. The second panel of Table II describes borrower characteristics for each size group. There does not seem to exist significant differences in borrowers’ observable risk
14
Borrower size is based upon each year of sales. Borrowers are divided into terciles based upon the level of sales each year with the top tercile designated as large borrowers, the middle tercile as the medium borrowers and the bottom tercile as the small borrowers. This allows each borrower to be designated as large, medium and small, respectively.
20 characteristics as measured by the leverage ratio (LEV) which is total debt to total assets. Using debt credit ratings is not likely to help us in distinguishing credit risk effects, as the average borrower in each group is not rated (NR). However, there is a distinction in DIP loan amount to total liabilities, (DIPLEV), which represents a non-traditional measure of leverage. 15 This measure declines monotonically from 54%, 28% to 20%, respectively for small, medium and large firms. Possible measures of borrower’s informational opacity are, the ratio of EBITDA to sales (CFSA), sales to total assets ratio (SATA), and the firm’s tangible assets to total assets ratio (PPETA) all of which are presented in this section of the table. The table reveals that medium size firms have the lowest level of CFSA compared to small and large borrowers. We find for the average small firm, the DIP loan represents 48% of sales to total assets while for the medium and large firm the percentage is 35% and 40% respectively. PPETA is highest among medium size firms compared to small and large firms. A potential collateral measure is the ratio of receivables and inventory to total assets (RECINVTA) and this ratio is also differs across borrowers’ size suggesting smaller firms are required to hold more marketable collateral than larger firms. The last panel of the table presents an analysis of lender’s characteristics. There are some slight differences in the percentage of firms in each size group that receive loans from a lender ranked in the top 25% of DIP lenders. However, we do observe a relation between type of lender and size of borrower. Thirty nine percent of small borrowers obtain a loan from a non-bank company (defined as a finance, an investment, or insurance company) while only 15% of the large borrowers obtain a loan from a non-bank company. 15
We also investigated one more additional measures suggested by the referee, the DIP loan amount to EBITDA. This is a measure which focuses on the cash flow of the firm. Our results for this measure was qualitatively the same as the debt measure but we focus on the debt measure for ease of exposition.
21 Thus, large borrowers receive DIP loans from banks in much larger proportion than medium or small borrowers. Similarly, large firms obtain DIP loans from large lenders in higher proportions than small firms. DIP loans for small firms are associated with smaller lenders. In addition, we include the number of lenders, the market concentration of the lead lender as measured by the Herfindahl index, and two key economic characteristics. DIP loans to large borrowers have on average 6 lenders while the number of lenders for loans to medium and small borrowers is 4 and 2, respectively. Large borrowers are also associated with lead lenders and a higher level on market concentration. For the full sample quarterly GDP growth average 0.74% and the slope of the yield curve is 191 basis points during the sample period.
5.3. Lender Characteristics in the DIP Lending Market Extant research shows that the type of lender, its size, and its ability to more efficiently monitor the borrower are important determinants of credit quantity, quality and pricing in particular for the corporate revolving credit market. We use a four-way classification of lenders in this section: banks, non-banks, large, and small lenders. Extant literature hypothesized that banks are unique in monitoring. For example, Mester et. al. (2003) finds that banks are able to gain an information advantage over other type of lenders through checking accounts that in turn are used to monitor accounts receivables and inventories. Many other papers presenting this argument are comprehensively discussed in Gorton and Winton (2002). Thus, our first classification for lender type in our
22 analysis is bank versus non-bank lenders. Carey et. al. (1998) have used similar classification in their analysis. Our second classification of lenders is between small and large lenders. Marquez (2002) shows that an increase in bank’s capacity lead to an increase in the banks’ informational advantage. Larger lenders are more likely to lead the front on technological progress, e.g. sophisticated credit scoring systems and data processing capabilities. There is also an extensive amount of papers arguing that large organizations favor the use of “hard” information resulting in a preference for lending to large firms. A full discussion of these issues is presented in Berger, Demsetz, and Strahan (1999) and Gorton and Winton (2002). Thus, we use lender size (large vs. small) to reflect economies of scope and the intermediaries’ efficiency in the production of information and monitoring activities. A lender is classified as large (small) if total assets are greater (smaller) than the median value of total assets for the sample year. 16 Results are presented in Table III. Table III about here We find that DIP loans by bank lenders relative to DIP loans by non-banks lenders are in general larger with shorter maturities and lower spreads, but are syndicated more often. Further, non-banks and banks seem to lend to firms with similar levels of secured loans. The traditional leverage ratio (LEV) is similar across all four lender dimensions and ranges between 94% and 99%. However, the ratio of the DIP loan amount to total liabilities (DIPLEV) of borrowers for banks is 24% which contrast to the ratio for 16
Total assets values are obtained from Bank Holding Company Y9, Bank Call Reports, Disclosure, SEC filings, and Lexis-Nexis database. When total assets data was not found, we assumed that the lender was too small to be reported and we classified it as such. We conducted a difference in mean test for sales and other variables in our sample between the group with total assets below the median and the ones missing total assets and found no significant difference. Thus, we are confident that our classification of lenders with missing information on total assets as small lenders is appropriate and robust.
23 borrowers of non-banks at 57%. This same pattern is also revealed between small and large lenders as smaller lenders are associated a higher DIPLEV of 45%, while large lenders have a DIPLEV of 27%. We find that banks tend to lend to firms with slightly better cash flows (CFSA). Our other measures for information opacity of the firm reveal a slightly smaller percentage for banks relative to non-bank lenders. Also, loans to small firms represent 47% of the loans by non-banks and 29% of the loans by banks. The percentages for loans to medium firms are 34% and 33% and for large firms are 20% and 38%, respectively. Similar conclusions are obtained for large vs small lenders. DIP loans by large lenders are larger loan amounts with shorter maturities and lower spreads, but also syndicated more often. However, in terms of collateral, small lenders require a large percentage of loan secured compared to large lenders. This would support our earlier inference that small lenders have low-informational production capabilities and may require more collateral to compensate for this disadvantage. Our measures for informational opacity show that small lenders lend primarily to the more profitable firms and firms with less information opacity given that CFSA is higher for small lender relative to large lenders. In summary, this analysis indicates that borrower characteristics and lender dimensions play a significant role in the DIP loan market. We argue that lending specialization is the result of both borrowers’ information asymmetries along with the financial intermediaries’ capabilities for the production of information and monitoring activities. We next explore this possibility using a multivariate analysis.
24
VI. Multivariate Analysis 6.1
A model for lender choice and DIP loan pricing Prior research on corporate revolving lending has mainly focus on two separate areas:
choice of lender and loan pricing. Typically, in a lender’s choice model, the decision is a function of a set of borrower’s observable risk, information proxies, and a set of time and industry control variables [Denis and Mihov (2003) and Carey et. al. (1998)]. In a typical loan pricing model, loan spreads are a function of a set of loan and borrower characteristics (observable risk and information effects) as in Strahan (1999), Shockley and Thakor (1997) or as in Ortiz-Molina and Penas (2005) or Dennis et. al. (2000) using simultaneous equations for collateral, maturity, and loan pricing. 17 However, the choice of lender and the loan pricing are inter-related. For example, Yasuda (2005) argues that firms with low reputation (more information-sensitive firms in our case) choose banks based on the bank’s ability to monitor and produce information. Staten, Unbeck and Gilley (1989) propose a theory of self-selection in credit markets and Barron and Chong (2003) provide a theoretical model in which borrowers self-select into seeking financing from banks and finance companies. To overcome this selectivity bias problem, we use an extension of the two-stage estimator developed by Heckman (1979) for the DIP loan pricing model resulting in a two-stage least-square (2SLS):
17
Li* = β1 Z’i + εi
(Lenders Choice Model)
Si = β4X’i + δ1λ1 + μi
(DIP Loan Pricing Model)
Other researchers that used simultaneous estimations are Booth and Booth (2006) and Brick and Palia (2005) for collateral and yields.
25 Li = 1 if Li* > 0 and Li = 0. Li* is the unobservable benefits from the lender’s decision to lend, Li is the lender’s observed choice, Zi is a vector of variables determining the lender decision. Si is the observed drawn all-in-spread, and Xi is a vector of variables determining the loan pricing. Also, εi and μi are normally distributed disturbances. Similar to Lu (2007) in the first-stage, we regress the type of lender (probit regression), on a set of control variables. This specification takes into account that the lender’s decision is endogenous and LAMBDA1, λ1 (the inverse mills ratio produced by the lender’s decision model) provides a measure of the marginal effect of information processing capabilities of different financial intermediaries on loan spreads. This allows us to explicitly control for the interdependence between the type of lender in the DIP loan pricing model using a two-stage least-square procedure similar to Brick and Palia (2007), Booth and Booth (2006), and Dennis et. al. (2000).
6.2 Empirical Results 6.2.1 Lender’s Choice model One of the main focuses of the paper is to investigate lending specialization in the DIP financing market and therefore attention is placed on the first model. The functional form for the lender decision equation is: DBANK = α0 + β1 (DLARGE) + β2(NUMLEND) + β3(DSYND) + β4(RANK25) + β5(HI) + β6(LSALES) +β7(PPETA) + β8(CFSA) + β9 (RECINVTA) + β10 (LEV) +β11(DIPLEV) + β12(DRATING) + β13(LSIZE) + β14(MAT) + β15(DSEC) + β16(DTERM) + β17-25(DIND1-DIND9) + β26-44(D88-D04) + εi .
(1)
26 DBANK is a variable that captures the choice of lender or lending specialization. Even though we have used so far two dimensions of lender to capture the information capabilities of the lender, we only use the banks versus non-bank lenders category because overwhelmingly most of the large lenders are likely to be banks. In this case DBANK is a dummy variable that takes the value of one if lender is a bank and zero if is a non-bank institution. Results of the estimation of equation (1) are presented in table IV.
6.2.1.1 Hypothesis 1 To test this hypothesis, we use a set of lender characteristics represented by the the size of the lender (DLARGE), the
number of lenders (NUMLEND), syndication
(DSYND), an indicator of the top 25 lead arranger (RANK25), and the Herfindahl Index of the lead lender (HI). We use DLARGE to capture the lender’s infrastructure in information processing. Another area of lender’s importance is syndication. Sufi (2007) finds a positive relation between firm information levels and the structure of the syndicate. He finds that the lead arranger selects the participants based on the participant’s familiarity with the borrowing firm, which suggests that the loan syndicate conducts an active search to reduce information asymmetry. Thus, we use a dummy capturing whether the DIP loan is syndicated, DSYND, and the number of lenders, NUMLEND, to capture the relation between borrower’s information sensitivity and lender’s characteristics. 18 The last two
18
Firms without developed reputations (more informationally opaque) are less likely to be funded through a syndicate. Dennis and Mullineaux (2000) and Lee, Whi, and Mullineaux (2004) uses syndication of a loan as a measure of borrower’s transparency. More transparent borrowers should have lower information costs and thus lower DIP loan fees.
27 variables capture the impact of lead arranger reputation and lead lender concentration, respectively. Our results in Table IV show that the lending characteristics of banks are significantly different than those of non-bank lenders. Large lenders are associated with banks capturing the bank’s information processing ability and a top 25 lead arranger is less likely to be a bank. More importantly, banks use more lenders in their syndicates to handle the informational asymmetries of DIP loans. Syndication is also more often found in banks than in non-bank lenders as banks are more dependent upon the syndicate structures to handle information asymmetries in DIP loans. Banks also, relative to non-bank lenders, use syndicates with greater lead lender concentrations. These results are consistent with the view that lending specialization may be induced by lender characteristics.
6.2.1.2 Hypothesis 2 Like Carey et. al. (1998), Strahan (1999), and Ortiz-Molina and Penas (2005), we use log of sales, LSALES, as measure of firm size to proxy for asymmetric information (opacity) of the firm. Similarly, Shockley and Thakor (1997) uses this variable to proxy for how “well known” the firm is - a measure of firm’s transparency. Because there is no consensus on a proxy for information opacity, we also use two additional variables in an attempt to measure effects not captured by LSALES. The ratio of property, plant, and equipment to total assets, PPETA, may also proxy for information as tangible assets may capture the firms’ degree of asset opaqueness (Strahan (1999) and Himmelberg and Morgan (1995)). The other variable is the ratio of cash flows to sales, CFSA. While some researchers have used CFSA to proxy for observable risk as it relates to the probability of
28 bankruptcy, DIP firms are already in bankruptcy. For DIP firms, CFSA may instead capture the probability of emerging from bankruptcy or the extent of the DIP firm’s economic viability. In this sense, CFSA captures the level of uncertainty or information opacity as to the likelihood of the firm to emerge from Chapter 11. We also include RECINVTA (Accounts receivable plus inventory divided by total assets) to capture additional informational value of typical collateral on DIP loans. From the results in Table IV, we observe a significant relationship between our measure of information opacity and the type of lender. The coefficient for LSALES is significant at the 5 percent level. The coefficients for PPETA, CFSA, and RECINVTA are not significant suggesting these two variables are not able to capture any additional information effects beyond those captured by LSALES. The results provides support to the view that there exists lending specialization in private markets (banks vs. non-bank lenders) based on information considerations. Banks have a greater propensity to lend to large borrowers as larger firms are in general more transparent and likely to borrow from a bank, while non-bank lenders have a greater propensity to lend to small borrowers.
6.2.1.3 Hypothesis 3 We use three variables to capture the role of credit risk on lending specialization in the DIP market. Traditionally credit risk is measured with credit ratings and/or leverage ratios. Only a small fraction of DIP loans have bank loan credit ratings and a significant proportion (83%) of the DIP firms in our sample do not have firm credit ratings and the rest have a D rating 19 . Even under this type of scenario, it is still possible
19
There were several cases where the reported rating in LPC was a B rating. Since we were not able to confirm this rating, we dropped these observations.
29 to have information about the credit quality of the firm by analyzing the unrated nature of the sample. For example, Haung and Ramírez (2008) find that even though most convertible debt is unrated, the use of an unrated dummy variable help explain the impact of credit quality on the choice of security, markets, and offering yields. Thus, we use a dummy variable, DRATING, that takes the value of zero if the firm has a D rating and one if the firm does not have a rating. We also use total debt to total assets ratio (LEV), as in previous studies, to proxy for the traditional credit risk measure. In addition, we investigate the impact of a nontraditional credit risk measure, DIPLEV. DIPLEV is the DIP loan amount divided by the total liabilities and thus more likely to better capture the impact of the loan on the firm’s leverage – a perhaps more significant credit risk proxy for DIP financing. The coefficients for LEV and DRATING are not significant at the 5 percent level suggesting that there is no credit risk segmentation in DIP lending markets. In addition, the coefficient for DIPLEV is also not significant at the 5 percent level confirming the absence of credit risk segmentation. This result is contrary to Carey et. al. (1998) who find that observable risk proxies have predictive power of the lender type. 6.2.1.4 The role of Contract Terms as Control Measures Rajan and Winton (1995) argue that maturity is used by lenders as a way to handle information asymmetries - small and lesser known (more informational opaque) firms use short-term loans and well known firms use long-term loans. Thus, to the extent that lending practices are determined by the lender’s information opacity, maturity and lender choice are related. We would expect a positive relation between bank and maturity. Booth and Booth (2006) argue that collateral is related to credit risk and to the level of
30 information opacity of the firm. Manove and Padilla (1999, 2001) and Jimenez and Saurina (2004) argue that lenders may ask for collateral as a condition to provide a loan as an alternative to screen and evaluate the borrower. Similarly, in Rajan and Winton (1995)’s model, firms which requires extensive monitoring are more likely to be asked by lenders to provide collateral. Thus, lenders with a lower level of expertise and/or scarce resources to evaluate the borrower will have more incentives to use collateral as a substitute for firm monitoring. We expect that banks would have a higher level of expertise and more resources to evaluate the borrower and therefore expect a negative relation between collateral and the likelihood of bank as lender. Correspondingly, we use MAT (loan maturity), and DSEC (dummy capturing collateral) as control variables in the lender’s decision. Also, loan size (LSIZE) is one of the often used non-price terms and therefore we control for it in the probit regression. Revolving lines of credit and short-term facilities are more monitoring intense than term loans, thus we control for the type of loan using a dummy variable, DTERM, that takes the value of one if the DIP loan is a term loan and zero otherwise. In summary, we find that contract design terms are in general not significant determinants of the lender choice as shown by the coefficients of MAT, DSEC and DTERM variables. We also present evidence that credit risk is not a significant determinant of lending specialization in the DIP market consistent with Hypothesis 3. We do find evidence that lending specialization may be determined by the information considerations of both the borrower and the lender. Consistent with Hypothesis 1 we find that the profile of the lender has an impact on the type of lender and consistent with
31 Hypothesis 2, we find that the information opacity and size of the borrower has an impact on the type of the lender.
6.2.2
Loan pricing model Chatterjee et. al. (1997) document that DIP loans are mostly revolving lines of
credit for working capital and Berger and Udell (1995) point out that lines of credit are relationship lending where information considerations are most important. Thus, we compare our study of the DIP lending market with existing research on corporate revolver lending. Based on the work of Hubbard et. al. (2002), Dennis et. al. (2000), Strahan (1999), Blackwell and Winters (1997), Shockley and Thakor (1997), and Berger and Udell (1995), among others, on the determinants of loan pricing and in particular revolver loans, we use the following model to represent the loan pricing equation: AIS = α0 + β1(LAMBDA) + β2(HI) + β3 (LSALES) + β4 (PPETA) + β5 (CFSA) + β6(RECINVTA) + β7 (SATA) + β8 (LEV) + β9 (DIPLEV) + β10(DRATING) + β11(LSIZE) + β12(MAT) + β13(DSEC) + β14(DTERM) + β15 (GROWTH)+ β16(SLOPE) + β17-25(DIND1-DIND9) + β26-39(D88-D04) + wi Loan spreads measured by the drawn AIS represents the observed component of the price of a DIP loan. Observable risk and information proxies, non-price loan terms, and control variables are defined in previous equations. 6.2.2.1 Hypothesis 4 The variable of particular interest to us is, LAMBDA1, λ1, our proxy for effects of information processing capabilities of banks and to some extent borrower’s information considerations on loan spreads. This coefficient captures the private information revealed
32 by a bank (an indication of whether self-selection bias concerning the type of lender) and the impact of lender choice on loan pricing. As extensively presented in the literature, informational considerations are recognized to determine both the dynamics of lending specialization and the pricing of loans. In particular, as shown in the model of Dell’Aricia and Marquez (2004), spreads on bank loans are directly related to the borrowers’ level of information asymmetry. Thus, we expect a negative relationship between LAMBDA1, λ1, and drawn AIS indicating that DIP loans made by banks carry lower drawn AIS than DIP loans made by non-bank companies. We also use the Herfindahl Index of the lead lender, HI, to capture the impact of lead lender concentration of loan pricing. Also, to investigate the impact of lender size we use DLARGE as a control variable in the bank only and nonbank only subsamples. As in the choice model, we use LSALES, CFSA, and PPETA as measures of firm’s asymmetric information. In addition, we include two financial variables to capture other possible firm characteristics associated with the loan pricing. We use RECINVTA to capture the collateral (asset-based lending) potential of the borrower 20 and SATA to capture firm’s profitability. Most extant research includes a battery of financial ratios, firm characteristics, and credit ratings to proxy for borrower’s risk. Many of these variables are often not statistically significant [See Blackwell and Winters (1997), Berger and Udell (1995), to some extent Carey et. al. (1998), and Straham (1999)]. What has consistently shown to be significant are the leverage of the firm, and credit rating indicators. We segment our credit
20
This variable could also proxy for information or credit risk. Mester, Nakamura, and Renault (2003) find that banks use checking accounts to monitor accounts receivable and inventories and RECINVTA may capture the potential exclusive access to this information by banks.
33 risk indicators into traditional (DRATING, LEV) and nontraditional measures (DIPLEV) to proxy for borrower’s credit risk. Dennis et. al. (2000) considers the endogeneity of maturity, collateral, and loan spreads. However, we argue in this paper that because DIP loans are of very short maturity and almost all have collateral, there is no econometric need to have a simultaneous system of equations 21 . Hubbard et. al. (2002), Dennis et. al. (2000) and Strahan (1999) argue that lenders limit their potential exposure and risk by limiting the loan amount; shortening the maturity of the loan; and requiring collateral (secured loans). 22 Therefore, we use LSIZE (loan size), MAT (loan maturity), and DSEC (dummy capturing collateral) to proxy the lender’s contractual controls. From extant literature, we expect LSIZE, and MAT to be negatively and DSEC to be positively related to AIS. Second, loans take the form of term loans, revolving credit lines, 364-day facilities, and other tranches. Revolving facilities have significant lender’s exposure uncertainty (takedown risk) but the shorter term portion of the facility is not subject to regulatory capital requirements while term loans typically have longer maturities, all of which impact the cost of the loan (See Moerman (2005)). Thus, we use a dummy variable, DTERM, that equals one if loan is a term loan and zero otherwise. As a final observation, market conditions may also play a role in the determinants of loan pricing and should not be overlooked. Petersen and Rajan (1994) find that interest-rate variables and bank concentration ratios are significantly associated with loan rates. Berger and Udell (1995) also use several macroeconomic variables to control for 21
In an earlier version of this paper we investigated an endogenous model similar to Dennis et. al. (2000) and our results did not change qualitatively. A simpler model is presented as requested by an anonymous reviewer. 22 See Rajan and Winton (1995) for a theoretical model and Hubbard et. al. (2002), Carey et. al. (1998), and Berger and Udell (1990) for empirical evidence on the use of collateral to limit lender’s exposure.
34 market conditions. We will similarly use controls in our analysis. We include dummies for the year of loan issuance, industry, as well as SLOPE and GROWTH to control for macroeconomic events. As in Jimenez and Saurina (2004), we argue that economic variables may play a role in loan pricing as lenders anticipate the business cycle and the monetary conditions of the economy. Estimation results are presented in Table V. Table V about here Panel 1 reports the second stage estimation results for the DIP loan pricing model controlling for the endogenous type of lender (bank versus non-bank) and Panel 2 presents the OLS estimation as a robustness test. To investigate the lender size effect we present Panel 3 with the results of the second-stage estimation for the DIP loan pricing model for the bank only sub-sample and control for lender size with DLARGE. Similarly, we investigate lender size in Panel 4 with the empirical results of the second-stage for the nonbank only sample. Results from Panel 1 show that the type of lender has an impact on DIP drawn AIS (after controlling for loan, borrower and other lender characteristics) as indicated by the negative and significant coefficient for LAMBDA1 (at the 5 percent level). If a DIP loan is made by a bank, the drawn AIS on the loan is 28.74 basis points lower than if it was made by a non-bank lender. This provides support for Hypothesis 4. Also, we find additional support for the view that firm’s information opacity is a determinant of the DIP loan pricing. 23 In Panel 1, the coefficients for CFSA and PPETA are negative and significant at the 5 percent level. CFSA and PPETA are measures of
23
As a robustness test, we performed a variance components test of the drawn AIS. A variance components tests determines the contribution of each regressor to the total variance. For our sample we found that the information effects of the type of bank contributed more to the total variance than the credit risk measures. The leverage ratio and the ratings variable did not make any contribution to the variance of the drawn AIS. We take this as evidence that the informational effect of the type of lender play a significant role in pricing DIP loans. The results are available from the author upon request.
35 firm’s asymmetric information and the significant negative coefficients of 91.90 and 60.41 basis points, respectively, suggest that the firm’s information opacity contributes to DIP loan pricing in an economically significant manner. Further, the nontraditional credit risk measure, DIPLEV is positive and significant at the 5 percent level. We also find that the market concentration of the lead arranger does have a significant negative impact on DIP loan pricing. This is consistent with Brick and Palia (2007) who argue and find that banks in more concentrated markets may charge a lower explicit rate. The variable SLOPE is also positive and significant at the 5 percent level, which is consistent with previous studies, indicating that lenders consider the term premium in their DIP loan pricing decision, while the quarterly GDP growth is not a factor. In Panel 2, we perform an OLS estimation as a robustness test using the dummy variable, DBANK. The coefficient for DBANK is also negative and significant at the 5 percent level. Accordingly for the OLS estimate, the drawn AIS on the DIP loan is -51.59 basis points lower if a DIP loan is made by a bank indicating there is a downward bias in the OLS estimate. Clearly, the endogenous nature of the lender is an important factor which must be controlled and accounted for. In our case, the economic bias from the endogenous nature of the lender is over 20 basis points, which is economically significant. We next investigate whether lender size is captured by the type of lender category. We perform a subset of OLS regression in Panel 3 and Panel 4 based on the bank and nonbank sub-samples, respectively. Each OLS regression estimates the DIP loan pricing model while controlling for the size of lender, DLARGE. The coefficient on DLARGE is
36 not significant at the 5 percent level for the DIP pricing model in each sub-sample. This provides evidence that banks possess unique information about borrowers that allows them to lend at a comparative advantage. In short, borrowers obtain significantly lower borrowing costs from different informational processing capabilities of banks.
VII. Conclusions The DIP lending market provides an excellent opportunity to study determinants of lending specialization (e.g. lender choice) and loan pricing in corporate revolving debt markets. We argue that given the impact on credit risk resulting from the legal environment of DIP financing, we are able investigate the role that information considerations play in the lending practices and pricing of DIP loans. We find a significant borrower and lender size effects wherein non-bank lenders tend to lend to smaller firms while banks lend to larger firms. We argue that this is the result of lender specialization based on information considerations. Further, we do not find any evidence of lending specialization based of the credit risk of the DIP borrowers. This result contrasts with the conclusions that ex-ante observable risk, and not informational considerations, are the differential effect leading to lending specialization in the corporate credit market. Further, using lender type to evaluate the impact of differential information among intermediaries on DIP loan pricing we show that the more information-savvy a lender is, the more likely it will provide DIP financing at lower costs. This informational advantage has a clear impact on DIP loan pricing as banks are able to offer lower DIP loan spreads than nonbank lenders.
Our results are consistent with the interpretation that banks provide
important and useful services. While our results provide evidence that is consistent with
37 lender specialization, these results can also arise if there are strong lender-borrower relationships which is not explored by us.
38 Appendix A Description of Variables AIS : The drawn all-in-spread. It measures the cost of the DIP loan. AIS is as reported in DealScan database which is the sum of coupon spread, annual fee, and upfront fee) measured as a mark-up over LIBOR. DSEC : a dummy variable coded 1 indicating that DIP loan is secured (as reported in DealScan database); 0 otherwise. DTERM : is a dummy variable coded 1 indicating that the loan was a term loan; 0 otherwise. LSIZE : is the log of the facility size. LSALES : is the log of firm sales as reported by LPC. MAT : is the maturity of the DIP loan. LEV : is computed as book value of total liabilities over book value of total assets DIPLEV : is the DIP loan amount divided by the total liabilities. DRATING : is a dummy variable indicating the long term debt rating of the reference. A dummy variable is coded 1 if the reference is not rated and 0 if the reference has a D rating. CFSA : is computed as EBITDA over sales. RECINVTA : is computed as receivable and inventories over book value of total assets. PPETA : is computed as property, plant and equipment over book value of total assets. SATA : is computed as sales over book value total assets. DSYND : is a dummy variable coded 1 indicating the loan is syndicated; 0 otherwise. RANK25 : is a dummy variable coded 1 indicating the arranger is a TOP 25 lead arranger; 0 otherwise. NUMLEND : is an ordinal number indicating the number of lenders as reported in DealScan HI : is the Herfindahl Index. GROWTH : is the quarterly growth rate of GDP. SLOPE : the slope of the default-free term structure of interest rates, measured as the difference between the ten year US Treasury bond and the three month US Treasury bill. DBANK : is a dummy variable coded 1 indicating the lead firm is a bank; 0 otherwise. DLARGE : is a dummy variable coded 1 indicating the lead lender is a large lender with assets greater that the median value total assets of the sample; 0 otherwise. LAMBDA1(λ1) : The inverse mills ratio is different in each regression and represents the degree of information processing that is possessed by each respective lender type. The IMR is calculated based upon the dummy variables DBANK and DLARGE. DIND1 : is a dummy variable coded 1 indicating the borrower is in Other Industries, 0 otherwise. DIND2 : is a dummy variable coded 1 indicating the borrower is in the Mining Industry, 0 otherwise. DIND3 : is a dummy variable coded 1 indicating the borrower is in the Construction Industry, 0 otherwise. DIND4 : is a dummy variable coded 1 indicating the borrower is in the Manufacturing Industry, 0 otherwise. DIND5 : is a dummy variable coded 1 indicating the borrower is in the Transportation Industry, 0 otherwise. DIND6 : is a dummy variable coded 1 indicating the borrower is in the Communication Industry, 0 otherwise. DIND7 : is a dummy variable coded 1 indicating the borrower is in the Wholesale Industry, 0 otherwise. DIND8 : is a dummy variable coded 1 indicating the borrower is in the Retail Industry, 0 otherwise. DIND9 : is a dummy variable coded 1 indicating the borrower is in the Finance Insurance and Real Estate Industry, 0 otherwise. DIND10 : is a dummy variable coded 1 indicating the borrower is in the Service Industry, 0 otherwise. D88-D04 : are respective dummy variables indicating the year of the DIP loan (1988-2004), respectively.
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45 Table I DIP Loan Characteristics-1988-2004
Panel A presents number of DIP loans, mean, and total loan amount by year. The total loan size represents the aggregate dollar volume for the each sample year. AIS is the drawn all-in-spread measured in basis points. The AIS distribution is characterized by a mean and standard deviation of 386 and 153 basis points, respectively. Panel B describes the number of DIP loans, the mean, and total loan size by industry category. The total loan size represents the aggregate dollar volume for the each industry category in Panel B. Panel A: # of DIPs
(%)
Mean
Total
AIS
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
5 7 19 46 17 16 15 32 23 19 25 23 53 67 74 44 66
0.91% 1.27% 3.45% 8.35% 3.09% 2.90% 2.72% 5.81% 4.17% 3.45% 4.54% 4.17% 9.62% 12.16% 13.43% 7.99% 11.98%
$ 64 20 210 76 92 102 46 99 52 229 131 285 134 220 312 142 232
$ 320 137 3,990 3,505 1,564 1,638 697 3,170 1,186 4,359 3,269 6,562 7,120 14,763 23,130 6,255 15,319
363 445 454 439 430 350 341 360 397 293 288 295 343 358 401 411 466
TOTAL
551
100%
$176
$96,984
386
Panel B: # of DIPs Mining Construction Manufacturing Transp/Utilities Telecomm. Wholesale Retail FIRE Services Other
6 14 213 51 20 15 151 8 63 10
(%)
Mean
Total
AIS
1.09% 2.54% 38.66% 9.26% 3.63% 2.72% 27.40% 1.45% 11.43% 1.81%
$150 65 146 292 498 217 187 134 98 54
$897 903 30,991 14,930 9,950 3,249 28,277 1,070 6,180 538
413 521 388 419 347 296 362 294 442 275
46 Table II Descriptive Statistics by Borrower Size Groups This table presents mean and median values (in parenthesis) for several key variables for the DIP Small
Medium
Large
All
Contract characteristics Facility size Maturity Drawn AIS Fraction secured Fraction syndicated Revolver > 1 yr Revolver < 1 yr Term Loan Other Loan Type Sample Size
$56.5 ($30.6) 18 (12) 420 (400) 82% 69% 45% 34% 14% 7% 191
$114.56 (100.0) 18.6 (18) 386 (350) 75% 84% 68% 16% 6% 10% 182
$367.14 (250.0) 18.4(18) 350 (350) 71% 91% 60% 20% 7% 12 178
$176.02 (96.7) 18 (17) 384 (350) 76% 81% 57% 24% 9% 10% 551
Borrower characteristics Leverage Ratio Rating EBITDA to Sales: CFSA Sales to total assets: SATA PPE to total assets: PPETA Rec. and Inv. to total assets DIP loan amt. to total liab.
94% NR -1.19% 48% 14% 40% 54%
98% NR -6.65% 35% 25% 34% 28%
99% NR -1.39% 40% 19% 29% 20%
96% NR -3.06% 41% 19% 32% 34%
45% 56% 39% 4% 1% 40% 60% 2 6
50% 67% 29% 4% 0% 50% 50% 4 6
58% 80% 15% 4% 1% 80% 20% 6 14
51% 68% 28% 4% 1% 56% 44% 4 9
0.80% 173
0.81% 218
0.60% 184
0.74% 191
Lender Characteristics Top 25 Rank Banks Finance company Investment company Insurance company Large lender Small lender Number of Lenders Herfindahl Index Economic Characteristics Quarterly GDP Growth Slope of Yield Curve in bps
47 Table III Descriptive Statistics by Type and Size of Lender This table presents mean and median values (in parenthesis) for several key variables for the DIP analysis between January 1, 1988 and December 31, 2004. Descriptive statistics are presented by type of lender. Data are obtained from the LPC database. Dollar figures are in millions. Bank and Non-Bank lender indicates if the lead lender is a bank or non-bank lender, respectively. Large lender indicates the lead lender is a large firm defined as a lender with assets greater than the median value of total assets for the sample year, otherwise the lender is characterized as a small lender. Type of Lender Bank Non-Bank Lender Contract characteristics Facility size Facility size to Sales Maturity Drawn AIS Fraction secured Fraction syndicated Revolver > 1 yr Revolver < 1 yr Term Loans Other Loan Types Borrower characteristics Leverage Ratio: LEV EBITDA to Sales: CFSA Sales to total assets: SATA PPE to total assets: PPETA Rec. and Inv. to total assets DIP loan amt. to total liabilities % that are small firms % that are medium firms % that are large firms Lender Characteristics Top 25 Rank Herfindahl Index Number of Lenders
Lender Size Small Large Lender Lender
$194.99 ($100.00) 0.19 (0.12) 17.65 (17) 364.99 (350) 76% 85% 53% 27% 9% 11%
136.58 (75.00) 0.37 (0.15) 19 (18) 429.79 (375.00) 75% 72% 66% 16% 11% 7%
112.37 (50.00) 0.30 (0.13) 19.62 (16) 422.84 (400.00) 80% 72% 59% 20% 11% 10%
225.50 (1125.50) 0.20 (0.13) 17 (17) 357 (350) 72% 88% 55% 27% 8% 10%
97% -3.02% 43% 18% 34% 24% 29% 33% 38%
95% -3.15% 38% 21% 35% 57% 47% 34% 20%
94% -2.10% 46% 17% 36% 45% 47% 38% 15%
99% -3.80% 38% 21% 33% 27% 24% 29% 46%
47% 13 5
58% 0.04 3
48% 0.05 3
53% 15 5
48 Table IV First Stage Estimation of Probit Model This table presents the determinants of lender type for the full sample in first stage. In the second stage, the endogenous nature of lender type is controlled for and used as determinants in the AIS DIP pricing equation. Panel 1 shows the probit regression estimation of Lender Type using the DBANK dummy variable as the dependent variable. Fixed effects for each year and industry were included in the regression but are not reported to save space. See Appendix A for a full description of each variable.
Variables Intercept Lender Characteristics DLARGE NUMLEND DSYND RANK25 HI Information opacity LSALES PPETA CFSA RECINVTA Credit risk LEV DIPLEV DRATING Other controls INDUSTRY DUMMIES YEAR DUMMIES Fraction of Correct Predictions Psuedo R2
Panel 1 Lender Type: Banks vs Non-Bank Lender Coeff. -4.33
t-value 1.29
0.94 0.08 0.62 -0.39 0.96
5.16* 2.41* 2.30* 2.28* 2.57*
0.35 -0.49 0.36 0.13
3.53* 1.24 0.53 0.27
0.13 0.05 0.05
0.58 0.34 0.22
Yes Yes 83% 46%
49 Table V Second Stage Estimation of AIS DIP Pricing Model This table presents the determinants of the AIS DIP pricing model where we concentrate on the endogenous nature of the lender type and examine the impact of lender size within the bank and non-bank samples. We control for the endogenous nature of the lender type in the first stage and use the IMR, λi, , to assess the impact of the lender choice on AIS DIP pricing. We estimate an AIS DIP pricing equation using one classification of lenders, banks versus non-banks. We also present for robustness test the OLS regression which is without the endogenous effect of the lender type. The dependent variable in each multivariate analysis is the drawn AIS in basis points. Fixed effects for each year and industry were included in the regression but are not reported to save space. See Appendix A for a full description of each variable. Panel 1 Panel 2 Panel 3 Panel 4 With Without Endogenous Endogenous Bank Non-Bank Lender Lender Sample Sample Type Type Only Only Coeff. 569.64
tvalue 2.88*
Coeff. 563.46
tvalue 2.88*
Coeff. 381.99
tvalue 1.88
Coeff. 1031.19
tvalue 2.19*
-28.74 -1.12
3.02* 4.99*
---0.79 -51.59
3.40* 3.91*
-0.89
3.50*
-94.74
1.58
71.88
3.11*
73.35
3.21*
-16.13 23.55
1.02 1.31
-21.34 148.67
0.71 2.89*
-3.95 -60.41 -91.90 -13.07 -8.03
0.80 2.46* 3.19* 0.41 1.19
1.63 -67.41 -92.57 -7.24 -8.67
0.33 2.70* 3.22* 0.23 1.30
-4.75 -54.59 -91.33 -11.23 -5.52
0.71 2.24* 4.81* 0.36 0.73
13.29 -132.91 -129.49 -107.48 -77.57
1.26 2.71* 0.93 1.93 1.61
6.40 0.16 8.26
0.48 7.39* 0.59
7.04 0.14 4.37
0.52 6.33* 0.31
1.91 0.18 23.67
0.13 6.86* 1.81
25.69 6.24 40.31
0.91 1.07 1.01
-9.02 25.91 Yes
0.84 2.75*
-9.12 25.51 Yes
0.80 2.74*
-10.92 23.53 Yes
0.99 2.51*
-2.02 33.89 Yes
0.74 1.43
Variables Intercept Lender Characteristics IMR - λ1 HI DBANK DLARGE DTERM Information opacity LSALES PPETA CFSA RECINVTA SATA Credit risk LEV DIPLEV DRATING Economic Factors GROWTH SLOPE INDUSTRY DUMMIES YEAR DUMMIES Adjusted R2
30%
30%
40%
21%
F-Value Observations
6.96* 551
7.16* 551
7.22* 372
2.23* 179
Yes
Yes
Yes
Yes
