Pricing and Performance of Loans Bundled with Underwriting

Transcription

1 Pricing and Performance of Loans Bundled with Underwriting Yang Lu * Department of Finance Stern School of Business New York University Job Market Paper January 12, 2007 ABSTRACT Banks provide loans and underwriting services to the same corporate customer with increasing frequency. Previous literature finds that loans that are bundled with underwriting deals carry lower interest rates, consistent with either strategic behavior by banks ( pay to play ) or informational economies of scope. However, I find that there is no interest rate discount in bundled loans after adjusting for endogeneity arising from a bank s decision to manage its risk exposure to a client. My results support the story that banks choose to provide bundled lending and underwriting services to higher-quality customers. Tests of subsequent performance show that borrowers receiving bundled services perform better than other borrowers in terms of future changes in credit risk (proxied using KMV s distance to default measure and Altman s (1968) Z-score). Such borrowers are also significantly less likely to default or receive credit rating downgrades. The effects are stronger for smaller and unrated companies. Moreover, using a new database of secondary market loan prices, I find that bundled loans have better return performance in the secondary market. * Department of Finance, Stern School of Business, New York University, 44 West 4th Street, Suite 9-193, New York, NY Phone: ylu1@stern.nyu.edu. I would like to thank my Ph.D. committee members Anthony Saunders, Alexander Ljungqvist, Yakov Amihud, Kose John and Daniel Wolfenzon for constant encouragement and support. I also thank Edward Altman, Steven Drucker, Amar Gande, Bill Greene, Victoria Ivashina, Michael Roberts, Rik Sen, Andre de Souza, Ingo Walter, Johnathan Wang and seminar participants at NYU and FDIC for help and comments. Finally, I thank Brooks Brady, Steven Miller, and Matthew Sanderson from Standard & Poor s for help with the data. All errors are mine.

2 The appropriate scope of banking activities has long been controversial in both academic and regulatory circles. 1 The debate has been especially heated because of recent changes in the industry. Consolidation among banks in the late 1990s alongside the repeal of the Glass-Steagall Act in 1999 have increased banks ability to compete for corporate customers by offering them both commercial loans and investment banking services. For instance, between 1995 and 2004, the incidence of bundling loans with underwriting services tripled, from 5% to 15% of corporate loans. The fraction was even larger in terms of dollar value. This greater freedom raises the concern that commercial banks might discount their loans to support their investment banking affiliates, which might distort the competitive structure of underwriting markets. There are also concerns that in bundled transactions, banks might lower their lending standards and extend loans to otherwise unqualified clients in the pursuit of underwriting fees. If true, such behaviors could increase the risk of large defaults and potentially hurt the stability of the banking system. Despite its apparent importance to the banks, their customers, and the financial system, we know relatively little about the economic consequences of this aspect of banking deregulation. This paper examines these consequences by studying the pricing and performance of loans that are bundled with underwriting deals. The following questions from Congressman John D. Dingell to the Federal Reserve Board and the Office of the Comptroller of the Currency in 2002 illustrate the concerns: since it appears that credit is being offered as a loss leader by commercial banks to facilitate or leverage the extension of their investment banking business, what are the implications of such mispricing on the supply of and demand for credit? What are the implications of this underpricing for the financial health of the smaller banks who participate in these syndicated facilities? To what degree is this tying activity a cause of the increased losses being realized by large banks on loans to borrowers such as Enron who were 1 See Drucker and Puri (2005) for an excellent survey on this topic.

3 known to pay large investment banking fees? Is the pay to play practice leading to a concentration of bad credit risks among an increasingly smaller number of banks? What are the systematic implications of this distortion? These concerns are shared among corporate executives, 2 investment banks and the financial press. 3 To summarize, this tying or pay to play story suggests that banks charge lower interest rates for loans that are bundled with underwriting deals and might extend credit to otherwise unqualified borrowers, which could adversely affect the banks themselves as well as the whole financial system. 4 Another popular explanation for lower interest rates charged in bundled loans is the existence of informational economies of scope. When a bank jointly provides lending and underwriting services, it can use the same company-specific information on both fronts, thus reducing its information acquisition costs. If it so chooses, the bank can then pass on the cost saving to the customer in the form of a lower interest rate. While both the pay to play story and the informational scope economies story can potentially explain why banks offer discounted interest rates in bundled loans, previous studies that test their validity have ignored the possibility that bundling may not be exogenous. Specifically, what if some unobserved company characteristics that lead a bank to provide the bundled transaction also lead the bank to charge a lower interest on the loan in the bundled transaction? If we do not control for this selection, we might overestimate the effects of bundling on loan pricing. Therefore, it is quite possible that 2 See the survey conducted by the Association for Financial Professionals in For example, according to The Economist of January 9, 2003, many of the biggest banks have used cutprice loans to win lucrative business previously reserved for investment banks. 4 Although bundling does not necessarily imply tying, which is illegal for commercial banks, it is very hard to tell one from another in practice. For example, in its interpretation of section 106 of the Bank Holding Company Act Amendments of 1970 (see 68 Fed. Reg (August 29, 2003)), Federal Reserve gives the conditions under which bundling of lending and underwriting services constitutes illegal tying. However, these conditions are very hard to verify due to the lack of explicit tying agreements between bank and companies. This is probably the reason why the U.S. General Accounting Office (GAO) finds no evidence of the tying practices (see the report at 2

4 the interest rate discount charged in bundled loans might simply reflect the banks selection based on their private information. 5 Having laid out the selection story, next I would like to get a better idea about which unobserved company characteristics are most likely to affect both the bundling decision and the loan pricing. Since possessing these characteristics is associated with receiving lower interest rates, a natural candidate is probably borrower quality. It is reasonable to assume that bank select better quality clients for bundled services. People have found that when lending-relationship banks provide underwriting services to the borrowers, the underwritten securities are generally better priced (Kroszner and Rajan (1997) and Gande et. al. (1997), etc.). This is consistent with lending banks providing underwriting services to higher-quality borrowers. One of the reasons why client quality affects bank s bundling decision is that when a bank provides bundled lending and underwriting services to a customer, it leaves itself exposed to the same client on two fronts: it puts its financial capital at risk on the lending front, and it puts its reputation capital at risk on the underwriting front. If the borrower in question later performs badly, the bank will be impacted adversely on both fronts. Thus, due to the increased risk, it is reasonable to think a bank would be more careful in doing its due-diligence and would choose higherquality clients to whom to provide joints services. A bank assesses a client s quality based not only on public information but also on its private information, which is not observable to the econometrician. Say a bank has two clients with almost identical publicly observable characteristics, but the bank has more favorable private information about one company. As a result, the bank might provide bundled services to this client and charge a fair interest rate. However, this fair interest rate charged in the bundled loan will appear lower to us as econometricians, since we do not have access to bank s private information. Therefore, it is quite possible that the yield discount observed in prior work may then simply reflect the selection bias due to the 5 There is a close analogy between this story and Campa and Kedia s (2002) argument for the diversification discount. I argue that bundling is not exogenous and unobserved characteristics of borrowers both cause a bank to provide bundled loans and to charge lower interest. Thus, the interest discount in bundled loans might be due to selection bias. Campa and Kedia (2002) argue that firms self-select to diversify. Certain unobserved firm characteristics, which cause firms to diversify, also cause them to be discounted. Self selection may thus explain the diversification discount. 3

5 bank s private information about the unobserved higher-quality of bundled-loan clients. I call this the private information story or unobserved higher-quality story. The unobserved higher-quality story provides new predictions about the pricing of bundled loans and the quality of bundled-loan clients. Testing this story is not easy, since it involves testing for the effects of unobservables. Here I employ two strategies. The first strategy is to use an econometric method to explicitly correct for the bank s selection in its bundling decision. If the higher client quality hypothesis is correct, we would expect to see no interest rate discount after correcting for the selection. The second strategy examines the ex-post performance of bundled-loan clients or bundled loans; it is based on the simple intuition that a bank s private information regarding unobserved client quality will be revealed eventually. If banks provide bundled loans predominantly to clients for whom they have more favorable private information, then bundled-loan clients and bundled loans should perform better ex-post. In addition, ex-post performance analysis could also shed light on how bundling of lending and underwriting services affects the health and stability of the financial system. This is the very reason why bundling has attracted significant regulatory attention. Following previous literature, I define a bundled loan as a loan to a borrower that issues bonds or shares around the loan origination date and for which the lead lender acts as lead underwriter. To make an apples-to-apples comparison, I define a non-bundled loan as a loan with a security issue around the loan origination date that is underwritten by a financial institution other than the lead lender. Therefore, all loans in my sample have security issues around their origination dates, and the only difference between bundled loans and non-bundled loans is whether or not the lead lender acts as lead underwriter. My sample consists of 10,053 loan facilities from 1994 to 2004 covering 2,896 companies. Of these, 2,486 loans (25%) are classified as bundled. Using OLS, I reproduce the results in previous studies and find that bundled loans have lower yields than comparable non-bundled loans. However, the interest rate discount in bundled loans disappears when I control for the selectivity in the bundling using a treatment effects model. My instrument is based on time series and cross-sectional 4

6 variation in exogenous regulatory constraints on different commercial banks ability to underwrite securities issues. Moreover, I find that the coefficient of the correction term for selection, the inverse mills ratio, is negative and significant. This suggests that bank s private information about bundled-loan clients is negatively related to the interest rate. The unobserved higher quality of bundled-loan clients allows a bank to offer them bundled loans and to charge lower interest. Thus, the bundled-loan interest discount found in previous studies appears to be due to unobserved higher client quality, not to bundling per se. This finding is consistent with the unobserved higher quality story I propose. Examining the ex-post performance of borrowers that receive bundled services allows me to further discriminate among the three candidate explanations. If, as the pay to play story and anecdotal evidence suggest, banks take on bad credit or disregard high default probability for the sake of their higher-margin underwriting business, bundled-loan clients should perform worse ex-post compared to other borrowers. The informational economies of scope story offers no clear prediction about ex-post performance, for it does not take a stand on the quality of the customers involved. The unobserved higher quality story, however, predicts that bundled-loan clients should perform better ex-post since banks provide bundled service predominantly to their higher-quality clients; and higher quality should eventually be revealed in the form of better ex-post performance. To examine ex-post performance, I look at three different sets of performance proxies. The first two are the distance to default (DD) measure (based on the KMV-Merton model) and Altman s (1968) Z-score measure, which are popular proxies for credit risk. I find that after loan origination, the distance to default and Z-score measures improve for bundled-loan clients and deteriorate for non-bundled-loan clients. My third performance proxy is based on borrowers default rates and credit rating downgrade probabilities. Using Standard & Poor s default and rating migration data, I find that bundled-loan clients default less frequently and are less likely to receive rating downgrades than are non-bundled-loan clients in the sample. Superior performance among bundled-loan clients is consistent with the unobserved higher-quality story. Interestingly, these differences in ex-post performance are more pronounced among smaller and unrated 5

7 borrowers. This is consistent with the view that smaller companies and unrated companies are generally more informationally opaque; therefore, a bank s private information should play a bigger role in differentiating a good client from a bad client. Finally, the recent rapid development of a secondary market for syndicated loans provides an opportunity to study the ex-post performance of the loans directly. Using loan quote data from the LSTA/LPC secondary market price database, I compare the return performance of bundled loans and non-bundled loans using the cumulative abnormal return (CAR), buy-and-hold abnormal return (BHAR), and calendar-time portfolio methods. Each shows that bundled loans perform better than non-bundled loans. These results further support the hypothesis that banks provide bundled services predominantly to higher quality clients. This paper makes several contributions to the existing literature and to the public debate. First, the majority of previous papers on universal banking focus the study on underwritten securities. My paper is among the first few to study bundled loans, which are an important component of bundled transactions. Second, this paper takes into account a bank s selection based on its private information. This had been ignored in previous studies. That banks have private information about their clients is one of the main reasons banks are viewed as special (Fama (1985)). My results show that ignoring banks specialness may lead to incorrect inference. Third, this paper is the first to investigate how bundling affects the ex-post performance of loans. Better ex-post performance of bundled loans and bundled-loan clients is consistent with the conjecture that banks provide bundled transactions predominantly to higher-quality clients. This provides support for giving commercial banks more commercial freedom and suggests that concerns about the possible negative effects of bundling on the health of financial system seem unfounded. Fourth, my results add to the large credit risk literature by highlighting that when studying default risk, it is important to take into account whether a loan is bundled. This paper also adds to our understanding of the secondary loan market by identifying an important performance driver in this market. Finally, this study adds to the on-going discussion in regulatory circles and the academic literature concerning the practice of product-tying by universal banks. Once I account for the decision to bundle, I 6

8 find that there is no interest rate discount in bundled loans. Thus, there is little evidence of tying. The remainder of the paper is structured as follows. The next section briefly discusses prior related literature. Section II presents the data and sample construction. Section III presents the results for the pricing of bundled loans. Section IV presents the results concerning the ex-post performance of bundled-loan clients. Section V looks at the expost performance of bundled loans in the secondary market. Section VI concludes. I. Literature Review Theoretical papers about bundling lending and underwriting services (e.g. Kanatas and Qi (1998, 2003), Puri (1999) and Rajan (2002)) model the tradeoff between the costs and benefits of providing joint services. I briefly summarize their main points. Joint provision of lending and underwriting services has three main potential benefits. The private information banks collect through the lending relationship can be used to certify the borrower s value to the public market. This helps mitigate adverse selection problems, possibly allowing the firm to sell its securities at higher prices. This is often referred to as the certification hypothesis. Second, using the same information for different products allows a bank to achieve informational economies of scope. Third, tying lending to underwriting by discounting loans may benefit the bank through expansion of its investment banking business. On the cost side, the literature has focused on potential conflicts of interest. Chiefly, when a bank has negative private information about a firm, it may help the firm issue public securities to repay its outstanding loans. Most of the empirical work has focused on testing the certification hypothesis against the conflicts of interest hypothesis. Researchers have used data from before the 1933 Glass- Steagall Act (which separated lending and underwriting) and from the late 1980s (when Glass-Steagall constraints began to be eased) to examine the ex-ante pricing and ex-post performance of underwritten securities. Puri (1996), Kroszner and Rajan (1997), Gande et. al. (1997), Roten and Mullineaux (2002), and Schenone (2004) investigate how prior lending relationships affect the ex-ante pricing of underwritten public securities, such as 7

9 corporate bonds and IPOs of equity. Ang and Richardson (1994), Kroszner and Rajan (1994), and Puri (1994) examine how lending relationships affect the default probability of corporate bonds. Benzoni and Schenone (2004) examine the long-run performance of equity offerings underwritten by lending-relationship banks. In general, these papers find little evidence supporting the existence of conflicts of interest. Securities underwritten by relationship banks are generally priced no worse and sometimes better than similar issues by non-relationship banks. Overall, public securities underwritten by relationship banks perform better than those underwritten by non-relationship banks. In addition, previous studies also investigate whether bundling of lending and underwriting services affects underwriting fees. Sufi (2004) and Drucker and Puri (2005) find that banks charge lower underwriting fees when they jointly provide lending and underwriting services. Nearly all these empirical papers analyze the underwriting part of the deal. Few have examined key issues about the loan part. Noted exceptions include Brav et. al. (2006), Calomiris and Pornrojnangkool (2006), and Drucker and Puri (2005). Brav et. al. (2006) compare loans issued right after an IPO or an SEO with other loans and find no interest rate differential between them. Note that given their focus on potential risk explanations for long-run underperformance following equity issues, Brav et al. do not require that the same bank provides the lending and underwriting services; thus the underwriter may not be the lender. However, to test the stories outlined in the introduction, in this paper I examine cases where the lending and underwriting services come from the same bank. Calomiris and Pornrojnangkool (2006) investigate how the banking relationships that combine lending and underwriting services affect the terms of lending and the underwriting costs. They find that banks price loans and underwriting services in a strategic way to extract value from their relationships. Drucker and Puri (2005) investigate cases where banks jointly provide lending and SEO underwriting services to the same customer around the same time. They use the propensity score matching method to compare the spreads of bundled loans with those of other loans. They find bundled loans have lower interest rates than other loans. One important limitation of these studies is that they ignore a bank s selection based on its private information. If the selection of 8

10 clients for bundled transactions is not random, then one cannot say for sure how bundling affects loan pricing without adjusting for the selection carefully. The secondary loan market has grown dramatically in recent years. However, relatively few studies have used secondary market loan data. Altman, Gande and Saunders (2004) compare the informational efficiency of the secondary loan market with the bond market by checking the market reaction to news events like bankruptcy and default, and find that the loan market is informatively more efficient. Allen and Gottesman (2005) investigate the informational efficiency of the loan market compared to the equity market, and find the equity market and syndicated loan market are highly integrated such that information flows freely across markets. Moerman (2005) investigates how the information asymmetry and financial reporting quality of a company affect the bid-ask spread of its loans in the secondary market. She finds that bid-ask spread is positively related to information asymmetry and timely incorporation of economic losses into financial statements reduces the bid-ask spread. II. Data and Sample A. Sample Selection and Definition of Bundled Loans My dataset combines data from different sources. Loan information (such as borrower identity, lenders, origination date, yield spread, amount, maturity, loan purpose, loan type, and borrower credit rating) comes from the Loan Pricing Corporation s (LPC) DealScan database. Secondary market data for syndicated loans are from the Loan Syndications and Trading Association (LSTA) and LPC mark-to-market pricing service. Underwriting information (such as issuer identity, underwriters, issue date, and security type) comes from Thomson Financial s Securities Data Corporation (SDC) Platinum database. Rating migration and default data are from Standard & Poor s Credit Pro database. I also use CRSP and Compustat to retrieve relevant company information. Linking the different databases together is not an easy task, especially since the loan databases only have 9

11 borrower names as the identifier. 6 Therefore, I carefully hand-match the borrowers in Dealscan to the issuers in SDC, and then I match to the companies in Compustat/CRSP. My sample period runs from 1994 to The main reason for this is data availability. The earliest loans that show up in the secondary market loan database were originated in Moreover, the loan data in Dealscan became comprehensive after These are major reasons why my sample period begins in That there were few cases of bundled lending before 1994 should allay any concern regarding my sample start time. I use the following definition to capture instances in which a bank bundles lending and underwriting services and jointly provides them to a customer. If a bank gives a solelender loan or leads a loan syndicate and also underwrites a security issue for the same company in the time period from one year before to one year after the loan origination date, I classify the loan as a bundled loan. Definition of the comparison group (i.e. non-bundled loans ) is very important to get meaningful inference. To compare bundled loans with stand-alone loans is not fair in the sense that borrowers that also issue securities around the loan may be fundamentally different from borrowers that do not issue securities. To make an apples-to-apples comparison and provide a stronger test of the unobserved higher-quality story against other stories, I define non-bundled loans as follows. If a bank gives a sole-lender loan or leads a loan syndicate to a company and the same company issues securities underwritten by a bank other than the loan lead lender in the time period from one year before to one year after the loan origination date, I classify the loan as a non-bundled loan. Therefore, all loans in my sample have security issues around the loan origination dates, and the only difference between bundled loans and non-bundled loans is whether underwriting is provided by the lead lender or not. 7 The choices of one year before and one year after are arbitrary. As a robustness check, I also run the analyses using six-month intervals in the bundling definition. The results are qualitatively similar. The definition of bundled loans is similar to that used in Drucker 6 Some of the loans have ticker information for the borrowers, but many of these prove unreliable. 7 Including stand-alone loans leads to stronger results. 10

12 and Puri (2005) with the exception that they only consider seasoned equity offerings (SEO), whereas I consider all underwritten transactions, including all debt and equity underwriting, to give a complete picture of bundling. 8 I also apply several filters to the loan data. First, I only consider dollar-denominated, completed loans to US companies. Second, I remove loans to borrowers with one digit SIC code 6 (financial institutions) and 9 (government agencies, etc.) Third, since most bundled loans involve public companies, I only consider loans involving them. B. Loan Characteristics and Borrower Characteristics The LPC DealScan database from which I obtain loan data has been extensively documented in the literature. 9 LPC reports loan data at the facility level as well as the deal level. A deal can be structured into different facilities. Facilities differ in origination date, type, amount, and maturity. The unit of observation used in this study is a loan facility. All empirical results in this paper are qualitatively unchanged if I do the analysis at the deal level, using the facility with the largest amount and earliest origination date in the deal as a proxy. The first part of the paper looks at loan pricing. To measure pricing, I use the variable All in Spread Drawn (AISD), which is total annual spread paid over LIBOR for each dollar drawn down. To control for other loan characteristics that have been shown to affect loan pricing, I include loan amount, loan maturity, whether the loan is syndicated or not, loan type, and loan purpose. To control for borrower credit risk and information opacity, I include credit rating, size, leverage, equity return volatility, and profitability. Dealscan provides the borrower s long term debt credit rating at loan origination. I supplement this with the rating information from the S&P Credit Pro database. The second part of the paper examines the ex-post change in credit quality. Here I use two proxies for credit quality. The first one is the distance to default (DD) measure based on the KMV model and ultimately on the structural model of Merton (1974). Following 8 Removing private offerings and removing shelf-registered offerings don t affect the results. 9 For detailed information about the Dealscan database, see Carey, Post, and Sharpe (1998). 11

13 KMV, I define distance to default based on how many standard deviations a company s asset value is currently above its debt value. See Appendix B for a detailed definition. The second proxy is Altman s (1968) Z-score, which is an index calculated from accounting ratios. I compute both the distance to default measure and the Z-score measure up to My accounting data are from Compustat. To ensure I use accounting information that is publicly available at loan origination, I use the following procedure similar to Bharath et. al. (2005). For a loan made in calendar year t, I use fiscal year t data only if the loan origination date is at least 6 months after the fiscal year end. Otherwise, I use fiscal year t-1 data. For more detailed variable definitions, see Appendix A. C. Lender Characteristics and Previous Lending Relationships In order to define bundled loans and control for lender characteristics and previous relationships with borrowers, I must solve two issues. First, I need to identify the lead banks in every loan. For sole-lender loans, this is trivial. For syndicated loans, since many features of loan contracts are not standardized, grouping lenders into lead banks and participants requires a few subjective criteria. Following Ivashina (2005), the administrative agent is defined to be the lead bank whenever available. If the administrative agent is not identified, I go down the list of book runner, lead arranger, lead bank, lead manager, agent, and arranger. Second, in the late 1990s, many mergers and acquisitions took place in the banking industry. I carefully track all mergers and acquisitions among lenders, and following Ljungqvist, Marston, and Wilhelm (2006), I assume that acquiring banks inherit the prior relationships and market shares of the target banks. Following previous literature, variables that capture lender reputation and relationship strengths are constructed as follows. I use loan market share to proxy for bank 12

14 reputation. 10 The loan market share of bank i in year t is defined as the dollar amount of loans in LPC arranged by bank i in year t divided by the total dollar amount of loans made that year. Following Ljungqvist, Marston, and Wilhelm (2006), the lending relationship strength of bank i with company j is defined as bank i s share of company j s previous loans. 11 If a loan is lead-managed by more than one bank, each lead bank is credited with an equal fractional share. Note my relationship strength variables vary from zero (no relationship) to one (exclusive relationship). Thus, in addition to capturing the existence of a relationship, these strength variables capture relationship intensity as well. 12 D. Summary Statistics Table 1 shows the distribution and summary statistics of bundled loans. There are a total of 10,053 loan facilities from 1994 to 2004 satisfying the condition to be included in the study, i.e. there are security issues in the time period from one year before to one year after the loan origination. These loans are extended to 2,896 companies. Of these, 2,486 (25%) are classified as bundled loans, i.e. the security issue around the loan is underwritten by the lead lender. Panel A shows that overall, bundled lending trends positively with time. In 2002, more than 42% of loans in my sample are classified as bundled. Panel B breaks the sample based on loan type. I use 3 groups: Revolver (including 364-day facility), Term loan (including term loan B-D (institutional term loan)), and others % of the loans in my sample are revolvers. Revolving lines of credit and term loans have similar fraction of bundled loans. Panel C breaks the sample based on loan purpose, using seven groups: Acquisition lines, LBO/MBO, Takeover, Debt Repay/Recapitalization, Corporate Purpose, Working Capital, and other purposes This is similar to Megginson and Weiss (1991). 11 For company j at time t, I sum the loan amounts lead-managed by bank i and its predecessors in the previous 5 years, then divide it by the total amount of loans borrowed by company j in the previous 5 years. 12 All empirical results continue to hold if I use the number of loans instead of dollar amounts in the definition of market shares and relationship strength variables. In the cases where a loan facility has more than one lead bank, I sum up the market shares and relationship strength variables across lead banks. Results are robust to using the mean value or the maximum value across lead banks. 13 The empirical results are robust to other grouping schemes. For example, grouping 364-day facilities and revolvers separately and grouping term loans and term loans B-D separately give similar results. 14 The empirical results are robust to grouping Acquisition lines, LBO/MBO and Takeover together; they are also robust to grouping Corporate Purpose and Working Capital together. 13

15 The fraction of bundled loans differs across the loan purpose groups. I include year fixed effect, loan type, and loan purpose control in all the analyses. Panel D breaks the sample by credit rating. Investment-grade borrowers are more likely to receive bundled loans. In addition, compared to non-bundled loans, the distribution of ratings for bundled loans is tilted toward investment-grade borrowers: 39% of bundled loans are extended to investment-grade borrowers, whereas only 28% of non-bundled loans are extended to investment-grade borrowers. Superficially, this feature of the data is consistent with the conjecture that banks select their clients more prudently when choosing bundling services clients. Panel E contrasts various loan, borrower, and lender characteristics between bundled loans and non-bundled loans. Univariate comparison suggests that bundled loans yields are lower than non-bundled loan yields. The median yield spread for bundled loans is 100 basis points, while the median yield spread for non-bundled loans is 150 basis points. This difference is statistically significant, as is the difference in means. In addition, bundled loans are generally larger in size, longer in maturity, and more likely to be syndicated. Borrowers receiving bundled services are usually better rated, larger, more highly leveraged, less volatile, and more profitable. The lead banks in bundled loans are generally more reputable and have closer relationships to the borrowers. And the fraction of loans lead managed by financial institutions other than commercial banks (say investment banks) is higher in bundled loans. The key element in the story I propose is a bank s private information about unobserved client quality. To test this story, I will carefully control for the observable differences between bundled loans and non-bundled loans when examining their pricing and ex-post performance. III. The Pricing of Bundled Loans A. Empirical Model Drucker and Puri (2005) document a yield discount between bundled loans and other loans using the propensity score matching method. However, as the authors acknowledge, matching models assume that unobservable private information does not affect loan 14

16 pricing. 15 My unobserved higher-quality story says that the private information, which banks use in deciding to jointly provide lending and underwriting services, is also used to price bundled loans. Thus, one needs to adjust for the endogeneity present in the decision to bundle before assessing interest rate differentials. 16 I implement a treatment effects model 17 to explicitly adjust for the endogeneity of bundling decision. I model loan yield spread as YieldSpread = δ 0 + δ1 X + δ 2 BundledLoan + ε (1) where X is a set of exogenous observable characteristics of loan, borrower, and lender, and BundledLoan is a dummy variable taking the value one if the loan is bundled, and zero otherwise. Coefficient δ 2 is the key parameter of interest. It estimates the interest rate difference between bundled loans and non-bundled loans. According to my unobserved higher-quality hypothesis, bundling is not exogenous. I assume the bank s decision model is BundledLoa n = 1 if β Z + υ > 0 (2) BundledLoa n = 0 if β Z + υ <= 0 where Z is a set of observable variables that can potentially affect whether the loan is bundled, and υ is an error term. Following the standard assumption in Heckman s (1979) two stage procedure, I assume the error terms ε and υ follow a bivariate normal distribution with means zero and standard deviations σ e and 1 and correlation ρ. Under this assumption, 15 See Li and Prabhala (2005) for an excellent survey of matching and self-selection models in finance. 16 I replicate the propensity score matching used in Drucker and Puri (2005) on my sample. Even in my sample (which includes both equity and debt issues, and requires that all loans have underwriting around), their results hold: Bundled loans have lower interest rates than matched non-bundled loans. These results are available on request. 17 The same model has also been used in Campa and Kedia (2002) among others. 15

17 E YieldSpread BundledLoan = 1) = δ + δ X + δ + E( ε BundledLoan ( = φ( β Z) X where λ 1 = E( υ BundledLoan = 1) = Φ( β Z) = δ 0 + δ1 + δ 2 + ρ σ e λ1 1) and E YieldSpread BundledLoan = 0) = δ + δ X + E( ε BundledLoan 0) ( 0 1 = φ( β Z) X where λ 2 = E( υ BundledLoan = 0) = 1 Φ( β Z) = δ 0 + δ1 + ρ σ e λ2 OLS estimate of δ 2 is given by E( YieldSpread BundledLoan = 1) E( YieldSpread BundledLoan = 0) φ( β Z) = δ 2 + ρ σ e Φ( β Z)(1 Φ( β Z)) (3) Therefore, if the error terms ε and υ are correlated (i.e. ρ 0 ), then the OLS estimate of δ 2 is biased, and the direction of bias depends on the sign of ρ. My unobserved higher-quality hypothesis says that banks provide bundled services to higher-quality clients. A bank s private information about unobserved higher quality of clients, which induces the bank to provide bundled services, also causes the bank to charge lower interests. In equation (2), error termυ includes variables affecting the bank s decision of bundling not explained by observables. Thus, υ can be viewed as the bank s private information about client quality. If, as hypothesized by my story, a bank s private information and the interest rate charged are negatively correlated (i.e. correlation ρ between error terms ε and υ is negative), then the estimated interest rate discount for bundled loans using OLS is downward biased. To account for the effects of selection bias, I follow a two-step estimation procedure detailed in Maddala (1983). I first estimate equation (2) using a Probit model to get a consistent estimator of β. I then use the estimated β to calculate λ 1 and λ 2, the correction terms for bank s selection. In the second step, I estimate δ by estimating 16

18 YieldSpread = δ 0 + δ1 X + δ 2 BundledLoan + δ λ lambda + µ (4) where δ λ = ρ σ and lambda is the correction term for selection and defined as e lambda = λ 1 BundledLoan + λ2 (1 BundledLoan) In this equation, the coefficient δ 2 indicates whether there is an interest rate discount after correcting for selection, and the coefficient δ λ captures the relation between bank s private information and loan interest rate. B. Identification and Instrumental Variable For identification, the bundled-lending decision equation (2) must include one or more instrumental variables not included in the loan yield equation (1). 18 An instrument must satisfy two conditions: (a) it affects whether the loan is bundled or not; and (b) it is not directly related to the interest rate. My choice of instrument is guided by economic considerations and is based on suitably exogenous changes in regulation. Recall that the difference between bundled and non-bundled loans is whether the security issue around the loan origination date is underwritten by the lead lender. I use as instrument time series and cross-sectional variation in regulatory constraints on a lender s ability to underwrite such securities. This variable is constructed from the graduated way in which commercial banks were allowed to (re-) enter the underwriting market. On January 18, 1989, the Federal Reserve began to allow so called Section 20 subsidiaries of commercial banks to underwrite first corporate debt and later equity securities subject to a 5 percent annual revenue cap. This cap was raised to 10 percent on September 14, 1989 and then to 25 percent on March 6, 1997 (announced on December 20, 1996). On November 12, 1999, the cap was lifted following the passage of the Gramm-Leach-Bliley Act. In addition to this time series variation, there is cross-sectional variation in the dates 18 Without instrumental variables, the inverse mills ratio terms are simply non-linear function of X, so the model can still be identified by assuming normality. However, it is well known that identification by functional form alone in this model often leads to very unstable and unreliable estimates of the parameters (Little, 1985). 17

19 on which banks were granted underwriting authority for the first time, and these dates sometimes varied for different types of securities for a given bank. How binding the revenue cap is at a particular time clearly affects a lending bank s underwriting decision, which translates into whether a loan is bundled or not. Unfortunately, commercial banks Section 20 underwriting revenues are not publicly disclosed, so it is not possible to directly measure directly how constrained each bank is at any point in time. Instead, to measure how constrained a bank might be at the time of a loan client s security issue, for each lead lender, I measure how long the bank has operated under its then-current cap. Consider a hypothetical bank receiving Section-20 underwriting approval in 1990 and examine its underwriting situation over time. In 1991, the bank was probably not bound by the 10% revenue cap since it just received permission to underwrite securities. However, in 1996, having had five more years to grow its underwriting business, the bank probably felt more constrained by the cap. The increase of the revenue cap in 1997 loosened the constraint dramatically because the bank suddenly received greater underwriting freedom. Thus, the longer the current cap has been in place, the more likely it is that it will bind, affecting a bank s probability of bundling a loan in ways that are unrelated to any characteristics of the borrower, and hence to the required interest rate. Formally, I measure this as follows: 19 ( Tnext _ dereg Tissue ) Constra int = 1 (5) ( T max( T, T )) next _ dereg prev _ dereg sec 20 _ app _ date where T next _ dereg is the next regulatory change date after the security issue, issue T is the security issue date, T _ is the previous regulatory change date before the security pre dereg issue, and T sec 20 _ app _ date is the Section 20 subsidiary approval date of the lead lender. 20 If 19 If there are more than two security issues around the loan, I will consider loan date. Using any of the security issue dates (earlier date or later date) doesn t change the results. 20 For investment banks, the constraint is set to be 0, since the regulation is only applied to commercial banks. For loans before the approval of Section 20 subsidiary, the constraint is set to be 1 since the bank is not eligible to underwrite the security at that time. By construction, the constraint variable is bounded 18

20 a security issue is closer to the next regulatory change date (so that the current cap has been in effect for longer), the lender s underwriting constraint is more likely to bind, so the lender is less likely to underwrite the client s security issue and the loan is less likely to be bundled. At the same time, since this instrument is constructed from exogenous regulatory changes, it is difficult to see how it would affect loan yields directly. Univariate comparison in Table 1 shows that the constraint variable differs significantly for bundled loans and non-bundled loans. Banks that offer bundled loans have a lower underwriting constraint. The mean and median differences are both statistically significant. Table 2 presents the first-stage probit results predicting whether a loan is bundled or not as a function of the instrument and other controls. The coefficient on the instrument is significant with the expected sign. Lead lenders with a higher underwriting constraint at the time of the security issue are less likely to underwrite the deal. 21 C. Regression Results In Table 3, the OLS results suggest that bundled loans offer a yield discount of between 11 and 38 basis points depending on the specification. However, after I adjust for endogeneity using a treatment effects model, the coefficient on the bundled lending variable ( δ 2 ) becomes insignificant. The negative coefficient on δ 2 in the OLS specification is soaked up by the coefficient on lambda, the inverse mills ratio. This sign change of the coefficient on δ 2 indicates that there is a downward bias in the OLS estimate. More importantly, the negative coefficient on lambda suggests that banks private information about unobserved characteristics of the borrower is negatively correlated to the interest rate. Therefore, the private information most likely concerns the borrower s unobserved good quality, which induces the bank to provide both lending and underwriting services to the same customer; it also causes the bank to charge a lower interest rate. So the interest rate discount of bundled loans found in previous studies is between 0 and 1 to make it comparable across different loans. Value 0 corresponds to no constraint or very low constraint; on the other hand, value 1 corresponds to very high constraint or complete ineligibility. 21 Note that the instrument captures intrinsic variation in underwriting constraints; extrinsic variation (between commercial banks and investment banks) is separately controlled for in Model 4 of Table 2. 19

21 due to the unobserved higher client quality, not the bundling. This result is consistent with the unobserved higher-quality story. 22 Other variables behave as expected and concur with findings in the existing literature. Generally, I find larger loans, syndicated loans, and those of longer maturity have lower yield spreads. Loans given to larger companies and companies with better credit ratings, lower leverage, lower equity return volatility, or higher profitability offer lower yield spreads. Bank characteristics and bank-company relationships also affect loan yields. Loans from banks with better reputation, from banks with closer lending relationships with the borrower, and from commercial banks offer lower yield spreads. In conclusion, the results in Table 3 show that after adjusting for the endogeneity present in the decision to bundle, there is no interest rate discount. The coefficient on the selection correction term lambda is significant and negative. These results are consistent with the unobserved higher-quality story. IV. Ex-Post Performance of Bundled-loan clients The findings of the previous section are generated using an econometric model. The reliability of the results depends on several assumptions (e.g. bi-variate normality of the errors in selection and valuation model). To directly support the unobserved higher quality story, I examine the ex-post performance of bundled-loan clients and bundled loans. In the next two sections, I ask whether bundled-loan clients and bundled loans perform better after loan origination. This will help to further distinguish the unobserved higher-quality story from the other stories. According to the unobserved higherquality story, banks predominantly provide bundled services to clients for whom banks have more favorable private information. If true, bundled-loan clients and bundled loans will perform better ex-post. Moreover, examining ex-post performance can also shed 22 For robustness check, I also implement the traditional instrumental variable (IV) model. Again, I find no interest rate discount using this model. To implement the IV model, I follow Procedure 18.1 in Wooldridge (2001, page 623). This procedure is different from the traditional 2SLS, since the first stage is not a linear model. Instead of using the fitted probability from the first stage to directly replace the dummy variable BundledLoan in the second stage, this procedure uses the fitted probability as an instrument for bundling status. Wooldridge argues that this procedure is better than directly replacing the dummy variable with the fitted probability. See Wooldridge (2001) for more details. 20

22 light on the question how the practice of bundling affects the health and stability of the financial system. A. Ex-post Change in Distance to Default and Altman s Z-score To the extent that the relevant unobserved characteristic is a client s credit quality, I consider the distance to default (DD) measure (based on the KMV-Merton model) and Altman s (1968) Z-score; these are the popular proxies for credit quality. I measure expost performance starting at one year after the loan origination date, since I use a oneyear window after loan origination to define bundled loans. I track performance for the next 2, 3, 4 and 5 years, respectively. For example, the change in distance to default for the next 4 years is measured as DD 4 years from the loan origination minus DD 1 year from the loan origination. Distance to default measures how many standard deviations a company s asset value is currently above its debt value. Altman s Z-score is an index calculated from accounting ratios. Higher values of DD and Z-score are generally associated with higher credit quality. My story predicts one should observe a larger increase (or a smaller decrease) in the DD and Z-score measures for bundled-loan clients than for non-bundled-loan clients. Univariate results in Table 1 show a clear difference in the ex-post performance of bundled-loan clients vs. non-bundled-loan clients. Starting one year from loan origination, the distance to default measure rises for bundled-loan clients and falls for non-bundledloan clients. The difference is both statistically and economically significant. For example, looking at the change from t+1 to t+4, on average, distance to default for bundled-loan clients rises by and decreases by for non-bundled-loan clients. The difference is 0.644, which is large given the average distance to default level is 2.5. Table 4 reports regression results with ex-post changes in DD and Z-score as dependent variables. The unit of observation is still a loan facility. In addition to the controls used before, I also control for the level of DD or Z-score measured at the month-end before the loan origination date. The regression results mirror the univariate results and show that distance to default rises significantly more for bundled-loan clients over the next 2 to 5 21