Institutional Demand Pressure and the Cost of
Corporate Loans
Victoria Ivashina*
Harvard Business School
Zheng Sun **
University of California, Irvine
This draft: February, 2010
Abstract
Between 2001 and 2007, annual institutional funding in highly leveraged loans went up from $32 billion to $426 billion, accounting for nearly 70% of the jump in total
syndicated loan issuance over the same period. Did the inflow of institutional funding in the syndicated loan market lead to mispricing of credit? To understand this relation, we look at the institutional demand pressure defined as the number of days a loan remains in syndication. Using market-level and cross-sectional variation in time-on-the-market, we find that a shorter syndication period is associated with a lower final interest rate. The relation is robust to the use of institutional fund flow as an instrument. Furthermore, we find significant price differences between institutional investors’ tranches and banks’ tranches of the same loans, even though they share the same underlying fundamentals. Increasing demand pressure causes the interest rate on institutional tranches to fall below the interest rate on bank tranches. Overall, a one-standard-deviation reduction in average time-on-the-market decreases the interest rate for institutional loans by over 30 basis points per annum. While this effect is significantly larger for loan tranches bought by structured investment vehicles (CDOs), it is not fully explained by their role.
JEL classification: G11, G14, G21, G22, G23
Keywords: Institutional investors; syndicated loans; LBO; credit crisis
** P mail: [email protected].
We are grateful for helpful comments to Malcolm Baker, Josh Coval, David Hirshleifer, Alberto Manconi, David Scharfstein, Jeremy Stein and participants at the Federal Reserve Bank of Boston, University of Wisconsin, University of Illinois at Urbana-Champaign, Harvard Business School, MIT, Bentley College, UC-Irvine, Federal Reserve Board seminars, Early Career Women in Finance Conference and FIRS conferences.
USC FBE FINANCE SEMINAR presented by Victoria Ivashina
FRIDAY,March 26, 2010
1. Introduction
Prior to the credit crisis that started in late 2007, the corporate loan market was characterized by decreasing interest rates. Spreads (interest margin paid over LIBOR) for leveraged buyout (LBO) financing dropped from over 375 basis points in 2001 to under 250 basis points in 2007. Contraction of loan spreads is at the heart of recent economic developments, including an LBO boom and its subsequent contraction. However, the factors that contributed to the decline in interest rates are poorly understood. This paper shows that variation in institutional investors’ demand for corporate loans (supply of funds) is a key explanatory factor for loan spreads.1
Credit expansion in the U.S. between 2001 and first half of 2007 was driven almost exclusively by the inflow of institutional (non-bank) funding. The participation of a wide range of institutional investors (including structured funds known as CDOs, hedge funds, mutual funds, pension funds, and insurance companies) in the corporate loan market was the result of the development of loan syndication.
2
1
Throughout the paper, we use the terms “institutional demand” and “institutional demand pressure” to refer to the institutional demand or appetite for corporate loans (i.e., supply of funds) as opposed to the borrowers’ demand for funding.
In general, institutional investment in loans tends to concentrate in the leveraged segment of the market; i.e., loans to non-investment-grade firms or non-rated firms with high committed or
outstanding leverage such as financings of LBOs or of mergers and acquisitions. In 2007, institutional investors funded 62% of primary leveraged loan issuance, up from 15% in
2 A syndicated loan is held pro-rata by a group of investors, but typically is originated and administered by one bank known as the lead arranger. The borrower has a traditional banking relationship with the lead; the rest of lenders are just passive investors. The introduction of secondary market trading standards and loan credit ratings facilitated institutional entry.
2001.3
Did an increase in the institutional supply of money have an impact on loan spreads? Practitioners’ view is suggested in the Standard and Poor’s (S&P) 2007 guide to the loan market: “In pricing loans to institutional investors, it’s a matter of the spread of the loan relative to credit quality and market-based factors. This second category can be divided into liquidity and market technicals (i.e., supply/demand)… Market technicals, or supply relative to demand, is a matter of simple economics. If there are a lot of dollars chasing little product, then, naturally, issuers will be able to command lower spread. If, however, the opposite is true, then spread will need to increase for loans to clear the market.” Our goal is to identify the price impact of institutional money chasing loans from other factors that can affect loan pricing.
That same year, leveraged lending represented 41% of the overall syndicated loan issuance, up from 20% in 2001.
We measure institutional demand pressure—imbalance between amount of fund chasing a given loan relative to the supply of the loan—as the number of days from the start of syndication to completion of the loan, at which point funds are available to the borrower; we call this variable time-on-the-market (TOM). The sequence of events that precedes syndication of a loan closely resembles the “book-building” procedure common in the equity and bond markets (we explain it in more detail in the next section). After conducting due diligence on the borrower, the lead bank first syndicates a fraction of the loan to banks and then opens syndication process to institutional investors by proposing an initial range of loan spreads. In a nutshell, time-in-syndication is the time that the lead bank needs to learn about the investors’ demand. Syndication is a sealed-bid auction with
3
Institutional investors also acquire loans through the secondary loan market. The figures based on the primary issuance are probably underestimating the overall importance of the institutional funding.
a ceiling spread (the equivalent of a floor price). If there is excess demand, the spread will be adjusted and syndicated in the first round. If the first-day demand is low, the auction remains open until there is a sufficient demand or until the lead bank decides to revise the spread up. The positive relation between time-in-syndication and spread is created after the initial auction. The rationale for the relation between pricing and time-in-syndication is best formalized by the Lazear (1986) retail pricing model (i.e., the dynamic is similar to the pricing of goods in a department store or housing). Following Lazear, time-on-the-market is a function of (i) heterogeneous investors’ valuations and (ii) searching costs. Based on the previous empirical literature, searching costs should be correlated with the size of the lender, the size of the borrower, and the size of the loan. Our analysis controls for all of these variables. Therefore, we argue that fluctuation in the time-in-syndication of institutional loans is a proxy for fluctuation in institutional
investors’ valuations. A “hot” deal will be subscribed more quickly than a less attractive one.
Consistent with the institutional demand pressure hypothesis, our results indicate a significant positive relation between loan spread and time-on-the-market. Specifically, if overall institutional demand cools and the average time-on-the-market goes up by four days (one standard deviation in the aggregate time-on-the-market), the loan spread will increase by over 34 basis points. For an average loan of $471 million and six years maturity, this represents approximately $8.23 million in interest expenses.4
4
Increase in interest expense corresponds to the net present value of six annual payments using LIBOR as a discount rate. The LIBOR rate averages 4.6 percent per year during our sample period.
Similarly, if a given loan remains unsold for 17 days above the market mean (a one standard deviation
increase in loan-specific time-on-the-market), it would result in a $3.15 million increase in interest payment.
There could be alternative explanations to our findings that warrant further exploration. First, the fact that time-on-the-market is an equilibrium outcome raises the concern that it could be measuring the availability of investment opportunities at the firm level (i.e., firms’ supply of loans) rather than the effect of the institutional funds chasing the deal (i.e., institutions’ demand for loans). However, if time-on-the-market is indeed correlated with firms’ investment opportunities, then we should find that it explains loan spreads for all the investors (institutions and banks). In fact, we find that the length of time in syndication is only relevant in explaining the spread on institutional tranches and its deviation from the spread charged by the banks.
To further disentangle the institutional demand for loans from the firms’ demand for funds we directly look at the flow of funds to different institutional investors. As flow of funds reflects the funding availability of institutional investors, it is likely to be
uncorrelated with the specific loans they picked. We find that time-on-the-market is highly correlated with the flow of funds of major institutional investors in the loan market, especially CDOs. Moreover, the positive association between the time-on-the-market and loan spread remain unchanged when we use flow of funds as an instrument for time-on-the-market.
Another alternative explanation of our findings is asymmetric information. Specifically, time-on-the-market and the initial and final spreads are likely to be
endogenous and reflect not only institutional demand but also the degree of asymmetric information between the lead bank and the institutional investors. The lead bank conducts
borrower’s due diligence and has better information about the loan quality than the participant investors. Thus, adverse selection could delay the syndication process and make institutional investors demand a higher spread. Generally speaking, it could be that cross-sectional variation in time-on-the-market and in the final spread reflect an omitted characteristic of the loan, as opposed to the institutional demand pressure. However, let us stress that such an omitted characteristic would have to be correlated not only to time-on-the-market but also to the spread.
We address this issue in three ways. First, in addition to the effect of loan-specific number of days on the market until loan sale, we also focus on market-level time-on-the-market (a measure of time-on-the-market-level investors’ sentiment), defined as the average number of days that institutional loans remained in the syndication pipeline in the quarter preceding a given loan. While the loan-specific measure could be linked to an omitted loan characteristic related to the spread, cross-sectional heterogeneity in loan
characteristics cannot explain market-wide time-variation in institutional demand. Second, throughout this paper, we control for a comprehensive set of borrowers’ and loans’ characteristics, including loan credit rating, secondary market liquidity, and ex-post performance. Finally and most importantly, we look at the pricing difference between institutional tranches and bank tranches for the same loans. Since institutional and bank facilities are claims to the same cash flows, both are governed by the same contract and have the same seniority; the difference in spread between the two cannot be explained by missing fundamental variables. We find that institutional tranches, on average, charge lower spreads than bank tranches of the same loans (controlling for mechanical difference in their loan contracts). More importantly, the difference in spread
between institutional and bank tranches increases when institutional loans are allocated more quickly; the higher the buyer pressure, the greater the compression in spread. This is consistent with our hypothesis that bank tranches set a rational price and institutional demand drives prices of institutional tranches away from fundamentals.
In the final part of the paper, we further investigate the role of collateralized debt obligations (CDOs) in generating institutional demand pressure. The period of our analysis coincides with an explosive growth of CDO funding. By 2007, it was the
dominant form of institutional investment in the leveraged loan market; according to S&P Leveraged Lending Review, 63% of primary loan syndications included a CDO investor. Arguably, CDOs have different incentives than other institutional investors and,
therefore, it is worth understanding if our results are specific to CDOs. We find that the relationship between time-on-the-market and loan spread is significantly stronger for loans syndicated to CDO investors. However, CDOs do not explain the overall effect of institutional demand pressure on loan spreads. Thus, our results reflect a broad supply effect; institutional sentiment is important regardless of the state of the securitization market.
Preceding the collapse of the financial markets in 2007, there was an important corporate credit expansion that was driven almost exclusively by the inflow of
institutional funding. However, research on this subject remains very limited. Notable exceptions include Kaplan and Strömberg (2009), Axelson, Jenkinson, Strömberg, and Weisbach (2007), and Shivdasani and Wang (2009) argue that credit market conditions contributed to the recent LBO boom. By linking institutional investors’ demand for corporate loans to the reduction in spreads, our paper provides further evidence that the
market conditions during the credit boom can be attributed to the shift in capital supply and an explanation why. Although loans financing LBOs represented nearly 60% of the high-yield market during the credit boom, we find that institutional investors’ preferences affected pricing on highly leveraged transactions in general. We conclude that there was a general shift in credit supply driven by institutional funding.
More broadly, we contribute to the literature on the economic effects of capital supply. As noted above, adjustment of the loan spread to institutional investors’ tastes is perceived by practitioners as “a matter of simple economics.” However, if the financial market is efficient, the demand for any security should be infinitely elastic and shifts in fund supply should not affect securities’ valuations.5 Thus, our results are an indication of downward-sloping investor demand for corporate loans. Our findings contribute to the vast literature documenting the effects of capital inflow, notably Shleifer (1986) on the stock-price impact of inclusion in the S&P 500 Index; Warther (1995), Wermers (1999), Frazzini and Lamont (2006), and Coval and Stafford (2007) on stock-price pressure generated by mutual fund flows; Gompers and Lerner (2000) on private-equity valuations due to fund inflows; Cornelli, Goldreich, and Ljungqvist (2006) on small investors impact on post-IPO stock prices; and Massa, Yasuda, and Zhang (2007) on firms’ financing choices in response to uncertainty of institutional demand for bonds.6
The rest of the paper is organized as follows: Section two provides background on loan syndication and outlines the empirical strategy. Section three summarizes the data. Section four presents the empirical results and examines alternative explanations. Section
5
See Modigliani and Miller (1958) and Scholes (1972). 6
See Baker (2009) for a comprehensive survey of the literature studying the effect of capital supply on equity and credit markets.
five examines the role of structured finance in driving the institutional demand. Section six concludes.
2. Empirical framework
2.1 Institutional background
Two primary types of investor participate in the syndicated loan market: banks and institutional investors. Institutional investors, among others, include structured investment vehicles, also known as collateralized debt obligations (CDOs), hedge funds, mutual funds, and insurance companies. Institutional capital inflow was especially pronounced in the leveraged segment of the market, broadly defined as loans to
borrowers with a high existing or committed leverage and typically associated with low credit quality.7
The overall growth of the syndicated loan market was largely due to the expansion of the leveraged segment of the market. Between 2001 and 2002, total high-yield loan issuance grew at a cumulative annual rate of 21%. In contrast, investment-grade loan issuance had slightly contracted over the same period. The 2001-2007 rise in leveraged loan volume was primarily funded by institutional investors. As can be seen in Figure 1, while the dollar amount of bank funding remained more or less at the same level, institutional funding over this period increased nearly twelve times.
These loans mainly financed LBOs, stock repurchases, and mergers and acquisitions.
[FIGURE 1]
7
The definition varies by institution. DealScan defines as leveraged any loan with a credit rating of BB+ or lower and any unrated loan; however, Leveraged loans are often referred to as “high-yield loans”; we use these terms interchangeably.
The key factors that attracted institutional investors to corporate loans were floating interest rates, the senior and secured nature of bank loans, relatively low volatility, low correlation with other assets classes, historically low default rates, and high recovery rates (Taylor and Sansone, 2007). There are several reasons why
institutional investment concentrated in the high-yield loans. First, in the non-investment segment of the loan market, there is a greater need for institutional money. The main reason for loan syndication is risk sharing. Loans are primarily originated by banks; however, banks are under regulatory pressure to provide capital in proportion to their risk-weighted assets. Hence, non-regulated institutional investors are a cheaper source of capital for leveraged loans. Second, institutional investors that buy loans tend to actively manage their portfolios. As a result, liquidity is an important factor for institutional investors. Because leveraged loans are large and have multiple lenders, they tend to have active secondary market trading, attracting institutional investors. Third, the driving force behind the expansion of institutional investment as well as the improving liquidity in the leveraged loan market was the growth of CDOs—special-purpose vehicles created specifically to invest in pools of non-investment-grade securities.8
Evidence consistent with the institutional demand effect on loan spread can be seen in the difference in spread behavior between investment-grade and non-investment-grade loans. Figure 2, Panel A plots average quarterly loan spreads for two adjoining credit ratings—investment grade (BBB), primarily funded by banks, and non-investment-grade (BB), primarily funded by institutional investors. The correlation between the two series is only 0.06. In Figure 2, Panel B, the correlation between BBB and BB-rated
8
A detailed description of the differences between loans with and without institutional investment can be found in Nandy and Pei (2007).
corporate bond yields over same period is 0.88 (0.95 for yields net of LIBOR). On the other hand, Figure 2, Panel C indicates a strong negative correlation between the loan amount funded by institutional investors and the spread on leveraged loans, also suggesting an institutional demand effect on loan spreads. However, these pictures represent an equilibrium outcome; further analysis is needed to measure the effect of institutional demand on corporate loans.
[FIGURE 2]
2.2 Syndication process
The central explanatory variable in our analysis—a measure of institutional investors’ demand for loans—is the number of days that a loan remains unsold (
time-on-the-market), or the duration of the syndication process. Figure 3 outlines the typical
syndication timeline. After the lead bank concludes due diligence on the borrower, the starting date of syndication and the loan structure (the amounts of different tranches, their maturities, and other non-price characteristics) are announced to potential investors. A credit rating is assigned, based on these characteristics, and the “price talk” (or “road show”) begins. During the price talk, the lead arranger informally polls a group of investors to assess market appetite for the loan. This information helps the bank set the target rate announced at the syndication launch.
[FIGURE 3]
Broadly speaking, there is no direct parallel between the pricing of syndicated loans at origination and the pricing of other goods and services. One potential explanation for this is that, while most goods require only one buyer, a large group of investors is required to allocate a loan and the clearing interest rate is the same for all investors.
There are some similarities with the pricing of public stock offerings. But loans, unlike stocks, are not fully underwritten at syndication and the lead bank is not responsible for absorbing the excess supply (S&P, 2007).9
We can think of the mechanism of demand discovery (syndication to institutional investors) as having two stages. The first stage is similar to a traditional sealed-bid auction with a floor price; the difference is that it cannot be cleared by one buyer.
So, if there is not enough demand on the first day, the syndication remains open. Although, we take the pricing model as given, the basic idea is that the syndication process matches borrowers with the set of investors with the highest valuations. Before the syndication, the lead bank does not know the demand with certainty; it only has a prior notion of the demand distribution. This is similar to many other situations, including stock pricing in IPOs (e.g., Benveniste and Spindt, 1989.)
10
9 We take as exogenous the practice of syndicating loans under best-effort agreement. However, one reason why best-effort agreements might be preferred over fully underwritten contracts is that the capital
requirement makes holding loans costly.
The interest rate for a syndicated loan is determined as spread (typically expressed in basis points) over London interbank offered rate (LIBOR), so the bidding takes place in terms of spread. At the beginning of the syndication, the lead bank announces a ceiling spread. In the auction, the spread can be bid down but not up. (Of course, bidding spread down is equivalent to bidding price up.) Once the syndication is open, investors submit their demand schedules, consisting of the maximum amount of loan they are willing to buy at given spreads. The bids are electronically submitted directly to the lead bank and are not observable by other investors. At the end of the first day, a minimum clearing spread is
determined and investors who bid that spread or below are awarded a share of the loan on a pro-rata basis.11
If the initial auction is undersubscribed, the syndication remains open. This second stage of the syndication is better described by a “retail” pricing model (Lazear, 1986) than by an auction model. For example, retail goods and—if we set aside
haggling—the housing market follow a retail pricing model. This means that the selling takes place at an announced price that is maintained for some period of time before it is discounted. Loan investors can submit their bids at any time until the syndication is closed. Typically, offers—including bids submitted by the investors at the initial
auction—are valid until some expiration date. The lead bank implicitly agrees to sell the loan to the first set of investors willing to buy it at the specified spread or below.
12
If all investors have the same valuation, then lack of demand on the first day of the auction could only mean that the loan is overpriced and there is no point in waiting;
The rationale for why the spread remains fixed for some period of time is that, if the loan is not syndicated in the first round, the lead bank still does not know whether or not the loan is overpriced (i.e., there are not enough investors that meet the reservation spread). The factors which can make it difficult to infer the demand and can therefore lead to variation in time-in-syndication are (i) heterogeneous valuations among investors and (ii) searching costs.
11 For example, say the lead bank needs to syndicate a $10 million loan and the initial spread is 300 bps over LIBOR. At syndication, two institutions submit their bids. The first bidder’s demand is $7 million at 300 bps, $6 million at 275 bps, and $4 million at 250 bps. The second bidder’s demand is $6 million at 300 bps and $5 million at 275 bps. The clearing spread is 275 bps. The first bidder receives $5.45 million of the loan and the second bidder receives the rest.
12 The implicit commitment to sell to the first set of buyers deters collusion between potential investors, given that there are many of them and they do not know who are the potential buyers. Overall, in 2006, there were 254 non-bank investor groups participating in the syndicated loan market, accounting for 720 different investment vehicles. In this context, if an investor’s valuation exceeds the reservation price, it is optimal for him not to wait to submit the bid.
the spread should be increased immediately. However, if investors have heterogeneous valuations, then insufficient demand at the initial auction might simply indicate that some of the investors that looked at the loans were not the right type of investors. Borrowing terminology from Lazear (1986), a certain fraction of investors at a given point in time are “shoppers” (their valuation is below the reservation value) and the rest are “buyers.”13
The second rationale for variation in time-on-the-market is related to search costs. If search costs are high, investors are less informed and less can be inferred from a lack of demand. It might be costly for individual investors to find and analyze information about loans because they lack the time or (more likely) the expertise or because, this being a private market, the information is limited. Continued marketing to the investors in this context might therefore be justifiable, leading to a longer syndication time. This idea is tied to the specialized nature of the assets (loans) and to the lead bank’s role as a specialist in this market (Shleifer and Vishny, 1992). This interpretation, however, is not directly tied to the fluctuation in institutional investors’ demand and it is important for us to distinguish between two alternative explanations for time-on-the-market. To do so, we The lead bank needs to assess whether or not there are enough buyers to allocate the loan. However, if the institutional demand is low and fewer investors are buyers or if there are fewer potential investors in general, then first-period demand is less informative. In that case, a longer time-on-the-market is needed to learn about the distribution of buyers. For example, in the housing market, September through February are considered to be the slowest trading months. Weather, holidays, and the beginning of the school year
contribute to a thin buyers’ market. As a result, properties often remain on the market for a longer period of time before the price is discounted.
control for lender fixed effects, size of the borrower, and size of the loan. Previous literature on fund flows for mutual funds has shown that size of the fund family is a good proxy for searching costs. In particular, Sirri and Tufano (1998) find that fund flows are directly related to both the size of a mutual fund’s family and the current media attention received by the fund.
To sum up, if the lead bank’s initial spread is high, the loan will be
oversubscribed, the syndication will close in the first round, and the spread will be adjusted down. However, regardless of investor demand, formalizing a syndication is a time-consuming process. Phone calls, delivery of the documents, and meetings with lawyers, among other necessary steps, require several days, even for oversubscribed loans. Hence, the lower bound on in-syndication is not zero. The variation in time-on-the-market comes from loans that had insufficient initial demand. If the demand is indeed insufficient, the empirical prediction is that the loan will remain outstanding longer and the spread will be adjusted up. However, the reason why the syndication remains open for some time without adjustment to the spread is that the lead believes that enough investors will appear for syndication to close. We see the same intuition at work in a department store when items that do not sell in the first week remain on sale at the full price. Notice that, in a syndication, the spread can still can be adjusted down even if it was initially undersubscribed.14
14 The implied assumption in the retail model is that not all potential investors are examining the loan simultaneously. (This idea is likely to be familiar to anybody who has tried to sell or rent out a house.) Similarly, time constraint is justifiable in a loan market. Many institutional investors are small
organizations and many others are small loan desks in large institutions. Because loans are complex assets which have limited secondary-market liquidity and for which limited information is available, it is likely to be time consuming for investors to look at any given loan. Another point to notice is that Lazear’s model implies that starting spread (price in his model) should be set up endogenously. If high demand is expected (i.e., if initial demand is more informative), the starting spread will be set lower. This is consistent with the countercyclical nature of the starting spreads that we observe in the data.
Although institutional funding is widespread, a fraction of the loans is funded by banks. The bank tranche is syndicated before syndication to institutional investors and, in that sense, syndication to banks seems to play a role similar to that of book-building in the stock market. Both the underwriter in the stock market and the lead bank in the loan maker must gauge the demand by potential investors. Much of the IPO literature focuses on the asymmetric information between sophisticated and unsophisticated investors that could lead to adverse selection; this is a key friction in price discovery (Rock, 1986). Accordingly, book-building in the stock market is often seen as the mechanism that allows the underwriter to extract information from sophisticated investors, thereby reducing adverse selection among investors (e.g., Benveniste and Spindt, 1989; Spatt and Srivastava, 1991; Cornelli and Goldreich, 2001.) In loan syndication, banks are the informed investors and non-bank institutions are the less-informed investors, hence pre-syndication to banks is similar to pre-allocation to large sophisticated investors (i.e., book-building) in the stock market. Of course, despite book-building practice in the stock market, IPOs are consistently underpriced and there is significant time and
cross-sectional variation in underpricing. This is a clear parallel with what we find for syndication loans.
Although, bank tranche is syndicated first, banks can adjust the spread before the loan is closed. However, as Figure 4 shows, while spread adjustment is infrequent for banks (roughly 10% of loans), institutional tranches have spread adjustments on approximately 60% of loans. It is even more striking that the final spread on the
institutional tranche of the loan deviates from the spread on the bank tranche of the same loan in a pro-cyclical pattern (see Figure 4, Panel C).
[FIGURE 4]
Our findings generally echo the inventory-control models which predict that a dealer will have a preferred inventory level due to credit constraint or risk aversion. (e.g., Garman, 1976; Stoll, 1978; Amihud and Mendelson, 1980.) Dealers adjust bid and ask quotes in order to maintain a balance of order flow. Similarly, when demand for a loan is unexpectedly low/high, lead banks will adjust the spread down/up in order to reach equilibrium. Our paper can also be related to the price-discovery process described in the microstructure literature. When there are both informed and uninformed traders in the market, market makers constantly adjust their expectations on the probability of informed trading based on observed market order flow. Such expectations are reflected in the bid and ask quotes set by the market makers. The idea of trades as “signals” of information is developed in papers by Glosten and Milgrom (1985) and Easley and O’Hara (1987). However, the parallel with this literature might not be direct as it is unlikely that lead banks use the syndication process to learn about the loan; rather, they are learning about the demand. Furthermore, the information-based microstructure model predicts that trades from informed traders will have a permanent price impact that helps price truly reflect fundamentals. We will argue that the institutional demand pressure pushes price away from fundamentals.
Overall, although there are some clear parallels between our paper and the IPO and microstructure literatures, we should emphasize that the concept of time-on-the-market was not treated in the previous literature.
Based on the framework outlined above, we argue that the length of time it takes for a deal to be fully subscribed by investors is affected by the amount of money chasing deals (i.e., institutional sentiment). If demand for a loan is high, competition will impel investors to go for the deal as quickly as possible and the deal will soon reach full subscription. In a tight credit market in which investors are very cautious, it is likely to take longer for a loan to attract enough funding to reach full subscription. Furthermore, after some time, the lead might have to revise the spread up to attract enough investors. This leads to our main hypotheses that (a) higher market-level institutional demand— lower average time-on-the-market in the preceding calendar quarter—should be
associated with a lower final spread and (b) higher loan-specific institutional demand— lower time-on-the market for a given loan—should be associated with a lower final spread.
3. Data and distribution of the key variables
Our sample consists of completed dollar-denominated loans originated to U.S. companies, excluding financial industries identified as SIC 60 through 64. Large syndicated loans are typically structured in several tranches, also called facilities. Each observation in the analysis corresponds to a specific facility, for which we collected data from Reuters’s DealScan database.15
Our focus is on the fraction of a loan funded by institutional investors, for which we use two alternative definitions based on (a) loan type and (b) lender or loan type. A
15
DealScan registers loans at origination. Because league tables are a powerful marketing tool in the syndicated loan market, lenders have an incentive to report this data. For more detail on potential biases introduced by reporting in DealScan, see Ivashina (2006).
typical leveraged loan includes a revolving credit line and several term-loan facilities (TLs). Under the TL tranches, the borrower draws the full amount committed and the loan is canceled once it is repaid. By contrast, a revolving line, designed to optimize the availability of working capital, allows the borrower to draw and repay committed funds at its own discretion. If the loan remains undrawn, the borrower only pays a commitment fee. For an institutional investor, it is costly to commit funds to an undrawn loan; therefore, institutional funding tends to concentrate in TLs. Specifically, institutional money backs term loan B (TLB), with term loan A (TLA) typically held by banks. The difference between these two types of loans is not very clear; the labels seem to indicate the primary investor’s constituency—and, therefore, the liquidity of a particular
tranche—rather than contractual differences. TLs A and B are of the same seniority and are backed by the same collateral. Barnish, Miller, and Rushmore (1997) argue that, in the mid-1990s, TLBs had longer maturity and possible prepayment fee. We do not observe provisions related to early loan prepayment; however, according to S&P, only 16% of the loans included prepayment fees. As we discuss later, we do not find a difference between the maturity of A and B tranches of the same loans to be
economically or statistically significant. Overall, there might be unobservable contractual differences between TLB and TLA that would lead to a different spread (e.g., prepayment option), but this could not explain why the spread on the institutional tranche would be a function of time-on-the-market.
A loan package often includes a second-lien tranche. These tranches are also typically allocated to institutional investors; however, they are considered subordinated debt and are priced at a premium. It is important to note that we exclude second-lien term
loan facilities (approximately 2% of the sample) from the analysis. Thus our sample includes only senior facilities and differences in spreads on the institutional and bank fractions of the loans cannot be explained by differences in their seniority.
Several of the institutional investors also invest, to a much lesser degree, in revolver lines and term loans in general. To identify non-TLB loans that have
institutional investors, we expand our definition to encompass the type of lenders in the lending syndicates in addition to loan type. For example, if a mutual fund or CDO investor is listed in the original lending syndicate for a revolving line, we also categorize the facility as an institutional loan. Throughout this paper we report the results for both definitions.
Our main dependent variable is the interest-rate margin, also called all-in-drawn spread, measured in basis points. Our spread variable includes an annual fee that is received by all syndicated members on a pro-rata basis but is net of any differential fees (i.e., fees that might go to some but not all members of the syndicate are excluded). It is constructed using DealScan and represents the total annual cost, including fees and fixed spread, paid over LIBOR for each dollar used under the loan commitment. The lead bank receives an upfront fee for syndicating the loan and a direct fee every time there is a trade or renegotiation; these fees are not reflected in our spread variable. In addition to the DealScan database, we use sources such as Standard and Poor’s and Moody’s credit ratings, secondary loan market quotes, and Compustat data.
Our main explanatory variable, time-on-the-market, is constructed using weekly records of outstanding loans published by Reuters Loan Pricing Corporation. These calendars are only available after 2002. Because we use lagged market-level
time-on-the-market calculated quarterly, our final sample consists of loans launched between the second quarter of 2002 and the end of 2007, and closed by the end of the first quarter of 2008.
Table 1, Panel B presents the distribution of our main proxy for institutional demand, time-on-the-market, measured as the number of days that a loan remains unsold after the launch day. We identify 2,283 institutional loans for which launch date is available; this number includes 1,077 TLBs. On average, 26 days elapse after the launch date until a loan is completely sold. Consistent with the peak of the last LBO boom, the shortest subscription period occurs in the first quarter of 2007, when it takes fewer than 20 days for a loan to be fully funded by institutional investors. In the second half of 2007, however, we see a sharp increase in time-on-the-market; the average number of days increases to more than 33, a reversion to the 2002 level.16
The literature documents that “hot” IPOs arrive in waves (Ritter, 1984) and we find that “hot” loan deals do, too. Time-on-the-market for a new loan in the current quarter can be predicted by the average time-on-the-market of loans issued in the previous quarter. This finding supports out interpretation of time-on-the-market as a prevailing market-level institutional sentiment.
[TABLE 1]
Secondary market liquidity is a critical factor for institutional investors. It could be the case thattime-on-the-market is a proxy for a loan’s liquidity rather than for
institutional interest; the shorter the launch time, the better the liquidity and the lower the
16Time-on-the-market in the last quarter of 2007 might be biased downward, given that loans that were not closed by the end of the first quarter of 2008 are not included in our sample. However, we do not use the last quarter of 2007 for an aggregate measure of institutional sentiment, nor do our results depend on the inclusion of the last quarter of 2007.
liquidity premium in the spread. To address this issue, in Table 2 we directly compare time-on-the-market with several alternative liquidity measures. The secondary loan market daily quotes are from the Reuters SMi database for the period between 2002 and 2004. We look at five alternative liquidity measures: the average number of trading days in a given month, the average number of bid quotes reported per month, the average number of ask quotes reported per month, the percentage bid-ask quoted spread, and the number of days from the deal’s active date to the first trading day. As can be seen in Table 2, the liquidity proxies are highly correlated among themselves, but none is
correlated with time-on-the-market. Therefore it is unlikely that our results are explained by unobservable expected liquidity.
[TABLE 2]
4. Pricing of institutional loans
4.1 Baseline model
Our focus is on the impact of the institutional investors’ demand for corporate loans on spreads. Hence, we first establish a benchmark loan-pricing model without including institutional demand proxies. Although the current literature does not have a defined set of loan-pricing factors, borrower’s credit risk and several loan characteristics have been found to affect the valuation of loans (for example, see Carey and Nini, 2007 or Ivashina, 2009).
We use the following benchmark specification, where each observation is a different facility:17
17
Except when we compare institutional to bank-funded facilities, each loan only appears once in our sample.
Final spread (All-in draw) = f (Loan, Firm, Bank, Market)
where
Loan = a set of loan characteristics: loan purpose, size, maturity, number of
tranches, presence of collateral and pricing grid, secondary loan market liquidity; where institutional loans are identified using investors’ identity, we also include loan type.
Firm = a set of borrowers’ characteristics: credit ratings at the time of
origination, ex-post change of credit risk proxied by probability to default, industry, sales size, leverage, return on assets, and number of past interactions with the same lead bank.
Bank = Lead bank fixed effects.
Market = S&P500 Composite Index and Lehman average corporate bond yield
to control for market-wide time trend. (Because market-wide indicators are quarterly, the market wide variables are left out when time fixed-effect is included.)
Loans issued by the same borrower may have correlated spreads and loans issued during the same quarter may be correlated. To account for these effects, we cluster standard errors by borrower and calendar quarter throughout the analysis.
Specifications (1) and (4) of Table 3 summarize the regression results of the baseline model for institutional loans defined by loan type and by loan and investor type, respectively. A few observations are in order: First, loan-specific characteristics are important determinants of loan prices. In particular, loans issued for the purpose of LBO have a significantly higher spread, which may reflect the higher risk associated with LBO deals. Larger or longer-maturity loans have lower spread, which is consistent with the
previous literature and likely reflects credit rationing. Loans with performance-related pricing provisions such as a pricing grid have lower spread. The secondary market liquidity is also an important factor for primary market loan spread. Loans with better secondary market liquidity (as proxied by lower average bid-ask spread) have lower spreads.18
Second, of the firm-level characteristics, credit ratings and industry are the most important factors.
19
Spreads are almost monotonically decreasing with deterioration in firms’ credit ratings. However, as pointed out by Carey and Nini (2006), agency ratings as indicators of credit risk may suffer from several drawbacks, such as stable
measurement and not incorporating private information owned by banks about the credit quality. To mitigate the imperfectness of the credit ratings, we follow the ex-post
performance of the loans after origination. We look at the probability of default measure proposed by Bharath and Shumway (2008). The measure is based on the Merton (1974) bond pricing model, but does not require solving for the implied asset value and
volatility. A higher value of probability to default suggests that a loan is of worse credit quality. We track the change in the distance to default measure from the year of loan origination to two years after the loan origination. We generally do not find a significant relationship between the exposed change of probability of default and loan spread.20
18 Because the available data is limited and only a fraction of loans is traded on the secondary market, the sample in which liquidity measures are available is small. We include a dummy (Not traded) to identify those facilities for which we do not have secondary market information.
19 To save space, we don’t report the individual coefficient on the 14 credit-ratings dummies and 65 industry dummies.
20 In an earlier version of the paper, we used the distance to default measure computed similarly to Crosbie(1999) and Vassalou and Xing (2004). The computation involves iteratively solve the implied firm asset and firm volatility. There was a marginal significant negative association between change the distance to default and loan spread.
Finally, market-level factors measured by the S&P 500 index and average bond yield also play an important role in loan spreads. For example, a lower S&P 500 rate is associated with significantly higher loan spreads, which may be due to the “flight to quality” or “flight to liquidity” effect during a down market.21
[TABLE 3]
4.2 The effect of demand pressure on loan spreads
To study the price impact of institutional demand, we add to the baseline model two more variables: TOM-market and TOM-loan.
Final spread (All-in draw) = f (TOM-market, TOM-loan, Loan, Firm, Bank, Market)
where
TOM-market = average time-on-the-market, measured using loans syndicated to
institutional investors and closed in the preceding calendar quarter.22
TOM-loan = loan-specific time-on-the-market, measured as the number of days
between syndication launch and closure.
Specifications (2)-(3) and (5)-(6) in Table 3 show results for the extended model. Consistent with our hypothesis, we find a significant positive relation between market-level and loan-specific time-on-the-market and loan spread, controlling for other factors that are known to affect loan spreads. The model’s goodness-of-fit as measured by adjusted R-squared improves significantly after including the two TOM variables. The
21
See, for example, Lang and Nakamura (1995) and Longstaff (2004). 22
Since market wide time-on-the-market is the same across firms within a quarter, we leave it out when time fixed effect is included in the panel regression.
coefficients indicate that, on average, if the syndication time for an individual loan is prolonged by 17 days (i.e., one standard deviation of individual time-on-the-market), the loan spread will be increased by 13 basis points (0.78×17).
The loan-specific time-on-the-marketcould reflect an unobserved quality of an individual loan. However, the cross-sectional heterogeneity in loan characteristics cannot explain market-level shifts in institutional sentiment. Indeed, we find an even stronger relationship between individual loan spread and market-level institutional demand
pressure (TOM-market). In particular, if an average loan in the market remains unsold for four days longer than the average number of days-in-syndication in the quarter prior to the launch date of a new loan (i.e., one standard deviation of market level time-on-the-market), the new loan will be required to pay an additional 30 basis points (7.48×4).
4.3Alternative explanations
4.3.1 Unobservable investment opportunities (borrower’s demand)
The observed loan spread is an equilibrium price determined by the interaction of the borrowers’ demand for funds and the institutional investors’ demand for loans. Loans that back better investment opportunities could be subscribed faster and also have a lower rate. Therefore, the positive association between time-on-the-market and loan spread may reflect the borrower’s demand for funds rather than the institutional demand for loans. To disentangle these alternative explanations, we first test whether there is a differential effect of time-on-the-market for institutional investors. The institutional demand pressure hypothesis suggests that the sensitivity of loan spread to time-on-the-market is higher for loan tranches funded by institutions than for tranches funded by banks. In contrast, if the
effect we are measuring is driven by the borrowers’ investment opportunities, the effect of time-on-the-market on loan spread should be the same for both banks and institutions.
The results are presented in Table 4. For bank-funded loans, we include all first-lien non-TLB facilities where launch date is available. The coefficients of the interaction terms between the dummy that identifies institutional loans and time-on-the-market reflect the marginal effect of our key explanatory variables for the subset of institutional tranches. We find that the positive relationship between time-on-the-market variables is more than two times stronger for institutional facilities. Moreover, loan-specific time-on-the-market only has statistical power in explaining the spread on institutional tranches.23
[TABLE 4]
This difference in the pricing behavior of the investor groups supports time-on-the-market as a measure of institutional demand as opposed to a measure of time-on-the-market-wide demand for credit.
The second strategy to separate an investment opportunities effect from an institutional demand effect is instrumental variable regression. Specifically, we use shocks to institutional investors’ capital, proxied by the net quarterly funds flow, as an instrument for institutional demand. Institutional investors may need to increase/decrease their investment in the loan market as they face large inflow/outflow from their
investors.24
23 As a group, institutional loans, have a significantly lower spread than bank-funded loans. This is not, however, a matched sample of bank and institutional facilities, so a lower spread could be the result of fundamentals. Tables 6 and 7 provide a detailed analysis of the differences in spread for the matched sample.
We include fund flow information for CDOs, prime funds, hedge funds,
24 The studies of the fund flows for institutional investors almost exclusively focus on mutual funds (e.g., Warther, 1995). The standard finding is that fund flow is a function of fund performance. There are relatively few studies on factors that drive fund inflow for other type of investors. Del Guercio and Tkac (2002) study pension fund flow and also find a positive relationship between past performance and capital
mutual funds, and finance companies. Between 2002 and 2007, CDOs were by far the largest institutional investors in the loan market, buying over half of the overall leveraged loan issuance. The second largest investor group is prime rate funds, which invest in 17% of the loans. High-yield and distressed hedge funds and mutual funds together were approximately 12% of the primary loan investors.25
Generally speaking, there can be two kinds of flow-of-funds measure: funds flow into institutional investors (changes on the right-hand side of the investor’s balance sheet) and funds flow from institutional investors into specific investments (changes on the left-hand side of the investor’s balance sheet). To proxy institutional investors’ demand for loans (credit supply), we would ideally use fund flow from institutional investors into the loan market. However, with the exception of CLOs and prime funds, we only observe funds flow into institutional investors. The implied assumption for using it as an
instrument for credit supply is that the allocation of funds across different asset classes is constant.
Flow of funds data comes from three sources. CDO and prime funds data comes from S&P Leveraged Lending Review and corresponds to the fund flow from these investors into the corporate loan market. Flow of funds for hedge funds is from the TASS database and is defined as the total net inflow of money into the hedge fund industry (i.e., capital additions to the total assets under management minus redemptions). Despite being based on voluntary reporting, the data covers a large cross-section of hedge funds. Flow of funds for financial companies and mutual funds are from the quarterly Flow of Funds
inflows. Pesando (1974) shows interest rates as a key factor affecting the flow of funds of life insurance companies.
Accounts published by the Federal Reserve.26
Table 5, Panel A reports the first-stage regression, where quarterly market-level
TOM is regressed on the contemporaneous flow of funds into different institutional investors. Due to measurement issues, the coefficients for CDOs and other institutional investors are not directly comparable, but the interpretation of the significance of individual coefficients should not be affected. Consistent with the institutional demand pressure hypothesis, we find that new CDO issuance and prime funds shopping for loans are both highly correlated with time-on-the-market. A one-standard-deviation increase in CDO issuance and prime fund flow leads to a decrease in the average loans’ syndication time of 1.5 days and 1.8 days, respectively. The effects of hedge fund and mutual fund flows are statistically insignificant. This is likely due to the inaccuracy of using the aggregate fund flow as a proxy for flow into the loan market.
These data provide the level of assets managed by each group of institutional investors. We calculate the flow of funds as a quarterly percentage change in assets. We do not know the total assets under management by CDOs and prime funds. Thus, CDO and prime funds’ fund flow is measured as a dollar amount, while other institutional fund flows are expressed as a percentage of the total assets under management. As was done for market-level time-on-the market, fund flow variables are lagged. Overall, we have fund flow information through the first quarter of 2007, which narrows our analysis to the second quarter of 2002 through the second quarter of 2007.
27
26
The data is available at
The adjusted R-squaredis
appreciation of the assets in the portfolio; in other words, an increase in assets can be attributed to either new money inflow to the industry or to capital appreciation of the existing assets.
27 Although we include fund flow for finance companies as a control, we do not count them as institutional investors because these are not lenders that are likely to be subject to non-fundamental sentiment.
According to Carey and Sharpe (1998), these firms act as traditional banks and tend to invest in smaller deals—$25 million to $200 million. In general, finance companies’ presence in the leveraged segment of
46%, which suggests that fund flow variables are a valid instrument for market-level time-one-the-market. We then use the predicted value of TOM-market from the first-stage regression as the explanatory variable for loan spread. The coefficient on the fitted value
of TOM-market remains positive and significant. The model’s goodness of fit, measured
by adjusted R-squared is also similar in magnitude to that of the original regression. The overall results suggest that institutional demand plays an important role in determining loan spread.
[TABLE 5]
4.3.2 Asymmetric information about loan quality (omitted fundamental variables)
The positive relationship between time-on-the-market and loan spread may also be due to asymmetric information between the lead banks and the institutional investors which participate in loan syndications. Although the literature has emphasized the
superior information owned by lead banks, the asymmetry of information can actually go both ways. First, lead banks have better information about the quality of the loans and may have an incentive to pass on low-quality loans to institutional investors. In this case, institutional investors face the traditional adverse selection problem: The more uncertain they are about a loan, the less willing are they to invest, and hence the higher spread required by the institutional investors. Second, institutional investors may know more about a borrower than the lead bank does. The fact that a loan is subscribed quickly may signal the quality of the loan; lead banks learn from the signal and decrease the spread. Since we do not observe the full information owned by banks and institutions, both cases point to the concern of missing fundamental variables.
the market is relatively small—they are responsible for the funding of approximately five percent of the entire leveraged loan market (Taylor and Sansone, 2007). Accordingly, we do not find fund flow to finance companies to be related to time-on-the-market.
To ensure that our results are not explained by asymmetric information about the quality of the loans, we look at the pricing difference between institutional and bank (pro-rata) tranches of the same loan. We expect that higher market-level and loan-specific institutional demand—higher time-on-the-market—result in lower loan spread for the institutional tranche than for the bank tranche of the same loan. In other words, we expect that the spread on institutional tranche deviates from the spread on bank tranche as a function of institutional sentiment. Given that bank and institutional tranches of a loan represent senior claims to the same cash flows—we excluded junior loan tranches from the analysis,—it is unlikely that the difference in spreads on the two tranches would be explained by an unobservable factor that is also explaining time-on-the-market.
Therefore, by looking at the price difference between the two tranches, we can potentially eliminate the effect of any missing factors related to fundamental cash flows.
In the sample unconstrained by availability of a control variable, there are 1,331 loans with loan spreads available for both institutional and bank tranches. We sort the observations into quartile portfolios according to time-on-the-market of the institutional tranche. For each quartile portfolio, we compute the average spread difference between institutional and bank tranches. The results are presented in Table 6. We find that, over our sample period, bank tranches consistently charge higher spreads than the institutional tranches for the same borrower. Moreover, the spread difference is monotonically
increasing in time-on-the-market. This result is true for both the market-level and individual-level institutional demand. Using individual level time-on-the-market as an example, for the first quartile (highest institutional demand, shortest time-on-the-market), it takes less than 12 days for a loan to be syndicated to institutional investors and these
institutional investors would be willing to accept the loan with a spread that is 84 basis points lower than the bank tranche of the same deal.28
In Table 1, the summary statistics on distribution of TOM-loan shows that loans syndicated in the earlier sample period have longer TOM. In Table 6 Panel B, because we sort all loans together, loans launched in earlier periods might be included in the longest time-on-the-market quartile and loans issued in the later period might be included in the shortest quartile. To mitigate this problem, we also sort loans issued in the same quarter and report the mean and standard deviation of the difference in spread across different quarters (i.e., this procedure is similar to Fama-MacBeth (1973) analysis). As expected, the spread differences decrease, but the monotonic relationship between the time-on-the-market and the spread difference remains.
In contrast, for the fourth quartile (lowest institutional demand, longest time-on-the-market), it takes more than 54 days for a loan to be fully subscribed by institutional investors and the institutional investors charge a spread only 14 basis points below its bank counterpart.
[TABLE 6]
It is interesting to note that if the loan remains unsubscribed for longer enough, the spread between bank and institutional tranches converges. This goes back to Lazear (1986) model. The loan stays on the market, because, in expectation, the lead bank believes that it can find enough investors to allocate the loan at the original spread. If that would not be the case, the spread would be adjusted up immediately. Also, if we think
28 The average difference between institutional and banks tranches is 43 basis points. An increase of 43 basis points translates to a 7% increase in the interest rate (based on 6.1% average LIBOR and the average spread). This is economically large, which is the point of our paper, but does seem to be a reasonable magnitude. One way to put our results in perspective is to think about IPO underpricing. According to Ritter and Welch (2002), the average first-day underpricing during the period 1995-2001 was 31%. Using median maturity and spread in our sample and using LIBOR as a discount rate, an increase in spread of 43 basis points approximately translates to a 2% increase in loan price (an increase of 84 basis points is a 4% increase in loan price).
about banks as informed agents setting the price at a fundamental value and institutions responding to the high demand by driving the spread below the fundamentals, this is exactly the result we would expect. Of course, this evidence is only suggestive. The prevalence of downward spread adjustment is likely to be a consequence of 2002-2007 being a credit expansion period.
Table 7 re-examines the results in Table 6 in a multivariate framework. The dependent variable is the spread difference between the institutional tranche and bank tranche of the same loan (all-in-drawn spread charged on the institutional tranche minus all-in-drawn spread charged on the bank tranche). We include facility-level information such as maturity, size, collateral, and secondary market liquidity to control for any mechanical difference between the institutional and bank tranches along these
dimensions. The positive relationship between time-on-the-market and spread difference found in Table 6 prevails after controlling for the facility-level characteristics.
[TABLE 7]
Our finding that institutional and bank tranches of the same loan charge different prices even though they share the same fundamentals indicates segmentation of pricing for banks and institutional investors.29
29 Market segmentation and the resulting mispricing have been extensively studied in both asset-pricing and banking literature. See, for example, Carey and Nini (2007) for the pricing difference between the
European and U.S. loan markets. Other papers that find evidence that assets with the same fundamentals don’t trade at the same prices include Froot and Dabora (1999), Lamont and Thaler (2003), and Mitchell, Pulvino and Stafford (2004).
This is a puzzling finding and one must wonder why borrowers do not take advantage of cheaper pricing by institutional investors. Conversely, if banks have the power to lock in a higher spread, why don’t they take advantage of it by fully funding the loan and selling it later to institutional investors? For the pricing difference to persist, there must be market frictions. Practitioners indicate that,
over the period of our analysis, borrowers indeed shifted away from banks and started to rely more on institutional funding. The share of TLB as a fraction of total loan increased from 49% to 66% between 2002 and 2006 (it was 26% in 1995). However, this
adjustment was slow compared to inflow of institutional cash into the leveraged loan market following the burst of the tech bubble. In addition, from the borrowers’ perspective, institutional investors’ reaction to sentiment can have a downside; for example, institutions could be more reluctant to renegotiate or refinance loans. Banks, on the other hand, have different incentives and are likely to price loans based on a
relationship with the borrower. A bank has to hold a fraction of the loan on its balance sheet in order to have an incentive to screen and monitor.30
The test reported in Tables 6 and 7 provides a broad solution to the omitted-variable problem. However, it is worth thinking about specific alternative explanations of our result. In particular, time-on-the-market could be driven by cross-sectional
differences in crafting the loan. More time might be needed to create documents for complicated borrowers and, at the same time, loans to such borrowers are riskier and command higher spread. Although both bank-tranche and institutional-tranche spreads should rise in this scenario, they might not rise in the same proportion. If there is a time trend in deal complexity and risk (for example, an increasing presence of LBO deals in our sample), then time fixed effects should control for it. To do deal with the cross-sectional variation, we try to measure loan “complexity” directly. Specifically, we search SEC filings for loan documents for all the loans in our sample that closed in 2006.
31
30
As part of this study, we interviewed Meredith Coffey at the Loan Syndications and Trading Association and Steven Miller at Standard and Poor’s.
For each loan, we then count (i) the number of pages in the contract, (ii) the number of pages
dedicated to any type of covenants, (iii) the number of pages dedicated to the financial covenants, and (iv) the number of financial covenants. We expect that complexity is reflected in a larger number of pages as it takes more words to express complex concepts. We also expect that financial covenants for complex loans would be more detailed, especially given that the type of complexity we address here has to be correlated with risk. We report the results in Table 8. The loans with the longest time-in-syndication appear, on average, to be eight pages longer than the loans with the shortest time-in-syndication, but this difference is driven by one particularly long contract and the result is reversed if that contract is removed from the sample. In addition, loans with longer time-on-the-market consistently have fewer financial covenants and fewer pages dedicated to financial covenants. Overall, however, we find that there are no statistical differences on any of the mentioned dimensions between contracts for the loans with the longest and the shortest time-on-the-market.
[TABLE 8]
To better understand at what point the information on the “hotness” of the deal is taken into account by the lead bank, we look at the change in spread on the institutional tranche of a loan between the launch date and the closing date. We expect that market-level institutional sentiment is incorporated into the initial spread and that the adjustment in spread is a function of loan-specific time-on-the-market. As can be seen in Table 9, we find that the market-level time-on-the-market indeed does not affect the change of spread, suggesting that banks have taken into account the market-level sentiment at the time they announce the deals. However, when the lead faces an unexpectedly high institutional
demand for a particular loan (as proxied by the time-on-the-market of an individual loan), the spread on the institutional tranche will be lowered accordingly.
[TABLE 9]
5. The role of structured financing
As mentioned earlier, CDOs were an increasing source of funding in the leveraged loan market over the period of our analysis.32
It is not clear would CDOs be more likely to overprice “hot” loans. One explanation could be lower information. As opposed to other institutional investors, CDOs’ incentives might be driven by fees, as opposed to the performance of the underlying portfolio. Investors buying tranches of CDOs do not have access to private information about the loan. CDOs, therefore, might have different incentives and be less informed about the fundamentals of the loans. This is consistent with a popular believe.
Not surprisingly, there is a tendency to attribute anything unusual that took place over this period to the role of securitization. For example, a recent paper by Shivdasani and Wang (2009) argues that the LBO boom was caused by CDO funding. Also, as shown in Table 5, CDO fund flow is important in explaining market-level time-on-the-market. So, in this last section, we investigate if the effect of institutional demand pressure on loan spreads is a result of securitization.
33
32 Strictly speaking, a CLO would include only loans while a CDO portfolio could include both bonds and loans. But the term CDO is often used to refer to any portfolio of collateralized fixed-income claims.
However, Benmelech, Dlugosz, and Ivashina (2009), find there is no difference in performance of loans with and without CDO investors. One could also argue that CDOs
33
See “Seeds of Credit Crunch Grow in LBO Loan Market,” Reuters, June 19, 2007 or “Easy Money: Behind the Buyout Surge, a Debt Market Booms—CLOs Spark Worries of Volatility and Risk; Loan Standards Loosen,” Wall Street Journal, June 26, 2007.
