Catering with Multiple Maturities

Nishant Dass* Massimo Massa**

Abstract

We study how firms choose their debt maturity structure. We argue that because of lower information-gathering costs, institutional investors prefer to invest in firms with bonds outstanding across multiple maturities. We show that, in segmented markets, this preference for firms with bonds of multiple maturities generates excess demand by institutional investors for these bonds. This greater demand is especially due to larger institutions, and mostly mutual funds and insurance companies. This results in lower bond yields, both in the primary as well as the secondary bond markets. Aware of these benefits, firms try to respond by issuing bonds across the spectrum of maturities. Geographical financial markets segmentation constrain the ability of the firms to exploit such a strategy.

JEL Classification: G12, G2, G32

Keywords: Bonds, Catering, Debt Maturity Structure, Bond Yields and Spreads, Financial Institutions.

* College of Management, Georgia Institute of Technology, 800 W. Peachtree St. NW, Atlanta, GA 30308; Phone: +1-404-894-5109; Email: nishant.dass@mgt.gatech.edu ** Finance Department, INSEAD, Boulevard de Constance, 77305 Fontainebleau, France; Phone: +33-1-6072-4481; Email: massimo.massa@insead.edu . We thank C. Lee, M. Campello, L. Cohen, R. Greenwood, H. Hong, and M. Roberts, as well as seminar participants at Georgia State and Georgia Tech for their helpful comments. All remaining errors are ours.

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Introduction

In 2007, IBM had outstanding $266 million of bonds with a maturity of less than 5 years,

$701 million of bonds with a maturity of between 5 and 10 years, and $1.875 billion of bonds

with a maturity of greater than 10 years. What is the rationale behind such a structure of

maturities?

There are few explanations and almost all are rooted in the demand for capital. Firms

engaging in asset and liability matching relate the debt maturity structure to the maturity of

their cash flows. Alternatively, different maturities may either be related to the overall choice

between private and public debt, or be affected by firm-specific characteristics such as

ratings, probability of distress, transparency, tangibility of the assets, and stability of the cash

flows (Rajan, 1992, Bolton, and Scharfstein, 1996, Hovakimian, Opler, and Titman, 2001,

Rauh and Sufi, 2009). The overall market conditions may also help explain the choice

between long-term and short-term financing if the firms act as providers of macro liquidity

and absorb the large supply shocks associated with changes in the maturity structure of

government debt. “When the government funds itself with relatively more short-term debt,

firms fill the resulting gap by issuing more long-term debt, and vice-versa” (Greenwood,

Hanson and Stein, 2008).

However, to our knowledge, there has been scarcely any effort to link the multiple-

maturity bond structure to the characteristics of the supply of capital: i.e., the institutions

investing in the bonds. This is all the more surprising considering that, unlike equity, bonds

are overwhelmingly held by institutions presiding over professionally managed portfolios.1

In this paper, we take a first step to address this issue. We study multiple-bond issuance

as a way of catering to the needs of institutional investors that try to minimize their

informational transaction costs. Consider a large institutional investor managing different

bond funds – e.g., Fidelity. Suppose this investor manages a short-term and a long-term fund,

both fully invested in corporate bonds. If the bond-issuing firms specialize in a particular

maturity – e.g., some in short-term debt and some in long-term debt – and the investor needs

to invest in both long-term and short-term bonds, then he will have to incur the information

gathering cost twice for investing in two firms that are specialized in specific maturities. If, on

the contrary, one firm issues across the spectrum of maturities, then the investor may focus on

collecting information only on such a firm, enjoying a more efficient information collection.2

1 Institutional investors, such as mutual funds and insurance firms, hold almost $4.3 trillion of corporate bonds, or about 85% of total outstanding amount. 2 This suggests that the manager may not necessarily have better information than the competitors. However, it would have collected its information in cheaper way.

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This would make the institutional investor prefer, ceteris paribus, the firm issuing bonds

across multiple maturities. A long-run relationship between the investor and such a firm may

thus arise, reducing the information costs for the institutional investor and creating a sort of

captive market for the issuer.

The implicit cost of this strategy for the institutional investor is the increased

concentration in the bonds issued by fewer firms. However, this does not seem to be an

overwhelming concern for institutional investors. Indeed, there is already substantial evidence

showing that while portfolio diversification considerations would induce portfolio managers

to diversify their holdings across firms, information considerations in general prevail, pushing

them to concentrate in fewer stocks (Kacperczyk, Sialm and Zheng, 2005) or hold locally

biased portfolios (Coval and Moskowitz, 1999, 2001).

We argue that this should be even more true for bond funds as the possibilities of

exploiting the information about the same firm are more numerous. Information on the default

probability and riskiness of a specific firm can be employed for investing in short-term,

medium term, and long term bonds, as well as in bonds with different covenants or collateral

features. Given that information is in general gathered at the family-headquarters level (e.g.,

Elton, Gruber and Green, 2007) and exploited by all the funds of the group, the coordinated

fund structure would make the aforesaid incentives stronger. All these elements increase the

benefit of focusing on fewer firms that issue bonds of multiple maturities and therefore raise

the demand for bonds of such firms.

The benefits should be higher for big financial institutions with funds operating across the

entire maturity spectrum. In particular, multiple maturity catering is more effective in the

presence of institutions that can exploit it and benefit more from it. These are active managers

for whom the cost of information collection is more relevant – e.g., mutual funds and

insurance companies – as well as institutions with more opportunities to invest across

different maturities – large institutions with a lot of money under management and/or

presiding over a family of specialized funds.

In the presence of market frictions and market segmentation, the higher demand by more

diversified investors should allow firms issuing bonds of multiple maturities to experience

higher bond prices and correspondingly command lower offering yields, regardless of the

maturity niche they target. In the presence of market segmentation and limited supply of

capital in each segment, spanning the spectrum of maturities would confer the firm a

competitive advantage. Aware of these benefits, firms would try to pursue a multiple-maturity

bond-issuance strategy. This incentive should be stronger if the firms compete for capital with

other similar firms – similar in terms of rating category and industry – that already have

bonds across multiple maturities.

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Of course, firms will be trading off the benefits of multiple-maturity catering with its

costs – mainly the transaction costs involved with multiple maturities issuance, the inability to

concentrate on the maturity more suited to the firm characteristics both in terms of maturity of

cash flows and risk (e.g., Diamond, 1991). Moreover, if geography and proximity investment

do effectively segment the potential demand each firm can cater to, the lack of proximity to

large asset managing groups – e.g., mutual fund families – restricts the firm ability to pursue a

multiple-maturity catering strategy. These factors would explain why only some pursue a

multiple-maturity strategy.

We study these issues using a sample of US corporations for the period 1998-2007. We

create a proxy for the degree of the firm’s “multiple-maturity catering”. It represents the

number of maturity niches in which the firm has already issued bonds. For example, a firm

may issue short term (maturity less than 5 years), medium term (maturity between 5 and 10

years), and long term (maturity longer than 10 years) bonds. A firm that has bonds

outstanding in 2 out of the 3 potential niches has a measure of multiple-maturity catering

equal to 2. A firm with bonds outstanding in all maturities has a multiple-maturity catering

measure equal to 3.

We start by testing whether institutional investors’ demand for bonds is affected by the

degree of multiple-maturity catering of the issuing firm. We show that this is indeed the case.

Overall, the more maturities the firm has issued in, the higher is the institutional investor

demand. In particular, the filling of one additional maturity niche increases the institutional

investor demand by 9%. These results are robust to controlling for market saturation, the

relative supply of bonds in a specific maturity niche as well as the possibility of a spurious

larger supply of bonds. They are also robust using this alternative measure of demand that

explicitly controls for the supply of bonds. This helps us rule out the possibility of a spurious

relation between multiple-maturity catering by the issuer and the demand from the

institutional investors.

Also, the demand for each firm is directly affected by the degree of multiple-maturity

catering by other firms competing for capital in the same market segment. This suggests that

firms that have issued across a wider maturity spectrum attract more demand than the ones

that issue across fewer maturities.

More importantly, in line with our expectations, the impact of multiple-maturity catering

on demand is relevant mostly when demand is represented by big institutions as well as

mutual funds and insurance companies. In particular, the filling of one additional maturity

niche increases the institutional investor demand by an insignificant amount for small

institutions and by 25% for large institutions. The difference between small and large

institutions is statistically significant. Also, multiple-maturity catering seems to affect mostly

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the behavior of mutual funds and insurance companies. The filling of one additional niche

increases the institutional investor demand by 6.5% for mutual funds and by 14% for

insurance companies. The effect of multiple-maturity catering on pension funds is significant

in general, but loses significance when we condition on a significant presence in a maturity

niche.

Also, the impact of multiple-maturity catering on demand is stronger if there is less

informational asymmetry or uncertainty about the firm – defined in terms of the firm being

listed on the NYSE, being rated, being older than average in its industry, and being closer to

the bond-holding investor. While more uncertainty increases the value of information and

therefore would increase the value of multiple-maturity catering, higher uncertainty also

increases the concentration risk. We interpret this result as due to the institutions’

unwillingness to concentrate their portfolio in issuers that are of questionable quality.

One important finding is that multiple-maturity catering does not affect the demand for

bonds by an institution whose affiliated bank has a lending relationship with this same firm.3

This can be explained as the benefit of multiple-maturity catering – the ability to use

information across different maturities – not being valuable in the case in which information

already comes almost freely from the lending arm. Also, if the main drawback of excessive

holdings of bonds of multiple-maturity catering firms is greater concentration risk, we expect

a financial group to be less willing to concentrate its positions – both loans and bonds – in the

same firm.

Next, we focus on whether firms exploit the investors’ preference for multiple-maturity

catering. We first document that the probability of issuing bonds in a niche that is either

empty or less exploited by the firm is 17% higher than average. We then relate the amount

issued in a specific maturity niche to the amount of bonds the firm already has outstanding in

that niche as well as to the competitors’ degree of multiple-maturity catering. We find that the

propensity of issuing bonds is directly related to the firm’s degree of multiple-maturity

catering. Firms are more likely to issue bonds in a specific maturity niche if they do not yet

have bonds outstanding in that maturity, and if the market is not yet saturated in that maturity.

A firm with 10% more coverage in the other niches (relative to the given niche) is likely to

issue 5.6% more in the given niche.

Also, in line with the demand side results, the incentive of issuing bonds across multiple

maturities is related to the degree of multiple-maturity catering by the other firms competing

for capital in the same market segment. The more maturity niches are filled by the

competitors, the higher is the incentive for the firm to engage in multiple-maturity catering. 3 It is interesting to note that a similar information argument would apply to the institutions also holding an equity stake in the sake firm. And indeed we find that multiple-maturity catering is less relevant in the case in which the institution has a sizable equity stake in the firm.

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Firms that are not rated, not listed on the NYSE, are younger in age, and are further from the

investing institution (i.e., firms with more informational uncertainty) discard multiple

maturity catering and concentrate in specific maturities.

We then identify a main exogenous determinant of multiple-maturity catering: the

availability of catering-interested capital. That is, given that multiple-maturity catering

appeals more to larger institutions/mutual funds and insurance companies, we expect firms to

be more willing to engage in multiple-maturity catering if the close-located institutional

capital is mostly managed by big institutions or managed by insurance and mutual funds. And

indeed, we find this to be the case. Also, a 10% higher coverage in the other niches (relative

to the given niche) makes the firm to offer a 6% larger issue in the given niche if there is

more potential local demand from larger institutions as compared to small institutions.

This provides an exogenous source of variation to study the effect of multiple-maturity

catering on bond yields. We find that multiple-maturity catering has significant pricing

implications. The more maturity niches an issuer has filled, the lower is the bond yield spread

at the time of issue. In particular, the filling of one additional niche translates into 19 fewer

basis points in the cost of the next issue. This is also reflected in the overall cost of borrowing

– firms with a more dispersed maturity structure command a lower yield in the secondary

bond market. The filling of one additional maturity niche translates into 83 basis points lower

yield spread.

In line with the findings on demand, the favorable impact of multiple-maturity catering on

offering yields is more pronounced when there is less informational uncertainty about the

issuer, and when there is a bigger exogenous supply of institutional capital of larger

institutional investors. Also, the impact of multiple-maturity catering on yields is

monotonically decreasing with maturity. One incremental filled maturity niche reduces the

offering spread by 9 bp for long term bonds, 18 bp for medium term bonds, and by 54 bp for

short-term bonds. This suggests that multiple-maturity catering mostly helps in the short-term

issues and is consistent with the fact that the main trade-off is concentration of risk.

Institutions deal with this issue by appreciating multiple-maturity catering only in the case of

relatively safer situations – i.e., firms that have lower uncertainty and bond issues that have

shorter maturity.

Given that the trade-off for the investor is to have a more concentrated portfolio, the

investor’s demand for multiple-maturity catering firms will be reduced when there is some

exogenous variation in the cost of holding a concentrated portfolio. We test this by exploiting

the natural experiment of the 2005 GM and Ford downgrade. This is a a unique experiment

involving an exogenous shock on the cost of multiple-maturity catering to the institutions.

While involving just two firms (GM and Ford), this event had vast repercussions for the entire

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market. The specific downgrade suddenly and drastically increased the cost of holding a

concentrated portfolio, and reduced the benefits of multiple-maturity catering. This should

reduce the price advantage of firms that have bonds outstanding across multiple maturity-

niches.

And indeed, we document that after the downgrade, multiple-maturity catering firms

experienced an increase in yield spread 7% higher (less negative) than that of single-maturity

firms. This is a particularly strong finding as most of the potential alternative hypotheses –

e.g., multiple-maturity being a proxy for size of the firm, recognition in the market, age,

rating, probability of default, riskiness, tangibility of the assets, cash flows, etc. – would have

predicted the very opposite!

Our findings provide a direct link between bond maturity and the institutional investors’

demand, and thereby offer a first attempt at a supply-side explanation of the bond maturity

structure. The extant literature has related the choice of bond financing to firm specific

characteristics (such as rating, probability of default, riskiness, tangibility of the assets, cash

flows). However, analysis of the supply-based determinants has been largely ignored (Rajan,

1992; Bolton and Scharfstein, 1996; Hovakimian, Opler, and Titman, 2001). Bond financing

in itself is often considered structurally similar to loan-financing. The focus in the literature

has mostly been on the determinants of the firm’s choice of the type of debt (e.g., Diamond,

1984; James, 1987; James and Wier, 1988; Diamond, 1991; Rajan, 1992; Houston and James,

1996; Cantillo and Wright, 2000; Hovakimian, Opler, and Titman, 2001; Denis and Mihov,

2004) and on the firm’s debt maturity choice (e.g., Bolton and Scharfstein, 1996). We

contribute to this vast literature by directly considering the choice of multiple-maturity

issuance and its implications for bond value.

Second, our findings add to the literature on “catering”. Baker and Wurgler (2005) have

shown that firms choose the payout policy to cater to the shareholders. Baker and Wurgler

(2000) and Baker, Greenwood, and Wurgler (2003) show how security issuance is a response

to mispricing, while Baker and Wurgler (2002) provide evidence that the firm’s capital

structure is influenced by managers’ market-timing behavior. Baker, Stein, and Wurgler

(2003) show that financially constrained firms display a higher sensitivity of issuing equity to

current market valuation. Greenwood, Hanson, and Stein (2008) document that when the

government funds itself with relatively more short-term debt, firms fill the resulting gap by

issuing more long-term debt, and vice-versa. All these studies share the same hypothesis that

firms act as arbitrageurs in the market to take advantage of temporary “mispricing”. We

contribute to this literature by showing evidence of catering in bond issues and its cross-

sectional implications.

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Finally, our results also help to explain bond yield spreads in terms of market-induced

reasons. The empirical asset pricing literature has not yet been able to properly identify the

determinants of the yield spread. Recently, Chen et al. (2007) and Collin-Dufresne et al.

(2001) have argued that there is a common component but they do not identify it. We provide

some evidence on one of the factors that explain yield spreads: the issuing firm’s multiple-

maturity catering dimension.

The remainder of the paper is structured as follows. Section 2 lays out our main testable

hypotheses. Section 3 describes the sample and the main variables we use. Section 4 studies

bondholder demand and how it is affected by the degree of multiple-maturity catering by the

issuing firm. Section 5 relates offering yields in the primary market and effective yields in the

secondary market to the degree of multiple-maturity catering by the issuing firm. Section 6

studies the supply side by analyzing the determinants of the firm’s choice to fill an additional

niche. Section 7 analyses a case study: the GM and Ford bond downgrade. A brief conclusion

follows.

2. Main Hypotheses and Testable Propositions

We start by considering the demand side. Most of the bonds are held by professional investors

– i.e., mainly mutual funds and insurance companies – that follow specialized investment

strategies. While portfolio diversification considerations dictate that portfolio managers

should diversify their holdings across firms, asset managers do instead hold concentrated

portfolios if this helps them to better exploit their informational advantage (e.g., Kacperczyk,

Sialm, and Zheng, 2005). This is also consistent with mutual funds exhibiting a strong

preference by investing in locally headquartered firms, where they appear to have

informational advantages (Coval and Moskowitz, 1999, 2001).

If this is the case for equity, we argue that this should be true even more in the case of

bonds. And indeed, similar and even stronger results hold for the bond mutual funds and

insurance-managed funds (Massa and Zhang, 2008). Indeed, unlike equity, bonds provide

more flexibility to the investment managers. Better information on the cash flows of IBM can

be exploited by investing in IBM stocks. Better information on IBM’s default probability and

riskiness can be employed by investing in short-term, medium-term, and long-term bonds, as

well as in bonds with different covenants, or collateral, or other features. Therefore, while the

potential gains are lower than those that would accrue to an equity investment, still the

potential strategies in which they can be employed are numerous.

Moreover, information is generally gathered at the group-headquarters level – mutual

fund family, insurance group, asset management concern – and exploited by all the funds of

the group (e.g., Elton, Gruber, and Green, 2007). For example, mutual funds families manage

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many different bond funds that differ in terms of maturity (e.g., money market funds, short-

term, medium-term, and long-term corporate bond funds) but they are likely to be in the same

rating class. Therefore, information about IBM’s prospects can be used by the family’s short-

term, medium-term, and long-term funds to invest in IBM’s short-term, medium-term, and

long-term bonds, respectively. There should therefore be an incentive for the financial group

to specialize and collect information on firms whose bonds span multiple maturities as this

provides it with a more versatile way of exploiting its information.

In the case of bonds, there are also additional benefits accruing from concentrating the

investment in few firms. Theory posits that lender concentration reduces the risk of

coordination failure among multiple claimants at the stage of renegotiation or default (Rajan

1992; Bolton and Scharfstein, 1996; Preece and Mullineaux 1996; Berglöf et al., 2000). Also,

concentration of holdings reduces the free-riding problem of monitoring with many

bondholders, letting the concentrated bondholder to internalize the benefits of monitoring

(Leland and Pyle, 1977; Diamond, 1984; Holmstrom and Tirole, 1997; Boot and Thakor,

2000; Sufi, 2007).

Overall, these considerations suggest that institutional investors should prefer to

concentrate their investment in firms that issue bonds across multiple maturities. A long-run

relationship between the institutional investor and the firm may arise, reducing the

information costs for the institutional investor and creating a sort of captive market for the

issuer. This lets us to propose our first hypothesis.

H1a: Institutional investors’ demand is positively related to the number of maturity

niches the firm has issued bonds in.

We argue that multiple maturity catering increases both the probability that institutional

investors invest in all the maturity issues of the same firm, as well as the overall demand of

the institutional investor in the specific firm. That is, not only multiple-maturity catering

makes institutions to invest in all the maturity niches issued by the firm, but it also increases

the overall demand of the institutions for the specific firm. This can be justified in different

ways. First, investors are less likely to hold a stake in firms that do not pursue multiple-

maturity catering. This would automatically increase the share in the portfolio of the firms

who do it. Second, multiple-maturity catering makes it more efficient the collection of

information on a firm, presumably allowing a bigger collection, as well as increasing

familiarity with it. And indeed, we will show that one of the side-effects of multiple-maturity

catering is to create a long-term relationship with the lenders. Both higher information and

higher familiarity reduce the sensitivity to risk of the investor, increasing the percentage

investment in the multiple-maturity catering firm.

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Given that the benefits of multiple-maturity catering are related to the ability of the

investor to exploit the information about the firm across many maturities, we expect the

impact of multiple-maturity catering on demand to be stronger for larger institutions – e.g.,

families that have funds which invest across different maturities, bigger groups. The intuition

is that larger institutions have more opportunities of exploiting the synergies accruing from

multiple-maturity catering, as compared to smaller ones. They would therefore be more

interested in multiple-maturity catering firms than focused firms. For example, compare a

large family of funds managing short-, medium- and long-term funds to a small boutique

fund-manager specialized in short-term funds. The large fund-family will be able to exploit

the information acquired from examining a single firm, and have its short-term funds invest in

the short-term bonds of this firm, have its medium-term funds invest in the medium-term

funds, and so on. Instead, the boutique fund-manager, being only focused on short-term

bonds, will not care about multiple-maturity catering. This allows us to provide cross-

sectional implications to the previous hypothesis.

H1b: The effect of multiple-maturity catering on demand is stronger for larger fund

families.

Also, multiple-maturity catering should be more appealing for active managers for whom

the cost of information collection is more relevant – e.g., mutual funds and insurance

companies.

What are the price implications of this for the issuing firm? In the presence of market

frictions, market segmentation and limited supply of capital, multiple-maturity catering may

increase the demand for a particular firm at the expense of focused issuers. In addition,

multiple-maturity catering may induce a substitution towards institutional ownership as well

as towards bigger and more diversified fund families. These families may have a lower

sensitivity to risk per dollar invested either because they are more diversified and manage

bigger portfolios – i.e., they can invest larger sums of money in the same asset without it

representing a sizable share of their portfolio – or because they can coordinate the behavior of

their funds and provide liquidity to the fund facing withdrawal needs. This suggests that

multiple-maturity catering should translate into higher prices, and therefore lower yields. This

allows us to propose our second hypothesis.

H2: The cost of borrowing is negatively related to the number of maturities the firm has

issued bonds in.

Finally, what are the corporate policy implications for the issuing firm? If multiple-

maturity issuance augments investor demand and reduces the cost of borrowing, firms should

have an incentive to issue bonds across multiple maturity niches. This allows us to propose

our third hypothesis.

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H3: The firm’s propensity to issue bonds in a given maturity-niche is positively related to

how empty the given niche is for the firm relative to its other maturity niches.

In all these cases, the firm’s multiple-maturity issuance is just determined by its

incentives to meet the market demand. We can therefore interpret this behavior as a form of

catering with multiple maturities (e.g., Baker and Wurgler, 2005).

One important question is: if multiple-maturity catering is an effective way of reducing

the cost of borrowing, why not all the firms pursue it? This is related to the financial markets

constrains. Multiple maturity catering is effective in the presence of institutions that can

exploit it and benefit more form it. We argued that these are active managers for whom the

cost of information collection is more relevant – e.g., mutual funds and insurance companies

– as well as institutions with more opportunities to invest across different maturities – large

institutions with a lot of money under management and/or presiding over a family of

specialized funds. Given that institutions tend to invest in close firms (Coval and Moskowitz,

1999, 2001, Massa and Zhang, 2008), the potential demand is constrained by geography

consideration. If markets are geographically segmented, the benefits of pursuing a multiple

maturity catering should be there only for firms that are located in a regional/local capital

markets with a high presence of insurance/mutual funds and big institutions and investment

groups. That is, if geography and proximity investment do effectively segment the potential

demand each firm can cater to, the close proximity to large complexes of insurance or mutual

funds restricts the ability to pursue a multiple-maturity catering.

There is another important element to consider: competition. If bond markets are

segmented and the demand for bonds slopes down, then issuing bonds across multiple

maturities becomes a strong tool to acquire capital market share at the expense of competing

firms. For example, assume that Pimco’s Long-term Fund invests in a set of firms with BBB

rating. If BBB-rated firms A, B, and C have already issued bonds across multiple maturities,

and therefore have been selected for investment by Pimco, then the ability of a similarly-rated

firm D to attract the investment from Pimco will be limited. This is more so if firm D only has

bonds of a particular maturity, as opposed to bonds of multiple maturities. So, the incentive

for D to become a multiple-maturity issuer is stronger, the fiercer the competition for

financing is. This suggests that the institutional demand for the bonds of a firm is negatively

related to the degree of multiple-maturity catering of its direct competitors in the financial

market. While we do not consider this as a separate hypothesis, we will explicitly control for

it throughout the paper.

We now turn to the empirical testing, starting with a description of our measure of bond

issuance across multiple maturities.

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3. Data and Empirical Testing Issues

3.1 Main sources of data

We draw our data from multiple sources: Compustat, CRSP, Lipper’s eMAXX, and

Mergent/FISD Corporate Bond Dataset. Data on ownership of corporate bonds are from

Lipper’s eMAXX fixed-income database. This dataset contains details of fixed-income

holdings for nearly 20,000 entities that include U.S. and European insurance firms; U.S.,

Canadian, and European mutual funds; and leading U.S. banks as well as public pension

funds. It provides information on quarterly ownership of more than 40,000 fixed-income

issuers, with $5.4 trillion in total fixed income par amount. These data cover the first quarter

of 1998 through the second quarter of 2007.

We focus on US institutional investors and their holdings of bonds issued by US firms

(about 1800 institutional investors every quarter, holding a total face value of about $80

billion on average). For these institutions, Lipper EMAXX reports the holdings based on

regulatory disclosure to the National Association of Insurers Commissioners (NAIC, for

insurance companies) and the Security and Exchange Commission (SEC, for mutual funds

and asset managers), and on voluntary disclosure by the major pension funds. Thomson

Financial’s 13F dataset contains information on the equity positions of investment companies

holding US equities.

The dataset reports both the holdings at the individual fund level and their aggregated

value at the family (of funds) level. The fund holdings list down the individual holdings of

various funds in bonds of different firms (identified by CUSIP), while the family holdings list

aggregate values at the managing firm level. LIPPER also provides data for the different

issues of bonds by a given firm.

Information on corporate bond issues is drawn from Mergent’s Fixed Investment

Securities Database (FISD), between 1998 and 2006. Mergent’s database provides extensive

bond information on approximately 68,000 issues. It reports: Full Bond Description,

Prospectus Issuer Name, Maturity Date, Offer Date, Offer Amount, Coupon Rate, Security

Level (Senior, Junior, etc.), Offer Amount/Full Amount Outstanding History, Offer Price and

Yield, Callability Features, Ratings, Convertible Debt Information, Underwriters, Private

Placement information, and Trustees and Fiscal Agents.

We first match Lipper and Mergent databases. Then, we merge these data with the

CRSP/Compustat database. We consider all non-financial, non-utility firms that appear in the

merged CRSP/Compustat database as well as have bonds reported by Lipper in the period

1998Q1-2007Q2. We exclude firms with book equity below $250,000 or assets below

$500,000. We further require all the variables and relevant lags thereof to be available for all

12

observations. We use Compustat to construct the standard measures of firm characteristics:

Firm Size, Leverage, Market-to-Book ratio, Cash Balances, Cashflows, Capital Expenditure,

Z-score, and Tangibility.

3.2 The construction of the main variables

The main variable that we focus on is the degree of multiple maturity issuance or the issuer’s

“multiple-maturity catering”. For each firm, this variable captures the number of maturity

niches in which it has already issued. We consider three main maturity niches: short term

(less than 5 years), medium term (between 5 and 10 years), and long term (more than 10

years). For a firm with bonds outstanding in 2 out of the 3 potential niches, the multiple-

maturity catering measure equals 2. A firm with bonds outstanding in all the 3 maturities has

a multiple-maturity catering measure equal to 3.4 Summary statistics reported in Table 1

show that in our sample the median (average) number of maturity niches filled is 2 (2.156).

3.3 Firm characteristics

Here, we provide a brief description of several firm-characteristics that we employ as control

variables. Size is the logarithm of sales (item 12). Leverage is the long-term debt (item 9) plus

debt in current liabilities (item 34) to assets (item 216 + item 9 + item 34) ratio. Cash is total

cash (item 1) to lagged assets (item 6) ratio. Capital Expenditure is capital expenditures (item

128) to lagged assets (item 6) ratio. Market-to-Book is market equity (item 25 x item 199) to

book equity (item 60) ratio. These data are obtained from CRSP-Compustat Merged database.

We also use year- and 48 industry-dummies (Fama and French, 1997).

To control for the credit riskiness of the firm, we use a Z-score, Tangibility ratio, and

credit ratings. Z-score is constructed as: [3.3 x pre-tax income (data170) + sales (data12) +

1.4 x retained earnings (data36) + 1.2 x (current assets (data4) – current liabilities (data5)) +

0.6 market equity (data25 x data199)] / book assets (data6). Tangibility ratio is: net PPE

(data8)/book assets (data6). The ratings are retrieved from the Lipper database, and we use

two proxies for measuring ratings. The first is an indicator variable that equals to 1 if the bond

has an S&P rating, and 0 otherwise. The second is built by defining the ratings as ranging

from “no rating” through “AAA-rated or higher”, and dividing the issuing firms into the

following five groups: those with no rating belong to group 0 and the rest are divided into

four equal groups.

We also consider some characteristics of the bonds issued, such as whether it is a private

placement (i.e., an indicator variable equal to 1 if the bond is privately placed and 0

otherwise), whether it has covenants attached (i.e., an indicator variable equal to 1 if the bond

4 We also experimented with using 5 niches and our main results hold as well. However, given that the average number of niches is around 2, we think 3 to be a more appropriate number of potential niches.

13

has covenants attached to it and 0 otherwise). Data on covenants are retrieved from the

Mergent Corporate Bond Database or Fixed Investment Securities Database (FISD) from

Mergent Inc., between 1998 and 2006. Callability and convertibility of bonds are defined

similarly. We use these variables as dummies when the analysis is carried out at the bond

level (in the yield regressions), while we take the value-weighted average at the firm level if

the analysis is at the issuing firm level (in the demand regressions). In this case, the weight is

the size of the issue.

In order to separate the sample into firms that have a banking relationship and those

which do not, we also construct a proxy of “banking relationship”. It is a dummy variable that

equals 1 if firm i has completed a relationship-lending deal (defined as a deal in which at least

one of the lead arrangers has lent to the borrower in the three years prior to the deal date) in

the past five years, and 0 otherwise. The construction of this variable is analogous to Bharat et

al. (2005), and is in line with the literature on relationships between firms and lenders (e.g.,

Boot, 2000; Boot and Thakor 2000; Berger and Udell, 1995; Petersen and Rajan 1994, 1995;

Yasuda, 2005). To construct this dummy, we obtain individual loan-origination data from the

DealScan database of the Loan Pricing Corporation (LPC) for the years 1989-2005.

4. Multiple-maturity Catering and Investor Demand

We now look at how multi-maturity catering affects institutional investor demand. We first

consider the overall relationship (H1a) and then we qualify it in terms of the characteristics of

the investors (H1b).

4.1 The Overall Relationship

We start by testing whether there is an overall link between the demand of the institutional

investors and the degree of multiple-maturity catering of the firm (H1a). We estimate:

tjitjitjtjtj,i, XNN F ,,,,3,2,1 εβββα ++++= − , (1)

where Fi,j,t is either the number of maturity niches issued by the firm in which the ith

institutional investor5 has invested6 or the fraction of institutional investor i’s portfolio

invested in the bonds of firm j at time t. In the latter case, Fi,j,t is the ratio between the

investment by investor i in all the bonds of the jth firm and all the other bonds that this

investor holds in its portfolio that are characterized by the same rating. Nj,t is the degree of

multiple-maturity catering (as defined above) by the jth firm, and N–j,t is the average degree of

5 All the funds managed by the same fund manager or insurance group or pension fund group are aggregated. The intuition is that multiple-maturity catering should affect the family as a whole as it allows to economize the transaction costs across all the funds it manages. 6 This variable ranges from 0 to 1 in the case the institution has invested in all the maturities niches in which the firm has issued.

14

multiple-maturity catering by the other firms competing for capital with the jth firm. We

define “competing firms” as those other firms whose bond-issues are closer substitutes of the

firm under consideration. Specifically, “competing” firms are those that have similar credit

ratings and belong to the same industry (48 industries as in Fama and French (1997)).

Xi,j,t is a vector of control variables that includes firm characteristics, such as size,

leverage, market-to-book, cash holdings, cash flows, Z-score, tangibility ratio, as well as a

dummy variable for bond-specific characteristics, such as whether the bonds are convertible,

callable, privately-placed, and have covenants. We also include a variable that proxies for the

overall excess supply of bonds in the specific maturity niche. This is constructed as the ratio

between the total amount of bonds outstanding in the niche (issued by all the firms in the

market) and the total amount of bond outstanding in the market across all the niches. This

variable measures the excess supply in a specific niche and the potential demand imbalance

and helps to control for the potential spurious correlation with the firm’s supply of bonds.

We estimate equation (1) both as a pooled regression with quarter, industry, rating-group

and family-type fixed-effects as well as a Fama MacBeth specification. Given that the results

are consistent, we report only the former. The errors are clustered at the family level.

We report the results in various panels of Table 2. We first consider bonds of all

maturities together and then we break the sample down into three groups by maturity. Bonds

of maturity less than 5 years are classified as short-term (ST), those with maturity greater than

or equal to 5 years but less than 10 years are classified as medium-term (MT), and those with

maturity greater than or equal to 10 years are classified as long-term (LT). The independent

variables Issuer’s Multiple-maturity catering and Competitors’ Average Multiple-maturity

catering in the first four columns are based on the mere presence in a niche while in the next

four columns, a niche is counted only when the issuer has a “significant” presence (at least

20% of its outstanding bonds) in that maturity niche.

In Panel A of Table 2, we report the main specification, while in Panels B, C, and D, we

break down the analysis in terms of whether the issuer has less or more informational

uncertainty/asymmetry (Panel B), the size of the institutional investor (Panel C), as well as

the type of institutional investor (Panel D). In Panel E of Table 2, we look at the effect that an

existing loan from an investor-affiliated bank has on the demand in response to multiple-

maturity catering. In Panel F of Table 2, we show that our results are robust to explicitly

controlling for the supply of bonds by the issuing firm. Finally, in Panel G of Table 2, we

measure the persistence of families and show that it is positively related to the issuer’s

multiple maturity catering. In Panels B–G of Table 2, we include all the control variables in

our tests that are shown in Panel A, but we leave their coefficients unreported in these

subsequent Panels for brevity.

15

We start with Table 2, Panel A. The results show that the demand by institutional

investors is strongly positively related to the degree of multiple-maturity catering by the firm

and negatively related, albeit in a somewhat weaker form, to the degree of multiple-maturity

catering by the competing firms. The more niches the firm has filled, the higher is the

probability that the institution has invested in all of them and the higher is its overall

investment in the firm.

The results hold across the different specifications and different maturities, and are robust

to the inclusion of the different sets of control variables. In particular, the fact that they are

robust to the inclusion of the proxy for the overall excess supply of bonds in the specific

maturity niche suggests that our variable of multiple-maturity catering does not spuriously

proxy for a simple excess supply in a specific niche.

We also rule out any spurious relation between the demand by investors and the supply of

bonds by a given firm. We do this by redefining the dependent variable as the ratio of the

investor’s holdings in the firm to the outstanding bonds of the specific firm. That is, instead of

measuring the portfolio weight of the firm in the investor’s portfolio, we measure the

institutional ownership in the firm. Again, we find statistically and economically similar

results – the filling of an additional niche leads to greater demand by the institutions, as

measured by their ownership in the firm. These results are left unreported for brevity. Besides

being robust to controlling for market saturation and relative supply of bonds in a specific

maturity niche, the results are also economically significant. In particular, the filling of one

additional niche increases institutional investor demand by 15%.

In Table 2, Panel B, we test whether this effect on demand is stronger when there is less

uncertainty or informational asymmetry about the firm. We measure asymmetry by: Listed on

the NYSE, which is a dummy variable equal to 1 if the issuer is listed on the NYSE, and 0

otherwise; Issuer is Rated, which is a dummy variable equal to 1 if the issuer has a credit

rating, and 0 otherwise; Firm’s Age, which is the number of years since the firm first

appeared in CRSP-Daily database; and Distance between Issuer and bond-holding Family,

which is the distance in miles between the issuer and the bond-holding family. We find that

the institutional investors hold more bonds of a firm issuing multiple maturities only when

there is less uncertainty or informational asymmetry about the issuer. We interpret this as the

institutions’ unwillingness to concentrate their portfolio in issuers that are of questionable

quality.

These results are also economically significant. The filling of one additional maturity

niche increases the institutional investor demand by 4% more compared to the base case, if

the firm is older than average or closer to the institution. For the case of being listed or rated,

16

we see that multiple-maturity catering has a statistically significant effect on the demand only

for listed or rated firms.

4.2 Conditioning on the Type of Potential Demand

We now focus on the characteristics of the investors and see how they affect demand. We

expect the demand due to multiple-maturity catering to be positively related to the ability of

the institutional investor to exploit it – i.e., its size (H1b). We measure the size of the

institution as the face value of all its bond holdings, expressed as a logarithm. We categorize

the institutions as small (medium and large) if their total assets are in the bottom (middle and

top, respectively) tercile in that quarter.

In Table 2, Panel C, we test whether the effect on demand depends on the size of the

institutional investor. The results show that the impact of multiple-maturity catering is mostly

concentrated in the large-sized institutions, with some weak evidence seen in medium-sized

institutions. In particular, if we focus on large-sized institutions, the previous results become

even more significant. The filling of one additional niche increases the institutional investor

demand by an insignificant amount for the small institutions and by 25% for large institutions.

The difference between small and big institutions is statistically significant.

This result is important as it helps to rule out the spurious correlation between higher

demand for a firm and the fact that the firm may have more bonds outstanding than other

similar firms. Indeed, if that were the case, the results would have opposite sign and would

be stronger for small institutions because the portfolio weight of small institutions would be

more easily affected by a higher supply of bonds than that of bigger institutions.

In Table 2, Panel D, we study the impact of multiple-maturity catering on demand by

classifying institutions into the different types. We consider banks, brokers, insurance

companies, mutual funds and pension funds. The results show that the impact is mostly

concentrated in mutual funds and insurance companies. The filling of one additional niche

increases institutional investor demand by 6.5% for mutual funds and by 14% for insurance

firms. This can be explained by the fact that these two types of investors tend to be more

active managers. This reduces the latter’s interest in multiple-maturity catering firms. Pension

funds are significant in the first set of specifications, but lose significance when we condition

on a significant presence in a niche. Banks, on the other hand, are not significant in the

former, but are in the latter set of specifications.

Next, we study whether the presence of a loan from a bank affiliated with the institutional

investor alters that investor’s appetite for multiple-maturity catering by the firm. As we

argued, we expect the impact of an extra maturity niche filled on the demand for bonds to be

weaker in the case where the investor has a bank lending relationship with that firm.

17

We define the existence of a bank loan as one that was originated in the two years before

the quarter under observation and has a tenor that is longer than 2 years – i.e., the loan is still

active in the current quarter.7 The results are reported in Table 2, Panel E and support our

working hypothesis. Niche-filling affects the demand only in the case where there is no

affiliated bank loan. An additional niche filled has a null effect on the demand by the

institution whose affiliated bank is in a lending relationship with the firm. That is, institutions

are reluctant to concentrate their bond portfolio in the bonds of a firm whom they have

already indirectly lent to via a bank loan.

Next, in Table 2, Panel F, we check for the robustness of our main demand results by

redefining the dependent variable such that it explicitly accounts for the supply of bonds by

the issuing firm. This is done by taking the aggregate institutional bond-ownership in the firm

as a fraction of the firm’s total bonds outstanding. The results are consistent with those

reported in Panel A of Table 2 above. The results are also economically significant – filling

an additional maturity niche increases overall institutional bond-ownership by 5.5%. This

rules out the possibility that the demand results are being spuriously driven by a greater

supply of bonds by the firm.

Finally, in Table 2, Panel G, we directly test whether the issuers generate a captive market

by catering with multiple maturities. We do this by specifically defining a dependent variable

that reflects the investors’ persistence – i.e., the ratio of the holdings or number of investors in

the current quarter that were also holding bonds of this issuer in the previous quarter. The

results are reported in Panel G of Table 2. They show that the persistence of the investors

increases with the filling of an additional niche. The results are economically significant

besides being statistically strong; we find that the filling of an additional niche increases

persistence by 3.3%.

Overall, these results are consistent with our working hypothesis and show a statistically

strong and economically significant relationship between the degree of multiple-maturity

catering of the issuer and the multiple-maturity interest of the investor (H1a). The cross-

sectional variation is in the direction suggested by our working hypothesis (H1b).

We then consider the effect of the competing firms. We argued that the demand for the

bonds of a firm should be diminished when there is more competition for capital in a

segmented market. And indeed, we find that the demand for each firm is directly affected by

the average degree of multiple-maturity catering by the other firms that are competing for

capital in the same industry-ratings segment. Specifically, as shown in Panel A of Table 2, the

filling of one additional maturity niche by the average firm in the industry-rating segment

7 Our results are qualitatively similar if we alternatively define the existence of a bank loan either by dropping the condition on the tenor or selecting loans that started in the last 5 years.

18

reduces institutional investor demand for bonds of the given firm by 3.6% in general and by

4.3% if we measure multiple-maturity catering by a significant presence in the niche.

The analysis of the different subsamples provided in Panel A of Table 2 reveals that

similarly demand is lowered by 11% (5.6% and an insignificant amount) for long-term bonds

(medium-term and short-term bonds, respectively). From Panel D we see that only the

demand of mutual funds is affected by the competing firms’ average multiple-maturity

catering. Specifically, with one more niche filled by the competitors, the mutual funds’

demand goes down by 4.9%. Finally, Panel E shows that the competitors’ average multiple-

maturity catering has a significantly negative impact of 3.7% on the demand, even after

controlling for the presence of a loan from the bond-holding investor.

5. The Choice of Multiple-Maturity Catering

Overall, the previous findings document an interest of the institutional investors in multiple-

maturity issuance firms. In the next section, we will see that this preference leads to lower

yields for these firms (H2). In this section, we consider how the firms react to this preference

shown by the institutional investors and the subsequent benefit that the bond market sees in

multiple-maturity catering. That is, do firms try to cater to this demand (H3)? Even if

logically after, we choose to consider hypothesis H3 before hypothesis H2 as the estimation

of the firm decision allows us to set up the econometric framework that will allow us to

properly test the effect on the yields.

We relate the decision to issue in a specific maturity to the amount of bonds the firm

already has outstanding in such a niche as well as to the competitors’ degree of multiple-

maturity catering. We consider two alternative specifications. The first relates the issuance

choice in one maturity niche to the amount offered in other niches. That is, it assesses whether

the firm pursues multiple-maturity issuance. The second specification relates the degree of

multi-maturity choice of a firm to its main determinants. The first specification estimates:

tnjtnjtjtnj,n,t XNE A ,,1,,101,91,8 εβββα ++++= −−−− , (2)

where the dependent variable is the dollar-amount offered in a bond issue, standardized by the

firm’s asset size. En,t-1 is a measure of how “empty” the particular niche, in which this bond is

issued, is relative to other niches (i.e., it’s a proxy for the excess supply in that niche). N-j,t-1 is

the average degree of multiple-maturity catering of the other firms competing for capital with

the jth firm. The definition of “competing firms” is the same as above. Xi,j,t-1 is a vector of

control variables. These are the same firm characteristics that we used in the demand

regressions above.

19

We augment it by adding the market yield spreads for the three maturity niches. These

variables are meant to capture the firm’s decision to time the market issuing bonds when their

yields are lower. We expect the decision to issue in a specific maturity niche to be positively

related to how empty that niche is (En,t-1). We also include the amount of potential bond

demand in the region. We rely on the literature on geography (e.g., Coval and Moskowitz,

1999, 2001, and Huberman, 2000) and argue that the greater the capital available to be

invested close-by, the higher is the potential demand for the bonds of the firm. We define the

capital available as simply the logarithm of the assets managed by the “local” institutional

investors8, where proximity is defined as being within 250 miles from the issuer’s location.9

As in the previous case, we estimate both a pooled specification with time and industry fixed

effect and clustering at the firm level as well as a Fama MacBeth specification. Given that the

results are consistent, for brevity we report only the former.

The main results are reported in Table 3, Panel A. The first five columns in Panel A only

use the sample of bond-issuing firms and the next five columns look at the entire universe of

Compustat firms, while controlling for the selection bias. These results show a strongly

positive relation between the amount of bonds issued in a specific maturity niche and how

empty that niche is. A firm with a 10% more “empty” maturity niche (i.e., with a 10% more

coverage in the other niches) is likely to offer a 6% larger issue in this emptier niche. This

holds across the different specifications and for different control variables. For example, in

columns (3)-(5) and (8)-(10), we also control for any simultaneous bonds offerings made by

competing firms, and our results go through. More importantly, this holds even after

controlling for the selection bias inherent in our sample of firms that issue bonds at all. It is

worth noting that Heckman’s (1979) Lambda, reported in the last five columns of Table 3,

Panel A, is highly significant in most cases, thus attesting to this bias. This supports our

working hypothesis that firms do respond to the greater demand. The signs of the control

variables are also as expected. More profitable and levered firms are less likely to issue bonds

while firms with more growth opportunities are more likely to issue bonds.

We expect firms to be more willing to engage in multiple-maturity catering when there is

less uncertainty about them and when there is more institutional capital of larger investors

available close to them. We test these hypotheses in Panels B and C, respectively, of Table 3.

In Panels B–D of Table 3, we include all the control variables in our tests that are shown in

Panel A, but we leave their coefficients unreported in these subsequent Panels for brevity. In

Panel B, we interact our variable of interest (En,t-1) with the level of uncertainty about the firm

(whether it is high or low). As before, we use four measures of informational uncertainty

8 We also experimented by standardizing the amount of bond capital by the book value of the firms located in the region as well as by their book value of debt. The (unreported) results are consistent with the reported ones. 9 Again, the results are robust to altering the proximity radius.

20

about the issuing firm – listing on the NYSE, a credit rating, greater than average firm age,

and smaller than average distance from the institutional investor (all of which reflect less

uncertainty about the issuer).

The results support our working hypothesis that firms with less information asymmetry

are more likely to respond to the investor’s preference for multiple-maturity catering firms. In

particular, a 10% emptier niche (or 10% higher coverage in the other niches) makes the

NYSE-listed firm likely to issue a 5.6% larger offering in the emptier niche than the firm that

is not NYSE-listed. Likewise, the offering size of an older firm is 4.8% larger than a younger

firm’s in response to an emptier niche. Similar results hold for our other measures of

informational asymmetry.

In Panel C of Table 3, we interact our variable of interest (En,t-1) with the amount of

capital that small/medium/large institutions located close-by have available to invest in the

firm. As before, we define the capital available as the fraction of total bond holdings of all

these institutional investors (irrespective of whether they hold bonds of the given firm) and

proximity as being within 250 miles from the issuer’s location.10 The results reported in Panel

C support our working hypothesis. They show that firms are more likely to engage in

multiple-maturity catering if they have more potential demand in their local market. A 10%

emptier niche (or 10% higher coverage in the other niches) is related with a 6% larger

offering in the emptier niche if there is more potential local demand by larger institutions as

compared to smaller ones.

The second specification relates the degree of multi-maturity choice of a firm to its main

determinants. The specification estimates:

tjtjtjj,t DX N ,,12,11 εββα +++= , (3)

where Nj,t is the degree of multiple-maturity catering of the jth firm and Xj,t is the vector of

control variables defined as above. Dj,t is a vector that proxies for local preferences for firms

engaging in multiple-maturity bond-issuance. The results in the previous section showed that

preference for multiple-maturity bond-issuance is mostly concentrated within mutual funds

and insurance companies and within big institutions. We therefore consider three proxies. The

first is the ratio between mutual fund and insurance companies over total institutional

investors (defined either in terms of institutions or in terms of asset managed by them). The

second is the ratio between big and small institutions (also defined either in terms of

institutions or in terms of asset managed by them). The third is the average niche-filling by

other firms in the same industry-rating group. We expect the decision to issue in a specific

10 Again, the results are robust to altering the proximity radius.

21

maturity niche to be positively related to these three proxies, and indeed that is what we find.

These results are left unreported for brevity.

Next, we test the firm’s choice of multiple-maturity directly as a catering decision.

Specifically, we measure the “benefit” of filling all three niches versus filling only one niche,

and test whether firms are more likely to fill an additional niche when this benefit is higher.

The benefit of filling three niches versus filling only one niche is calculated as the difference

between the residuals from the regression of yields of those firms that have filled all three

niches and the residuals from the regression of yields of those firms that have filled only one

of the three niches. The difference between these residuals is our independent variable,

Benefit of Three Niches. The dependent variable is a dummy variable that equals 1 if the firm

issues bonds in an empty niche or alternatively, a dummy variable that equals 1 if the firm

issues bonds in niches that are less than fully filled.11

These results are reported in Panel D of Table 3. In columns (1) and (2), the dependent

variable equals 1 if the firm issues bonds in a so-far empty niche. In the remaining columns,

the dependent variable equals 1 if the firm issues bonds in a niche that is not empty but is less

than fully-filled. We find a significantly positive relationship between the Benefit of Three

Niches and the propensity to fill a niche when that niche is less than fully-filled. The odds of

issuing bonds in a niche that is less than 30% full are 17% higher (as of column (8)).

5.1 Robustness checks

One potential econometric issue is selection bias. Indeed, all the estimates in Table 3 are

based on the firm having already decided to issue bonds. This may induce a selection bias if

the variables that determine the size of bond-issue are the same variables that explain the

decision to issue bonds in the first place. To address this issue, we adopt a two-step

procedure, in which we first estimate a Probit model of the probability of issuing bonds and

then we estimate an expanded specification of equation (3) that contains the inverse Mills

ratio (Heckman’s (1979) Lambda) constructed from the first stage.12

The results confirm the previous ones. Given that this is not the main focus of the paper,

we will not dwell on these results. They are however reported along with the main results in

Panels A and B. It is just worth mentioning that the firms more likely to issue bonds are the

ones that have lower cash flow volatility, are larger in size, have higher leverage and

11 Given that there are three potential niches, we consider a niche as less than fully filled if it is less than one-third full. 12 The dependent variable in the first-stage regression is a dummy variable taking the value of 1 if the firm issues bonds and zero otherwise. The explanatory variables in this first stage are the firm-specific control variables, lagged by one year. These include: Relationship Banking, Change in MB, Cashflow Volatility, Size, Leverage, Cash, Capital Expenditure, Cashflow, Market-to-Book, Z-score, and Tangibility ratio. We also include year and 48 industry-dummies.

22

tangibility ratio as well as higher capital expenditures. In contrast, firms with more cash and a

higher z-score are less likely to issue bonds.

Overall, these results are consistent with the demand and yield results documented above,

and show a strongl positive relationship between the amount of bonds issued in a niche and

the emptiness of that particular niche.

6. Multiple-Maturity Catering and Bond Yields

We are now ready to estimate the impact of investor preferences for multiple-maturity

catering on the yields of the firms pursuing such a strategy. We saw that multiple-maturity

catering has the effect of increasing the demand of institutional investors and in particular of

big institutions, mostly mutual funds and insurance companies. We now study if this

translates into more favorable market prices (H2) or it is just substitution of demand from one

type of investors to another.

We start by relating the bond yields of the firm to whether it pursues multiple-maturity

catering. This provides evidence on the correlation. To properly pin down causality, we also

use some exogenous source of variation of multiple-maturity catering. Throughout the

analysis, we consider both the initial bond offering (primary market) and the secondary bond

market. We estimate:

tbjtbjttjtjtbj XTYNNY ,,,,76,5,4,, εββββα +++++= − , (4)

where Yj,b,t is the yield on the bth bond issued by the jth firm at time t and TYt is the yield on a

Treasury bond of similar maturity. In an alternative specification, we also replace the yield on

the left-hand side with a yield spread. This bond yield spread is constructed as the difference

between the bond yield and the yield of an equivalent maturity Treasury bond. The firm-

specific control variables, denoted by Xj,b,t, are defined as before. Nj,t is the degree of multiple-

maturity catering of the jth firm and defined as it was above, and N–j,t is the average degree of

multiple-maturity catering of the other firms competing for capital with the jth firm. As above,

we define “competing firms” as those other firms whose bond-issues are closer substitutes of

the firm under consideration. Competing firms have both similar credit ratings and belong to

the same industry.

Given that the analysis is done at the bond level, we augment the set of control variables

by adding bond specific characteristics such as maturity, convertibility, existence of

covenants. The data on yield, time-to-maturity, and convertibility are obtained from Mergent

FISD. The data on Treasury constant maturity interest rates are obtained from the FRED

database of the Federal Reserve Bank in St. Louis. The data on secondary market yields on

bonds is from Bloomberg. The observations in equation (2) are at the issue-level.

23

We estimate equation (4) both for the primary and the secondary market. For the case of

primary market, Yj,b,t is the offering yield or offering yield-spread, while for the secondary

market, it represents the yield or spread for the traded bond in the secondary market. We

expect the yield to be negatively related to the firm’s degree of multiple-maturity catering.

We estimate a pooled specification with time, ratings, and industry fixed-effects and cluster

the standard errors at the issuing firm level.

The results are reported in Tables 4 and 5, respectively, for the primary and secondary

markets.

6.1 The Effect of Multiple-maturity catering in the Primary Market

We start with the results on the primary market reported in Table 4, Panel A. The dependent

variable in the first four columns is the percentage yield on the bonds issued by the firm and

in the next four columns it is the spread over a Treasury Bond that’s closest in maturity. The

results show that the degree of multiple-maturity catering reduces the offering yield. This

holds across all the alternative specifications as well as after instrumenting. The results are

not only statistically significant, but also economically relevant. One incremental filled

maturity niche reduces the offering spread by 19 bp in general.

In Panel B of Table 4, we split the sample according to the maturity of the bond issued.

Again, we classify bonds as short-term (ST) if their maturity is less than 5 years, as medium-

term (MT) if their maturity is greater than or equal to 5 years but less than 10 years, and as

long-term (LT) if their maturity is greater than or equal to 10 years. The results show that the

impact is monotonically decreasing with maturity. One incremental filled maturity niche

reduces the offering spread by 9 bp for long-term bonds, 18bp for medium-term bonds, and

by 54 bp for short-term bonds. This suggests that multiple-maturity catering mostly helps in

the short-term issues. This is consistent with the intuition that the main trade-off is

concentration of risk. Institutions deal with this risk by concentrating only on relatively less

uncertain and safer firms.

In the previous section, we showed that the impact of multiple-maturity catering on the

demand is stronger for the firms with lower informational uncertainty as well as for larger

institutional investors. This greater impact on demand should then translate into a bigger

impact on yields. Therefore, we expect multiple-maturity catering to reduce the offering

yields more in the case in which there is less informational asymmetry or uncertainty about

the issuing firm and there is more institutional capital of larger institutional investors in the

proximity. We test this hypothesis in Panels C and D of Table 4. In Panels B–D of Table 4,

we include all the control variables in our tests that are shown in Panel A, but we leave their

coefficients unreported in these subsequent Panels for brevity.

24

In Panel C, we interact the firm’s multiple-maturity catering with the informational

asymmetry about the firm. As before, we use four measures of uncertainty or information

asymmetry – listing on the NYSE, having a credit rating, the firm’s greater than average age

in years since it first appeared on CRSP Daily, and the lower than average distance from the

institutional investor (all of which reflect less information asymmetry about the issuer). We

find that the yield-reducing effect of filling an additional maturity niche is present only in the

case where there is less informational asymmetry about the issuing firm. This result reinforces

the earlier claim – made in Table 2, Panel B – that the institutions are not willing to

concentrate their bond-portfolios in those multiple-maturity catering firms that are of

questionable quality. The effect of filling an additional maturity niche ranges between 9 and

14 bp for the less-uncertainty subsample and is not different from zero for the remaining

subsample.

6.2 The Effect of Multiple-maturity catering in the Secondary Market

We now look at the secondary market. We report the results in Table 5. The dependent

variable in the first four columns of Panel A in Table 5 is the percentage yield on the issuing

firm’s bonds in secondary-market trading. In the next four columns, it is the spread over a

Treasury Bond that’s closest in maturity. In columns (1)-(2) and (5)-(6), multiple-maturity

catering is measured by the mere presence in a niche, while in the remaining columns, a niche

is only counted when the firm has a significant presence (at least 20% of outstanding bonds)

in that maturity niche.

The results are consistent with those found for the primary market and document a

distinct and sizable effect of multiple-maturity catering on the cost of borrowing for the firm.

Firms that have filled more niches command a lower yield in the secondary bond market. The

results hold across the different specifications. After controlling for industry, time, and ratings

fixed-effects and measuring multiple-maturity catering with a significant presence (in column

(4)), we find that the incremental effect of filling one additional maturity niche reduces the

effective yield by 45 bp. The results are economically and statistically similar if the dependent

variable is the yield spread.

In Panel B of Table 5, we break the analysis down by the maturity of the bonds. In the

interest of space, we only report the results using the multiple-maturity catering measure that

is based on the mere presence in a niche. Unlike the primary market, the impact is not

monotonically decreasing by maturity – the secondary-market yield is 90 bp lower for short-

term bonds, while the reduction is highest for the medium-term bonds (110 bp) and only

about 22 bp for the long-term bonds. The estimations based on a significant presence in the

niche deliver similar results and hence left unreported – we find a 57 bp lower yield for the

25

short-term bonds, while the reduction is highest for medium-term bonds (61 bp) and almost

non-existent for the long-term bonds.

6.3 Dealing with Endogeneity and the GM and Ford Downgrade Experiment

We now address the endogeneity of the multiple-maturity catering decision of the firm. The

fact that the firm has filled three maturity niches as opposed to just one is influenced by the

external demand around the firm, as described in equation (2) above. We consider two

exogenous sources of variations: the first is cross-sectional and is based on the exogenous

local investor preferences and the second is based on an experiment that changes investor’s

preferences.

We start with the cross-sectional variation in the determinants of multiple-maturity

catering. We rely on the results from equation (2) and use them to generate an exogenous

variation in multiple-maturity catering. In particular, we estimate an instrumental variable

specification in which we instrument the multiple-maturity catering of the firm with the

multiple-maturity catering tendency of competing firms in the same industry-rating group as

well as two proxies for institutional preferences: the ratio between mutual fund and insurance

companies over total institutional investors and the ratio between big and small institutions.

These are ideal instruments as they directly affect the firm’s multiple-maturity catering,

but should not affect the yields through any other channel than their impact on multiple-

maturity catering. And indeed, the results reported in Panel C of Table 5 show that the

Hansen test of overidentification supports the exogeneity restriction, while the F-test of the

first stage shows that they are above the threshold for weak instruments (Staiger and Stock,

1997).

The column-headings in Panel C describe the specific sample we analyze. Overall, our

results hold for all bonds put together but they are clearly strongest for the short-term bonds

and become monotonically weaker as the maturity lengthens. This monotonic effect of

maturity is consistent with the results reported earlier. Besides, the results are equally strong

if we use secondary-market spreads instead of yields as the dependent variable.

The second approach to deal with endogeneity relies on an exogenous source of variation

identified through an experiment. We argued that investor demand for multiple-maturity

catering firms is related to the degree of concentration of its portfolios. Therefore, if the cost

of holding a concentrated portfolio suddenly increases, then the demand for the bonds of

multiple-maturity catering firms should decrease and this should then reduce the advantages

of multiple-maturity catering. We can test this by using a unique experiment: the reaction to

the GM and Ford bonds’ downgrade.

26

On May 2005, the public debts of GM and Ford were downgraded to junk bond status.

While the downgrading had been expected, the severity of the cut, especially in the case of

GM, which went down two grades from BBB- to BB, sent a shock to the market, triggering a

wide-spread sell-off affecting as much as $400 billion worth of debt. We use the downgrade

and the subsequent broader sell-off as an exogenous shock and test how it affected the

demand for the bonds of multiple-maturity catering firms. Given that the higher uncertainty

and risk increased the cost of holding a concentrated portfolio, this represents an exogenous

shock that should reduce the appeal of multiple-maturity catering. This provides an ideal

scenario to assess the value of multiple-maturity issuance for institutional investors as the cost

of holding a concentrated portfolio shot up.

We focus on the change in yield around the event. We define a crisis dummy that equals 1

for the second quarter of 2005 and 0 otherwise. Then, we regress the change in yield on the

crisis dummy, the degree of multiple-maturity catering, their product, and the standard set of

control variables. The dependent variable is either the quarterly yield of each bond issue

calculated by averaging monthly yields during a quarter or the quarterly yield change

calculated by first averaging monthly yields during a quarter and then subtracting the average

of the previous quarter.

The results are reported in Panel D of Table 5. In the first four columns, we estimate the

effect of the mere presence of the issuer in a particular niche while in the next four columns,

we count only a “significant” presence of the issuer in a particular niche – i.e., where the

issuer has at least 20% of its outstanding bonds. The results show that the degree of multiple-

maturity catering is not always related to quarterly changes in the spread before the

downgrade event. However, it becomes quite related for the quarter when the crisis happens.

In particular, if we consider the period around the crisis (from one quarter before to one

quarter after the event – in second, fourth, sixth, and eighth columns of Table 5, Panel D), the

interaction between the event dummy and the degree of multiple-maturity catering is

significantly positive. After the downgrade, multiple-maturity catering firms experienced an

increase in yield spread 6% higher than that of single-niche issuance firms.

Conclusion

We study how firms choose the maturity structure of their debt. We argue that the multiple

maturity choice is due to the desire of firms to exploit the information transaction costs of the

institutional investors holding bonds. Institutional investors have a preference for firms that

issue bonds across many maturities as this reduces the information gathering costs. If one firm

issues across all the spectrum of maturities, then the investor may specialize in collecting

information only on that firm. A long-run relationship between the investor and such a firm

27

may arise reducing the information costs for the institutional investor and creating a captive

market for the issuer.

We study these issues using a sample of US corporations for the period 1998-2007. We

create a measure of “multiple-maturity catering”. For each firm, this represents the number of

maturity niches in which it has already issued. We show that institutional investor’s

preference for multiple-issuance explains higher demand for multiple-issuance firms. The

effect is stronger the lesser is the uncertainty about the firm. Indeed, the investors are

unwilling to concentrate their portfolios in firms that are of questionable quality. This

generates excess demand and lower bond yields, both in the primary market – offering yields

– and in the secondary market – effective yields.

Aware of these benefits, the firms then try to pursue a multiple-maturity catering strategy.

The probability of issuing in a specific maturity niche is negatively related to the firm having

already issued in such a niche and positively related to the other firms with which they

compete for capital already follow a multiple issuance strategy.

We use an event – the GM and Ford downgrading – to see how the preferences of

institutions for multiple-maturity catering decreases when the cost of portfolio concentration

drastically increases. We show that after the downgrade of GM and Ford, the costs of holding

a concentrated portfolio increased suddenly, which reduced the price advantage of multi-

issuance strategies.

Overall, our findings provide a first analysis of the effect of supply side on the bond

financing maturity structure, and provide a direct link between the maturity structure and the

institutional investor demand.

28

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32

Appendix: Variable Definitions

Market value of assets: stock price (data199) * shares outstanding (data25) + short term debt(data34) + long term debt(data9) + preferred stock liquidation value (data10) – deferred taxes and investment tax credits (data35).

Market-to-Book Ratio: market value of assets/book assets (data6)

Total Debt: long term debt (data9)+short term debt (data34)

Book Leverage: total debt/book assets(data6)

Market Leverage: total debt/market value of assets

Altman’s z-Score: 3.3 * pre-tax income (data170) + sales (data12) + 1.4 * retained earnings (data36) + 1.2* (current assets (data4) – current liabilities (data5)) / book assets (data6)

Firm Size: log(sales) (data12)

Asset Tangibility: net PPE (data8)/book assets (data6)

Cash Holding: cash and short-term investments (data1)/lagged assets(data6)

Cash Flow: (depreciation and amortization (data14)+ income before extraordinary items(data18))/lagged assets (data6)

Investment: capital expenditures(data128)/lagged assets(data6)

Cash Flow Volatility: Cashflow volatility is the standard deviation of all annual cashflows before the year of issue, with at least 10 consecutive observations available in that pre-bond sample period.

Offering Bond Yield Spread: Spread between a bond’s yield to maturity at the offering date and the yield to maturity on a government bond with similar maturity. The bond’s yield at the offering date is retrieved from the Mergent database; yields on government bonds are retrieved from The data on Treasury constant maturity interest rates come from the FRED database (Federal Reserve Bank of St. Louis).

Bond Yield Spread: Our data on bond yield are obtained from Bloomberg database. The relevant treasury rates come from FRED database. We adjust bond yield in the following way: for bond i at month t, we first calculate the time to maturity and subtract the corresponding treasury constant maturity rate from its bond yield. In detail, the adjustment is the 90-day T-bill rate if the time to maturity is less than one year, the 2-year T-bill rate if the time to maturity is between 1 and 5 years, and the 10-year treasury constant maturity rate if time to maturity is larger than 5 years.

Credit Ratings Dummies: four dummy variables created according to credit rating (thin).

Industry Dummies: Two-digit SIC industry dummies

Rating Availability: Indicator variable equal to 1 if the bond has a S&P rating, and 0 otherwise. Rating data are retrieved from the Lipper database.

Private placement: Indicator variable equal to 1 if the bond is a private placement and 0 otherwise. Private placement data are retrieved from the Lipper database.

Covenants Indicator: Variable equal to 1 if the bond has covenants attached to it and 0 otherwise. Data on covenants are retrieved from the Mergent database.

33

Table 1: Summary Statistics Table 1 provides summary statistics characterizing the main variables used in our analyses. Panels A, B, and C exhibit only those variables that are used in the analysis of the demand, supply, and yields, respectively, of bonds. These demand, supply, and yields results are shown in Tables 2, 3, and 4, respectively. The summary statistics are presented separately because of the different databases used in the respective portions of the analyses. Variable definitions are provided in the Appendix.

Table 1, Panel A: Variables used in the analysis of the demand for bonds

DEPENDENT VARIABLES

Units N Mean Median Std. Dev. Institutional Investor's Overall Holdings of an Issuer's Bonds % 200482 0.275 0.102 0.494 Institutional Investor's Holdings of an Issuer's ST Bonds % 200442 0.256 0.000 1.334 Institutional Investor's Holdings of an Issuer's MT Bonds % 200336 0.301 0.018 1.103 Institutional Investor's Holdings of an Issuer's LT Bonds % 199077 0.304 0.000 1.794

INDEPENDENT VARIABLES

Issuer’s Multiple-maturity catering 0/1 200482 2.156 2.000 0.835 Competitors’ Average Multiple-maturity catering 0/1 200482 2.130 2.273 0.623 Firm Size logarithm 200482 8.454 8.526 1.657 Leverage fraction 200482 0.523 0.499 0.207 Market-to-Book Ratio fraction 200482 9.518 3.659 79.323 Cash fraction 200482 0.122 0.038 1.230 Cashflows fraction 200482 0.107 0.084 1.762 Z-Score fraction 200482 1.449 1.388 1.101 Tangibility fraction 200482 0.644 0.360 13.073 Relationship Banking 0/1 200482 0.776 1.000 0.417 Convertible 0/1 200482 0.118 0.000 0.253 Covenants 0/1 200482 0.785 0.848 0.237 Callable 0/1 200482 0.727 0.861 0.327 Private Placement 0/1 200482 0.003 0.000 0.034 NYSE 0/1 200482 0.836 1.000 0.370 Rating Group integer 200482 2.812 3.000 0.890 Firm’s Age years 200482 30.273 25.500 25.239 Distance miles 192585 988.066 826.114 723.666 Institutional investor is Small-sized 0/1 200482 0.319 0 0.466 Institutional investor is Medium-sized 0/1 200482 0.337 0 0.473 Institutional investor is Large-sized 0/1 200482 0.344 0 0.475

34

Table 1, Panel B: Variables used in the analysis of the supply of bonds

DEPENDENT VARIABLE

Units N Mean Median Std. Dev. Offer Amount, as a fraction of issuer’s asset size logarithm 958 2.885 3.198 1.586

INDEPENDENT VARIABLES

Units N Mean Median Std. Dev. How Empty is this Niche relative to other Niches fraction 958 16.537 1.918 35.000 Overall Average Investor Holdings in this Niche % 958 31.828 29.166 8.747 Competitors’ Average Multiple-maturity catering 0/1 958 2.109 2.235 0.595 Simultaneous Bond Offerings by Competitors logarithm 958 1.195 0.070 1.673 Firm Size logarithm 958 9.032 8.983 1.302 Leverage fraction 958 0.500 0.507 0.156 Market-to-Book Ratio fraction 958 4.617 3.793 6.207 Cash fraction 958 0.048 0.022 0.073 Capital Expenditure fraction 958 0.079 0.059 0.075 Cashflow Volatility 958 0.710 0.064 30.88 Z-Score fraction 958 1.645 1.508 0.859 Tangibility fraction 958 0.447 0.411 0.298 Asset Maturity 958 12.027 10.609 8.642 Convertibility of Outstanding Bonds 0/1 958 0.060 0.000 0.173 Covenants on Outstanding Bonds 0/1 958 0.733 0.767 0.249 Private Placement of Outstanding Bonds 0/1 958 0.001 0.000 0.017 Callability of Outstanding Bonds 0/1 958 0.435 0.419 0.341 NYSE 0/1 958 0.975 1.000 0.156 Rating Group integer 958 3.014 3.000 0.918 Firm’s Age years 958 48.729 47.125 20.534 Distance miles 852 878.626 703.104 435.316 Small institutional investor in the proximity (within 250 miles) fraction 871 0.010 0.002 0.069 Medium institutional investor in the proximity (within 250 miles) fraction 871 0.113 0.059 0.187 Large institutional investor in the proximity (within 250 miles) fraction 871 0.877 0.937 0.208

35

Table 1, Panel C: Variables used in the analysis of the yields of bonds

DEPENDENT VARIABLE

Units N Mean Median Std. Dev. Bond Yields at the time of Offer in the Primary Market % 2185 6.880 6.570 1.857 Bond Spreads at the time of Offer in the Primary Market % 2185 1.801 1.210 1.662 ST-Bond Yields at the time of Offer in the Primary Market % 284 5.760 5.610 1.787 ST-Bond Spreads at the time of Offer in the Primary Market % 284 1.297 0.845 1.468 MT-Bond Yields at the time of Offer in the Primary Market % 826 7.006 6.540 2.091 MT-Bond Spreads at the time of Offer in the Primary Market % 826 2.180 1.550 1.950 LT-Bond Yields at the time of Offer in the Primary Market % 1040 7.037 6.700 1.514 LT-Bond Spreads at the time of Offer in the Primary Market % 1040 1.607 1.185 1.349

INDEPENDENT VARIABLES

Units N Mean Median Std. Dev. Issuer’s Multiple-maturity catering 0/1 2185 1.952 2.000 0.862 Competitors’ Average Multiple-maturity catering 0/1 2185 2.054 2.087 0.641 Simultaneous Bond Offerings by Competitors logarithm 2185 1.431 0.053 1.993 Firm Size logarithm 2185 8.573 8.612 1.826 Leverage fraction 2185 0.511 0.496 0.201 Market-to-Book Ratio fraction 2185 9.618 3.800 64.718 Cash fraction 2185 0.161 0.037 1.946 Capital Expenditure fraction 2185 0.111 0.067 0.185 Tangibility fraction 2185 0.500 0.442 0.375 Z-Score fraction 2185 1.444 1.442 1.270 Convertible 0/1 2185 0.043 0.000 0.203 Rule 415 Regulation 0/1 2185 0.610 1.000 0.488 Rule 144A 0/1 2185 0.315 0.000 0.465 Covenants 0/1 2185 0.405 0.000 0.491 NYSE 0/1 2011 0.866 1.000 0.341 Rating Group integer 2011 2.738 3.000 1.089 Firm’s Age years 2011 29.327 25.500 24.993 Distance miles 1570 965.997 798.863 477.813 Small institutional investor in the proximity (within 250 miles) fraction 830 0.024 0.001 0.134 Medium institutional investor in the proximity (within 250 miles) fraction 830 0.160 0.068 0.245 Large institutional investor in the proximity (within 250 miles) fraction 830 0.816 0.925 0.278

36

Table 2, Panel A: Demand for Bonds by Institutional Investors and Issuer’s Multiple-maturity catering

Table 2, Panel A shows the impact of the issuer’s multiple-maturity catering behavior on the institutional investors’ demand for that issuer’s bonds. The dependent variable is the fraction of an institutional investor’s portfolio invested in an issuer in a given quarter and the independent variable of interest is Issuer’s Multiple-maturity catering. It is a discrete variable that measures the presence of an issuer across the various niches of bond maturities – the more categories or niches of maturities that the issuer has outstanding bonds in, the higher the multiple-maturity catering variable. We define three niches of maturities – short term, medium term, and long term – and therefore, the multiple-maturity catering variable takes values 1, 2, or 3. The control variable Competitors’ Average Multiple-maturity catering is the average of multiple-maturity catering across all other issuers in the same industry and rating group; all firms are categorized into 48 industries as per Fama and French (1997). All firms without ratings are assigned to a rating group 0, and those with ratings are divided into four groups. Issuer’s Multiple-maturity catering and Competitors’ Average Multiple-maturity catering in the first four columns are based on the mere presence in a niche while in the next four columns, a niche is counted only when the issuer has a “significant” presence (defined as at least 20% of the issuer’s outstanding bonds) in that niche. Overall Average Investor Holdings in this Niche is the average of the fraction of an investor’s holdings in that particular niche, where the average is calculated across all institutional investors. Institutional Investor’s TNA is a proxy for the investor’s size, and is measured as the logarithm of aggregate bond holdings. Issuer-specific control variables – Firm Size is logarithm of sales, Leverage is the ratio of debt to debt plus equity, Market-to-Book is the ratio of market equity to book equity, Cash is the cash holdings as a fraction of lagged assets, Cashflow is the sum of earnings and depreciation as a fraction of lagged assets, Z-score is the Altman’s (1968) Z-score for bankruptcy risk, Tangibility is the ratio of fixed assets to lagged total assets, Relationship Banking is a dummy variable that marks whether the issuer has any relationship banking currently, Convertible is the average of a dummy variable measuring convertibility across bonds of that issuer, Covenants, Private Placement, and Callable are measured similarly. The Appendix provides more details on the variable definitions. Also included are four sets of dummy variables that control for fixed effects of ratings, type of institutional investor, time (quarter), and issuer’s industry. The column headings indicate the sample that we analyze – ST, MT, and LT refer to short term, medium term, and long term bonds.

Presence in a Niche Significant Presence (>20%) in a Niche

All Bonds ST Bonds MT Bonds LT Bonds All Bonds ST Bonds MT Bonds LT Bonds Issuer's Multiple-maturity catering 0.0250*** 0.0814*** 0.0345*** 0.1535*** 0.0263*** 0.0890*** 0.0483*** 0.0864*** [6.79] [9.05] [4.66] [9.40] [7.05] [10.86] [5.53] [6.20] Competitors' Avg Multiple-maturity catering -0.0100** 0.0122 -0.0168** -0.0350* -0.0119*** -0.0097 -0.0173* -0.0111 [-2.59] [0.87] [-2.06] [-1.78] [-2.75] [-0.66] [-1.68] [-0.56] Number of other Niches in which the Investor holds Bonds of same Issuer (=1 or 2)

-0.3025*** -0.2340*** -0.3586*** -0.2995*** -0.2355*** -0.3541*** [-10.42] [-8.54] [-9.43] [-10.37] [-8.57] [-9.26]

Overall Average Investor Holdings in this Niche

0.001 0.0056** 0.0115** 0.0027 0.0052** 0.0135*** [0.22] [2.40] [2.41] [0.62] [2.34] [2.93]

Institutional Investor's TNA -0.1234*** -0.1163*** -0.1374*** -0.1206*** -0.1235*** -0.1167*** -0.1373*** -0.1222*** [-16.18] [-10.86] [-13.39] [-8.27] [-16.21] [-10.89] [-13.35] [-8.39] Firm Size 0.0296*** 0.0328*** 0.0422*** 0.0483*** 0.0313*** 0.0370*** 0.0421*** 0.0745*** [10.36] [5.32] [7.57] [5.36] [11.11] [5.53] [8.04] [8.63] Leverage 0.0166 0.0146 -0.0053 0.0478 0.0175 0.0162 -0.008 0.0844* [1.22] [0.47] [-0.23] [1.00] [1.30] [0.53] [-0.35] [1.77] Market-to-Book 0.0000 -0.0001*** 0.0001 0.0000 0.0000 -0.0001*** 0.0001 -0.0001** [0.80] [-2.67] [1.31] [-1.13] [0.64] [-3.39] [1.19] [-2.08]

37

Cash 0.0164*** 0.0026 0.0349*** -0.0001 0.0172*** 0.0038 0.0350*** 0.0122 [3.52] [0.33] [3.32] [-0.01] [3.70] [0.46] [3.33] [1.13] Cashflow -0.0343* 0.1017*** -0.0222 -0.0728 -0.0298 0.1148*** -0.0184 -0.0368 [-1.83] [3.53] [-0.71] [-1.36] [-1.58] [3.85] [-0.59] [-0.71] Z-score -0.0101*** -0.0356*** -0.0146** -0.0112 -0.0124*** -0.0423*** -0.0170*** -0.0303*** [-3.13] [-5.01] [-2.48] [-0.98] [-3.85] [-5.81] [-2.83] [-2.74] Tangibility 0.003 -0.0142*** -0.0004 0.01 0.0024 -0.0160*** -0.0009 0.0041 [1.09] [-3.43] [-0.08] [1.25] [0.85] [-3.73] [-0.20] [0.53] Relationship Banking 0.0097** 0.0165 0.0352*** -0.0069 0.0107*** 0.0209* 0.0368*** -0.0008 [2.38] [1.48] [4.04] [-0.43] [2.63] [1.87] [4.24] [-0.05] Convertible 0.0236 0.1296*** -0.0159 0.1562*** 0.0245 0.1329*** -0.0126 0.1511*** [1.08] [3.10] [-0.54] [2.71] [1.13] [3.17] [-0.43] [2.64] Covenants 0.0304*** 0.0574 0.0624*** 0.0794** 0.0304*** 0.0569 0.0598*** 0.0980*** [3.07] [1.31] [3.17] [2.38] [3.07] [1.33] [3.08] [2.90] Private Placement -0.0437 -0.1489 0.1425 -0.0327 -0.0523 -0.1725* 0.1212 -0.0512 [-0.83] [-1.48] [0.66] [-0.25] [-0.99] [-1.71] [0.56] [-0.39] Callable -0.0156* -0.0332* 0.0006 -0.0406 -0.0197** -0.0426** -0.0029 -0.0820** [-1.74] [-1.83] [0.03] [-1.15] [-2.24] [-2.45] [-0.13] [-2.34] Constant 2.4443*** 1.8624*** 2.5098*** 1.2763*** 2.4370*** 1.8896*** 2.5169*** 1.1077*** [10.56] [9.42] [10.01] [5.12] [10.54] [9.69] [10.09] [4.53] Observations 200482 200442 200336 199077 200482 200442 200336 199077 Adjusted R-squared 0.22 0.06 0.08 0.05 0.22 0.06 0.08 0.05 Ratings Dummies Yes Yes Yes Yes Yes Yes Yes Yes Investor-type Dummies Yes Yes Yes Yes Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes Robust t-statistics, clustered at institutional investor level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

38

Table 2, Panel B: Demand for Bonds by Institutional Investors and the Issuer’s Information Asymmetry Table 2, Panel B shows how the institutional investor’s appetite for issuer’s multiple-maturity catering behavior changes with the level of information asymmetry surrounding the issuer. The dependent variable is the institutional investors’ demand for bonds of all maturities, and is measured as the fraction of their portfolio invested in an issuer in a given quarter. The differential impact of issuer’s information asymmetry on the institutional investors’ demand for bonds is reflected in the interactions between issuer’s multiple-maturity catering and various measures of information asymmetry. These measures of information asymmetry are indicated at the head of each column. Specifically, issuing firms that are listed on the NYSE, or have a credit rating, or are older than the average firm, or have less than average distance from the institutional investor, are defined as having less information asymmetry. All the other variables as well as the Significant Presence (>20%) in a Niche are defined in the same manner as in Panel A of Table 2 above. These tests include all the control variables used in Table 2, Panel A, but these control variables are left unreported for brevity. Also included are four sets of dummy variables that control for fixed effects of ratings, type of institutional investor, time (quarter), and issuer’s industry.

Presence in a Niche Significant Presence (>20%) in a Niche

NYSE Rating Firm's Age Distance NYSE Rating Firm's Age Distance Issuer's Multiple-maturity catering 0.0112 0.0023 0.0177*** 0.0138*** 0.0294*** -0.0029 0.0118** 0.0075 [1.46] [0.43] [3.96] [2.62] [2.73] [-0.44] [2.34] [1.21] (Issuer's Multiple-maturity catering) x (Issuer has less information asymmetry)

0.0157** 0.0325*** 0.0111** 0.0113* -0.0033 0.0360*** 0.0189*** 0.0207*** [2.02] [5.28] [2.11] [1.85] [-0.30] [4.94] [3.44] [2.73]

Issuer has less information asymmetry -0.0253* -0.0563*** -0.0024 -0.0134 0.004 -0.0503*** -0.0119 -0.0257** [-1.83] [-3.94] [-0.21] [-1.06] [0.25] [-3.66] [-1.09] [-2.03] Competitors' Average Multiple-maturity catering

-0.0092** -0.0090** -0.0087** -0.0065 -0.0120*** -0.0104** -0.0107** -0.0099** [-2.41] [-2.39] [-2.24] [-1.58] [-2.81] [-2.42] [-2.48] [-2.15]

Constant 2.4657*** 2.4499*** 2.4528*** 2.5129*** 2.4332*** 2.4375*** 2.4463*** 2.5100*** [10.66] [10.57] [10.59] [10.73] [10.52] [10.54] [10.58] [10.73] Observations 200482 200482 200482 178424 200482 200482 200482 178424 Adjusted R-squared 0.22 0.22 0.22 0.23 0.22 0.22 0.22 0.23 Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Ratings Dummies Yes No Yes Yes Yes No Yes Yes Investor-type Dummies Yes Yes Yes Yes Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes Robust t-statistics, clustered at institutional investor level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

39

Table 2, Panel C: Demand for Bonds by Small, Medium, and Large Institutional Investors The dependent variable in Panel C of Table 2 is the fraction of an institutional investor’s portfolio invested in an issuer in a given quarter. The results in this Panel show how small, medium, and large institutional investors respond differently to the issuer’s multiple-maturity catering behavior. This difference due to the institutional investor’s size is measured by interacting the Issuer’s Multiple-maturity catering with three dummy variables for investor size, essentially splitting the issuer’s multiple-maturity catering into three separate components. Investor is small/medium/large are three dummy variables that equal to 1 if the investor is small/medium/large, respectively, and 0 otherwise; the investors are grouped as such according to terciles of their aggregate bond holdings. All the other variables as well as the Significant Presence (>20%) in a Niche are defined in the same manner as in Panel A of Table 2 above. These tests include all the control variables used in Table 2, Panel A, but these control variables are left unreported for brevity. Also included are four sets of dummy variables that control for fixed effects of ratings, type of institutional investor, time (quarter), and issuer’s industry. The column headings indicate the sample that we analyze – ST, MT, and LT refer to short term, medium term, and long term bonds.

Presence in a Niche Significant Presence (>20%) in a Niche

All Bonds ST Bonds MT Bonds LT Bonds All Bonds ST Bonds MT Bonds LT Bonds (Issuer's Multiple-maturity catering) x (Investor is small)

-0.0054 0.0317 -0.0334** 0.1177*** -0.0049 0.0336* -0.02 0.031 [-0.56] [1.65] [-2.18] [4.78] [-0.45] [1.65] [-1.07] [1.14]

(Issuer's Multiple-maturity catering) x (Investor is medium)

0.0042 0.0560*** 0.0112 0.1213*** 0.0029 0.0614*** 0.0220* 0.0536*** [0.69] [5.01] [1.07] [7.00] [0.43] [5.33] [1.79] [3.36]

(Issuer's Multiple-maturity catering) x (Investor is large)

0.0708*** 0.1535*** 0.1256*** 0.2170*** 0.0815*** 0.1762*** 0.1541*** 0.1783*** [8.74] [10.08] [7.95] [9.03] [8.43] [10.48] [8.24] [7.51]

Competitors' Average Multiple-maturity catering

-0.0109*** 0.0101 -0.0185** -0.0363* -0.0129*** -0.0117 -0.0192* -0.0127 [-2.85] [0.72] [-2.27] [-1.84] [-3.03] [-0.80] [-1.86] [-0.64]

Number of other Niches in which the Investor holds Bonds of same Issuer (=1 or 2)

-0.3185*** -0.2546*** -0.3694*** -0.3137*** -0.2545*** -0.3660*** [-10.73] [-9.09] [-9.41] [-10.64] [-9.04] [-9.27]

Overall Avg Investor Holdings in this Niche 0.0005 0.0051** 0.0113** 0.0024 0.0049** 0.0130*** [0.12] [2.19] [2.36] [0.55] [2.18] [2.84] Constant 2.9282*** 2.6309*** 3.5232*** 1.9218*** 2.8875*** 2.6267*** 3.4241*** 1.9016*** [11.44] [9.04] [11.00] [5.15] [11.31] [9.09] [10.73] [4.95] Observations 200482 200442 200336 199077 200482 200442 200336 199077 Adjusted R-squared 0.24 0.06 0.08 0.05 0.23 0.06 0.08 0.05 Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Ratings Dummies Yes Yes Yes Yes Yes Yes Yes Yes Investor-type Dummies Yes Yes Yes Yes Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes Robust t-statistics, clustered at institutional investor level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

40

Table 2, Panel D: Demand for Bonds by Different Types of Institutional Investors

Table 2, Panel D shows the impact of the issuer’s multiple-maturity catering behavior on the demand for the issuer’s bonds by institutions of different types. Five main categories of institutions are identified – banking institutions, brokerage houses, pension funds, mutual funds, and insurance companies; these are appropriately marked at the head of each column. The dependent variable is the fraction of an institutional investor’s portfolio invested in an issuer in a given quarter. All the other variables as well as the Significant Presence (>20%) in a Niche are defined in the same manner as in Panel A of Table 2 above. These tests include all the control variables used in Table 2, Panel A, but these control variables are left unreported for brevity. Also included are three sets of dummy variables that control for fixed effects of ratings, time (quarter), and issuer’s industry.

Presence in a Niche Significant Presence (>20%) in a Niche Banks Broker Pension MutualFund Insurance Banks Broker Pension MutualFund Insurance Issuer's Multiple-maturity catering

0.008 0.010 0.036* 0.019*** 0.036*** 0.049*** 0.009 0.024 0.015** 0.035*** [0.65] [0.91] [3.04] [3.19] [6.57] [3.53] [0.91] [1.41] [2.30] [7.00]

Competitors' Avg Multiple-maturity catering

-0.006 0.011 0.010 -0.014** -0.008 -0.023 0.001 0.001 -0.023*** 0.001 [-0.36] [0.82] [0.86] [-2.18] [-1.48] [-1.14] [0.05] [0.06] [-2.87] [0.14]

Constant 1.927*** 2.717*** 0.966** 1.973*** 1.848*** 1.994*** 2.735*** 0.958** 1.970*** 1.824*** [4.85] [3.40] [6.34] [4.86] [8.87] [5.01] [3.43] [6.20] [4.85] [8.76] Observations 16513 12349 2701 85714 82978 16513 12349 2701 85714 82978 Adjusted R-squared 0.19 0.25 0.35 0.18 0.34 0.20 0.25 0.35 0.18 0.34 Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Ratings Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Robust t-statistics, clustered at institutional investor level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

41

Table 2, Panel E: Demand for Bonds When There is an Existing Bank-Loan by the Same Investor

Table 2, Panel E shows the impact of the issuer’s multiple-maturity catering behavior on the demand for the issuer’s bonds by institutions when there is an outstanding loan that was issued by a bank affiliated with the bond-holding institution. Specifically, we identify banks that lend to the bond-issuing firm, and check whether these banks are a part of the financial conglomerate affiliated with the bond-holding institutional investor. If the bank loan was issued in the last two years and has a tenor longer than two years (i.e., it is an active loan at the time of the bond issue), then we mark it with a dummy, Existing Loan, equal to 1; if there is no such loan, then Existing Loan is equal to 0. We form a complementary dummy variable, No Existing Loan, that equals 1 when there is no loan, and equals 0 in the presence of a loan identified as above. The dependent variable is the fraction of an institutional investor’s portfolio invested in an issuer in a given quarter. All the other variables as well as the Significant Presence (>20%) in a Niche are defined in the same manner as in Panel A of Table 2 above. These tests include all the control variables used in Table 2, Panel A, but these control variables are left unreported for brevity. Also included are four sets of dummy variables that control for fixed effects of ratings, type of institutional investor, time (quarter), and issuer’s industry. The column headings indicate the sample that we analyze – ST, MT, and LT refer to short term, medium term, and long term bonds.

Presence in a Niche Significant Presence (>20%) in a Niche

All Bonds ST Bonds MT Bonds LT Bonds All Bonds ST Bonds MT Bonds LT Bonds (Issuer's Multiple-maturity catering) x (Existing Loan)

-0.0072 0.0581*** 0.0009 0.1296*** -0.0124 0.0632*** 0.0094 0.0594 [-0.48] [3.12] [0.045] [3.17] [-0.67] [2.74] [0.36] [1.25]

(Issuer's Multiple-maturity catering) x (No Existing Loan)

0.0251*** 0.0815*** 0.0347*** 0.1536*** 0.0268*** 0.0890*** 0.0485*** 0.0865*** [6.83] [9.06] [4.68] [9.40] [7.15] [10.9] [5.54] [6.20]

Competitors' Avg Multiple-maturity catering -0.0100*** 0.0122 -0.0168** -0.0350* -0.0107*** -0.0098 -0.0174* -0.0111 [-2.60] [0.86] [-2.06] [-1.78] [-2.75] [-0.67] [-1.68] [-0.56]

Number of other Bins in which the Investor holds Bonds of same Issuer (=1 or =2) -0.3026*** -0.2340*** -0.3585*** -0.2995*** -0.2354*** -0.3541***

[-10.4] [-8.54] [-9.43] [-10.4] [-8.57] [-9.26] Overall Avg Investor Holdings in this Bin 0.0010 0.0056** 0.0115** 0.0027 0.0052** 0.0135***

[0.22] [2.41] [2.41] [0.61] [2.34] [2.93] Constant 2.4435*** 2.2036*** 2.5088*** 1.2755*** 2.4321*** 2.1124*** 2.5161*** 1.1069*** [10.6] [3.85] [10.0] [5.12] [10.5] [3.70] [10.1] [4.53] Observations 200482 200442 200336 199077 200482 200442 200336 199077 Adjusted R-squared 0.22 0.06 0.08 0.05 0.22 0.06 0.08 0.05 Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Ratings Dummies Yes Yes Yes Yes Yes Yes Yes Yes Investor-type Dummies Yes Yes Yes Yes Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes

Robust t-statistics, clustered at institutional investor level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

42

Table 2, Panel F: Demand for Bonds Calculated After Accounting for Supply of Bonds

Table 2, Panel F defines the demand for the issuer’s bonds by institutions in a different manner. This is done to check that our results above are robust to controlling for the supply of bonds and are not spuriously driven by the supply. Therefore, the dependent variable is the fraction of all institutional investors’ holdings in the issuer in a given quarter, i.e., it reflects the overall institutional bond-ownership in the firm. Since the observations are aggregated across investors, they are now at the issuer-quarter level and therefore much fewer than those in the tables above. Overall Average Investor Holdings in this Niche is the average of the fraction of an investor’s holdings in that particular niche, where the average is calculated across all institutional investors. All the other variables as well as the Significant Presence (>20%) in a Niche are defined in the same manner as in Panel A of Table 2 above. These tests include all the control variables used in Table 2, Panel A, but these control variables are left unreported for brevity. Also included are four sets of dummy variables that control for fixed effects of ratings, type of institutional investor, time (quarter), and issuer’s industry. The column headings indicate the sample that we analyze – ST, MT, and LT refer to short term, medium term, and long term bonds.

Presence in a Niche Significant Presence (>20%) in a Niche

All Bonds ST Bonds MT Bonds LT Bonds All Bonds ST Bonds MT Bonds LT Bonds Issuer's Multiple-maturity catering 0.0153** 0.0710*** 0.0757*** 0.0569*** 0.0197** 0.0783*** 0.0648*** 0.0477*** [2.17] [3.13] [8.64] [8.94] [2.26] [3.69] [4.19] [6.09] Competitors' Avg Multiple-maturity catering

0.0026 -0.0013 0.0028 -0.0171** 0.0013 0.0003 -0.0061 -0.0110 [0.42] [-0.05] [0.24] [-2.39] [0.18] [0.01] [-0.56] [-1.27]

Number of other Bins in which the Investor holds Bonds of same Issuer (=1 or =2)

-0.0970*** -0.1517*** -0.0879*** -0.0881*** -0.1507*** -0.0855*** [-4.11] [-10.86] [-10.52] [-3.50] [-10.02] [-10.30]

Overall Avg Investor Holdings in this Bin 0.0009 0.0003 0.0035** 0.0019 -0.0000 0.0036** [0.26] [0.20] [2.08] [0.59] [-0.02] [2.22] Constant 0.2151*** 0.5340** 0.0475 -0.1103** 0.2119*** 0.5133** 0.0341 -0.1409*** [4.22] [2.42] [0.50] [-2.33] [4.31] [2.49] [0.37] [-2.69] Observations 6145 814 4182 4424 6145 814 4182 4424 Adjusted R-squared 0.24 0.28 0.32 0.34 0.24 0.28 0.31 0.32 Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Ratings Dummies Yes Yes Yes Yes Yes Yes Yes Yes Investor-type Dummies Yes Yes Yes Yes Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes

Robust t-statistics, clustered at institutional investor level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

43

Table 2, Panel G: Persistence of Institutional Investors in Issuers that Cater with Multiple Maturities

Table 2, Panel G tests whether issuers do find a captive market by catering with multiple maturities. This is done by defining a dependent variable that is the ratio of the holdings (or number) of institutional investors in this quarter to that of the same investors in the previous quarter. I.e., if there were ten investors holding the bonds of this issuer in the last quarter, and eight of these ten investors also hold bonds of this issuer in the current quarter, then 8/10 is a measure of Persistence. Alternatively, we define Persistence as a fraction of these investors’ holdings as opposed to just the number. Columns (1) and (2) use Persistence based on holdings and columns (3) and (4) use Persistence based on number of investors. Columns (1) and (3) measure Multiple Maturity Catering by the mere presence in a niche while in columns (2) and (4), it is measured by a significant (at least 20%) presence in that niche. Since the observations are aggregated across investors, they are now at the issuer-quarter level and therefore much fewer than those in the tables above. All the other variables are defined in the same manner as in Panel A of Table 2 above. These tests include all the control variables used in Table 2, Panel A, but these control variables are left unreported for brevity. Also included are three sets of dummy variables that control for fixed effects of ratings, time (quarter), and issuer’s industry.

Persistence

[1] [2] [3] [4] Issuer's Multiple Maturity Catering 0.0333*** 0.0316*** 0.0247*** 0.0192** [4.59] [4.09] [3.47] [2.53] Competitors' Avg Multiple Maturity Catering 0.0081 0.0039 0.0074 0.0129 [0.76] [0.35] [0.74] [1.05] Constant 0.046 0.062 1.1882*** 1.1911*** [0.72] [0.97] [22.33] [21.89] Observations 7528 7528 7558 7558 Adjusted R-squared 0.05 0.05 0.06 0.06 Control Variables Yes Yes Yes Yes Ratings Dummies Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Robust t-statistics, clustered at firm-level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

44

Table 3, Panel A: Issuer’s Multiple-maturity catering Behavior in the Supply of Bonds

Table 3, Panel A analyzes new bond issues and shows evidence of issuer’s multiple-maturity catering behavior. The dependent variable, expressed as a logarithm, is the face value of the bonds issued as a fraction of the issuing firm’s asset size, and the independent variable of interest is How Empty is this Niche Relative to other Niches. The latter is measured for a given niche as the ratio of bonds outstanding in the other two niches to the bonds outstanding in the given niche. A higher value of this variable thus indicates that for this issuer, the given niche is more empty than the other two niches. The control variable Competitors’ Average Multiple-maturity catering is the average of multiple-maturity catering across all other issuers in the same industry and rating group; all firms are categorized into 48 industries as per Fama and French (1997). All firms without ratings are assigned to a rating group 0, and those with ratings are divided into four groups. The control variable Simultaneous Bond Offerings by Competitors is a measure of the average size of bond offerings made by a competitor (in the same industry-rating group) in the same quarter as the given firm’s issue. The interactions of How Empty is this Niche Relative to other Niches with the two control variables reflect whether the multiple-maturity catering tendency of a given firm is stronger when the competitors are also multiple-maturity catering. The first five columns only use the sample of bond-issuing firms and the next five columns look at the entire universe of Compustat firms, while correcting for the selection bias. Overall Average Investor Holdings in this Niche is the average of the fraction of an investor’s holdings in that particular niche, where the average is calculated across all institutional investors. Issuer-specific control variables – Firm Size is logarithm of sales, Leverage is the ratio of debt to debt plus equity, Market-to-Book is the ratio of market equity to book equity, Cash is the cash holdings as a fraction of lagged assets, Capital Expenditure is ratio of capital expenses to lagged assets, Cashflow Volatility is the standard deviation of annual cashflows in all the years before the year of this bond issue, Z-score is the Altman’s (1968) Z-score for bankruptcy risk, Tangibility is the ratio of fixed assets to lagged total assets, Asset Maturity is a measure of asset maturity based on Guedes and Opler (1996), Convertibility of Outstanding Bonds is the average of a dummy variable measuring convertibility across the outstanding bonds of that issuer, Covenants, Private Placement, Callability, and Ratings of Outstanding Bonds are measured similarly. Lambda is the inverse Mill’s ratio from the first-stage regression in columns [6]-[10]. The Appendix provides more details on the variable definitions. Also included are dummy variables that control for fixed effects of time (quarter) and issuer’s industry.

Sample of Bond-issuing Firms Universe of Firms (Correcting for Selection Bias) [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] How Empty is this Niche relative to other Niches

0.0036*** 0.0032** 0.0036*** 0.0031** 0.0025 0.0035*** 0.0030*** 0.0035*** 0.0030*** 0.0018 [3.23] [2.38] [3.21] [2.35] [0.42] [4.39] [3.44] [4.37] [3.41] [0.50]

(How Empty is this Niche) x (Comp. Avg Multi-maturity catering)

0.0074** 0.0082*** [2.19] [2.78]

Competitors' Average Multiple-maturity catering

0.0364 0.0408 0.2939** 0.0245 0.0277 0.2692** [0.35] [0.39] [2.10] [0.37] [0.41] [2.19]

(How Empty is this Niche) x (Simultaneous Bond Offer. by Comp.)

-0.0044 -0.0042 [-0.86] [-1.27]

Simultaneous Bond Offerings by Competitors

-0.0104 -0.0102 0.5941*** -0.0072 -0.0074 0.6019*** [-0.54] [-0.49] [3.25] [-0.40] [-0.39] [5.37]

Overall Average Investor Holdings in this Niche

-0.0046 -0.0057 -0.0048 -0.0059 -0.0096 -0.0039 -0.0057* -0.0041 -0.0058* -0.0109** [-1.17] [-1.45] [-1.24] [-1.49] [-1.48] [-1.25] [-1.72] [-1.28] [-1.75] [-2.26]

Firm Size -0.4899*** -0.4692*** -0.4926*** -0.4719*** -0.4619*** -0.3095*** -0.3221*** -0.3122*** -0.3254*** -0.5014***

45

[-9.00] [-8.11] [-8.96] [-8.09] [-6.00] [-4.27] [-4.34] [-4.29] [-4.35] [-4.85] Leverage -1.0292*** -1.1493*** -1.0326*** -1.1543*** -2.2910*** -0.6967** -0.8044*** -0.7004** -0.8111*** -2.4375*** [-2.70] [-2.93] [-2.69] [-2.93] [-4.06] [-2.45] [-2.72] [-2.46] [-2.74] [-5.09] Market-to-Book 0.0123** 0.0120** 0.0121** 0.0118** 0.0457 0.0122** 0.0115** 0.0121** 0.0114** 0.0518* [2.27] [2.14] [2.24] [2.10] [1.14] [2.44] [2.30] [2.41] [2.27] [1.92] Cash -0.9809 -1.1852 -0.9791 -1.1845 -0.3032 -1.9374*** -1.7776*** -1.9316*** -1.7717*** -0.1509 [-1.00] [-1.31] [-0.99] [-1.31] [-0.34] [-3.53] [-3.22] [-3.51] [-3.21] [-0.23] Capital Expenditure -1.2186 -0.5774 -1.2085 -0.5675 -2.5485 -0.7334 -0.1510 -0.7273 -0.1466 -2.6199** [-0.90] [-0.47] [-0.89] [-0.46] [-1.50] [-1.01] [-0.20] [-1.00] [-0.19] [-2.55] Cashflow Volatility 0.0370** 0.0254 0.0368** 0.0252 0.0263 0.0241** 0.0139 0.0241** 0.0138 0.0292* [2.35] [1.47] [2.32] [1.45] [1.09] [2.17] [1.18] [2.17] [1.18] [1.68] Z-score 0.4068*** 0.4181*** 0.4102*** 0.4219*** 0.3148*** 0.3615*** 0.3791*** 0.3639*** 0.3821*** 0.3111*** [5.55] [6.21] [5.59] [6.25] [3.05] [7.09] [7.28] [7.09] [7.26] [3.95] Tangibility 0.0225 -0.1032 0.0201 -0.1053 0.2478 0.0989 -0.0726 0.0965 -0.0748 0.1781 [0.07] [-0.35] [0.06] [-0.36] [0.74] [0.47] [-0.33] [0.46] [-0.34] [0.61] Asset Maturity -0.0173* -0.0191* -0.0172* -0.0190* 0.0068 -0.0179*** -0.0188*** -0.0179*** -0.0188*** 0.0089 [-1.69] [-1.88] [-1.69] [-1.87] [0.50] [-2.91] [-2.96] [-2.90] [-2.95] [0.89] Convertibility of Outstanding Bonds 0.128 0.1328 0.1315 0.1365 -0.7646 0.1221 0.1336 0.1245 0.1361 -0.8068*** [0.48] [0.49] [0.50] [0.50] [-1.49] [0.66] [0.71] [0.67] [0.72] [-2.63] Covenants on Outstanding Bonds 0.0208 0.1983 0.0174 0.1949 0.2286 -0.0049 0.1797 -0.0072 0.1774 0.2436 [0.11] [0.99] [0.09] [0.98] [0.58] [0.04] [1.20] [-0.05] [1.18] [0.92] Private Placement of Outstanding Bonds 1.0882 1.138 1.1377 1.1851 0.2101 0.9595 1.0428 0.9953 1.0786 0.3484 [0.56] [0.51] [0.58] [0.52] [0.09] [0.58] [0.63] [0.60] [0.65] [0.18] Callability of Outstanding Bonds 0.0525 0.1514 0.0514 0.149 0.6151** 0.0659 0.1551 0.0651 0.1534 0.6460*** [0.29] [0.84] [0.29] [0.82] [2.10] [0.63] [1.42] [0.62] [1.40] [3.75] Rating of Outstanding Bonds -0.0805*** -0.0877*** -0.0805*** -0.0881*** -0.1269*** -0.0803*** -0.0870*** -0.0803*** -0.0872*** -0.1295*** [-7.75] [-6.22] [-7.75] [-6.22] [-5.51] [-10.58] [-9.17] [-10.58] [-9.17] [-8.27] Constant 9.0208*** 8.2848*** 9.0468*** 8.2990*** 8.2232*** 5.7197*** 5.9028*** 5.7604*** 5.9434*** 8.8095*** [16.71] [12.34] [16.63] [12.28] [9.08] [4.55] [4.43] [4.57] [4.45] [5.80] Lambda 0.5039*** 0.4165** 0.5018*** 0.4128** -0.0981 [2.71] [2.20] [2.70] [2.17] [-0.43] Observations 1032 958 1032 958 495 1022 948 1022 948 490 Adjusted R-squared / Chi-sq. p-value 0.71 0.71 0.71 0.71 0.80 [0.00] [0.00] [0.00] [0.00] [0.00] Qtr Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Robust t-statistics, clustered at the issuing firm level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

46

Table 3, Panel B: Issuer’s Multiple-maturity catering Behavior and the Issuer’s Information Asymmetry

Table 3, Panel B shows how the issuer’s multiple-maturity catering behavior changes with the level of information asymmetry surrounding the issuer. The dependent variable, expressed as a logarithm, is the face value of the bonds issued as a fraction of the issuing firm’s asset size, and the independent variable of interest is the interaction of How Empty is this Niche Relative to other Niches with Issuer has less/more Information Asymmetry. The measures of issuer’s information asymmetry are indicated at the head of each column. Specifically, issuing firms that are listed on the NYSE, or have a credit rating, or are older than the average firm, or have less than average distance from the institutional investor, are defined as having less information asymmetry. These corresponding variables are also included as controls in the regressions. All the other variables are defined in the same manner as in Panel A of Table 3 above. These tests include all the control variables used in Table 3, Panel A, but these control variables are left unreported for brevity. Also included are dummy variables that control for fixed effects of time (quarter) and issuer’s industry.

Sample of Bond-issuing Firms Universe of Firms Correcting for Selection Bias NYSE Rating Firm's Age Distance NYSE Rating Firm's Age Distance (How Empty is this Niche) x (Issuer has less information asymmetry)

0.0036*** 0.0041** 0.0037** 0.0026* 0.0035*** 0.0040*** 0.0036*** 0.0025** [2.62] [2.25] [2.56] [1.94] [3.80] [3.61] [3.75] [2.39]

(How Empty is this Niche) x (Issuer has more information asymmetry)

-0.003 -0.0088** -0.0002 0.0021 -0.0028 -0.0092** -0.0004 0.0023 [-0.90] [-2.04] [-0.09] [1.22] [-0.76] [-2.42] [-0.22] [1.42]

Issuer has less information asymmetry -0.5920* 0.0307 -0.0052 -0.0003* -0.5768** 0.0507 -0.0055*** -0.0003*** [-1.71] [0.16] [-1.46] [-1.69] [-2.46] [0.39] [-2.69] [-3.06] Competitors' Average Multiple-maturity catering

0.0449 0.0110 0.0470 0.0224 0.0333 -0.0061 0.0344 0.0195 [0.43] [0.10] [0.45] [0.22] [0.50] [-0.08] [0.52] [0.28]

Overall Average Investor Holdings in this Niche

-0.0058 -0.0079** -0.0069* -0.0069** -0.0057* -0.0078** -0.0069** -0.0068** [-1.49] [-2.09] [-1.77] [-2.10] [-1.75] [-2.40] [-2.09] [-1.99]

Lambda 0.3977** 0.4568** 0.4447** 0.2158 [2.10] [2.41] [2.35] [1.15] Constant 8.7521*** 8.6073*** 8.2260*** 8.6022*** 6.4129*** 6.0017*** 5.7471*** 7.1154*** [12.83] [12.62] [12.18] [12.17] [4.77] [4.52] [4.32] [5.89] Observations 958 958 958 852 23811 23811 23811 23705 Adjusted R-squared / Chi-sq. p-value 0.71 0.71 0.71 0.73 [0.00] [0.00] [0.00] [0.00] Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes Robust t-statistics, clustered at the issuing firm level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

47

Table 3, Panel C: Issuer’s Multiple-maturity catering Behavior and the Size of Institutional Investors in the Proximity

Table 3, Panel C shows the effect that the exogenous presence of small/medium/large institutional investors in the proximity of a bond-issuing firm has on that issuer’s multiple-maturity catering behavior. This difference due to the institutional investor’s size is measured by interacting the variable How Empty is this Niche with three exogenous measures for investor size, essentially splitting the issuer’s multiple-maturity catering into three separate components. The three variables, Small/Medium/Large Institutional Investors in the Proximity, are measured as the fraction of total bond-holdings controlled by small/medium/large institutional investors located in the proximity of the issuer. Since the three variables sum to one, only two of these are included as controls. All the other variables are defined in the same manner as in Panel A of Table 3 above. These tests include all the control variables used in Table 3, Panel A, but these control variables are left unreported for brevity. Also included are dummy variables that control for fixed effects of time (quarter) and issuer’s industry.

[1] [2] [3] [4] [5] (How Empty is this Niche) x (Small Institutional Investors in the Proximity)

0.004 0.0044 0.0032 0.0043 0.0628 [0.21] [0.24] [0.17] [0.24] [1.07]

(How Empty is this Niche) x (Medium Institutional Investors in the Proximity)

-0.0029 -0.0034 -0.0029 -0.0036 -0.0303 [-0.98] [-1.03] [-0.98] [-1.08] [-1.55]

(How Empty is this Niche) x (Large Institutional Investors in the Proximity)

0.0040*** 0.0033** 0.0039*** 0.0033** 0.0127** [3.43] [2.43] [3.28] [2.40] [2.62]

Fraction of all Institutional Investors in the Proximity that are Small

-0.9521 -1.0137 -0.9014 -0.9975 -5.6525 [-0.76] [-0.81] [-0.72] [-0.81] [-1.43]

Fraction of all Institutional Investors in the Proximity that are Large

-0.7517*** -0.7587*** -0.7520*** -0.7732*** -1.1288*** [-3.42] [-3.11] [-3.41] [-3.16] [-2.80]

(How Empty is this Niche) x (Competitors' Avg Multiple-maturity catering)

0.0037 [1.07]

Competitors' Average Multiple-maturity catering 0.1542 0.1604 0.5854*** [1.38] [1.42] [3.86] (How Empty is this Niche) x (Simultaneous Bond Offerings by Competitors)

-0.0118*** [-3.03]

Simultaneous Bond Offerings by Competitors -0.0104 -0.017 0.4385** [-0.48] [-0.74] [2.54] Overall Average Investor Holdings in this Niche -0.0027 -0.0039 -0.0024 -0.0042 -0.0032 [-0.82] [-1.18] [-0.73] [-1.25] [-0.60] Constant 11.0733*** 11.0040*** 9.4061*** 11.0671*** 9.3849*** [16.74] [12.23] [13.78] [12.08] [8.76] Observations 871 805 871 805 422 Adjusted R-squared 0.75 0.75 0.74 0.75 0.85 Control Variables Yes Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Robust t-statistics, clustered at the issuing-firm level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

48

Table 3, Panel D: Issuer’s Multiple-maturity catering Behavior and the Benefit of Filling Three Niches

Table 3, Panel D shows how the issuer’s multiple-maturity catering responds to the market’s pricing of firms that have filled all three niches in comparison to those that have only filled one niche. Specifically, this is a logit regression of a dummy variable that indicates whether the firm is issuing bonds in an empty niche or a niche that is not quite one-third full and the main independent variable of interest is Benefit of Three Niches. In columns (1) and (2), the dependent variable is equal to 1 if the firm issues bonds in a maturity-niche for the first time while in columns (7) and (8), the dependent variable equals 1 if the firm issues bonds in a niche that is not empty but is less than 30% full. The dependent variables in columns (3)-(6) are defined similarly. We go up to 30% because, given three potential niches, a niche would be considered full if it’s 33% full. The independent variable Benefit of Three Niches is constructed by regressing the yields of firms that have already filled three niches and also regressing the yields of firms that have only filled one of the three potential niches. The difference between the residuals from these two regressions is used to proxy for the Benefit of Three Niches. All the other variables are defined in the same manner as in Panel A of Table 3 above. These tests include all the control variables used in Table 3, Panel A, but these control variables are left unreported for brevity. Also included are dummy variables that control for fixed effects of time (quarter).

Filling an Empty Niche

Filling a <10% Full Niche

Filling a <20% Full Niche

Filling a <30% Full Niche (1) (2) (3) (4) (5) (6) (7) (8) Benefit of Three Niches 0.068 0.051 0.092 0.086 0.109* 0.148* 0.160** 0.170** [0.84] [0.50] [1.23] [0.89] [1.67] [1.74] [2.10] [1.97] T-Bill Rates -0.074 0.441 -0.052 0.385 -0.530*** -0.627 -0.284* -0.515 [-0.46] [1.53] [-0.35] [1.47] [-2.71] [-1.40] [-1.92] [-1.23] Simultaneous Bond offerings by matched firms -0.017 -0.038 -0.018 -0.047 [-0.22] [-0.53] [-0.32] [-0.91] Constant 7.413*** 6.918*** 6.017*** 5.625*** 6.958*** 8.841*** 3.181** 5.127* [4.64] [3.03] [4.21] [2.80] [4.67] [3.09] [2.33] [1.96] Observations 1030 1030 1030 1030 1030 1030 1030 1030 Pseudo R-squared 0.21 0.26 0.18 0.22 0.11 0.14 0.06 0.09 Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Qtr Time Dummies No Yes No Yes No Yes No Yes Robust z-statistics, clustered at the firm-level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

49

Table 4, Panel A: Impact of Issuer’s Multiple-maturity catering on the Primary-Market Yields and Spreads

Table 4, Panel A shows the impact of the issuer’s multiple-maturity catering behavior on the primary-market yields and spreads (at the time of offer). The dependent variable in the first four columns is the percentage yield on the bonds issued by the firm and in the next four columns, it is the spread over a Treasury Bond that’s closest in maturity. The independent variable of interest is Issuer’s Multiple-maturity catering, which is a discrete variable that measures the presence of an issuer across the various niches of bond maturities – the more categories or niches of maturities that the issuer has outstanding bonds in, the higher the multiple-maturity catering variable. We define three niches of maturities – short term, medium term, and long term – and therefore, the multiple-maturity catering variable takes values 1, 2, or 3. The control variable Competitors’ Average Multiple-maturity catering is the average of multiple-maturity catering across all other issuers in the same industry and rating group; all firms are categorized into 48 industries as per Fama and French (1997). All firms without ratings are assigned to a rating group 0, and those with ratings are divided into four groups. The control variable Simultaneous Bond Offerings by Competitors is a measure of the average size of bond offerings made by a competitor (in the same industry-rating group) in the same quarter as the given firm’s issue. T-Bill Rates is the percentage rate offered on a contemporaneous Treasury Bill that is closest in maturity to the bond issued by the firm. Issuer-specific control variables – Firm Size is logarithm of sales, Leverage is the ratio of debt to debt plus equity, Market-to-Book is the ratio of market equity to book equity, Cash is the cash holdings as a fraction of lagged assets, Cashflow is the sum of earnings and depreciation as a fraction of lagged assets, Z-score is the Altman’s (1968) Z-score for bankruptcy risk, Tangibility is the ratio of fixed assets to lagged total assets. Bond-specific control variables – Medium-/Long-term Maturity are two dummy variables that are equal to 1 if the bond offered is a medium or long term bond (along with a similarly defined Short-term Maturity dummy, these would sum to 1), Convertible is a dummy variable indicating that the offered bond is convertible, Rule 415 Regulation is a dummy variable indicating that it is a delayed or continuous bond offering, Rule 144A is a dummy variable indicating that the private placement of the bond offering, and Covenants is a dummy variable indicating that the offered bond has covenants attached. The Appendix provides more details on the variable definitions. Also included are dummy variables that control for fixed effects of ratings, time (quarter), and issuer’s industry.

Primary-market Yields Primary-market Spreads [1] [2] [3] [4] [5] [6] [7] [8] Issuer's Multiple-maturity catering -0.198*** -0.100** -0.098** -0.100** -0.193*** -0.098** -0.096* -0.098** [-4.44] [-2.02] [-1.97] [-2.01] [-4.41] [-1.99] [-1.93] [-1.97] Competitors' Average Multiple-maturity catering -0.044 -0.041 -0.044 -0.042 [-0.58] [-0.54] [-0.58] [-0.55] Simultaneous Bond Offerings by Competitors -0.008 -0.007 -0.007 -0.006 [-0.46] [-0.41] [-0.38] [-0.33] T-Bill Rates 0.984*** 0.541*** 0.540*** 0.541*** -0.027 -0.485*** -0.486*** -0.486*** [17.75] [4.92] [4.92] [4.92] [-0.50] [-4.65] [-4.65] [-4.65] Firm Size -0.265*** -0.286*** -0.288*** -0.288*** -0.265*** -0.283*** -0.285*** -0.285*** [-8.70] [-8.45] [-8.35] [-8.34] [-8.84] [-8.38] [-8.27] [-8.26] Leverage 1.918*** 1.960*** 1.958*** 1.958*** 1.928*** 1.979*** 1.978*** 1.977*** [7.48] [8.07] [8.07] [8.06] [7.51] [8.10] [8.11] [8.09] Market-to-Book 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 [-0.31] [-0.38] [-0.39] [-0.38] [-0.30] [-0.37] [-0.38] [-0.38] Cash -0.296** -0.144 -0.146 -0.145 -0.301** -0.146 -0.149 -0.147 [-2.36] [-1.28] [-1.30] [-1.29] [-2.38] [-1.30] [-1.32] [-1.30]

50

Cashflow -1.077** -0.563 -0.57 -0.565 -1.095** -0.57 -0.577 -0.573 [-2.47] [-1.44] [-1.46] [-1.44] [-2.50] [-1.45] [-1.47] [-1.45] Capital Expenditure 0.051 -0.322 -0.316 -0.32 0.067 -0.288 -0.283 -0.286 [0.19] [-1.33] [-1.31] [-1.33] [0.25] [-1.19] [-1.17] [-1.19] Tangibility 0.095 0.043 0.043 0.044 0.094 0.033 0.034 0.034 [0.63] [0.26] [0.26] [0.27] [0.63] [0.20] [0.20] [0.21] Z-score -0.003 0.002 0.002 0.003 -0.002 0.003 0.003 0.003 [-0.06] [0.05] [0.05] [0.06] [-0.04] [0.07] [0.06] [0.08] Medium-term Maturity 0.169* 0.311*** 0.314*** 0.314*** 0.119 0.268*** 0.270*** 0.271*** [1.82] [3.46] [3.47] [3.48] [1.30] [2.98] [2.99] [3.00] Long-term Maturity -0.246** 0.270** 0.277** 0.276** -0.292*** 0.239* 0.244** 0.244** [-2.33] [2.27] [2.32] [2.31] [-2.76] [1.96] [1.99] [1.98] Convertible -1.445*** -1.696*** -1.696*** -1.697*** -1.459*** -1.710*** -1.710*** -1.711*** [-6.69] [-7.61] [-7.60] [-7.62] [-6.76] [-7.70] [-7.69] [-7.70] Rule 415 Regulation -0.847*** -0.638*** -0.641*** -0.639*** -0.858*** -0.653*** -0.657*** -0.654*** [-5.59] [-4.67] [-4.68] [-4.67] [-5.61] [-4.70] [-4.70] [-4.70] Rule 144A 0.455*** 0.234 0.233 0.233 0.439*** 0.22 0.219 0.219 [2.89] [1.56] [1.55] [1.55] [2.76] [1.45] [1.44] [1.44] Covenants 0.481*** 0.251*** 0.252*** 0.251*** 0.475*** 0.249*** 0.250*** 0.249*** [5.21] [2.65] [2.67] [2.66] [5.16] [2.63] [2.64] [2.63] Constant 5.289*** 8.380*** 8.421*** 8.403*** 5.448*** 8.582*** 8.619*** 8.601*** [7.95] [9.54] [9.56] [9.55] [8.28] [10.07] [10.08] [10.06] Observations 2185 2011 2011 2011 2185 2011 2011 2011 Adjusted R-squared 0.58 0.67 0.67 0.67 0.48 0.57 0.57 0.57 Ratings Dummies No Yes Yes Yes No Yes Yes Yes Qtr Time Dummies No Yes Yes Yes No Yes Yes Yes Industry Dummies No Yes Yes Yes No Yes Yes Yes Robust t-statistics, clustered at the issuing-firm level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

51

Table 4, Panel B: Impact of Issuer’s Multiple-maturity catering on the Primary-Market Yields and Spreads of Different Maturity Bonds

Table 4, Panel B shows the impact of the issuer’s multiple-maturity catering behavior on the primary-market yields and spreads (at the time of offer), separately for short-term, medium-term, and long-term bonds. The dependent variable in the first three columns is the percentage yield on the bonds issued by the firm and in the next three columns, it is the spread over a Treasury Bond that’s closest in maturity. The column headings indicate the sample that we analyze – ST, MT, and LT refer to short term, medium term, and long term bonds. All the other variables are defined in the same manner as in Panel A of Table 4 above. These tests include all the control variables used in Table 4, Panel A, but these control variables are left unreported for brevity. Also included are dummy variables that control for fixed effects of ratings, time (quarter), and issuer’s industry.

Primary-market Yields Primary-market Spreads ST Bonds MT Bonds LT Bonds ST Bonds MT Bonds LT Bonds Issuer's Multiple-maturity catering -0.543** -0.184** -0.086* -0.555** -0.178** -0.086* [-2.02] [-2.25] [-1.72] [-2.08] [-2.19] [-1.72] Competitors' Average Multiple-maturity catering -0.307 0.009 0.051 -0.309 0.012 0.051 [-1.37] [0.06] [0.61] [-1.36] [0.09] [0.61] Simultaneous Bond offerings by Competitors 0.153 0.019 -0.030 0.165 0.018 -0.030 [1.39] [0.68] [-1.42] [1.51] [0.63] [-1.42] Constant 4.348** 9.932*** 6.951*** 4.449** 9.902*** 6.951*** [2.00] [7.69] [3.53] [2.02] [7.70] [3.53] Observations 274 756 948 274 756 948 Adjusted R-squared 0.69 0.70 0.68 0.50 0.65 0.60 Control Variables Yes Yes Yes Yes Yes Yes Ratings Dummies Yes Yes Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Yes Robust t-statistics, clustered at the issuing-firm level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

52

Table 4, Panel C: Impact of Issuer’s Multiple-maturity catering on the Primary-Market Yields and Spreads, and the Issuer’s Information Asymmetry

Table 4, Panel C shows how the impact of the issuer’s multiple-maturity catering behavior on the primary-market yields and spreads (at the time of offer) changes with the level of information asymmetry surrounding the issuer. The dependent variable in the first four columns is the percentage yield on the bonds issued by the firm and in the next four columns, it is the spread over a Treasury Bond that’s closest in maturity. The independent variable of interest is the interaction of Issuer’s Multiple-maturity catering with Issuer has less/more Information Asymmetry. The measures of issuer’s information asymmetry are indicated at the head of each column. Specifically, issuing firms that are listed on the NYSE, or have a credit rating, or are older than the average firm, or have less than average distance from the institutional investor, are defined as having less information asymmetry. These corresponding variables (except firm’s age, which has a correlation of 0.70 and 0.85 with the interaction terms) are also included as controls in the regressions. All the other variables are defined in the same manner as in Panel A of Table 4 above. These tests include all the control variables used in Table 4, Panel A, but these control variables are left unreported for brevity. Also included are dummy variables that control for fixed effects of ratings, time (quarter), and issuer’s industry.

Primary-market Yields Primary-market Spreads NYSE Rating Firm's Age Distance NYSE Rating Firm's Age Distance (Issuer's Multiple-maturity catering) x (Issuer has less information asymmetry)

-0.092* -0.116** -0.124** -0.138** -0.090* -0.114** -0.119* -0.137** [-1.84] [-2.29] [-2.00] [-2.11] [-1.81] [-2.26] [-1.91] [-2.09]

(Issuer's Multiple-maturity catering) x (Issuer has more information asymmetry)

-0.001 0.701 -0.060 -0.105 0.001 0.705 -0.060 -0.109 [-0.01] [1.56] [-1.03] [-1.23] [0.01] [1.57] [-1.02] [-1.28]

Issuer has less information asymmetry -0.374 -0.231 0.000 -0.373 -0.224 0.000 [-1.29] [-0.52] [0.04] [-1.29] [-0.51] [0.05] Competitors' Average Multiple-maturity catering -0.037 -0.046 -0.030 -0.044 -0.038 -0.046 -0.030 -0.046 [-0.50] [-0.60] [-0.38] [-0.49] [-0.51] [-0.61] [-0.39] [-0.51] Simultaneous Bond offerings by Competitors -0.005 -0.010 0.002 -0.015 -0.004 -0.009 0.004 -0.013 [-0.30] [-0.58] [0.12] [-0.75] [-0.22] [-0.50] [0.19] [-0.66] Constant 8.343*** 7.654*** 10.622*** 9.847*** 8.540*** 7.850*** 10.817*** 10.048*** [9.29] [7.87] [10.33] [7.85] [9.78] [8.26] [10.82] [8.26] Observations 2011 2011 2031 1570 2011 2011 2031 1570 Adjusted R-squared 0.67 0.67 0.67 0.66 0.58 0.58 0.58 0.57 Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Ratings Dummies Yes Yes Yes Yes Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes Robust t-statistics, clustered at the issuing-firm level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

53

Table 4, Panel D: Impact of Issuer’s Multiple-maturity catering on the Primary-Market Yields and Spreads, and the Size of Institutional Investors in the Proximity

Table 4, Panel D shows how the impact of the issuer’s multiple-maturity catering behavior on the primary-market yields and spreads (at the time of offer) changes with the exogenous presence of small/medium/large institutional investors in the proximity of a bond-issuing firm. This difference due to the institutional investor’s size is measured by interacting the variable Issuer’s Multiple-maturity catering with three exogenous measures for investor size. The three variables, Small/Medium/Large Institutional Investors in the Proximity, are measured as the fraction of total bond-holdings controlled by small/medium/large institutional investors located in the proximity of the issuer. Two of these three investor-size variables are included as controls, and so is the total bond-holdings controlled by all institutional investors located in the proximity of the issuer. The dependent variable in the first four columns is the percentage yield on the bonds issued by the firm and in the next four columns, it is the spread over a Treasury Bond that’s closest in maturity. All the other variables are defined in the same manner as in Panel A of Table 4 above. These tests include all the control variables used in Table 4, Panel A, but these control variables are left unreported for brevity. Also included are dummy variables that control for fixed effects of ratings, time (quarter), and issuer’s industry.

Primary-market Yields Primary-market Spreads [1] [2] [3] [4] [5] [6] [7] [8] (Issuer's Multiple-maturity catering) x (Small Institutional Investors in the Proximity)

0.270 -1.062 -1.003 -1.045 0.268 -1.085 -1.025 -1.068 [0.85] [-1.40] [-1.32] [-1.37] [0.84] [-1.42] [-1.34] [-1.39]

(Issuer's Multiple-maturity catering) x (Medium Institutional Investors in the Proximity)

-0.365 -0.108 -0.116 -0.108 -0.371 -0.117 -0.126 -0.117 [-1.38] [-0.43] [-0.47] [-0.43] [-1.40] [-0.47] [-0.50] [-0.47]

(Issuer's Multiple-maturity catering) x (Large Institutional Investors in the Proximity)

-0.255** -0.242** -0.243** -0.240** -0.248** -0.234** -0.235** -0.232** [-2.34] [-2.11] [-2.09] [-2.09] [-2.28] [-2.04] [-2.02] [-2.02]

Fraction of all Institutional Investors in the Proximity that are Small

-2.002** 0.038 -0.091 0.005 -2.000** 0.055 -0.076 0.022 [-2.36] [0.03] [-0.08] [0.00] [-2.36] [0.05] [-0.07] [0.02]

Fraction of all Institutional Investors in the Proximity that are Large

-0.381 0.091 0.076 0.081 -0.414 0.048 0.033 0.038 [-0.55] [0.14] [0.12] [0.12] [-0.59] [0.07] [0.05] [0.06]

Total Bond-holdings of all Institutional Investors in the Proximity

-0.064 -0.060 -0.060 -0.059 -0.061 -0.056 -0.057 -0.056 [-1.27] [-1.22] [-1.22] [-1.22] [-1.21] [-1.15] [-1.15] [-1.15]

Competitors' Avg Multiple-maturity catering 0.129 0.134 0.132 0.136 [1.00] [1.04] [1.02] [1.06] Simultaneous Bond offerings by matched firms -0.009 -0.011 -0.009 -0.011 [-0.34] [-0.43] [-0.34] [-0.42] Constant 8.425*** 10.249*** 10.248*** 10.264*** 8.488*** 10.307*** 10.306*** 10.322*** [5.88] [6.55] [6.55] [6.57] [5.89] [6.54] [6.53] [6.56] Observations 830 760 760 760 830 760 760 760 Adjusted R-squared 0.50 0.61 0.61 0.61 0.43 0.55 0.55 0.55 Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Ratings, QtrTime, and Industry Dummies No Yes Yes Yes No Yes Yes Yes

54

Robust t statistics in brackets; *** p<0.01, ** p<0.05, * p<0.1

Table 5, Panel A: Impact of Issuer’s Multiple-maturity catering on the Secondary-Market Yields and Spreads

Table 5, Panel A shows the impact of the issuer’s multiple-maturity catering behavior on the secondary-market yields and spreads. The dependent variable in the first four columns is the percentage yield and in the next four columns, it is the spread over a Treasury Bond that’s closest in maturity. The independent variable of interest is Issuer’s Multiple-maturity catering, which is a discrete variable that measures the presence of an issuer across the various niches of bond maturities – the more categories or niches of maturities that the issuer has outstanding bonds in, the higher the multiple-maturity catering variable. We define three niches of maturities – short term, medium term, and long term – and therefore, the multiple-maturity catering variable takes values 1, 2, or 3. The control variable Competitors’ Average Multiple-maturity catering is the average of multiple-maturity catering across all other issuers in the same industry and rating group; all firms are categorized into 48 industries as per Fama and French (1997). All firms without ratings are assigned to a rating group 0, and those with ratings are divided into four groups. In columns (1)-(2) and (5)-(6), multiple-maturity catering is counted by the mere presence in a niche, while in the remaining columns (3)-(4) and (7)-(8), it is counted only if the issuing firm has a significant presence (>20%) in that niche. Issuer-specific control variables – Firm Size is logarithm of sales, Leverage is the ratio of debt to debt plus equity, Market-to-Book is the ratio of market equity to book equity, Cash is the cash holdings as a fraction of lagged assets, Cashflow is the sum of earnings and depreciation as a fraction of lagged assets, Z-score is the Altman’s (1968) Z-score for bankruptcy risk, Tangibility is the ratio of fixed assets to lagged total assets. Bond-specific control variables – Convertibility of Outstanding Bonds is the dummy variable indicating convertibility, averaged across all outstanding bonds of the issuing firm; Covenants, Private Placement, and Callability of Outstanding Bonds are similarly defined. The Appendix provides more details on the variable definitions. Also included are dummy variables that control for fixed effects of ratings, time (quarter), and issuer’s industry.

Secondary-market Yields Secondary-market Spreads

[1] [2] [3] [4] [5] [6] [7] [8] Issuer's Multiple-maturity catering -1.274*** -0.846*** -1.340*** -0.934*** [-5.80] [-4.76] [-6.03] [-5.18] Competitors' Avg Multiple-maturity catering

-1.246*** -0.263 -1.023*** -0.237 [-5.50] [-1.46] [-4.67] [-1.31]

Issuer's Multiple-maturity catering (Significant Presence of at least 20%)

-0.907*** -0.454*** -0.831*** -0.452*** [-4.74] [-3.28] [-4.22] [-3.21]

Competitors' Avg Multiple-maturity catering (Sig. Presence of at least 20%)

-1.284*** -0.053 -1.037*** -0.043 [-4.90] [-0.26] [-4.04] [-0.21]

Firm Size -0.285** -0.117 -0.573*** -0.283** -0.233* -0.130 -0.555*** -0.324*** [-2.06] [-0.86] [-5.11] [-2.44] [-1.65] [-0.96] [-4.92] [-2.82] Market-to-Book 0.000 -0.001 0.000 -0.001 0.000 -0.001 0.000 -0.001 [-0.01] [-0.64] [0.29] [-0.46] [-0.08] [-0.67] [0.23] [-0.47]

55

Leverage 3.663*** 2.020** 3.372*** 1.903** 3.771*** 2.089** 3.469*** 1.951** [3.29] [2.25] [3.07] [2.16] [3.34] [2.30] [3.14] [2.20] Cash -0.672** -0.482** -0.760*** -0.524** -0.709** -0.474** -0.799** -0.523** [-2.36] [-2.35] [-2.63] [-2.50] [-2.27] [-2.22] [-2.53] [-2.39] Cashflows -2.256** -1.589** -2.529** -1.711** -2.490** -1.575** -2.763** -1.715** [-2.25] [-2.24] [-2.48] [-2.36] [-2.28] [-2.13] [-2.50] [-2.26] Capital Expenditure 1.608 1.396 2.163** 1.457 1.409 1.051 1.952* 1.112 [1.61] [1.44] [2.13] [1.49] [1.23] [0.87] [1.68] [0.92] Tangibility -0.860 -1.027* -1.277** -1.081* -0.943 -0.782 -1.347** -0.837 [-1.54] [-1.71] [-2.24] [-1.79] [-1.40] [-0.99] [-1.97] [-1.05] Z-score -0.968*** -0.972*** -0.890*** -0.850*** -0.998*** -0.945*** -0.896*** -0.804*** [-3.83] [-3.83] [-3.51] [-3.51] [-3.88] [-3.64] [-3.47] [-3.24] Convertibility of Outstanding Bonds 0.125 0.480 0.095 0.256 0.677 0.565 0.583 0.306 [0.13] [0.62] [0.10] [0.32] [0.75] [0.73] [0.63] [0.39] Covenants on Outstanding Bonds 0.927* 1.486*** 0.759 1.495*** 0.693 1.611*** 0.524 1.618*** [1.94] [3.19] [1.56] [3.16] [1.43] [3.44] [1.06] [3.40] Private Placement of Outstanding Bonds -0.895 1.566* -0.606 1.460* -0.693 1.774** -0.623 1.591** [-0.42] [1.88] [-0.28] [1.76] [-0.35] [2.00] [-0.30] [1.98] Callability of Outstanding Bonds 0.289 0.395 0.478 0.456 0.902** 0.336 1.103*** 0.408 [0.72] [1.16] [1.15] [1.34] [2.30] [1.00] [2.67] [1.21] Constant 15.092*** 13.116*** 16.066*** 13.772*** 9.336*** 7.320*** 10.325*** 7.163*** [13.4] [7.48] [14.1] [7.84] [8.29] [3.81] [9.14] [3.74] Observations 35526 35526 35526 35526 35528 35528 35528 35528 Adjusted R-squared 0.19 0.32 0.18 0.32 0.19 0.31 0.18 0.31 Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Ratings Dummies No Yes No Yes No Yes No Yes Qtr Time Dummies No Yes No Yes No Yes No Yes Industry Dummies No Yes No Yes No Yes No Yes Robust t-statistics, clustered at issuing-firm level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

56

Table 5, Panel B: Impact of Issuer’s Multiple-maturity catering on the Secondary-Market Yields and Spreads of Different Maturity Bonds

Table 5, Panel B shows the impact of the issuer’s multiple-maturity catering behavior on the secondary-market yields and spreads. The dependent variable in the first four columns is the percentage yield and in the next four columns, it is the spread over a Treasury Bond that’s closest in maturity. The column-headings indicate the subsample that we analyze – short-term (ST), medium-term (MT), or long-term (LT) bonds. All the other variables are defined in the same manner as in Panel A of Table 5 above. These tests include all the control variables used in Table 5, Panel A, but these control variables are left unreported for brevity. Also included are dummy variables that control for fixed effects of ratings, time (quarter), and issuer’s industry.

Secondary-market Yields Secondary-market Spreads ST Bonds MT Bonds LT Bonds ST Bonds MT Bonds LT Bonds Issuer's Multiple-maturity catering -0.924*** -1.099*** -0.218** -0.898*** -1.107*** -0.218** [-2.81] [-4.24] [-2.05] [-2.74] [-4.17] [-2.05] Competitors' Avg Multiple-maturity catering

-0.111 -0.071 -0.134 -0.108 -0.069 -0.135 [-0.40] [-0.27] [-1.07] [-0.39] [-0.26] [-1.09]

Constant 14.943*** 8.648*** 4.967*** 10.811** 4.191** 0.063 [3.13] [4.27] [4.43] [2.27] [2.04] [0.056] Observations 9808 15115 10603 9808 15117 10603 Adjusted R-squared 0.37 0.34 0.39 0.36 0.32 0.37 Control Variables Yes Yes Yes Yes Yes Yes Ratings Dummies Yes Yes Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Yes Robust t-statistics, clustered at the issuing-firm level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1

57

Table 5, Panel C: Endogenously-Determined Issuer’s Multiple-maturity catering and Secondary-Market Yields

Table 5, Panel C shows the impact of the issuer’s multiple-maturity catering behavior on the secondary-market yields and spreads, except the issuer’s multiple-maturity catering behavior is instrumented because it is endogenously determined. The dependent variable in the first four columns is the percentage yield and in the next four columns, it is the spread over a Treasury Bond that’s closest in maturity. All the other variables are defined in the same manner as in Panel A of Table 5 above. These tests include all the control variables used in Table 5, Panel A, but these control variables are left unreported for brevity.

Secondary-market Yields

Secondary-market Spreads All Bonds ST Bonds MT Bonds LT Bonds All Bonds ST Bonds MT Bonds LT Bonds Issuer's Multiple-maturity Catering (Significant Presence of at least 20%)

-16.164* -20.315*** -12.154** -0.425 -15.132 -19.007*** -10.039* -0.525 [-1.71] [-2.90] [-2.04] [-0.18] [-1.63] [-2.68] [-1.90] [-0.23]

Constant 11.702*** 12.463** 13.405*** 9.051*** 6.372* 7.816 7.946*** 3.151 [3.25] [2.09] [5.38] [2.80] [1.85] [1.39] [3.71] [0.97] Observations 29428 8245 12245 8938 29428 8245 12245 8938 Control Variables Yes Yes Yes Yes Yes Yes Yes Yes QtrTime/Industry Dummies No No No No No No No No First-stage F-stat > Critical Value Yes Yes Yes Yes Yes Yes Yes Yes Hansen's J (p-value) 0.19 0.86 0.77 0.64 0.25 0.91 0.82 0.69 Robust t statistics in brackets; *** p<0.01, ** p<0.05, * p<0.1

58

Table 5, Panel D: Issuer’s Multiple-maturity catering and the Impact of GM/Ford Downgrade on Secondary-market Trading

Table 5, Panel D shows the impact of the issuer’s multiple-maturity catering behavior on the yields in the secondary market around the exogenous downgrade of GM and Ford bonds to junk status, which we call a “crisis”. The dependent variable is the percentage yield on the issuing firm’s bonds in the secondary-market trading and the independent variable of interest is the interaction between Issuer’s Multiple-maturity catering and the Crisis. The variable Crisis is a dummy variable equal to 1 in 2005Q2 and 0 otherwise, marking the quarter in which GM and Ford bonds were downgraded to junk status. The control variable Previous Quarter’s Yield is the yield on the bond during the previous month. All the other variables and Significant Presence (>20%) in a Niche are defined in the same manner as in Panel A of Table 5 above. These tests include all the control variables used in Table 5, Panel A, but these control variables are left unreported for brevity. Also included are dummy variables that control for fixed effects of ratings, time (quarter), and issuer’s industry.

Presence in a Niche Significant Presence (>20%) in a Niche Yield ∆Yield Yield ∆Yield [1] [2] [3] [4] [5] [6] [7] [8] Issuer's Multiple-maturity catering -0.200*** -0.081 -0.009*** -0.015*** -0.041 -0.108*** -0.005*** -0.016*** [-4.35] [-1.44] [-5.11] [-3.78] [-1.43] [-2.85] [-2.99] [-4.32] (Issuer's Multiple-maturity catering) x (Crisis) 0.073*** 0.045*** 0.036*** 0.021*** 0.084*** 0.069*** 0.045*** 0.028*** [3.33] [2.61] [23.53] [12.61] [3.57] [3.16] [23.00] [12.46] Competitors' Avg. Multiple-maturity catering -0.164*** 0.037 -0.017*** 0.011 -0.031 -0.082 -0.011*** -0.004 [-3.05] [0.33] [-7.24] [1.54] [-0.57] [-0.97] [-4.54] [-0.62] Previous Quarter's Yield 1.069*** 1.222*** 0.001*** -0.000 1.071*** 1.224*** 0.001*** 0.000 [33.84] [33.16] [2.65] [-0.04] [33.90] [32.83] [2.75] [0.06] Constant 1.988*** -1.438*** 0.070** 0.016 1.828** -1.374*** 0.080*** 0.029 [2.70] [-2.99] [2.35] [0.32] [2.48] [-3.04] [2.68] [0.57] Observations 24766 2927 24639 2923 24766 2927 24639 2923 Adjusted R-squared 0.81 0.91 0.05 0.09 0.81 0.91 0.05 0.09 Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Rating Dummies Yes Yes Yes Yes Yes Yes Yes Yes Qtr Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes Robust t-statistics, clustered at issuing-firm level, are in brackets; *** p<0.01, ** p<0.05, * p<0.1