Download Why firms issue callable bonds: Hedging

Journal of Corporate Finance 16 (2010) 588–607
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Journal of Corporate Finance
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j c o r p f i n
Why firms issue callable bonds: Hedging investment uncertainty
Zhaohui Chen a,⁎, Connie X. Mao b,⁎, Yong Wang c,1
a
b
c
McIntire School of Commerce, University of Virginia, Charlottesville, VA 22903, United States
Department of Finance, Fox School of Business and Management, Temple University, Philadelphia, PA 19122, United States
Department of Accounting and Finance, School of Business, Western New England College, Springfield, MA 01119, United States
a r t i c l e
i n f o
Article history:
Received 23 June 2009
Received in revised form 15 March 2010
Accepted 16 June 2010
Available online 23 June 2010
JEL Classification:
G31
G32
Keywords:
Callable bond
Debt agency problem
Risky shifting
Investment uncertainty
a b s t r a c t
This paper analyzes a firm's dynamic decisions: i) whether to issue a callable or non-callable
bond; ii) when to call the callable bond; and iii) whether to refund it when it is called. We argue
that a firm uses a callable bond to reduce the risk-shifting problem in case its investment
opportunities become poor. Our empirical findings support this argument. We find that a firm
facing poorer future investment opportunities is more likely to issue a callable bond than a firm
facing better investment opportunities. In addition, a firm with a higher leverage ratio and
higher investment risk is more likely to issue a callable bond. Finally, after a callable bond is
issued, a firm with a poor performance and a low investment activity tends to call back a bond
without refunding; a firm with the best performance and highest investment activity tends to
call back a bond and refund its call; and a firm with mediocre performance and investment
activity tends to not call its bonds.
© 2010 Elsevier B.V. All rights reserved.
1. Introduction
When a firm issues a bond, it must decide whether to issue a callable bond or a non-callable bond. A callable bond includes a
call provision, which gives the issuer an option to buy back the bond at a predetermined price during a predetermined time period.
Callable bonds are commonly used by U.S. corporations in the public debt market. For example, in the Fixed Investment Securities
Database (FISD), 42% of fixed rate U.S. corporate bonds issued between 1970 and 2000 are callable.
Why does a firm issue a callable bond? The most common explanation is to hedge interest rate risk (e.g., Pye, 1966). The
argument is that once the interest rate goes down, the issuing firm can refund the bond at a lower interest rate.2 This argument,
however, has difficulty explaining the empirical finding that most firms do not refund their bonds when they call them back. For
example, King and Mauer (2000) report that 77% of bonds being called in their sample are not refunded. How can a firm benefit
from lower interest rates without refunding? In other words, if a firm is willing to borrow at a higher interest rate, say 8%, when it
issues a bond, why isn't it willing to borrow at a lower interest rate, say 6%, when it calls back the bond? Other explanations for
why a firm would issue a callable bond do not explain the refunding decision of the firm. Explaining refunding decisions calls for a
new theory on why a firm would issue a callable bond in the first place.
⁎ Corresponding author. Tel.: + 1 434 243 1188, +1 215 204 4895.
E-mail addresses: [email protected] (Z. Chen), [email protected] (C.X. Mao), [email protected] (Y. Wang).
1
Tel.: + 1 413 796 2162; fax: 413 796 2068.
2
In frictionless financial markets, the option to refund at lower interest rates does not add any value to the firm. It is merely a transfer from the bond investors to the
firm, as argued by Kraus (1973). However, in the presence of market frictions, hedging can increase firm value by reducing friction costs, as argued by Froot et al. (1993).
0929-1199/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.jcorpfin.2010.06.008
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
589
In the first part of the paper, we develop a theory on a firm's ex ante choice between issuing a callable or non-callable bond, its
ex post decisions whether to call back a callable bond, and whether to refund it. On the one hand, our theory explains the existing
empirical findings in the current literature, such as the lack of refunding of called bonds. On the other hand, it produces a variety of
novel testable hypotheses, which we examine empirically in the second part of the paper.
In our model, an equity-value-maximizing firm needs to raise money to invest in a current project and possibly a future project.
The current project has a positive NPV but it is uncertain whether the future project has a positive NPV. The firm decides whether
to issue a callable bond or a non-callable bond to competitive investors. After the current project generates a cash flow, the firm
and the investors observe more information about the future project. Based on the new information, the firm then decides
whether or not to invest the cash in the future project. Because the firm tries to maximize its equity value, the investment decision
may not be efficient if the bond is non-callable. More specifically, the firm may want to invest in a negative NPV but risky future
project. This is because although investing in the project will lower the firm's value, it will lower the bond value even more and
equity holders can capture the difference. Anticipating that situation, investors would pay a lower price (or equivalently demand a
higher yield) for the firm's bond when it is issued than they would if the firm could commit to an efficient investment decision.
This is the well-known risk-shifting problem first studied by Jensen and Meckling (1976).
Issuing a callable bond may alleviate this risk-shifting problem. The key point is that a callable bond gives the issuing firm an
option to reduce its debt obligation if it finds out that the future project has a negative NPV. If the firm's bond is non-callable, as
discussed above, the firm may still want to invest in the project. Instead, if the firm has an option to buy back the bond at a lower
price than its value, the firm may have an incentive to not invest in the negative NPV project but pay out cash by calling back the
bond. The reason is that now the debt obligation is reduced so that the firm can keep a larger portion of its value, most of which
would go to the bond holders if it is a non-callable bond. In other words, a callable bond essentially enables the bond holders to
bribe the firm into making an efficient investment decision.
There is, however, a cost associated with issuing a callable bond. When the future project turns out to be good, the firm would
invest in the project. If the project is better than good, the firm then would want to call back the bond and refund it at a lower cost.
In this case, however, the firm incurs a refunding cost.3 Therefore, the firm faces the following trade-off when it decides whether to
issue a callable bond or a non-callable bond. The benefit of issuing a callable bond is that it would reduce the agency cost of debt if
the investment opportunities turn out to be bad. The cost is that the firm would incur the refunding cost if the investment
opportunities turn out to be good. This implies that a firm expecting better investment opportunities would issue a non-callable
bond while it would issue a callable bond if it is expecting poorer investment opportunities.
Our model also characterizes the firm's behavior after it issues a callable bond. First, if the firm finds out that its future project is
bad, it would not invest in the project but call back the bond without refunding it. We thus provide an explanation to the observed
lack of refunding of called bonds discussed above. Secondly, if the firm finds out that the future project is good, it would invest in
the project, call back the bond, and refund it at a lower cost. Finally, if the firm finds out that its future project is mediocre, it would
choose to invest in the project without calling back the bond. This is because i) the project has a positive NPV so it is worth
continuing; and ii) the benefit from refunding the bond is not high enough to offset the refunding cost.
Our analysis yields a variety of testable hypotheses that differentiate our theory from the alternative theories in the existing
literature. In the second part of the paper, we test those hypotheses empirically. In the ex ante (at issue) study, we examine the relation
between a firm's decision of issuing a callable bond versus a non-callable bond and its expected future investment opportunities,
leverage ratio, and investment risk. In the ex post (at call) study, we examine the relation between a firm's current investment
performance and its decision whether or not to call back the bond, along with whether or not to refund it. We find strong empirical
support for our theory. We find that a firm expecting worse future investment opportunities and/or with higher leverage ratio and
investment risk is more likely to issue a callable bond. As a firm calls back its bond, the firm with the poorest performance and the
lowest investment activity is not likely to refund a call. In contrast, a firm with the best performance and the highest investment
activity is likely to refund it. A firm with mediocre performance and investment activity tends to not call their bonds. Our findings are
also economically significant. We estimate, for example, that an increase of one standard deviation in the market/book ratio (proxy for
future investment opportunities) corresponds to a 36% decrease in the firm's probability to issue a callable bond versus a non-callable
one. In addition, we find that, as a firm calls back a bond, a non-refunding call is associated with poorer performance and lower
investment activity. A decrease of one standard deviation in its ROA corresponds to a 15% decrease in the firm's probability of
refunding its called bond. Our findings are robust to various model specifications and different measures of key variables.
The rest of the paper is organized as follows. Section 2 is a review of the relevant literature. In Section 3, we use a numerical
example to develop the theoretical argument that a firm can use callable bonds to reduce the risk-shifting problem. Empirical
hypotheses are also derived. A formal model is available upon request. Section 4 describes our data, sample, and variables. In
Section 5, we examine the hypotheses concerning the likelihood of issuing callable versus non-callable bonds. In Section 6, we test
the hypotheses concerning the likelihood to call with refund, call without refund, and not call. Section 7 concludes.
2. Literature review
The literature offers five theories explaining why a firm issues a callable bond. The first is the “hedging interest rate risk theory” in
which a callable bond provides a firm with the opportunity to refund at a lower interest rate (Pye, 1966). The second is the “signaling
3
Refunding costs can be significant for some firms. Gande et al. (1999) document an average gross spread of 2.5% for junk bonds issued from 1985 to 1996.
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Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
theory” in which a callable bond allows a higher quality firm to reduce the cost associated with asymmetric information (Robbins and
Schatzberg, 1986, 1988). The reason is that even though a higher quality firm has to issue a bond at a lower price due to asymmetric
information, it can capture the price appreciations by calling back and refunding the bond after its true quality is revealed. The third
explanation is the “resolving debt overhang theory,” which indicates that a callable bond allows the issuing firm to overcome the debt
overhang problem, as identified by Myers (1977). If it suffers from a debt overhang problem, a firm (acting in the interest of its equity
holders) would not invest in positive NPV projects because part of the benefits from the new projects would go to the existing bond
holders. One way to resolve this underinvestment problem is to allow the firm to call back its outstanding debt at the time of
investment and reissue debt that reflects the improved prospects of the firm (Bodie and Taggart, 1978). The fourth explanation,
“removing restrictive covenants theory,” posits that a callable bond allows a firm to remove undesirable restrictive covenants in the
bond indentures so that the firm can engage in value-adding activities that are otherwise impossible (Vu, 1986).
The last explanation is that a firm issues a callable bond to reduce the risk-shifting problem. Equity holders can expropriate
wealth from bondholders by increasing the risk of the firm. Barnea et al. (1980) show that because the call option value of a
callable bond declines as the firm value decreases, equity holders will have less incentive to transfer wealth. We call it the
“reducing risk-shifting theory.”
Our theory differs from the existing theories in two important ways. First, our theory provides an explanation for why some
firms refund their called bonds but others don't. Second, our model formally studies a firm's trade-off between issuing a callable
bond versus a non-callable bond.
There is a small empirical literature on callable bonds (e.g., Vu, 1986; Kish and Livington, 1992; Crabbe and Helwege, 1994;
King and Mauer, 2000; Guntay, et al., 2004). The studies provide mixed evidence for each of the five explanations that explain why
a firm issues a callable bond.
Overall, we think our study makes three important contributions to the current literature. First, it derives firms' equilibrium
decisions whether to issue callable bonds or non-callable bonds, when to call back the callable bonds, and whether to refund them.
Secondly, it documents empirical findings that are consistent with our theory, but inconsistent with other theories. Lastly, to the
best of our knowledge, our study is the first to examine a firm's commitment to payout cash by calling back its bond under poor
performance conditions.
3. Theoretical analysis
3.1. Our model
For simplicity, we use a numerical example to demonstrate the main trade-off in our model. The formal analysis is available
upon request. Consider a firm at the beginning of the first period in a two-period risk-neutral economy. The sequence of events is
depicted in Fig. 1 and numerical analysis is presented in Table 1. The firm has a profitable investment project to undertake
immediately at Date 0. If undertaken, the firm has to invest $50 and the project will generate a fixed cash flow of $55 to the firm at
the end of the first period (Date 1). The firm also has a future investment project in the second period. However, whether it will be
profitable or not is uncertain at the beginning of the first period (Date 0). It is only at the end of the first period (Date 1) that the
uncertainty will resolve. After the firm learns about the expected NPV of the project at Date 1, it then decides whether to invest $55
in the second period project or not. For simplicity, we assume that the risk-free rate is zero.
We assume that the manager of the firm tries to maximize the firm's equity value (for example, the manager is the owner of the
firm). For simplicity, we assume that even though all agents in the economy observe the expected NPV of the second period project
when the uncertainty resolves at Date 1, the project NPV is not contractible. More specifically, a contract that requires the manager
to invest in the second period project only if it has a positive NPV cannot be enforced by a court.4 The project can be in three
possible states at Date 1: bad, mediocre, and good. The second period project is risky because it can yield a cash flow of either $100
or $0. If the second period project is bad, it will yield a cash flow of $100 with probability of 0.2, and a $0 cash flow with probability
0.8. If it is mediocre, the probability of a $100 cash flow is 0.7. If it is good, the probability of a $100 cash flow is 0.9. Notice that it is
not efficient for the firm to invest if the project is bad because its NPV is −$35.
None of the agents in the economy knows the state of the second period project at Date 0; however, they have a belief about it.
They believe that the probability of a bad project is 0.25, a mediocre project is 0.5, and a good project is 0.25.
We focus on two debt financing contracts of the firm to finance the initial $50 investment: a non-callable bond maturing at the
end of the second period, or a callable bond with the same maturity but is callable at the end of the first period.5 Neither pays any
coupon. We assume that the bond investors are competitive and require their investment to at least break even. We further
assume that each bond holder only buys a small fraction of the bond issued. As a result, non-callable bonds cannot be bought back
at Date 1 because of a hold-up problem (Gertner and Scharfstein, 1991). We will discuss this problem in more details later.
There are two factors determining the firm's financing choices: bond issuing cost and the risk-shifting cost. Bond issuing cost is
assumed to be a fixed fee whenever the firm issues a new bond. That is, if the firm calls back its bond and issues a new bond to
invest in the second period, it has to incur another issuing cost. We also call the issuing cost of a new bond to refinance the old one
a refunding cost. Clearly, if the firm issues a non-callable bond, it will never incur the refunding cost. Risk-shifting cost will be the
negative NPV incurred by the firm due to equity holders' risk-shifting incentive.
4
5
This is the common assumption in the incomplete contract literature (Grossman and Hart, 1986).
We implicitly assume that the firm will issue debt rather than equity because of the benefit of the debt, e.g., tax benefit. See footnote 10 for more detail.
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
591
Fig. 1. Sequence of events.
Suppose the firm issued the non-callable bond at Date 0 with a face value $80 and raised $50 to invest at Date 0. The firm has
two ways to spend the $55 cash that the first period project generates at Date 1: investing in the risky second period project or
investing in a risk-free T-bill. If the second period project is bad, clearly, investing in a T-bill has a higher NPV, zero. However, if the
firm invests in the T-bill, it will get a cash flow of $55 at Date 2 and all the money goes to the debt holders (because the face value of
the debt is $80) and the equity holders get zero. On the other hand, if the firm invests in the risky project, with 0.2 probability, the
firm will generate a $100 cash flow at Date 2. In this case, the equity holders get $100 − $80 = $20; with 0.8 probability, the firm
will generate $0 cash flow at Date 2. In this case, the equity holders get zero. So the equity holders get an expected payoff of $4 at
Date 1 if the firm invests in the risky project. Comparing the two payoffs, equity holders are better off investing in the negative NPV
risky project. This is the well-known risk-shifting problem (Jensen and Meckling, 1976). Given that the firm will always invest in
the second period project, the investors are willing to pay at Date 0 the expected payoff for the bond, which will be $50, based on
the bond value of 16, 56, and 72 in bad, mediocre, and good states, respectively.6 They just break even.
The risk-shifting problem arises because of the large amount of debt that the firm has to pay regardless of the state of the
project. One obvious way to reduce this problem is for the firm to buy back or renegotiate the debt when the investment
opportunity becomes worse. Unfortunately, when there is a large amount of bond holders, the firm cannot buy back the bond at
Date 1 because of a hold-up problem. Suppose there are 100 bond holders and each holds 1% of the bond (face value 80/100 = 0.8).
Also suppose that the firm offers the bond holders the opportunity to buy back the bond at the equilibrium price of the bond,
which is 0.2 × $80 = $16. That is, each bond holder gets 0.16. If all the bond holders accept, we can check that the firm would not
invest in the risky project. However, for individual bond holder, it is not optimal to accept the offer if all the other bond holders
accept it. The reason is that if all the other bond holders accept the offer and the firm does not invest, the firm would have $55 −
$16 = $39 cash, which is more than enough to pay the remaining bond holder the face value of his bond, 0.8. As a result, it is
optimal for a bond holder to not accept the offer. This is the well-known hold-up problem in debt renegotiation (e.g., Gertner and
Scharfstein, 1991, among others). As a result, it is impossible for the firm to solve the risk-shifting problem via buying back noncallable debt in the open market, making an exchange offer, or restructuring in some other manner.
On the other hand, a carefully designed callable bond can mitigate this risk-shifting problem. If the firm issues a bond with a
face value of $72.22 that is callable at Date 1 at a price of $49.44, the firm would not have an incentive to invest in the bad project.
This is because investing in the bad project gives it 0.2 × ($100 − $72.22) = $5.556 but calling back the bond without investing
gives it $55 − $49.44 = $5.56.7 The callable bond thus eliminates the risk-shifting cost. However, there is a cost of the callable bond
over the non-callable bond when the second period project is good at Date 1. In this state, the firm would invest in the project and
thus the debt value is $72.22 × 0.9 = $65 if not called. Thus, if the firm calls back the bond at $49.33 and then refunds the debt at a
cost of $4, it can save $65 − $4 − $49.44 = $11.56. Comparing it with the non-callable bond, we can see that the firm incurs an
additional refunding cost of $4 in this state.
We next examine the state in which the second period project is mediocre. In this state, the firm will invest because the project
NPV is positive. The debt value if not called is thus $72.22 × 0.7 = $50.554. So the firm can save $50.554 − $49.44 = $1.114 by
calling back the bond. But because the firm will have to refund the debt, it has to incur a refunding cost of $4. Thus, the firm is
better off by not calling back the bond and refunding.8
Now we study the trade-off between issuing the callable and the non-callable bond at the beginning of the first period (Date 0).
The expected risk-shifting cost that the firm can save by issuing a callable bond is 0.25 × ($55 − 0.2 × $100) = $8.75. The expected
6
So the expected payoff for the bond holders is 0.25 × $16 + 0.5 × $56 + 0.25 × $72 = $50.
We derive the call price $49.44 by making the firm just indifferent to whether or not to invest in the bad project. This is in general the best arrangement
because it minimizes the firm's incentive to refund the bond at other states, thus minimizes the expected refunding cost. One can design a callable bond in this
example to make the firm strictly better off calling back the bond and not investing.
8
The bond holders' expected payoff is $50 because in both good and bad state the bond value is $49.44 and in mediocre state the bond value is $50.554. So the
expected payoff is 0.5 × $49.444 + 0.5 × $50.554 = $50.
7
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Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
Table 1
Firm's investment and financing choices in the presence of a non-callable or callable bond (a numerical example). Consider a firm in a two-period risk-neutral
economy. The firm has a profitable investment project to undertake at Date 0, the beginning of the first period. If undertaken, the firm has to invest $50 and the
project will generate a fixed cash flow of $55 to the firm at the end of the first period (Date 1). The firm also has a future investment project in the second period.
However, whether it will be profitable or not is uncertain at Date 0, and the uncertainty is resolved at Date 1. aEx ante all agents have a common belief at Time 0
that the probability of a bad project is 0.25, a mediocre project is 0.5, and a good project is 0.25. After the manager (in the interest of equity holders) learns about
the expected NPV of the project at Date 1, he then decides whether to invest the cash flow of $55 in the second period project or not. We analyze the payoffs for the
firm, bond holders, and equity holders in bad, mediocre, and good state at Date 1, as well as different actions the manager undertakes, given that the firm issues a
non-callable or callable bond at Date 0 that matures at Date 2. The cells highlighted in grey color are the optimal choices that the manager would undertake given
various prospects of the second period project, in the presence of a non-callable or callable bond. b
Financing choice at Date 0 Decision at Date 1
(begin of the 1st period)
(end of the 1st period)
Issue non-callable bond:
Par of $80
(1) Invest $55 in T-bill
with a yield of 0%
(2) Invest $55 in the
project
Issue callable bond: Par of
$72.22 and callable at a
call price of $49.44 at
Date 1 c
(3) Call without refund
and not invest in project
(4) Not call and invest
$55 in project
(5) Call with refund and
invest $55 in the project
Second period project state (Date 1)
Bad
Mediocre
Good
Prob[100] = 0.2,
Prob[0] = 0.8
Prob[100] = 0.7,
Prob[0] = 0.3
Prob[100] = 0.9,
Prob[0] = 0.1
Expected NPV:
0.2 × $100 − 55 = −$35
Expected NPV:
0.7 × $100 − $55 = $15
Expected NPV:
0.9 × $100 − $55 = $35
Firm value: $55
Firm value: $55
Firm value: $55
Outstanding bond value: $55
Equity value: 0
Firm value: 0.2 × $100 = $20
Outstanding bond value: $55
Equity value: 0
Firm value: 0.7 × $100 = $70
Outstanding bond value: $55
Equity value: 0
Firm value: 0.9 × $100 = $90
Outstanding bond value:
0.2 × $80 = 16
Equity value: $4
Firm value: $55
Outstanding bond value:
0.7 × $80 = $56
Equity value: $14
Firm value: $55
Outstanding bond value:
0.9 × $80 = $72
Equity value: $18
Firm value: $55
Outstanding bond value: $49.44
Equity value: $55 − $49.44 =
$5.56
Firm value: 0.2 × $100 = $20
Outstanding bond value: $49.44
Equity value: $55 − $49.44 =
$5.56
Firm value: 0.7 × $100 = $70
Outstanding bond value: $49.44
Equity value: $55 − $49.44 =
$5.56
Firm value: 0.9 × $100 = $90
Outstanding bond value:
0.2 × $72.22 = $14.444
Equity value: $5.556
Firm value: 0.2 × $100 = $20
Outstanding bond value:
0.7 × $72.22 = $50.554
Equity value: $19.446
Firm value: 0.7 × $100 = $70
Outstanding bond value:
0.9 × $72.22 = $65
Equity value: $25
Firm value: 0.9 × $100 = $90
Outstanding bond value: $49.44 Outstanding bond value: $49.44 Outstanding bond value:
$49.44
Refunding cost: $4
Refunding cost: $4
Refunding cost: $4
Equity value: −$33.44
equity Value: $16.56
Equity value: $36.56
a
The project can be in three possible states at Date 1, bad, mediocre, and good. The second period project is risky because it can yield a cash flow of either 100 or
0. If the second period project is bad, it will yields a cash flow of $100 with probability of 0.2, and a $0 cash flow with probability 0.8. If it is mediocre, the probability
of a $100 cash flow is 0.7. If it is good, the probability of a $100 cash flow is 0.9. It is not efficient for the firm to invest if the project is bad because its NPV is negative.
b
For simplicity, we assume that the risk-free rate is zero.
c
When the firm issues a callable bond, the firm has two more choices not listed here: 1) not call but invest in T-bill; and 2) call and refund but invest in T-bill.
However, it can be shown that they are dominated by the other choices in each state of the second period project. Therefore, we do not report the analysis of those
two choices here to save space.
extra refunding cost of the callable bond is only 0.25 × $4 = $1. It is thus optimal for the firm to issue a callable bond.9 On the other
hand, if the firm expects a better second period project in the sense that it is more likely to be good and less likely to be bad, then
the trade-off may go the other way. For example, suppose the probabilities of the project being bad, mediocre, or good are 0.05, 0.5,
and 0.45, respectively. If the firm issues the callable bond, the savings from risk-shifting is 0.05 × ($55 − 0.2 × 100) = $1.75, but the
expected refunding cost is 0.45 × $4 = $1.8. So the firm is better off issuing a non-callable bond.10
What would happen if the firm chooses the third financing alternative: issuing a short-term straight bond in the first period?
Similar to the callable bond, it will eliminate the risk-shifting problem when the project is bad. This is because the firm has to
reissue a new bond at the end of the first period to invest in the second period project. Because the new bond is issued after the
9
We assume for simplicity that the first period investment always generates enough cash flow for the firm to call back its bond. Including a state at which the
firm does not have enough cash to call back the bond does not change the basic results in our model. This is because in this state, the firm cannot call back the
bond, thereby would invest in a negative NPV project regardless whether it issued a callable or non-callable bond. As a result, adding this state in our model does
not change the trade-off between the cost and benefit of a callable and a non-callable bond.
10
Notice that so far we have assumed that the firm chooses financing alternatives to commit to maximize the total value of the firm (debt value plus equity
value), rather than maximize the equity value. The reason is that the two objectives are equivalent in equilibrium. More specifically, because the bond investors
are rational, they break even in equilibrium (remember that we assume that the bond investors are competitive). That is, they do not capture any value created
by the firm, nor do they bear any costs — either the risk-shifting cost or the refunding cost. As a result, all the value created and the costs incurred by the firm are
captured by the equity holders. Thus maximizing the total value of the firm is equivalent to maximizing the equity value in equilibrium (the details are available
upon request).
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
593
bond investors learn about the project, the bond will be fairly priced. As a result, the gain to the equity holders is the project NPV
minus the refunding cost. The firm would thus invest in the project only if it is either good or mediocre. However, in these two
states, the firm has to reissue another short-term bond to finance the second period project at an issuing cost of $4. Comparing this
alternative with the callable bond, we can see that the callable bond dominates because it eliminates the risk-shifting cost while
the firm needs to incur an issuing cost only when the project is good.11
3.2. Testable hypotheses
Based on our analysis, we offer the following hypotheses.
H1. A firm expecting poorer future investment opportunities is more likely to issue a callable bond.
H2. A firm with higher leverage is subject to greater risk-shifting problems, thus is more likely to issue a callable bond.
H3. A firm with greater investment risk is more likely to issue a callable bond.
H4. Conditional on calling a bond, a firm with poorer performance is less likely to refund.
H5. Conditional on calling a bond, a firm with less active investments is less likely to refund.
H6. A firm with the best performance and most active investments tends to call and refund its bonds; a firm with the poorest
performance and least active investments tends to call without refund; a firm with mediocre performance and investments tends
to not call at all.
H2 and H3 are not unique to our model. Both theories of solving debt overhang and reducing risk shifting suggest a positive
relation between leverage and the likelihood of issuing callable bonds. The signaling theory suggests that firms suffering from
more severe asymmetric information (e.g., firms with greater investment risk) are more likely to issue callable bonds. H1, H4, H5,
and H6 are unique to our model since none of the existing theories offer the same empirical implications. For example, the
signaling theory predicts that firms with better private information about future performance are more likely to issue callable
bonds. The solving debt overhang theory predicts that firms expecting better future investment opportunities would incur higher
costs of forgone investment due to debt overhang, thus would be more likely to issue callable bonds. The theory of removing
restrictive covenants predicts that firms with better investment opportunities should be more likely to issue callable bonds
because they would value the option to remove the restrictive covenants more. These theories all predict a positive relation
between a firm's future investment opportunities and its likelihood of issuing a callable bond, which is the opposite of H1.
Furthermore, the theory of reducing risk shifting in Barnea et al. (1980) is silent regarding a firm's refunding decision upon their
calls, and other existing theories suggest that a firm should always refund its call. In contrast, H4 and H5 predict when a firm
should refund its calls and when it shouldn't. H6 predicts when a firm would call with refund, when it would call without refund,
and when it would not call at all. These four hypotheses help differentiate our theory from the others.
4. Data, variable construction, and descriptive statistics
4.1. Our sample of bonds
To investigate a firm's decision of issuing callable versus non-callable bonds, we obtain data on 13,784 nonconvertible fixed
rate U.S. corporate bonds issued between January 1980 and December 2003 from the Fixed Investment Securities Database (FISD).
The FISD database (which is provided by LDS Global Information Services, Inc., currently owned by Mergent) contains issue- and
issuer-specific information, such as coupon rate, maturity, and credit rating, on all U.S. corporate bonds maturing in 1990 or later.
We use the FISD database instead of the New Issue Database of Securities Data Company (SDC) as the FISD database specifies
bonds as callable or non-callable, while the SDC database does not. More importantly, we find that using information in the SDC
database to infer whether a bond is callable or non-callable may not lead to accurate categorization.12
11
Another alternative is all-equity financing. We rule out this alternative because of the benefits of debt financing, e.g., tax benefits, which are not modeled in
our setting. However, if we consider the tax benefits of debt financing, we have to consider the extra cost of the callable bond because when the firm calls back
the bond without refunding, the firm loses its tax benefit. The loss of tax benefits will make the callable bond less favorable. But as long as the loss is not too big
relative to the risk-shifting cost, which is more likely to be the case when the firm does not have many good investment opportunities, it is still optimal for the
firm to issue a callable bond than a non-callable bond or equity financing. The details are available from the authors.
12
For example, Guntay et al. (2004) classify their bond sample into callable and non-callable bonds by examining the difference between the call protection
period and time to maturity. They define a bond as being callable if the call protection period is less than one year, five years, seven years, or ten years as the bond
will mature respectively within three to seven years, seven to ten years, ten to fifteen years, or more than fifteen years. To examine the validity of this
classification, we take all the nonconvertible fixed rate bonds in the FISD being called up to year 2004, and hand match them to the bond issues from the SDC
database based on issuer cusip, issuer name, issuance date, maturity dates, and coupon rate. Thirty-five percent of these bonds are actually being categorized as
non-callable bonds according to the above classification scheme. In contrast, only 1% of the bonds being called in FISD are misclassified as non-called bonds in
FISD. Therefore, we believe the definition of callable bonds in FISD is more reliable than the approximate classification based on the call protection period and
time to maturity in the SDC database.
594
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
Fig. 2. Bond sample distribution over time. The sample consists of 13,784 nonconvertible fixed rate U.S. corporate bonds issued between January 1980 and
December 2003 obtained from the Fixed Income Securities Database (FISD). We report the percentage of callable bonds, the ten-year Treasury rate (obtained from
the constant-maturity Treasury bond yield from the H.15 release of the Federal Reserve System) ateach year-end, and the number of bonds issued every year.
Fig. 2 presents the distribution of all the 13,784 nonconvertible fixed rate U.S. corporate bonds over time. Callable bonds were
very popular debt instruments in the 1980s, accounting for on average 70% of total public debt. The proportion of callable bonds
drops significantly to only 23% in 1990s and early 2000s. The transition from high to low usage of callable bonds in early 1990s
accompanies a rapid growth in bond issuance, as shown in Fig. 2. On average, callable bonds constitute 42% of total public debt
issued between 1980 and 2003. We also plot market interest rate (10-year Treasury rate) in Fig. 2. The significant drop in the
percentage of callable bond issuances is coincident with the decreasing interest rate in our sample period. This evidence is
consistent with the theory of hedging interest rate risk.
After we merge the bond sample with the CRSP/Compustat database as well as excluded those bonds issued by firms without
operating income beta, we lost 4554 bonds.13 Utility and financial firms often use callable bonds to hedge interest rate exposure
due to their duration gaps; however, they may not be appropriate targets for our study as we evaluate our theory on hedging
investment risk. Thus we exclude 3556 bonds issued by utility firms (SIC Codes between 4800 and 4999) and financial firms (4digit SIC Codes between 6000 and 6999). As a result, our sample is reduced to 5674 bonds. To be included in our final analysis, we
require a bond with complete issue-specific information, (e.g., issue amount and S&P credit rating). Furthermore, a bond issuer
must have stock prices available in the CRSP database and relevant accounting information available in the Compustat database
(e.g., total assets and long-term debt). This yields a final sample of 3156 bonds issued between 1980 and 2003.
4.2. Variable construction for callable and non-callable bonds
4.2.1. Measuring future investment opportunities
We adopt several ex ante proxy variables to measure an issuing firm's future investment opportunities; the market/book ratio
(MB), the price/earnings ratio (PE), return on assets (ROA), analyst earnings forecasts (FORECAST), and growth rate of investment
( CAPEX and CAPEXRD). Unless otherwise noted, all variables are measured as of the year ending just prior to the bond issuance
date. Variable definitions are in the Appendix. Hypothesis H1 suggests that the probability of a firm issuing callable bond would be
negatively related to these proxy variables of future investment opportunities. In contrast, theory of signaling, solving debt
overhang, and removing restrictive covenants all predict that the likelihood of issuing callable bonds would be positively related to
future investment opportunities.
4.2.2. Measuring leverage
Leverage is measured as the book value of either long-term debt or total debt (long-term debt plus debt in current liabilities)
divided by the book value of total assets. Our choice of book (rather than market) leverage is influenced by Welch (2004), who
points out that market leverage may change passively simply because of changes in stock price performance.14 Hypothesis H2
suggests that the probability of a firm issuing callable bond would be positively related to leverage.
13
14
About 2000 of them are lost due to the fact that some firms do not have sufficient quarterly data to compute operating income beta.
Using market leverage ratio yields similar results.
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
595
Table 2
Summary statistics. Our sample includes 3156 bonds issued between 1980 and 2003. In Panel A, we report sample distribution in each one-digit SIC coded
industry. In Panel B, we report the sample distribution over time. In Panel C, we provide descriptive statistics on the issue-specific and firm-specific variables. All
variables are winsorized at the 1st and 99th percentile. Call dummy is a binary variable that equals one for callable bonds and zero for non-callable bonds. Issue
amount is the dollar proceeds of each bond issue. First-time issuer dummy equals one if this is the first time for a firm to issue a bond in the US public bond market
since January of 1975, and zero otherwise. Time to maturity is measured as the logarithm of the difference in years between the issuance date and maturity date.
Rating is the score of S&P rating, which is computed using a conversion process in which AAA+-rated bonds are assigned a value of 23 and D-rated bonds receive a
value of 1. Leverage is measured as the book value of either long-term debt or total debt (long-term debt plus debt in current liabilities) divided by the book value
of total assets. Firm size is defined as the logarithm of total assets. The market/book ratio (MB) is defined as the market value of total assets (sum of book value of
debt and market value of equity) divided by the book of value of equity. The price/earnings ratio (PE) is defined as stock price divided by earnings per share. ROA1
is the ratio of operating income before interest, tax, and depreciation (EBITD) and the book value of total assets. ROA2 is net income scaled by the book value of
total assets. FORECAST1 is the median value of the most recent annual earnings forecasts for the forthcoming fiscal year-end provided by all analysts. FORECAST2 is
the median value of the most recent annual earnings forecasts for the fiscal year-end of next year provided by all analysts. Both FORECAST1 and FORECAST2 are
scaled by the year-end book value of equity. ΔCAPEX (ΔCAPEXRD) are the first difference of capital expenditures (capital expenditures plus R&D expenses) scaled
by total sales. Risk-free rate is the constant-maturity Treasury bond yield from the H.15 release of the Federal Reserve System matching the maturity of each bond
issue. If the maturity of a corporate bond does not match that of a Treasury bond, we linearly interpolate the Treasury rates for maturities of one, three, five, seven,
ten, twenty, and thirty years. Operating income beta is the slope coefficient from a regression in which we regress the quarterly changes in operating income
before depreciation normalized by total assets over the last 7 years preceding the debt issue on changes in 1-year T-bill rates. Operating income volatility is defined
as the standard deviation of the first difference in quarterly earnings before interest, depreciation, and tax over the last 7 years preceding the debt issue,
normalized by the average value of total assets over the same time period. Unless otherwise noted, all variables are measured as of the year ending just prior to the
bond issuance date.
Panel A. Sample distribution over industry
One-digit SIC Code
Industry
NOBS
0
1
2
3
4
5
7
8
Agriculture, forestry, and fishing
Mining
Construction
Manufacturing
Transportation
Wholesale Trade
Agricultural Services
Forestry
6
269
955
728
534
408
167
89
Panel B. Sample distribution over time
Year
NOBS
% callable bonds (in terms of # of bonds)
% callable bonds (in terms of issue amount)
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
39
26
48
32
38
92
166
188
93
162
154
170
216
245
101
140
177
197
223
156
93
148
118
134
1.000
1.000
0.854
0.938
0.895
0.793
0.608
0.277
0.591
0.142
0.026
0.100
0.269
0.347
0.297
0.179
0.266
0.198
0.152
0.179
0.075
0.108
0.127
0.187
1.000
1.000
0.830
0.979
0.885
0.787
0.689
0.536
0.750
0.318
0.037
0.109
0.244
0.340
0.399
0.187
0.235
0.162
0.140
0.131
0.057
0.054
0.104
0.155
Panel C. Descriptive statistics
Variable
NOBS
Mean
Std. dev.
Minimum
Maximum
Call dummy
Issue amount ($ million)
First-time issue dummy
Time to maturity
Rating
Total assets ($ million)
Total market value ($ million)
3156
3156
3156
3156
3156
3156
3074
0.2864
195.32
0.1518
13.3279
13.5612
7960.69
11955.67
0.4522
162.31
0.3589
8.4665
3.3910
9466.95
15836.14
0.0000
0.37
0.0000
2.0164
1.0000
78.95
105.36
1.0000
1000.00
1.0000
40.0329
22.0000
70349.00
114839.09
(continued on next page)
596
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
Table 2 (continued)
Panel C. Descriptive statistics
Variable
NOBS
Mean
Std. dev.
Minimum
Maximum
Firm size (Ln Assets)
Leverage (long-term debt)
Leverage (total debt)
PE
MB
FORECAST1
FORECAST2
ROA1
ROA2
ΔCAPEX
ΔCAPEXRD
Risk-free rate
Operating income beta
Operating income volatility
3156
3134
3156
3068
3035
2688
2649
3102
3090
3040
3040
3156
3123
3156
8.3483
0.2679
0.3128
15.4538
1.4796
0.0976
0.1216
0.1461
0.0439
0.0069
0.0071
7.0083
0.0007
0.0137
1.2368
0.1339
0.1349
21.8513
0.6257
0.1804
0.1758
0.0547
0.0429
0.0489
0.0502
1.9826
0.0266
0.0090
4.3688
0.0084
0.0382
− 112.5000
0.8141
− 2.0239
− 2.0239
− 0.0049
− 0.1847
− 0.3576
− 0.3576
1.6682
− 0.1345
0.0023
11.1612
0.7945
0.8450
227.5735
4.5558
1.7613
1.7613
0.3078
0.1679
0.3052
0.3208
15.1599
0.1376
0.0616
4.2.3. Measuring investment risk
We employ several variables to proxy for investment risk, including firm size, a first-time issuer dummy, and operating income
volatility. Smaller firms, first-time issuers, and firms with larger operating income volatility would have greater level of
investment risk. Our hypothesis H3 suggests that the probability of a firm issuing a callable bond would be negatively related to
firm size, but positively related to the first-time issuer dummy and operating income volatility.
A firm's investment risk or overall risk is also reflected in bond credit rating. RATING is the S&P credit rating score, which is
computed using a conversion process in which AAA+-rated bonds are assigned a value of 23 and D-rated bonds receive a value of
1. Since credit rating may incorporate part or all of future investment opportunities, we orthogonalize this variable by regressing it
against each of the variables proxied for investment opportunities, and use the residual term (Rating Residual) as the regressor in
the model.15 Hypothesis H3 suggests that the probability of a firm issuing a callable bond would be negatively related to Rating
Residual.
4.2.4. Other control variables
Guntay et al. (2004) show that the choice of issuing a callable bond is positively related to the market interest rate and a firm's
interest rate sensitivity of operating income. Based on this evidence, they argue that a firm uses a callable bond to hedge operating
income fluctuations. To control for the confounding effects of market interest rate and a firm's operating income exposure to
interest rate, we also include the risk-free rate and the operating income beta, which measures a firm's interest rate sensitivity to
operating income.
We also include a few issue-specific variables that might affect the choice of whether to issue a callable or a non-callable bond,
including time to maturity and issue size. According to the theory of hedging interest rate risk, there is a substitution effect
between using a call option and shortening maturity. Thus we expected a positive relation between maturity and the probability of
issuing callable bonds. In addition, larger issues are more likely to be associated with callable bonds since they create higher
interest rate exposure for a firm. It is worth mentioning that our theory also suggests a positive relation between issue size and
maturity, and the probability of issuing callable bonds. This is because larger issues and longer maturities would subject the issuers
to greater investment risk.
5. Choices of issuing a callable bond
Table 2 reports descriptive statistics for our final bond sample.16 Panel A presents sample distribution in each one-digit SIC
coded industry; Panel B presents sample distribution over time; Panel C offers summary statistics on the variables used in the
analysis. The bond sample contains about 29% callable bonds;17 15% are first-time issue. The average issue size is $195 million,
while the average time to maturity is approximately 13 years, suggesting a large proportion of long-term bonds in the sample. The
average S&P credit rating score is 13.6, equivalent to a rating between BBB+ and BBB. Our sample seems to be filled with large
companies. The average total asset of bond issuers is $7.9 billion, and their average market value is about $12 billion.
5.1. Univariate results
In Table 3, we examine the difference in mean and median value of issue-specific and firm-specific variables between callable
and non-callable bond issues. We observe significant differences in both mean and median values of proxy variables of future
investment opportunities between the two groups. Callable bonds are issued by firms with a lower market/book ratio (MB), lower
15
16
17
The residual term from the regression captures the credit rating information without the influence of investment opportunities.
To minimize the effect of outliers, we winsorize all the variables at the 1st and 99th percentiles.
Callable bonds account for 24% of the total issue amount in our sample.
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
597
price/earnings ratio (PE), lower ROA, and lower analyst forecasts for future earnings. Growth rate in capital expenditure and R&D
expenses is lower in firms issuing callable bonds than that in firms issuing non-callable bonds. These results support hypothesis
H1: Firms with poorer future investment opportunities are more likely to issue callable bonds. Furthermore, callable bond issuers
have greater mean and median values of leverage, supporting hypothesis H2. Callable bonds are issued by smaller firms with lower
credit ratings and greater operating income volatility, and are more likely to be first-time issues. These results are consistent with
hypothesis H3: Firms with greater investment risk are more likely to issue callable bonds. Both types of bond issuers have a very
small mean or median operating income beta; although the mean is not statistically significantly different between the two
groups, the median operating income beta of callable bond issuers is significantly higher than that of non-callable bond issuers.
Consistent with the theory of hedging interest rate risk, callable bond issuances are associated with a significantly higher interest
rate. In addition, we find callable bond issuances are associated with longer maturity and smaller issue size.
5.2. Logistic regressions explaining the likelihood of issuing callable bonds
We employ logistic regressions to explore the cross-sectional relation between a firm's likelihood of issuing a callable bond and
variables that proxy for future investment opportunities, leverage, and investment risk. The dependent variable in the logistic
models is a binary variable equal to one for callable bonds and zero for non-callable bonds. The results are reported in Table 4.18
As shown in Table 4, the explanatory power of our logit models is substantial, as evidenced by the Pseudo-R2 exceeding 62% in
each regression. The first variable of interest is market/book ratio (MB), which proxies for future investment opportunities. The
coefficient estimate on MB is negative and statistically significant at the 1% level in all models. This result is consistent with
hypothesis H1, suggesting that firms with better future investment opportunities are less likely to issue callable bonds.19
Our theoretical analysis indicates that callable bonds could resolve the agency problem of risk shifting when a firm's future
investment opportunities are poor. Hypothesis H2 suggests that a firm with a higher leverage ratio is more likely to issue a callable
bond, since it is subject to a greater debt agency problem. Consistent with H2, we observe a positive and significant relation
between the total leverage ratio and the probability of issuing a callable bond in model (1). To test the robustness of this leverage
effect, we include in model (2) a long-term leverage ratio, and the result remains.20
We include several variables to proxy for investment risk. Firm size is significantly negatively related to the probability of
issuing a callable bond, since a larger firm is often subject to less investment risk. First-time issuers tend to be smaller firms, or
firms with less experience and reputation (or access) in the public debt market. The coefficient estimate of the first-time issuer
dummy is positive and significant. Operating income volatility, however, is not significantly related to the usage of a callable bond.
Rating residual is negatively related to the probability of issuing a callable bond, and the coefficient estimate is highly significant. A
firm with a higher credit rating residual is facing lower investment risk, and hence, it is less likely to issue a callable bond. These
results support hypothesis H3: a firm with greater investment risk is less likely to issue a callable bond. Kish and Livington (1992)
and Crabbe and Helwege (1994) document a significant negative relation between credit rating and the use of callable bonds.
To assess the economic impact of each variable on the choice of issuing callable bonds, we compute an odds ratio that
represents the change in probability of issuing a callable bond given the change of one standard deviation of each independent
variable. Change in probability for MB is −0.3564 in model (1), implying that an increase of one standard deviation in MB would
decrease the probability of issuing a callable bond by 36%.21 Change in probability for total leverage ratio and rating residual is
0.2388 and −0.5594, respectively. These results suggest an economically significant effect of future investment opportunities,
leverage, and rating residual on the likelihood of issuing a callable bond.
The coefficient estimate of the risk-free rate is positive and significant at the 1% level in most regressions, suggesting that
interest rate risk may be a significant consideration in corporate usage of callable bonds.22 Guntay et al. (2004) argue that if
callable bonds are used for hedging interest rate risk, firms with higher interest rate sensitivity (operating income beta) would be
more likely to issue callable bonds. Our evidence does not support their argument. We find that the coefficient estimates on
operating income beta are mostly positive; however, they are not significant in any of the regressions.23 Overall, our analysis offers
mixed evidence with respect to the theory of hedging interest rate.24
18
To control for time and industry effects, we include in the logistic regressions dummy variables for each calendar year and each industry based on two-digit
SIC Codes.
19
One might argue that MB might capture the risk aspect of a firm since a firm with high growth potential would have a large MB but also a high level of
investment risk. In our logistic models, we include several variables to control for investment risk as discussed above; therefore, the relation we observe between
MB and the probability of issuing a callable bond should reflect the impact of future investment opportunities rather than investment risk on the usage of a
callable bond. Furthermore, since the relationship between investment risk and callable bond usage is expected to be positive, the negative relation between MB
and the probability of issuing a callable bond is likely the impact of future investment opportunities that is captured by MB.
20
Kish and Livington (1992) have documented a similar result.
21
Given that the mean probability of issuing a callable bond is 14.31%, as indicated in model (1) in Table 4, this is equivalent to an increase of unconditional
probability of issuing a callable bond by 5.2%.
22
This result is consistent with the findings documented in the literature (e.g., Kish and Livington, 1992: Guntay et al., 2004).
23
To take into account the statistical significance of the beta estimate, as in Graham and Rogers (2002), we define an operating income exposure variable that is
zero if the operating income beta is not significant at the 10% level. Otherwise, it takes the value of − 1 or + 1, depending on the sign of the coefficient. Our
results are similar as we use the operating income exposure variable in the analysis.
24
To assess whether the difference between our results on operating income beta and those in Guntay et al. (2004) is driven by different sample periods, we
estimate the same regression models in Table 3 of Guntay et al. (2004) based on a sample period of 1981 through 1997. The coefficient estimate of operating
income beta remains insignificant. Nevertheless, the difference between our results and those in Guntay et al. (2004) might be driven by the use of different
databases and/or different methods of defining a callable bond, as discussed in footnote 10.
598
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
Table 3
Univariate statistics of callable and non-callable bonds. This table reports the mean and median of issue-specific and firm-specific variables for callable and noncallable bonds issued between 1980 and 2003. T-tests (Wilcoxon rank tests) are used to examine the null hypothesis that the mean (median) of each variable is the
same between callable and non-callable bonds.
Variable
MB
PE
ROA1
ROA2
FORECAST1
FORECAST2
ΔCAPEX
ΔCAPEXRD
Leverage (total debt)
Firm size (Ln assets)
Rating
Operating income volatility
First-time issue dummy
Operating income beta
Risk-free rate
Time to maturity
Issue amount ($ million)
Mean
Median
Callable
Non-callable
T-statistics
Callable
Non-callable
Z-statistics
1.308
11.520
0.144
0.038
0.058
0.100
0.001
0.002
0.350
7.609
11.887
0.015
0.254
0.002
8.049
15.742
166.097
1.545
16.986
0.147
0.046
0.110
0.128
0.009
0.009
0.298
8.645
14.233
0.013
0.111
0.000
6.590
12.359
207.052
−11.530
−6.280
−1.120
−4.630
−5.050
−3.080
−3.850
−3.480
8.540
−20.690
−15.290
5.170
9.030
1.110
16.770
9.680
−7.580
1.184
11.275
0.146
0.042
0.053
0.070
0.001
0.001
0.321
7.600
12.000
0.012
0.000
0.002
7.520
10.025
148.800
1.323
15.218
0.146
0.047
0.077
0.094
0.002
0.003
0.293
8.742
14.000
0.011
0.000
−0.002
6.494
10.016
200.000
−10.023
−10.884
−0.494
−2.919
−11.346
−8.291
−3.763
−3.832
6.042
−19.270
13.537
5.086
10.240
2.560
15.082
8.001
−5.519
As with Kish and Livingston (1992), Crabbe and Helwege (1994), and Guntay et al. (2004), we find that larger bond issues and
those with longer maturities are more likely to be callable. In model (3), we replace time to maturity with duration, that is, the
discount time-weighted cash flows of the bond divided by bond price, and obtain similar results. The coefficient estimate on
duration is significantly positive. This finding is consistent with the theory of hedging interest rate risk. Since larger bond issues
and issues with longer maturities are associated with greater interest rate risk, the issuing firm is more likely to use a callable bond
to hedge interest rate risk. This finding is also consistent with our theory that longer maturity and larger issue size may be
associated with greater future investment risk. Therefore, a firm would be more likely to issue a callable bond to minimize the
agency problem according to H3.
Our logistic analysis above focuses on a firm that issues callable and non-callable bonds; however, the decision of whether or
not to issue a bond could itself be endogenous. Hence, we investigate the possibility that our results are spuriously driven by an
unobserved but nonrandom selection criterion. To test (and if necessarily correct) for selection bias, we estimate a maximum
likelihood version of a Heckman (1979) selection model of regression, and the result is reported in model (4) of Table 4. In
particular, we take all the firm-year observations in the Compustat database in 1980 through 2003, and construct a dummy
variable “issuing bond” based on whether or not a firm issued a bond (callable or non-callable) in a particular year as recorded in
the FISD database. Then we estimate a selection probability model (results not reported) that relates the probability of issuing
bonds to firm size, MB, R&D expenses (normalized by assets), proportion of tangible assets, leverage, ROA, Altman z-score, and
operating income volatility.25 These variables are chosen based on Denis and Mihov (2003). As shown in model (4), the inverse
Mills ratio from the selection probability model is only marginally significant at the 10% level. After controlling for potential
selection bias, our results are robust. The selection-adjusted coefficient estimates are similar to those from model (1).
In addition, to assess whether our results are driven by firm size, we divide our sample into three equal groups based on size
(small, medium, and large), and a logistic regression is conducted in each sub-sample. Our results are robust in each group. The
coefficient estimates on MB (measure of future investment opportunities) are significantly negative in all three sub-samples, and
the magnitudes of coefficients are also comparable among groups (results available upon request).
5.3. Additional tests of hypothesis H1
To further investigate the relation between future investment opportunities and the probability of issuing callable bonds (H1),
we employ several alternative ex ante measures of investment opportunities; however, regressions results are not reported but
available upon request. The coefficient estimates of price/earnings ratio (PE), ROA, and analyst earnings forecasts for the
forthcoming year and next year (FORECAST1 and FORECAST2) are all negative and statistically significant at the 1% level. Changes
in probability indicate that an increase of one standard deviation in one of these variables is associated with a decrease of the
probability of issuing a callable bond by 10% to 32%. These results lend strong support for hypothesis H1.
To assess the relation between a firm's decision to issue a callable bond and its expected future investments, we also include an
ex ante measure of growth in capital expenditure (ΔCAPEX) or growth in capital expenditure and R&D expenses (ΔCAPEXRD). We
find that the coefficient estimates of ΔCAPEX and ΔCAPEXRD are both negative and significant (results available upon request).
25
The dependent variable for the selection model is a binary variable “issuing bond” that equals one if a firm issued a callable or non-callable bond in a given
year, as recorded in the FISD database, and zero otherwise.
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
599
Table 4
Logistic regressions explaining issuance of callable bonds. This table presents the results of logistic models in which the dependent variable is a binary variable
equal to one for callable bonds and zero for non-callable bonds. Independent variables include issue-specific and firm-specific variables that proxy for firms' future
investment opportunities, leverage, and investment risk. All the explanatory variables are as defined in the Appendix. Regression (4) controls for sample selection
bias by estimating a MLE version of the Heckman (1979) selection model. To control for time and industry effects, we also include dummy variables for each
calendar year and each industry based on two-digit SIC Code. P-values are reported in parentheses. Mean probability is the predicted probability of issuing callable
bonds when all explanatory variables have their mean values. Change in probability is defines as the percentage change in the probability of issuing callable bonds
when the corresponding explanatory variable is increased by one STD, and all other variables are evaluated at their means and reported in { }.
Independent variables
1
2
3
4
Intercept
−7.0718
(b.0001)
−0.7946
(b.0001)
{−0.3564}
1.8859
(0.0002)
{0.2388}
−7.1908
(b.0001)
−0.7974
(b.0001)
{−0.3579}
−8.6560
(b.0001)
−0.7372
(b.0001)
{−0.3376}
1.3633
(0.0186)
{0.1707}
−8.1241
(0.0074)
−0.5317
(b.0001)
{−0.1971}
2.6990
(b.0001)
{0.1445}
−0.4917
(b.0001)
{−0.4222}
0.3261
(0.0997)
{0.1063}
−0.2318
(b.0001)
{−0.4773}
2.2243
(0.7615)
{0.0179}
0.5813
(b.0001)
{1.4651}
1.5955
(0.4924)
{0.0380}
0.7340
(b.0001)
{1.4582}
−0.2052
(0.3288)
{−0.149}
0.1959
(0.2976)
{0.0244}
−0.3217
(b.0001)
{−0.3682}
3.9593
(0.4935)
{0.0412}
−0.0187
(0.8748)
{−0.0129}
0.1488
(0.9132)
{0.0081}
0.7313
(b.0001)
{0.3009}
1.5264
(b.0001)
{0.25}
MB
Leverage (total debt)
Leverage (long-term debt)
Firm size
First-time issuer dummy
Rating residual
Operating income volatility
Risk-free rate
Operating income beta
Log(Issue amount)
Log(Time to maturity)
−0.5718
(b.0001)
{−0.4703}
0.3672
(0.0349)
{0.1188}
−0.2928
(b.0001)
{−0.5594}
2.2343
(0.7374)
{0.0178}
0.3567
(b.0001)
{0.7721}
0.4081
(0.8476)
{0.0095}
0.6463
(b.0001)
{1.2051}
1.3484
(b.0001)
{0.9246}
2.0520
(0.0001)
{0.2598}
−0.5590
(b.0001)
{−0.4629}
0.4039
(0.0213)
{0.1316}
−0.2876
(b.0001)
{−0.5532}
3.0814
(0.6446)
{0.0246}
0.3622
(b.0001)
{0.7892}
0.7013
(0.7417)
{0.0164}
0.6443
(b.0001)
{1.2049}
1.3487
(b.0001)
{0.9279}
Duration
0.1185
(b.0001)
{0.6285}
Inverse Mills Ratio
Mean probability
Industry dummy & Calendar year dummy
Pseudo-R2
NOBS
0.1431
Yes
0.6321
3056
0.1416
Yes
0.6318
3038
0.1332
Yes
0.6297
2685
0.9250
(0.0648)
0.6693
Yes
0.6465
3056
Assuming that an ex ante growth of investment is a proxy of future investment growth, our results suggest that a firm expecting
high growth in investments would be less likely to use a callable bond, since it faces less investment risk in the future.
In addition to these ex ante measures of investment opportunities, we adopt a few ex post variables to proxy for investment
opportunities. Based on rational expectations, observed investment opportunities should be a proxy for anticipated investment
opportunities (e.g., Pilotte, 1992). We include a few ex post variables that proxy for investment opportunities: Ex-ROA is the
average ROA over the three years following bond issuance and Ex-ΔCAPEX (Ex-ΔCAPEXRD) is computed as the average of ΔCAPEX
(ΔCAPEXRD) over the three years following bond issuance. The coefficient estimates of Ex-ROA, Ex-ΔCAPEX, and Ex-ΔCAPEXRD
are all negative and statistically significant (results available upon request). These results further confirm that a firm is less likely to
issue a callable bond when it expects future investment opportunities to be better.
Our empirical evidence on the relation between future investment opportunities and the probability of issuing callable
bonds lends strong support to our hypotheses, particularly H1. In contrast, this evidence is not consistent with the alternative
explanations, including the signaling theory, the theory of solving debt overhang, and removing restrictive covenants. As
discussed in Section 3, these three theories all predict that firms with better future investment opportunities are more likely to
issue callable bonds.
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Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
5.4. Robustness tests on the choice of issuing callable bonds
One caveat of our results is that it does not take into account other financial contracting devices, e.g., leverage and debt
maturity, which would also mitigate the risk-shifting incentive. While we have controlled for the impact of leverage and maturity
in our regression models above, the control might be problematic since the choice of a callable bond is likely jointly endogenous
with these “control” variables. As such, we conduct a few robustness tests to address the endogeneity issue of leverage and debt
maturity. 26
First we estimate a “reduced form” model that excludes Leverage and Log(Time to maturity) that are likely jointly endogenous,
and the results are reported in column (1) of Table A1. As with the results in model (1) of Table 4, the coefficient estimate on MB is
significantly negative. Firm size and Rating residual are both significantly negatively related to the probability of issuing a callable
bond.
Second we estimate a system of three simultaneous equations that recognizes that both leverage and debt maturity are
determined endogenously with the choice of issuing a callable bond. For the leverage and maturity equations in columns (3) and
(4) respectively, we use the explanatory variables that Johnson (2003) and Billett et al. (2007) employ in their system of leverage
and maturity equations. In particular, in the leverage equation we include MB (market-to-book), operating income volatility, debt
maturity (prop. short-term debt), interaction term of MB and debt maturity, fixed assets, profitability, firm size, investment tax
credit dummy, net operating loss carry forward dummy, and abnormal earnings. In the maturity equation, we include MB
(market-to-book), leverage, operating income volatility, firm size and the square of firm size, investment tax credit dummy, net
operating loss carry forward dummy, abnormal earnings, asset maturity, and rated firm dummy. In the equation explaining
the choice of issuing callable bonds (column 2), we include the same set of variables used in model (1) of Table 4, except that
we include firm level debt maturity (prop. short-term debt) instead of bond maturity. Following Johnson (2003), maturity
(prop. short-term debt) is defined as the fraction of a firm's total debt that matures in 3 years or less. We define all other variables in
Table A1. Each variable is measured at the fiscal year-end prior to the bond issuance date. The system of equations is estimated by
nonlinear two-stage least squares method for the pooled sample of callable and non-called bonds issued between 1980 and 2003.
After accounting for the endogenous choice of leverage and debt maturity, our results on the likelihood of issuing callable
bonds as reported in Table 4 remain robust. We find that MB, firm size, and rating residual are all significantly negatively related to
the probability of issuing a callable bond, which supports our hypothesis H1 and H3. Leverage remains significantly positively
related to the likelihood of issuing a callable bond, supporting our hypothesis H2.27
6. Firms' choices of call with refund, call without refund, and not call
In addition to the implications on a firm's choice of whether to issue a callable or a non-callable bond, our model also provides
explicit empirical implications on a firm's choice whether to call the bond, as well as whether to refund the call. To test those
implications, we first explore that, conditional on the call events, how a firm's performance and investment activity are related to
the decision on whether or not to refund the call. Second, we examine the impact of firm performance and investment activity on
the firm's choices of call with refund, call without refund, and not call at all.
6.1. Choice of refunding around the call events
Our sample of bonds being called is obtained from the file called “Amount Outstanding” in the FISD.28 To be included in our
analysis, we require that a called bond have issue-specific information available in FISD (e.g., issue amount and credit rating) and
relevant accounting information available in the Compustat database (e.g., total assets, long-term debt) around the call date. The
analysis yields 853 bonds being called between 1983 and 2004.
To measure a firm's refunding activities around a call event, we aggregate the total amount of new debt within a 12-month
period surrounding the call date (i.e., six months before or six months after the call), as reported in the SDC New Issue Database.
New debt includes public debt, private placements, 144A, shelf registration debt, and convertible debt. We find only 46% of firms
issuing new debt in this 12-month window.29 To define a refunding call, we follow King and Mauer (2000) and require the
refunding ratio (the total amount of new debt raised within a 12-month period divided by the book value of the bonds being
called) to be at least 110%.30 Otherwise, the call event is defined as a non-refunding call.
26
We thank an anonymous referee for suggesting this great point.
The signs of the coefficient estimates on variables in the leverage and maturity equations are in general consistent with those reported in Johnson (2003) and
Billett et al. (2007).
28
This file provides the date and amount of any changes to a bond issue's amount outstanding due to various actions (e.g., part of an issue called, entire issue
called, call with an equity clawback provision). This file allows us to identify those bond issues that are entirely called back up to 2004, including call date, call
price, and the amount of issue being called. We do not include bonds partially called or called with an equity clawback provision.
29
King and Mauer (2000) report that 23% of their call events in 1975–1994 are associated with raising new debt. We find a much higher percentage of
refunding calls in our sample, partly due to the fact that the SDC database offers a more comprehensive coverage of new debt issuance than Moody's manuals, the
Wall Street Journal Index, or LexisNexis that King and Mauer (2000) rely on to identify new financing activities.
30
The extra 10% in the refunding ratio could be thought of the residual financing activities for an average firm in a given 12-month period. Even in the absence
of refunding calls, an average firm may raise some funds in the financial market on a regular basis. We employ alternative definition of refunding calls (e.g., a
refunding ratio of 100% or 130%), and our results remain qualitatively the same.
27
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
601
Table 5
Summary statistics on called bond sample. Our sample includes 853 bonds being called back between 1983 and 2004. In Panel A, we provide descriptive statistics
on the called bond sample. Total new debt is the total amounts of new debt (public and private) issued within a 12-month period surrounding the call dates.
Refunding ratio is total amounts of new debt raised within a 12-month period divided by the book value of the bonds being called. Refund dummy is a binary
variable equal to one if the refunding ratio is 110% or greater. Otherwise, the refund dummy is zero. Tangible assets are defined as property, plant and equipment
divided by total assets. All other variables are as defined in the Appendix. In Panel B, we report for both refunding calls and non-refunding calls, the mean of
refunding ratio, change of interest rate (maturity matched Treasury rate at call date minus Treasury rate at issue date), credit rating at issue date, credit rating at
call date, the change of credit ratings between call date and issue date, and the number of years from the call date to the maturity date. T-tests are conducted to
examine the null hypothesis that the mean of these two groups is the same.
Panel A. Descriptive statistics
Variable
NOBS
Mean
Std dev
Minimum
Maximum
Total new debt
Refunding ratio
Refund dummy
ROA1
ROA2
ΔCAPEX
ΔCAPEXRD
Firm size
Tangible assets
Operating income volatility
Leverage
853
853
853
853
833
814
814
853
853
853
853
356.2121
3.0696
0.4021
0.1377
0.0301
−0.0048
−0.0048
8.2935
0.4377
0.0134
0.3601
607.3295
6.1880
0.4906
0.0551
0.0475
0.0410
0.0420
1.3425
0.2187
0.0088
0.1558
0.0000
0.0000
0.0000
−0.0075
−0.1702
−0.2525
−0.2525
4.7089
0.0213
0.0023
0.0610
4329.2000
64.0000
1.0000
0.2796
0.1379
0.2066
0.2066
11.0973
0.9053
0.0612
0.8886
Panel B. Mean difference between refunding calls and non-refunding calls
Non-refunding calls
Refunding calls
Difference
T-statistics
NOBS
Refunding
ratio
Change of
risk-free rate
Rating
at issue
Rating
at call
Rating at
call–Rating at issue
Years from maturity
date to call date
510
343
0.0752
7.5219
7.4467
−17.50
−2.1692
−2.3172
−0.1480
1.11
12.2176
14.3907
2.1731
−8.28
12.2294
14.3703
2.1409
−8.39
0.0118
−0.0204
−0.0322
0.31
7.6789
9.4608
1.7819
−3.35
Panel A in Table 5 reports descriptive statistics on our bond sample being called from 1983 to 2004. Despite the fact that only
46% of firms raise new debt within a 12-month period surrounding the call date, these firms raise about $356 million in new debt
on average, which is equivalent to 3.07 times the average amount of bonds being called back. Forty percent of the call events are
categorized as refunding calls since they raise new debt at least as large as 110% of the book value of the bonds. Therefore, there are
a significant proportion of firms (60%) not refunding their calls.
In Panel B of Table 5, we investigate the difference between refunding calls and non-refunding calls. For those refunding calls,
the mean refunding ratio is 7.52, suggesting that many firms raised a large amount of capital within a 12-month period around the
call date. Consistent with our sample construction, non-refunding calls are associated with little new debt. The mean refunding
ratio is 0.075 in this group, suggesting that non-refunding firms raise on average new capital of only 7.5% of the amount of bonds
being called. This raises an interesting question: Why are these firm calling back bonds without refunding? One might argue that it
is not worth refunding because the interest rate does not drop enough to outweigh the refunding costs. If this is true, we would
expect the change of interest rate between call date and issue date to be larger (or less negative) in the non-refunding group than
that in the refunding group. As shown in Panel B of Table 5, the mean change of interest rate between the call date and issue the
date is negative in both groups; a t-test indicates that the mean change is not statistically significantly different, suggesting that an
interest rate change is not the reason for a firm to not refund a call.
In Panel B of Table 5, we also examine the credit rating of these two groups of bonds at the time when they were issued and
called. When they were issued, bonds in the refunding group (average rating of BBB+) were rated two notches higher than those
in the non-refunding group (average rating of BBB−); upon their calls, they basically retain their rating level. These findings are
not consistent with the theories of signaling and solving the debt overhang problem, since both predict an improvement in a firm's
prospects upon calling a bond (i.e., an improvement in credit rating).31 In addition, the average number of years between the call
date and the maturity date is 7.70 and 9.37 years for the non-refunding and refunding group respectively. This suggests that the
call events in our sample are significant early terminations of bond maturity.
While we count both public and private debt in measuring refunding activities, we do not include bank debt. If the firms in our
sample refund public debt with bank debt, we may misclassify refunding calls as non-refunding. To address this issue, we examine
the change of total debt in the balance sheet from before to after the call event. We find that a non-refunding firm experiences a
significant decrease in total leverage (total debt divided by assets), while the refunding firm has a significant increase in leverage
31
Crabbe and Helwege (1994) and King and Mauer (2000) also document little significant rating improvement for a sample of bonds being called in 1975–
1994.
602
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
(results available upon request). Further analysis indicates that the change in total leverage largely comes from long-term rather
than short-term debt, indicating that our definition of refunding is accurate.
As we show in our model, as a firm faces deteriorating investment opportunities, it would choose to not invest in the project
and call back a bond without refunding. In contrast, when a firm has excellent investment opportunities, it would invest, call back
the bond, and refund the call. Hypotheses H4 and H5 suggest that a firm with poorer performance and less active investments is
less likely to refund when it calls a bond.
To test these two hypotheses, we employ logistic models to investigate the cross-sectional relation between a firm's likelihood
of refunding and its performance and investment activities. The dependent variable in the logistic models is a binary variable equal
to one for refunding a call and zero for not refunding a call. Explanatory variables include a measure of performance, ROA1 (EBITD/
Assets) or ROA2 (net income/assets), or a measure of investment activities, ΔCAPEX or ΔCAPEXRD. In addition, we include control
variables that proxy for financing costs, including firm size, tangible assets (defined as property, plant, and equipment divided by
total assets), total leverage ratio, and operating income volatility. All these explanatory variables are computed in the year-end
prior to the call date. Furthermore, we include the change of interest rate and the change of credit rating between the call date and
issue date to capture the potential benefit of refunding.32
The regression results are reported in Table 6. The variables of interest are the measures of performance and investment
activities. The coefficient estimates on ROA1 and ROA2 are both positive and statistically significant at the 5% level, suggesting that
a firm with poorer performance is less likely to refund a call. Furthermore, the growth rates of CAPEX and CAPEXRD are both
significantly positively related to the probability of refunding around a call event. The evidence indicates that a poor-performing
firm invests less in their project and pays out cash. Our results are also economically significant. A decrease of one standard
deviation in ROA2 and the growth rates of CAPEX are associated with a decrease of 26% and 12% in the likelihood of refunding,
respectively. These results support hypotheses H4 and H5.
The coefficient estimate of firm size is significantly positive in all the regressions, suggesting that a larger firm is more likely to
refund a call since it incurs fewer financing costs due to its better access to the capital market and the economy of scale effect. The
proportion of tangible assets is positively related to the probability of refunding around call events; however, the coefficient
estimates are statistically insignificant. The coefficient estimates of total leverage ratio and operating income volatility are not
statistically significant either. The change of interest rate between the call date and the issue date is not significantly related to the
probability of refunding. This result is due to the inclusion of dummy variables for each calendar year. If we leave out calendar year
dummy variables, the coefficient estimate on the change of interest rate becomes negative and statistically significant. This finding
suggests that the lower the interest rate at the call date, the more likely a firm is to refund a call because of the benefits of
refunding.33 Change in credit rating (rating at call minus rating at issue) is not significantly related to the choice of refunding. This
evidence is inconsistent with the theory of signaling and solving debt overhang problem.
6.2. Robustness tests
We conduct the following robustness checks of refunding choice. First, we restrict our sample to called bonds that are also
present in our analysis of the choices of issuing callable versus non-callable bonds, and this yields 492 called bonds. 34 The logistic
regression results are similar to those using the full bond sample, as reported in Table 6. Second, most of previous studies on
callable bonds are based on call events hand collected from Moody's Manuals (Vu, 1986; King and Mauer, 2000; Guntay et al.,
2004). As an alternatively sample source, we follow King and Mauer (2000) and consult Moody's Annual Bond Records, and hand
collect 489 bonds called by industrial firms from 1990 to 2005. The results are similar.
Another, perhaps more extreme way for a firm to reduce investment than reducing capital expenditures is asset sales. H5
suggests that a non-refunding firm is more likely to sell assets around the call event. To test this hypothesis, we collect asset sales
activities from the SDC M&A database for the six months before and six months after each call event. We find support for H5: the
average net asset sales (the amount of assets sold minus the amount of assets bought during the 12-months window) normalized
by the amount of debt being called are significantly larger in the non-refunding group than that in the refunding group (results
available upon request).
6.3. Choice to call with refund, call without refund, or not call
We next examine a firm's unconditional choices to call with refund, call without refund, or not call as its bond exits the call
protection period. Hypothesis H6 suggests that the choice is not monotonic with respect to firm performance and investment
activity. The best firms are more likely to call with refund. The worst firms are more likely to call without refund, while the
mediocre firms are more likely to not call their bonds.
We estimate a multinomial logit model to explore the impact of a firm's performance and investment activities on the three
choices of callable bonds: call with refund, call without refund, or not call. Following Denis et al. (1997), Shumway (2001), and
King and Mauer (2009), we start with a sample of all callable bonds in FISD. We track each callable bond starting from the year in
32
We include dummy variables for each calendar year and industry based on two-digit SIC Codes to control for the time and industry effects.
The benefit of refunding at a lower interest rate can be thought of as a deceased refunding cost in our model.
34
There are 53% of bonds (492) being called in FISD also present in the at issue analysis in section 5; the rest are either callable bonds issued before 1980 or
they do not have adequate data available for issue analysis.
33
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
603
Table 6
Logistic regressions explaining the likelihood of refunding around call events. This table presents the results of logistic models in which the dependent variable is a
binary variable equal to one for refunding calls and zero for non-refunding calls. Independent variables include firm size, leverage ratio, tangible assets, operating
income volatility, changes of interest rate and credit rating between call date and issue date, and ROA or growth rate of capital expenditures and R&D expenses. All
the explanatory variables are as defined in the Appendix. Change in credit rating is defined as the rating at call minus the rating at issue. To control for the time and
industry effects, we also include dummy variables for each calendar year and each industry based on two-digit SIC Code. P-values are reported in parentheses.
Mean probability is the predicted probability of refunding when all explanatory variables have their mean values. Change in probability is defines as the
percentage change in the probability of refunding when the corresponding explanatory variable is increased by one STD, and all other variables are evaluated at
their means and reported in { }.
Independent variables
1
2
3
4
Intercept
−7.7941
b.0001
3.8347
(0.0247)
{0.1452}
−8.1124
b.0001
−7.2683
b.0001
−7.2718
b.0001
ROA1
ROA2
7.5031
(0.0011)
{0.2597}
ΔCAPEX
5.0225
(0.0317)
{0.1199}
ΔCAPEXRD
Firm size
Tangible assets
Leverage (total debt)
Change in risk-free rate
Change in credit rating
Operating income volatility
Mean probability
Industry dummy and calendar year dummy
Pseudo-R2
NOBS
0.4773
b.0001
{0.4877}
0.0698
(0.8747)
{0.0101}
−0.0588
(0.9194)
{−0.0062}
−0.0158
(0.7210)
{−0.0206}
−0.0581
(0.3239)
{−0.0635}
−6.8539
(0.4906)
{−0.0423}
0.3355
Yes
0.2949
853
0.4738
b.0001
{0.4898}
0.2240
(0.6065)
{0.0330}
0.6941
(0.2690)
{0.0757}
−0.0287
(0.5212)
{−0.0377}
−0.0805
(0.1851)
{−0.0879}
−2.8477
(0.7778)
{−0.0178}
0.3301
Yes
0.3052
833
0.5445
b.0001
{0.4818}
0.7688
(0.1031)
{0.1024}
0.5939
(0.3467)
{0.0562}
−0.0114
(0.8001)
{−0.0138}
−0.0283
(0.6375)
{−0.0263}
−7.3007
(0.5258)
{−0.0402}
0.3831
Yes
0.2828
786
4.9442
(0.0312)
{0.1199}
0.5445
b.0001
{0.4817}
0.7751
(0.1004)
{0.1032}
0.5833
(0.3549)
{0.0552}
−0.0119
(0.7930)
{−0.0143}
−0.0286
(0.6343)
{−0.0266}
−7.0060
(0.5422)
{−0.0386}
0.3832
Yes
0.2829
786
which its call protection expires or 1980 (whichever comes later) until the year of being called or 2004 (whichever comes first).
Each bond in a year is categorized as either not called, called with refunding, or called without refunding.35 For example, if a bond
was called with refunding in 2002 but it became callable in 1995, we would have a time series of observations of the bond for 1995
through 2002. The bond would be categorized as “not called” in 1995 through 2001, and “called with refunding” in 2002.
Therefore, the choices in our study are both time series and cross-sectional.
An important caveat for our “not call” observations is that they might include financially distressed firms that cannot afford to
call their bonds, and our model does not account for this scenario (in our model, the firm always has enough cash to call the bond
without refund if it chooses to). Failure to account for the firm's inability to call would potentially bias the performance and
investment activities of our “not call” sample downward. To mitigate this potential problem, we impose a simple selection filter.
We restrict our sample firms to those that are present in the CRSP database at the end of 2004.36
Table 7 reports the results of the multinomial logit models.37 We use the observations of “not call” as the base case and evaluate
the other two outcomes (call with refund and call without refund). Models (1), (3), (5), and (7) evaluate the choices of call with
refund and not call. Models (2), (4), (6), and (8) evaluate the choices of call without refund and not call. Independent variables
35
Shumway (2001) shows that such a multi-period logit model (using multiple-period data before corporate events (e.g., bankruptcy or mergers) is equivalent
to a discrete-time hazard model, which produces more consistent and unbiased estimates than a static single-period logit model.
36
Since we impose this filter on all our sample firms, including those that call their bonds with refund, call their bonds without refund, and not call their bonds,
it should not bias our results in any systematic way. Among 198 firms that are not present in the CRSP database at the end of 2004 and thereby excluded in our
sample, 25% filed for bankruptcy by 2004.
37
Since we use panel data in Table 7, standard errors in multinomial logit models are corrected for firm clustering effect.
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Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
Table 7
Multinomial logistic regressions explaining the choice to call with refund, call without refund, or not call. This table presents the coefficient estimates from
multinomial logistic regressions explaining the three choices of callable bonds: call with refund, call without refund, or not call. Our sample includes all callable
bonds during the period right after call protection expires or 1980 (whichever comes later) until the year of being called or 2004, whichever comes first. We use
the observations of “not call” as the base case and evaluate the other two outcomes (call with refund and call without refund) as alternatives to this choice. Models
(1), (3), (5), and (7) evaluate the choices of call with refund and not call, and models (2), (4), (6), and (8) evaluate the choices of call without refund and not call.
Independent variables include firm size, leverage ratio, tangible assets, operating income volatility, changes of interest rate and changes of credit rating between
call date and issue date, STD of risk-free rate, time to maturity, and ROA or growth rate of capital expenditures and R&D expenses. STD of risk-free rate is the
standard deviation of the 30-year Treasury bond yield in each calendar year. All other explanatory variables are as defined in the Appendix. To control for the time
and industry effects, we also include dummy variables for each calendar year and each industry based on two-digit SIC Code. P-values are computed based on
standard errors corrected for firm clustering effect in panel data and are reported in parentheses.
Variable
Intercept
ROA1
Call with
refund vs. not
called
Call without
refund vs. not
called
Call with
refund vs. not
called
Call without
refund vs. not
called
Call with
refund vs. not
called
Call without
refund vs. not
called
Call with
refund vs. not
called
Call without
refund vs. not
called
1
2
3
4
5
6
7
8
0.502
(0.515)
3.498
(0.016)
3.598
b.0001
−1.125
(0.438)
1.026
(0.173)
3.783
b.0001
0.988
(0.190)
3.853
b.0001
1.040
(0.166)
3.821
b.0001
3.229
(0.074)
−4.318
(0.006)
5.977
(0.088)
−8.735
(0.010)
3.814
(0.230)
0.316
(0.000)
0.649
(0.049)
− 0.740
(0.209)
0.004
(0.875)
− 1.347
(0.005)
0.025
(0.714)
17.357
(0.042)
− 1.900
(0.000)
Yes
-8.118
(0.009)
-0.042
(0.522)
0.034
(0.920)
− 0.444
(0.444)
− 0.066
(0.033)
− 1.316
(0.009)
0.025
(0.692)
19.264
(0.020)
− 1.842
(0.000)
Yes
ROA2
ΔCAPEX
ΔCAPEXRD
Firm size
Tangible assets
Leverage (total debt)
Change in risk-free rate
STD of risk-free rate
Change in credit rating
Operating income
volatility
Ln (time to maturity)
Industry dummy and
calendar year dummy
Pseudo-R2
NOBS
0.315
(0.000)
0.369
(0.281)
-0.533
(0.362)
0.000
(0.991)
− 1.411
(0.003)
0.018
(0.802)
12.698
(0.135)
-1.855
(0.000)
Yes
0.165
2792
−0.010
(0.878)
0.212
(0.525)
0.025
(0.964)
− 0.071
(0.017)
− 1.367
(0.005)
0.051
(0.387)
14.578
(0.067)
− 1.835
(0.000)
Yes
0.306
(0.000)
0.490
(0.136)
− 0.536
(0.376)
0.000
(0.998)
− 1.428
(0.003)
0.017
(0.806)
14.512
(0.088)
− 1.894
(0.000)
Yes
0.169
2743
−0.016
(0.799)
0.342
(0.295)
− 0.597
(0.298)
− 0.066
(0.029)
− 1.371
(0.005)
0.060
(0.320)
16.035
(0.047)
− 1.830
(0.000)
Yes
0.321
(0.000)
0.681
(0.040)
− 0.749
(0.204)
0.007
(0.813)
− 1.350
(0.004)
0.029
(0.671)
17.727
(0.037)
− 1.898
(0.000)
Yes
0.167
2704
−0.040
(0.534)
0.043
(0.901)
− 0.472
(0.416)
− 0.066
(0.034)
− 1.327
(0.008)
0.025
(0.682)
19.423
(0.019)
− 1.853
(0.000)
Yes
0.165
2710
include firm size, leverage ratio, tangible assets, operating income volatility, changes of interest rate and changes of credit rating
between call date and issue date, STD of risk-free rate, time to maturity, and measures of firm performance and investment
activities, including ROA and growth rate of capital expenditures and R&D expenses. STD of risk-free rate is the standard deviation
of the 30-year Treasury bond yield in each calendar year. ROA or growth rate of capital expenditures and R&D expenses are the
main variables of interest.
Based on H6, we expect a positive coefficient estimate on ROA or growth rate of capital expenditures and R&D expenses in
models evaluating the choice of call with refund versus not call, and a negative coefficient estimate in models evaluating the choice
of call without refund versus not call. Note that because now we are comparing the two extremes cases, call with refund and call
without refund, with the middle case, not call, we expect that our results to be not as strong as those reported in Table 6, where we
directly compare the two extreme cases.
As shown in Table 7, we find significantly positive coefficient estimates on both ROA1 and ROA2 in models (1) and (3),
suggesting that better performing firm is more likely to call and refund a bond rather than not call. The coefficient estimates on
ROA1 and ROA2 in models (2) and (4) are both negative but only statistically significant in model (4), suggesting that a firm that is
performing poorly is more likely to call a bond without a refund rather than not call. The opposite effects of firm performance on
the choice of call with refund versus not call and call without refund versus not call is apparent, as predicted by H6. In models (5)
and (7), the growth rates of CAPEX and CAPEXRD are both positively related to the probability of call with refund versus not call,
though the relationship is only marginally significant in model (5). In contrast, the growth rates of CAPEX and CAPEXRD are
negatively related to the probability of call without refund versus not call, and both results are significant. This evidence suggests
that a firm with higher investment activity is more likely to call with refund than not call, but is less likely to call without refund
than not call. This evidence again lends support to hypothesis H6.
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
605
7. Conclusion
If a firm issues a non-callable bond, even when the firm's investment opportunity turns out to be poor, it may still have
incentives to invest because of the well-known risk-shifting problem. In this paper, we propose a theory that a firm could issue a
callable bond to reduce the risk-shifting problem. Because the call option enables the firm to reduce its debt obligation when its
investment opportunity turns out to be poor, this makes it more attractive for equity holders to forgo the negative NPV project and
repay the bond earlier. The cost to a firm of issuing a callable bond, however, is that it will have to incur a refunding cost if its
investment opportunity turns out to be excellent. Therefore, a firm would trade-off between the benefit of reducing risk-shifting
problem and the refunding cost, when it decides whether to issue a callable versus a non-callable bond.
Our model produces several unique empirical implications that help differentiate it from others. Our empirical findings offer
strong support to our model. We find that a firm with poorer future investment opportunities is more likely to issue a callable
bond. In addition, a firm with a higher leverage ratio and higher investment risk is more likely to issue a callable bond. Finally, we
find that a firm with the best performance and the highest investment activity is likely to call and refund its bond; a firm with the
worst performance and the lowest investment activity is likely to call without refunding its bond; and a mediocre firm is likely to
not call its bond. In contrast, our findings do not seem to support the alternative theories in the literature, such as hedging interest
rates risk, signaling, solving debt overhang problems, and removing restrictive covenants.
Acknowledgments
We would like to especially thank an anonymous referee, Andres Almazan, Aydogan Alti, Ilan Guedj, Jay Hartzell, Jean Helwege,
Richard Kish, Bob Parino, David Reeb, Laura Starks, Sheridan Titman, Paul Tetlock, and Seminar participants at Temple University,
University of Texas at Austin, and 2007 Financial Management Annual Meeting for their helpful comments and discussions. All
errors are solely ours.
Appendix A. Variable definitions
A.1. Measuring future investment opportunities
• Market/book ratio (MB) is the market value of total assets (sum of book value of debt and market value of equity) divided by the
book value of equity.
• Price/earnings ratio (PE) is stock price divided by earnings per share.
• ROA1 is the ratio of operating income before interest, tax, and depreciation (EBITD) and the book value of total assets.
• ROA2 is net income scaled by the book value of total assets.
• FORECAST1 is the median value of the most recent annual earnings forecasts for the forthcoming fiscal year-end provided by all
analysts, scaled by the book value of equity as of the year ending prior to the bond issuance date.
• FORECAST2 is the median value of the most recent annual earnings forecasts for the fiscal year-end of next year provided by all
analysts, scaled by the book value of equity as of the year ending prior to the bond issuance date.
• Ex ante growth rate of investment is the first difference in capital expenditures scaled by total sales (ΔCAPEX), and the first
difference in capital expenditures plus R&D expenses scaled by total sales (ΔCAPEXRD).
A.2. Measuring leverage
• Leverage is the book value of either long-term debt or total debt (long-term debt plus debt in current liabilities) divided by the
book value of total assets.
A.3. Measuring investment risk
• Firm size is the logarithm of total assets.
• First-time issuer dummy is equals one if this is the first time for a firm to issue a bond in the US public bond market since January
of 1975, and zero otherwise.
• Operating income volatility is the standard deviation of the first difference in quarterly earnings before interest, depreciation,
and tax over the last 7 years preceding the debt issue, normalized by the average value of total assets over the same time period.
• RATING is the S&P credit rating score, which is computed using a conversion process in which AAA+-rated bonds are assigned a
value of 23 and D-rated bonds receive a value of 1.
A.4. Other control variables
• Risk-free rate is the constant-maturity Treasury bond yield from the H.15 release of the Federal Reserve System matching the
maturity of each bond issue. If the maturity of a corporate bond does not match that of a Treasury bond, we linearly interpolate
the Treasury rates for maturities of one, three, five, seven, ten, twenty, and thirty years.
606
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
• Operating income beta is the slope coefficient obtained from the regression in which we regress the quarterly changes in
operating income before depreciation normalized by total assets over the last 7 years preceding the debt issue on the changes in
1-year T-bill rates.
• Time to maturity is measured as the difference in years between the issuance date and maturity date.
• Issue amount is computed as the dollar proceeds of each bond issue.
Table A1
Robustness tests on explaining the likelihood of issuing callable bonds. Column (1) presents results of a logistical model explaining the choice of issuing callable
bonds. The dependent variable is a binary variable equal to one for callable bonds and zero for non-callable bonds. This model is exactly the same as model (1) in
Table 4, except we leave out independent variables Leverage and Log(Time to maturity). Columns (2) through (4) report the results of simultaneous equation
regressions in which both leverage ratio and debt maturity are determined endogenously with the choice of issuing callable bonds. Leverage is the book value of
total debt (long-term debt plus debt in current liabilities) divided by the book value of total assets. Firm level debt maturity (prop. short-term debt) is the fraction
of a firm's total debt that matures in 3 years or less. Fixed assets are the ratio of net property, plant, and equipment to the book value of total assets. Profitability is
the ratio of earnings before interest, taxes, depreciation, and amortization (EBITDA) to the book value of total assets. Firm size is net sales in millions of dollars.
Asset maturity is the book value-weighted maturity of long-term assets and current assets, where the maturity of long-term assets is computed as gross property,
plant, and equipment divided by depreciation expense and the maturity of current assets is computed as current assets divided by the cost of goods sold. Abnormal
earnings is the difference between earnings per share in year t + 1 (excluding extraordinary items and discontinued operations and adjusted for any changes in
shares outstanding) minus earnings per share in year t, divided by the year t stock price. Rated firm dummy is equal to one for firms with credit rating available in
the Compustat database, and zero otherwise. All other explanatory variables are as defined in the Appendix. Each variable is measured at the fiscal year-end prior
to the bond issuance date. The system of equations is estimated by nonlinear two-stage least squares method for the pooled sample of callable and non-called
bonds issued between 1980 and 2003. P-values are reported in parentheses.
Logistic regression
Independent variable
Three-equation system (2SLS)
Call dummy
Call dummy
Leverage
(1)
(2)
(3)
Maturity (prop.
short-term debt)
−5.7341
(b.0001)
−0.8551
(b.0001)
0.4555
(0.0055)
−0.0622
(b.0001)
0.6699
(0.0029)
−0.0697
(b.0001)
0.0210
(0.4662)
−0.0420
(b.0001)
0.4477
(0.6818)
0.0163
(0.0321)
0.3176
(0.3378)
0.0053
(0.5208)
−0.0318
(0.5448)
1.2234
(b.0001)
−0.2899
(b.0001)
1.1159
(b.0001)
−0.0236
(0.0023)
−0.5242
(b.0001)
−1.2139
(b.0001)
−0.0605
(0.8090)
(4)
Intercept
MB(market-to-book)
Leverage
Firm size
First-time issuer dummy
Rating residual
Operating income volatility
Risk-free rate
Operating income beta
Log(Issue amount)
−0.5230
(b.0001)
0.1718
(0.3021)
−0.2870
(b.0001)
2.6638
(0.6735)
0.6679
(b.0001)
0.4574
(0.8251)
0.6063
(b.0001)
Maturity (prop. short-term debt)
(Market-to-book)*[Maturity (prop. short-term debt)]
Fixed assets
Profitability
Log(firm size)
−2.5162
(b.0001)
1.2219
(b.0001)
0.0511
(0.0344)
−0.0952
(0.4800)
−0.0353
(b.0001)
[Log(firm size)]2
−0.0356
(0.1401)
0.0063
(0.6566)
0.0243
(0.6645)
Investment tax credit dummy
Net operating loss carryforward dummy
Abnormal earnings
Asset maturity
Rated firm dummy
Pseudo-R2 or Adj-R2
NOBS
0.5961
3059
2226
0.3167
2226
−0.1391
(0.0030)
0.0075
(0.0150)
−0.0494
(0.0253)
0.0018
(0.8894)
−0.0028
(0.9548)
−0.0005
(0.5890)
−0.0787
(0.0002)
2226
Z. Chen et al. / Journal of Corporate Finance 16 (2010) 588–607
607
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