getwhyfirms issue callable bonds: hedging investment uncertainty
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8/10/2019 GetWhyfirms issue callable bonds: Hedging investment uncertainty
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In the rst part of the paper, we develop a theory on a rm'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 ndings 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-maximizingrm 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 rm decides whether
to issue a callable bond or a non-callable bond to competitive investors. After the current project generates a cash ow, the rm
and the investors observe more information about the future project. Based on the new information, the rm then decides
whether or not to invest the cash in the future project. Because the rm tries to maximize its equity value, the investment decision
may not be efcient if the bond is non-callable. More specically, the rm may want to invest in a negative NPV but risky future
project. This is because although investing in the project will lower the rm'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 rm's bond when it is issued than they would if the rm could commit to an efcient investment decision.
This is the well-known risk-shifting problem rst studied byJensen and Meckling (1976).
Issuing a callable bond may alleviate this risk-shifting problem. The key point is that a callable bond gives the issuing rm an
option to reduce its debt obligation if it nds out that the future project has a negative NPV. If the rm's bond is non-callable, as
discussed above, the rm may still want to invest in the project. Instead, if the rm has an option to buy back the bond at a lower
price than its value, the rm 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 rm 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 rm into making an efcient investment decision.
There is, however, a cost associated with issuing a callable bond. When the future project turns out to be good, the rm would
invest in the project. If the project is better than good, the rm then would want to call back the bondand refundit at a lower cost.
In this case, however, therm incurs a refunding cost.3 Therefore, therm faces the following trade-off when it decides whether to
issue a callable bond or a non-callable bond. The benet 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 rm would incur the refunding cost if the investment
opportunities turn out to be good. This implies that a rm 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 therm's behavior after it issues a callable bond. First, if the rm nds out that itsfuture 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 rmnds 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 rm nds 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 benet 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 secondpart of thepaper, we testthosehypotheses empirically. In the ex ante (atissue)study, we examinethe relation
between a rm'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 rm's current investment
performance and its decision whether or not to call back the bond, along with whether or not to refund it. We nd strong empirical
support for our theory. We nd that a rm expecting worse future investment opportunities and/or with higher leverage ratio and
investment risk is more likely to issue a callable bond. As a rm calls back its bond, the rm with the poorest performance and the
lowest investment activity is not likely to refund a call. In contrast, a rm with the best performance and the highest investment
activity is likely to refund it. A rm with mediocre performance and investment activity tends to not call their bonds. Our ndings are
also economically signicant. We estimate, forexample,thatan increase of onestandard deviation in the market/book ratio (proxyfor
future investment opportunities) corresponds to a 36% decrease in therm's probability to issue a callable bond versus a non-callable
one. In addition, we nd that, as a rm 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
rm's probability ofrefunding its called bond. Our ndings are robust to various model specications and different measures of key variables.
The rest of the paper is organized as follows.Section 2is a review of the relevant literature. In Section 3,we use a numerical
example to develop the theoretical argument that a rm can use callable bonds to reduce the risk-shifting problem. Empirical
hypotheses are also derived. A formal model is available upon request. Section 4describes 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 7concludes.
2. Literature review
The literature offers ve theories explaining why a rm issues a callable bond. The rst is the hedging interest rate risk theory in
which a callable bond provides a rm with theopportunity to refund at a lower interest rate (Pye, 1966). The second is the signaling
3 Refunding costs can be signicant for some rms.Gande et al. (1999)document an average gross spread of 2.5% for junk bonds issued from 1985 to 1996.
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theory in which a callable bond allows a higher quality rm to reduce the cost associated withasymmetric information (Robbins and
Schatzberg, 1986, 1988). The reason is that even though a higher quality rm 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 thata callable bond allows the issuing rm to overcome thedebt
overhang problem, as identiedby Myers (1977). If it suffers from a debt overhang problem, a rm (acting in theinterest of itsequity
holders) would not invest in positive NPV projects because part of the benets from the new projects would go to the existing bond
holders. One way to resolve this underinvestment problem is to allow the rm to call back its outstanding debt at the time of
investment and reissue debt that reects the improved prospects of the rm (Bodie and Taggart, 1978). The fourth explanation,
removing restrictive covenants theory,posits that a callable bond allows a rm to remove undesirable restrictive covenants in the
bond indentures so that the rm can engage in value-adding activities that are otherwise impossible (Vu, 1986).
The last explanation is that a rm issues a callable bond to reduce the risk-shifting problem. Equity holders can expropriate
wealth from bondholders by increasing the risk of the rm.Barnea et al. (1980)show that because the call option value of a
callable bond declines as the rm 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
rms refund their called bonds but others don't. Second, our model formally studies a rm'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 ve explanations that explain why
a rm issues a callable bond.
Overall, we think our study makes three important contributions to the current literature. First, it derives rms' 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 ndings that are consistent with our theory, but inconsistent with other theories. Lastly, to the
best of our knowledge, our study is the rst to examine a rm'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 rm at the beginning of the rst period in a two-period risk-neutral economy. The sequence of events is
depicted inFig. 1 and numerical analysis is presented in Table 1. The rm has a protable investment project to undertake
immediately at Date 0. If undertaken, therm has to invest $50 and the project will generate a xed cash ow of $55 tothe rmat
the end ofthe rst period (Date 1). The rm also has a future investment project in the second period. However, whether it will be
protable or not is uncertain at the beginning of the rst period (Date 0). It is only at the end of the rst period (Date 1) that the
uncertainty will resolve. After therm 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 rm tries to maximize the rm's equity value (for example, the manager is the owner of the
rm). For simplicity, we assumethat even though all agents in the economy observe the expected NPVof the secondperiod project
when the uncertainty resolves at Date 1, the project NPV is not contractible. More specically, 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 cashow of either $100
or $0. If the second period project is bad, it will yield a cashow of $100 with probability of 0.2, and a $0 cashow with probability
0.8. If it is mediocre, the probability of a $100 cashowis 0.7. If it is good, the probability ofa $100 cashow is 0.9. Noticethat it is
not efcient for the rm 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 nancing contracts of thermto nance 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 rst 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 therm's nancing choices: bond issuing cost and the risk-shifting cost. Bond issuing cost is
assumed to be a xed fee whenever the rm issues a new bond. That is, if the rm 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 renance the old one
arefunding cost. Clearly, if the rm issues a non-callable bond, it will never incur the refunding cost. Risk-shifting cost will be the
negative NPV incurred by the rm due to equity holders' risk-shifting incentive.
4
This is the common assumption in the incomplete contract literature (Grossman and Hart, 1986).5 We implicitly assume that the rm will issue debt rather than equity because of the benet of the debt, e.g., tax benet. See footnote 10 for more detail.
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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 rm would thus invest in the project only if it is either good or mediocre. However, in these two
states, the rm has to reissue another short-term bond to nance 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 rm 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 rm expecting poorer future investment opportunities is more likely to issue a callable bond.
H2. A rm with higher leverage is subject to greater risk-shifting problems, thus is more likely to issue a callable bond.
H3. A rm with greater investment risk is more likely to issue a callable bond.
H4. Conditional on calling a bond, a rm with poorer performance is less likely to refund.
H5. Conditional on calling a bond, a rm with less active investments is less likely to refund.
H6. Arm with the best performance and most active investments tends to call and refund its bonds; a rm with the poorest
performance and least active investments tends to call without refund; a rm with mediocre performance and investments tendsto not call at all.
H2 and H3are 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 rms suffering from
more severe asymmetric information (e.g.,rms with greater investment risk) are more likely to issue callable bonds.H1, H4, H5,
and H6are unique to our model since none of the existing theories offer the same empirical implications. For example, the
signaling theory predicts that rms with better private information about future performance are more likely to issue callable
bonds. The solving debt overhang theory predicts that rms 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 rms 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 rm's future investment opportunities and its likelihood of issuing a callable bond, which is the opposite ofH1.
Furthermore, the theory of reducing risk shifting inBarnea et al. (1980)is silent regarding a rm's refunding decision upon their
calls, and other existing theories suggest that a rm should always refund its call. In contrast, H4 and H5predict when a rmshould refund its calls and when it shouldn't. H6predicts when a rm 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 rm's decision of issuing callable versus non-callable bonds, we obtain data on 13,784 nonconvertible xed
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-specic 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 speci
esbonds as callable or non-callable, while the SDC database does not. More importantly, we nd 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 nancing. We rule out this alternative because of the benets of debt nancing, e.g., tax benets, which are not modeled in
our setting. However, if we consider the tax benets of debt nancing, we have to consider the extra cost of the callable bond because when the rm calls back
the bond without refunding, the rm loses its tax benet. The loss of tax benets 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 rm does not have many good investment opportunities, it is still optimal for the
rm to issue a callable bond than a non-callable bond or equity nancing. 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 dene a bond as being callable if the call protection period is less than one year, ve years, seven years, or ten years as the bond
will mature respectively within three to seven years, seven to ten years, ten to fteen years, or more than fteen years. To examine the validity of this
classication, we take all the nonconvertible xed 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- ve percent of these bonds are actually being categorized as
non-callable bonds according to the above classication scheme. In contrast, only 1% of the bonds being called in FISD are misclassied as non-called bonds in
FISD. Therefore, we believe the denition of callable bonds in FISD is more reliable than the approximate classication based on the call protection period andtime to maturity in the SDC database.
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Fig. 2presents the distribution of all the 13,784 nonconvertible xed 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 signicantly 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 inFig. 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 signicant 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 rms without
operating income beta, we lost 4554 bonds.13 Utility and nancialrms 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 rms (SIC Codes between 4800 and 4999) and nancialrms (4-
digit SIC Codes between 6000 and 6999). As a result, our sample is reduced to 5674 bonds. To be included in our nal analysis, we
require a bond with complete issue-specic 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 nal 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 rm'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 denitions are in the Appendix. Hypothesis H1 suggests that the probability of a rm issuing callable bond would benegatively 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 inuenced byWelch (2004), who
points out that market leverage may change passively simply because of changes in stock price performance.14 HypothesisH2
suggests that the probability of a rm issuing callable bond would be positively related to leverage.
Fig. 2. Bond sample distribution over time. The sample consists of 13,784 nonconvertible xed rate U.S. corporate bonds issued between January 1980 and
December2003 obtained from theFixedIncome Securities Database (FISD). We report thepercentage 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.
13
About 2000 of them are lost due to the fact that some rms do not have sufcient quarterly data to compute operating income beta.14 Using market leverage ratio yields similar results.
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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-specic and rm-specic 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 thedollarproceeds of each bond issue. First-timeissuerdummy equals oneif this is therst time for a rmto issue a bondin the USpublicbondmarket
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 thescoreof S&Prating, which is computed using a conversion process in which AAA+-rated bonds areassigned a value of 23 andD-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 dened as the logarithm of total assets. The market/book ratio (MB) is dened 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 dened as stock price divided by earnings per share. ROA1is 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. FORECAST1is themedian value of themost recentannual earnings forecasts forthe forthcoming scal year-end provided by all analysts. FORECAST2is
the median value of the most recent annual earnings forecasts for the scal year-end of next year provided by all analysts. Both FORECAST1 and FORECAST2 are
scaled by the year-end book value of equity. CAPEX (CAPEXRD) are the rst 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 theTreasury rates for maturities of one, three, ve, seven,
ten, twenty, and thirty years. Operating income beta is the slope coefcient from a regression in which we regress the quarterly changes in operating income
beforedepreciation normalized by total assetsover thelast 7 years precedingthe debt issue on changes in 1-yearT-bill rates.Operating incomevolatility is dened
as the standard deviation of the rst difference in quarterly earnings before interest, depreciation, and tax over the last 7 years preceding the debt issue,
normalized by theaverage value of total assetsover thesame time period. Unlessotherwisenoted, all variables aremeasuredas of theyear endingjust prior to the
bond issuance date.
Panel A. Sample distribution over industry
One-digit SIC Code Industry NOBS
0 Agriculture, forestry, and shing 61 Mining 269
2 Construction 955
3 Manufacturing 728
4 Transportation 534
5 Wholesale Trade 408
7 Agricultural Services 167
8 Forestry 89
Panel B. Sample distribution over time
Year NOBS % callable bonds (in terms of # of bonds) % callable bonds (in terms of issue amount)
1980 39 1.000 1.000
1981 26 1.000 1.000
1982 48 0.854 0.830
1983 32 0.938 0.979
1984 38 0.895 0.8851985 92 0.793 0.787
1986 166 0.608 0.689
1987 188 0.277 0.536
1988 93 0.591 0.750
1989 162 0.142 0.318
1990 154 0.026 0.037
1991 170 0.100 0.109
1992 216 0.269 0.244
1993 245 0.347 0.340
1994 101 0.297 0.399
1995 140 0.179 0.187
1996 177 0.266 0.235
1997 197 0.198 0.162
1998 223 0.152 0.140
1999 156 0.179 0.131
2000 93 0.075 0.0572001 148 0.108 0.054
2002 118 0.127 0.104
2003 134 0.187 0.155
Panel C. Descriptive statistics
Variable NOBS Mean Std. dev. Minimum Maximum
Call dummy 3156 0.2864 0.4522 0.0000 1.0000
Issue amount ($ million) 3156 195.32 162.31 0.37 1000.00
First-time issue dummy 3156 0.1518 0.3589 0.0000 1.0000
Time to maturity 3156 13.3279 8.4665 2.0164 40.0329
Rating 3156 13.5612 3.3910 1.0000 22.0000
Total assets ($ million) 3156 7960.69 9466.95 78.95 70349.00
Total market value ($ million) 3074 11955.67 15836.14 105.36 114839.09
(continued on next page)
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4.2.3. Measuring investment risk
We employ several variables to proxy for investment risk, including rmsize, a rst-time issuer dummy, and operating income
volatility. Smaller rms, rst-time issuers, and rms with larger operating income volatility would have greater level of
investment risk. Our hypothesisH3suggests that the probability of a rm issuing a callable bond would be negatively related to
rm size, but positively related to the rst-time issuer dummy and operating income volatility.
A rm's investment risk or overall risk is also reected 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 eachof the variables proxied for investment opportunities, and use the residual term (Rating Residual) as the regressor in
the model.15 HypothesisH3suggests that the probability of a rm 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 rm's
interest rate sensitivity of operating income. Based on this evidence, they argue that a rm uses a callable bond to hedge operating
income uctuations. To control for the confounding effects of market interest rate and a rm's operating income exposure to
interest rate, we also include the risk-free rate and the operating income beta, which measures a rm's interest rate sensitivity to
operating income.
We also include a few issue-specic 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 rm. 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 2reports descriptive statistics for our nal 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 theanalysis. The bond sample contains about 29% callable bonds;17 15% are rst-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 lled 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
InTable 3, we examine the difference in mean and median value of issue-specic andrm-specic variables between callable
and non-callable bond issues. We observe signicant differences in both mean and median values of proxy variables of future
investment opportunities between the two groups. Callable bonds are issued by rms with a lower market/book ratio (MB), lower
Table 2 (continued)
Panel C. Descriptive statistics
Variable NOBS Mean Std. dev. Minimum Maximum
Firm size (Ln Assets) 3156 8.3483 1.2368 4.3688 11.1612
Leverage (long-term debt) 3134 0.2679 0.1339 0.0084 0.7945
Leverage (total debt) 3156 0.3128 0.1349 0.0382 0.8450
PE 3068 15.4538 21.8513 112.5000 227.5735
MB 3035 1.4796 0.6257 0.8141 4.5558FORECAST1 2688 0.0976 0.1804 2.0239 1.7613
FORECAST2 2649 0.1216 0.1758 2.0239 1.7613
ROA1 3102 0.1461 0.0547 0.0049 0.3078
ROA2 3090 0.0439 0.0429 0.1847 0.1679
CAPEX 3040 0.0069 0.0489 0.3576 0.3052
CAPEXRD 3040 0.0071 0.0502 0.3576 0.3208
Risk-free rate 3156 7.0083 1.9826 1.6682 15.1599
Operating income beta 3123 0.0007 0.0266 0.1345 0.1376
Operating income volatility 3156 0.0137 0.0090 0.0023 0.0616
15 The residual term from the regression captures the credit rating information without the in uence of investment opportunities.16
To minimize the effect of outliers, we winsorize all the variables at the 1st and 99th percentiles.17 Callable bonds account for 24% of the total issue amount in our sample.
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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 rms issuing callable bonds than that in rms 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 rms with lower
credit ratings and greater operating income volatility, and are more likely to be rst-time issues. These results are consistent with
hypothesisH3: 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 signicantly different between the two
groups, the median operating income beta of callable bond issuers is signicantly higher than that of non-callable bond issuers.
Consistent with the theory of hedging interest rate risk, callable bond issuances are associated with a signi cantly higher interest
rate. In addition, we nd 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 rm'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 inTable 4, the explanatory power of our logit models is substantial, as evidenced by the Pseudo-R2 exceeding 62% in
each regression. The rst variable of interest is market/book ratio (MB), which proxies for future investment opportunities. The
coefcient estimate on MB is negative and statistically signicant at the 1% level in all models. This result is consistent with
hypothesisH1,suggesting that rms 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
rm's futureinvestment opportunities are poor. Hypothesis H2 suggests that a rm 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 signicant 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 signi cantly negatively related to the probability of
issuing a callable bond, since a larger rm is often subject to less investment risk. First-time issuers tend to be smaller rms, or
rms with less experience and reputation (or access) in the public debt market. The coefcient estimate of the rst-time issuer
dummy is positive and signicant. Operating income volatility, however, is not signicantly related to the usage of a callable bond.
Rating residual is negatively related to the probability of issuing a callable bond, and the coefcient estimate is highly signicant. A
rm 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 rm with greater investment risk is less likely to issue a callable bond.Kish and Livington (1992)
and Crabbe and Helwege (1994)document a signicant 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 thatrepresents 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 signicant effect of future investment opportunities,
leverage, and rating residual on the likelihood of issuing a callable bond.
The coefcient estimate of the risk-free rate is positive and signicant at the 1% level in most regressions, suggesting that
interest rate risk may be a signicant consideration in corporate usage of callable bonds.22 Guntay et al. (2004)argue that if
callable bonds are used for hedging interest rate risk, rms with higher interest rate sensitivity (operating income beta) would be
more likely to issue callable bonds. Our evidence does not support their argument. We nd thatthe coefcient estimates on
operating income beta are mostly positive; however, they are not signicant 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-digitSIC Codes.19 One might argue that MB might capture the risk aspect of a rm since a rm 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 reect 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 ndings documented in the literature (e.g., Kish and Livington, 1992:Guntay et al., 2004).23 To take into account the statistical signicance of the beta estimate, as inGraham and Rogers (2002), we dene an operating income exposure variable that is
zero if the operating income beta is not signicant at the 10% level. Otherwise, it takes the value of 1 or +1, depending on the sign of the coefcient. 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 inGuntay et al. (2004)is driven by different sample periods, we
estimate the same regression models inTable 3ofGuntay et al. (2004)based on a sample period of 1981 through 1997. The coefcient estimate of operating
income beta remains insignicant. Nevertheless, the difference between our results and those inGuntay et al. (2004)might be driven by the use of differentdatabases and/or different methods of dening a callable bond, as discussed in footnote 10.
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As withKish and Livingston (1992), Crabbe and Helwege (1994), and Guntay et al. (2004), we nd 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 ows of the bond divided by bond price, and obtain similar results. The coefcient estimate on
duration is signicantly positive. This nding 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 rm is more likely to use a callable bond
to hedge interest rate risk. This nding is also consistent with our theory that longer maturity and larger issue size may be
associated with greater future investment risk. Therefore, a rm would be more likely to issue a callable bond to minimize the
agency problem according toH3.
Our logistic analysis above focuses on a rm 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 aHeckman (1979) selection model of regression, and the result is reported in model (4) ofTable 4. In
particular, we take all the rm-year observations in the Compustat database in 1980 through 2003, and construct a dummy
variable issuing bondbased on whether or not arm 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 rm 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 onDenis and Mihov (2003). As shown in model (4), the inverse
Mills ratio from the selection probability model is only marginally signicant at the 10% level. After controlling for potential
selection bias, our results are robust. The selection-adjusted coefcient estimates are similar to those from model (1).
In addition, to assess whether our results are driven by rm 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
coefcient estimates on MB (measure of future investment opportunities) are signicantly negative in all three sub-samples, and
the magnitudes of coefcients are also comparable among groups (results available upon request).
5.3. Additional tests of hypothesisH1
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 coefcient 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 signicant 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 rm's decision to issue a callable bond and its expected future investments, we also include an
ex ante measure of growth in capital expenditure (CAPEX) or growth in capital expenditure and R&D expenses (CAPEXRD). We
nd that the coefcient estimates ofCAPEX and CAPEXRD are both negative and signicant (results available upon request).
25
The dependent variable for the selection model is a binary variable
issuing bond
that equals one if a rm issued a callable or non-callable bond in a givenyear, as recorded in the FISD database, and zero otherwise.
Table 3
Univariate statistics of callable and non-callable bonds. This table reports the mean and median of issue-speci c andrm-specic variables for callable and non-
callable bonds issuedbetween 1980and 2003. T-tests (Wilcoxonrank tests)are used to examine thenull hypothesis that themean (median) of each variable is the
same between callable and non-callable bonds.
Variable Mean Median
Callable Non-callable T-statistics Callable Non-callable Z-statistics
MB 1.308 1.545 11.530 1.184 1.323 10.023
PE 11.520 16.986
6.280 11.275 15.218
10.884ROA1 0.144 0.147 1.120 0.146 0.146 0.494
ROA2 0.038 0.046 4.630 0.042 0.047 2.919
FORECAST1 0.058 0.110 5.050 0.053 0.077 11.346
FORECAST2 0.100 0.128 3.080 0.070 0.094 8.291
CAPEX 0.001 0.009 3.850 0.001 0.002 3.763
CAPEXRD 0.002 0.009 3.480 0.001 0.003 3.832
Leverage (total debt) 0.350 0.298 8.540 0.321 0.293 6.042
Firm size (Ln assets) 7.609 8.645 20.690 7.600 8.742 19.270
Rating 11.887 14.233 15.290 12.000 14.000 13.537
Operating income volatility 0.015 0.013 5.170 0.012 0.011 5.086
First-time issue dummy 0.254 0.111 9.030 0.000 0.000 10.240
Operating income beta 0.002 0.000 1.110 0.002 0.002 2.560
Risk-free rate 8.049 6.590 16.770 7.520 6.494 15.082
Time to maturity 15.742 12.359 9.680 10.025 10.016 8.001
Issue amount ($ million) 166.097 207.052 7.580 148.800 200.000 5.519
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Assuming that an ex ante growth of investment is a proxy of future investment growth, our results suggest that a rm 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-CAPEX (Ex-CAPEXRD) is computed as the average ofCAPEX
(CAPEXRD) over the three years following bond issuance. The coefcient estimates of Ex-ROA, Ex-CAPEX, and Ex-CAPEXRD
are all negative and statistically signicant (results available upon request). These results further conrmthata rm 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, particularlyH1. 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 inSection 3, these three theories all predict that
rms with better future investment opportunities are more likely toissue callable bonds.
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 onefor callable bonds andzero fornon-callable bonds.Independent variables include issue-specic and rm-specic variables that proxyfor rms' future
investment opportunities, leverage, and investment risk. All the explanatoryvariables are as denedin the Appendix. Regression (4) controls forsample selection
bias by estimating a MLE version of theHeckman (1979)selection model. To control for time and industry effects, we also include dummy variables for each
calendar year andeach industry based on two-digitSIC Code. P-values arereportedin parentheses. Mean probabilityis thepredicted probabilityof issuingcallable
bonds when all explanatory variables have their mean values. Change in probabilityis denesas the percentage change in the probability of issuingcallable 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 7.1908 8.6560 8.1241
(b.0001) (b.0001) (b.0001) (0.0074)
MB 0.7946 0.7974 0.7372 0.5317
(b.0001) (b.0001) (b.0001) (b.0001)
{0.3564} {0.3579} {0.3376} {0.1971}
Leverage (total debt) 1.8859 1.3633 2.6990
(0.0002) (0.0186) (b.0001)
{0.2388} {0.1707} {0.1445}
Leverage (long-term debt) 2.0520
(0.0001)
{0.2598}
Firm size 0.5718 0.5590 0.4917 0.2052
(b.0001) (b.0001) (b.0001) (0.3288)
{0.4703} {0.4629} {0.4222} {0.149}
First-time issuer dummy 0.3672 0.4039 0.3261 0.1959
(0.0349) (0.0213) (0.0997) (0.2976)
{0.1188} {0.1316} {0.1063} {0.0244}
Rating residual 0.2928 0.2876 0.2318 0.3217
(b.0001) (b.0001) (b.0001) (b.0001)
{0.5594} {0.5532} {0.4773} {0.3682}
Operating income volatility 2.2343 3.0814 2.2243 3.9593
(0.7374) (0.6446) (0.7615) (0.4935)
{0.0178} {0.0246} {0.0179} {0.0412}
Risk-free rate 0.3567 0.3622 0.5813 0.0187
(b.0001) (b.0001) (b.0001) (0.8748)
{0.7721} {0.7892} {1.4651} {0.0129}
Operating income beta 0.4081 0.7013 1.5955 0.1488
(0.8476) (0.7417) (0.4924) (0.9132)
{0.0095} {0.0164} {0.0380} {0.0081}
Log(Issue amount) 0.6463 0.6443 0.7340 0.7313
(b
.0001) (b
.0001) (b
.0001) (b
.0001){1.2051} {1.2049} {1.4582} {0.3009}
Log(Time to maturity) 1.3484 1.3487 1.5264
(b.0001) (b.0001) (b.0001)
{0.9246} {0.9279} {0.25}
Duration 0.1185
(b.0001)
{0.6285}
Inverse Mills Ratio 0.9250
(0.0648)
Mean probability 0.1431 0.1416 0.1332 0.6693
Industry dummy & Calendar year dummy Yes Yes Yes Yes
Pseudo-R2 0.6321 0.6318 0.6297 0.6465
NOBS 3056 3038 2685 3056
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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 nancial 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 controlvariables. 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) ofTable A1. As with the results in model (1) ofTable 4, the coefcient estimate on MB is
signicantly negative. Firm size and Rating residual are both signicantly 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 thatJohnson (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, xed assets, protability, rm 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, rm size and the square ofrm size, investment tax credit dummy, net
operating loss carry forward dummy, abnormal earnings, asset maturity, and rated rm dummy. In the equation explaining
the choice of issuing callable bonds (column 2), we include the same set of variables used in model (1) ofTable 4, except that
we include rm level debt maturity (prop. short-term debt) instead of bond maturity. Following Johnson (2003), maturity
(prop. short-term debt) is dened as the fractionof a rm's total debt that matures in 3 years orless. Wedene all other variablesin
Table A1. Each variable is measured at the scal 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. Wend that MB, rm size, and rating residual are all signicantly negatively related to
the probability of issuing a callable bond, which supports our hypothesis H1 and H3. Leverage remains signicantly 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 rm's choice of whether to issue a callable or a non-callable bond, our model also provides
explicit empirical implications on a rm's choice whether to call the bond, as well as whether to refund the call. To test those
implications, we rst explore that, conditional on the call events, how a rm's performance and investment activity are related to
the decision on whether or not to refund the call. Second, we examine the impact ofrm performance and investment activity on
the rm'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 le called Amount Outstandingin the FISD.28 To be included in our
analysis, we require that a called bond have issue-specic 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 rm'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 nd only 46% ofrms
issuing new debt in this 12-month window.
29
To de
ne a refunding call, we follow King and Mauer (2000) and require therefunding 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 dened as a non-refunding call.
26 We thank an anonymous referee for suggesting this great point.27 The signs of the coefcient estimates on variables in the leverage and maturity equations are in general consistent with those reported inJohnson (2003) and
Billett et al. (2007).28 This le 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 le 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 19751994 are associated with raising new debt. We nd 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 thatKing and Mauer (2000)rely on to identify new nancing activities.30 The extra 10% in the refunding ratio could be thought of the residual nancing activities for an average rm in a given 12-month period. Even in the absence
of refunding calls, an average rm may raise some funds in the nancial market on a regular basis. We employ alternative denition of refunding calls (e.g., arefunding ratio of 100% or 130%), and our results remain qualitatively the same.
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Panel A inTable 5reports descriptive statistics on our bond sample being called from 1983 to 2004. Despite the fact that only
46% ofrms raise new debt within a 12-month period surrounding the call date, these rms 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 arecategorized 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 signicant proportion ofrms (60%) not refunding their calls.
In Panel B ofTable 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 rms 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-refundingrms raise on average new capital of only 7.5% of the amount of bonds
being called. This raises an interesting question: Why are these rm 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 ofTable 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 signicantly different, suggesting that an
interest rate change is not the reason for a rm to not refund a call.
In Panel B ofTable 5,we also examine the credit rating of these two groups of bonds at the time when they were issued andcalled. 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 ndings are
not consistent with the theories of signaling and solving the debt overhang problem, since both predict an improvement in a rm'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 signicant 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 rms 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 nd that a non-refunding rm experiences a
signicant decrease in total leverage (total debt divided by assets), while the refunding rm has a signicant increase in leverage
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 dened as property, plant and equipment
divided by total assets. All other variables are as dened 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 853 356.2121 607.3295 0.0000 4329.2000
Refunding ratio 853 3.0696 6.1880 0.0000 64.0000
Refund dummy 853 0.4021 0.4906 0.0000 1.0000
ROA1 853 0.1377 0.0551 0.0075 0.2796
ROA2 833 0.0301 0.0475 0.1702 0.1379
CAPEX 814 0.0048 0.0410 0.2525 0.2066
CAPEXRD 814 0.0048 0.0420 0.2525 0.2066
Firm size 853 8.2935 1.3425 4.7089 11.0973
Tangible assets 853 0.4377 0.2187 0.0213 0.9053
Operating income volatility 853 0.0134 0.0088 0.0023 0.0612
Leverage 853 0.3601 0.1558 0.0610 0.8886
Panel B. Mean difference between refunding calls and non-refunding calls
NOBS Refunding
ratio
Change of
risk-free rate
Rating
at issue
Rating
at call
Rating at
callRating at issue
Years from maturity
date to call date
Non-refunding calls 510 0.0752 2.1692 12.2176 12.2294 0.0118 7.6789
Refunding calls 343 7.5219 2.3172 14.3907 14.3703 0.0204 9.4608
Difference 7.4467 0.1480 2.1731 2.1409 0.0322 1.7819
T-statistics 17.50 1.11 8.28 8.39 0.31 3.35
31
Crabbe and Helwege (1994) and King and Mauer (2000)also document little signicant rating improvement for a sample of bonds being called in 1975
1994.
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(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 denition of refunding is accurate.
As we show in our model, as a rm faces deteriorating investment opportunities, it would choose to not invest in the project
and call back a bond without refunding. In contrast, when a rm has excellent investment opportunities, it would invest, call back
the bond, and refund the call. Hypotheses H4 and H5suggest that a rm 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 rm'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, CAPEX or CAPEXRD. In addition, we include control
variables that proxy for nancing costs, includingrm size, tangible assets (dened 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 benet of refunding.32
The regression results are reported inTable 6. The variables of interest are the measures of performance and investment
activities. The coefcient estimates on ROA1 and ROA2 are both positive and statistically signicant at the 5% level, suggesting that
a rm with poorer performance is less likely to refund a call. Furthermore, the growth rates of CAPEX and CAPEXRD are both
signicantly positively related to the probability of refunding around a call event. The evidence indicates that a poor-performing
rm invests less in their project and pays out cash. Our results are also economically signicant. 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 hypothesesH4 and H5.
The coefcient estimate ofrm size is signicantly positive in all the regressions, suggesting that a larger rm is more likely to
refund a call since it incurs fewernancing 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 coefcient
estimates are statistically insignicant. The coefcient estimates of total leverage ratio and operating income volatility are not
statistically signicant either. The change of interest rate between the call date and the issue date is not signicantly 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 coefcient estimate on the change of interest rate becomes negative and statistically signicant. This nding
suggests that the lower the interest rate at the call date, the more likely a rm is to refund a call because of the benets of
refunding.33 Change in credit rating (rating at call minus rating at issue) is not signicantly 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 followKing and Mauer (2000)and consult Moody's Annual Bond Records, and hand
collect 489 bonds called by industrial rms from 1990 to 2005. The results are similar.
Another, perhaps more extreme way for a rm to reduce investment than reducing capital expenditures is asset sales. H5
suggests that a non-refundingrm 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 nd support forH5: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 signicantly 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 rm's unconditional choices to call with refund, call without refund, or not call as its bond exits the call
protection period. HypothesisH6 suggests that the choice is not monotonic with respect to rm performance and investment
activity. The best rms are more likely to call with refund. The worst rms are more likely to call without refund, while the
mediocrerms are more likely to not call their bonds.
We estimate a multinomial logit model to explore the impact of a rm'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.33 The benet 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 orthey do not have adequate data available for issue analysis.
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which its call protection expires or 1980 (whichever comes later) until the year of being called or 2004 (whichever comes rst).
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
nancially distressed
rms that cannot afford tocall their bonds, and our model does not account for this scenario (in our model, the rm always has enough cash to call the bond
without refund if it chooses to). Failure to account for the rm's inability to call would potentially bias the performance and
investment activities of our not callsample downward. To mitigate this potential problem, we impose a simple selection lter.
We restrict our sample rms 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 ofnot 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
Table 6
Logistic regressions explainingthe likelihoodof refundingaround call events. This table presents theresults of logistic models in which thedependent variable is a
binary variable equal to one for refunding calls and zero for non-refunding calls. Independent variables include rm size, leverage ratio, tangible assets, operating
incomevolatility, changes of interest rate andcredit ratingbetween call date andissue date, andROA or growth rate of capitalexpenditures andR&D expenses. All
theexplanatoryvariables areas denedin the Appendix. Changein creditratingis dened asthe rating atcallminus the ratingat issue. Tocontrol for thetime 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 de nes 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 8.1124 7.2683 7.2718
b.0001 b.0001 b.0001 b.0001
ROA1 3.8347
(0.0247)
{0.1452}
ROA2 7.5031
(0.0011)
{0.2597}
CAPEX 5.0225
(0.0317)
{0.1199}
CAPEXRD 4.9442
(0.0312)
{0.1199}
Firm size 0.4773 0.4738 0.5445 0.5445
b.0001 b.0001 b.0001 b.0001
{0.4877} {0.4898} {0.4818} {0.4817}
Tangible assets 0.0698 0.2240 0.7688 0.7751
(0.8747) (0.6065) (0.1031) (0.1004)
{0.0101} {0.0330} {0.1024} {0.1032}
Leverage (total debt) 0.0588 0.6941 0.5939 0.5833
(0.9194) (0.2690) (0.3467) (0.3549)
{0.0062} {0.0757} {0.0562} {0.0552}
Change in risk-free rate 0.0158 0.0287 0.0114 0.0119
(0.7210) (0.5212) (0.8001) (0.7930)
{0.0206} {0.0377} {0.0138} {0.0143}
Change in credit rating 0.0581 0.0805 0.0283 0.0286
(0.3239) (0.1851) (0.6375) (0.6343)
{0.0635} {0.0879} {0.0263} {0.0266}
Operating income volatility
6.8539
2.8477
7.3007
7.0060(0.4906) (0.7778) (0.5258) (0.5422)
{0.0423} {0.0178} {0.0402} {0.0386}
Mean probability 0.3355 0.3301 0.3831 0.3832
Industry dummy and calendar year dummy Yes Yes Yes Yes
Pseudo-R2 0.2949 0.3052 0.2828 0.2829
NOBS 853 833 786 786
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 lter on all our sample rms, 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 rms that are not present in the CRSP database at the end of 2004 and thereby excluded in our
sample, 25% led for bankruptcy by 2004.37 Since we use panel data inTable 7, standard errors in multinomial logit models are corrected for rm clustering effect.
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includerm 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 ofrm 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 onH6, we expect a positive coef
cient estimate on ROA or growth rate of capital expenditures and R&D expenses inmodels evaluating the choice of call with refund versusnot call, and a negative coefcient 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 nd signicantly positive coefcient estimates on both ROA1 and ROA2 in models (1) and (3),
suggesting that better performing rm is more likely to call and refund a bond rather than not call. The coefcient estimates on
ROA1 and ROA2 in models (2) and (4) are both negative but only statistically signicant in model (4), suggesting that a rmthat is
performing poorly is more likely to call a bond without a refund rather than not call. The opposite effects ofrm 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 signicant 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 signicant. This evidence suggests
that a
rm with higher investment activity is more likely to call with refund than not call, but is less likely to call without refundthan not call. This evidence again lends support to hypothesis H6.
Table 7
Multinomial logistic regressions explaining the choice to call with refund, call without refund, or not call. This table presents the coefcient 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 rst. We use
the observations ofnotcall as thebase case andevaluate theothertwo outcomes (call withrefund andcall withoutrefund) as alternatives to thischoice. 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 includerm 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 dened 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 onstandard errors corrected for rm clustering effect in panel data and are reported in parentheses.
Variable 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
Intercept 0.502 3.598 1.026 3.783 0.988 3.853 1.040 3.821
(0.515) b.0001 (0.173) b.0001 (0.190) b.0001 (0.166) b.0001
ROA1 3.498 1.125
(0.016) (0.438)
ROA2 3.229 4.318
(0.074) (0.006)
CAPEX 5.977 8.735
(0.088) (0.010)
CAPEXRD 3.814 -8.118(0.230) (0.009)
Firm size 0.315 0.010 0.306 0.016 0.321 0.040 0.316 -0.042
(0.000) (0.878) (0.000) (0.799) (0.000) (0.534) (0.000) (0.522)
Tangible assets 0.369 0.212 0.490 0.342 0.681 0.043 0.649 0.034
(0.281) (0.525) (0.136) (0.295) (0.040) (0.901) (0.049) (0.920)
Leverage (total debt) -0.533 0.0 25 0.536 0.597 0.749 0.472 0.740 0.444
(0.362) (0.964) (0.376) (0.298) (0.204) (0.416) (0.209) (0.444)
Change in risk-free rate 0.000 0.071 0.000 0.066 0.007 0.066 0.004 0.066
(0.991) (0.017) (0.998) (0.029) (0.813) (0.034) (0.875) (0.033)
STD of risk-free rate 1.411 1.367 1.428 1.371 1.350 1.327 1.347 1.316
(0.003) (0.005) (0.003) (0.005) (0.004) (0.008) (0.005) (0.009)
Change in credit rating 0.018 0.051 0.017 0.060 0.029 0.025 0.025 0.025
(0.802) (0.387) (0.806) (0.320) (0.671) (0.682) (0.714) (0.692)
Operating income
volatility
12.698 14.578 14.512 16.035 17.727 19.423 17.357 19.264
(0.135) (0.067) (0.088) (0.047) (0.037) (0.019) (0.042) (0.020)
Ln (time to maturity) -1.855
1.835
1.894
1.830
1.898
1.853
1.900
1.842(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Industry dummy and
calendar year dummy
Yes Yes Yes Yes Yes Yes Yes Yes
Pseudo-R2 0.165 0.169 0.167 0.165
NOBS 2792 2743 2704 2710
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7. Conclusion
If a rm issues a non-callable bond, even when the rm'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 rm could issue a
callable bond to reduce the risk-shifting problem. Because the call option enables the rm 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 rm 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 rm would trade-off between the benet 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 ndings offer
strong support to our model. We nd that a rm with poorer future investment opportunities is more likely to issue a callable
bond. In addition, a rm with a higher leverage ratio and higher investment risk is more likely to issue a callable bond. Finally, we
nd that a rm with the best performance and the highest investment activity is likely to call and refund its bond; a rm with the
worst performance and the lowest investment activity is likely to call without refunding its bond; and a mediocre rm is likely to
not call its bond. In contrast, our ndings 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,
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