Non-Financial Firms as Cross-Market
Arbitrageurs∗
Yueran Ma†
Harvard University
Abstract
I demonstrate that non-financial corporations act as cross-market arbitrageurs
in their own securities. Firms simultaneously issue in one market and repurchase in
another in response to relative valuations, inducing large and negatively-correlated
financing flows in different markets. Specifically, net equity repurchases and net
debt issuance both increase when the expected returns on debt are especially low,
or when the expected returns on equity are relatively high. Credit valuations affect
equity financing as much as equity valuations do, and vice versa. Additionally,
cross-market corporate arbitrage counteracts market segmentation, helps account
for merger dynamics, and has implications for unconventional monetary policies.
JEL classification: G32, G10, G35, G34.
Key words: Non-financial firms; Cross-market corporate arbitrage; Capital market
frictions; Financial policies.
∗
I am grateful to Robin Greenwood and Andrei Shleifer for their invaluable guidance, and to Malcolm
Baker, John Campbell, Ed Glaeser, Chen Lian, Gordon Liao, David Scharfstein, Alp Simsek, Erik
Stafford, Jeremy Stein, Adi Sunderam, Chunhui Yuan, Luis Viceira, Yao Zeng, seminar participants at
Harvard, and especially Sam Hanson for very helpful suggestions. Previous versions of the paper have
been circulated under the title “Non-Financial Firms as Arbitrageurs in Their Own Securities”. All
errors are mine.
†
Email: [email protected].
1
Introduction
Since 2010, the US market has witnessed a surge in equity repurchases by non-financial
corporations. In large part, these equity repurchases have been accompanied by debt
issuance. According to corporate executives, the debt-financed equity repurchases were
motivated by the extraordinarily low cost of financing in credit markets, which has made
it appealing to replace equity capital with debt. These transactions are described as “a
great trade” by Home Depot’s CFO in comments about her company’s debt-financed
repurchase program.1
In this paper, I present evidence that a significant portion of financing flows in the
US arise from non-financial corporations acting as cross-market arbitrageurs in their own
securities. While previous studies of corporate market timing mostly focus on a single
asset class,2 firms issue securities in several different capital markets, each of which may
experience distinct pricing fluctuations. As I show, firms do not time a single market
in isolation. Rather, they jointly time multiple markets and actively arbitrage among
them by simultaneously issuing in one market and repurchasing in another, in response
to variations in the relative valuation of different securities. Financing activities in each
market are driven not only by conditions in that particular market, but also by valuations
in other markets. For instance, when credit markets are a particularly cheap source of
funding, firms not only issue additional debt, but also repurchase more equity. Conversely,
when the cost of equity is especially low, firms issue equity to retire debt.
1
The Economist, September 13, 2014. Other comments by corporate executives and market participants see, for example, Reuters, September 6, 2013; CNBC, October 26, 2013.
2
Ritter (1991), Spiess and Affleck-Graves (1995), Loughran and Ritter (1995), and Baker and Wurgler
(2000) show that firms issue more equity prior to periods of low stock returns, while Hong, Wang, and
Yu (2008) show that firms repurchase equity when their stocks are temporarily undervalued. In the
bond market, Baker, Greenwood, and Wurgler (2003a) show that firms issue more long-term bond prior
to periods of low bond returns, and Harford, Martos-Vila, and Rhodes-Kropf (2014) find that firms
also issue more debt when credit ratings appear inflated. Jenter, Lewellen, and Warner (2011) provide
evidence for firms timing the issuance of derivative securities such as put options.
1
This type of cross-market arbitrage helps to explain the otherwise puzzling strong
negative correlation between financing activities in equity and debt markets. Figure 1
below shows aggregate net equity repurchases (negative one times net equity issuance)
and net debt issuance by US non-financial firms, both normalized by total assets. These
two series rise and fall with each other over the past three decades—the raw correlation
is about 0.5. The same relationship also holds at the firm level. These patterns suggest
that financing activities are, to a significant extent, driven by firms actively issuing some
securities to retire others, as opposed to issuing to finance investment or repurchasing to
distribute excess cash. I document that an important fraction of financing flows can be
explained by the relative valuation of debt and equity, after traditional determinants of
-.2
0
% of Total Asset
.2
.4
.6
.8
financing decisions are taken into account.
1980
1990
2000
2010
Time
Net Equity Repurchase/Asset
Net Debt Issuance/Asset
Source: Flow of Funds. Levels are quarterly rates.
Figure 1: Aggregate Net Equity Repurchases and Net Debt Issuance by US Non-Financial Firms.
In what follows, I first develop a simple model building on Stein (1996) to explore
how firms would organize financing activities when there are separate valuation shocks to
equity and debt markets. The model highlights two key features of cross-market corporate
arbitrage. First, financing activities in each market are influenced by both debt and equity
pricing shocks. Second, variations in relative valuation induce financing flows in debt and
2
equity markets that move in opposite directions. The model also suggests that financially
unconstrained firms will be most likely to engage in cross-market arbitrage.
I use several complementary empirical strategies to document the prevalence of crossmarket corporate arbitrage, both in the aggregate and at the firm level. I begin by
regressing equity and debt financing activities on proxies for both equity valuations (i.e.
variables known to predict equity returns) and debt valuations. I show that net equity
repurchases increase when the expected returns on debt are particularly low, and when
the expected returns on equity are relatively high. At the same time, net debt issuance
also increases by a similar amount. These findings are consistent with the two key features
of cross-market corporate arbitrage highlighted in my simple model: financing activities
in each market are influenced by both debt and equity valuations, and equity and debt
financing flows move in opposite directions. Moreover, I find that credit market conditions
affect equity financing as much as equity market conditions do, and vice versa. Consistent
with the model’s prediction, I also find that cross-market arbitrage is most prevalent
among large and unconstrained firms.
Then I show that firm actions have predictive power for future relative returns of
debt and equity. For instance, when firms increase debt and reduce equity, future debt
returns tend to be lower than what would be predicted given the level of stock returns. In
particular, a capital structure arbitrage trading strategy that mimics firms’ trades—i.e.
shorting debt and buying equity, with positions weighted by hedge ratios that reflect
the sensitivity of debt returns to equity returns under theoretical benchmarks—earns
abnormal returns.
At the most basic level, my evidence shows that pricing dynamics in multiple markets jointly determine financing patterns. The level of financing activity in each market
responds not only to conditions in the same market; it is also strongly influenced by
3
valuations in other markets, holding fixed valuations in the market of interest.
In addition, my findings suggest that firms’ cross-market arbitrage may play a role
in integrating dispersed markets. Specifically, consider a simple view of financial market
mispricing which holds that different markets are well-integrated and they experience
common misvaluations of firm cash flows. In this case, each type of security would
be mispriced depending on its sensitivity to total firm value, with equity being more
mispriced than debt (Dong, Hirshleifer, and Teoh, 2012; Gao and Lou, 2013). It then
follows that firms may want to issue debt to repurchase equity when their assets are
undervalued, as equity would be particularly undervalued compared to debt, and vice
versa.
This integrated-mispricing view does not, however, square with several features of the
data. First, firms often engage in debt-financed equity repurchases not necessarily because equity is severely undervalued, but because the cost of debt is extraordinarily low.
Second, variance decompositions suggest that debt and equity valuations both account
for a significant fraction of variations in financing activities. Neither of these findings
fits well with the integrated-mispricing narrative where debt and equity valuations are
perfectly linked, and measures of debt and equity market conditions are largely redundant. Moreover, firm actions have forecasting power for future relative returns of debt
and equity in excess of the integrated markets benchmark. In light of these results, a
more natural perspective is that capital markets are partially segmented, and debt and
equity securities frequently experience separate valuation shocks.3 As firms actively exploit pricing discrepancies across different markets, issuing in markets that require low
compensation for risks and repurchasing from markets that require high compensation,
3
Considerable evidence from asset pricing studies points to partial segmentation between debt and
equity markets (e.g. Collin-Dufresne, Goldstein, and Martin (2001), Yu (2006), Duarte, Longstaff, and
Yu (2007), Kapadia and Pu (2012), among others). Recent studies of credit market sentiment also find it
to be important but largely distinct from equity market sentiment (e.g. Greenwood and Hanson (2013),
Lopez-Salido, Stein, and Zakrajsek (2015)).
4
their action could help to integrate otherwise segmented markets.
One possible concern with my analysis is that financing activities and relative valuations of debt and equity might be correlated due to omitted variables that are unrelated
to cross-market corporate arbitrage. I examine a variety of alternative explanations in
detail, including dynamic capital structure considerations, time-varying borrowing constraints, and agency problems. I do not find evidence that adjustments to the financing
mix predicted by these theories can account for the close connection between financing
activities and the relative expected returns on debt and equity.
Finally, I show that firms’ cross-market arbitrage has broader economic implications.
I begin by presenting additional evidence on firms’ response to pricing shocks resulting
from imperfect market integration. I study an important shock of this sort induced by
variations in the supply of government bonds, which is often thought to have a significant
impact on bond market conditions and the cost of debt financing (Greenwood, Hanson,
and Stein, 2010; Graham, Leary, and Roberts, 2015). I find that as government bond
supply falls, firms (especially large firms) not only issue more debt but also repurchase
more equity. My findings hint that unconventional monetary policies such as “Quantitative Easing”—a deliberate effort to lower credit market risk premia through purchases of
long-term government bonds—might have unintended consequences: firms may respond
to the unusually low cost of credit by issuing debt to repurchase more equity, especially
if they do not think investment opportunities are attractive.
I then show that the notion of cross-market corporate arbitrage is also useful for
analyzing merger and acquisition waves, and in particular the dynamics of cash mergers.
Through cash mergers, firms can exploit market-wide differences in the pricing of debt
and equity by issuing debt to buy other firms’ equity. I document that the volume of cash
mergers as well as their share of total mergers increases significantly when the cost of
5
debt is low compared to the cost of equity. Conversely, when the cost of debt is relatively
high and when equity appears to be particularly overvalued, firms shift away from cash
mergers and substitute towards stock mergers.
My findings contribute to the growing literature on capital market-driven corporate
finance (see Baker (2009) for a summary). Relative to existing research which focuses on
valuations in a single asset class (e.g., Loughran and Ritter (1995), Baker and Wurgler
(2000), Greenwood and Hanson (2013), Harford et al. (2014), among many others), my
evidence highlights that financing activities are jointly determined by pricing dynamics
in multiple markets. This perspective shows that financing decisions in each market can
be better understood by considering valuations in several different markets. It also helps
us to see the important connection between financing flows across major markets.
A contemporaneous paper by Gao and Lou (2013) also considers the case where equity and debt can both be misvalued. My paper is different from their work in several
ways. First, Gao and Lou (2013) assume that equity and debt misvaluations are perfectly correlated, with equity always more mispriced than debt. My results suggest that
this integrated-mispricing view has limitations, and it can miss interesting aspects of
firm behavior in the real world where this assumption may not hold.4 Second, while
the analysis in Gao and Lou (2013) is entirely cross-sectional, I show that cross-market
corporate arbitrage is relevant not only for studying firm behavior at the micro level, but
also for understanding the aggregate dynamics of financing flows. Third, I also show that
cross-market corporate arbitrage is useful for analyzing several other issues of practical
and academic interests, such as the implications of unconventional monetary policies and
merger dynamics.
4
Gao and Lou (2013) also propose the term “cross-market timing”, but use it in a different way.
In their use, the term refers to looking at non-fundamental shocks in a firm’s stock to infer about debt
misvaluation, which can then guide debt issuance decisions. As explained previously, my analysis does
not impose the assumption of integrated mispricing or restrict to equity valuation shocks only. I find that
firms’ cross-market arbitrage takes diverse forms, and corporations respond actively to separate pricing
shocks arising from both equity and debt markets.
6
The remainder of the paper is organized as follows. Section 2 presents a set of motivating facts. Section 3 provides an analytical framework for cross-market corporate arbitrage
and lays out the key predictions. Section 4 describes the data. Section 5 presents the
main empirical evidence. Section 6 addresses a set of alternative explanations, and provides additional evidence on the broader implications of cross-market corporate arbitrage.
Section 7 concludes.
2
Motivating Facts
In recent decades, there has been a strong negative correlation between debt and
equity financing activities by US non-financial firms. At the aggregate level, net equity
repurchases rise and fall with net debt issuance. Figure 2 Panel A reproduces the aggregate time series for the post-1980 period using data from the Flow of Funds, and
Panel B plots the same series for the pre-1980 period. The comovement is particularly
pronounced after the early 1980s, as the SEC adopted new rules (rule 10b-18, effective
since late 1982) which substantially lowered the legal risks of equity repurchases.
Similar comovement between net equity repurchases and net debt issuance also appears at the firm level. A simple regression of quarterly net equity repurchases on net
debt issuance:
N et debt issuanceit
N et equity repurchaseit
= αi + β
+ it
Asseti,t−1
Asseti,t−1
(1)
in the post-1985 quarterly Compustat dataset yields β = 0.11 with t-statistic of 13.76
when using firm fixed-effects and clustering standard errors by both firm and time. Although the slope coefficient β is smaller here than that in the aggregate time series (which
is about 0.43), the strength of the relationship is impressive nonetheless.
7
Figure 3 further confirms that a significant fraction of total financing activities involves
firms who are simultaneously issuing in one market while repurchasing in another. Figure
3 part (a) and part (b) show that about 35% of quarterly equity repurchases come from
firms that are net issuing debt in the current or previous quarter, and about 35% of
(seasoned) equity issuance comes from firms that are net retiring debt.5 Similarly, parts
(c) and (d) show that about 20% of debt issuance (repurchases) comes from firms that
are concurrently net repurchasing (issuing) equity.
Finally, there appear to be significant differences between large and small firms, and
the aggregate negative correlation between equity and debt financing activities is primarily driven by large firms. Figure 4 plots net equity issuance and net debt issuance
by small (assets below median) and large (assets above median) firms in the Compustat
universe. It shows that small public firms are primarily net equity issuers, and their debt
market activities are much smaller in comparison. In contrast, large public firms demonstrate the type of behavior shown in Figure 2: their net equity repurchases increase (i.e.
net equity issuance decreases) with net debt issuance.
These stylized facts show that financing activities in debt and equity markets often
move in opposite directions. One rationale corporate executives provide is that firms face
a menu of securities and opportunistically substitute between them to exploit relative
valuations across dispersed markets. For example, Intel issued $5 billion debt in 2011Q3
and $6 billion debt in 2012Q4, stating in its 424B2 filings that the proceeds will be used to
fund stock repurchase programs. In public comments, Intel’s Treasurer Ravi Jacob says
that issuing debt to repurchase equity is appealing because the cost of debt at the time
appears too low compared to the stock’s dividend yield. Table 1 shows Intel’s transactions
as recorded in Compustat. A number of other firms (e.g. Home Depot, FedEx, IBM,
5
Farre-Mensa, Michaely, and Schmalz (2015) perform a similar calculation about equity repurchases
financed by debt and find very close estimates.
8
Lowe’s, Macy’s, Merck, Microsoft, Pepsi, Priceline, Sony, etc.) undertook similar actions
in recent years. CFOs and analysts express the view that “if a company is able to issue
a bond at a level that looks cheap relative to the dividend yield, then issuing bonds to
fund share repurchases is attractive.”6
These anecdotes suggest that firms pay close attention to valuations in multiple asset
classes, and attempt to arbitrage across different markets to take advantage of perceived
pricing misalignments. In the following section, I provide a simple framework to analyze
firms’ cross-market arbitrage and derive the empirical predictions.
3
A Model of Firms as Cross-Market Arbitrageurs
This section outlines the basic mechanisms of cross-market corporate arbitrage. I
explain the idea with the help of a simple model.
3.1
Firms’ Natural Advantage as Arbitrageurs
For firms to play a role as arbitrageurs, it must be the case that arbitrage by private
investors is incomplete due to frictions in capital markets. Thus, before delving into the
details of the model, it would be helpful to consider the difference between arbitrage by
issuing firms and arbitrage by private investors. Why might firms be natural arbitrageurs
when private arbitrage is constrained?
It is well known that a variety of transactional, legal, and institutional constraints
can make it harder for investors to short corporate securities than for firms to issue them.
Moreover, the arbitrage problem for issuing firms could be a degree simpler than that for
private investors, since firms produce the cash flows underlying their financial securities,
and pay back the securities with cash flows. This helps to reduce firms’ problem, to
6
Wall Street Journal, March 25, 2013; Reuters, September 6, 2013.
9
a large extent, to one about the difference between a security’s current market price
and its fundamental cash flow value. When firms engage in cross-market arbitrage, for
instance, they are simply re-packaging the same stream of operating cash flows into
different securities to exploit pricing discrepancies. In contrast, private arbitrageurs do
not naturally have the securities’ cash flows, and they need to put up additional capital
for their arbitrage trades. If mispricing persists or worsens, private arbitrageurs will suffer
capital losses. Without sufficient capital to sustain the positions, they would be forced
to unwind precisely at the wrong time, as shown in Shleifer and Vishny (1997).
To illustrate further, consider the classic problem facing a hedge fund manager who
finds the current market price Pt of, for instance, Tesla’s security to be overvalued relative
to its fundamental cash flow value C, and decides to short the security. If the market price
fails to converge back to C or becomes even higher in the near term, the fund manager
has to put up more capital against his short position. At the same time, the hedge fund
may also experience outflows from return-chasing investors. In this situation, the fund
manager could be forced to liquidate his positions at a loss. In contrast, suppose Tesla
decides to issue an additional unit of the security at time t. At issuance, Tesla receives
the market value of the security (≈ Pt ). Tesla will ultimately pay back buyers of the
newly issued security with cash flows that the security is entitled to (≈ C), and the net
gain is approximately the difference between the security’s current market value and its
fundamental cash flow value.7 This gain would not change in the event that security price
goes up further at t + 1. If it wishes, Tesla can issue more at t + 1, but rarely would it
be under any pressure to undo the issuance and forgo the gains.
Thus, to the extent that issuing firms are less affected by some of the key frictions
that constrain private investors, they may play a natural role as arbitrageurs in their own
securities. This observation provides an alternative perspective to the classical limits to
7
The net gain will ultimately accrue to original shareholders and likely also to managers.
10
arbitrage problem raised by Shleifer and Vishny (1997). Arbitrage in financial markets
is not necessarily only performed by private investors. When private arbitrage is limited,
corporate issuers may step in and act as an important group of arbitrageurs.
3.2
Security Valuations and Financial Policies
In this section, I provide a simple model to analyze cross-market corporate arbitrage.
I build on the classical market timing framework in Stein (1996). I extend Stein’s model
to examine how a firm would behave when separate valuation shocks can affect both
equity and debt markets.
Consider a firm which begins with existing debt of d dollars and existing equity
of 1 − d dollars. Given current market conditions and investment opportunities, the
firm can choose to net issue an additional amount of debt D, net repurchase equity S
(S < 0 means the firm net issues equity), and invest K. Let k be the discount rate
of the firm’s future cash flows given their risk properties. Let PD∗ and PE∗ denote the
fundamental value of the firm’s debt and equity, and P̃D and P̃E the market prices. The
corresponding market timing gain of issuing one dollar of debt or one dollar of equity is
equal to δD = 1 − PD∗ /P̃D and δE = 1 − PE∗ /P̃E respectively. At this point, the model does
not make restrictions about the relationship between δD and δE : they could represent
either integrated mispricing, or security-specific valuation shocks in segmented markets.
Section 3.3 will examine how integrated mispricing imposes additional structure on δD
and δE , as well as its implications.
The firm’s objective is to maximize the net present value of its investment plus market
timing gains, also taking into account the costs and benefits associated with changes in
11
capital structure:
θ
max f (K)/(1 + k) − K + (−S)δE + DδD − [D − dK]2
K,D,S
2
(2)
The first term f (K) broadly refers to returns on a set of possible investment, including
real investment and other uses of funds, such as precautionary savings. As long as
the firm also has diminishing marginal benefits from cash holdings,8 explicitly making
cash holdings another choice variable does not change the intuition and the qualitative
predictions of the model, so I abstract away from it. The firm’s budget constraint implies
K = D − S.
As in Stein (1996), I take into consideration that in reality capital structure may not
be fully flexible. For example, firms can have a range of desirable capital structure due to
the trade-off that too much debt will raise bankruptcy probabilities to dangerously high
levels, yet too little debt means giving up sizeable tax benefits. For algebraic convenience,
I assume that the firm starts with the target leverage ratio d, and the cost of deviation
is quadratic in the distance to target leverage. Specifically, when investment is K, the
target level of net debt issuance is dK, thus if actual debt issuance is D, the firm would
be overleveraged by D − dK; the associated cost would be θ[D − dK]2 /2, where θ is a
parameter of capital structure flexibility.
One can also include a set of other considerations, such as the benefits of equity
repurchases and the costs of equity issuance (e.g. due to signaling in the presence of
information asymmetries), or a general preference for issuance choice. These additions
can affect the average level of net equity repurchases and net debt issuance, and relatedly
the overall level of payout and external financing positions as discussed by Farre-Mensa
8
This can happen because precautionary savings have diminishing marginal returns, and excess cash
holdings have carry costs (Azar, Kagy, and Schmalz, 2015). It can also result from other frictions
including tax treatments and agency problems (Opler, Pinkowitz, Stulz, and Williamson, 1999; Bolton,
Chen, and Wang, 2011).
12
et al. (2015). However, they will not change the central predictions about how financial
policies vary in response to pricing shocks.
Some simple derivations lead to the following result:
Proposition 1. 1) Net debt issuance is always increasing in debt valuations (∂D∗ /∂δD >
0), and net equity repurchases are always decreasing in equity valuations (∂S ∗ /∂δE < 0).
2) Net equity repurchases are increasing in debt valuations (∂S ∗ /∂δD > 0) and net debt
issuance decreasing in equity valuations (∂D∗ /∂δE < 0) if f 00 (K ∗ )/(1+k)+θd(1−d) < 0.
Proposition 1 shows the firm will always tilt towards net issuing more debt (equity)
when there is a positive valuation shock in that particular market. However, whether the
firm will engage in cross-market arbitrage—that is, issuing debt to repurchase more equity
when there is a favorable debt valuation shock, and similarly issuing equity to retire more
debt when there is a favorable equity valuation shock—depends on the parameters. The
firm does so if f 00 (K ∗ ) + θd(1 − d) < 0. This condition comes from a trade-off between
the costs and benefits of cross-market arbitrage.
For instance, after the firm issues more debt following a positive debt pricing shock,
it can use the proceeds to fund additional investment or to repurchase equity. When
making this decision, the firm has two considerations. First, it compares the benefits of
increasing investment to the benefits of reducing its cost of capital by repurchasing equity
with overvalued debt. If the firm is financially constrained and there are many profitable
investment opportunities that it has not taken (i.e. f 00 (K ∗ ) not very negative), then
the benefits of increasing investment would dominate. In contrast, for an unconstrained
firm that has exhausted most of its investment opportunities (i.e. f 00 (K ∗ ) very negative),
reducing financing costs through cross-market arbitrage would be more appealing.
Second, the firm also seeks to maintain a desirable capital structure. Issuing debt
means an increase in leverage, and equity repurchases will lead to a further increase in
13
leverage. If capital structure is very inflexible (i.e. θ very large), then following the debt
issuance, the firm might even want to issue more equity to keep the capital structure
in control. If, instead, capital structure is completely flexible (i.e. θ → 0), then the
condition f 00 (K ∗ ) + θd(1 − d) < 0 always holds, and the firm will issue a substantial
amount of debt to both invest more and substitute out equity capital. Indeed, in a world
where capital structure adjustments are costless, absent of any valuation shocks, firms
should be indifferent between alternative financial structures, as in Modigliani and Miller
(1958). In this setting, as soon as we introduce valuation shocks in securities markets,
firms will lean against them all the way until they are eliminated.
Taken together, when increasing investment is not very attractive, and when balance
sheet is flexible, firms engage in cross-market arbitrage to exploit pricing discrepancies
between different markets. In this case, financing activities display two key features.
First, financing decisions in each market are influenced by conditions in both debt and
equity markets. We would expect an increase in net equity repurchases not only when
equity valuations are low, but also when credit valuations are high. Similarly, net debt
issuance would not only respond to credit valuations but also lean against equity valuations. Second, the same set of pricing shocks will induce financing flows in debt and
equity markets that move in opposite directions (i.e. net equity repurchases and net debt
issuance respond to δD and δE with the same sign). These two features are interrelated,
and they reflect two aspects of cross-market corporate arbitrage: one from the perspective
of what determines financing decisions in a given market, and another from the perspective of how financing activities across different markets are connected. In Section 5, I
take these predictions to the data and present supporting evidence both in the aggregate
and at the firm level.
14
3.3
Cross-Market Corporate Arbitrage in Integrated and Segmented Capital Markets
This final section discusses how the assumption of integrated versus segmented markets can affect the structure and implications of firms’ cross-market arbitrage.
In Section 3.2, the model does not impose any restriction on the relationship between
δD and δE . If capital markets are segmented, a large set of combinations of {δD , δE } are
possible. However, if markets are integrated, in the sense that investors price a firm’s
different securities with the same beliefs about firm cash flows, and all market-specific
pricing frictions are eliminated through private arbitrage, then δD and δE would be closely
connected.
Specifically, suppose the fundamental value of the firm’s total cash flows, equity, and
debt is V ∗ , VE∗ , and VD∗ respectively (where VE∗ and VD∗ are functions of V ∗ ). If investors
in both markets all misperceive firm value to be Ṽ = V ∗ (1 + δV ) (i.e. firm assets are
mispriced by δV per dollar), and δV is not too large,9 then we have
δE =
δD =
(∂VE /∂V )δV V ∗
P̃E − PE∗
≈ ∗
=
VE + (∂VE /∂V )δV V ∗
P̃E
P̃D − PD∗
(∂VD /∂V )δV V ∗
≈ ∗
=
VD + (∂VD /∂V )δV V ∗
P̃D
δV V ∗
VE∗
∂VE /∂V
+ δV V ∗
δV V ∗
∗
VD
∂VD /∂V
+ δV V ∗
(3)
(4)
To the extent that equity value is convex in firm value and debt value is concave in firm
value, VE∗ < V ∗ (∂VE /∂V ) and VD∗ > V ∗ (∂VD /∂V ). As a result, when δV > 0, we have
δE > δD > 0; when δV < 0, we have δE < δD < 0; or equivalently ∂δE /∂δV > ∂δD /∂δV .
In this case, δD and δE would comove closely (although it turns out that they are not
perfectly linearly correlated). In particular, δE would tend to be the leading indicator of
More precisely, |(∂VE /∂V )δV V ∗ | < VE∗ . That is, the magnitude of mispricing in the firm’s equity
does not exceed the fundamental value of firm equity.
9
15
mispricing that plays a dominant role, and the additional impact of δD would be small.
It also follows that the firm would only issue debt to repurchase equity when δV < 0 and
all of its claims are undervalued, but debt less so than equity.
In contrast, when markets are segmented, debt and equity investors may have different
beliefs about firm cash flows, different risk appetites, or different constraints. In this
setting, equity valuations do not necessarily reflect all pricing shocks, and credit market
conditions can play an important independent role. Debt-financed equity repurchases
can arise, for instance, when credit markets experience positive valuation shocks (due
to exuberant sentiment and underestimation of default risks, reaching for yield, etc.).
Accumulating evidence has shown that real world equity and debt markets seem to be
far from well integrated (e.g. Yu (2006), Duarte et al. (2007), Kapadia and Pu (2012),
Greenwood and Hanson (2013)): at the firm level, pricing discrepancies appear quite
common; in the aggregate, credit booms do not go hand in hand with equity bubbles. In
my empirical analysis, I do not impose a priori assumptions about market integration.
My findings, as will be discussed in Sections 5 and 6, suggest the importance of market
segmentation in driving cross-market corporate arbitrage.
Finally, it may be worth clarifying the relationship between cross-market corporate
arbitrage and the Modigliani and Miller (1958) theorem. At first glance, the possibility
for a firm to benefit from cross-market arbitrage when markets are integrated might
seem inconsistent with Modigliani and Miller (1958). There is, however, an interesting
and subtle qualification to the Modigliani-Miller statement when markets are integrated
but inefficient. On the one hand, as long as markets hold common views of firm cash
flows and the Modigliani-Miller style arbitrage is frictionless, the firm’s market value is
always independent of financial structure, as originally stated in Modigliani and Miller
(1958). On the other hand, variations in the financing mix that exploit the differential
16
misvaluations of equity and debt (i.e. ∂δE /∂δV > ∂δD /∂δV ) can still create value for
existing shareholders in the long run.10 Thus, when markets are integrated (and there
are no other frictions from taxes, bankruptcy costs, etc.), the firm’s market value will be
independent of financial structure, but its fundamental shareholder value may not be, and
cross-market corporate arbitrage could increase shareholder value. If markets are instead
segmented, then by exploiting relative valuations in different markets via cross-market
arbitrage, the firm can both create value for its shareholders and change the total market
value of its securities (or equivalently the total cost of capital).
4
Data
The data for my empirical analysis fall into two main categories: 1) data on the pricing
of stocks and bonds, and 2) data on firm financials and corporate policies. I collect both
types of data at the aggregate level and at the firm level. Most tests are at quarterly
frequencies; I turn to annual frequencies only when it is necessary. The tests focus on
the post-1985 period because firms can issue and repurchase in both debt and equity
markets without major regulatory constraints in this period, as discussed in Section 2.
In addition, quarterly Compustat data on issuance and repurchases are available since
1985. The sample ends at the end of 2012.
10
To see this, consider an example where equity and debt markets share a common downward-biased
perception of firm value: the fundamental value of the firm, its equity, and debt is V ∗ , E ∗ , and D∗
respectively, whereas the market’s perception of firm value is Ṽ < V ∗ , and correspondingly the market
value of equity and debt is Ẽ and D̃. (Thus E ∗ = Ẽ(1 − δE ), D∗ = D̃(1 − δD ), with δE < δD < 0.) Now
if the firm repurchases α fraction of its total N shares, which costs αẼ, and finances it entirely by issuing
debt, then the total market value of the firm’s securities, as well as the share price, would stay the same.
Namely, the market perception of equity value will be Ẽ − αẼ = (1 − α)Ẽ in total, or Ẽ/N per share,
and the market value of the firm remains [D̃ + αẼ] + (1 − α)Ẽ = Ṽ . However, the fundamental value of
each remaining share will increase, since every dollar of the repurchased equity has a higher fundamental
value than every dollar of the newly issued debt, given that equity is more undervalued than debt: the
fundamental value of the remaining equity becomes V ∗ − D∗ − αẼ(1 − δD ) , and the fundamental value
∗
∗
∗
∗
∗
−αẼ(1−δD )
−αẼ(1−δE )
−αE ∗
∗
> V −D(1−α)N
= E(1−α)N
= PE∗ . In other words,
of every share becomes PE,N
= V −D(1−α)N
buying back more undervalued equity and replacing it with less undervalued debt creates a positive
transfer to the remaining shareholders, from the perspective of a rational observer. Nevertheless, market
investors, given their biased beliefs, do not recognize this transfer, so the price of equity and the market
value of the firm stay the same.
17
4.1
Stock and Bond Data
Aggregate data on corporate bonds come from several sources: data on yields are
from Moody’s, and data on returns are from Morningstar/Ibbotson and Barclays Capital;
these data are assembled by Greenwood and Hanson (2013).11 Yields on Treasury bonds
and bills are from the Federal Reserve Economic Database (FRED). Aggregate data on
historical stock returns and valuations are from Robert Shiller’s dataset.
Firm-level bond data come from the Trade and Reporting Compliance Engine (TRACE)
database, which was launched in July 2002. TRACE has very comprehensive coverage
of corporate bond pricing by transaction, but it can only provide information for firms
with traded public debt, which, as discussed below, tend to be relatively large firms in
the Compustat universe.
Because firms often have more than one corporate bond outstanding, I compute the
face-value-weighted average of bond yields, spreads, and returns at the firm level.12
Results are very similar using equal-weighted or trading-volume-weighted averages—
variables constructed using these three different weighting methods are more than 0.97
correlated. Specifically, in every month, I first take each bond’s monthly median yield
and price from the raw TRACE file to reduce data error. I then compute firm-level
monthly bond yield as the face-value-weighted average of each bond’s yield. For spreads,
I first compute bond-level credit spread as the difference between the bond’s yield and the
11
The series in Greenwood and Hanson (2013) end in December 2009. For subsequent periods,
Moody’s yield data is available through the Federal Reserve Economic Database (FRED), and I supplement corporate bond returns data using returns on Bank of America Merrill Lynch corporate bond
indices also available through FRED. Merrill Lynch also provides yield data for different classes of bonds
through Thomson Reuters Datastream, which produces almost identical results.
12
I follow standard procedures and exclude convertible bonds, callable/putable bonds, asset-backed
securities, Yankee bonds, Canadian bonds, and bonds issued in foreign currencies. Bond features are
obtained through the Mergent’s Fixed Income Security Database (FISD). Prior to November 2008,
TRACE requires reporting bond yield, in addition to bond price, in every transaction. After November
2008, reporting yield is no longer mandatory. When yield is not available, I use bond price and coupon
information to impute bond yield. I verify that yields provided by TRACE are highly reliable: they are
almost identical to yields imputed from coupon information and have fewer outliers than imputed yields.
18
contemporaneous yield on its nearest-maturity Treasury, and bond-level term spread as
the yield difference between its nearest-maturity Treasury and the three-month Treasury
bill. Then, I compute monthly firm-level credit spread and term spread as the face-valueweighted average of bond-level spreads. Similarly, for bond returns, I first calculate the
returns on each bond, and then take the face-value-weighted average at the firm level.
For end-of-quarter firm-level bond yield and spread, I use the last available monthly observation in each quarter. I keep all bonds with maturity greater than or equal to one
year, and winsorize the top and bottom one percent outliers.
Finally, firm-level data on stock returns and market valuation are from Center for
Research in Security Prices (CRSP).
4.2
Firm Decisions and Characteristics
My analysis focuses on non-financial corporations in the United States. Aggregate
data on their financing activities, investment, and balance sheet characteristics come
from the Flow of Funds. In particular, net equity repurchases and net debt issuance are
from Table F.102 of non-financial corporate businesses,13 so are other flow measures such
as capital expenditures and profits. Balance sheet measures such as total assets and cash
reserves are from Table B.102.
The firm-level analysis covers firms in the quarterly Compustat dataset. Following
standard practices, I exclude financial firms (SIC from 6000 to 6999), utilities (SIC from
4000 to 4949), foreign-based firms, and government-sponsored agencies. Firm-level net
equity repurchases are defined as Purchase of Common and Preferred Stock (PRSTKC)
minus Sale of Common and Preferred Stock (SSTK). Net debt issuance is defined as
13
Net equity repurchases is the negative of the item “net equity issues”. Net debt issuance is the
sum of the net issuance of corporate bonds, depository institution loans not elsewhere classified, finance
company loans, and syndicated loans.
19
Long-term Debt Issuance (DLTIS) minus Long-term Debt Reduction (DLTR).14 Other
firm-level balance sheet and cash flow variables are also from the Compustat dataset.
Appendix C provides details about the sources and definitions of the main variables.
Table 2 reports summary statistics of firms in the TRACE sample and the full Compustat sample. For comparability, the statistics of the full Compustat sample are based on
the same time period as that of the TRACE sample. Table 2 shows firms in the TRACE
sample are much larger than the average Compustat firm. This tilt towards larger firms
does not interfere with the analysis, as the model in Section 3 suggests that cross-market
corporate arbitrage, if it exists, should be most pronounced among larger firms.
5
Empirical Evidence
In this section, I provide empirical evidence for firms’ cross-market arbitrage, both at
the aggregate level and at the firm level.
5.1
Aggregate Evidence
I start with a basic test of how aggregate financing activities in each market respond
to valuations across debt and equity markets, using quarterly regressions of the following
form:
St = α1 + βD1 XD,t−1 + βE1 XE,t−1 + γ1 Zt + ut
(5)
Dt = α2 + βD2 XD,t−1 + βE2 XE,t−1 + γ2 Zt + vt
(6)
The dependent variables St and Dt are net equity repurchases and net debt issuance in
quarter t by all non-financial corporations, normalized by their total assets. The main
14
I primarily focus on long-term debt given that equity and long-term debt are closer substitutes than
equity and short-term debt from a maturity-matching perspective. Most examples of firms’ equity-debt
swaps are between equity and long-term debt.
20
independent variables XD,t−1 and XE,t−1 capture valuations in debt and equity markets
respectively, measured as of the end of quarter t − 1. Zt are control variables.
I use three sets of proxies for debt and equity valuations. The first set includes the
credit spread (yield difference between high yield corporate bonds and ten-year Treasury
bonds) and the term spread (yield difference between ten-year Treasury bonds and threemonth Treasury bills) as proxies for debt valuations, and E/P10 (the inverse of CampbellShiller P/E10) as a proxy for equity valuations. This set of proxies are straightforward,
widely used, and well known to be strong predictors of future excess returns on corporate
bond and equity. A shortcoming of these proxies, especially those for debt valuations, is
that they may not cleanly separate the expected excess returns component from other
components (such as expected default frequencies and recovery rates in the case of the
credit spread, and expected future interest rates in the case of the term spread).
The second set of proxies address this issue by breaking the credit spread into a
component of credit premium—the part that cannot be explained by expected default
probabilities and major bond characteristics—and a component of fundamentals, following Gilchrist and Zakrajsek (2012). The term spread is similarly decomposed into
the term premium component and the expected future interest rate component. Credit
premium and term premium are then used as proxies for debt valuations, and the fundamental components are included as controls. The calculation of credit premium and
term premium is explained in detail in Appendix C.
The third set of proxies proceed by forming “best forecasters” of future 12-month
excess returns on corporate bond and equity. These proxies for expected returns are fitted
values from forecasting regressions of future bond and equity returns.15 As mentioned
15
Specifically, the predicted excess return is the fitted value from a regression of future 12-month
0
0
excess returns on current valuation ratios and past returns: rx12
t = α + Xt β + Wt γ + t . Excess returns
refer to returns in excess of the risk free rate. Xt includes credit spread and term spread in predicting
future bond returns, and E/P10 and dividend yield in predicting future equity returns. Wt includes past
12-month and 24-month excess returns of bond (equity) in predicting future bond (equity) returns. For
predicted future bond returns I use high-yield corporate bonds because they are most sensitive to the
21
earlier, all valuation proxies are measured at the end of quarter t − 1.
Table 3 presents the aggregate results: columns (1) to (3) examine net equity repurchases, and columns (4) to (6) examine net debt issuance. Panel A reports regressions
without controls. Panel B adds a set of control variables, which include other factors
that may influence financing activities. In corporate finance theories, an important consideration for raising or paying down external funds is the state of cash balances. Thus
I control for total cash reserves by the end of quarter t − 1. It is also well known that
contemporaneous cash flows tend to affect financial decisions (e.g. Fazzari, Hubbard, and
Petersen (1988), Almeida, Campello, and Weisbach (2004)), so I also control for current
corporate profits. Another important consideration for financing decisions is investment
expenditures. Brav, Graham, Harvey, and Michaely (2005) show that firms make financial decisions conditional on their investment plans. It has been well documented that
near-term actual investment largely reflects ex ante plans (Lamont, 2000; Gennaioli, Ma,
and Shleifer, 2015), thus I use capital expenditures in quarter t to control for financing
flows driven by investment plans. Lastly, I add the output gap as a control for other business cycle related variations (Covas and Den Haan, 2011; Begenau and Salomao, 2015).16
Together, these controls help to assess the fraction of financing activities accounted for
by traditional determinants, relative to the fraction that appears better explained by
valuation conditions across debt and equity markets. Standard errors in these time series
regressions are Newey-West with eight lags.
From Table 3 we see a clear overall pattern: net equity repurchases and net debt
issuance both increase when the cost of debt is especially low (i.e. bond spreads and
pricing of credit risk, and therefore are a strong indicator of credit valuations. Results are very similar
using Baa bonds. Standard errors for the use of generated regressors are corrected using GMM.
16
I do not use Tobin’s Q to proxy for investment opportunities because the construction of Q means
it would be highly correlated with equity valuations. I use the output gap instead of HP-filtered GDP as
filtering can induce look-ahead biases that complicate the time series analysis. Using alternative proxies
such as recent GDP growth or HP-filtered GDP produce similar results. Output gap is computed as the
log difference between actual GDP and potential GDP.
22
bond premia are low and expected bond returns are low), and when the cost of equity is
relatively high (i.e. earnings-to-price are high and expected returns on equity are high).
Notably, the coefficients in regressions of net equity repurchases and net debt issuance
are very similar in magnitude, suggesting an almost dollar-for-dollar substitution between
equity and debt in response to changes in relative valuations. This pattern is highly
consistent with predictions of cross-market corporate arbitrage. The results are little
different with and without controls.
Then, looking at financing activities in each market specifically, columns (1) to (3)
highlight the importance of credit market conditions for equity financing: it is not simply that firms issue more equity when equity is overvalued and repurchase when equity
is undervalued; equity financing is also significantly affected by credit valuations. For
instance, a one standard deviation decrease in the credit spread is associated with a 0.24
standard deviations increase in net equity repurchases, a one standard deviation decrease
in the credit premium is associated with a 0.33 standard deviations increase in net equity
repurchases, and a one standard deviation decrease in the term premium is associated
with a 0.55 standard deviations increase in net equity repurchases.17 These results speak
to the considerable amount of credit market-driven equity repurchases, which appear to
play a prominent role in firms’ financial activities but have received limited attention in
previous research. In addition, the evidence suggests that the integrated-mispricing view
may not account for patterns in the data: it is not necessarily the case that equity valuations reflect all pricing shocks and play a dominant role. Instead, there can be separate
mispricings of debt and equity, and the spillover of credit market conditions on equity
financing decisions is important.
17
Lopez-Salido et al. (2015) extend this observation and show that the impact of credit market sentiment can be detected not only with contemporaneous measures of credit valuations, but also with reversions in credit market risk premia predicted using past market conditions. In the sample period, the standard deviations of aggregate net equity repurchases, credit spread, credit premium, and term premium
are 0.002, 0.02, 0.0056, and 0.008 respectively. 0.02 × 0.0238/0.002 = 0.24; 0.0056 × 0.1166/0.002 = 0.33;
0.008 × 0.1365/0.002 = 0.55.
23
Symmetrically, columns (4) to (6) show that equity market conditions have an independent influence on debt financing. This result is consistent with cross-market corporate
arbitrage, and it adds to existing findings on the impact of equity valuations. For example, Baker and Wurgler (2000) document that the equity share in total new issues
increases when equity is overvalued, but variations in the equity share do not directly
reveal how the level of debt financing changes. Evidence in Table 3 shows that, with
firms acting as cross-market arbitrageurs, they issue equity to reduce debt when equity
valuations are favorable, which leads to a decrease in the level of net debt issuance. In
terms of economic magnitudes, all else equal, a one standard deviation decrease in the
earnings-to-price ratio (i.e. a one standard deviation increase in P/E10) is associated
with a decline in net debt issuance of about 0.25 standard deviations.18 Relative to the
observation in Gao and Lou (2013), Table 3 shows that equity valuations have a distinct
impact on debt financing beyond debt market conditions, as opposed to being a signal
for debt valuations.
To further assess the relative importance of different factors (cost of debt, cost of
equity, and control variables) in explaining financing activities, I calculate a simple variance decomposition for regressions in Table 3 Panel B. Specifically, I decompose the
explained variations in financing activities into parts that come from each set of explanatory variables. For example, the variance share of debt valuations is computed
as v(debt) = Var(debt) , where debt = βD XD , equity = βE XE , control = γZ, and
Var(total)
total = debt + equity + control (the variance shares of equity valuations and control
variables are defined analogously). The end of Panel B reports the variance shares.19
18
In the sample period, the standard deviations of aggregate net debt issuance and E/P10 are 0.002
and 0.016 respectively. 0.016 × 0.0282/0.002 = 0.23 (using coefficient on E/P10 from Panel A column
(4)); 0.016 × 0.0371/0.002 = 0.3 (using coefficient on E/P10 from Panel A column (5)).
19
This decomposition is essentially a decomposition of the regression R-squared. In principle, one can
also start with a univariate regression, for example, of net equity repurchases on equity valuations, then
add debt valuations, finally add controls, and look at the incremental R-squared. To make the results
compact and to stay close to specifications following from the model in Section 3, I report multivariate
regressions, and show the relative importance of the three sets of factors through the decomposition.
24
The results show that valuation conditions in debt and equity markets are both highly
relevant, and the cross-market impact is strong: variations in the cost of debt affect equity
financing as much as variations in the cost of equity, and vice versa. In addition, the influence of pricing dynamics in each market seems largely distinct. For instance, the second
last row reports the variance share that comes from the part of debt valuations which
is orthogonal to equity market conditions.20 This part is also substantial, and generally
not much smaller than v(debt). The last row performs the same orthogonalization for
equity valuations, and results are similar. Finally, the decomposition (rows 3 and 4) finds
that debt and equity valuations combined can account for about a half of total variations
in financing flows, while control variables (such as cash reserves, cash flow conditions,
business cycle fluctuations, etc.) account for another half.
Taken together, the findings suggest that firms jointly time debt and equity markets,
and cross-market corporate arbitrage appears to be an important contributor to aggregate
financing dynamics. Consistent with predictions in Section 3, the evidence shows that
financing activities in each market are influenced by both debt and equity valuations, and
the cross-market spillovers are strong. Moreover, equity and debt financing flows move
in opposite directions, with about the same magnitude, in response to changes in relative
valuations. As I document in the next section, the same results hold at the firm level.
5.2
Firm-Level Evidence
In this section, I test cross-market corporate arbitrage at the firm level. The baseline firm-level regressions parallel those at the aggregate level. In addition, I examine
the model’s prediction about the heterogeneous propensity of different types of firms to
∗
I first orthogonalize debtt on XEt and obtain debt⊥
t = debtt − E [debtt |XEt ]. Then I compute
⊥
the variance share of this orthogonalized component: v(debt ) = Var(debt⊥ )/Var(total). Note that
V ar[W − E(W |X)] = E[(W − E(W |X))2 ] − (E[W − E(W |X)])2 = E[V ar(W |X)], and we can see the
connection between Var(debt⊥
t ) and Var(debtt ) from the law of total variance V ar(W ) = E[V ar(W |X)]+
V ar[E(W |X)]; Var(debt⊥
)
isolates
the variations in debtt that cannot be explained by equity valuations.
t
20
25
engage in cross-market arbitrage. Furthermore, as firm-level information makes it easier
to pinpoint how the same firm act across different markets, I present additional results
on the simultaneity of equity and debt financing flows in opposite directions.
A. Baseline Firm-Level Results
To begin, I analyze quarterly firm-level regressions that are analogous to equations
(5) and (6):
Sit = α1 + βD1 XD,it−1 + βE1 XE,it−1 + γ1 Zit + uit
(7)
Dit = α2 + βD2 XD,it−1 + βE2 XE,it−1 + γ2 Zit + vit
(8)
The dependent variables Sit and Dit are firm-level quarterly net equity repurchases and
net debt issuance, normalized by firm assets. The main independent variables XD,it−1
and XE,it−1 are firm-level proxies for debt and equity valuations, measured as of the end
of quarter t − 1. Zit is firm-level controls.
Similar to the aggregate tests, I use three sets of valuation proxies at the firm level.
The first set uses the average credit spread and term spread on firm bonds to proxy for
firm-level debt valuations, and the book-to-market ratio to proxy for firm-level equity
valuations. In addition, previous studies show that recent stock performance strongly
affects firms’ issuance and repurchase decisions (Stephens and Weisbach, 1998; Korajczyk
and Levy, 2003); thus I also include past quarter stock returns following Stephens and
Weisbach (1998). The second set of proxies isolate the credit premium and the term
premium (based on nearest-maturity Treasuries) of each individual bond; the average
credit premium and term premium on firm bonds are then used to proxy for firm-level debt
valuations. The third set of proxies again are firm-level “best forecasters” for expected
future bond and stock excess returns.21
21
At the firm level, the predicted excess return is the fitted value from a regression of future 12-month
0
excess returns of the form: rx12
it = α + Xit β + it . Xit includes firm-level credit spread and term spread
in predicting future bond returns, and book-to-market ratio and past quarter stock returns in predicting
26
Firm-level controls are also similar to those at the aggregate level, which include cash
holdings as of the end of the previous quarter, current profits and capital expenditures,
and the output gap. In addition, I add some firm-specific controls. I address adjustments
driven by deviations from target capital structure by controlling for firms’ ex ante distance to target leverage estimated following Fama and French (2002), which incorporates
elements of both the pecking order theory and the trade-off theory.22 I also control for
past year asset growth as a proxy for expansion tendency, as well as firm size as of the
end of the previous quarter. Because Compustat data are not seasonally adjusted (unlike
aggregate Flow of Funds data), I include quarter-of-year dummies (i.e. first quarter, second quarter, etc.) in all firm-level regressions. Lastly, I include firm fixed effects to focus
on the behavior of a given firm under different market conditions, and standard errors
are clustered by both firm and time.
Table 4 reports firm-level results. Net equity repurchases and net debt issuance both
increase when bond spreads and bond premia are low, and when prior stock returns are
low, consistent with predictions; the response to the book-to-market ratio is weaker and
sometimes ambiguous. Net equity repurchases and net debt issuance also increase when
the predicted future firm-level bond returns are low, and when the predicted future firmlevel equity returns are high.23 The coefficients in regressions of net equity repurchases
and net debt issuance are mostly close in magnitude. The bottom of these panels presents
future equity returns.
22
The estimation procedure is the same as equation (8) in Fama and French (2002), except on the
right-hand-side I do not include the target payout ratio, since I do not jointly estimate target payout
with target leverage. In addition, I also include the firm-level distance to insolvency as computed in
Atkeson, Eisfeldt, and Weill (2014) as a better proxy for expected distress costs.
23
Korajczyk and Levy (2003) perform an analysis where they select all firms that are issuers of
either debt or equity, and regress the probability of the issuance being debt rather than equity on
aggregate credit spread, term spread, and recent stock returns. They show an interesting finding that
the probability of debt issuance relative to equity issuance is decreasing in aggregate credit spread, term
spread, and recent stock returns. However, their finding does not directly reveal whether firms act as
cross-market arbitrageurs. For example, the result on the relative likelihood of debt versus equity issues
could obtain even if debt issuance only responds to debt market conditions and equity issuance to equity
market conditions. It is also not immediately clear if there are negatively-correlated financing flows
across markets.
27
variance decompositions. Similar to the aggregate evidence, firm-level results again show
that financing activities in each market are affected by conditions in both debt and equity
markets, and the influence of these two markets is largely distinct. In addition, as before,
valuation variables and controls roughly split the variance shares.24
B. Cross-Market Arbitrage by Firm Type
The model in Section 3 predicts that cross-market corporate arbitrage would be more
prevalent among unconstrained and strong-balance-sheet firms. In particular, these firms’
financing activities in a given market will lean more strongly against valuations in other
markets: their net equity repurchases should increase by more in credit booms, and net
debt issuance decrease by more when the stock market performs especially well. I test this
proposition in Table 5. I group firms by four relevant characteristics, repeat the analysis
in Table 4, and report the sensitivity of financing activities to valuations in other markets
for each group of firms. Firm groups are formed based on size, profitability, recent capital
expenditures growth (as a proxy for untapped investment opportunities), and the fourvariable KZ index from Baker, Stein, and Wurgler (2003b) (I use the four-variable KZ
excluding the term with Q because Q is highly correlated with equity valuations by
construction).
The evidence suggests that net equity repurchases are more sensitive to credit market
conditions, and net debt issuance more sensitive to equity market conditions, among large
firms and those that are more likely to be unconstrained (firms with better cash flows,
slower recent growth of capital expenditures, and lower KZ index values). The difference
is more pronounced when the sample is split by firm size and profitability, and weaker (but
24
The decomposition for firm-level regressions is similar to that in Korajczyk and Levy (2003). The
PI
debt variance share is computed as v(debt) = Var(debt)/Var(total), where debt = (1/I) i=1 βD XD,i ,
PI
PI
equity = (1/I) i=1 βE XE,i , other = (1/I) i γZi , and total = debt + equity + other. i is the
firm index and I is the total number of firms. For v(debt⊥ ), I first orthogonalize βD XD,it on XE,it ,
then take debt⊥ to be the cross-sectional average of the residuals, and finally calculate v(debt⊥ ) =
Var(debt⊥ )/Var(total⊥ ). The variance shares of equity valuations and controls are defined analogously.
28
in the predicted direction) when the sample is split by capital expenditures growth and
the KZ index. Note that relative to the average firm in Compustat, companies in TRACE
already tend to be much larger, more profitable, and less constrained. Nontheless, among
these firms there still appear to be some differences in their propensity to engage in
cross-market arbitrage, and the overall results are in line with predictions of the model.
C. The Simultaneity of Issuance and Repurchases
The baseline regressions present evidence that financing decisions respond strongly to
valuations across debt and equity markets in ways consistent with cross-market corporate
arbitrage. To make the arbitrage behavior more explicit, it is ideal to show the simultaneous occurrence of financing activities in debt and equity markets in opposite directions.
In previous tests, this comovement is implied by the fact that both net equity repurchases
and net debt issuance respond to valuation variables with the same sign, and mostly with
similar magnitudes. In the following, I take an alternative approach to identify the arbitrage behavior and the comovements. To do so, I define two dummy variables. The first
dummy variable equals one when the firm has both abnormally high net equity repurchases and abnormally high net debt issuance, where “abnormal” is defined as deviating
from the firm’s average quarterly level (over all post-1985 quarters) by one percent of
asset; that is, the variable takes the form I1,it = 1{Sit > S̄i + 0.01, Dit > D̄i + 0.01},
where Sit (Dit ) is the quarterly net equity repurchases (net debt issuance) normalized
by assets.25 The second dummy variable equals one when the firm has both abnormally
low net equity repurchases (or equivalently abnormally high net equity issuance) and
25
I compare Sit and Dit to their average level because, as discussed in Section 3, firms could have
non-zero average issuance or repurchases due to other reasons. For instance, some large firms routinely
repurchase equity as a form of payout (e.g. firms like Coca Cola, Dell, Gap, Kimberly-Clark, McDonald’s, Pfizer, etc. have high average equity repurchases). Suppose in a given quarter, keeping regular
repurchases unchanged, they issue debt to fund corporate projects. Without comparing Sit to S̄i , this
quarter would be mistakenly categorized as firms deliberately issuing debt to repurchase equity. In comparison, having both Sit and Dit deviate from their regular level may be a better way to capture firms
deliberating issuing some securities to retire others. However, for more than 80% of firms in my sample,
S̄i and D̄i are less than 0.005 (i.e. 0.5% of total assets) and have little impact.
29
abnormally low net debt issuance (or equivalently abnormally high net debt reductions):
I2,it = 1{Sit < S̄i − 0.01, Dit < D̄i − 0.01}.
Table 6 presents firm-level logit regressions of the form:
P (Iit = 1|XD,it−1 , XE,it−1 , Zit ) = Φ(βD XD,it−1 + βE XE,it−1 + γZit )
(9)
for Iit = I1,it and Iit = I2,it . The control variables (Zit ) are identical to those in firm-level
panel regressions in Table 4. Logit regressions with firm fixed effects are used (the number
of observations in Table 6 is smaller than that in the full TRACE sample because fixed
effects logit only uses firms whose outcome variable is not always equal to zero or one).
Table 6 Panel A shows that the first type of behavior, namely replacing equity with
debt, is more likely to happen when the cost of debt is especially low (e.g. bond spreads
and expected returns are low), and when the cost of equity is relatively high (e.g. poor
recent stock performance and high expected returns on equity). Table 6 Panel B shows
that the second type of behavior, namely replacing debt with equity, is more likely to occur
when the cost of debt is high (e.g. bond spreads and expected returns are high), and when
the cost of equity is low (e.g. good recent stock performance and low expected returns on
equity). These results complement the baseline tests and provide further evidence that
firms arbitrage across debt and equity markets in response to relative valuations.
5.3
Forecasting Regressions
Previous tests show the relationship between financing activities and capital market
conditions using ex ante valuation proxies. A complimentary approach is to assess valuations through future securities returns, as overvalued securities tend to have particularly
low returns going forward. In this section, I study how financing activities connect to
future returns across markets.
30
To take a first look, I examine the extent to which equity financing is driven by
relative valuation shocks in the debt market by connecting net equity repurchases to
future debt returns. Cross-market corporate arbitrage predicts that an increase in net
equity repurchases not explained by equity market conditions tends to be associated
with overvalued credit, and correspondingly would forecast low future debt returns. To
tease out variations in net equity repurchases driven by equity valuations and isolate the
influence of credit market conditions, I control for future returns of firm equity; I can
alternatively control for ex ante proxies of equity valuations and results are very similar.
Analogously, I test how debt financing is related to future equity returns, controlling for
future debt returns. Specifically, I test:
12
rx12
D,it = α1 + β1 Sit + ζ1 rxE,it + γ1 Zit + uit
(10)
12
rx12
E,it = α2 + β2 Dit + ζ2 rxD,it + γ2 Zit + vit
(11)
12
where rx12
D,it (rxE,it ) is returns on firm debt (equity) in excess of the risk free rate in the
twelve months following the end of quarter t. Results are almost the same using raw
returns instead of excess returns. As before, Sit (Dit ) is quarterly net equity repurchases
(net debt issuance) and Zit is the same set of controls as those in Table 4.
Table 7 reports the forecasting results. Panel A shows that higher net equity repurchases are on average associated with lower future bond returns, as well as future
increases in bond spreads, controlling for future stock returns. To better disentangle the
part of future returns that comes from corrections to non-fundamental shocks, in columns
(2) and (4) I control for future corporate profitability and output gap. Symmetrically,
Panel B shows that higher net debt issuance tends to be associated with an upward correction in equity prices, controlling for bond market conditions.26 Together, the results
26
Spiess and Affleck-Graves (1999) find evidence of long-run stock under-performance following debt
31
provide further evidence that financing activities in each market appear to be influenced
by independent variations of valuations in other markets.
Then, I test how firm actions connect to future relative returns of debt and equity
against the integrated markets benchmark. To do so, I adopt the framework of the
“capital structure arbitrage” trading strategy, a common strategy private arbitrageurs
use to exploit discrepancies between debt and equity pricing when markets are segmented.
Specifically, this strategy takes opposite positions in a firm’s debt and equity, weighting
the equity position by a hedge ratio h = (DV /EV )(E/D), where V , E, D represent the
market value, equity value, and debt value of the firm respectively. The hedge ratio
proxies for the sensitivity of debt returns to equity returns when markets are integrated.
If there are indeed pricing discrepancies, then realized debt returns would deviate from
hedge-ratio weighted equity returns. As shown by Schaefer and Strebulaev (2008), hedge
ratios implied by a simple Merton model can properly account for returns on corporate
12
12
+it ,
= α+βhit rE,it
bonds and stocks on average (in the sense that in a regression like rD,it
the null hypothesis β = 1 cannot be rejected), although models of credit risk often fail to
match the level of credit spreads. Thus I also compute the hedge ratio using the Merton
model following Schaefer and Strebulaev (2008). Panel A column (1) in Table 8 confirms
12
12
= α + βhit rE,it
+ it , β is close to one.
that in the simple regression rD,it
In the rest of Table 8 Panel A, I test forecasting regressions of the form:
12
12
= α + βhit rE,it
+ λ1 1{Sit > S̄i + 0.01, Dit > D̄i + 0.01}
rD,it
|
{z
}
I1,it
(12)
+λ2 1{Sit < S̄i − 0.01, Dit < D̄i − 0.01} +uit
|
{z
}
I2,it
issuance. In particular, they find this phenomenon to be concentrated in small, young, NASDAQ-listed
firms. This is consistent with the prediction that small firms are unlikely to be cross-market arbitrageurs,
and they would take every opportunity to issue securities. In contrast, firms in the TRACE sample are
predominantly large and mature firms which are more representative of firms that act as cross-market
arbitrageurs. For these firms, conditional on a give level of debt pricing, they reduce net debt issuance
if equity is more overpriced and vice versa.
32
In Panel B, I impose the restriction that β is equal to one and test:
12
12
= α + λ1 1{Sit > S̄i + 0.01, Dit > D̄i + 0.01}
− hit rE,it
rD,it
|
{z
}
I1,it
(13)
+λ2 1{Sit < S̄i − 0.01, Dit < D̄i − 0.01} +vit
|
{z
}
I2,it
These regressions examine whether when we observe cross-market arbitrage by firms,
there tend to be larger misalignments between the pricing of their debt and equity. The
results show that when firms replace equity with debt (I1,it = 1), future debt returns tend
to be lower than hedge-ratio weighted equity returns, indicating that debt is relatively
more overvalued ex ante. When firms replace debt with equity (I2,it = 1), future debt
returns tend to be higher than hedge-ratio weighted equity returns, indicating that equity
is relatively more overvalued ex ante, though the effect is weaker. These findings provide
further evidence that firms actively exploit asynchronized pricing dynamics in imperfectly
integrated capital markets. They step in precisely when private arbitrage is incomplete.
In this way, firms can play an interesting role in helping to integrate partially segmented
securities markets. Nevertheless, it appears that firms’ arbitrage is still imperfect; otherwise, future returns in equations (12) and (13) would not be predictable. Firms do not
eliminate pricing discrepancies all the way as their arbitrage is not costless (due to capital
structure constraints, transaction costs, etc.). Thus, they require to earn a premium from
the arbitrage, leaving behind some residual predictability of future returns.
33
6
Discussion
6.1
Alternative Interpretations
In this section, I address alternative interpretations of the main results. Specifically,
there could be concerns that firms’ issuance and repurchases vary over time due to other
reasons, which happen to comove with the relative returns of debt and equity. While
the analysis in Section 5 already considers a set of issues through controls and different
empirical strategies, I perform more robustness checks below to further distinguish crossmarket corporate arbitrage from other theories.
A. Dynamic Capital Structure Adjustments
In the framework of dynamic capital structure theories, firms have time-varying optimal target leverage due to changes in economic conditions and default probabilities, which
can affect their financing decisions. For example, when economic conditions improve and
bankruptcy risks decrease, optimal leverage would increase. To move towards this higher
optimal leverage, firms might issue debt and repurchase equity. It is less straightforward,
however, why this would coincide with ex ante expected returns on corporate debt being
particularly low relative to expected returns on corporate equity.
Furthermore, one feature of dynamic capital structure models is that leverage adjustments should be optimal within the considerations of the models. For instance, when
firms optimally lever up due to a decrease in asset growth volatility in the classical model
of Leland (1994), future default rates should be lower (shown in the supplementary appendix). Bhamra, Kuehn, and Strebulaev (2010) consider optimal financing decisions
in a much richer dynamic capital structure model with transitions between good and
bad macroeconomic states, as well as stochastic cash flow growth and volatility. In their
model, firms’ optimal leverage increases in good states. Nonetheless, they show that con34
ditioning on current leverage, macroeconomic conditions and financing decisions should
not be correlated with future default rates.
Table A1 examines the empirical relationship between financing activities and future
default rates. Because default rates are only available at annual frequencies, I also aggregate net equity repurchases and net debt issuance by year, and use the financing variable
in year t to forecast default rates in year t + 1 and year t + 2. Table A1 shows that
an increase in either net equity repurchases or net debt issuance forecasts higher future
default rates as well as future increases in default rates, even after controlling for current
leverage. The results indicate that firms are possibly levering up for reasons other than
classic dynamic capital structure considerations. Greenwood and Hanson (2013) show
that aggregate default rates tend to be high following periods of credit overvaluation.
Thus, one interpretation of the evidence is that firms increase net equity repurchases and
net debt issuance to exploit the particularly low cost of credit.
In addition, Bhamra et al. (2010) point out that, in a dynamic capital structure
model, changes in underlying economic conditions are important for the target leverage
of constrained firms, but almost irrelevant for that of unconstrained firms. However, as
shown in Section 5, my results are stronger among large and unconstrained firms, which
is more consistent with predictions of cross-market corporate arbitrage.
B. Time-Varying Borrowing Constraints
One version of the pecking order theory postulates that firms always prefer debt to
equity in their capital structure, but there can be time-varying borrowing constraints
that limit firms’ ability to have as much debt as they want to. Thus when borrowing
constraints loosen, firms may want to replace equity with debt. Jermann and Quadrini
(2012) use this idea to develop a model with stochastic borrowing capacity and suggest
that it can explain firms’ issuance and investment activities. Begenau and Salomao (2015)
35
extend the work of Jermann and Quadrini (2012) by endogenizing different borrowing
capacities for small and large firms. In these models, corporate securities have constant
required returns. Thus, taken at face value, they cannot account for the evidence of crossmarket corporate arbitrage where firms exploit pricing discrepancies between different
securities as reflected by their expected returns and actual returns.
Nonetheless, there could be alternative narratives that relax the assumption of constant required returns, and it is worth checking that my results are not simply driven
by variations in borrowing constraints. For example, if firms have time-varying collateral
value, it is possible that borrowing capacity increases and required returns on debt decrease when the collateral value is high. It is also possible that the strength of financial
institutions’ balance sheet is time-varying: when banks are in weaker conditions, they
cut back on lending and they also charge particularly high risk premia on loans. Alternatively, it might be that when expected future cash flows are high, borrowing constraints
loosen (due to an increase in pledgeable income or lower expected default), and creditors
demand very low risk premia.
In Table A2, I follow standard specifications and address the concern of time-varying
collateral value by controlling for the value of tangible assets (plants, real estate, equipments, and inventories). I also address the concern of time-varying bank lending capacity
by controlling for the fraction of loan officers reporting tightening lending standards for
commercial and industrial loans from the Federal Reserve’s Senior Loan Officer Opinion
Survey. In all specifications, as in the analysis in Section 5, I include controls of business
cycles, current profitability, etc. The additional controls do not affect the main results
(and the fraction of loan officers tightening even sometimes comes in with the wrong
sign). Finally, in Table A3, I examine how financing activities forecast future cash flows,
and find no evidence that financing decisions can be explained by (rationally anticipated)
36
cash flow prospects.
C. Time-Varying Agency Problems
Agency theories of corporate finance point out that managers sometimes divert firms’
funds to self-interested projects, such as empire building. Because debt requires firms
to make periodic payments, it can decrease the free cash flows that managers have at
their disposal (Jensen and Meckling, 1976; Jensen, 1986). It then follows that firms
may optimally lever up when they anticipate good future cash flows. However, Table
A3 shows that future profitability is not significantly higher following an increase in net
debt issuance or net equity repurchases. The evidence suggests that optimal financial
structure adjustments driven by (rationally) expected cash flows do not seem to account
for the empirical patterns.
One might also think that managers may derive personal benefits, for instance, from
share repurchases as their compensation is tied to nominal share prices or to earnings
per share (EPS). While this type of considerations can increase firms’ propensity to
repurchase shares in general, it does not directly imply that net equity repurchases, as
well as net debt issuance, would vary with the relative valuation of debt and equity.27
However, it is worth checking that other incentives for share repurchases do not happen
to comove with the relative valuation of debt and equity. For example, there are time
variations in the dilutive effect of employee stock options which can influence managers’
equity repurchase decisions. I address this and related issues below.
D. Employee Stock Option Exercises and EPS Management
For firms that use option-based compensation, equity issuance and repurchases could
be affected by employee stock options. For example, employees exercising stock options
27
If we include in the model of Section 3 a term γv(S) that represents managers’ personal preferences
for net equity repurchases, it turns out that its impact on S 0 (δD ) and D0 (δE ) depends on v 00 (S). If
v 00 (S) = 0, then v(S) only contributes to the average level of net equity repurchases and net debt
issuance, but not their sensitivity to valuation shocks.
37
leads to equity issuance and increases shares outstanding. In response to the dilutive
impact of employee stock options, firms also sometimes repurchase shares. I perform
detailed checks to assess the potential impact of these issues.
First, the magnitude of employee stock option exercises appears small relative to
firms’ net equity repurchases. In aggregate, the market value of shares exercised through
employee stock options (which is an upper bound on the impact of employee stock option
exercises) is less than 0.1% of total firm assets, and less than 0.05% post dot-com boom.
In comparison, corporate net equity repurchases are sometimes more than ten times as
large. Second, in Table A4 I repeat regressions in Section 5 controlling for the amount of
employee stock option exercises (either in terms of the number of shares exercised relative
to total shares outstanding, or in terms of the market value of shares exercised normalized
by firm assets). In addition, some evidence suggests that firms manage diluted EPS, and
repurchase shares when option-based compensation has a significant impact on diluted
EPS (Bens, Nagar, Skinner, and Wong, 2003; Brav et al., 2005), even if the employee
stock options are not yet exercised. Thus, in Table A4 I also include controls for the
dilutive effect of outstanding employee stock options. This test helps to check whether
there is particularly serious option dilution putting pressure on managers to repurchase
shares (and possibly finance it with debt) which happens to coincide with debt and equity
market conditions. In all cases, the results are not affected by the additional controls.
In sum, I do not find that issues related to employee stock options influence my results.
In principle, it seems hard to think of reasons why either employee stock option exercises
or option dilution might be correlated with debt valuations in a way that makes equity
financing activities display patterns of cross-market corporate arbitrage. In unreported
results, I also examine several other possible motives for EPS management, such as recent
EPS growth and missing analyst forecasts (Almeida, Fos, and Kronlund, 2015). A priori,
38
it does not appear that these motives have strong reasons to coincide with capital market
conditions and can mechanically generate patterns of cross-market corporate arbitrage;
nor do I find that they affect my results.
Taken together, the alternative explanations do not seem to account for the evidence
of cross-market corporate arbitrage. Of course, firms may substitute equity with debt
and vice versa due to other considerations such as adjustments towards target leverage.
Nevertheless, findings in Section 5 and extensive robustness checks above suggest that a
significant fraction of these substitutions appears to be driven by firms actively exploiting variations in the relative valuation of debt and equity and acting as cross-market
arbitrageurs. In the following, I will discuss additional results which further extend the
evidence and, moreover, speak to the broader implications of cross-market corporate
arbitrage.
6.2
The Impact of Time-Varying Government Bond Supply
This section explores how firms respond to one particular type of supply shocks in the
bond market. It complements the analysis in Section 5 and provides further evidence for
firms’ reaction to market-specific pricing shocks in imperfectly integrated capital markets.
It also sheds light on one of the broader implications of firms’ cross-market arbitrage.
A growing number of studies document that bond markets experience price pressure
due to the time-varying supply of government bonds. Specifically, when markets are
partially segmented and bonds are mostly held by specialist investors, the demand for
bonds may not be perfectly elastic. In this case, an increase in the supply of government
bonds tends to raise the excess returns investors demand for holding bonds (Greenwood
and Vayanos, 2014), leading to a higher cost for firms to finance through corporate bonds.
As a result, when an increase in the supply of government bonds raises the cost of
39
corporate debt, firms might reduce debt and shift towards equity financing. Similarly,
when bond premia decrease as government bond supply declines, firms can take advantage
by issuing more debt and reducing equity capital.
Table 9 examines the empirical relationship between long-term government bond issuance, and corporate net debt issuance and net equity repurchases. I compute long-term
government bond issuance as the change in outstanding Treasury notes and Treasury
bonds, excluding those held by monetary authorities and foreigners (such as sovereigns
like Japan and China), normalized by quarterly GDP. The exclusion is meant to approximately capture the amount of long-term Treasuries that have to be held by active private
investors.28 In addition, this analysis focuses on the post-1985 period, when firms are allowed to repurchase and issue relatively freely in both equity and debt markets. Column
(1) shows the results for all non-financial firms: holding equity valuations and other control variables constant, net equity repurchases decrease when government bond issuance
increases, and net corporate debt issuance decreases as well.29 A decomposition by firm
size in columns (2) and (3) shows that the effect is concentrated in large firms, consistent
with previous results that large firms act more readily as cross-market arbitrageurs.
In recent years, there are concerns that the Federal Reserve’s interventions in the
bond market, such as large-scale bond purchases through Quantitative Easing, have resulted in especially low bond risk premia and have contributed to a wave of debt-financed
equity repurchases (e.g. Stein (2012)). An extremely preliminary back-of-the-envelope
calculation using results in Table 9 suggests that, all else equal, purchases of long-term
bonds on the magnitude of $85 billion per month (which is the maximum size of monthly
28
Directly using the change in total outstanding public debt and normalizing by total corporate
assets, as in Graham et al. (2015), produces very similar results. Long-term government bonds can be
particularly relevant as they account for the majority of duration risks from government bond supply,
but in practice long-term government bond supply and total government debt are highly correlated.
29
The negative relationship between government bond supply and corporate net equity repurchases
does not exist pre-1980 when firms face strict equity repurchase restrictions. Nor does it exist in a long
time series predominantly affected by the pre-1980 subsample, consistent with Graham et al. (2015).
40
purchases during QE3) could lead to an annual increase in net equity repurchases of
about 0.8 standard deviations, or 0.6% of total corporate assets (roughly 1.2% of annual GDP).30 Results in this section, along with those in Section 5, show it is plausible
that macroeconomic policies which disproportionately affect the bond market could come
with an unintended consequence of spurring firms’ cross-market arbitrage, as opposed to
increasing real investment. This is particularly likely to happen when firms do not perceive many profitable investment opportunities. In this way, firms’ cross-market arbitrage
can have implications for the transmission and effectiveness of unconventional monetary
policies, which could be worth further research.
6.3
Cross-Market Corporate Arbitrage and Merger Dynamics
Finally, I show that the idea of cross-market corporate arbitrage also sheds light on
aggregate merger and acquisition dynamics. Results in the main analysis primarily focus on firms’ arbitrage through issuance and repurchases of their own securities. Another
way in which firms can arbitrage across debt and equity markets is through debt-financed
cash mergers, which allow firms to exploit market-wide differences between the pricing
of debt and equity by issuing debt to buy other firms’ equity. Specifically, when credit
markets are a very cheap source of funding and the overall level of equity prices is not
too high in comparison, firms may find it appealing to issue debt and initiate cash mergers. Conversely, when equity markets are especially overvalued, stock mergers come into
fashion (Andrade, Mitchell, and Stafford, 2001; Shleifer and Vishny, 2003; Rhodes-Kropf,
Robinson, and Viswanathan, 2005; Dong, Hirshleifer, Richardson, and Teoh, 2006).
30
$85 billion per month translates into $255 billion per quarter, and $255 billion is about 6.4% of US
quarterly GDP. 0.064 × 0.0232 × 4 ≈ 0.6% (the calculation has “×4” because the left-hand-side variable
in Table 9 is quarterly net equity repurchase/asset), and total corporate assets is about twice the size of
US annual GDP. This estimate is also largely consistent with results in Table 3. If bond purchases of this
size affect the term premium by 120 basis points, which is plausible given Fed economists’ estimates of
the impact of large-scale asset purchases (LSAPs) (see references in Stein (2012)), then results in Table 3
show that, all else equal, annual net equity repurchase/asset will increase by 0.012 × 0.108 × 4 ≈ 0.52%.
41
In Figure 5, I plot aggregate merger activities of US non-financial firms.31 Panel
A plots the value of stock mergers, which had an extraordinary surge during the doccom boom when equity valuations reached historical heights. Meanwhile, Panel B shows
that cash mergers track net debt issuance very closely, just like net equity repurchases.
(However, the value of cash mergers is generally less than a third of net debt issuance, and
I check that results in previous sections are not simply driven by acquisitions; previous
results all hold if we control for cash mergers or subtract them out from net debt issuance.)
Table 10 presents regressions of how aggregate merger activities connect to valuations
across debt and equity markets. I look at both the value of cash mergers (normalized by
total firm assets), and the fraction of total mergers paid by cash. Results show that the
amount of cash mergers, both in normalized values and as a fraction of total mergers,
increases when debt is a particularly cheap source of funding (i.e. bond spreads and
premia are especially low and expected bond returns are low), and decreases when equity
valuations are elevated (i.e. stocks have low expected returns and E/P10 is low).
These findings add to existing theories and evidence on market valuations and merger
waves. They show that aggregate merger dynamics are significantly influenced by the
relative valuation of debt and equity, as firms look across different markets when making
decisions about initiating mergers and about forms of payment. These results also provide
broader evidence that non-financial firms act as active cross-market arbitrageurs. Crossmarket corporate arbitrage takes multiple forms, and this perspective could be useful for
understanding a set of corporate activities.32
31
Merger data come from SDC Platinum. I include all completed mergers of US targets by US
acquirers, excluding acquirers that are financial or utility firms. I restrict to US firms because my analysis
focuses on valuation dynamics in US capital markets. Aggregate stock (cash) merger is calculated by
summing over deal value times percentage stock (cash) across all deals in a given time period.
32
The evidence also suggests that large non-financial firms seem to behave similarly to private equity
firms studied in Axelson, Jenkinson, Stromberg, and Weisbach (2013) who borrow aggressively to fund
buyouts when the cost of credit is particularly low. Of course, even large and unconstrained non-financial
corporations may not be as aggressive as private equity firms, and the median non-financial firm would
be much less likely to mimic private equity investors.
42
7
Conclusion
In this paper I present evidence that non-financial corporations act as cross-market
arbitrageurs in their debt and equity securities. Firms jointly time multiple markets and
actively issue some securities to replace others in response to relative valuations, inducing
strongly negatively correlated financing flows across markets. Aggregate and firm-level
results show that net equity repurchases and net debt issuance both increase when the
expected returns on debt are especially low, and when the expected returns on equity are
relatively high. In particular, financing decisions in a given market are heavily influenced
by pricing dynamics in other markets: credit valuations affect equity financing as much
as equity valuations do, and vice versa. Cross-market corporate arbitrage appears to
account for an important fraction of financing flows both in the aggregate and at the firm
level. More broadly, it sheds further light on merger dynamics, and may have implications
for unconventional monetary policies.
Additionally, to the extent that firms actively exploit pricing discrepancies in partially
segmented capital markets, shifting the supply of cash flow risks from markets that require
higher compensation to markets that require lower compensation, they can play a role
in integrating dispersed markets. Future research may shed further light on how firm
actions influence asset prices across markets. The evidence on cross-market corporate
arbitrage also suggests that non-financial firms are a very active force in financial market
activities. Recent research documents that a set of firms even go so far as to take large
speculative positions in a variety of financial instruments (such as other firms’ corporate
bonds, MBS, ABS, sovereign debt, etc.) under the name of cash holding (Duchin, Gilbert,
Harford, and Hrdlicka, 2014). Future research on the diverse forms of financial activities
by non-financial corporations can provide new insights into the role of firms in capital
markets and its implications for efficiency and stability.
43
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47
A
Figures
Figure 2: Aggregate Net Equity Repurchases and Net Debt Issuance
These figures plot quarterly aggregate net equity repurchases (solid line) and net debt issuance (dashed
line) by all non-financial corporate businesses, normalized by their total assets. The series are taken
from the Flow of Funds. Panel A is post-1980 and Panel B is pre-1980. Levels are quarterly rates.
-.2
0
% of Total Asset
.4
.6
.2
.8
Panel A. Post-1980
1980
1990
2000
2010
Time
Net Equity Repurchase/Asset
Net Debt Issuance/Asset
-.5
% of Total Asset
0
.5
1
Panel B. Pre-1980
1950
1960
1970
Time
Net Equity Repurchase/Asset
Net Debt Issuance/Asset
48
1980
Figure 3: Simultaneous Issuance and Repurchases Across Markets
.8
.6
.4
.2
0
0
.2
.4
.6
.8
Plot (a) shows the fraction of total equity repurchases (by value) that are done by firms which are also net issuing debt in the
current or previous quarter. (Debt issuance in both the current and previous quarter is considered because contemporaneous
issuance and repurchases are not always done precisely in the same quarter, as shown by the example of Intel in Table 1.)
Plot (b) shows the fraction of total equity issuance done by firms which are also net retiring debt in the current or previous
quarter. Plot (c) shows the fraction of total debt issuance done by firms which are also net repurchasing equity in the current
or previous quarter. Plot (d) shows the fraction of total debt reductions done by firms which are also net issuing equity in
the current or previous quarter. Issuance and repurchases are restricted to those that are greater than 1% of assets in net
terms in a given quarter. Equity issuance does not include IPOs. Debt is restricted to long-term debt.
1985
1990
1995
2000
Time
2005
2010
1985
1995
2000
Time
2005
2010
.6
.4
.2
0
0
.2
.4
.6
.8
(b) Fraction of Equity Issued by Debt Repurchasers
.8
(a) Fraction of Equity Repurchased by Debt Issuers
1990
1985
1990
1995
2000
Time
2005
2010
1985
(c) Fraction of Debt Issued by Equity Repurchasers
1990
1995
2000
Time
2005
2010
(d) Fraction of Debt Repurchased by Equity Issuers
49
Figure 4: Financing Activities by Small and Large Firms
These figures plot total net equity issuance (solid line) and net debt issuance (dashed line) by small and
large firms in the Compustat universe, normalized by total assets of each group. Small firms are defined
as those with book assets less than the median in the Compustat cross section. Large firms are those
with book assets above the median. Levels are quarterly rates.
-2
0
% of Total Asset
2
4
6
8
Panel A. Small Compustat Firms
1985
1990
1995
2000
Time
2005
2010
Net Equity Issuance/Asset
Net Debt Issuance/Asset
-1
-.5
% of Total Asset
0
.5
1
1.5
Panel B. Large Compustat Firms
1985
1990
1995
2000
Time
2005
Net Equity Issuance/Asset
Net Debt Issuance/Asset
50
2010
Figure 5: Aggregate Dynamics of Mergers and Acquisitions
Panel A shows the total value of quarterly mergers paid with stock, and Panel B shows the total value of
quarterly mergers paid with cash. Only completed deals of US targets by US acquirers in non-financial,
non-utilities industries are included. The aggregate value of stock mergers in Panel A is the sum of
merger value times percentage paid by stock, and the aggregate value of cash mergers in Panel B is the
sum of merger value times percentage paid by cash. Both are divided by total non-financial firm assets
from the Flow of Funds. Levels are quarterly rates.
0
% of Total Asset
.5
1
1.5
Panel A. Aggregate Stock Mergers
1985q1
1990q1
1995q1
2000q1
Time
2005q1
2010q1
0
-.2
.1
.2
0
.4
.6
Net Debt Issuance/Asset
Cash Merger/Asset
.2
.4
.3
.8
.5
Panel B. Aggregate Cash Mergers
1985q1
1990q1
1995q1
2000q1
Time
2005q1
Cash Merger/Asset
Net Debt Issuance/Asset
51
2010q1
B
Tables
Table 1: Example: Intel (Compustat Records)
This table shows quarterly net debt issuance and net equity repurchases by Intel from 2011 to 2012, as recorded
in Compustat. The unit is one million dollars.
Year-Quarter
Net Debt Issuance
Net Equity Repurchases
Assets
2011Q1
2011Q2
2011Q3
2011Q4
2012Q1
2012Q2
2012Q3
2012Q4
0
0
4,962
0
0
0
0
5,999
3,767
1,826
3,669
3,033
275
959
881
884
65,552
66,089
70,551
71,119
71,817
72,352
74,441
84,351
Table 2: Summary Statistics
Summary statistics for firms covered in firm-level analysis. Mean, median, standard deviation, and selected
percentiles are presented. The TRACE sample is my main sample. For comparability, the statistics for the
Compustat sample are presented based on the same time period as the TRACE sample (2003Q1 to 2012Q4).
Mean
Std. Dev.
Credit spread
Term spread
Book-to-market ratio
Book asset
Market capitalization
Cash/asset
Capx/asset
Net income/asset
Net equity repurchase/asset
Net debt issuance/asset
0.032
0.013
0.509
14,837.5
15,249.0
0.086
0.014
0.011
0.004
0.002
0.034
0.010
0.422
40,402.7
31,296.5
0.095
0.016
0.033
0.018
0.034
Book asset
Market capitalization
Cash/asset
Capx/asset
Net income/asset
Net equity repurchase/asset
Net debt issuance/asset
3,436.5
4,081.5
0.224
0.012
-0.006
-0.007
0.002
18,390.4
18,458.7
0.232
0.015
0.091
0.056
0.038
5%
25%
Median
75%
TRACE Sample
0.006
0.012
0.022
0.041
-0.003
0.005
0.013
0.020
0.091
0.262
0.417
0.646
704.5 2,247.0 5,418.4 13,946.0
317.9 1,650.1 4,667.9 14,508.2
0.005
0.024
0.057
0.116
0.002
0.005
0.009
0.017
-0.019
0.005
0.014
0.023
-0.004 -0.001
0.000
0.006
-0.038 -0.006
0.000
0.002
Full Compustat Sample
18.6
100.8
403.2
1,675.8
19.2
119.7
469.5
1,794.6
0.006
0.042
0.138
0.338
0.001
0.003
0.007
0.014
-0.122 -0.005
0.010
0.022
-0.022 -0.002
0.000
0.000
-0.035 -0.003
0.000
0.000
52
95%
N
0.081
0.031
1.214
49,579.0
63,528.5
0.266
0.048
0.040
0.030
0.052
10,866
10,866
10,866
10,866
10,866
10,866
10,866
10,866
10,866
10,866
13,510.5
15,124.9
0.728
0.039
0.048
0.024
0.047
79,894
79,894
79,894
79,894
79,894
79,894
79,894
Table 3: Aggregate Financing Activities and Capital Market Conditions
Time series regressions of financing activities on proxies for debt and equity valuations:
Dependent variablet = α + βD XD,t−1 + βE XE,t−1 + γZt + ut .
In columns (1) to (3), the dependent variable is net equity repurchases by all non-financial firms in quarter
t, normalized by their total assets. In columns (4) to (6), the dependent variable is net debt issuance by
all non-financial firms in quarter t, normalized by their total assets. XD and XE are proxies for valuations
in debt and equity markets: XD includes credit spread and term spread in columns (1) and (4), credit
premium and term premium in columns (2) and (5), and predicted next 12-month excess returns on high
yield corporate bonds in columns (3) and (6); XE uses E/P10 (inverse of P/E10) in columns (1), (2), (4)
and (5), and predicted next 12-month excess stock returns in columns (3) and (6). All values are measured
as of the end of quarter t − 1. Zt is a set of controls, including cash holdings by the end of quarter t − 1, capx
spending and corporate profits in quarter t, and the output gap as of the end of quarter t − 1. Quarterly
from 1985Q1 to 2012Q4. Standard errors are Newey-West with eight lags.
Credit spread
Term spread
Credit premium
Term premium
E/P10
Ê[rx12
D]
Ê[rx12
E]
Observations
R-squared
Panel A. No Controls
Net Equity Repurchases
(1)
(2)
(3)
-0.0238
[-2.56]
-0.0856
[-2.82]
-0.1166
[-3.65]
-0.1365
[-6.21]
0.0245
0.0663
[2.53]
[6.13]
-0.0152
[-3.83]
0.0087
[2.95]
112
112
112
0.255
0.416
0.291
Net Debt Issuance
(4)
(5)
(6)
-0.0435
[-5.35]
-0.1030
[-3.82]
-0.0772
[-1.76]
-0.1278
[-4.26]
0.0282
0.0371
[1.89]
[1.88]
-0.0214
[-5.23]
0.0113
[2.61]
112
112
112
0.406
0.325
0.465
Panel B. With Controls
Net Equity Repurchases
Net
(1)
(2)
(3)
(4)
Credit spread
-0.0270
-0.0227
[-2.83]
[-2.31]
Term spread
-0.1030
-0.0833
[-4.54]
[-2.61]
Credit premium
-0.0139
[-0.37]
Term premium
-0.1080
[-4.44]
E/P10
0.0603
0.0643
0.0661
[5.95]
[5.74]
[5.13]
12
Ê[rxD ]
-0.0173
[-5.51]
12
Ê[rxE ]
0.0135
[5.94]
Observations
112
112
112
112
R-squared
0.547
0.532
0.542
0.505
v(debt)
0.74
0.79
0.79
0.44
v(equity)
0.45
0.75
0.30
0.48
v(control)
0.67
1.04
0.50
0.52
v(debt+equity)
0.79
0.52
0.74
0.59
cov(debt+equity, control)
-0.23
-0.28
-0.12
-0.06
v(debt⊥ )
0.65
0.44
0.69
0.38
v(equity⊥ )
0.39
0.40
0.26
0.41
Newey-West t-statistics in brackets.
53
Debt Issuance
(5)
(6)
-0.0088
[-0.18]
-0.0860
[-2.55]
0.1106
[6.80]
112
0.546
0.51
1.20
1.40
0.68
-0.54
0.29
0.64
-0.0159
[-3.85]
0.0172
[4.99]
112
0.551
0.54
0.38
0.31
0.59
0.05
0.46
0.33
Table 4: Firm-Level Financing Activities and Capital Market Conditions
Firm-level panel regressions of financing activities on proxies for debt and equity valuations:
Dependent variableit = α + βD XD,it−1 + βE XE,it−1 + γZit + uit .
In columns (1) to (3), the dependent variable is firm-level net equity repurchases in quarter t, normalized by
firm assets. In columns (4) to (6), the dependent variable is firm-level net debt issuance in quarter t, normalized
by firm assets. XD,i and XE,i are proxies for debt and equity valuations at the firm level: XD,i includes average
credit spread and average term spread on firm bonds in columns (1) and (4), average credit premium and term
premium on firm bonds in columns (2) and (5), and predicted next 12-month excess returns on firm bonds in
columns (3) and (6); XE,i includes book-to-market ratio and stock returns in quarter t − 1 in columns (1), (2),
(4) and (5), and predicted next 12-month excess returns on firm equity in columns (3) and (6). All values are
measured as of the end of quarter t − 1. Zit is a set of controls, including cash holdings by the end of quarter
t − 1, capx and profits in quarter t, as well as estimated deviation from target leverage, log firm asset, past one
year asset growth, and the output gap by the end of quarter t − 1. Quarter-of-year dummies and firm fixed
effects are included. Quarterly from 2003Q1 to 2012Q4.
Panel A. No Controls
Net Equity Repurchases
Net Debt Issuance
(1)
(2)
(3)
(4)
(5)
(6)
Credit spread
-0.0574
-0.0596
[-6.57]
[-3.20]
Term spread
-0.1121
-0.1952
[-3.04]
[-3.49]
Credit premium
-0.0377
-0.0531
[-6.64]
[-2.47]
Term premium
-0.1089
-0.2361
[-2.57]
[-3.82]
Book-to-market ratio
0.0006
0.0011
-0.0019 -0.0017
[0.63]
[0.95]
[-1.23]
[-1.14]
Past quarter stock returns -0.0077 -0.0076
-0.0068 -0.0065
[-1.95]
[-1.93]
[-4.76]
[-4.67]
]
-0.0242
-0.0296
Ê[rx12
D
[-4.42]
[-4.20]
12
0.0133
0.0061
Ê[rxE ]
[2.09]
[1.28]
Observations
10,866
10,866
10,866
10,866
10,866
10,866
Panel B. With Controls
Net Equity Repurchases
Net Debt Issuance
(1)
(2)
(3)
(4)
(5)
(6)
Credit spread
-0.0556
-0.0495
[-6.25]
[-3.71]
Term spread
-0.1182
-0.1340
[-3.79]
[-1.82]
Credit premium
-0.0371
-0.0501
[-6.38]
[-3.30]
Term premium
-0.1140
-0.1398
[-3.04]
[-1.63]
Book-to-market ratio
0.0009
0.0013
0.0001
0.0001
[1.03]
[1.20]
[0.06]
[0.07]
Past quarter stock returns -0.0074 -0.0073
-0.0087 -0.0086
[-1.92]
[-1.90]
[-4.27]
[-4.21]
Ê[rx12
]
-0.0207
-0.0196
D
[-4.35]
[-3.26]
Ê[rx12
]
0.0136
0.0132
E
[2.32]
[2.32]
Observations
10,866
10,866
10,866
10,866
10,866
10,866
v(debt)
0.79
0.24
0.83
0.21
0.11
0.20
v(equity)
0.32
0.26
0.17
0.11
0.10
0.04
v(control)
0.07
0.62
0.24
0.64
0.69
0.74
v(debt+equity)
0.86
0.49
0.45
0.28
0.21
0.10
cov(debt+equity, control)
0.03
-0.06
0.15
0.04
0.05
0.08
v(debt⊥ )
0.51
0.18
0.42
0.13
0.08
0.10
v(equity⊥ )
0.22
0.17
0.04
0.08
0.07
0.01
t-statistics in brackets. Standard errors clustered by firm and time.
54
Table 5: Sensitivity of Financing Activities to Conditions in Other Markets:
Results by Firm Type
These tables report the sensitivity of financing activities in a given market to conditions in another
market for firms with different characteristics. Firms are sorted into bottom 30% and top 30% based on
their size (market value), profitability (net income/assets), past year capx growth, and the KZ index.
The groups are formed using firm characteristics by the end of quarter t − 1. In Panel A, the regression
in column (1) of Table 4 Panel A is estimated for each group of firms, and the coefficients on credit
spread and term spread are reported along with the respective t-statistics. In Panel B, the regression in
column (4) of Table 4 Panel A is estimated, and the coefficient on past stock returns is reported along
with the respective t-statistics. The results are bolded for groups expected to have stronger propensity
of cross-market corporate arbitrage (i.e. expected to have coefficients larger in magnitude). abs(dif) is
the absolute difference between the coefficient for the bottom 30% group and the coefficient for the top
30% group, and p-val is the associated p-value that the difference is statistically significant.
Panel A. Net Equity Repurchases and Credit Market Conditions
Coefficient on credit spread
Bottem 30%
Top 30%
Difference
Full sample
Size
Profitability
Capx growth
KZ index
b
[t]
-0.057
[-6.57]
-0.041
-0.043
-0.061
-0.128
[-2.82]
[-1.97]
[-3.11]
[-3.73]
b
[t]
abs(dif)
p-val
-0.201
-0.101
-0.016
-0.051
[-5.60]
[-3.44]
[-0.64]
[-1.96]
0.160
0.058
0.045
0.077
0.001
0.096
0.182
0.089
Coefficient on term spread
Bottem 30%
Top 30%
Difference
Full sample
Size
Profitability
Capx growth
KZ index
b
[t]
-0.112
[-3.04]
0.051
0.061
-0.120
-0.150
[0.94]
[0.85]
[-3.12]
[3.83]
b
[t]
abs(dif)
p-val
-0.121
-0.192
-0.100
0.055
[-2.75]
[-3.72]
[-1.75]
[1.04]
0.172
0.253
0.020
0.205
0.011
0.001
0.485
0.001
Panel B. Net Debt Issuance and Equity Market Conditions
Coefficient on past quarter stock returns
Bottem 30%
Top 30%
Difference
Full sample
Size
Profitability
Capx growth
KZ index
b
[t]
-0.007
[-4.76]
-0.002
-0.005
-0.009
-0.007
[-1.06]
[-1.84]
[-3.05]
[-1.94]
b
[t]
abs(dif)
p-val
-0.018
-0.010
-0.005
-0.004
[-5.54]
[-2.53]
[-1.22]
[-1.79]
0.016
0.006
0.004
0.003
0.001
0.080
0.182
0.376
55
Table 6: Simultaneous Financing Activities and Capital Market Conditions
Firm-level logit regressions of financing activities on proxies for debt and equity valuations:
P (Iit = 1|XD,it−1 , XE,it−1 , Zit ) = Φ(βD XD,it−1 + βE XE,it−1 + γZit ).
In Panel A, Iit = I1,it = 1{Sit > S̄i + 0.01, Dit > D̄i + 0.01}, where Sit and Dit are net equity repurchases and
net debt issuance in quarter t normalized by firm assets and S̄i and D̄i are their averages (averaged over all post1985 quarters where firm data are available in Compustat). In Panel B, Iit = I2,it = 1{Sit < S̄i − 0.01, Dit <
D̄i − 0.01}. XD,i , XE,i , and Zi are the same as those in Table 4. Logit regressions are estimated with firm fixed
effects (thus the number of observations is smaller than that in the baseline sample in Table 4). Quarterly from
2003Q1 to 2012Q4.
Panel A. Increase Debt and Reduce Equity: P (Sit > S̄i + 0.01, Dit > D̄i + 0.01)
Credit spread
Term spread
-8.498
[-2.66]
-23.54
[-4.24]
-7.302
[-2.05]
-24.72
[-3.58]
Credit premium
Term premium
Book-to-market ratio
Past quarter stock returns
-0.198
[-0.74]
-0.846
[-2.96]
0.0318
[0.12]
-0.720
[-2.46]
-7.300
[-2.10]
-32.80
[-4.53]
-0.112
[-0.41]
-0.837
[-2.87]
-6.465
[-1.74]
-30.11
[-3.69]
0.0921
[0.36]
-0.715
[-2.40]
Ê[rx12
D]
Ê[rx12
E]
Controls
Observations
No
Yes
No
5,738
5,738
5,738
t-statistics in brackets.
Yes
5,738
-4.503
[-5.59]
0.956
[1.33]
No
5,738
-3.690
[-3.48]
1.380
[1.93]
Yes
5,738
Panel B. Increase Equity and Reduce Debt: P (Sit < S̄i − 0.01, Dit < D̄i − 0.01)
Credit spread
Term spread
8.845
[3.46]
6.350
[0.63]
9.286
[3.24]
24.86
[1.97]
Credit premium
Term premium
Book-to-market ratio
Past quarter stock returns
-0.186
[-0.71]
0.908
[3.54]
-0.104
[-0.39]
0.989
[3.62]
7.886
[2.67]
27.00
[2.11]
-0.172
[-0.64]
0.738
[2.74]
8.837
[2.82]
40.31
[2.64]
-0.110
[-0.41]
0.877
[3.07]
Ê[rx12
D]
Ê[rx12
E]
Controls
Observations
No
Yes
No
2,276
2,276
2,276
t-statistics in brackets.
56
Yes
2,276
2.863
[3.93]
-1.903
[-2.21]
No
2,276
3.136
[3.55]
-1.946
[-2.22]
Yes
2,276
Table 7: Financing Activities and Future Cross-Market Returns
Firm-level forecasting regressions of future cross-market returns. Panel A uses net equity repurchases to
forecast firm-level average excess bond returns in the twelve months following the end of quarter t (rx12
D,it )
12
as well as future 12-month changes in firm-level average bond spread (∆spreadit ). Panel B uses net
debt issuance to forecast future 12-month firm-level excess stock returns (rx12
E,it ). Excess returns refer to
returns in excess of the risk free rate. Control variables are the same as those used in previous firm-level
regressions and are explained in Table 4. Forward controls include one-year ahead firm profitability and
output gap. Regressions include quarter-of-year dummies and firm fixed effects.
Panel A. Net Equity Repurchases and Future 12-month Bond Returns
rx12
D,it
Net equity repurchase/asset
rx12
E,it
∆spread12
it
(3)
(4)
(1)
(2)
-0.2450
[-2.10]
0.1892
[4.86]
-0.2827
[-2.69]
0.1973
[5.53]
0.0677
[3.04]
-0.0409
[-7.46]
0.0636
[2.58]
-0.0387
[-7.46]
Other controls
Yes
Yes
Yes
Yes
Forward controls
No
Yes
No
Yes
Observations
9,085
9,085
9,085
9,085
t-statistics in brackets. Standard errors clustered by firm and time.
Panel B. Net Debt Issuance and Future 12-month Stock Returns
(1)
Net debt issuance/asset
rx12
D,it
0.3730
[1.75]
1.6997
[9.65]
∆spread12
it
rx12
E,it
(2)
(3)
0.5094
[2.30]
1.7520
[10.48]
(4)
0.5121
[2.43]
0.5531
[3.00]
-7.2921
[-5.55]
-7.1167
[-4.80]
Other controls
Yes
Yes
Yes
Yes
Forward controls
No
Yes
No
Yes
Observations
9,085
9,085
9,085
9,085
t-statistics in brackets. Standard errors clustered by firm and time.
57
Table 8: Financing Activities and Future Capital Structure Arbitrage Returns
Firm-level forecasting regressions of future returns on capital structure arbitrage trading strategies. Panel
12
A uses firm actions to forecast future 12-month firm-level bond returns (rD,it
), controlling for hedge ratio12
weighted firm equity returns (hit rE,it ). Firm-level hedge ratio hit is the average of bond-level hedge ratios,
with the same weighting as firm-level average bond returns. Panel B uses firm actions to forecast future
12-month returns on a capital structure arbitrage trade that buys firm bond and shorts firm equity
12
12
(rD,it
− hit rE,it
). The firm action variables are the same as those use in Table 6: I1,it indicates firms
that simultaneously increase debt and reduce equity; I2,it indicates firms that simultaneously increase
equity and reduce debt. Standard errors are clustered by both firm and time.
Panel A
12
rD,it
(1)
(2)
I1,it
(4)
0.0359
[1.36]
0.9874
[4.67]
-0.0111
[-2.06]
0.0353
[1.34]
0.9857
[4.68]
-0.0116
[-2.10]
I2,it
12
hit rE,it
(3)
0.9887
[4.67]
0.9868
[4.67]
Observations 9,085
9,085
9,085
9,085
t-statistics in brackets. Standard errors clustered by firm and time.
Panel B
12
12
− hit rE,it
rD,it
(1)
(2)
(3)
I1,it
-0.0148
[-2.01]
I2,it
0.0275
[1.07]
-0.0139
[-1.99]
0.0258
[1.01]
Observations
9,085
9,085
9,085
t-statistics in brackets. Standard errors clustered by firm and time.
58
Table 9: Corporate Financing Decisions and Government Bond Supply
Time series regressions of corporate net equity repurchases and net debt issuance on government bond
issuance:
Yt = α + βGt−1 + ηWt−1 + γZt + t .
In the top panel Yt is net equity repurchases; in the bottom panel Yt is net debt issuance. The dependent
variable in the first column is the aggregate amount by all non-financial corporations, normalized by
their total assets, using data from the Flow of Funds. The dependent variables in the second and third
columns are the aggregates of Compustat firms whose market value is among the top 70% and bottom
30% in each quarter. The Compustat aggregates are normalized by total assets of each group. Gt−1 is
issuance of long-term government bond in quarter t−1, computed as the quarterly change in outstanding
Treasury notes and Treasury bonds (excluding those held by the monetary authority and foreigners),
normalized by quarterly GDP. Wt−1 is a proxy for equity valuations, which uses E/P10 in the first
column, and total book equity/total market equity of the respective Compustat group in the last two
columns, all measured by the end of quarter t − 1. Zt is the same set of controls as those in Table 3 in
the first column, and their counterparts computed using the aggregates of each Compustat group in the
last two columns (e.g. the control for cash holdings in the case of the large firms is total cash of large
firms normalized by total assets of large firms). Quarterly from 1985Q1 to 2012Q4. Standard errors are
Newey-West with eight lags.
Net Equity Repurchases
All Non-Financial Large Firms Small Firms
Government issuance
-0.0232
[-2.99]
-0.0365
[-3.11]
0.0214
[1.37]
Net Debt Issuance
All Non-Financial Large Firms Small Firms
Government issuance
-0.0205
-0.0346
[-2.87]
[-2.96]
Newey-West t-statistics in brackets.
59
0.0219
[0.58]
Table 10: Aggregate Merger Dynamics and Valuations of Debt and Equity
Time series regressions of merger activities on proxies for debt and equity valuations:
Ct = α + βD XD,t−1 + βE XE,t−1 + γZt + ut .
In columns (1) to (3), Ct is the total value of quarterly cash mergers, normalized by total assets of
non-financial firms from the Flow of Funds. In columns (4) to (6), Ct is the fraction of quarterly mergers
paid by cash (in value terms). Merger data are taken from SDC Platinum; only completed deals of US
targets by US acquirers in non-financial, non-utilities industries are included. XD and XE are proxies
for valuations in debt and equity markets, which are the same as those in Table 3. Zt includes a time
trend to control for a gradual increase in the fraction of total mergers paid by cash over time, as well as
the output gap to control for potential variations in merger payments over the business cycle. Quarterly
from 1985Q1 to 2012Q4. Standard errors are Newey-West with eight lags.
Cash Merger/Asset
(1)
(2)
(3)
Credit spread
Term spread
-0.0138
[-5.54]
-0.0264
[-4.12]
Credit premium
Term premium
Cash Merger/Total Merger
(4)
(5)
(6)
-1.2994
[-1.83]
-3.6561
[-2.35]
-0.0290
[-1.89]
-0.0308
[-4.92]
0.0201
[2.72]
E/P10
0.0194
[2.89]
Ê[rx12
D]
-0.0054
[-4.55]
0.0045
[2.69]
112
112
112
112
0.304
0.252
0.286
0.338
Newey-West t-statistics in brackets.
Ê[rx12
E]
Observations
R-squared
7.5044
[2.84]
60
0.1249
[0.04]
-4.7180
[-2.86]
9.7150
[4.76]
112
0.411
-0.7102
[-3.00]
1.5804
[2.32]
112
0.289
C
Definition of Main Variables
Aggregate Level
Variable
Construction
Source
Net equity repurchase
-FA103164103.Q
Flow of Funds
Net debt issuance
FA103163003.Q+FA103168005.Q+FA103169535.Q
+FA103169803.Q+FA103165005.Q
Flow of Funds
Net income
FA146110005.Q
Flow of Funds
Capital expenditure
FA145050005.Q
Flow of Funds
Cash holding
FL103020005.Q+FL103030003.Q+FL103091003.Q
+FL103061103.Q+FL103034003.Q
Flow of Funds
Tangible asset
FL105035005.Q+FL105015205.Q+FL105020015.Q
Flow of Funds
Total asset
FL102000005.Q
Flow of Funds
Output gap
log(GDPC1)-log(GDPPOT)
FRED
Government bond issuance
Quarterly change in (FL313161125.Q+FL313161400.Q
-FL713061125.Q-FL263061125.Q)
Flow of Funds
Net percentage of domestic
banks tightening lending
standards for C&I loans
DRTSCILM
FRED
Merger dollar amount
Quarterly sum of merger deal values (times percentage paid with cash (stock) when calculated aggregate
amount of cash (stock) mergers).
SDC Platinum
Expected future 12-month
stock returns
Predicted value of future 12-month stock returns using P/E10, dividend yield, past 12-month and 24-month
stock returns as predictors.
Expected future 12-month
bond returns
Predicted value of future 12-month bond returns using
credit spread, term spread, past 12-month and 24-month
bond returns as predictors.
Excess bond premium
From Gilchrist and Zakrajsek (2012)
Term premium
Follows Sack (2006); derives from Cochrane and Piazzesi (2005). Can also compute from a macro VAR.
The Cochrane-Piazzesi estimate and the VAR estimate
are 0.9 correlated in my data, and they yield almost
identical results.
Gilchrist’s website
Note: Flow of Funds occasionally updates historical time series. The values used here are retrieved
in July 2013.
61
Firm Level
Variable
Construction
Source
Net equity repurchase
PRSTKC-SSTK
Compustat
Net debt issuance
DLTIS-DLTR
Compustat
Net income
NI
Compustat
Capital expenditure
CAPX
Compustat
Cash holding
CHE
Compustat
Leverage
AT/SEQ
Compustat
Distance to target leverage
Difference between actual leverage and
predicted leverage using net income, depreciation, size, R&D outlays, Q, and distance
to insolvency as predictors, adapted from
Fama and French (2002) equation (8).
Tangible asset
PPENT+INVT
Compustat
Total asset
AT
Compustat
Distance to insolvency
Follows Atkeson et al. (2014)
CRSP
KZ index
Four-variable KZ from Baker et al. (2003b)
equation (5).
Compustat
Employee stock option exercises
Quarterly number of shares exercised is the
sum of all employee exercises.
Thomson Reuters
transactions dataset
Expected future
stock returns
12-month
Predicted value of future 12-month stock
returns using book-to-market ratio and
past quarter firm stock returns as predictors.
Expected future
bond returns
12-month
Predicted value of future 12-month (facevalue-weighted) average firm-level bond returns using firm (face-value-weighted) average credit spread and term spread as predictors.
Credit premium
Calculation of bond-level credit premium
follows Gilchrist and Zakrajsek (2012).
Firm-level credit premium is face-valueweighted average of bond-level estimates.
CRSP & Compustat
Term premium
Bond-level term premium is calculated using nearest-maturity Treasuries. Firmlevel term premium is face-value-weighted
average of bond-level estimates.
CRSP & Compustat
insider
Note: Compustat variables used to compute net equity repurchases, net debt issuance, and capital
expenditure are year-to-date data items. Thus I use the original value for the first fiscal quarter,
and compute quarterly flows for the second to the fourth fiscal quarters by differencing the original
year-to-date data.
62