Does dynamic tradeoff theory explain high-frequency debt issuers?∗
B. Espen Eckbo†
Michael Kisser‡
January 2015
Abstract
We contrast funding policies and leverage dynamics of high versus low-frequency net-debt issuers
(HFIs and LFIs). These are defined, respectively, as publicly traded industrial companies in the top
and bottom quartiles of the annual net-debt issue distribution (net of debt retirements). Firms tend
to remain in their initial quartile for years after public listing, suggesting that HFIs persistently draw
benefits from debt financing in excess of debt issue costs, which LFIs do not. HFIs are large, highly
leveraged growth firms with intensive capex programs. Classical dynamic tradeoff theory suggests
that, relative to LFIs, HFIs should undertake smaller net-debt issues, have less volatile leverage ratios,
and greater speed-of-adjustments to target leverage ratio deviations. We find little support for these
predictions. Overall, net-debt issue frequencies are driven mainly by firms’ capex programs, sometimes
with transitory debt (debt issues when overleveraged). Leverage ratios are lowered following transitory
debt issues through equity issues and retained earnings rather than by debt repurchases.
∗
We have benefitted from the comments and suggestions of Harry DeAngelo, Pierre Chaigneau, Michael Hertzel, Michael
Roberts and Karin Thorburn, and seminar participants at the 2013 meetings of the Financial Management Association, the
European Finance Association, the SFS Cavalcade, the Southern Finance Association, Concordia University, the Norwegian
School of Economics, Tuck School of Business at Dartmouth, University of Adelaide, University of Bristol, University of
Stavanger Corporate Finance Conference, and the Vienna Graduate School of Finance. This project has received funding
from Lindenauer Center for Corporate Governance at the Tuck School of Business.
†
Tuck School of Business, Dartmouth College. b.espen.eckbo@dartmouth.edu
‡
Norwegian School of Economics. michael.kisser@nhh.no
1
Introduction
It is well established that the distribution of leverage ratios among public nonfinancial U.S. firms has a
persistent and sizable left tail consisting of firms with zero or near-zero debt (Strebulaev and Yang, 2013).
What is much less known, however, is the right tail of the distribution: the characteristics and dynamic
leverage behavior of firms that persistently fund themselves by raising debt. We show that such firms
exist and persist in their high-frequency debt-issue behavior over much of their lifetime as public firms.
Moreover, we use these high-frequency debt issuers to provide new empirical perspective on several of
the basic predictions of the class of dynamic tradeoff theory pioneered by Fischer, Heinkel, and Zechner
(1989) and extended to include investment by DeAngelo, DeAngelo, and Whited (2011).
All theories of debt financing presume the existence of some form of debt benefits (whether from taxes,
low issue costs, agency benefits, etc.). Public companies that have been observed to persistently fund
themselves with new net debt issues (net of debt retirements) therefore form a relatively powerful place to
test these theories. That is, in contrast to zero- or near-zero leverage firms, we know that high-frequency
net-debt issuers view debt financing as sufficiently beneficial (for whatever reason) to cover issue costs
most of the time. For these firms, the remaining issue is whether the dynamic behavior (frequency and
size) of net-debt issues and of leverage ratios conform to to the theoretical predictions.
In dynamic capital structure theory such as Fischer, Heinkel, and Zechner (1989), issuance costs
directly influence the frequency and size of capital structure adjustments. Temporarily underleveraged
firms with high fixed debt issue costs remain inactive for long spells before issuing large amounts to
restore leverage targets. In contrast, underleveraged firms with largely proportional issue costs issue
more frequently—they become high-frequency issuers—and they issue relatively small amounts each time
(Leary and Roberts, 2005; Korteweg and Strebulaev, 2014).
This cost-optimizing behavior further implies that high-frequency debt issuers will exhibit low leverage
ratio volatility relative to low-frequency debt issuers. Moreover, high-frequency net-debt issuers are
expected to more quickly revert to an optimal leverage ratio over time—exhibiting relatively greater
speed-of-adjustment back to leverage targets in the vernacular of Fama and French (2002), Flannery and
Rangan (2006) and others. We address these predictions using publicly traded industrial firms over the
period 1971-2012, while systematically benchmarking the capital structure decisions of high-frequency
net debt issuers with that of low-frequency issuers.
1
We define high-frequency net-debt issuers (henceforth HFIs) as firms in the upper quartile of the
annual net-debt issue frequency distribution distilled from cash-flow statements (which record private
and public debt issues from all sources). Low-frequency net-debt issuers (LFIs) are firms in the lower
quartile of this distribution. We eliminate debt rollovers (and use net debt) because we are primarily
interested in theoretical implications for dynamic debt changes. Optimal debt levels are captured by the
usual factors driving empirical cross-sectional leverage targets (Parsons and Titman, 2008; Frank and
Goyal, 2009).
Furthermore, we require our sample firms to go public during the sample period. Conditioning the
issue frequency distribution on firm age since public listing avoids the confounding effect of pooling older
firms with newly listed companies, where the latter have not yet had much time to issue securities. In
the process, we also provide new and interesting evidence on the lifecycle funding behavior of public
industrial companies from the year of public listing.
As it turns out, our simple issue-frequency classification works well as it reveals a substantial spread
between HFIs and LFIs, both in terms of firm characteristics and issue dynamics. For example, across
firm-years, leverage ratios average as much as 35% for HFIs versus only 10% for LFIs. Moreover, while
32% of the LFIs have zero leverage in a typical firm-year, only 1% of HFIs ever reduce leverage to zero
in a firm-year. Also important, the composition of firms classified as HFIs and LFIs tend to persist
over time: firms classified as either HFIs or LFIs early on are highly likely to retain that classification
throughout their lifecycle as public companies.
Who are the high-frequency issuers? They are large relative to LFI, both in terms of sales and
total assets. More important, they have significantly higher annual average capital expenditures (capex),
which results in a substantially higher asset- and sales growth. Capex is by far the most important
firm characteristic predicting the net-debt issue probability and the time between net-debt issues (the
issue hazard). Notwithstanding their relatively intensive capex program, HFIs exhibit significantly lower
Tobin’s Q and lower research and development (R&D) expenses than LFIs.
In terms of corporate funding, HFIs and LFIs receive a similar contribution from operating profits,
averaging about 40% of all sources funds. HFIs rely much less on sale of equity and assets, making up
the difference largely in the form of debt issues. For both HFIs and LFIs, the average combined proceeds
from the sale of equity and debt securities—total external finance—is approximately the same as proceeds
from total asset sales (illiquid assets plus cash draw-downs and reductions in net working capital). In a
2
typical firm year, these two sources of funds (sale of securities and total asset sales) each average about
30% of total funds (median about 15%). Our evidence suggests that costly asset sales are higher up in
the financing pecking order than previously anticipated by the capital structure literature. This overall
funding pattern appears soon after public listing.
Under dynamic tradeoff theory, the greater the fixed component of issue costs, the longer the inactive
periods and the greater the net-debt issue when the firm recapitalizes. This suggests that the net-debt
issue size of LFIs should on average be larger compared to those of HFIs. The data tells a different story,
however, as the issue sizes of these two categories of firms are roughly equal. Second, the leverage ratio
variability is higher for HFIs than for LFIs—and not lower as expected. We suspect HFIs drive much of
the firm-level leverage instability highlighted by DeAngelo and Roll (2013) as net-debt issuers below the
top quartile have both low and relatively stable leverage ratios. Third, when using net-debt issues as the
dependent variable in the estimation of speed-of-adjustment (SOA) to target leverage deviations, we fail
to find a higher SOA coefficient for HFIs than for LFIs.1
These results fail to support the dynamic tradeoff theory of Fischer, Heinkel, and Zechner (1989).
To better understand this failure, we investigate several additional issues. The most important is the
role of investments in driving the timing and size of net-debt issues, and which may mask tradeoff
behavior. In particular, DeAngelo, DeAngelo, and Whited (2011) shows that overleveraged firms may
issue “transitory” debt to finance investment. The debt is transitory in the sense that it is optimally
retired following the investment shock. Interestingly, we do find that overleveraged firms issue debt to
finance capex. However, we do not observe the predicted debt retirements. Rather, firms adjust down
leverage ratios by issuing new equity and/or retaining profits. In sum, HFIs are high-grow firms funding
a relatively aggressive capex program with debt. The timing of net-debt issues appears to be largely
driven by the arrival of investment projects rather than by the type of recapitalization events suggested
by classical tradeoff theory.
The rest of the paper is organized as follows. Section 2 identifies HFIs and LFIs and their firm
characteristics. Section 3.2 describes how HFIs and LFIs differ in their overall funding mix, followed by
our tests of dynamic tradeoff theory in Section 4. Section?? discusses alternative hypotheses and possible
reasons for our rejection of the tradeoff theory, while Section 6 concludes the paper.
1
The extant literature estimates SOA coefficients using leverage ratio changes as dependent variable. Leverage ratios
change in response to “active” net-debt issues and retirements as well as to “passive” asset growth (Welch, 2004). Our
estimation concentrates on the active (net-debt issue) part.
3
2
Lifecycle net-debt issue frequencies
2.1
Sample selection
Our sample consists of 12,131 nonfinancial U.S. domiciled corporations and an unbalanced panel of 93,031
firm-years from the period 1971-2012. To arrive at this sample, we start with the merged Crisp/Compustat
(CCM) database (22,853 firms and 250,053 firm-years) and then impose seven sample requirements in the
following order: U.S domiciled firms only (eliminates 2,158 firms and 20,473 firms-years); nongovernmental industrial firms only (eliminates 5,593 firms and 65,011 firm-years for utilities (SIC codes 4899-5000),
financial firms (SIC 5999-7000), and government entities (SIC above 8999); no change of fiscal year (-0
and -1,817); no missing book values of total assets (-16, -449), firm age positive (-382, -5,463)2 ; consistent
cash flow statement data (-333 and -3,888);3 consistent balance sheet data (-57, -2,528);4 firm must go
public during sample period (-2,411, -39,449), contiguous balance sheet data only (-0; -16,944). Table 1
displays formal definitions of the main variables employed in this paper.
2.2
Annual issue frequencies
Table 2 sorts the sample firms by firm age since public listing (year 0) and displays annual cumulative
number of net-debt and gross equity issues using two issue size thresholds: 2.5% and 5% of the current
book value of assets, respectively. For each size threshold, the table shows the frequency of the population
average firm (mean), the firm in the 25th percentile firm (p(25)), the median firm (p(50)), and the firm
in the 75th percentile (p(75)). Throughout the paper, we identify HFIs (LFIs) as the firms in the
top (bottom) quartile of the net-debt issue frequency distribution.5 The average issue (or retirement)
frequency of both HFIs and LFIs is also displayed in the Table. Panel A lists the net-debt issue frequency
(N DI + ) while Panel B shows the equity issue frequency (EI).
2
Firm age is defined as the difference between the reporting date of the financial statement and the date of the first
month a company is reported in the CCM monthly stock price database. The reported age is rounded to the next smaller
integer.
3
We first drop observations with negative values for the following Compustat variable names (see Table 1 for variable
definitions): dltis, dltr, sstk, prstkc, dv, capx, aqc, ivch, sppe and siv (eliminates 3,493 firms). We then set missing entries
for items in the cash flow statement to zero and drop observations in case total sources or uses of funds equal zero (eliminates
1,165 firms) or deviate by more than 1% from each other (eliminates 230 firms).
4
We set missing entries for deferred taxes and investment tax credit (txditc) and preferred stock liquidation value (pstkl)
equal to zero and subsequently require non-missing data for the market value of the firm’s equity (prcc f × csho), Tobin’s Q
(lt + pstkl - txditc + prcc f × csho)/at), total debt (dltt + dlc), cash holdings (che), property plant and equipment (ppent)
and further drop observations in case the book leverage ratio is outside the unit interval or cash holdings are negative.
5
Each firm is reclassified annually over the lifecycle following public listing as HFI or LFI.
4
Consider first the central tendency (mean and median) in the issue frequency distribution. According
to Panel A, firms that have been publicly traded for ten years on average undertake 3.1 net debt issues
exceeding 2.5% of current book assets (median 3). Firms that have been listed for twenty years make 5.8
issues on average (median 6). With a 5% threshold the average and median number of issues is 2.4 and 2
after ten years of listing, and 4.3 and 4 after twenty years, respectively. Thus, firms typically undertake
a single positive net-debt issue exceeding 5% of total assets every five years.6
The annual frequency in Table 2 is about half the frequency reported by Leary and Roberts (2005)
based on quarterly data over the period 1984-2001, implying that debt issues often cluster within a single
year.7 Since the annual frequency averages roughly once every five years (Panel A of Table 2), it follows
that quarterly issues tend to cluster with roughly two in a given year on average. Welch (2004, 2013)
factors out annual movements in market leverage ratios caused by stock returns from total changes in
market leverage ratios. This isolates what he calls the “active” managerial capital structure adjustment
embedded in leverage ratio changes, and which occurs with a greater frequency than what is reported in
Table 2. However, our direct evidence in Panel A of Table 2 shows that the actual annual net-debt issue
frequency is lower than what he reports indirectly.
Next, we turn to HFIs and LFIs: firms above the 75th and below the 25th percentiles, respectively.
After ten years of public listing, and using the 5% issue threshold, HFIs have made on average 4.9 net
debt issues. This exceeds the average frequency of LFIs by a factor of ten (the average net debt issue
frequency for LFIs is 0.49). After twenty years of listing, HFIs have made 7.6 issues and LFIs 1.1 issues.
This difference in relative issue frequency is slightly increased if we reduce the issue-size threshold to
2.5% of current assets.
Panel B shows the frequency distribution of equity issues, which on average occur less frequently
than net debt issues. The low number of large equity offerings is consistent with prior studies (Eckbo
and Masulis, 1995; Fama and French, 2005b; Eckbo, Masulis, and Norli, 2007; Leary and Roberts, 2010).
However, the lifecycle issue-frequency information is new to the literature. With the 5% threshold, after
6
Untabulated results further show that net-debt retirements are only slightly less frequent than net-debt issues: with a
5% threshold and after ten years of public listing, the average firm has undertaken 1.6 net-debt retirements (median 1).
After twenty years of listing, the average number of net-debt retirements is 2.8 (median 3). Thus, firms typically undertake
a net-debt retirement exceeding 5% of total assets every seven years.
7
Leary and Roberts (2005) report that firms on average issue debt once every 8 quarters. They restrict their sample firms
to have at least 16 quarters (4 years) of contiguous data, which cuts the sample in nearly one half relative to our requirement
of only four contiguous quarters (one year). Our less restrictive sample brings in younger and less established companies
relative to theirs. The average firm in Leary and Roberts (2005) has approximately 36 quarters of data and issues net debt
4.2 times (on average one net-debt issue every 8.57 quarters).
5
ten years of listing, the bottom quartile have issued equity only once, the median firm twice and the top
quartile three times. After twenty years, these three categories of firms have issued equity exceeding 5%
of total assets once, twice and four times, respectively. Interestingly, the cumulative number of equity
issues is largely similar for LFIs and HFIs. Reducing the issue threshold from 5% to 2.5% only marginally
increases the number of equity issues regardless of the age since listing.
2.3
Issue-frequency persistence
Table 2 indicates that the annual differences between HFIs and LFIs tend to persist across listing age and
across issue-size thresholds. That is, although the typical firm is relatively inactive in terms of issuing
positive net debt, HFIs are highly active regardless of age.
Table 3 further indicates a high degree of persistence of HFIs (Panel A) and LFIs (Panel B). The table
presents various backward and forward looking persistence measures. For example, column (1) in Panel
A shows that 100% of the firms that were classified as HFI after five years of listing were also classified as
HFI one year earlier, 88% were classified as HFI two years earlier, and 65% were classified as HFI three
years earlier. For firms that were classified as HFI after ten years of listing, the corresponding percentages
are 100%, 92% and 82%, respectively.8 Turning to the forward looking measures, column (6) shows that,
after five years of listing, only 1% of all HFIs reduce their subsequent issue frequencies sufficiently to ever
be classified as LFI in the future. Furthermore, there is a 70% chance of staying HFI (with the difference
of 29% becoming medium frequency issuers). After ten years of listing, these percentages are 0%, 81%
and 19% respectively.
Panel B also shows that the composition of LFIs hardly changes over time, with the only exception
being rebalancing years. For example, five years following public listing the threshold for being classified
as a LFI increases from 0 to 1 (Table 2), which changes the portfolio composition of LFIs such that, in
this year, only 59% of LFIs were also classified as low frequency in the previous year. However, with the
exception of those events, the issue frequency classification is highly persistent and this is also reflected in
the forward looking persistence measures. Column (6) shows that only 2% of all LFIs increase subsequent
issue frequencies sufficiently to be classified as high frequency issuers in the future. Eighty-two percent
of the LFIs stay low frequency, while 16% become medium frequency issuers.
8
These percentages tend to shift only in the years in Table 2 showing a discrete increase in the cumulative number of
issues.
6
3
Who are the high-frequency issuers?
3.1
Distinguishing characteristics of high-frequency issuers
Table 4 displays descriptive information for HFIs (Panel A) and LFIs (Panel B) sorted by the year since
public listing. There are several interesting differences between the two issuer types. First, LFIs are
much less leveraged and have higher cash balances than HFIs. Focusing on the grand average values at
the end of each panel, the market leverage ratio (L) in column (1) is 35% for HFIs and 10% for LFIs.
Book leverage ratios in column (2) are virtually identical. This difference in average leverage ratios is also
reflected in column (3) which shows the fraction of the sample firms that are all-equity financed (AE):
it is a much as 32% for LFIs and 1% for HFIs. Moreover, the cash ratio C in column (6) is 31% for LFIs
and 10% for HFIs.
Second, the asset structure and growth rates also differ substantially among the two issuer types,
with LFIs being smaller firms with greater Tobin’s Q and lower asset tangibility. The book value of total
assets (assets) in column (7) is $345 million for LFIs and $804 million for HFIs, while Q in column (10)
is 2.56 versus 1.80 for the two issuer types, respectively. The greater Q value for LFIs is also reflected in
higher research and development spending in percent of total assets (RD in column (11)), which is 8%
for LFIs and 3% for HFIs. On the other hand, HFIs have greater capital expenditures (capex in column
(12)): 10% of total assets versus 6% for LFIs. The greater investment rate of HFIs also shows up in the
growth rate of both total assets (gA ) and total sales (gS ) in the last two columns.
3.2
Overall funding policy of high-frequency issuers
This section presents evidence on the overall funding pattern of HFIs relative to LFIs. This includes the
sale of equity as well as debt securities, reduction in working capital, cash draw-downs and and sale off
assets. This description, which is new to the capital structure literature, serves to solidify the notion that
HFIs differ from LFIs primarily in the debt-financing dimension. For this purpose, we calculate so-called
financing ratios. The financing ratio Rj represents the non-negative funding source Sj+ and is defined as:
Sj+
Rj ≡ P7 + ,
i Si
7
(1)
where the denominator sums over the following seven available financing sources in the cash-flow statement:
7
X
Si+ = CF + + EI + N DI + + ∆C − + I − + ∆W − + O+ .
(2)
i
CF + is the positive portion of operating cash flow; EI is proceeds from equity issues; N DI + is positive
net debt issues (debt issues exceeding debt retirements), ∆C − is draw-down of cash balances; I − is asset
divestitures including sale of property, plant and equipment (PPE); ∆W − is reduction in net working
capital;9 and O+ is a small residual that maintains the cash flow identity. Table 1 shows how each
of these variables are defined using Compustat cash-flow statement mnemonics, while Table 5 lists the
annual values on each of these funding ratios and their components.10
Financing ratios are interesting as they, unlike leverage ratios, measure the evolution of capital structure using market values (cash flows) and not book value. Moreover, the ratios themselves are not
confounded by asset growth. While changes in asset growth drive changes in equity values and leverage ratios even when managers do not actively rebalance capital structure, our financing ratios respond
exclusively to managerial issuance activity.
The detailed evolution of the individual financing ratios is shown Table 5. To simplify the exposition,
Figure 1 aggregates the contribution of liquid asset sales (cash draw-downs and reductions in net working
P
capital) a single Liquid Asset Sales ratio: RAS ≡ (∆C − + ∆W − + O+ )/ 7i Si+ . The other three ratios
P
shown in the figure are, respectively, the Net-Debt Issue ratio RN DI + ≡ N DI + / 7i Si+ , the Equity Issue
P
P
ratio REI ≡ EI/ 7i Si+ , positive Operating Cash Flow ratio RCF + ≡ CF + / 7i Si+ and the Illiquid Asset
P
Sales ratio RI − ≡ I − / 7i Si+ . By construction, these five ratios sum (vertically) to one.
Panel A of Figure 1 illustrates that net debt issues constitute a significant source of funds for HFIs.
Even in the year of public listing, net debt issues are as large as equity issues (the contribution of net
debt issues is 33%, equity issues account for 36% of all sources of funds). However, and this is contrary
9
In 1988, Statement of Financial Accounting Standards (SFAS) instituted a new and uniform reporting system for
working capital, including its component assets and liabilities. We work with net working capital over the entire sample
period. Separate analysis on the post-1988 period shows that splitting net working capital into assets and liabilities does
not affect our main conclusions below.
10
Debt and equity issues derived from the cash flow statement may differ from balance sheet induced changes of debt and
equity. This happens when a transaction does not have cash flow implications. Examples of such transactions include the
use of stock as a payment method in take-overs, the consolidation of debt and equity in acquisitions, exercise of convertible
securities and stock option related compensation policies. Computing the difference between net debt issues and balancesheet implied positive changes in debt, shows that this effect is small: the mean (median) difference (scaled by assets) is
0% (0%). For equity issues, the distribution is slightly more skewed with a mean (median) of -4% (0%). One of the main
contributions of this paper is to disentangle the cash flow effects of security issues from other changes in debt and equity
and we control for these effects in the dynamic target leverage ratio regressions below.
8
to equity issues, they continue to be highly significant in the following years, raising close to 30% of
all sources of funds. For LFIs, shown in Panel B, the story is different. The contribution of net debt
issues is zero in the year of public listing, whereas equity issues account for 60% of all funding sources.
Over the entire life cycle, the average contribution of net debt issues is merely 2%. whereas the median
contribution is zero. While the external funding policy thus differs strongly across the two groups of
firms, Table 5 shows that the contribution of operating cash flow is comparable and equal to 40%.
3.3
Investment as a predictor of net-debt issues
Table 6 presents estimates of the determinants of becoming HFI after T years of listing in Panel A, and
of the time between successive net-debt issues (the issue hazard or financing spell) in Panel B and C. The
probit estimation in Panel A uses firm characterists in the year of public listing in the following model:
YiT∗
= α + βXi0 + iT
YiT
= 1 if YiT∗ ≥ 75th percentile and 0 otherwise
(3)
where YiT∗ is the latent variable for the probability of firm i being a HFI after T years of listing, and
YiT is the dummy variable for YiT∗ . The vector Xi,0 of firm characteristics is the same as in Table 4 but
now measured in the year of going public (year 0). In addition, the estimation of (3) includes industry
dummies for eight of the 12 Fama-French (FF12) industries (excluding financial firms and regulated
utilities).
Panel A of Table 6 shows the parameter estimates for forecasting periods of 3, 6, 9, 12 and 15
years following public listing. These estimates strongly suggest that the issue frequency classification far
out in time is predictable based on year-0 values of several of the characteristics. Moreover, the most
powerful characteristics increasing the probability of becoming a HFI are initial Capex and leverage ratio
L. Moreover, initial R&D and cash balance C significantly reduce this probability.11
In Panel B of Table 6 we use hazard analysis to investigate whether corporate investments also drive
the time between successive net-debt issues (net-debt financing spells). Moreover, we expect this effect
to differ for LFIs and HFIs. That is, if the arrival process of new investment projects requiring external
11
The average marginal effects on the probability are as follows: a 10 percentage point increase in Capex (L) increases the
probability of being classified as a HFI nine years following public listing by 7.4 points (3.8 points). A similar increase in
R&D decreases the probability by 4.8 percentage points.
9
finance is relatively slower for LFIs than for HFIs, then the sensitivity of the issue hazard to investment
should be greater for LFIs. As in Hertzel, Huson, and Parrino (2012), we estimate a standard exponential
hazard model model of the form:
hi = h0 exp(β0 + βxi )αi ,
(4)
where h0 is the baseline hazard (when all covariates are equal to zero and assumed constant in Panel B),
and αi captures unobserved heterogeneity analogous to a regression error term.12 The firm characteristics
(xi ) now enter after subtracting the median value across all firms each year (Leary and Roberts, 2005).13
Moreover, we perform the hazard rate estimation separately for LFIs and HFIs.
In Panel B, a hazard ratio which is statistically indistinguishable from unity means that the control
variable does not change the likelihood of the financing event taking place the following year (up or down).
The tabulated results confirm the findings in the Panel A: Capex again has a strong and significant impact
on net-debt issue decision. Specifically, a unit increase in capital expenditures (relative to the median
firm) raises the issue hazard by a factor of seventeen for HFIs and a factor of nineteen LFIs. In other
words, for both HFIs and LFIs, investment and net-debt issues tend to be driven by investment funding
needs. Panel B also shows that the availability of internal funds—either C or prof —reduces issue hazards
with similar marginal effects for LFIs and HFIs.
The exponential hazard model can easily be adapted to investigate the role of time on issue hazards
by simply extending the set of control variables,
hi (t) = h0 exp(β0 + βxi + γf (t))αi and f (t) = t + t2 + t3
(5)
We follow Leary and Roberts (2005) and parameterize the function f (t) as a cubic function of time
(f (t) = t + t2 + t3 ) and then fit this function to our data on actual net debt issue activity. As shown in
Panel C, this estimation exacerbates the difference in the impact of Capex in the issue hazards of LFIs
and HFIs. This effect reflects both the persistently lower investment activity for LFIs and the use of
net-debt issues to fund new investment projects as they materialize.
≥t)
If T is a random variable measuring the time between issues, h(t) = limm→0 Pr(t≤T <t+m|T
, where h(t) is the instanm
taneous rate at which a firm issues net debt conditional on not having done so for t periods. For example, h(t)m at t = 5
gives the probability that a firm will issue over the next m periods, conditional on not having done so for the last five years.
13
This also implies that the baseline hazard is ĥ0 = exp(β̂0 ) for the median firm.
12
10
4
Does dynamic tradeoff theory explain high-frequency debt issuers?
Classic dynamic trade-off theories separate financing and investment decisions (Fischer, Heinkel, and
Zechner, 1989; Goldstein, Ju, and Leland, 2001; Strebulaev, 2007). In those models, there is no room for
externally financed investment and recapitalizations are thus purely driven by trade-off considerations.
Observed issue frequencies reflect the magnitude and functional form of debt issuance costs. In general,
higher costs increase the period of optimal inactivity and the existence of fixed costs further slows down
issue frequencies.14
Dynamic financing and investment models incorporate investment decisions into a dynamic tradeoff framework (Hennessy and Whited, 2005, 2007; DeAngelo, DeAngelo, and Whited, 2011). Trade-off
considerations are relevant for the existence of a long-run leverage target, but the arrival of investment
opportunities further drives issue behavior. For example, more persistent investment shocks result in
more frequent debt issues.15 While debt financing is optimal even in case current leverage exceeds a
long-run target, the additional debt associated with such a project is only predicted to be transitory. In
general, issue frequencies are jointly driven by investment shocks and issuance costs.
Figure 2 displays dynamic issue hazards for our HFIs (Panel A) and LFIs (Panel B). The graphs
show that estimated hazards differ considerably across the two groups of firms. For HFIs, the probability
of debt issuance is highest when the firm has just issued debt in the previous period. The more time
elapses since the last issue, the lower the probability that the firm will issue again in the current period.
For LFIs, the picture is different as overall debt issue probabilities are lower and the hazard rate is more
hump-shaped. The shape of the hazard functions is broadly consistent with Leary and Roberts (2005)
who estimate issue hazards based on simulated data generated from the tradeoff framework of Fischer,
Heinkel, and Zechner (1989) under fixed and proportional issue cost regimes. In their simulations, variable
costs generate a downward sloping hazard rate while fixed costs imply a hum-shaped form of the hazard
function.
However, the visual similarity of the hazard functions is not sufficient to conclude that observed issue
behavior for HFIs and LFIs is actually driven by pure trade-off considerations. For example, we can’t
reject the possibility that more persistent investment shocks lead to more frequent and clustered net debt
14
For details on the overall effect of issuance costs on issue frequencies, see Table IV in (Fischer, Heinkel, and Zechner,
1989). For details on the impact of the structure of issuance costs on issue frequencies, see Leary and Roberts (2005).
15
For details on the effect of investment shock persistence on issue frequencies, see Table 4 in DeAngelo, DeAngelo, and
Whited (2011).
11
issues of HFIs (DeAngelo, DeAngelo, and Whited, 2011).
In this section, we therefore develop several hypotheses to examine whether the issue behaviour of
HFIs is consistent with classical dynamic trade-off theories or dynamic financing and investment models.
We split the predictions into two parts. The first part deals with classic dynamic trade-off theory, as
summarized in Proposition 1 below. The second part uses the predictions of dynamic financing and
investment models, detailed in Proposition 2.
4.1
Classical dynamic trade-off theory
Proposition: Suppose HFIs face lower total and fixed debt issuance costs than LFIs. Dynamic tradeoff
theory predicts the following:
(1) HFIs make smaller and more frequent net-debt issues than LFIs.
(2) Irrespective of issue frequencies, over-levered firms do not issue net debt
(3) HFIs have less volatile leverage ratios than LFIs.
(4) HFIs will exhibit greater speed-of-adjustment to target leverage ratio deviations than LFIs.
Discussion: The Proposition is a simple restatement of the comparative statics presented by Fischer,
Heinkel, and Zechner (1989) under the assumption that HFIs have lower total and fixed issue costs than
LFIs. The intuition is simple: let firm value follow a random walk with a positive drift, and suppose
debt issue costs are fixed (our LFIs). If the firm is currently at the optimal leverage ratio, firm value
has to drift upwards by at least the issue-cost amount before triggering another recapitalization, which
takes time. Moreover, at the recapitalization point, the net-debt issue will be large enough to bring the
firm value back to its maximum, where net marginal issue benefits and marginal issue costs both equals
zero. Conversely, if issue costs are largely proportional (our HFIs), the optimal issue size is smaller,
resulting in relatively frequent but small issues, where each issue drives only the movement back to the
recapitalisation boundary. In both cases, over-levered firms are not expected to issue net debt. Finally,
lower issuance costs result in tighter recapitalization boundaries, which directly implies lower leverage
volatility and consequently a greater speed-of-adjustment to target leverage deviations.16
16
See Table IV in (Fischer, Heinkel, and Zechner, 1989) for a numerical analysis producing these effects within their
dynamic tradeoff theory. The table shows that the existence of even small proportional costs can lead to wide periods of
inactivity. However, note that their analysis assumes that once a leverage boundary is hit, the firm retires the entire old
12
4.2
Dynamic financing and investment theory
Proposition: Suppose HFIs face more persistent investment shocks than LFIs. Dynamic financing and
investment theory predicts the following:
(1) HFIs issue transitory debt more frequently than LFIs
(2) Irrespective of issue frequencies, firms retire transitory debt in periods following investment
(3) HFIs have lower but more volatile (net) leverage ratios than LFIs
(4) Irrespective of issue frequencies, net debt issues respond to deviations from target if optimal investment outlays are low
Discussion: The Proposition is a restatement of the comparative statics presented by DeAngelo,
DeAngelo, and Whited (2011) under the assumption that HFIs face more persistent investment shocks
than LFIs.17 The intuition is as follows: more persistent investment shocks lead to a higher frequency of
observed net debt issues. HFIs issue net debt more frequently in order to finance investment, thereby also
increasing the frequency of transitory debt (i.e. cases where current leverage exceeds a long-run target)
relative to LFIs. The transitory debt model of DeAngelo, DeAngelo, and Whited (2011) further shows
that, irrespective of issue frequency, firms retire transitory debt in periods following investment to bring
leverage back to a long-term target.18 The high persistence of investment shocks makes debt a valuable
source of external funding. As a consequence, firms optimally keep debt low in order to preserve debt
capacity (resulting in low average leverage ratios) but the occurrence of transitory debt at the same time
increases leverage ratio volatility relative to LFIs. Finally, dynamic financing and investment models
imply that a classical adjustment to a long-run target is most likely, in case optimal investment outlays
are low.
4.3
Test results of classical dynamic tradeoff theory
The evidence in Panel A of Table 7 fails to support the first two predictions in Proposition 1. The table
shows net-debt issue sizes (relative to the lagged market value of the firm) for the HFIs and LFIs in our
debt and issues new debt. As a result, proportional issue costs are paid on the total face value of the new debt and not
just on the difference between old and new debt—exacerbating the period of inactivity between rebalancing events shown
in their Table IV.
17
See Table 4 in DeAngelo, DeAngelo, and Whited (2011) for detailed comparative statics.
18
Note, however, that new investment shocks can lead to further debt issues even though the old transitory debt has not
been fully retired.
13
sample. Net-debt issues are similar and, on average, substantial for both categories of firms: 16% and
17%, respectively. Results are further robust to using median values (10% versus 9%), thereby rejecting
the first prediction of Proposition 1.
To benchmark the size of net debt issues, Table 7 further includes information on the firm’s investment
outlays. Average capital expenditures equal 12% (8%) of the lagged market value of the firm for high (low)
frequency issuers. While postponing a more formal examination of dynamic financing and investment
models to Section 4.4, these results are indicative that the higher frequency of net debt issues for HFIs
is correlated with higher investment outlays. The last column displays the fraction of net debt issues
occurring in periods when HFIs (LFIs) are already over-levered. Contrary to the predictions implied by a
classical dynamic trade-off model, net debt issues of already over-levered firms occur frequently for both
groups of firms (30% for HFIs, 26% for LFIs), consistent with Hovakimian (2004).
Turning to the third prediction of the Proposition 1, Panel A of Table 8 links issue frequencies to
the stability of corporate leverage ratios. The table is structured as follows. First, Initial leverage (L0 )
is the leverage ratio in year 0 for a firm still listed in year t (t = 1, .., 30). For example, the average
initial leverage ratio for LFIs five years following public listing (t = 5) is L0 = 10%, while it is 20% for
HFIs. Second, Change in leverage is the change in the leverage ratio from year 0 to year t (Lt − L0 ). For
example, in listing year five, the leverage ratio change is +2 percentage point for LFIs (up to 12% from
the 10% in year 0) and +20 percentage points for HFIs (up to 40% from the 20% in year 0). Third, follows
the definition of instability in DeAngelo and Roll (2013), Leverage instability is the fraction of firms for
which the change in leverage from year 0 to year t exceeds +/− 20 percentage points (|Lt − L0 | > 0.2).
Finally, Leverage volatility denotes the time-varying volatility estimate of firm i’s leverage ratio.19
Panel A of Table 8 shows that the degree of leverage instability differs substantially across LFIs and
HFIs, but in the wrong direction. According to Proposition 1, leverage ratios of HFIs should be less
volatile than those of HFIs. Instead, Table 8 shows clearly that leverage ratios of HFIs are substantially
more volatile than those of LFIs (16% HFIs versus 8% LFIs). The higher volatility of leverage ratios is
further reflected in the alternative instability measure. To illustrate, for HFIs, 50% of the individual leverage changes up through listing year 5 exceed +/− 20 percentage points. The corresponding percentage
19
Unlike the analysis in DeAngelo and Roll (2013), Table 8 conditions on firm age since listing and on the net-debt issue
frequency (LFI and HFI). The frequency count in Table 8 is based on the issue-size threshold of 2.5% of the current book
value of assets.
14
for LFIs is only 16%.20 Panel B shows that the difference in leverage ratio volatility is eliminated when
net leverage ratios are used (net leverage ratio volatility equals 19% for both HFIs and LFIs). However,
the leverage instability measure of DeAngelo and Roll (2013) continues to suggest that net leverage ratios
of HFIs are more unstable (43% of all HFIs experience significant changes in leverage ratios relative to
the year of public listing, whereas the corresponding fraction is only 26% for LFIs). In sum, HFIs do not
have less volatile leverage ratios. If anything, there ratios are more volatile and more unstable.
Table 9 presents corresponding information on the evolution of target leverage ratios. Contrary to
above, target leverage ratio instability is the exception, not the rule: Only 4% of all firms experience a
target leverage ratio change in excess of 20 percentage points and the volatility of target leverage ratios
is similar for LFIs and HFIs. This is true across issue frequencies and suggest that the observed leverage
ratio instability for high frequency issuers is driven by leverage ratio changes that are unrelated to changes
in target leverage ratios.
We next turn to the final prediction of Proposition 1 and test whether HFIs manage their net-debt
issues towards a leverage target. Several studies estimate the following speed-of-adjustment parameter
φ:
Li,t − Li,t−1 = α + ηi + φ L∗i,t (βXi,t−1 ) − Li,t−1 + i,t ,
(6)
where the determinants Xi,t−1 of the target leverage ratio include asset size, profitability, Q, cash
ratio, tangibility, depreciation, R&D expenses, capital expenditures and the median industry leverage
ratio. Estimation of equation (6) requires taking into account the presence of a firm fixed effect, the
challenge that the target leverage ratio L∗i,t must be estimated and that the lagged dependent variable
features as a regressor.21 Failure to account for these complications introduces a “dynamic panel bias”
which we address through the use of system GMM estimation (Blundell and Bond, 1998; Lemmon,
Roberts, and Zender, 2008; Flannery and Hankins, 2013).22
20
Given the relatively stable leverage ratios of LFIs, this suggests that much of the leverage ratio instability reported by
DeAngelo and Roll (2013) is driven by the subset of HFIs.
21
The equivalent regression in leverage levels is: Li,t = α + ηi + φL∗i,t (βXi,t−1 ) + (1 − φ)Li,t−1 + i,t . Thus, the SOA
parameter φ is identified as the negative value of the parameter estimate for the lagged book leverage ratio Li,t−1 .
22
The dynamic panel bias can be addressed through system GMM estimation (Blundell and Bond, 1998), long difference
estimates (Hahn, Hausman, and Kuersteiner, 2007; Huang and Ritter, 2009), or bias correction methods (Kiviet, 1995; Bruno,
2005). The application of these methods is often complicated by the fact that corporate finance panels are unbalanced and
suffer from non-contiguous data due to missing observations. Flannery and Hankins (2013) simulate data with similar
properties and compare the performance of these estimates. Their simulations suggest that bias correction methods and
system GMM estimates emerge as the most accurate methodologies. Since bias correction methods are computationally
15
The first column in Panel A of Table 10 shows the SOA estimate for market leverage ratios when
estimating regression (6) across the 80,900 available firm-years in our sample.23 The GMM coefficient
estimate of 0.259 suggests that it takes the average firm about 3 years to recover half of the target leverage
deviation (the “half-life” implied by a SOA parameter φ is ln(0.5)/ln(1 + φ)). This estimate is very close
to the GMM estimates of 0.25 reported by Lemmon, Roberts, and Zender (2008) for U.S. companies.
Columns 2 and 3 of Panel A display separate SOA coefficients for low and high frequency issuers.
The estimates suggest that high frequency issuers have a similar SOA coefficient (32% for high frequency
issuers, 27% for low frequency issuers) and the difference is statistically insignificant.24 These findings
are inconsistent with the final prediction of Proposition 1.
As pointed out by Welch (2004), leverage ratio changes may be driven by passive asset growth.
To understand whether active net debt issues are related to deviations from target leverage ratios, we
therefore replace the dependent variable and instead estimate the following regression equation:
N DIi,t
Ai,t
+
N DIi,t
Ai,t
= α1 + η1,i + φ1 L∗i,t (βXi,t−1 ) − Li,t−1 + 1,i,t
(7)
= α1 + η1,i + φ1 L∗i,t (βXi,t−1 ) − Li,t−1 + 1,i,t
(8)
Note that these regressions come without a specific hypothesis. The simple reason is that speed-ofadjustment estimates to leverage targets depend on both issue size assumptions and the frequency of net
debt issues. Unconditionally, we expect that HFIs make smaller issues than LFIs (see prediction 1 in
Proposition 1) which, holding frequency constant, would imply lower SOA estimates of such a net debt
issue regression. However, lower issue frequencies also reduce SOA estimates (by increasing the periods
intensive (requiring smaller samples), we use system GMM. We implement system GMM in Stata using the command
xtabond2 and treat lagged leverage and the vector Xi,t−1 as predetermined and use a maximum of 3 (lagged leverage) and 5
(Xit,−1 ) lags when constructing instruments. Changing the specification and modelling Xi,t as endogenous does not change
our results.
23
See Table 11 for a robustness check using book leverage ratios.
24
These findings contribute to a more recent literature on SOA estimation that uses a model such as (6) to investigate
the stability of SOA-estimates over time, across firms and across countries. Halling, Yu, and Zechner (2012) find that SOAestimates are lower during recessions and that this effect is more pronounced for financially constrained firms. Oztekin and
Flannery (2012) compare SOA-estimates across countries and find that higher adjustment costs (proxied by cross-country
variation in trading costs) significantly reduce the speed of adjustment. Hovakimian and Li (2012) estimate adjustment
speeds at refinancing points and conclude that results are inconsistent with the premise of a partial adjustment model.
Faulkender, Flannery, Hankins, and Smith (2012) estimate target leverage and the associated SOA parameter in two steps
(where the first step is estimated using system GMM) and find that over-levered firms exhibit higher SOA-parameters than
under-levered firms.
16
of inactivity) such that it is not clear whether the SOA of LFIs should be higher or lower than of HFIs.
The results of the GMM estimation for equation 7 are shown in Panel B of Table 10 and show that net
debt issues of HFIs are more sensitive to deviations from leverage targets than for LFIs (the estimated
SOA coefficient φ equal 32% for HFIs and 19% for LFIs). These findings are robust to alternative issue
frequency classifications. For example, results are unchanged if we classify HFIs and LFIs based on year
10 following public listing as opposed to the annual reclassification underlying Table 10.
Panel C displays results of the positive net debt issue regression (equation 8). Note that the regression
estimates the sensitivity of positive net debt issues for periods in which firms issue net debt. In other
words, the coefficient φ can not be interpreted as a speed-of-adjustment parameter (given that the regression excludes periods of inactivity) but rather as an observed conditional sensitivity of positive net debt
issues to deviations from leverage targets. All three coefficient estimates φ are statistically insignificant
from zero, suggesting that positive net debt issues do not respond to deviations from leverage targets.
Summing up, all four implications of classic dynamic trade-off models are not supported by our
analysis of HFIs. While those firms actively issue net debt, those issues do not seem to be driven by
classic trade-off considerations. Going beyond the four predictions, the perhaps most surprising and
contradictory evidence is that while the size of net debt issues of HFIs is economically substantial, the
associated net debt issues do not respond to deviations from leverage targets.
4.4
Test results of dynamic financing and investment theory
Dynamic financing and investment theory implies that over-levered firms may issue debt to finance
investment. The associated debt is predicted to be transitory and firms are expected to start retiring
debt once investment shocks level out (DeAngelo, DeAngelo, and Whited, 2011).
Panel A of Table 7 has shown that debt issues of over-levered firms happen more frequently for HFIs
relative to LFIs (30% versus 26%). Panel B presents corresponding issue, investment and transitory debt
characteristics of those over-levered firms (those with ∆L∗ < 0). Both issue and investment size are
similar to the values reported in Panel A. Interestingly, issue size and investment outlays are similar for
HFIs, but not for LFIs. The third column displays the deviation from the target leverage ratio in the
year following the net debt issue (i.e. L∗t+1 − Lt ): as expected, both group of firms are substantially
over-levered as leverage exceeds the target by 9 percentage points (HFIs) and 8 percentage points (LFIs).
What happens over the next three periods is interesting. Superficially, the evidence seems consistent
17
with the transitory debt argument as the deviation from the target is reduced to 1 percentage point
(HFIs) and 2 percentage points (LFIs) in three years from now. Surprisingly, however, the adjustment
does not happen through net debt retirements. In fact, for HFIs cumulative net debt issues are even
positive over the next three years and equal to 8% of the current market value of assets. Instead, both
groups of firms engage in substantial equity issues over the coming three years: HFIs (LFIs) raise new
equity equal to 14% (11%) of the market value of assets. Those external equity issues, in conjunction
with retained earnings, reduce excess leverage back to the target. Consequently, the term transitory
leverage may be more appropriate. These findings are interesting and difficult to frame within a narrow
interpretation of the dynamic financing and investment theory.
The evidence on the first two predictions of Proposition 2 are thus mixed. Over-levered firms issue
debt to finance investment and reduce deviations from target leverage ratios substantially over the next
three years. However, debt retirements have no role in this adjustment as firms instead issue equity and
retain profits.
Turning to the third prediction of Proposition 2, Table 8 presents direct evidence on average leverage
ratios and leverage ratio volatility. Results are again mixed. As shown before, HFIs have indeed more
volatile leverage ratios but the difference vanishes if net leverage ratios are employed. In addition, Table
8 clearly shows that HFIs have substantially higher leverage ratios. This holds true for gross leverage
ratios (Panel A) and net leverage ratios (Panel B). The findings thus do not support the prediction that
HFIs keep leverage ratios low in order to preserve debt capacity.25
Finally, dynamic financing and investment theory implies that adjustment to target leverage ratios is
most likely when optimal investment outlays are low. DeAngelo, DeAngelo, and Whited (2011) state that
”< ... >, we find that firms in our model rebalance aggressively toward target in some, but not all, states
of the world, most notably when optimal investment outlays are low.” Panel D of Table 10 estimates the
sensitivity of net debt issues to deviations from target leverage ratios for periods in which investment
outlays are low. Comparing the SOA-coefficients to those in Panel B, we can see that speed-of-adjustment
to target is indeed higher in periods for which observed investment outlays are low.
25
Note that equity issue costs also generate strong variation in optimal leverage ratios in DeAngelo, DeAngelo, and Whited
(2011). It would be interesting to see whether lower equity issuance costs offset the effect more persistent investment shocks
on optimal leverage ratios.
18
5
Discussion and alternative explanations
5.1
Are low-frequency issuers financially constrained?
In economic terms, a financially constrained firm faces relatively high (perhaps even prohibitive) costs
of external funding. With this definition, firms that are known to have raised debt at a consistently
high frequency throughout their lifecycle as public companies are almost surely truly unconstrained
relative to firms that rarely raise external funding. The extant literature offers several types of financial
constraint classifications, including the sensitivity of investment to cash flow (Fazzari, Hubbard, and
Petersen, 1988), quantitative indices based on financial statement and stock market information (Kaplan
and Zingales, 1997; Lamont, Polk, and Saa-Requejo, 2001; Whited and Wu, 2006), univariate sorts on
firm characteristics (Almeida, Campello, and Weisbach, 2004), and a classification based on firm size and
age (Hadlock and Pierce, 2010).
Table 12 displays the Kaplan-Zingales (KZ) index, the Whited-Wu (WW) index and the size-age (SA)
index proposed by Hadlock and Pierce (2010) for our LFIs and HFIs. For all three indices, higher values
indicate greater financial constraints.26 The WW-index and the SA-index give similar scores to LFIs and
HFIs, thus failing to detect the greater financial flexibility revealed by HFIs. Moreover, the KZ-index
wrongly scores HFIs as more financially constrained than LFIs.
The KZ-index attaches a relatively large weight to leverage, and HFIs are indeed highly leveraged
(Table 4). The WW-index also accounts for sales growth and firm size, and we have shown that HFIs
grow substantially and are relatively large. In addition, it loads substantially less on leverage. As it
turns out, the negative effect of firm size and sales growth on the financial constraint score dominates the
positive effect of leverage, to the point where the difference between HFIs and LFIs becomes small under
the WW index. Under the SA-index, the effect of firm size is non-linear, and this index also gives older
(longer listed) firms a lower score. However, it can be seen that age dominates in the construction of the
SA index as dollar differences in the book value of assets are mitigated by the logarithmic transformation
of the size variable. The net effect of size and age is such that the SA-index is again similar for HFIs and
LFIs.
26
KZ = −1.001909×prof +3.139193×L−39.36780×div −1.314759×C +0.2826389×Q where div is the ratio of dividends
to assets. W W = −0.091 × prof − 0.062 × divpos + 0.021 × tldt − 0.044 × size + 0.102 × ISG − 0.035 × SG where divpos is
a dummy variable equal to one in case the firm paid dividends, tldt is the ratio of long-term debt to assets, ISG is industry
sales growth (defined using 3-digit SIC codes) and SG is sales growth. SA = −0.737 × size + 0.043 × size2 − 0.04 × age.
19
Summing up, existing measures of financial constraints do not detect differences between high and
low frequency issuers.
5.2
Does pecking-order behaviour drive issue decisions?
While dynamic financing and investment theory incorporates pecking order behaviour due to reduced
form external financing costs (which make equity issues the most costly source of funds), the empirical
test design of the pecking order is different. Those tests are typically based on regressing debt issues on
the financing deficit (Shyam-Sunder and Myers, 1999; Frank and Goyal, 2003), i.e.
∆Di,t = α + βDEFi,t + i,t
(9)
The simple idea is that debt issues should mirror the financing deficit and thereby result in a sensitivity
parameter β that is close to one. These tests are subject to much controversy. For example, Chirinko and
Singha (2000) show that the test lacks statistical power to discriminate among alternative explanations.
In addition, several empirical studies have produced contradicting results (Shyam-Sunder and Myers,
1999; Frank and Goyal, 2003; Fama and French, 2005a; Lemmon and Zender, 2010). Finally, those static
tests do not account for the option of firms to preserve debt capacity. Using a reduced-form model,
Lemmon and Zender (2010) include the squared financing deficit as an additional regressor to account
for debt capacity and further show that this test also increases statistical power. Leary and Roberts
(2011) instead propose a new testing strategy that is based on estimating debt capacity for each firm.
However, irrespective of which model is being used, all tests are subject to the endogeneity induced by
using contemporaneous values for the financing deficit.
To estimate whether high or low frequency issuers are more likely to exhibit pecking order behaviour,
we follow Lemmon and Zender (2010) and estimate a reduced-form extension of the pecking order theory
that accounts for debt capacity by including the squared financing deficit:
N DIi,t
= α + βDEFi,t + γDEF2i,t + i,t
Ai,t
(10)
Table 13 presents corresponding results and shows that net debt issues of HFIs exhibit a substantially
larger sensitivity to the financing deficit than LFIs (the sensitivity equals 75% for HFIs under firm-fixed
effect estimation, compared to 10% for LFIs). These results are consistent with our previous findings
20
that issue decisions of HFIs are to a large degree driven by investment expenditures, which themselves
form a major component of the financing deficit.
6
Conclusion
Using forty years of annual cash flow statements, we employ cross-sectional differences in net debt issue
frequencies and identify a subset of firms that frequently issues net debt. The identification of those
firms is interesting because we know that those high-frequency net debt issuers view debt financing as
sufficiently beneficial to cover issue costs most of them time. Exploiting this observation, we test whether
issue activity of HFIs fit the type of issue activity predicted by dynamic trade-off theory.
Specifically, we contrast implications stemming from classic dynamic trade-off theory with those implied by dynamic financing and investment models. Classic dynamic trade-off theory of capital structure
predicts that HFIs should exhibit low issue size, low leverage volatility and high speed-of-adjustment to
leverage targets relative to LFIs. The data strongly reject these predictions. Net debt issues of HFIs are
far from marginal and instead are equal in magnitude to LFIs. On average, net debt issues of HFIs (LFIs)
raise 16% (17%) of the lagged market value of the firm. We also find that, if anything, leverage ratios
of HFIs are more unstable and volatile. This is particularly interesting given that the target leverage
ratios are stable and less volatile for both HFIs and LFIs. Finally, we show that SOA estimates from
a dynamic adjustment models are statistically indistinguishable between HFIs and LFIs. Going beyond
the predictions, the perhaps most surprising and contradictory evidence is that while the size of net debt
issues of HFIs is economically substantial, the associated net debt issues do not respond to deviations
from leverage targets.
Turning to dynamic financing and investment models, the empirical evidence is mixed. We do find
that net debt issues of over-levered firms are more common for HFIs than for LFIs and we also show that,
consistent with the theory, firms indeed reduce leverage in periods following investment. However, the
leverage adjustment does not happen via debt repurchases but instead through equity issues and retained
earnings. As such, the term transitory leverage might be more appropriate.
Dynamic financing and investment models also predict that HFIs should have lower average leverage
ratios in order to preserve debt capacity. However, the data clearly shows that HFIs employ substantially
more debt than LFIs in each year of public listing. Finally, we confirm that SOA estimates are larger in
21
periods when observed investment outlays are low, which is consistent with the predictions from dynamic
financing and investment theory.
The issue frequency classification is highly persistent and firm characteristics in the year of public
listing predict future high- and low-frequency issuers. High frequency issuers have higher initial leverage
ratios, lower cash holdings and relatively high capital expenditures in the year of public listing. Over the
life cycle, high frequency issuers substantially increase leverage, while low frequency issuers tend to have
stable and low leverage ratios. The average increase in leverage ratios for high frequency issuers masks
substantial variation in individual leverage ratios: 38% of all high-frequency issuers change leverage by
more than 20 percentage points relative to the year of public listing.
Moreover, duration analysis shows that capital expenditures are strongly related to issue hazards,
both for high and low frequency issuers, suggesting that debt issue activity is to a large degree driven by
real investment. This finding is further confirmed by reduced form pecking order tests which show that
net debt issues of HFIs are significantly more sensitive to changes in the financing deficit.
Overall, while positive net debt issue activity is responsive to investments, the observed leverage
ratio instability and the statistical independence between positive net debt issues and deviations from
(hypothetical) leverage targets challenges standard tradeoff theories of debt. While fixed issue costs may
cause firms to take long swings away from a leverage target, the relative issue size does not reflect the
issue frequency. Dynamic financing and investment models help to better explain issue activity of high
frequency issuers, yet new puzzles arise: high frequency issuers reduce transitory leverage by issuing
(costly) external equity.
22
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25
Table 1: Definition and value of financing ratios derived from Compustat cash-flow statements
Variable
Description (Compustat mnemonics)
Mean
Median
St.Dev.
27.4
36.5
16.0
5.8
74.2
1.3
2.8
0.5
0.5
3.0
421.2
310.4
249.9
121.2
667.2
Capital expenditures
Acquisitions
Increase in investments
Sale of Investments
Sale of property, plant and equipment
Short-term investments - change
Investing activities - other
capx + aqc + ivch - siv - sppe - ivstch - ivaco
47.5
18.8
41.6
37.4
3.2
-1.3
0.7
67.8
3.2
0.0
0.0
0.0
0.0
0.0
0.0
4.3
382.6
186.6
1113.1
979.9
86.0
82.2
141.3
531.3
Sale of common and preferred stock
Purchase of common and preferred stock
Cash dividends
Long-term debt - issuance
Long-term debt - reduction
Changes in current debt
Other financing cash flow [ = (txbcof + fiao) ]
sstk + prstkc + dv + dltis + dltr + dlcch + fincf oth
15.8
18.5
10.0
76.8
62.9
0.1
-1.1
0.2
0.4
0.0
0.0
0.1
0.8
0.0
0.0
0.3
103.8
240.2
136.4
622.3
559.0
85.4
93.1
347.9
6.5
0.0
154.1
15.8
79.8
82.8
8.7
49.5
6.6
2.5
0.4
0.4
4.9
0.0
0.1
0.0
0.0
103.8
633.0
690.8
87.6
1001.3
56.8
38.5
28.5
65.8
2.9
15.2
117.3
12.3
3.56
0.0
1.0
0.0
0.0
6.7
0.5
0.00
326.6
570.6
30.6
125.8
1290.7
106.2
85.46
13.97
25.52
11.55
0.00
0.00
0.00
246.18
216.86
113.96
I: Compustat cash flow items
ibc Income before extraordinary items
dpc Depreciation and amortization
ocf otha Other operating cash flow ( = xidoc + txdc +esubc + sppiv + fopo + fsrco + exre)
nwc invb Investment into net working capital [ = (recch + invch + apalch + txach + aoloch)*(-1)]
oancf ibc + dpc + ocf oth - nwc inv
capx
aqc
ivch
siv
sppe
ivstch
ivacoc
ivncf
sstk
prstkc
dv
dltis
dltr
dlcch
fincf othd
fincf
chech
Change in cash and cash equivalents
II: Sources of funds
EI Equity Issues: sstk
DI Debt Issues: dltis + max[dlcch,0]
CF+ Positive operating Cash Flow: max[oancf + nwc inv,0]
∆C − Draw-down of Cash balance: max[chech*(-1),0]
I− Asset sales: siv + min[ivstch,0] + min[ivaco,0] + sppe
∆W − Decrease in net Working capital: max[nwc inv*(-1),0]
O+ Other sources: max[fincf oth,0]
III: Uses
ER
DR
CF−
∆C +
I+
∆W +
O−
of funds
Distributions to equity-holders: dv + prstkc
Debt Retirements: dltr + min[dlcch,0]*(-1)
Negative operating Cash Flow: max[(oancf + nwc inv)*(-1),0]
Build-up of Cash balance: max[chech,0]
Investments: ivch + aqc + min[ivstch*(-1),0] + min[ivaco*(-1),0] + capx
Increase in net Working capital: max[nwc inv,0]
Other uses: max[fincf oth*(-1),0]
IV: Composite variables
N DI Debt issue minus debt retirement: DI - DR
N DI + Positive portion of debt issue minus debt retirement: max[DI - DR,0]
N DI − Negative portion of debt issue minus debt retirement: max[DR - DI,0]
a
ocfoth is the sum of extraordinary items and discontinued operations (xidoc), deferred taxes (txdc), equity in net loss (esubc), loss from sale of
PPE and investments (sppiv), funds from operations–other (fopo), other sources of funds (fsrco) and exchange rate effects (exre). The item fsrco
is 0 if the company reports according to format code 7 (scf=7), exre is zero in case of format codes scf=1, 2 or 3.
b
nwcinv is constructed as follows: For format code 7, it is the sum of (multiplied by minus 1) accounts receivable-decrease(recch), inventory-decrease
(invch), accounts payable and accrued liabilities-increase (apalch), income taxes-accrued-increase (txach), assets and liabilities-other (aoloch). For
format code 1, it is the variable wcapc. In case of format codes 2 and 3, it is wcapc ∗ (−1).
c
ivaco is replaced by fuseo*(-1) in case of format codes 1, 2 or 3.
d
fincfoth is the sum of excess tax benefits of stock options (txbcof) and other financing activities (fiao).
26
Table 2: Lifecycle net-debt and gross equity issues following public listing
All firms went public during the sample period (event year 0). The issue size thresholds (2.5%, 5% and 0%) are in percent
of current book value of assets. p(25), p(50) and p(75) are, respectively the number of issues in the 25th , 50th and 75th
percentiles of the issue frequency distribution. Sample of 12,131 U.S. public firms, 1971-2012.
Year
Firms
Mean
2.5% Size Threshold
LFI p(25) p(50) p(75)
A: Cumulative number of net-debt issues
0
12131
0.3 0.0
0
0
1
10783
0.6 0.0
0
0
2
9264
0.9 0.0
0
1
3
7918
1.2 0.0
0
1
4
6848
1.5 0.0
0
1
5
5921
1.8 0.5
1
2
6
5046
2.1 0.5
1
2
7
4344
2.3 0.5
1
2
8
3815
2.6 0.5
1
2
9
3345
2.9 0.5
1
3
10
2931
3.1 0.5
1
3
11
2585
3.4 1.1
2
3
12
2307
3.6 1.1
2
3
13
2021
3.9 1.1
2
4
14
1783
4.2 1.1
2
4
15
1549
4.5 1.1
2
4
16
1370
4.8 1.7
3
5
17
1160
5.1 1.7
3
5
18
1020
5.3 1.6
3
5
19
906
5.5 1.6
3
5
20
782
5.8 1.6
3
6
5% Size Threshold
LFI p(25) p(50) p(75)
HFI
Mean
(N DI + )
1
1.0
1
1.2
2
2.2
2
2.5
2
2.7
3
3.6
3
3.8
4
4.7
4
4.9
4
5.1
5
6.0
5
6.2
5
6.4
6
7.2
6
7.4
6
7.5
7
8.4
7
8.6
7
8.7
8
9.6
8
9.8
0.2
0.5
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.7
3.8
3.9
4.1
4.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
1.1
1.1
1.1
1.1
1.0
1.1
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
2
2
2
2
2
2
0
0
1
1
1
1
1
2
2
2
2
2
2
3
3
3
3
4
4
4
4
0
1
1
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
0.2
1.2
1.4
2.4
2.6
2.7
3.6
3.8
3.9
4.1
4.9
5.1
5.2
5.3
6.2
6.2
6.3
6.5
7.4
7.4
7.6
0.7
1.0
1.2
1.4
1.5
1.7
1.8
1.9
2.1
2.2
2.3
2.4
2.4
2.5
2.6
2.7
2.7
2.7
2.7
2.7
2.7
0.8
1.0
1.2
1.4
1.5
1.7
1.8
1.9
2.0
2.1
2.2
2.4
2.5
2.6
2.7
2.7
2.7
2.7
2.8
2.8
2.6
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
2
2
2
2
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
0.7
1.0
1.2
1.4
1.6
1.8
2.0
2.1
2.2
2.2
2.5
2.6
2.6
2.7
2.9
2.9
2.9
2.8
3.1
3.1
3.2
B: Cumulative number of equity issues (EI)
0
12131
0.8 0.8
1
1
1
1
10783
1.0 1.1
1
1
2
2
9264
1.3 1.3
1
1
2
3
7918
1.5 1.6
1
1
2
4
6848
1.8 1.9
1
1
3
5
5921
2.0 2.0
1
2
3
6
5046
2.2 2.3
1
2
3
7
4344
2.4 2.6
1
2
4
8
3815
2.6 2.9
1
2
4
9
3345
2.7 3.1
1
2
4
10
2931
2.9 3.3
1
2
4
11
2585
3.0 3.4
1
2
4
12
2307
3.2 3.6
1
2
4
13
2021
3.3 3.8
1
3
5
14
1783
3.4 3.9
1
3
5
15
1549
3.6 4.0
1
3
5
16
1370
3.6 4.0
1
3
5
17
1160
3.6 3.9
1
3
5
18
1020
3.6 4.0
1
3
5
19
906
3.8 4.2
1
3
5
20
782
3.8 4.1
1
3
5
0.7
1.0
1.3
1.5
1.7
2.0
2.1
2.3
2.5
2.5
2.8
2.9
3.0
3.2
3.3
3.4
3.5
3.5
3.5
3.7
3.8
27
HFI
Table 3: Persistence of firms in the HFI and LFI issue frequency classifications.
The table lists various backward and forward looking persistence measures of the firms in the HFI (Panel A) and LFI
(Panel B) frequency classification. Columns (1) through (3) are backward looking (“Persistence over past n years”): these
columns show the fraction of firms classified as HFI and LFr I that were also classified as HFI and LFI in the previous
year, in the previous two years, and i the previous three years, respectively. Columns (4) through (6) are forward looking
(“Future classification”): these columns displays the fraction of HFIs and LFIs that are classified as LFI, medium frequency
issuer, or HFI in any of the remaining years of the firm’s life as a public company in our sample period . The classification
of HFI and LFI is performed each year and is based on the 2.5% size threshold. Sample of 12,131 U.S. public firms, 1971-2012.
Age
Persistence over
past n years
n=1 n=2 n=3
(1)
(2)
(3)
Future
classification
LFI MFI HFI
(4)
(5)
(6)
A: High frequency issuers (HFIs)
0 n.A. n.A. n.A. 0.07 0.31
1 0.51 n.A. n.A. 0.02 0.21
2 1.00 0.59 n.A. 0.02 0.27
3 0.69 0.69 0.41 0.03 0.34
4 0.80 0.55 0.55 0.01 0.23
5 1.00 0.88 0.65 0.01 0.29
6 0.81 0.81 0.71 0.00 0.16
7 1.00 0.88 0.88 0.00 0.20
8 0.84 0.84 0.75 0.00 0.26
9 0.84 0.70 0.70 0.00 0.16
10 1.00 0.92 0.82 0.00 0.19
11 0.87 0.87 0.81 0.00 0.24
12 0.86 0.75 0.75 0.00 0.15
13 1.00 0.94 0.84 0.00 0.19
14 0.88 0.88 0.83 0.00 0.26
15 0.88 0.76 0.76 0.00 0.15
0.62
0.77
0.71
0.63
0.77
0.70
0.83
0.79
0.74
0.84
0.81
0.75
0.85
0.81
0.74
0.85
Av.
0.23
0.76
B: Low frequency issuers (LFIs)
0 n.A. n.A. n.A. 0.63 0.26
1 1.00 n.A. n.A. 0.72 0.22
2 1.00 1.00 n.A. 0.79 0.18
3 1.00 1.00 1.00 0.84 0.14
4 1.00 1.00 1.00 0.71 0.25
5 0.59 0.59 0.59 0.75 0.22
6 1.00 0.65 0.65 0.80 0.19
7 1.00 1.00 0.69 0.84 0.15
8 1.00 1.00 1.00 0.88 0.11
9 1.00 1.00 1.00 0.91 0.09
10 1.00 1.00 1.00 0.81 0.18
11 0.67 0.67 0.67 0.84 0.16
12 1.00 0.70 0.70 0.87 0.12
13 1.00 1.00 0.73 0.91 0.09
14 1.00 1.00 1.00 0.92 0.08
15 1.00 1.00 1.00 0.87 0.13
0.11
0.06
0.03
0.02
0.04
0.02
0.02
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Av.
0.02
0.81
0.94
0.76
0.89
0.69
0.83
28
0.01
0.82
0.16
Table 4: Lifecycle firm characteristics of low and high-frequency net-debt issuers.
The table lists the fraction of firms classified as LFI (HFI) in the previous year (f ract−1 ), average annual
P values of
book leverage ratio (L), target leverage ratioPL∗ (βXi,t−1 ), cumulative frequency of net-debt issues ( ti=0 N DI + ),
cumulative frequency of net debt-retirements ( ti=0 N DI − ), fraction of all-equity financed firms (AE), the cash ratio
(C), book value of assets (assets), operating cash flow (prof ), tangibility (tan), Tobin’s Q (Q), R&D expenditures
(RD), capital expenditures (capex), asset growth rate (gA ) and sales growth rate (gS ). The target leverage ratio
L∗ is estimated using lagged values of size, profitability, Q, cash ratio, tangibility, depreciation, R&D expenses,
capital expenditures and the median industry leverage ratio, and firm-fixed effects. The issue frequency classification
(HFI and LFI) is performed each year and is based on the 2.5% size threshold. Sample of 12,131 U.S. public firms, 1971-2012.
Year
L
(1)
BL
(2)
AE
(3)
C
(4)
assets
(5)
prof
(6)
tan
(7)
Q
(8)
RD
(9)
capex
(10)
gA
(11)
gS
(12)
A: High frequency issuers (HFIs)
0 0.26 0.34 0.01 0.19
294
1 0.30 0.33 0.01 0.13
276
2 0.39 0.40 0.00 0.09
391
3 0.38 0.37 0.01 0.09
392
4 0.37 0.35 0.02 0.10
404
5 0.41 0.39 0.01 0.08
500
6 0.39 0.37 0.02 0.08
512
7 0.41 0.40 0.02 0.07
581
8 0.39 0.37 0.02 0.08
581
9 0.39 0.36 0.02 0.08
877
10 0.38 0.37 0.01 0.08
960
11 0.36 0.35 0.02 0.08 1,021
12 0.36 0.34 0.01 0.08 1,093
13 0.37 0.36 0.01 0.07 1,081
14 0.36 0.34 0.02 0.07 1,488
15 0.35 0.32 0.01 0.08 1,540
-0.01
-0.02
0.02
0.03
0.04
0.05
0.05
0.06
0.06
0.06
0.06
0.08
0.08
0.08
0.08
0.08
0.33
0.34
0.37
0.36
0.35
0.37
0.37
0.38
0.38
0.37
0.38
0.36
0.36
0.37
0.36
0.35
2.56
2.16
1.79
1.72
1.69
1.55
1.57
1.56
1.63
1.52
1.60
1.55
1.53
1.51
1.55
1.47
0.04
0.05
0.03
0.03
0.03
0.03
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.14
0.12
0.11
0.09
0.08
0.09
0.08
0.09
0.09
0.08
0.08
0.08
0.08
0.08
0.07
0.07
n.A.
0.44
0.38
0.30
0.23
0.22
0.18
0.79
0.15
0.17
0.15
0.18
0.13
0.14
0.13
0.13
n.A.
1.92
0.68
1.62
0.36
0.32
0.18
0.20
0.35
0.20
0.14
0.15
0.19
0.13
0.15
0.12
Avg. 0.35 0.35 0.01 0.10
804
B: Low frequency issuers (LFIs)
0 0.11 0.14 0.21 0.33
181
1 0.11 0.11 0.28 0.31
206
2 0.10 0.09 0.33 0.31
236
3 0.09 0.08 0.39 0.33
256
4 0.07 0.06 0.45 0.34
281
5 0.12 0.11 0.30 0.28
311
6 0.11 0.10 0.34 0.28
368
7 0.10 0.09 0.37 0.29
428
8 0.08 0.08 0.39 0.31
468
9 0.08 0.07 0.42 0.31
387
10 0.06 0.06 0.44 0.32
406
11 0.09 0.09 0.36 0.28
447
12 0.08 0.08 0.37 0.29
478
13 0.06 0.08 0.41 0.29
512
14 0.06 0.07 0.42 0.29
553
15 0.06 0.06 0.44 0.29
451
0.03
0.36
1.80
0.03
0.10
0.27
0.72
0.01
-0.02
-0.02
0.00
0.01
0.02
0.03
0.04
0.04
0.04
0.05
0.06
0.06
0.06
0.05
0.06
0.23
0.22
0.22
0.21
0.20
0.22
0.22
0.21
0.21
0.20
0.20
0.22
0.22
0.21
0.22
0.22
3.17
2.70
2.55
2.54
2.51
2.27
2.28
2.26
2.23
2.19
2.27
2.08
2.15
2.22
2.21
2.14
0.06
0.08
0.09
0.10
0.10
0.08
0.08
0.09
0.09
0.09
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.06
0.05
0.05
0.06
0.06
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
n.A.
0.25
0.20
0.18
0.16
0.18
0.17
0.17
0.19
0.09
0.10
0.14
0.11
0.11
0.11
0.09
n.A.
0.94
1.02
0.60
0.33
0.28
0.35
0.39
0.20
0.13
0.99
0.79
0.16
0.14
0.12
0.11
Avg.
0.02
0.22
2.56
0.08
0.06
0.17
0.53
0.10
0.10
0.32
0.31
345
29
Table 5: Lifecycle financing ratios following public listing
The annual (non-negative) cash contribution of the i’th funding source is the ratio Rj ≡ Sj /
is the sum of the seven individual funding sources in the firm’s total cash flow statement:
7
X
P7
i
Si , where the denominator
Si = EI + N DI + + CF + + ∆C − + ∆W − + I − + O+
i
The four columns are: RN DI + is the net debt issue ratio (N DI + in the numerator), REI is the equity issue ratio, RCF + is
the operating cash flow contribution, R∆C − is the contribution from cash draw-downs, R∆W − is contribution of reductions
in net working capital and RI − is the fraction of funds provided by illiquid asset sales. Variable definitions are in Table 1.
Sample of 12,131 U.S. public firms, 1971-2012.
RN DI +
Year
REI
RCF +
R∆C −
R∆W −
RI −
RO+
HFI
LFI
HFI
LFI
HFI
LFI
HFI
LFI
HFI
LFI
HFI
LFI
HFI
LFI
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
0.33
0.31
0.35
0.27
0.23
0.25
0.23
0.25
0.21
0.21
0.21
0.21
0.20
0.20
0.20
0.18
0.19
0.18
0.16
0.16
0.17
0.00
0.01
0.01
0.01
0.00
0.04
0.04
0.03
0.03
0.03
0.01
0.04
0.04
0.04
0.04
0.04
0.05
0.04
0.03
0.04
0.05
0.36
0.13
0.12
0.12
0.11
0.10
0.09
0.09
0.10
0.08
0.11
0.08
0.08
0.07
0.07
0.08
0.06
0.06
0.06
0.06
0.05
0.60
0.17
0.16
0.14
0.14
0.13
0.13
0.12
0.12
0.10
0.11
0.11
0.09
0.10
0.09
0.09
0.07
0.08
0.09
0.09
0.08
0.21
0.29
0.31
0.36
0.40
0.40
0.42
0.42
0.44
0.44
0.46
0.46
0.46
0.47
0.47
0.47
0.47
0.48
0.50
0.51
0.50
0.27
0.40
0.42
0.44
0.44
0.46
0.46
0.47
0.47
0.47
0.48
0.47
0.47
0.45
0.46
0.45
0.47
0.47
0.50
0.49
0.49
0.02
0.12
0.06
0.06
0.06
0.05
0.06
0.04
0.05
0.05
0.04
0.05
0.07
0.06
0.05
0.05
0.07
0.06
0.07
0.06
0.06
0.03
0.20
0.14
0.14
0.14
0.12
0.12
0.11
0.11
0.11
0.14
0.12
0.12
0.13
0.14
0.13
0.12
0.10
0.11
0.09
0.11
0.04
0.07
0.08
0.09
0.10
0.09
0.09
0.08
0.10
0.11
0.08
0.09
0.09
0.09
0.09
0.11
0.08
0.10
0.10
0.10
0.11
0.04
0.10
0.11
0.10
0.09
0.10
0.09
0.09
0.08
0.10
0.08
0.09
0.09
0.09
0.09
0.08
0.08
0.11
0.09
0.10
0.09
0.03
0.07
0.07
0.09
0.09
0.09
0.10
0.10
0.10
0.09
0.10
0.11
0.10
0.10
0.12
0.10
0.12
0.10
0.10
0.11
0.11
0.04
0.12
0.15
0.16
0.17
0.14
0.15
0.15
0.17
0.18
0.17
0.16
0.18
0.17
0.18
0.20
0.19
0.19
0.18
0.18
0.17
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
Mean
Median
0.26
0.19
0.02
0.00
0.13
0.01
0.24
0.04
0.38
0.36
0.41
0.35
0.06
0.00
0.11
0.00
0.08
0.00
0.08
0.00
0.09
0.01
0.13
0.00
0.01
0.00
0.01
0.00
0.11
0.09
0.12
0.10
0.02
0.00
0.04
0.00
0.03
0.00
0.03
0.00
0.03
0.00
0.07
0.00
0.00
0.00
0.01
0.00
Standardization by lagged assets
Mean
0.11 0.01
0.11 0.22
Median 0.03 0.00
0.00 0.01
30
Table 6: Investment outlays as a determinants of issue frequencies and financing spells.
Panel A presents coefficient-estimates determining the probability of becoming a HFI T years following public listing (year
0) conditional on the year-0 covariates:
YiT = α + βXi,0 + iT ,
where YiT = 1 if firm i is classified as a HFI in T years, T = 3, 6, 9, 12, 15. Panel B presents coefficient estimates determining
the number of years between successive net-debt issues (financing spells) for LFIs and HFIs, respectively, using the following
exponential shared frailty hazard model:
hi = h0 exp(β0 + βxi,t−1 )αi ,
where h0 denotes a constant baseline hazard, and αi is a frailty term capturing unobserved observation-specific effects. Panel
C allows a time-varying baseline hazard by extending the hazard model
hi = h0 exp(β0 + βxi,t−1 + γf (t))αi
where f (t) = t + t2 + t3 . The covariates are investment Capex, book leverage ratio (L), the cash ratio (C), book value
of assets (assets), operating cash flow (prof ), tangibility (tan), Tobin’s Q (Q), R&D expenditures (R&D). In Panel
A, the estimation includes industry dummies, based on the Fama-French 12 Industry Classification. In Panel B and
C, the covariates xi,t−1 enter subtracting their median values. All covariates are winsorized at the 1(99) percent level.
The high and low-frequency classification (HFI and LFI) is performed annually and based on a 2.5% issue size threshold. *, **, *** indicate significance at the 10%, 5% and 1% level, respectively. Sample of 12,131 U.S. public firms, 1971-2012.
Firm-specific explanatory variables (X)
N
Capex
R&D
L
C
assets
tan
Q
depr
Pseudo-R2
-0.694***
-0.740***
-0.868***
-1.075***
-0.561*
-0.21
-0.264
-0.316
-0.239
-0.462
-0.022**
-0.036***
-0.036**
-0.042**
-0.028
-1.732**
-1.131
-0.469
-1.861
-1.607
0.117
0.123
0.133
0.139
0.131
0.411***
0.806***
0.651*
0.699***
1.031*
1.025***
0.135*
0.042***
0.392***
0.966
0.662*
0.926
1.037**
1.023***
0.115*
0.201***
prof
A: Probability of becoming HFI T years from public listing (X measured in year 0)
T
T
T
T
T
=
=
=
=
=
3 years
6 years
9 years
12 years
15 years
7,918
5,046
3,345
2,307
1,549
2.653***
2.465***
2.310***
2.059***
1.897***
-1.103***
-1.263***
-1.493***
-2.174***
-2.259**
1.066***
1.096***
1.197***
1.345***
1.475***
-0.998***
-1.026***
-1.169***
-1.080***
-1.140***
-0.024*
-0.046***
-0.043**
-0.055**
-0.050*
B: Time between net-debt issues,with constant baseline hazard
LFI only
HFI only
29,421
28,346
19.217***
17.319***
1.602
1.293
0.657
0.932
0.064***
0.511***
1.034
1.003
C: Time between net-debt issues, with time-varying baseline hazard
LFI only
HFI only
29,421
28,162
22.442***
3.601***
1.385
1.015
1.074
0.490***
0.066***
0.643***
31
1.03
1.000
Table 7: Size of net debt issues and transitory debt for HFIs and LFIs
The table displays net debt issues (N DI) and capital expenditures (Capex) for high and low frequency net debt issuers.
Both variables are standardized relative to the lagged market value of the firm. Results are displayed separately for issue
and non-issue periods. Issue periods and the issue frequency classification are based on a 2.5% threshold of current assets.
Sample of 12,131 U.S. public firms, 1971-2012.
A: Classic dynamic trade-off theory
NDI
Issue periods (NDI > 2.5%)
Capex
∆L∗ < 0
NDI
Non-issue periods (NDI ≤ 2.5%)
Capex
∆L∗ < 0
Year
HFI
LFI
HFI
LFI
HFI
LFI
HFI
LFI
HFI
LFI
HFI
LFI
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
n.A.
0.17
0.19
0.17
0.16
0.16
0.16
0.15
0.16
0.14
0.14
0.18
0.14
0.14
0.14
0.14
n.A.
n.A.
n.A.
n.A.
n.A.
0.19
0.18
0.18
0.17
0.17
0.12
0.23
0.14
0.16
0.26
0.10
n.A.
0.13
0.14
0.13
0.12
0.14
0.13
0.12
0.13
0.12
0.11
0.11
0.10
0.12
0.10
0.11
n.A.
n.A.
n.A.
n.A.
n.A.
0.12
0.10
0.08
0.08
0.08
0.07
0.09
0.06
0.07
0.06
0.05
n.A.
0.21
0.26
0.30
0.29
0.37
0.32
0.42
0.39
0.34
0.36
0.35
0.36
0.32
0.37
0.27
n.A.
n.A.
n.A.
n.A.
n.A.
0.15
0.16
0.21
0.25
0.20
0.31
0.33
0.24
0.28
0.29
0.39
n.A.
-0.04
-0.06
-0.06
-0.06
-0.06
-0.06
-0.06
-0.06
-0.05
-0.06
-0.06
-0.05
-0.05
-0.06
-0.06
n.A.
-0.01
-0.01
-0.01
-0.01
-0.02
-0.02
-0.01
-0.02
-0.01
-0.01
-0.02
-0.01
-0.01
-0.01
-0.01
n.A.
0.07
0.07
0.07
0.06
0.07
0.07
0.09
0.07
0.07
0.07
0.06
0.07
0.06
0.07
0.06
n.A.
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.04
0.04
0.04
0.04
0.04
0.04
0.04
n.A.
0.49
0.46
0.59
0.60
0.58
0.62
0.62
0.62
0.59
0.69
0.59
0.54
0.58
0.53
0.54
n.A.
0.51
0.43
0.40
0.34
0.36
0.34
0.33
0.36
0.32
0.42
0.40
0.34
0.39
0.39
0.35
Avg.
Median
0.16
0.10
0.17
0.09
0.12
0.07
0.08
0.04
0.30
0.00
0.26
0.00
-0.05
-0.03
-0.01
0.00
0.07
0.04
0.05
0.03
0.56
1.00
0.41
0.00
B: Dynamic financing and investment theory
NDI
Current Period
Capex
L∗t+1 − Lt
L∗t+4 − Lt+3
Future Periods
Pt+3
s=t+1 N DIs
Pt+3
s=t+1 N EIs
Year
HFI
LFI
HFI
LFI
HFI
LFI
HFI
LFI
HFI
LFI
HFI
LFI
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
n.A.
0.13
0.17
0.16
0.16
0.15
0.13
0.15
0.15
0.11
0.15
0.29
0.14
0.14
0.12
0.12
n.A.
n.A.
n.A.
n.A.
n.A.
0.13
0.17
0.15
0.13
0.12
0.12
0.24
0.06
0.10
0.66
0.04
n.A.
0.12
0.15
0.14
0.16
0.17
0.15
0.14
0.13
0.13
0.11
0.11
0.11
0.14
0.11
0.12
n.A.
n.A.
n.A.
n.A.
n.A.
0.07
0.09
0.04
0.06
0.12
0.11
0.14
0.07
0.07
0.08
0.03
n.A.
-0.08
-0.10
-0.08
-0.09
-0.08
-0.09
-0.10
-0.11
-0.09
-0.09
-0.11
-0.11
-0.14
-0.09
-0.10
n.A.
n.A.
n.A.
n.A.
n.A.
-0.08
-0.04
-0.07
-0.05
-0.11
0.03
-0.06
-0.08
-0.07
-0.08
-0.07
n.A.
0.00
0.01
0.00
0.00
0.00
-0.03
-0.02
0.03
0.00
-0.01
-0.03
-0.01
-0.02
-0.03
-0.02
n.A.
n.A.
n.A.
n.A.
n.A.
0.08
0.02
0.04
-0.01
-0.03
0.02
0.01
-0.03
-0.03
-0.02
0.02
n.A.
0.12
0.13
0.11
0.07
0.09
0.11
0.09
-0.02
0.07
0.05
0.03
0.01
-0.02
0.10
0.00
n.A.
n.A.
n.A.
n.A.
n.A.
-0.01
-0.02
0.00
0.04
-0.09
0.16
0.12
-0.05
-0.01
0.33
0.01
n.A.
0.26
0.20
0.13
0.13
0.13
0.07
0.10
0.10
0.09
0.07
0.06
0.05
0.06
0.11
0.01
n.A.
n.A.
n.A.
n.A.
n.A.
0.28
0.01
0.26
0.25
0.25
0.15
0.36
0.10
-0.05
0.03
0.17
Avg.
Median
0.15
0.09
0.14
0.07
0.13
0.08
0.07
0.04
-0.09
-0.07
-0.08
-0.06
-0.01
0.01
-0.02
-0.01
0.08
0.00
0.00
-0.02
0.14
0.01
0.11
0.00
32
Table 8: Lifecycle net-debt issue frequency and leverage instability
Initial leverage (L0 ) is the leverage ratio in year 0 for a firm still listed in year t. Change in leverage is the leverage ratio
change from year 0 to year t (Lt − L0 ). Leverage instability is fraction of firms for which the leverage ratio change from year 0
to year t exceeds +/− 20% (|Lt − L0 | > 0.2) and leverage volatility is the standard deviation of the leverage ratio up to year
t. The issue frequency classification is performed annually and uses the 2.5% threshold. LFI and HFi are the bottom and
top quartiles of the issue frequency distribution. Year 0 is the year of public listing. Panel A presents corresponding statistics based on actual leverage ratios, Panel B is based on target leverage ratios. Sample of 12,131 U.S. public firms, 1971-2012.
Initial leverage
L0
Year
All
LFI
HFI
Change in leverage
(Lt − L0 )
Leverage instability
|(Lt − L0 )| > 0.2
Leverage volatility
All
LFI
HFI
All
LFI
HFI
All
LFI
HFI
A: L is the actual leverage ratio
0
0.15 0.11 0.26
n.A.
1
0.15 0.10 0.20
0.06
2
0.15 0.09 0.21
0.09
3
0.15 0.09 0.20
0.10
4
0.15 0.08 0.19
0.10
5
0.15 0.10 0.20
0.10
6
0.15 0.09 0.21
0.10
7
0.15 0.09 0.22
0.09
8
0.15 0.08 0.22
0.09
9
0.16 0.07 0.22
0.09
10
0.16 0.07 0.23
0.07
11
0.15 0.09 0.22
0.07
12
0.15 0.08 0.22
0.07
13
0.15 0.07 0.23
0.06
14
0.15 0.07 0.22
0.06
15
0.15 0.07 0.22
0.07
16
0.15 0.08 0.22
0.06
17
0.16 0.08 0.22
0.06
18
0.16 0.08 0.23
0.05
19
0.16 0.08 0.24
0.05
20
0.17 0.08 0.23
0.03
Avg.
0.15 0.10 0.21
0.07
n.A.
0.01
0.01
0.00
-0.01
0.02
0.01
0.01
0.00
0.01
-0.01
0.01
0.00
-0.01
0.00
-0.01
-0.01
-0.01
-0.02
-0.01
-0.01
0.00
n.A.
0.10
0.18
0.18
0.18
0.20
0.19
0.20
0.17
0.17
0.15
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.13
0.11
0.08
0.14
n.A.
0.15
0.25
0.28
0.31
0.31
0.33
0.33
0.32
0.32
0.31
0.31
0.31
0.31
0.32
0.33
0.33
0.32
0.34
0.35
0.33
0.25
n.A.
0.06
0.09
0.09
0.09
0.16
0.16
0.15
0.14
0.13
0.12
0.18
0.16
0.16
0.15
0.16
0.18
0.20
0.18
0.19
0.20
0.10
n.A.
0.24
0.45
0.45
0.46
0.50
0.49
0.50
0.48
0.47
0.44
0.44
0.45
0.47
0.48
0.46
0.47
0.45
0.48
0.47
0.44
0.38
n.A.
n.A.
n.A.
n.A.
n.A.
0.12
0.12
0.12
0.12
0.12
0.12
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
n.A.
n.A.
n.A.
n.A.
n.A.
0.08
0.07
0.07
0.07
0.06
0.06
0.08
0.08
0.08
0.07
0.07
0.09
0.08
0.08
0.08
0.08
0.08
n.A.
n.A.
n.A.
n.A.
n.A.
0.17
0.17
0.17
0.17
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.17
0.16
B: L is the net market leverage ratio
0
0.01 -0.05 0.17
n.A. n.A.
1
0.00 -0.08 0.09
0.05 -0.01
2
0.01 -0.09 0.12
0.07 -0.05
3
0.01 -0.10 0.10
0.07 -0.11
4
0.01 -0.11 0.09
0.09 -0.09
5
0.02 -0.07 0.11
0.09 -0.02
6
0.02 -0.07 0.11
0.08 -0.04
7
0.03 -0.08 0.13
0.07 -0.06
8
0.03 -0.09 0.13
0.07 -0.07
9
0.03 -0.10 0.13
0.06 -0.09
10
0.03 -0.10 0.13
0.04 -0.08
11
0.03 -0.07 0.13
0.04 -0.07
12
0.03 -0.07 0.13
0.04 -0.08
13
0.04 -0.08 0.15
0.04 -0.08
14
0.03 -0.09 0.14
0.04 -0.06
15
0.03 -0.09 0.13
0.05 -0.08
16
0.04 -0.07 0.13
0.04 -0.06
17
0.05 -0.06 0.14
0.03 -0.09
18
0.05 -0.06 0.14
0.03 -0.08
19
0.05 -0.06 0.16
0.02 -0.09
20
0.06 -0.07 0.15
0.02 -0.08
Avg.
0.02 -0.07 0.12
0.05 -0.04
n.A.
0.11
0.20
0.20
0.20
0.22
0.20
0.21
0.19
0.19
0.17
0.16
0.15
0.16
0.15
0.14
0.17
0.15
0.13
0.10
0.10
0.15
n.A.
0.26
0.38
0.42
0.44
0.45
0.47
0.46
0.47
0.47
0.45
0.45
0.46
0.45
0.46
0.47
0.47
0.46
0.47
0.47
0.44
0.36
n.A.
0.20
0.30
0.35
0.35
0.38
0.39
0.39
0.41
0.41
0.36
0.40
0.41
0.41
0.41
0.43
0.41
0.41
0.41
0.40
0.37
0.26
n.A.
0.31
0.49
0.51
0.52
0.55
0.56
0.57
0.55
0.53
0.52
0.52
0.53
0.51
0.54
0.52
0.51
0.49
0.51
0.51
0.47
0.43
n.A.
n.A.
n.A.
n.A.
n.A.
0.20
0.20
0.19
0.19
0.20
0.20
0.20
0.20
0.20
0.19
0.19
0.18
0.18
0.18
0.18
0.18
0.19
n.A.
n.A.
n.A.
n.A.
n.A.
0.20
0.20
0.19
0.20
0.20
0.19
0.19
0.20
0.19
0.19
0.19
0.18
0.18
0.18
0.18
0.18
0.19
n.A.
n.A.
n.A.
n.A.
n.A.
0.20
0.20
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.18
0.18
0.18
0.18
0.19
0.19
0.19
33
Table 9: Lifecycle net-debt issue frequency and target leverage instability
Initial leverage (L0 ) is the leverage ratio in year 0 for a firm still listed in year t. Change in leverage is the leverage ratio
change from year 0 to year t (Lt − L0 ). Leverage instability is fraction of firms for which the leverage ratio change from year 0
to year t exceeds +/− 20% (|Lt − L0 | > 0.2) and leverage volatility is the standard deviation of the leverage ratio up to year
t. The issue frequency classification is performed annually and uses the 2.5% threshold. LFI and HFi are the bottom and
top quartiles of the issue frequency distribution. Year 0 is the year of public listing. Panel A presents corresponding statistics based on actual leverage ratios, Panel B is based on target leverage ratios. Sample of 12,131 U.S. public firms, 1971-2012.
Initial leverage
L0
Year
All
LFI
HFI
Change in leverage
(Lt − L0 )
Leverage instability
|(Lt − L0 )| > 0.2
Leverage volatility
All
LFI
HFI
All
LFI
HFI
All
LFI
HFI
A: L is the target leverage ratio
0
n.A. n.A. n.A.
n.A.
1
0.20 0.11 0.30
n.A.
2
0.20 0.09 0.33
0.04
3
0.19 0.07 0.30
0.05
4
0.19 0.06 0.28
0.06
5
0.19 0.10 0.30
0.06
6
0.18 0.08 0.28
0.07
7
0.18 0.07 0.30
0.07
8
0.18 0.06 0.29
0.06
9
0.18 0.05 0.28
0.06
10
0.17 0.04 0.29
0.05
11
0.17 0.07 0.28
0.05
12
0.17 0.06 0.27
0.05
13
0.16 0.04 0.28
0.05
14
0.16 0.04 0.27
0.05
15
0.16 0.04 0.26
0.06
16
0.16 0.05 0.27
0.05
17
0.16 0.05 0.26
0.05
18
0.16 0.05 0.26
0.05
19
0.16 0.04 0.27
0.05
20
0.16 0.03 0.26
0.04
Avg.
0.18 0.08 0.29
0.04
n.A.
n.A.
0.03
0.04
0.04
0.05
0.05
0.05
0.05
0.05
0.04
0.04
0.04
0.04
0.03
0.04
0.04
0.04
0.04
0.04
0.02
0.03
n.A.
n.A.
0.04
0.06
0.07
0.08
0.08
0.09
0.08
0.08
0.07
0.07
0.07
0.07
0.07
0.08
0.08
0.07
0.07
0.08
0.06
0.06
n.A.
n.A.
0.01
0.03
0.04
0.05
0.07
0.07
0.07
0.08
0.06
0.07
0.08
0.07
0.07
0.09
0.09
0.08
0.09
0.08
0.07
0.04
n.A.
n.A.
0.01
0.02
0.03
0.04
0.04
0.06
0.05
0.06
0.05
0.07
0.07
0.05
0.06
0.07
0.07
0.07
0.07
0.06
0.06
0.03
n.A.
n.A.
0.01
0.03
0.06
0.07
0.09
0.09
0.08
0.09
0.07
0.08
0.09
0.07
0.07
0.09
0.10
0.09
0.10
0.10
0.08
0.05
n.A.
n.A.
n.A.
n.A.
n.A.
0.06
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
n.A.
n.A.
n.A.
n.A.
n.A.
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
n.A.
n.A.
n.A.
n.A.
n.A.
0.06
0.06
0.06
0.06
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
B: L is the target net leverage ratio
0
n.A. n.A. n.A.
n.A. n.A.
1
0.05 -0.09 0.20
n.A. n.A.
2
0.05 -0.13 0.26
0.02 0.01
3
0.05 -0.17 0.23
0.02 0.01
4
0.06 -0.17 0.20
0.04 0.02
5
0.06 -0.09 0.23
0.04 0.03
6
0.06 -0.11 0.21
0.05 0.03
7
0.06 -0.12 0.24
0.04 0.02
8
0.06 -0.14 0.23
0.04 0.01
9
0.06 -0.15 0.22
0.03 0.01
10
0.06 -0.16 0.23
0.01 -0.01
11
0.05 -0.11 0.22
0.02 0.00
12
0.05 -0.13 0.21
0.02 0.00
13
0.05 -0.14 0.23
0.03 0.01
14
0.05 -0.15 0.22
0.02 0.01
15
0.06 -0.16 0.21
0.03 0.00
16
0.06 -0.11 0.22
0.02 0.00
17
0.06 -0.11 0.21
0.01 -0.01
18
0.07 -0.12 0.21
0.02 0.00
19
0.06 -0.13 0.22
0.01 -0.01
20
0.07 -0.14 0.21
0.01 -0.01
Avg.
0.06 -0.12 0.22
0.02 0.01
n.A.
n.A.
0.03
0.04
0.05
0.07
0.07
0.07
0.06
0.05
0.04
0.05
0.04
0.06
0.05
0.06
0.05
0.04
0.05
0.04
0.04
0.04
n.A.
n.A.
0.01
0.02
0.03
0.04
0.05
0.05
0.06
0.06
0.05
0.06
0.07
0.07
0.07
0.08
0.08
0.08
0.08
0.09
0.09
0.04
n.A.
n.A.
0.00
0.01
0.01
0.03
0.02
0.03
0.03
0.04
0.04
0.05
0.05
0.05
0.05
0.06
0.06
0.06
0.06
0.06
0.06
0.02
n.A.
n.A.
0.01
0.03
0.05
0.07
0.08
0.08
0.10
0.08
0.08
0.08
0.09
0.10
0.09
0.11
0.10
0.10
0.12
0.13
0.12
0.05
n.A.
n.A.
n.A.
n.A.
n.A.
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.05
n.A.
n.A.
n.A.
n.A.
n.A.
0.04
0.04
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
n.A.
n.A.
n.A.
n.A.
n.A.
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
34
Table 10: Speed-of-adjustment to target leverage deviations
The table reports the estimated value of φ from the following type of regression
Yi,t = α + ηi + φ L∗i,t (βXi,t−1 ) − Li,t−1 + i,t .
where the dependent variable is the either the change in the leverage ratio or the net-debt issue by firm i at time t,
i,t is the regression error term, αi is the constant, ηi is firm fixed effect, Li,t−1 is the leverage ratio lagged one period,
L∗i,t (βXi,t−1 is the current period (estimated) target leverage ratio where the determinants Xi, t − 1 are the lagged values
of size, profitability, Q, cash ratio, tangibility, depreciation, R&D expenses, capital expenditures and the median industry
leverage ratio (all winsorized at the 1(99) percent level). Coefficients are estimated using system GMM, implemented
using the stata command xtabond2, assuming that all regressors are predetermined. We use a maximum number of lags
of 3 (5) for book leverage (target leverage ratio regressors). No investment periods are defined as those when the sum
of capital expenditures and acquisition outlays is less than 5% of the total book value of assets. *, **, *** indicate significance at the 10%, 5% and 1% level, respectively. Total sample of 12,131 U.S. public firms and 80,900 firm-years, 1971-2012.
Speed-of-adjustment parameter estimate φ
Dependent
variable Yi,t
All
HFI
LFI
HFI - LFI
Panel A: Yi,t ≡ Li,t − Li,t−1 (the leverage ratio change):
Coefficient
S.E.
Z-score
0.259***
0.009
0.272***
0.020
0.044
1.793
Panel B: Yi,t ≡
Coefficient
S.E.
Z-score
0.316***
0.014
N DIi,t
Ai,t
0.231***
0.016
(net-debt issues):
0.319***
0.032
0.194***
0.021
0.125
3.258
Panel C: Yi,t ≡
+
N DIi,t
Ai,t
(positive net-debt issues):
Coefficient
0.014
0.068
0.033
0.035
S.E.
0.014
0.532
0.029
Z-score
0.067
N DIi,t
Panel D: Yi,t ≡ Ai,t (net-debt issues; no investment):
Coefficient
S.E.
Z-score
0.282***
0.018
0.408***
0.035
0.223
127.961
0.185
0.001
35
Table 11: Robustness: Speed-of-adjustment to target leverage deviations (book leverage)
The table reports the estimated value of φ from the following type of regression
Yi,t = α + ηi + φ L∗i,t (βXi,t−1 ) − Li,t−1 + i,t .
where the dependent variable is the either the change in the leverage ratio or the net-debt issue by firm i at time t,
i,t is the regression error term, αi is the constant, ηi is firm fixed effect, Li,t−1 is the leverage ratio lagged one period,
L∗i,t (βXi,t−1 is the current period (estimated) target leverage ratio where the determinants Xi, t − 1 are the lagged values
of size, profitability, Q, cash ratio, tangibility, depreciation, R&D expenses, capital expenditures and the median industry
leverage ratio (all winsorized at the 1(99) percent level). Coefficients are estimated using system GMM, implemented
using the stata command xtabond2, assuming that all regressors are predetermined. We use a maximum number of lags
of 3 (5) for book leverage (target leverage ratio regressors). No investment periods are defined as those when the sum
of capital expenditures and acquisition outlays is less than 5% of the total book value of assets. *, **, *** indicate significance at the 10%, 5% and 1% level, respectively. Total sample of 12,131 U.S. public firms and 80,900 firm-years, 1971-2012.
Speed-of-adjustment parameter estimate φ
Dependent
variable Yi,t
All
HFI
LFI
HFI - LFI
Panel A: Yi,t ≡ Li,t − Li,t−1 (the leverage ratio change):
Coefficient
Standard error
Z-score
0.289***
(0.010)
Panel B: Yi,t ≡
N DIi,t
Ai,t
Coefficient
Standard error
Z-score
0.177***
0.014
Panel C: Yi,t ≡
Coefficient
S.E.
Z-score
Panel D: Yi,t ≡
Coefficient
S.E.
Z-score
0.373***
(0.016)
0.282***
(0.022)
0.090***
3.293
(net-debt issues):
0.222***
0.024
0.187
0.157
0.035
0.221
+
N DIi,t
Ai,t
(positive net-debt issues):
0.046
0.094
N DIi,t
Ai,t
-0.022
0.035
-0.047
0.037
-0.025
-0.503
(net-debt issues; no investment):
0.236***
0.019
0.341***
0.037
0.233
0.376
0.108
0.286
36
Table 12: Financial constraint scores for LFIs and HFIs
The financial constraint scores are the KZ-index (Kaplan and Zingales, 1997), the WW-index (Whited and Wu, 2006), and
the SA-index (Hadlock and Pierce, 2010). The low and high-frequency issuers (LFIs and HFIs) are classified using the 2.5%
issue size threshold. Sample of 12,131 U.S. public firms, 1971-2012.
Year
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Avg.
KZ-index
LFI HFI
WW-index
LFI HFI
SA-index
LFI HFI
0.54
0.56
0.42
0.30
0.18
0.32
0.24
0.13
0.02
-0.05
-0.13
-0.01
-0.04
-0.09
-0.10
-0.21
-0.07
-0.22
-0.26
-0.23
-0.42
1.24
1.33
1.47
1.33
1.22
1.32
1.24
1.30
1.25
1.19
1.25
1.15
1.10
1.17
1.10
1.02
1.08
1.02
0.95
1.02
0.96
n.a.
-0.19
-0.21
-0.20
-0.20
-0.20
-0.22
-0.22
-0.22
-0.22
-0.25
-0.25
-0.24
-0.24
-0.24
-0.25
-0.26
-0.26
-0.25
-0.26
-0.27
n.a.
-0.23
-0.21
-0.25
-0.21
-0.23
-0.23
-0.25
-0.25
-0.25
-0.25
-0.26
-0.26
-0.27
-0.27
-0.29
-0.29
-0.29
-0.29
-0.29
-0.30
-1.92
-2.02
-2.11
-2.21
-2.29
-2.37
-2.45
-2.53
-2.60
-2.66
-2.73
-2.80
-2.86
-2.94
-2.96
-3.00
-3.11
-3.15
-3.18
-3.26
-3.32
-1.95
-2.07
-2.26
-2.33
-2.41
-2.53
-2.60
-2.75
-2.79
-2.84
-2.94
-3.01
-3.07
-3.17
-3.22
-3.27
-3.36
-3.38
-3.44
-3.52
-3.56
0.28
1.24
-0.22
-0.25
-2.34
-2.56
37
Table 13: Pecking Order Financing Deficit Regressions
The table reports the estimated value of β from the following type of regression
N DIi,t
= α + βDEFi,t + γDEF2i,t + i,t .
Ai,t
where the dependent variable is total net debt issues (scaled by total assets) and the right hand side variable is the
financing deficit (dv + capx + aqc + ivch - siv - ivstch - sppe - ivaco - oancf), standardized by total assets. The
inclusion of the squared financing deficit follows Lemmon and Zender (2010). The financing deficit is trimmed at the 1
(99) percent level. Coefficients are estimated using OLS (Panel A) and fixed effects (Panel B). . *, **, *** indicate significance at the 10%, 5% and 1% level, respectively. Total sample of 12,131 U.S. public firms and 80,900 firm-years, 1971-2012.
Financing deficit parameter estimate β
Dependent
variable Yi,t
All
HFI
LFI
HFI - LFI
0.689***
0.007
0.077***
0.005
0.613
Panel A: OLS Estimation
Coefficient
Standard Error
Z-score
0.392***
0.004
68.813
Panel B: Fixed-effect Estimation
Coefficient
Standard Error
Z-score
0.433***
0.006
0.748***
0.008
0.104***
0.006
0.644
61.279
38
Figure 1: Lifecycle financing ratios for high and low frequency issuers
P
P
The figure plots four financing ratios Rj ≡ Sj+ / 7i Si+ , where 7i Si+ is the firm’s total cash contribution from each of its
P7 +
+
+ CF + + ∆C − + I − + ∆W − + O+ . EI is proceeds from
seven (non-negative) sources of funds:
i Si = EI + N DI
+
equity issues, N DI is positive net debt issues (net of debt retirements), CF + is positive operating cash flow, ∆C − is
cash drawdowns, I − is sale of illiquid assets (disinvestment of property, plant and equipment), ∆W − is reduction in net
working capital, and O+ is “other” sources of funds (a
residual closing the P
cash flow identity). By construction,
P7small
7 +
+
+ P7 +
+
S
,
R
=
N
DI
/
the four ratios sum vertically to one:
R
+
EI = EI/
N DI
i Si , and
i Si , RCF + = CF /
i i
P
RAS = (∆C − + I − + ∆W − + O+ )/ 7i Si+ . Year 0 is the year of public listing. Sample of 12,131 U.S. public industrial
firms and 93,101 firm-years, 1971-2012.
0
.1
Financing Ratio
.2
.3
.4
.5
Panel A: Average funding ratios (HFI)
0
5
10
Event Year
R(NDI+)
R(CF+)
R(I-)
15
20
R(EI)
R(AS)
0
Financing Ratio
.2
.4
.6
Panel B: Average funding ratios (LFI)
0
5
10
Event Year
R(NDI+)
R(CF+)
R(I-)
39
15
R(EI)
R(AS)
20
Figure 2: Dynamic issue hazards for low and high-frequency net-debt issuers
Estimation is based on an exponential shared frailty hazard model of the form
hi (t) = h0 (t)exp(β0 + βxi (t))αi
where t is the length of the spell (duration), h0 (t) denotes the baseline hazard, xi (t) a set of control variables, and αi is a
frailty term capturing unobserved observation-specific effects. The Firm-specific covariates are the same as in Table 6. The
baseline hazard is modelled using a quadratic functional form (h0 (t) = a1 t + a2 t2 ). The classification of a firm as LFI or
HFI is performed annually based on the 2.5% issue size threshold. Sample of 12,131 firms, 1971-2012.
.006
.008
Baseline Hazard
.01
.012
.014
.016
A: Estimated dynamic hazard for LFIs
0
5
10
Time Since Last Issue
15
20
.1
.2
Baseline Hazard
.3
.4
.5
.6
B: Estimated dynamic hazard for HFIs
1
2
3
4
Time Since Last Issue
40
5
6