1
Leverage adjustment in extremis: The case of
acquisitions
Abstract
This paper examines the leverage adjustments of Australian firms making
large acquisitions. Using a modified partial adjustment model we find that
firms actively manage their leverages toward target leverage ratios.
Further, we provide new evidence that the relative speed of adjustments is
related to important firm characteristics. The overall evidence in our study
supports the trade-off theory of capital structure.
JEL classification: G32 G43
Keywords: Adjustment to target leverage, Capital structure, Acquisitions
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Leverage adjustment in extremis: The case of
acquisitions
1. Introduction
What motivates a firm to adjust and maintain its capital structure? A widely accepted
view in modern corporate finance is the trade-off theory of capital structure. It postulates that
firms have optimal debt-to-equity ratios that balance marginal tax benefits of debt financing
against marginal financial bankruptcy costs (Modigliani & Merton 1963; Jensen & Meckling
1976). A particular prediction of trade-off theory is that firms will follow a target leverage ratio
in order to balance their leverages and minimise deviations from target leverage ratios. Despite
this strong theoretical underpinning, empirical evidence to corroborate the notion of trade-off
theory has been mixed and inconclusive.1 In this paper, using takeover financing as leverage
shocks and a new empirical methodology, we address the question of whether Australian firms
pursue target leverage ratios.
A recent study in Australia finds evidence in support of firms having target leverage
ratios (Koh et al. 2011). Koh et al. (2011) argue that firms take advantage of intertemporal firm
characteristics to issue debt, a behaviour supporting the notion of target leverage adjustment.
Their findings contradict the long held view that Australian firms followed pecking order
behaviour (Gatward & Sharpe 1996; Suchard & Singh 2006) in their financing activities. The
empirical findings of Koh et al. (2011) contribute to the ongoing general debate on the validity
of trade-off theory of capital structures.2
1 Shyam-Sunder and Myers (1999) and Chang and Dasgupta (2009) challenge the robustness of empirical
evidence that firms have target leverage ratios (We discuss this further in Section 2 below). 2 There is a number of competing theories of capital structure. Trade-off theory of capital structure
advocates the existence of target leverage ratios. Koh et al. (2011) support the theory that Australian firms
follow target adjustment behaviour. Pecking order theory and market timing theory offer alternative
explanations to interpret corporate financing behaviours. Pecking order theory suggests that firms prefer
internal financing and debt to equity due to information asymmetry between management and investors and
adverse selection cost (Myers and Majluf, 1984). Gatward and Sharpe (1996) and Surchard and Singh
(2006) report that Australian firms follow the pecking order financing strategy. That is, corporations
choose to fulfil the needs of new finance with their retained earnings before issuing debt or equity.
Therefore, the existing Australian empirical evidence in capital structure is a matter of debate. On the
other hand, market timing theory argues that firms tend to issue equity when their market values are
considered overvalued and repurchase equity when market values are low relative to book value or past
market values (Baker and Wurgler, 2002).
3
We provide further and new evidence that Australian firms have target leverage ratios,
supporting Koh et al. (2011). We go beyond Koh et al. (2011) and extend the empirical
literature in this area in two important ways. Firstly, we employ an extensive and most recent
Australian mergers and acquisitions sample where firms’ leverages are likely to deviate from
their target leverage ratios due to acquisition related financing transactions. Similar to Harford
et al. (2009), our sampling procedure allows us to determine the role of target leverage ratios in
capital structure decisions for Australian firms. Secondly, we utilise new measures of the speed
of leverage adjustment (from hereafter, SOA refers to speed of leverage adjustment) introduced
in Hovakimian and Li (2011) which addresses estimation bias commonly found in target
adjustment regressions.
If Australian firms have target leverage ratios, we would expect there to be clear
evidence that they move towards their target leverage ratios after corporate events which cause
substantial deviations from the target leverage ratio. We present evidence that this is the case in
Table 3. If Koh et al.’s argument in favour of Australian firms having target leverage ratios is
sound, we would expect Australian firms engaging in acquisitions with the potential to change
their leverages to follow their target leverage ratios and make financing decisions that would
approach their target leverage ratios. Thus, we would expect those over-levered (under-levered)
firms to reduce (increase) their leverages to a lower (higher) level in order to approach their
target leverage ratios. That is, if the acquisition payment method increases the acquirer’s
leverage to a level that is higher than the target leverage ratios, we would expect the acquirer to
issue equity to reduce leverage and move closer to the target leverage ratio. On the other hand,
if the payment method reduces leverage to a level lower than the target leverage ratio, the
acquirer is expected to issue debt in order to increase leverage and approaching the target
leverage ratio.
Our results support the existence of target leverage ratio. Using a sample of 1,133 firms
from the beginning of 2000 and the end of 2010, we find that Australian firms do take into
account their leverages when planning for large acquisitions. Similar to the behaviour of US
firms (Harford et al. 2009) and in accordance with the trade-off theory, Australian firms exhibit
the tendency to adjust leverage ratios in response to the leverage deviation caused by
acquisitions. This result is confirmed when we examine security issuance showing that the over-
and under-leveraged firms issue equity and debt primarily to move the firm along the leverage
continuum, as predicted by trade-off theory.
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We also investigate which firm characteristics are important drivers of the leverage
adjustment process. We find size, profitability and cash are significant firm attributes for the
leverage rebalancing process. In particular, we provide a new result related to the size of the
firm and the SOA. We find that the size variable is not only significant, as postulated by
Flannery and Rangan (2006), but also has a conditional effect on a firm’s leverage adjustment
process – the size is an augmenting factor for slow adjusters while it acts as a withholding factor
for quick adjusters.
We proceed as follows. The next section (Section 2) is a discussion of trade-off theory
and the leverage adjustment process under it. In Section 2 we also discuss empirical issues
associated with tests of target leverage. Section 3 outlines data selection and provides theory
descriptions. Section 4 contains our results related to the target adjustment process and security
issues. Section 5 provides further analyses of the relationship between the SOA and firm
characteristics. Section 6 concludes the paper.
2. Target adjustment behaviour
In US, there are a number of studies (e.g. Jalilvand & Harris 1984; Hovakimian et al.
2001; Flannery & Rangan 2006) which have found supportive empirical evidence to show that
when firms undertake leverage adjustments, they tend to move towards their target leverage
ratios.3 Leary and Roberts (2005) and Harford et al. (2009) find that the pattern in financial
behaviour is consistent with dynamic leverage adjustments and converge towards the target
leverage ratio after accounting for adjustment costs. These studies utilise the traditional target
adjustment models to examine if firms’ leverages shift toward their target leverage ratios in a
long horizon. One of the traditional tests of the target adjustment model is specified as:
levi,t = (1-𝜆) levi,t-1 + 𝜆 ̂i,t + ɛi,t (1)
The coefficient of the target leverage ratio ( ̂ i,t), 𝜆, is represented as the speed of
leverage adjustment (SOA), which is expected to be greater than 0 if there is a leverage
3 American studies such as Jalilvand and Harris (1984), Hovakimian et al. (2001), Fama and French (2002),
Flannery and Rangan (2006) and Kayhan and Titman (2007) find evidence that firms adjust their leverages
and move towards their target leverage ratios over time.
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adjustment undertaken by firms towards the target leverage ratio. Moreover, the higher the
coefficient of the target leverage ratio, the faster is the firm moves toward the target leverage
ratio. In a recent paper, Chang and Dasgupta (2009) undertake mean reversion tests of leverage
ratio and demonstrate that the SOA estimates from a traditional target adjustment model in
simulation samples generated via random financing is as high as when financing behaviour
follows the trade-off theory. Therefore, random data could be interpreted as purposeful
adjustments to the target and those studies supporting the notion that firms have target leverage
ratio using traditional target adjustment models have been the subject of considerable
controversy. We do not dispute this existing empirical evidence. Rather, we take into
consideration the impact of the mechanically mean reversion effect to improve our
understanding of corporate leverage adjustment behaviour.
Prior to Chang and Dasgupta (2009), Shyam-Sunder and Myers (1999) argue that
corporate financing policy is mainly driven by the need for external funds rather than motivation
to move towards the target leverage ratio. That is, firms issue (retire) debt when they face a
financial deficit (surplus), thus, the need for external funds is associated with internally
generated funds. Such financing behaviours appear to support the pecking order theory, rather
than any attempt to reach the target leverage ratio. In addition to this, Shyam-Sunder and Myers
(1999) demonstrate that the target adjustment model appears to produce significant empirical
results when actual financing behaviour follows the pecking order. They explain that there is
mean reversion in leverage ratios which generates spuriously significant results. Chen and Zhao
(2007) further support Shyam-Sunder and Myers’ (1999) proposition and demonstrate that
leverage ratios revert back to the mean mechanically even though the financing behaviours are
inconsistent with target adjustment behaviours. In other words, it provides no information which
reveals firms’ financing behaviours because leverage ratios revert to the mean even though they
do not follow their targets.
Chang and Dasgupta (2009) address the mean reversion issue and generate simulation
samples which are designed that firms do not behave to follow the target leverage ratio. They
provide strong evidence that estimates of SOA generated from the traditional approaches are
inappropriate. In particular, they show that the partial adjustment relationship between the
leverage ratio and other firm characteristics can be mimicked by alternate financing policies
including random financing. That is, the statistically significant estimates of SOA generated by
simulation samples are indistinguishable from estimates obtained from analysing real sample
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data. If both simulation samples and real sample data generate the same results, we cannot
distinguish between target adjustment behaviour and mechanical mean reversion. In other
words, the target adjustment models could be problematic and often generate positive and
significant SOA even when no target behaviour exhibits. Chang and Dasgupta (2009), as we
have noted, appear to invalidate existing studies supporting target behaviour studies.
Hovakimian and Li (2011) present a modified target adjustment process via a two-stage
process that addresses Chang and Dasgupta’s (2009) critique. Hovakimian and Li (2011)
suggest a number of working steps to be followed to avoid the issue of look-ahead bias and the
mechanically mean reversion effect. The first stage uses historical fixed firm effects regressions
to estimate the target leverage ratio. The second stage uses the estimate of the target leverage
ratio from stage 1 in the modified partial adjustment model, which potentially corrects for the
mean reversion and improves the ability to reject the target adjustment hypothesis when firms do
not behave in a way which follows the target leverage ratio. Hovakimian and Li (2011) exclude
those firms with a leverage ratio greater than 0.8 to reduce the bias in favour of target adjustment
behaviour. Following the working steps mentioned above, we can essentially eliminate the bias
in favour of target adjustment behaviour and avoid the risk of generating spuriously significant
estimates of SOA when firms do not follow the target leverage ratio.
An Australian study, Koh et al. (2011) utilise Hovakimian and Li’s (2011) methodology
to show evidence that Australian firms have target leverage ratios while they take advantage of
firm characteristics to raise capital in ideal circumstances. If Koh et al.’s argument in favour of
Australian firms having target leverage ratios is sound, we expect to find evidence supporting
Australian acquirers following their target leverage ratios. To ensure that our findings are not
driven by bias in favour of target adjustment behaviour, we adopt the modified partial
adjustment model used by Hovakimian and Li (2011) to examine if Australian acquirers have
target leverage ratios and undertake leverage adjustments toward a target leverage ratio. In the
existence of target adjustment behaviour studies, we would expect to find positive SOA which
indicates that Australian acquirers follow a target leverage ratio.
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3. Sample data
Using the Zephyr database, we collect a list of merger and acquisition transactions that
are completed throughout Australia between the beginning of 2000 and the end of 2010 to
analyse the acquirers’ target adjustment behaviours. Accounting data are drawn from Aspect
FinAnalysis database. For each observation in the acquisition sample we collect accounting data
from the beginning of 1996 to the end of 2010.4 The definitions for variables used in this paper
are reported in the appendix. Consistent with prior literature (e.g. Fama & French 2002;
Hovakimian & Li 2011), the majority of variables are scaled by total assets in the fiscal year.
The breakdown of the sample used in this paper is reported in Panel A of Table 1. We
start with 2,825 firm-year acquisition observations in our initial sample. Following previous
capital structure studies (e.g. Hovakimian et al. 2001; Koh et al. 2011), we drop acquirers from
the private sector, acquirers belonging to the financial sector (e.g. banks, diversified financials,
insurance and real estate industry group) because financial firms’ leverage ratios are likely to be
significantly different from the leverage ratios of other firms in the sample. Those acquisition
transactions that are not paid with equity only, cash only or a combination of cash and equity are
excluded from the sample. To minimise the effect of outliers, acquirers with a market to book
ratio (M/B) greater than 10, a book leverage (BL) and profitability (EBITDA) greater than 1 or
less than -1 are excluded from the sample (Koh et al. 2011). This process generates a final
sample of 1,133 firm-year acquisition observations for analysis.
From the final sample of 1,133 firm-year acquisition observations, Panel B in Table 1
shows the number of acquisitions conducted by every acquirer over the sample period - 87.4%,
9.6% and 3% of the final sample are initiated by acquirers who make only 1, 2, and 3 or more
acquisitions respectively over the sample period. It provides information on whether the
acquirer conducts more than one acquisition to rebalance its leverage towards the target leverage
ratio (Klasa & Stegemoller 2007; Harford et al. 2009). Finally, Panel C in Table 1 shows only
24.8% of the final sample is conducted with equity only, whereas 46.2% is paid with cash only.
This leaves the remaining (29%) conducted with mixed payment (hereafter, mixed payment
refers to transactions paid with a combination of cash and equity).
4 This allows us, where possible, to use this information to examine firms up to three years before they
engage in acquisition activity.
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--- Insert Table 1 here ---
We arrange the final sample of 1,133 firm-year acquisition observations into three
groups: cash acquirers, stock acquirers and mixed acquirers. This grouping allows us to examine
how methods of payment influence capital structure decisions. Cash acquirers are those
acquirers which offer cash only to settle their acquisition transactions. Stock acquirers pay
equity only and mixed acquirers pay a combination of cash and equity. Of the 1,133 firm-year
acquisition observations in the final sample, there are 523 cash acquirers, 281 stock acquirers
and 329 mixed acquirers.
Table 2 reports the descriptive statistics of acquirers’ characteristics in the pre-acquisition
year, t=-1. Cash acquirers appear to have higher book leverage (0.2123) than mixed acquirers
(0.1579) while stock acquirers have the lowest book leverage (0.1272). The finding is consistent
with Harford et al. (2009) that stock acquirers have a lower level of capital structures in the pre-
acquisition year. In addition to this, stock acquirers have a higher pre-acquisition market to
book ratio (1.8890), higher level of cash reserve (0.2650) and higher level of net equity issued
(0.4191). These phenomena are consistent with market timing theory, which states that
acquirers choose to issue equity to raise capital for investment needs when the market values of
their assets are relatively higher than book values (high market to book ratio) (Baker & Wurgler
2002). Firms with higher market to book ratios tend to hold more cash and grow faster
(Mikkelson & Partch 2003). Stock acquirers also hold a smaller firm size (17.0004): this is
consistent with borrowing decision that a smaller firm with higher default risk has limited access
to debt markets (Warner 1977).
The lower cash balance (0.1223) for cash acquirers suggests that they need to issue debt
to finance their acquisitions. Additionally, cash acquirers appear to hold higher levels of
tangible assets (0.2603) and have a larger firm size (19.5832). Larger firms face lower default
risk and find it advantageous to issue more debt (Titman & Wessels 1988). This is consistent
with Hovakimian et al. (2004) who find that debt issuers hold more tangible assets and are
significantly larger. Larger firms that have greater access to debt markets tend to hold cash
proportionally less than total non-cash assets (Opler et al. 1999). In particular, cash acquirers
are less profitable (0.2312) while their net equity issued (0.2346) is greater than net debt issued
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(0.1989). Profitable firms are predicted to borrow more due to lower financial distress costs
(Frank & Goyal 2009) whereas less profitable firms tend to issue more equity (Frank & Goyal
2008). As mentioned earlier as cash acquirers are required to issue debt to finance their
acquisitions, we would expect them to issue equity to offset the acquisition effect. The
explanation is that cash acquirers undertake immediate leverage adjustments after issuing debt to
finance the acquisition deals. According to trade-off theory, we would expect firms to undertake
leverage adjustments subsequent to acquisition deals if they have target leverage ratios. This
paper provides preliminary evidence of the importance of the target leverage ratio in financing
acquisitions.
--- Insert Table 2 here ---
4. Leverage adjustments surrounding acquisitions
In this section we examine leverage changes surrounding acquisitions as firms are forced
to make significant changes to their leverage policy as a result of making an acquisition. The
acquirer’s financing decisions will change a firm’s target leverage ratio. Consequently if the
firm’s leverage policy is to adjust the leverage to an ‘optimal’ level we should see these
adjustments in a time path relative to the acquisition year, t=0.
4.1. Measuring the Speed of Adjustment
As mentioned earlier we use the Hovakimian and Li (2011) measure of SOA surrounding
the acquisition year. Our use of Hovakimian and Li’s (2011) measure of SOA is a departure
from the methodologies traditionally employed to estimate SOA. Chang and Dasgupta (2009)
provide strong evidence that estimates of SOA generated by the traditional approaches are
inappropriate. In particular, they show that the partial adjustment relationship between leverage
ratio and other firm characteristics (including determinants suggested by theory) can be
mimicked by alternate financing policies including random financing. In addition, the target
adjustment model is susceptible to mechanical mean reversion since the leverage ratio is bound
between 0 and 1 at extreme values (Chen & Zhao 2007).
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Hovakimian and Li (2011) provide a modification of the target adjustment process via a
two-stage process. We follow Hovakimian and Li’s (2011) approach where the first stage is a
historical fixed firm effects regression to estimate the target leverage ratio based on firm
characteristics thought to be important to explain capital structure. It takes the following form:
BLi,t+1 = α + β1M/Bi,t + β2Sizei,t + β3EBITDAi,t + β4PPEi,t + β5Depi,t + β6IndDummy + δi,t+1 (2)
The second stage of Hovakimian and Li’s (2011) approach is to include the estimate of target
leverage ratio, ̂i,t+1, from equation (2) to explain the deviation from the target leverage ratio.
BLi,t+1 – BLi,t = α + 𝜆1 ̂i,t+1 + 𝜆2BLi,t + ɛi,t+1 (3)
Equation (3) above potentially corrects for the mean reversion by including a specific
proxy for the target leverage ratio. In all our analyses we report the values of 𝜆1 and refer to it as
the SOA. 𝜆2 is also an estimate of SOA but its statistical power is likely to be influenced by
mean reversion and hence not reported.
We measure book leverage as the total debt scaled by the total assets and use it as our
measure of leverage. We estimate the target leverage ratio for every firm in the sample by
running annual regressions of book leverage in year t+1 on the capital structure determinants in
year t considered in prior literature (Titman & Wessels 1988; Rajan & Zingales 1995;
Hovakimian et al. 2001; Fama & French 2002; Kayhan & Titman 2007). The capital structure
determinants are market to book ratio (M/B), firm size (Size), profitability (EBITDA), tangible
assets (PPE), depreciation (Dep) and industry dummy (IndDummy) in equation (2).
Firms with potentially profitable investment opportunities have an incentive to avoid
raising funds through debt in order to maintain financial flexibility. In other words, firms with
high growth opportunities tend to raise funds through issuing equity when the stock price is
relatively high. Therefore the target leverage ratio is likely to decrease for these firms (Titman
& Wessels 1988; Rajan & Zingales 1995; Baker & Wurgler 2002). To capture the effect of
growth opportunities on leverage, we use the market to book ratio (M/B) for the firms to proxy
for this effect. We include firm size (Size) in equation (2). Larger firms are more diversified
and tend to have less volatile cash flows. Therefore, large firms can afford more debt and
increase their target leverage ratios because they have greater access to debt markets while firms
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with greater tangible assets can use it as collateral to take on more debt (Rajan & Zingales 1995).
The inverse effect of profitability on leverage should become stronger when firm size increases
(Titman & Wessels 1988; Frank & Goyal 2009). Firms with high past profitability are more
likely to utilise their earnings to pay down their debt. These firms have lower target leverage
ratios compared with less profitable firms. Therefore, we also include tangible assets (PPE) and
profitability (EBITDA) in equation (2). Firms with high tax shield benefits are likely to borrow
more (DeAngelo & Masulis 1980). We include depreciation (Dep) in equation (2) to capture the
effect of interest tax benefits on debt. Finally, we also include an industry dummy (IndDummy)
to control for industry effects not captured by other independent variables. Following
Hovakimian and Li’s approach, acquirers with book leverage (BL) exceeding 0.8 are excluded in
estimating equation (3).
A positive SOA (𝜆1) represents an adjustment speed which is moving towards the
direction of the target leverage ratio, whereas a negative SOA indicates adjustment in an
opposite direction. Considering the levels of speed of adjustment, a high SOA implies that an
average firm undertakes a relatively quicker adjustment.
4.2. Leverage changes surrounding acquisitions
Table 3 shows the SOA, change in book leverage and book leverage of acquirers between
years t-3 and t+3 where the acquisition year is t=0. Panel A, which reports these variables for
our entire sample, shows that that acquirers actively adjust their leverage prior to, and following
the acquisition year. The SOA is consistently and significantly positive during the pre- and post-
acquisition years. The SOA is also highest during the acquisition year (SOA=0.3222). This
result confirms the notion that acquirers are cognizant of leverage effects and make adjustments
to their leverage. It is also interesting to note that the SOA are significantly positive prior to
acquisition years implying that, to some extent, acquirers anticipate future investment
opportunities and possibly ‘gear up’ for the investment needs (DeAngelo et al. 2011; Uysal
2011).
--- Insert Table 3 here ---
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In Table 3, we also provide estimates for subgroups based on the financing methods
employed for the acquisition. Grouping according to the financing method allows us to examine
leverage adjustments conditional on the anticipated direction of leverage rebalancing. In Table
3, we find that the general pattern of active leverage adjustment movement holds for all
financing groups. It is particularly strong for stock acquirers during the years immediately
preceding the acquisition. That stock acquirers find it easier to change their leverage while cash
acquirers do not may be an indication of the relative ease of capital raising in Australia. Since
cash acquirers are likely to issue debt and debt issuance requires more due diligence and
development of credit worthiness (Suchard & Singh 2006), cash acquirers appear to take a
longer time and plan ahead to make significant acquisitions.
As expected, the change in leverage in the acquisition year for all firms is large. Book
leverage goes up by 0.0084 in the acquisition year and is approximately four times higher than
the corresponding change in the previous year (0.0019). The leverage change increases (0.0153)
in the post-acquisition year, t=1 and this pattern of leverage change is similar across all
subgroups. More importantly, the leverage changes are in the predicted direction for our cash
and stock acquirers. For example, the leverage changes in years t=0 and t=1 are positive for
cash acquirers (0.0124 and 0.0208, respectively) while the corresponding changes for the stock
acquirers are negative (-0.0075 and -0.0016, respectively). These directional changes,
conditioned on the method of financing of acquisitions, confirm our postulated link between the
form of financing and leverage change. While it is clear that stock acquirers undergo downward
change in leverage, our evidence of leverage change shows that cash acquirers are likely to issue
debt to finance their acquisitions and experience an upward change in leverage.
The general level of debt is comparatively higher for cash acquirers for the pre- and post-
acquisition years as compared to those for stock acquirers. Book leverage ranges from 0.2372 to
0.2848 for cash acquirers while the corresponding range for stock acquirers is between 0.1684
and 0.1976. This evidence of generally consistent levels of debt across the years, and the
systematic difference between the cash and stock acquirers, provides indirect evidence of target
adjustment by Australian firms. If the ability to issue debt or equity is related to firms’ non-
financial characteristics such as information asymmetry (Bessler et al. 2011), capital proximity
and industry (MacKay & Phillips 2005), the financing decision can cluster around debt levels.
From evidences presented in Panels B and C of Table 3, it appears that firms making cash
acquisitions prefer to maintain higher levels of debt as compared to those firms making
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acquisitions via equity issue. Doukas et al. (2011) explains the role of capital market conditions
is determining the debt financing during “hot-debt” periods. They find that firms issue debt
when debt market conditions are favourable. They find that the cumulative change in book
leverage of these issuers persists for more than five years after the hot-debt issue year. Part of
the explanation for our evidence could be related debt market conditions which allows debt
issuers with larger firm size or better reputation in capital markets to issue debt relatively easily
as compared to stock issuers and sustain higher debt levels (Ferri & Jones 1979; Titman &
Wessels 1988; González & González 2012).
For our subgroup containing firms with mixed financing for making acquisitions, the
results in Table 3 does not convey any consistent and clear pattern. Since the directional effects
of mixed form of financing on leverage is neither clear in theory nor apparent in our analysis, we
do not use results from this subgroup to draw inferences regarding leverage change behaviour.
Nonetheless, we continue to include this sample in our overall sample to draw overall
implications regarding target adjustments of Australian firms.
Overall, the findings in Table 3 support the theory that Australian acquirers have target
leverage ratios. Our findings are consistent with Koh et al. (2011): that Australian firms take
advantage of opportunity available for them to achieve the target leverage ratio. The SOA
observed in Koh et al. (2011) are positive (except for debt issuers) and statistically significant.
Table 6 in Koh et al. (2011) shows the average SOA for debt issuers (-0.0503), equity issuers
(0.2231), dual issuers (0.1284) and non-issuers (0.0821). The SOA comparison between Table 6
in Koh et al. and our evidence in Table 3 suggests that Australian firms that engage in
acquisition activity are more active in engaging in leverage adjustments and appear to adjust
quicker. That is, acquisitions are corporate events which cause substantial deviations from the
target leverage ratio; acquirers appear to be more “keen” to undertake quicker adjustment.
4.3. Capital issues surrounding acquisitions
In this section we examine the capital issue activities of firms undergoing extreme
leverage change due to large acquisitions. This analysis complements the leverage change
analysis of the previous section and allows us to look closely at the interaction between the
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change in leverage caused by the acquisition and a firm’s attempts to move towards the target
leverage ratios through financing activities.
--- Insert Table 4 here ---
Table 4 reports the changes in net financing relative to the acquisition year and is similar
to the analyses in Table 3. We report changes in financing activities from three possible sources
of capital for a public firm: net debt issued, net equity issued and change in retained earnings.
Following convention, we classify a firm to be a net issuer if the firm issues debt or equity above
five percent of the previous year’s total assets (Hovakimian et al. 2001). As in our earlier
analyses, we also partition the sample by the form of financing employed for the acquisition.
As expected, the source of financing in all forms (equity, debt and retained earnings) is
noticeably different in acquisition year, t=0, as compared to the years surrounding it. This result
holds for all subgroups by acquisition methods of payment. For example, the median net debt,
equity and newly retained earnings for cash acquirers are 0.1562, 0.1562 and 0.0211 respectively
and are highest when compared across the years. This evidence is confirmation that large
acquisitions act as a financing shock (Harford et al. 2009). Comparing the debt and equity
issuance activities of cash versus stock acquirers, we find that stock acquirers issue both debt
and equity at higher levels than that by cash acquirers. The average (median) net equity and debt
issued in the acquisition year are 0.3727 and 0.2293 (0.3062 and 0.1993) for stock acquirers and
are higher than the corresponding values for cash acquirers (0.1923 and 0.1959 mean values;
0.1561 and 0.1562 median values). This pattern also holds for the two subgroups when
financing is obtained from newly retained earnings.
The evidence that firms that are financing their acquisitions via equity issues have more
ability to issue equity is not surprising. However, the evidence of the ability of stock acquirers
to issue debt as well may seem counterintuitive. But to the extent that the ability to issue debt
(along with the ability to issue equity) could be a function of the overall deal size in that large
acquisitions are predominantly financed by equity issues (Martin 1996), and it is relatively easier
to obtain debt financing in conjunction with equity issues rather than straight debt financing.
Bessler et al. (2011) find that firms exploit opportunities and make relatively large equity
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issuances and build up cash holding after declines in firm-level information asymmetry in order
to redeem debt and rebalance their leverages. In addition, recent studies point to the existence of
merger and acquisition waves (Duong & Izan 2011) which can account for the easier availability
of debt and equity capital in boom years. In our later cross sectional regression analyses, we
control for these effects.
5. Firm characteristics and the speed of adjustment
We have provided evidence that Australian acquirers have target leverage ratios. Our
findings confirm our expectation that firms change their leverages, presumably to target leverage
ratio, due to a financing shock caused by acquisitions. However, some firms may move towards
their target leverage ratios quicker than others. In this section we address the systematic
differences in the speed in which acquirers move towards their target leverage ratios. Such an
analysis is potentially important in understanding why Australian acquirers have target leverage
ratios.
We examine Australian acquirers’ SOA by estimating equation (4) where the dependent
variable is the residual, ɛi,t+1, from equation (3) and takes the following form:
ɛi,t+1 = α + β1Over + β2Rtni,t + β3Ln(Size)i,t + β4Taxi,t + β5Profiti,t + β6Cash/Mi,t + β7Reli,t +
β8Wave + β9(Profiti,t*Over) + β10(Cash/Mi,t*Over) + µi,t+1 (4)
If ɛi,t+1 is positive, an acquirer is moving faster to its target leverage ratio than predicted;
it is more “keen”, for example, to move to its target leverage ratio than an acquirer with a lower,
or negative value of, ɛi,t+1. For example, if an acquirer’s predicted change is 10% and a change
of 12% is observed, ɛi,t+1 is 2%; it is adjusting quicker than predicted. If an acquirer’s predicted
change is 10% and a change of 2% is observed, ɛi,t+1 is -2%; it is adjusting slower than predicted.
In effect the term ɛi,t+1, being the residual from a cross sectional predictive regression equation
for leverage deviation, captures the under- or over-shooting of the SOA for individual firms.
Thus the use of ɛi,t+1 allows us to build a cross-sectional relationship between the relative speed
of a firm’s adjustment to target leverage ratio with its intrinsic characteristics.
16
Analysing residual value, ɛi,t+1 on pre-acquisition firm characteristics allows us to provide
evidence on how pre-acquisition firm characteristics affect acquirers’ adjustments toward their
target leverage ratios. In order to conduct this analysis, we group residual values, ɛi,t+1 into two
groups: positive residual value and negative residual value. This grouping allows us to examine
the role pre-acquisition firm characteristics and distinguish between what motivates acquirers
that adjust quicker versus those adjust slower towards their target leverage ratios. We estimate
equation (3) via a least square regression method.
The variables we use in equation (4) are:
1. Over: Harford et al. (2009) show those acquirers who are over-levered are more likely to
reduce their leverage deviations subsequent to acquisition. Therefore, we include a dummy
variable for over-leverage to test if acquirers undertake quicker adjustment if they are
already overleveraged relative to their target leverage ratios in the pre-acquisition year. We
create an over-levered dummy which equals 1 when observed leverage is higher than the
target leverage ratio in pre-acquisition year, and 0 otherwise.
2. Rtn: A firm’s stock return incorporate the markets expectations of acquirers’ growth
opportunities (Shleifer & Vishny 2003). A firm experiencing an increase in its stock price is
more likely to issue equity and subsequently reduce its leverage to a lower level
(Hovakimian et al. 2001) and this may lead to a significant deviation from its target leverage
ratio (Kayhan & Titman 2007). In addition, Welch (2004) and Flannery and Rangan (2006)
have documented that firms tend to absorb the impact of share price changes on their
leverages. Therefore, we use stock return as an indicator for changes in leverage. We
measure the past two-year stock return to consider if firms take advantage of higher stock
prices to issue equity. The two-year stock return is defined as the average of past 2 years
percentage share return from the beginning of the pre-acquisition year.
3. Ln(Size): Larger firms face lower default risk and have easier access to debt markets (Titman
& Wessels 1988). Therefore, they can afford a higher level of debt capacity (Frank & Goyal
2009; Harford et al. 2009). Flannery and Rangan (2006) find evidence that larger firms tend
to adjust slower than smaller firms with the larger firms having the ability to issue public
debt. To control for acquirers’ size, we use the natural logarithm of CPI adjusted market
value of acquirers’ assets.
17
4. Tax: The trade-off theory predicts that firms take advantage of tax shields to issue more debt
(Frank & Goyal 2009). Therefore, tax shield benefits should motivate under-levered firms to
issue debt and approach their target leverage ratios at a quicker pace. We measure the
marginal tax rate as the change in tax liability scaled by the change in earnings before
interest and tax.
5. Profit: Firms with high past profitability tend use their accumulated retained earnings to
retire the debt (Titman & Wessels 1988). Assuming firms maintain high profitability, they
have less volatile cash flows and unused debt capacity (Frank & Goyal 2008). Therefore,
they can afford to issue more debt, or buyback their equity to adjust their leverage to reach
their target leverage ratio. We follow Hovakimian et al. (2001) to measure the past
profitability as the average of three years of earnings before interest, tax, depreciation and
amortisation scaled by market value of assets in respective fiscal years.
6. Cash/M: Cash plays an important role in recessions. Firms with sufficient cash holdings can
make sure they have sufficient funds to meet their unexpected contingencies or profitable
investment opportunities when cash flows are low or external financings are expensive
(Opler et al. 1999). Gao (2011) demonstrates that high excess cash holding acquirers spend
more in reducing debt but less on investments compared to acquirers with low excess cash
holdings. Such firms have adequate financial capacity (retire debt/buyback equity) to attain
their target leverage ratios. However, holding liquid assets can cause agency issues between
managers and shareholders which increase discretionary activities by managers and goes
against shareholders’ interest (Jensen 1986). Leary and Roberts (2005) also suggest that
firms promptly adjust their leverages to attain the target leverage ratio only when the
adjustment cost is lower than the benefits of adjustment. In our case, we include cash
balance which is measured by the sum of cash and current investment scaled by market value
of assets.
7. Rel: We also control the likelihood that when size of acquisition transaction is relatively
larger than the market value of acquirer’s assets, acquirers might be forced to undertake
external financing. Martin (1996) suggests that acquirers are more likely to issue equity to
finance acquisitions because equity financing conveys lower potential constraints on
managers. Deal size to acquirer is calculated as acquisition deal value divided by market
value of acquirer assets.
18
8. Wave: We create a high merger and acquisition wave dummy which equals 1 when the
number of merger and acquisition observation in calendar year is higher than the annual
average in the sample to control for merger and acquisition wave effect (Duong & Izan
2011).
9. We also include the interaction of Profit with Over and Cash/M with Over. Examination of
this interaction will provide insights into whether high past profitability or cash motivates
over-levered acquirers adjust quicker towards their target leverage ratios.
--- Insert Table 5 here ---
As in the preceding analyses, we run cross sectional regressions for each of the
subgroups we have examined previously: cash acquirers, stock acquirers and mixed acquirers.
The results of this analysis for acquirers moving faster to their target leverage ratios (that is,
acquirers with positive residual values) are reported in Table 5. The results for acquirers moving
slower (that is, acquirers with negative residual values) are reported in Table 6.
In Table 5, we present the results of multivariate analyses for acquirers with positive
residuals from equation (3) hence are characterised as fast adjusters relative to the average
acquirers in our sample. For all acquirers in this subgroup (Panel A), a statistically significant
negative coefficient (-0.0098) for the Ln(Size) suggest that the larger the market values of assets
for fast adjusters, the market value of assets has a negative effect on the adjustment process. Our
finding is consistent with Flannery and Rangan (2006, page 497) who argue that the external
pressure for larger firms to adjust their leverages is not as intense as smaller firms whose
financial institutions enforce relatively more covenants. Therefore, larger firms do not tend to
adjust as rapidly as smaller firms.
The statistically significant negative coefficient on Cash/M (-0.0889) shows that
acquirers with high cash balances tend to adjust slower. This finding explains that fast adjusters
do not take advantage of their high level of cash balances to approach their target leverage ratios
at a quicker pace. If these acquirers have adequate amount of cash for their normal operations,
19
they become less likely to undertake external financings (Leary & Roberts 2005) and, therefore,
adjust slower. An alternate explanation is that benefits of cash holdings could be higher than
leverage adjustment costs (Leary & Roberts 2005) causing the leverage adjustments to proceed
at a slower pace. In our case, support for this conjecture is found when we partition this
subgroup further according to acquisition financing method. We find the coefficient for cash is
negative and statistically significant (-0.0702) for cash acquirers only. This indicates that those
cash acquirers who hold a higher level of cash balance tend to adjust slower. Firms with high
cash balance are less likely to conduct external financing (Myers & Majluf 1984; Leary &
Roberts 2005), therefore, they prefer to maintain their cash balances rather than promptly adjust
their leverages to attain the target leverage ratio.
The negative and statistically significant coefficient (-0.0126) of the Ln(Size) suggests
that those faster cash acquirers with greater market values of assets tend to adjust slower while
the coefficients for stock acquirers and mixed acquirers are insignificant. Cash acquirers need to
issue debt to finance their acquisition transactions and our finding supports Flannery and Rangan
(2006) that large cash acquirers withstand less external pressure than others when they are away
from their target leverage ratios, therefore, they appear to adjust slower.
The coefficient of the Profit is positive and statistically significant (0.3408) for mixed
acquirers only. This indicates that faster mixed acquirers manage to adjust quicker when their
past profitability are high. Mixed acquirers pay a combination of cash and equity to finance
their acquisition deals, because of this, their leverage ratios are less likely to be higher than cash
acquirers. High profitable mixed acquirers build up high level of retained earnings when they
are making profits so they have sufficient cash inflows to retire the debt they issued for
acquisition transactions (Flannery & Rangan 2006; Frank & Goyal 2009). In other words, they
are more “keen” to adjust quicker towards their target leverage ratios.
The coefficients of Rel are positive and statistically significant for all subgroups in Table
5 and the highest absolute value (0.0891) is for cash acquirers. This indicates that the larger the
transaction size, cash acquirers appear to adjust quicker than stock acquirers and mixed acquirers
and highlights the asymmetric leverage adjustment speeds. Byoun (2008) among others has
pointed out the difference in the SOA towards the target leverage when a firm is above versus
below a target leverage ratio. The high value of the coefficient supports the notion that as cash
acquirers are more likely to borrow more when the transaction size is relatively larger, financial
20
distress costs would also increase in response to the accumulating leverage. For this reason, a
cash acquirer with large acquisition has strong incentives to reduce its leverage to a lower level
quickly. On the other hand, stock acquirers have lesser capital structuring constraints with
increasing debt from lower level or repurchase equity when they are below their target leverage
ratios (Flannery & Rangan 2006; Lemmon & Zender 2010).
The coefficients of the Wave (0.0344) and the interaction variable of the Profit with the
Over (-0.3461) are statistically significant for mixed acquirers only. Mixed acquirers appear to
have a significantly positive relationship with high acquisition wave. The result shows mixed
acquirers participate in high acquisition wave tend to adjust quicker. Rhodes-Kropf and
Viswanathan (2004) find that acquisition wave is correlated with market valuation. During high
acquisition wave, it is beneficial for mixed acquirers who offer a combination of cash and equity
to finance acquisition deals (which reduce their leverages also) while targets are also more
willing to accept a mixed offer rather than only equity because targets concern whether the value
of the stock only offer is mis-valued. The mixed acquirers appear to seize opportunities (that is,
take advantage of high stock prices during high acquisition wave) and adjust towards their target
leverage ratios. Those mixed acquirers are desperate to approach their target leverage ratios,
thus, they adjust quicker.
Acquirers are categorised as over-levered acquirers when their observed leverage is
relatively higher than the target leverage ratio in pre-acquisition. In other words, acquirers have
issued proportionally more debt than equity in pre-acquisition year. Harford et al. (2009)
demonstrate that acquirers are more likely to issue equity to finance acquisitions when they are
over-levered. The result is inconsistent with Harford et al. (2009). Interestingly when we
interact the Profit with the Over, we find that the coefficient is negative (-0.3461) only for the
mixed acquirers group. High profitable firms tend to have less volatile cash flows and unused
debt capacity (Frank & Goyal 2008). They can afford to issue more debt or buyback the equity
to attain their target leverage ratios. However, the fact is mixed acquirers with high profitability
appear to find it advantageous to issue debt rather than issue equity to reverse the pre-acquisition
leverage deviation.
--- Insert Table 6 here ---
21
We now turn our attention to regression analyses of acquirers which have negative
residuals and are likely to move slower than the average acquirers in adjusting to the target
leverage ratio. Thus, they are characterised as slow adjusters in Table 6. Our discussion starts
with the negative significant coefficient (-0.0047) for the Rtn for all acquirers in our sample. As
expected, acquirers with higher past stock returns are reluctant to issue debt (Hovakimian et al.
2004) and firms appear to accept any impact of share price changes on their leverages (Welch
2004; Flannery & Rangan 2006). Firms tend to take advantage of their high stock price to raise
capital (Baker & Wurgler 2002) and become less “keen” to encounter the acquisition effect on
their leverages. This finding is consistent with Zwiebel (1996, page 1213) which finds firms are
more likely move towards their target leverage ratios only when their stock prices are low to
prevent control challenges. The negative and statistically significant coefficient (-0.0088) of the
past stock returns for stock acquirers suggests that slow stock acquirers find its high past stock
returns advantageous and issue equity to finance their acquisition transactions which is
consistent with market timing theory (Baker & Wurgler 2002). The higher the past stock return,
stock acquirers prefer to take advantage of their high stock prices and so it slows down the
adjustment speed towards the target leverage ratio.
The coefficient of Ln(Size) is 0.0033 for all acquirers and 0.0052 for cash acquirers.
This result is in contrast to the observed effect of size on SOA for quick adjusters (Table 5).
Flannery and Rangan (2006, page 498) explain that larger firms have less volatile cash flows,
they bear costs that are lower than smaller firms when they are away from their target leverage
ratios. Large firms appear to adjust quicker when it is necessary to do so. Our evidence shows
that large firms adjusting quicker to minimise their leverage deviations and confirms the
postulated effect of the size variable. Combined with the results in Table 5, it seems that the size
variable is not only significant, as postulated by Flannery and Rangan (2006), has a conditional
effect on a firm’s leverage adjustment process - the size is an augmenting (withholding) factor
for slow (quick) adjusters.
The results in Table 6 also show that stock acquirers take advantage of high marginal tax
benefit and adjust quicker (0.0014) towards the target leverage ratio whereas cash acquirers
adjust slower (-0.0009). We expect acquirers financing acquisitions via equity to issue debt to
reverse the under-leveraged deviation and attain the target leverage ratio. Our evidence shows
that stock acquirers take advantage of high tax shield benefits to issue debt subsequent to
acquisition transactions and maintain the target leverage ratio. On the other hand, we expect
22
cash acquirers to issue equity subsequent to acquisition transactions to attain their target leverage
ratios. High tax shield benefits do not motivate cash acquirers to adjust quicker because tax
benefits are independent from equity financing.
The cash variable produces a negative and statistically coefficient for mixed acquirers
only. Slow mixed acquirers appear to be less “keen” to adjust their leverages although they hold
a higher level of cash balances. This finding is consistent with Table 5 in that fast adjusters with
high cash balances are less “keen” to undertake adjustments, they prefer to maintain cash
balances because benefits of cash balance are higher than costs of external financing (Opler et al.
1999).
The results in Table 6 also show that firms with high past profitability tend to adjust
quicker (0.0029) towards their target leverage ratios. It reflects that past profitability of
acquirers plays an important in deciding the adjustment speed towards the target leverage ratio.
Tsyplakov (2008) and Frank and Goyal (2009) suggest that more profitable firms tend to build
up high level of retained earnings before buying external funds so they generally have lower
level of capital structures. In other words, high profitable firms have more unused debt capacity
and equity capacity. Frank and Goyal (2008) show that high profitable firms tend to issue debt
to shield their profits. Therefore, high profitable acquirers appear to take advantage of its
financial flexibility to borrow more (buyback more), therefore, appear to adjust quicker towards
their target leverage ratios.
6. Conclusion
If firms have target leverage ratios, they will take action to reach their target leverage
ratios. The further firms are from their target leverage ratios, the greater their SOA will be.
Acquisitions are activities many firms engage in and such events can, quite naturally, drive firms
from their target leverage ratios. This paper exploits this idea and, utilizing a sample of
Australian acquirers from 2000 to 2010, confirms that Australian acquirers have target leverage
ratios. Our findings confirm those of Koh et al. (2011) but are consistent with our argument that
firms in extremis will have faster SOA: the estimated SOA presented in this paper are all greater
than those reported in Koh et al.
23
Like Koh et al., we utilize methodology which permits valid conclusions regarding the
existence of target leverage ratio. Chang and Dasgupta (2009) have demonstrated that the
conclusions drawn by a number of seminal works on target leverage ratio are flawed: the
methodology is biased towards finding adjustments to target leverage ratios when this behaviour
is not present. Hovakimian and Li (2011) introduce a methodology which addresses the critique
of Chang and Dasgupta. We utilize Hovakimian and Li’s methodology in our confirmation that
Australian acquirers have target leverage ratios.
We extend Hovakimian and Li’s methodology to consider unexpected, or abnormal,
adjustments to target. If Hovakimian and Li’s methodology perfectly captured firms’
behaviours, the equation used to estimate SOA (equation (2) in this paper) would have a perfect
fit (that is, its R2 would be 100%). However, the model’s fit is not perfect and there are errors;
positive errors indicate that firms adjust faster than average; negative errors indicate that firms
adjust slower than average. We find that analysing positive and negative errors separately
allows us to tease out the fine structure of capital adjustment.
Utilizing variables commonly used to explain capital structure, our models of unexpected
adjustment allow us to comment further on firms’ motivations as well as the applicability of a
range of capital structure theories. The picture is complex, but our findings confirm that, in
moving to their target leverage ratios “…Australian firms act opportunistically. Australian firms
issue debt when they can and equity when they must” (Koh et al. 2011, page 387). Firms tend to
exploit their size to overcome difficulties associated with information asymmetry. Larger
(smaller) cash holdings are associated with slower (quicker) adjustment. Profitability affects
firms’ adjustment in much the same way as cash when firms are adjusting faster than expected
but, when firms are adjusting slower than expected, the effect is the opposite. Profitability also
encourages firms to take on more debt as does a higher marginal tax rate. Firms engaged in
acquisitions in periods of higher takeover activity, however, appear to act opportunistically only
as far as acquisition is concerned; these firms are keen to move quickly to their target leverage
ratios. We also find evidence that acquirers may be motivated to move to their target leverage
ratios if their lower share price makes the acquirers a potential target leverage ratio.
24
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27
Table 1
Sample Selection
The sample is collected from the Zephyr database. The final sample consists of Australian firms completing an
acquisition between 1 Jan 2000 and 31 Dec 2010. Accounting variables are taken from Aspect FinAnalysis
database.
Panel A: Sample selection
Criteria
Sample
Initial Excluded Remaining
Number of acquisition observations 2825
Less:
1. Acquirers where ASX code is unavailable (e.g. private firms) (581)
2. Acquirers from financials sector (328)
3. Other methods of payment (546)
4. Outliers:
Book leverage and profitability greater than 1 or less than -1
Market to book value ratio greater than 10
(237)
Final sample (N) 1133
Panel B: Year wise distribution
Acquisition year, 0 N Percentage Acquirers making 1
acquisition
Acquirers
making 2
acquisitions
Acquirers making 3 or
more acquisitions
2000 57 5% 53 2 2
2001 79 7% 70 6 3
2002 61 5.4% 57 4 -
2003 130 11.5% 114 14 2
2004 148 13.1% 120 21 7
2005 147 13% 118 25 4
2006 95 8.4% 83 7 5
2007 138 12.2% 124 8 6
2008 115 10.2% 101 11 3
2009 58 5.1% 55 3 -
2010 105 9.3% 95 8 2
Final Sample (N) 1133
(%) 87.4% 9.6% 3%
Panel C: Sample firms by the method of payment
Method of payment Cash acquirers Stock acquirers Mixed acquirers Total
523 281 329 1133
Fraction sample (%) 46.2% 24.8% 29% 100%
28
Table 2
Descriptive Statistics
This table reports the averages and standard deviations of acquirers’ characteristics in the pre-acquisition year. Annual accounting variables are measured in the pre-
acquisition year, t=-1 except net equity issued, net debt issued and newly retained earnings which are measured in the acquisition year, t=0. Firm-year acquisition
observations where book leverage (BL) or profitability (EBITDA) are greater than 1 or less than -1 and market to book ratio (M/B) is greater than 10 are excluded. Book
leverage (BL) is total debt divided by total assets, profitability (EBITDA) is earnings before interest, tax, depreciation and amortisation divided by lagged total assets, firm
size (Size) is the natural logarithm of total assets, market to book ratio (M/B) is book debt plus market equity divided by total assets, tangible assets (PPE) is net plant,
property and equipment divided by total assets, depreciation (Dep) is sum of depreciation expenses and amortisation divided by lagged total assets, cash (Cash/A) equals to
the sum of cash and current investment divided by total assets. Acquirers are defined as issuing debt (equity) when net debt (equity) issued is greater than 5% of the pre-issue
total assets (Hovakimian et al. 2001). Net equity issued (e/A) is measured as the change in book equity minus the change in retained profits divided by total assets. Net debt
issued (d/A) is measured as the change in total debt divided by total assets. Newly retained earnings (RE/A) is measured as the change in retained profits divided by total
assets.
N BL EBITDA Size M/B PPE Dep Cash/A e/A d/A RE/A
All acquirers 1065 Average 0.1753 0.2122 18.4844 1.7974 0.2172 0.0503 0.1793 0.3090 0.2095 0.1481
S.D. 0.1667 0.1701 2.4471 1.2065 0.2286 0.0613 0.2247 0.3694 0.1780 0.7519
Cash acquirers 490 Average 0.2123 0.1925 19.5823 1.6549 0.2603 0.0509 0.1223 0.2346 0.1989 0.0598
S.D. 0.1610 0.1493 2.3855 1.0878 0.2340 0.0529 0.1779 0.3949 0.1442 0.3471
Stock acquirers 265 Average 0.1272 0.2313 17.0004 1.8890 0.1785 0.0497 0.2650 0.4191 0.2257 0.3829
S.D. 0.1782 0.1965 1.9120 1.3574 0.2296 0.0719 0.2710 0.4480 0.2505 1.3497
Mixed acquirers 310 Average 0.1579 0.2271 18.0176 1.9445 0.1821 0.0498 0.1943 0.3021 0.2124 0.0874
S.D. 0.1519 0.1738 2.1147 1.2268 0.2071 0.0638 0.2211 0.2322 0.1588 0.3730
29
Table 3
Leverage adjustments surrounding acquisitions
This table shows the means of speed of leverage adjustment estimates (SOA), changes in book leverage, book leverage,
number of observation and Fama-Macbeth t-statistics between year t-3 and year t+3 where the acquisition year is t=0.
The speed of leverage adjustment (SOA) is equal to the average of 𝜆1 in following equation.
BLi,t+1 – BLi,t = α + 𝜆1 ̂i,t+1 + 𝜆2BLi,t + ɛi,t+1 (2)
Following Hovakimian and Li (2011, page 31), acquirers with book leverage (BL) exceeding 0.8 are excluded. The
change in book leverage (Δ in BL) equals to the different of book leverage in year t and book leverage in year t-1. The
book leverage (BL) is the mean of book leverage. Fama-Macbeth t-statistics are in brackets. * and ** denote
significance at the 5% and 1% confidence levels respectively.
Year relative to acquisition
-3 -2 -1 0 +1 +2 +3
Panel A: All acquirers
SOA 0.1187 0.2035 0.3046 0.3222 0.2268 0.1511 0.2135
t-stat (3.2254)* (2.5730)* (3.3045)** (5.1654)** (3.5944)** (2.6432)* (3.4830)*
Δ in BL -0.0078 0.0008 0.0019 0.0084 0.0153 0.0064 0.0025
BL 0.2301 0.2236 0.2201 0.2194 0.2455 0.2559 0.2620
N 505 599 694 820 679 597 504
Panel B: Cash acquirers
SOA 0.1125 0.2462 0.2030 0.2629 0.1302 0.0293 0.1151
t-stat (1.0367) (2.5171)* (3.1898)** (2.8949)* (3.1293)* (0.4086) (0.9249)
Δ in BL -0.0036 -0.0082 -0.0009 0.0124 0.0208 0.0041 0.0040
BL 0.2576 0.2438 0.2372 0.2459 0.2710 0.2794 0.2848
N 299 342 377 421 372 332 299
Panel C: Stock acquirers
SOA -0.0639 0.1739 0.2764 0.2629 0.3866 -0.0811 -0.2436
t-stat (-0.3614) (1.1840) (1.6125) (1.4322) (2.5377)* (0.6879) (0.9781)
Δ in BL -0.0051 0.0033 0.0094 -0.0075 -0.0016 0.0242 -0.0118
BL 0.1871 0.1848 0.1811 0.1684 0.1764 0.1976 0.1934
N 81 102 126 157 117 92 68
Panel D: Mixed acquirers
SOA 0.0556 0.1375 0.3110 0.3321 0.2381 0.3099 0.3318
t-stat (0.6873) (2.6587)* (1.8122) (2.6571)* (2.6179)* (2.7366)* (1.7944)
Δ in BL -0.0197 0.0189 0.0027 0.0118 0.0151 0.0011 0.0064
BL 0.1923 0.2047 0.2122 0.2063 0.2381 0.2418 0.2464
N 125 155 191 242 190 173 137
30
Table 4
Capital issues surrounding acquisitions
This table shows net equity issued, net debt issued and newly retained earnings surrounding the acquisition year,
t=0. Acquirers are defined as issuing debt (equity) when debt (equity) issuance is greater than 5% of pre-issue
total assets (Hovakimian et al. 2001). Net equity issued equals (e/A) the change in book equity minus the change
in retained profits divided by total assets. Net debt issued (d/A) is measured as the change in book debt divided by
total assets. Newly retained earnings (RE/A) is measured as the change in retained profits divided by total assets.
Years relative to acquisition
-3 -2 -1 0 +1 +2 +3
Panel A: All acquirers
e/A Average 0.2089 0.2010 0.2190 0.2582 0.2122 0.1693 0.1575
Median 0.1362 0.1415 0.1436 0.1819 0.1502 0.1240 0.1151
N 205 246 295 456 299 193 168
d/A Average 0.1622 0.1784 0.1847 0.2080 0.1901 0.1487 0.1714
Median 0.1352 0.1405 0.1492 0.1616 0.1537 0.1231 0.1359
N 233 283 343 519 403 283 218
RE/A Average 0.0617 0.0615 0.0678 0.0818 0.0620 0.0632 0.0878
Median 0.0169 0.0192 0.0216 0.0247 0.0204 0.0171 0.0171
N 505 599 694 820 679 597 504
Panel B: Cash acquirers
e/A Average 0.1963 0.1726 0.1897 0.1923 0.1747 0.1609 0.1604
Median 0.1343 0.1291 0.1284 0.1561 0.1378 0.1187 0.1175
N 106 132 137 186 139 91 95
d/A Average 0.1685 0.1712 0.1772 0.1959 0.1913 0.1358 0.1721
Median 0.1404 0.1403 0.1403 0.1562 0.1556 0.1077 0.1340
N 138 149 178 241 211 168 127
RE/A Average 0.0287 0.0402 0.0501 0.0462 0.0604 0.0217 0.0306
Median 0.0130 0.0188 0.0168 0.0211 0.0193 0.0147 0.0144
N 299 342 126 421 372 332 299
Panel C: Stock acquirers
e/A Average 0.2671 0.2886 0.2762 0.3727 0.3240 0.1679 0.1629
Median 0.1359 0.2253 0.2294 0.3062 0.2359 0.1170 0.1053
N 28 42 55 106 61 37 27
d/A Average 0.1790 0.2019 0.1893 0.2293 0.1678 0.1723 0.1459
Median 0.1446 0.1765 0.1452 0.1993 0.1433 0.1451 0.1217
N 35 51 63 100 73 41 34
RE/A Average 0.1629 0.1042 0.1497 0.1852 0.0522 0.0959 0.6643
Median 0.0353 0.0262 0.0327 0.0322 0.0195 0.0259 0.0220
N 81 102 126 157 117 92 68
Panel D: Mixed acquirers
e/A Average 0.2049 0.2018 0.2274 0.2590 0.1960 0.1818 0.1486
Median 0.1386 0.1415 0.1437 0.1868 0.1619 0.1415 0.1091
N 71 72 103 164 99 65 46
d/A Average 0.1380 0.1768 0.1950 0.2123 0.2016 0.1650 0.1851
Median 0.1087 0.1260 0.1608 0.1614 0.1744 0.1403 0.1552
N 60 83 102 178 119 74 57
RE/A Average 0.0752 0.0805 0.0489 0.0768 0.0712 0.1256 -0.0735
Median 0.0154 0.0192 0.0264 0.0289 0.0251 0.0192 0.0213
N 125 155 191 242 190 173 137
31
Table 5
OLS Regression of SOA of Fast Adjusters on firm characteristics The dependent variable is the positive residual value for each firm, ɛi,t+1, obtained after estimating equation (2).
All accounting variables are measured in the pre-acquisition year, t-1, except deal size to acquirer (Rel) and the
high acquisition wave dummy variable (Wave) which are measured in acquisition year, t=0. The over-levered
dummy (Over) equals to 1 when if book leverage is higher than target leverage ratio, and 0 otherwise, past share
return (Rtn) is the average of the past 2 years share return (measured in percent) from the beginning of the pre-
acquisition year, CPI adjusted assets (Ln(Size)) is the natural logarithm of the Consumer Price Index adjusted
acquirer's market value of assets, marginal tax (Tax) is the change in tax on earnings before interest and tax
divided by change in earnings before interest and tax, past profitability (Profit) is the average of 3 years earnings
before interest, tax, depreciation and amortisation divided by the market value of assets in respective fiscal years,
cash (Cash/M) equals the sum of cash and current investment divided by market assets, market assets are
measured as the sum of book debt and market equity, deal size to acquirer (Rel) is calculated as acquisition deal
value divided by market value of acquirer assets, high acquisition wave dummy (Wave) equals to 1 when the
number of merger and acquisition observations in calendar year is higher than the sample average and 0
otherwise. The t-statistics are in brackets and calculated following White (1980). * and ** denote significance at
the 5% and 1% confidence levels respectively.
Independent variables
Panel A:
All acquirers
Panel B:
Cash acquirers
Panel C:
Stock acquirers
Panel D:
Mixed acquirers
Coefficients
(t-stat)
Coefficients
(t-stat)
Coefficients
(t-stat)
Coefficients
(t-stat)
Constant 0.3052 0.3893 0.3133 0.1026
(4.9586)** (4.5059)** (1.2260) (0.8128)
Over -0.0059 -0.0026 -0.0389 -0.0066
(-0.3804) (-0.1102) (-0.6153) (-0.2596)
Rtn -0.0048 -0.0088 0.0006 -0.0070
(-1.770) (-1.9093) (-0.1672) (-1.9438)
Ln(Size) -0.0098 -0.0126 -0.0094 -0.0018
(-4.0527)** (-3.9357)** (-0.9352) (-0.3455)
Tax -0.0005 0.0101 0.0057 0.0008
(-0.4009) (1.8115) (0.6647) (0.6183)
Profit 0.0655 0.0065 0.0342 0.3408
(0.6093) (0.0666) (0.1094) (2.0176)*
Cash/M -0.0889 -0.0702 -0.3077 -1.0137
(-2.7599)** (-2.3660)* (-1.4891) (-1.7476)
Rel 0.0005 0.0891 0.0013 0.0012
(1.0867) (2.5015)* (3.0420)** (8.1328)**
Wave 0.0085 -0.0018 0.0296 0.0344
(1.0308) (-0.1837) (1.2855) (2.0603)*
Profit*Over -0.0838 -0.1576 0.3688 -0.3461
(-0.7770) (-1.0993) (1.0099) (-2.0562)*
Cash/M*Over 0.0014 -0.0092 -0.1932 0.6693
(0.0232) (0.8736) (-0.9748) (1.1443)
Adjusted R2 0.1113 0.2152 0.0549 0.2693
Akaike Information Criterion -2.5204 -2.7661 -2.0598 -2.5459
N 277 156 44 77
32
Table 6
OLS Regression of SOA of Slow Adjusters on firm characteristics The dependent variable is the negative residual value for each firm, ɛi,t+1, obtained after estimating equation (2).
All accounting variables are measured in the pre-acquisition year, t-1, except deal size to acquirer (Rel) and the
high acquisition wave dummy variable (Wave) which are measured in acquisition year, t=0. The over-levered
dummy (Over) equals to 1 when if book leverage is higher than target leverage ratio, and 0 otherwise, past share
return (Rtn) is the average of the past 2 years share return (measured in percent) from the beginning of the pre-
acquisition year, CPI adjusted assets (Ln(Size)) is the natural logarithm of the Consumer Price Index adjusted
acquirer's market value of assets, marginal tax (Tax) is the change in tax on earnings before interest and tax
divided by change in earnings before interest and tax, past profitability (Profit) is the average of 3 years earnings
before interest, tax, depreciation and amortisation divided by the market value of assets in respective fiscal years,
cash (Cash/M) equals the sum of cash and current investment divided by market assets, market assets are
measured as the sum of book debt and market equity, deal size to acquirer (Rel) is calculated as acquisition deal
value divided by market value of acquirer assets, high acquisition wave dummy (Wave) equals to 1 when the
number of merger and acquisition observations in calendar year is higher than the sample average and 0
otherwise. The t-statistics are in brackets and calculated following White (1980). * and ** denote significance at
the 5% and 1% confidence levels respectively.
Independent variables
Panel A:
All acquirers
Panel B:
Cash acquirers
Panel C:
Stock acquirers
Panel D:
Mixed acquirers
Coefficients
(t-stat)
Coefficients
(t-stat)
Coefficients
(t-stat)
Coefficients
(t-stat)
Constant -0.1416 -0.2147 -0.1784 0.0042
(-3.4250)** (-3.4291)** (-2.1077)* (0.0661)
Over 0.0059 0.0209 -0.0121 -0.0227
(0.8057) (1.4976) (-0.5500) (-0.5769)
Rtn -0.0047 -0.0032 -0.0088 0.0004
(-2.4907)* (-1.8801) (-2.5701)* (0.1539)
Ln(Size) 0.0033 0.0052 0.0055 -0.0013
(2.0250)* (2.2023)* (1.4720) (-0.4983)
Tax -0.0002 -0.0009 0.0014 0.0025
(-0.4432) (-5.4044)** (2.7082)** (0.7405)
Profit 0.0029 0.1188 0.0021 0.0088
(2.6580)** (1.9644) (1.6210) (0.9264)
Cash/M -0.0046 0.0974 0.0399 -0.1707
(-0.0950) (1.9033) (1.6461) (-3.4596)**
Rel -0.0033 -0.0005 0.0011 -0.0156
(-0.3497) (-1.6035) (0.1581) (-0.6258)
Wave -0.0019 0.0005 -0.0195 -0.0227
(-0.3306) (0.0581) (-1.6921) (-2.1333)*
Profit*Over -0.0010 0.0001 -0.0322 -0.0087
(-0.9924) (0.0018) (-0.2540) (-0.8644)
Cash/M*Over -0.0329 -0.1133 -0.0322 -0.0834
(-0.4907) (-1.8926) (-0.3293) (-0.7523)
Adjusted R2 0.0403 0.1168 0.1059 0.2267
Akaike Information Criterion -3.2602 -3.3540 -3.0419 -3.5304
N 307 161 69 77
33
Appendix
Variable Sources and Definitions
Accounting data are collected from Aspect FinAnalysis database between 1 Jan 1996 and 31 Dec 2010. The accounting data in Panels A, B, C and D are collected from
FinAnalysis Annual Balance Sheet, FinAnalysis Ratio Analysis, Annual Profit and Loss and Annual Sundry Analysis respectively.
Variable FinAnalysis Definitions
Panel A: Annual Balance Sheet
Book equityi,t Retained profitsi,t + Paid in share capitali,t
Book debti,t Total assetsi,t – Book equityi,t
Book leveragei,t (BL) Total debti,t/Total assetsi,t
Market to Book ratioi,t (M/B) (Book debti,t + Market equityi,t)/Total assetsi,t
Firm Sizei,t (Size) Natural logarithm of Total assetsi,t
Market assetsi,t Book Debti,t + Market equityi,t
Cashi,t (Cash) Cashi,t + Non current investmenti,t
Net debt issuedi,t (d/A) (Book debti,t – Book debti,t-1)/Total assetsi,t
Net equity issuedi,t (e/A) [(Book equityi,t – Book equityi,t-1) – (Retained profitsi,t – Retained profitsi,t-1)]/Total assetsi,t
New retained earningsi,t (RE/A) (Retained profitsi,t – Retained profitsi,t-1)/Total assetsi,t
Panel B: Annual Ratio Analysis
Market equityi,t Market capitalisationi,t
Share Returni,t (Year end share pricei,t – Year end share pricei,t-1)/Year end share pricei,t-1
Past Share Returni,t (Rtn) (Share returni,t-1 + Share returni,t)/2
Panel C: Annual Profit and Loss
Profitabilityi,t (EBITDA) Earnings before interest, tax, depreciation and amortisationi,t/Total assetsi,t-1
Past Profitabilityi,t (Profit) [(EBITDAi,t-2/Market assetsi,t-2) + (EBITDAi,t-1/Market assetsi,t-1) + (EBITDAi,t/Market assetsi,t)]/3
Panel D: Annual Sundry Analysis
Tangible assetsi,t (PPE) Net plant, property and equipmenti,t/Total assetsi,t
Depreciationi,t (Dep) (Depreciationi,t + Amortisationi,t)/Total assetsi,t-1
Marginal Taxi,t (Tax) Change in tax on earnings before interest and taxesi,t/Change in earnings before interest and taxesi,t