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 acquisitionsAustralian mergers and acquisitions sample where firms’ leverages are likely to deviate from their target leverage ratios
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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
2
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
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.
4
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.
5
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.
8
--- 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
9
(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).
10
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:
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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%
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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
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