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).

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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