Download Home Country Macroeconomic Influences on Outward Cross

Home Country Macroeconomic Influences on Outward Cross-border Mergers and
Acquisitions: Evidence from the UK
Abstract
Prior studies examining the trends of mergers and acquisitions (M&As) have concentrated
on host country macroeconomic influences with relatively little attention on home country. In
this paper, we use a non-linear modelling approach, the Exponential GARCH (EGARCH)
model, to investigate dynamic effects of macroeconomic shocks on the UK outward M&As
over the period 1987Q1 to 2008Q1. Our results indicate that a number of home country
macroeconomic variables, including economic output, producer price, broad money supply
and real effective exchange rate play an important role in explaining the trends of crossborder mergers and acquisitions outflows by the UK firms. The findings support the notion
that home country macroeconomic factors can create the advantages to improve the outward
Cross-border M&A activities.
Keywords: cross-border, mergers and acquisitions, macroeconomic factors, EGARCH, UK
JEL Classification:
1. Introduction
The increasing significance of macroeconomic factors in explaining the location of
international production activity in the 1990s in the host country has been highlighted in the
literature (see Dunning, 2009; Vasoncellos and Kish, 1998; Uddin and Boateng, 2011). In
comparison, studies looking at the relationship between home country macroeconomic
fundamentals and aggregate mergers and acquisitions remain sparse (Neto et al., 2010,
Tolentino, 2010). The emphasis of host country macroeconomic influences on the inwards
M&As appears reasonable in that, macroeconomic factors constitute a major component of
location attractiveness of the host country in question (Dunning, 2009). Green and Meyer
(1997); Oxelheim et al., (2001) argue that the host country macroeconomic policy
environment may increase or decrease the cost of doing business in the host country. This is
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consistent to the theoretical prediction that, capital should flow into countries which offer
favourable environment in terms of macroeconomic attractiveness. In contrast, the
relationship between outward M&As and home country factors appears unclear and
controversial. In this study, we argue that the environmental factors associated with a firm’s
country of origin are crucial, even if partially, to the development of a firm’s competitive
advantages by providing the context in which firm choices are made. Our question therefore
is: do home country macroeconomic factors have explanatory power for the cross-border
mergers and acquisitions outflows? This paper attempts to model the relationship between a
selected home country-specific national macroeconomic factors and the level of CBM&A
activities by UK firms using nonlinear time series model. Specifically, we use the ARCH type
model to examine the dynamic effects of macroeconomic shocks on the UK outward M&As
over the period of 1987-2008. This is significant in that, financial time series, such as stock
prices and exchange rates have a tendency to exhibit clustering of volatility along with
leptokurtic unconditional variances. Moreover, prior studies have used linear time series
models to examine macroeconomic influences on M&As (see Shugart and Tollison, 1984;
McCann, 2001; Yagil, 1996). Relatively, few studies have used non-linear time series models
to capture wave behaviours in M&A (see Resende, 1999; Carow, Heron and Saxton, 2004;
Boateng, Naraidoo and Uddin, 2011). Town, 1992 and Resende, 1999 argue that a more
promising line of research is to extend the non-linear time series models that naturally
accommodate the dynamics associated with waves and patterns of mergers and acquisitions
behaviour. Responding to that call, this study uses EGARCH to capture the relationship
between macroeconomic factors and the outward M&As by UK firms. In this model, the
time-dependent volatility is modeled via some deterministic equations.
The choice of the United Kingdom is driven by two reasons: First, UK provides a unique
setting and dataset as a leading European country in the international market for corporate
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control for the analysis of macroeconomic factors as evidenced in Table 1. The Table
indicates that the UK holds the top position of CBM&As among the European Union
countries. Despite the rise and fall in trends in outward M&A activities, the Table indicates
that the UK is ahead of other leading countries in the EU such as Germany and France in
terms of volume of CBM&As.
(Insert Table 1 here)
Second, as a non-member of the European Single Currency, the UK sets it own interest rates
and other macroeconomic policies and that allow us to see the full and clear impact of the
role of macroeconomic influences on outward M&A outside the European Single Currency.
Our results show a strong explanatory power of a number of home country macroeconomic
fundamentals on outward M&A activities. The results show the important role played by
home country macroeconomic factors and support the notion that home country
macroeconomic factors create competitive advantages on which firms investment decision
are made.
The rest of the paper is organized along the following lines. The next section summarises the
literature and develops the hypotheses of study. Section 3 presents the data and the modelling
framework that accounts for the macro-economic influences on CBM&As. Section 4 presents
the results and discusses the findings of the study. The last section provides a summary of the
conclusion and discusses the implications of the study.
2. Literature Summary & Hypothesis
Scholars have examined and produced a myriad of reasons, at least in part, which may
contribute to the growth of M&As at aggregate level including stock prices and inflation
(Weston, 1953; Nelson, 1959; Evenett, 2003), gross domestic product (Becketti, 1986;
Resende, 2008); interest rates ((Becketti, 1986; Ali-Yrkko, 2002). However, most of these
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studies tend to examine the relationship between host country macroeconomic variables and
inward M&As with relatively limited literature probing the contribution of home country
macroeconomic factors influencing the growth of CBM&As (Nachum and Role, 1999;
Nachum, 2001). Systematic research evidence indicates that internal influences associated
with a firm’s assets and competencies are central to their competitive advantages and
predominately explain their overseas expansion (Hawanini and Schill, 1982; Dunning 1981,
1988). However, it is also reasonable to conjecture that external factors play a role, albeit
partially, in the development of a firm’s competitive advantages by providing the context in
which firm choices are made. More recently, Dunning (2009) has reinforced the importance
of external macroeconomic factors in explaining the international production activity within
the OLI paradigm. This point is shared by Kalotay and Sulstarova (2010) who suggested that
Dunning’s OLI paradigm is missing a fourth, ‘home country’ leg. This study builds on a
premise that some of the firm’s ownership-specific advantages that drive outward M&A
activities are likely to be derived, at least in part, from external factors associated with a
firm’s country of origin (see Nachum and Role, 1999; Kalotay and Sulstarova, 2010). We
provide the hypotheses of the study below.
2.1 Stock Prices
The pioneering work of Weston (1953) and the subsequent one by Nelson (1959) suggest that
stock price exert a significant influence on aggregate M&A activities. In a study of stock
prices over the 1895-1920 period in the U.S. market, Nelson (1959) found a positive
correlation between changes in the merger activities and changes in the stock prices. Using
overvaluation hypothesis, researchers such as Shleifer and Vishny, 2003; Baker et al., 2009
have rendered some support for the relationship between stock prices and M&A activities.
Shleifer and Vishny (2003) point out that during stock market boom, share prices of some
companies tend be overvalued. To protect the interest of the shareholders from any
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subsequent fall in share prices, managers exchange the firm’s overvalued shares to acquire
real assets via acquisitions. Baker et al. (2009) concur and argue that CBM&As could occur
for similar reasons, that is, overvalued stock in the home country may be used in acquisition
of firms abroad. Baker et al. (2009) termed this as ‘cheap financial capital hypothesis’ and
used similar arguments put forward by Shleifer and Vishny (2003) to explain the role of stock
prices on aggregate CBM&As activities. In similar vein, Vasconcellos and Kish (1998) in
their study found that a depressed US stock market relative to foreign stock market
encourages foreign acquisitions of US companies. Oster (1990) suggested that firms would
pursue M&As more than any other entry mode when the target shares are under priced. It is
important to point out that numerous empirical studies such as Vasconcellos and Kish (1998;
1996), McCann (2001) and Kish and Vasconcellos (1993) also found that stock price
movement influence CBM&As flow, however the direction of the movement appears
inconclusive.
In the context of the UK, Uddin and Boateng (2011) argue that there have been periods where
stock prices have tumbled such as the UK withdrawal from European Exchange Rate
Mechanism, the technology bubbles and the 1997 financial crisis in Asia. Likewise, they
pointed out that there have also been periods of buoyant stocks in bull market and all these
have implications for the behaviour patterns of investors. Given the length of growth
witnessed in the UK, we hypothesised that:
H1: The relationship between UK stock prices and the UK CBM&As outflows will be positive
2.2 Gross Domestic Product
A number of studies have found a positive relation between size of the host economy and FDI
inflow, however, we know very little about the relation between size of the home economy
and CBM&As. Drawing on the literature that relates GDP with outward FDI, Vasconcellos
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and Kish (1996) pointed that in times of prosperity, a firm is well positioned to engage in
international expansion through M&As. This is because higher GDP may result in higher
level of cash reserve in the hands of firms which may encourage them to acquire companies
abroad. Given that, the UK has witnessed a huge expansion in its economy, particularly, in
late 1990s and in the last decade, we hypothesised that:
H2: There will be a positive relationship between GDP and outwards M&As
2.3 Interest Rates
The role of interest rate in the home country in stimulating investments across the border has
been recognised in both finance and international business literature. According to Moeller
and Schlingemann (2005) the cost of financing influences acquisitions hence interest rate
becomes dominant factor in making international acquisition decisions.
The above is
supported by Tolentino (2010) who argued that, relatively low interest rates are associated
with a home country’s capital abundance and forms an important impetus for capital
investment outflow abroad to diversify and enhance the level of profitability. Empirical
studies that examined the effect of home country macroeconomic factors in respect of FDI
outflows also suggest that home country interest rate as a significant factor that explains part
of the FDI outflows. For example, Aliber (1970) and Grosse and Trevino (1996) pointed out
that lower financing cost is a major source of competitive advantage for firms to pursue FDI.
Using a broader measure of capital costs, Barrell and Pain (1996) found a positive
relationship between relative capital costs and outward foreign direct investment of which
CBM&As is an important part. Pablo (2009); Forssbaeck and Oxelheim (2008) support this
view and suggest that the ability of a firm to undertake cross-border investment can be
explained by the cost of and access to capital. For example, the availability of lower cost of
external funds creates a financial synergy and increases the likelihood of acquisitions (Uddin
and Boateng, 2011). On the relationship between interest rate and outward M&A activities,
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scholars argue that the relationship lies with the fact that lower interest rates reduces the cost
of financing at home due to capital abundance. On the other hand, it may be argued that, in
efficient financial markets, no firm has a financial advantage over another, since all firms
have equal access to finance at equal (risk-adjusted) cost (Forssbaeck and Oxelheim, 2008).
However, this argument is based on the assumption of imperfect capital markets that are at
least partially internationally segmented. In the light of the above argument, we hypothesised
that:
H3: Interest rate in the UK has a positive influence on CBM&As
2.4 Money Supply
Neoclassical theories emphasise the role of economic, technological and regulatory shocks in
explaining merger waves (Gort, 1969; Mitchell and Mulherin, 1996). However, Harford
(2005) points out that these shocks are not enough to generate merger waves and further
argue that there must be sufficient capital liquidity to accommodate the asset reallocation.
According to Harford (2005), the increase in capital liquidity and reduction in financing
constraints must be present to generate merger wave by economic, technological and
regulatory shocks. Following the earlier work by Shleifer and Vishny (1992) regarding
liquidity, this study uses money supply as a proxy for overall liquidity. Shleifer and Vishny
(1992) argued that one of the potential determinants of liquidity is the number of buyers in
the market. From a macroeconomic perspective, increased money supply in the economy in a
given period of time enhances the number of buyers in the market by affecting the disposable
income or the cost of borrowing. The role of liquidity in stimulating aggregate M&As
activities has also been emphasised by a number of scholars including Resende (2008),
Clarke and Ioannidis (1994) and Fishman (1989). However, the influence of liquidity in
respect of the aggregate flow of CBM&As is scarcely discussed in the literature. From a
theoretical perspective, increased level of liquidity should affect the CBM&As outflows
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positively. For example, an increased level of liquidity in the home country will facilitate
assets reallocation and given that the supply of domestic targets is constant, this leads to an
increase in outward CBM&As. From the above discussions, we put forward the following
hypothesis:
H4: Money supply is positively related to outward M&As by UK firms
2.5 Exchange Rate
Valuation hypothesis of M&As states that acquisitions will take place because of valuation
differences between target and bidding firms. Erel et al. (2011) pointed out that given that
markets in different countries are not perfectly integrated, valuation differences across
markets can motivate CBM&As. One of the potential sources of value difference is the
changes in currency value or exchange rates. For example, if the home currency appreciates
in value relative to host currency, then firms from home country will find firms in the host
country relatively cheaper leading to more acquisitions of firms in the host country. The
above is consistent with the view put forward by Vasconcellos and Kish (1996) who suggest
that the relative strength or weakness of the domestic currency pari-pasu the foreign currency
plays an important role in the M&A decision-making process. Exchange rate has impact on
the effective price of the transaction, its financing, the costs of managing the acquired firm
and the profits to be repatriated to the acquirer firm (Weston et al., 1990). The above
arguments also support the classical work of Froot and Stein (1991) who argue foreign firms
are wealthier relative to domestic ones when dollar is depreciating and we should observe a
negative relationship between inward acquisitions and exchange rate and the converse is true
if dollar is appreciating. Studies by Harris and Ravenscraft (1991), Kang (1993), Dewenter
(1995) and Goergen and Renneboog (2004) have also rendered some support for this notion
and pointed out that firms from countries with appreciating currency should act as an acquirer
whereas firms from countries with depreciating currency should be a target on the grounds
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that strong currency would reduce the acquisition price and transaction costs. Consistent with
these findings, Nisbet, Thomas and Barrett (2003) found that the UK firms acquired more
firms in U.S. when the pound was stronger against US dollar. In an attempt to shed to shed
more lights on the relationship between exchange rate movements and foreign acquisitions by
Japanese firms in the United States, Blonigen (1997) found that real exchange rate
depreciations of a dollar to the yen results in substantial increases in acquisition FDI
involving firms that tend to have firm-specific assets. While past empirical efforts such as
Caves, 1989; Froot and Stein, 1991 have found correlation between dollar depreciations and
increased FDI, others including Stevens, 1992; Healy and Palepu, 1993 have found little
support. To Cushman (1985) the relationship between exchange rate movement and
acquisitions remains unresolved and therefore an empirical question. Given that the pound
sterling has been a strong currency over the past two decades, we hypothesised that:
H5: The UK exchange rate will positively influence the outward M&As by UK firms
2.6 Inflation
Gugler et al. (2011) argue that when firm’s return on its capital exceeds cost of capital then q
is greater than one and this leads the firms to acquire more assets either in the form of capital
investments or acquisitions of other firms. Inflation in economy affects both the return on
investments and also the cost of capital and thereby affects the acquisition decision of any
individual firm. For example, McKinnon (1973) pointed out that at higher rates of inflation,
money is more costly to hold, so the net return from investment is lower. On the other hand,
Fisher equation of nominal interest rate shows that nominal interest rate which is a measure
of cost of capital is always higher than real interest rate in the presence of inflation. Therefore,
the presence of high inflation in the home country discourages domestic acquisitions by
negatively affecting the firm’s q thereby reducing return on investments and increasing cost
of capital. The alternative of firm is to invest abroad where the inflation is lower. Lower
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inflation in the host country relative to home country will help boost the q ratio and will
increase the volume of acquisitions activity. Sayek (2009) also found that changes in inflation
rates of the domestic or foreign country tend to alter the net returns and optimal investment
decisions of the multinational enterprises (MNEs). In the presence of inflation, MNE
minimises the negative effects of inflation by changing location of production based on the
extent of inflation between home and host country. Although the role of inflation in
explaining aggregate CBM&As flow is important, there exists few studies in the UK context.
Given the consistently low rate rates of inflation in the UK over the past decade, it is
important we investigate the relationship between inflation and the rising trends of CBM&As
in the UK. We hypothesise that:
H6: Inflation rate in the UK have positive influence on outward M&As
3. Data and Methodology
The data which comprises of the outwards UK CBM&A deals completed between the 1987 –
2008 period was derived from Thomson One Banker database and compared with data
provided in Acquisitions Monthly. In terms of the data relating to macroeconomic variables
used in the model, namely, UK real gross domestic product (GDP), UK money market
interest rate, exchange rate between the UK pound sterling and the U.S. dollar, the UK money
supply, UK all share price index and UK consumer price index were collected from
Economist Intelligence Unit (EIU) country database and cross checked with data available
from the UK national statistical database. The data was then organized to form a quarterly
time series, covering the period from first quarter of 1987 to first quarter of 2008, forming a
total of 85 observations.
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3.1 Variables Measurement
CBM&A outflow (CBMAOUTVAL) is measured by the value of M&As made by the UK
firms in foreign countries. FTSEALL is FTSE all-share price index in the UK, GDP is real
gross domestic product at constant 2001 market price, BASERATE is the percentage real
interest rate on three-month UK treasury bills, EFFECTIVEXRATE is the real effective
exchange rate index and CPI is the consumer price index. M4CHANGES measures the
changes in broad money supply M4 in the UK economy.
Figure 1 plots the value of CBMAOUTVAL and their first differences, and there are obvious
clustering patterns and time dependencies in volatility of both level and first differences of
CBMAOUTVAL.
Figure 1: Value of outward cross-border mergers and acquisitions and their first
differences
DCBMAOUTVAL
CBMAOUTVAL
120,000
140,000
120,000
80,000
100,000
40,000
80,000
0
60,000
-40,000
40,000
-80,000
20,000
-120,000
0
88
90
92
94
96
98
00
02
04
06
88
90
92
94
96
98
00
02
04
06
Table 2 provides the summary statistics for all the variables as well as their first differences.
The Table indicates that, over the sample period, most of the series evidence significant
skewness and kurtosis. Jarque–Bera test statistic suggests rejection of the null hypothesis of
normal distribution for many variables except GDP, DGDP, CPI, FTSEALL and
DOLLAREXRATE. We also found that there are significant fat tails in our dependent
variable, DCBMAOUTVAL. Just as the Ljung-Box–Pierce and ARCH effect test statistics
show, there are also significant autocorrelations in it, as well as many other independent
11
variables, and in their variances, while the latter is an indication of volatility clustering or
other time-dependencies.
(Insert Table 2 Here)
To capture the volatility clustering or other time-dependencies evident in DCBMAOUTVAL,
we adopt an EGARCH model, which is described in the next part.
3.2 Methodology
It is pertinent to note that, Figure 1 shows the volatility in DCBMAOUTVAL follows an
autoregressive conditional heteroskedastic process suggesting that ARCH type models are
more appropriate than OLS, GMM or VAR models. In recent years, ARCH type models have
become popular as a means of capturing observed characteristics of fat tails, changing
conditional variance, and volatility clustering in financial time series. These models use time
series data on returns to model conditional variance. Engle (1982) suggests an ARCH model
with an application to inflation time series. Bollerslev (1986, 1987) proposes a generalization
of Engle’s (1982) ARCH(q) model by including p moving average term in the autoregressive
equation for the conditional variance: the GARCH model. The ARCH models assume that the
variance of tomorrow’s return is a weighted average of the squared residuals and allow the
data to determine the best weights to be used in forecasting the variance; while the GARCH
models are also a weighted average of past squared residuals, but it has declining weights that
never go completely to zero (Engle, 2001, p.159). The GARCH models have proven
surprisingly successful in predicting conditional variances. Various extensions and
modifications of ARCH/GARCH models have been proposed, for example, the integrated
12
GARCH (IGARCH) model of Engle and Bollerslev (1986), the exponential GARCH
(EGARCH) model of Nelson (1991), the threshold ARCH (TARCH) of Rabemananjara and
Zakoian (1993), and the GARCH-NIG model of Forsberg and Bollerslev (2002).
Specifically, we employ a univariate EGARCH (1,1) model following Nelson (1991). The
symmetric assumption in GARCH has been questioned empirically however Nelson (1991)
argues for a model in which the conditional variance responds asymmetrically to positive and
negative innovations, namely, the EGARCH model. It overcomes two drawbacks in GARCH
for computation and simple effects of the past variance on the present one. Beside the ability
of modelling the leverage effect, the other advantage of EGARCH from an implementation
perspective is that, the estimation of the parameters does not require that the parameters
satisfy any inequality constraints. The log operator ensures that the conditional volatility is
positive all the time.
The EGARCH modelling equation is given by:
Yt  c  1Yt 1   i , 2 X i ,t   i ,3 X i ,t 1   t
(1)
 t  t 1 ,  t 2 , ~ N0, t2 
(2)
log  t2  1   2 zt 1   3  zt 1  E zt 1    4 log  t21
 t   t zt : zt ~ N0,1
where Yt is the dependent variable DCBMAOUTVAL,
(3)
(4)
X i ,t is a vector of explanatory
variables, such as DGDP, DCPI, DFTSEALL, DM4STOCK, and so on. The subscript t is a
time index ( t  1,, T ), i stands for a independent variable index ( i  1,, N ), c is the
constant,  is a scalar or a vector of parameters to be estimated, and  t is the stochastic error
2
term conditional on the past information set,  t is the variance of the error term  t .
In the EGARCH model, the persistence of variance is measured by the magnitude of  4 : the
closer the magnitude approaches unity, the greater the persistence of shocks to volatility. The
positivity or negativity of unanticipated excess returns determines future variance, which is
13
measured by  2 and  3 :  2 represents a magnitude effect. For  3 > 0, the innovation in
z
log  2 is then positive (negative) when the magnitude of t 1 is larger (smaller) than its
expected value. The  2 is a sign effect. For  2 < 0, the innovation in the conditional variance
is positive (negative) when returns innovations are negative (positive). The parameters in
EGARCH may be estimated by the log likelihood function log L
and the parameter
 c, 1 , ,  4  to maximize:
Max log L   
T
1 T
1 T 
log 2   log  t2   t2
2
2 t 1
2 t 1  t
2
(5)
The maximum likelihood estimates ˆ for  will be asymptotically normal and consistent with
a covariance matrix equal to the inverse of Fisher’s information matrix. Following Nelson
(1991) and Miyakoshi and Jalolov (2005), we also assume asymptotic normality and the
consistency of estimate ˆ and hence traditional inference procedures are appropriate.
4. Empirical results
Table 3 reports the empirical results of outward UK M&A activities using contemporary
macro-economic variables. As the table shows, the R-squared ranges from 0.318 to 0.352 for
contemporary macroeconomic variables suggesting that, the impact of macroeconomic
factors is able to explain partly the cross-border outflows in the UK. In most of the regression
results, the Durbin-Watson statistics is generally 2 or close to 2, which indicates that there is
little autocorrelation in the residuals.
(Insert Table 3 Here)
The regression results in Table 3 indicate that five macroeconomic factors, namely, DGDP,
DCPI, DFTSEALL, DBANKBASERATE, M4CHANGE are statistically and highly
significant across most sets of equations at the contemporary period. For example, the GDP
has a positive coefficient in almost all the equations and is positively related to outward
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M&As. This may imply that the growth in the GDP in home country leads to higher outward
acquisitions by the UK acquiring firms. The results may due to the improvement in the UK
GDP over the period under consideration and this is consistent with the conclusion drawn by
Pablo (2009), Coffee (1999) and Vasconellos et al. (1990).
DCPI is another consistently significant macroeconomic variable. The positive sign and the
statististically significant coefficients indicate that rising inflation leads to outward M&As.
Again as expected, higher inflation in the home country tends to make domestic targets
expensive and encourage potential acquirers to go to foreign countries where inflation rates
are low.
Another important finding is the consistently positive relationship between the UK stock
prices and the UK outward CBM&As. The finding renders support for the conclusion drawn
by Shleifer and Vishny (2003) and Rhodes-Kropf and Viswanathan (2005) that higher stock
valuation leads to more M&As formation. Shleifer and Vishny (2003) argued that
overvaluation of stock is a short term phenomenon prompted by rising stock market.
Managers therefore used their overvalued equity stocks to acquire cheaper foreign firms in
the international market for corporate control. However, this result appears to be at variance
with McCann (2001) who reported insignificant relationship between these two variables
using UK data.
The study also found a positive relationship between interest rate and outward CBM&As
with two of the six equations being statistically significant. The results render a moderate
support suggesting that, an increase in interest rate leads to an increase in the UK’s outward
CBM&As. This finding is expected on the grounds that, an increase in the home country
interest rate tends to increase the cost of doing business and the cost of raising external
finance such as debt capital. To reduce the cost of capital and improve the firms’ competitive
advantage, companies may seek locations where the costs of doing business are relatively
15
lower through international M&As. This is because M&As is viewed as a form of corporate
investment and the ultimate motivation of these investments is to increase the net present
value of the firms (Finn and Hodgson, 2005). As a result, when the interest rate in the local
market increases, firms may go for international acquisitions to get access to those markets
where the interest cost appears relatively lower. From the theoretical standpoint, this finding
appears to be consistent with efficient seeking motive of outward investment (see Dunning
1993). The most intriguing result comes from the inverse relationship between money supply
and outward M&As. The result appears contrary to our prediction that increased liquidity in
the economy should encourage the M&A formation.
The regression results using euro/pound exchange rates also suggest a positive and significant
relationship between sterling appreciation and outward CBM&As of UK and this is
consistent with the conclusion drawn by Pablo (2009), Coffee (1999), Vasconcellos, Madura
and Kish (1990), Froot and Stein (1991), Harris and Ravenscraft (1991) and Kang (1993) but
it is at variance with the results found by McCann (2001). This finding supports the notion
that a strong pound sterling relative to foreign currencies should result in upward trends in the
acquisition of foreign firms by the UK firms in that strong pound sterling makes foreign
assets cheaper to acquire. However, the regression results using effective exchange rates or
are more mixed.
4.1 Additional robustness checks
As seen from Table 3 we use different proxies of the independent variable of exchange rate,
namely DEUROEXRATE, DDOLLAREXRATE and DEFFECTIVEEXRATE for additional
robust checks. In general, the estimates from the alternative definition appear similar.
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5. Conclusion
This paper examines macroeconomic consequences of outward M&As using EGARCH
model. Our analysis find that home country macroeconomic factors, namely, GDP, inflation
rate, stock prices, base interest rates, money supply and effective exchange rate play an
important role in explaining the trends of cross-border mergers and acquisitions outflows by
the UK firms rendering support to H1 to H6. The results suggest that home country
macroeconomic factors drive CBM&A decisions by the UK firms. For example, the ability to
raise capital at preferential rate at home is a tremendous push for outwards M&As. Another
important conclusion to be drawn from our results is that the appreciation of home country’s
currency encourages outward M&As. The results support the findings of Blonigen, 1997;
Choi and Jeon, 2007 and Boilling et al., 2007. The results also indicate that stock prices have
positive impact on cross-border M&A outflows. For example, it is argued that the acquiring
firms prefer to pay for their acquisitions with stock when it is overvalued to minimize
valuation risk. The higher the price of acquirers’ stock the more they are likely to engage in
acquisitions. This appears support the view given by Sudarsanam and Sorwar (2010) who
argue that acquisition with a share as a medium of payment is one of the ways to minimise
valuation risk. The important conclusion to be drawn here is that, the acquiring firms in
cross-border M&A can minimise their costs and obtain financial synergies by paying
attention to the relative levels of share price, exchange rates and interest rates.
The study also finds that, GDP, inflation and money supply have significant impact on the
outward M&As in the UK. The positive impact of GDP and inflation on outward M&A
indicate that home country economic conditions appear to have a considerable influence on
firms’ international strategies. For example, economic prosperity as reflected in the country’s
GDP may lead the firms in that country to look towards international expansion. The period
under consideration has seen a high growth in GDP and mixed levels of inflation in the UK
17
and these may explain the rising trends of M&A activities. However, contemporary broad
money supply appears to have negative and significant effects on outward M&A activities.
The implication of this study is clear. Despite the claim that due to the forces of globalization,
the firm’s national origin, in an increasingly globalised economy, will be less relevant as a
source of competitive advantage, the study shows that environmental factors of the country of
origin matter in the firm’s international expansion strategy. The study raises a number of
interesting questions that are not explored in the present paper. do the macroeconomic factors
in the UK outside the Single Currency zone affect outward M&As differently compared to
Single Currency Zone countries? Is there a link between being a member of the Single
Currency zone and firm’s profitability?. These interesting questions are left for future
research. Despite the significant contribution, the limitation of this paper is the concentration
of our analysis on aggregate data only without analysing the macroeconmic influences of the
sectors involved in CBM&As. It would also be useful if future studies could analysis the
impact of macroeconomic factors sectoral pattern of M&As.
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Table 1: The Share of UK CBM&As Activities, 1990 – 2008 (As percentage of EU CBM&As
activities)
Year
EU CBM&A
UK CBM&A
UK
CBM&A
as % of France
& Germany
Deals
UK CBM&A as % of EU
CBM&A
No
of Value
No.
of Value
No. of Deals
Deals
Deals
1990
894
55 691
300
5 593
108.30
29.90
1991
692
9 295
203
2 031
90.22
21.43
1992
810
24 379
172
421
53.42
27.21
1993
751
21 544
212
10 899
88.33
49.12
1994
977
47 642
282
22 384
88.68
41.77
1995
1 429
57 500
389
10 701
87.22
36.41
1996
1 335
59 693
362
15 966
88.94
37.35
1997
1 565
82 807
489
23 308
136.21
41.08
1998
1 946
206 690 526
54 417
99.06
33.44
1999
2 728
416 301 614
164 103
60.43
41.40
2000
3 245
605 193 688
321 784
117.41
47.70
2001
2 275
221 031 527
36 655
142.82
34.15
2002
1 363
143 452 330
36 664
154.93
32.37
2003
1 005
47 417
279
36 565
124.0
47.64
2004
1 170
69 917
446
32 769
93.11
28.73
2005
1 828
210 111 544
50 170
110.12
23.41
2006
2 216
260 680 681
19 900
101.95
21.50
2007
2 782
537 890 814
222 984
122.04
29.26
2008
2 548
306 734 600
54 653
155.04
23.55
Source: Authors’ own calculation based on data available from UNCTAD (2010) FDI database.
Values are in million U.S. Dollars
Value
10.04
35.50
17.57
25.17
21.36
48.43
38.18
34.65
48.49
37.09
30.69
31.19
27.31
25.74
32.50
40.00
35.00
41.45
17.81
25
Table 2 Summary statistics for economic variables and their first differences
Variable
Mean
Std. Dev.
Skewnes
Kurtosis
Min
Max
s
CBMAOUTVAL
DCBMAOUTVAL
GDP
DGDP
CPI
DCPI
FTSEALL
DFTSEALL
BASERATE
DBASERATE
M4STOCK
DM4STOCK
EUROEXRATE
DEUROEXRATE
DOLLAREXRATE
DDOLLAREXRATE
EFFECTIVEEXRATE
DEFFECTIVEEXRATE
8814.0
164.24
4
21394
3031.6
7.75
88.03
0
0.55
2074.4
25.52
8
7.16
-0.06
79017
17261.
5.80
6.65
70
-0.11
1.67
0.01
96.03
0.05
16660.3
22168.8
6
71653.7
1
1283.55
3
11.44
0.61
733.21
150.99
3.19
0.66
367802.
13139.1
20
4.61
2
1.04
0.16
0.07
6.49
2.41
5.59
0.30
0.30
0.40
-0.61
1.39
0.06
-0.92
1.19
0.89
0.77
1.01
-0.17
-8.00
0.48
-1.31
-1.03
-1.23
37.50
19.91
1.96
3.51
2.57
7.02
1.70
4.78
3.28
11.46
2.80
4.33
1.14
70.18
2.39
8.66
2.83
9.44
1204.00
-109641
101015.
9641.00
-47.00
00
62.10
-0.50
927.00
-462.00
3.50
-2.33
271054.
-17298
00
1.32
-9.14
1.42
-0.33
81.10
-11.90
129390.
115989.
00
355669.
00
6754.00
00
106.20
3.30
3399.00
371.00
15.00
3.33
172103
55017.0
7.00
12.57
0
0.70
2.04
0.19
104.60
6.40
Jarque-
Ljung-
ARCH
bera
Box-
test
4657.7*
1002.1*
5.15
3.18
5.66
79.54*
5.64
21.48*
20.23*
261.78*
8.58**
20.42*
12.72*
16690*
4.61
136.28*
15.28*
166.36*
Pierce
38.44*
78.83*
812.34*
70.81*
622.64*
242.31*
489.01*
18.82
560.78*
44.68*
676.09*
287.56*
771.53*
3.63
232.15*
17.84
365.58*
25.49
0.00
20.15*
83.98*
3.17
79.79*
0.55
74.57*
0.05
80.00*
1.45
83.95*
48.99*
79.58*
0.01
67.23*
0.56
72.52*
0.08
Notes: 1. * and ** denote the rejection of normal distribution, no serial correlation or ARCH effects hypothesis at 1% and 5%
significant levels, respectively.
26
Table 3: Estimation Results for DCBMAOUTVAL with contemporary explanatory variables
Constant
DCBMAOUTNO(-1)
DGDP
DCPI
DFTSEALL
DBANKBASERATE
M4CHANGE
DEUROEXRATE
DDOLLAREXRATE
DEFFECTIVEEXRATE
δ1
δ2
δ3
δ4
Number of observations
R2
Durbin-Watson stat
(1)
(2)
(3)
(4)
(5)
(6)
-452.888
(697.325)
-0.724***
(0.051)
0.894***
(0.124)
1636.303***
(305.375)
6.914***
(1.041)
528.683
(391.328)
-0.166***
(0.028)
133.235
(517.623)
754.770**
(294.510)
-0.803***
(0.057)
0.679***
(0.110)
1242.326***
(176.404)
5.022***
(1.292)
768.757***
(273.144)
-0.209***
(0.024)
-801.378
(790.20)
-0.767***
(0.021)
0.926***
(0.151)
1490.94***
(398.595)
7.720***
(0.946)
31.198
(545.303)
-0.151***
(0.031)
-
-
255.874***
(15.396)
-0.766***
(0.094)
-0.300***
(0.067)
1910.55***
(342.987)
12.607***
(1.814)
1074.82***
(230.483)
-0.025
(0.028)
1533.03***
(446.065)
2585.66
(3888.646)
-3747.45***
(570.093)
-0.687***
(0.053)
0.521***
(0.182)
4174.483***
(447.026)
16.014***
(0.606)
560.186
(391.713)
0.095***
(0.016)
3324.14***
(845.697)
-534.454
(397.034)
-0.651***
(0.079)
0.872***
(0.130)
896.139***
(246.134)
6.398***
(1.091)
242.929
(253.420)
-0.111***
(0.026)
5319.45***
(1003.912)
26385.67***
(3997.533)
-1159.68***
(202.439)
7.049***
(0.861)
0.126***
(0.271)
2.220***
(0.223)
0.595***
(0.045)
79
0.341
1.862
-
-1207.56
(3637.396)
-
-
8.192***
(0.827)
0.321
(0.264)
2.092***
(0.168)
0.523***
(0.047)
79
0.352
2.073
6.737***
(0.829)
-0.184
(0.236)
2.070***
(0.184)
0.624***
(0.043)
79
0.318
1.990
8.628
(71.591)
8.061***
(0.032)
0.299
(0.189)
1.940***
(0.140)
0.529***
(0.003)
79
0.340
2.042
8.922***
(0.916)
-0.582*
(0.340)
2.362***
(0.311)
0.507***
(0.044)
79
0.336
1.993
-134.517
(121.847)
12.732***
(0.774)
-0.540**
(0.273)
2.698***
(0.246)
0.336***
(0.043)
79
0.349
1.935
Note: 1). ***, ** and * denote statistical significance at the 1%, 5% and 10% significance levels, respectively. 2). Standard
errors are reported in parentheses.
27