International Sources of Risk: Evidence from Cross-Border Mergers
Richard A. Brealey*
Ian A. Cooper**
Evi Kaplanis***
London Business School
First Version: August 1998
This Revision 18/8/98
* London Business School and Bank of England, Threadneedle Street, London EC2R
8AH
** (corresponding author) London Business School, Sussex Place, Regent’s Park,
London NW1 4SA, England, +44-171-262-5050, [email protected]
*** London Business School
1
1. Introduction
This paper studies the stochastic structure of international equity returns. In
particular, we are interested in the fact that some studies have found that share prices
of international companies may move more closely with their market of listing than
one would expect from the degree of international diversification of their operations
(Jacquillat and Solnik (1978), Froot and Dabora (1996)). Other studies find, however,
that the degree of international diversification of operations does have a significant
effect on the international factor structure of equity returns (Agmon and Lessard
(1977), Yang, Wansley and Lane (1985)).
Further evidence that the covariance structure of returns is affected by the country of
trading is provided by U.S. closed-end funds. Bonser-Neal et al (1990) and Bailey
and Lim (1992) have documented a relationship between the returns on closed-end
country funds and the market returns of the country in which the funds are traded.
Bodurtha, Kim and Lee (1995) also report a positive correlation between changes in
the premium over net asset value of country funds traded in the U.S. and the returns
on a U.S. market index. Similar results are reported by Bekaert and Urias (1996) and
Hardouvelis, La Porta and Wizman (1993).
Evidence that the location of listing has an influence is also apparent for firms with
foreign listings. Several studies of foreign listings have looked at the effect on the
variance of returns, but the only analysis to look at the covariance is Urias (1994).1
Urias examines the issuance of ADRs by seven Chilean and six Venezuelan
companies. In the case of the Chilean ADRs the stock’s exposure to the U.S. factor
increases in six cases and its exposure to the Chile factor decreases in five cases. In
the case of the Venezuelan ADRs the exposure to the U.S. factor declines in all cases
and the exposure to the Venezuelan factor declines in four out of six cases.
Our analysis is similar in motivation to that of Urias. Rather than testing directly
whether country factors in stock returns are related to the country of operations or
listing, we focus on cases where there is a discrete shift in domicile as a result of a
cross-border merger or acquisition.2 We compare the covariance with country factors
of the combined firm before and after merger. We hypothesise that the effect of the
merger is to bring about a shift in the stockholder clientele and that this will cause the
stock to move more closely with the local market than the combination of the two
stocks before merger.
2. Cross-border Mergers and Returns
Campbell and Mei (1993) show that the covariance of an asset’s returns with the
returns on the market portfolio can be broken into three components associated with
1
A number of studies have estimated the wealth effects of foreign listings, with inconclusive results.
See, for example, Lee (1991), Howe and Kelm (1987), Lau, Diltz and Apilado (1994), Alexander, Eun
and Janakiraman (1988), Jayaraman, Shastri and Tandon (1993), and Sundaram and Logue (1996).
2
Henceforth we use the terms ‘merger’ and ‘acquisition’ interchangeably.
2
changing expectations of future cash flows, real interest rates and future excess
returns. They use this decomposition to identify the components of domestic betas.
Their empirical estimates lead them to conclude that the covariances of expected
excess-returns with the market are typically much larger in absolute value than the
covariance of cash flows with the market. Thus, it appears that a high proportion of
the covariance of returns is due to common changes in discount rates rather than to the
comovement of the cash flows.
The Campbell-Mei finding concerns a single equity market. Its implications for the
comovement of international asset prices are unclear. For instance, the covariance of
the return to an individual equity with a foreign stock index will depend on the extent
to which shocks to cash flows, real interest rates, and expected excess returns are
correlated between countries. A theoretical model of an international covariance
decomposition along the lines of Campbell and Mei requires, therefore, a model of the
way that expected returns are related internationally.
One extreme would be that international equities are fully integrated and behave
essentially as a single market. In this case, we might expect that the component of an
equity return that is caused by a shock to its expected excess returns will be
independent of the market in which it is listed. It is well known, however, that equity
markets show some signs of being not fully integrated. In particular, investor
portfolios are not broadly diversified across different national markets, but instead are
subject to significant home bias (Cooper and Kaplanis (1986) and (1994), French and
Poterba (1991)). So international equity markets may be, at least partially, segmented
from each other.
Such segmentation may result in expected return factors in different markets that are
specific to the market and do not represent global factors. For instance, suppose that
investors in one country become more risk averse (perhaps as a result of a reduction in
wealth) and demand a higher premium for holding risky assets. This will result in a
common decline in the price of locally traded stocks and, depending on the portfolio
shifts that are forced on investors in other countries, a smaller adjustment in the prices
of internationally traded stocks and of the other countries’ locally traded stocks. Thus
changes in risk aversion that are country specific will induce common price
adjustments in locally traded stocks that are not highly correlated with changes in
expected returns on other markets. If expected excess returns depend thus on the
firm’s domicile and Campbell and Mei are correct in their suggestion that a large part
of stock comovement is due to shifts in the expected excess return, then country
factors in stock returns may depend as much on the firm’s domicile as on the countries
in which the firm does business.
Consider now the effect of changes in the domicile of a company brought about by an
acquisition. The impact of the fact that the merger is cross-border on the value of the
target company will be unpredictable. If international capital markets are not fully
integrated, there will be an initial adjustment in both portfolio holdings of this stock
and its value. This effect could either raise or lower the value of the acquired firm
(Black (l974), Cooper and Kaplanis (1997)). If the acquisition is of a company whose
shares are traded in a small capital market (‘local market’) by a company from a large
capital market (‘international market’), there will, on average, be an initial fall in the
3
required return, and a commensurate rise in value. Conversely, if an internationally
traded stock becomes only locally traded, there will be an initial fall in price. Where
the stock’s status is unchanged (for example, a locally traded stock remains only
locally traded), price may either rise or fall.
In contrast, the impact of the cross-border nature of the merger on the stochastic
structure of returns is more predictable. If markets are integrated, then the only effect
should be through the impact on the operations of the merging firms. On the other
hand, if the markets are segmented, then we would expect country-specific variations
in the expected excess return to cause the stock of the combined firm to comove more
closely with the market of the combined firm’s domicile and less with the market of
the target firm.
3. Cross-border Mergers
A number of studies have examined cross-border mergers, but their focus has been on
the consequent wealth changes rather than on the impact on the covariance structure.
Kang (1993) examined 102 Japanese acquisitions of U.S. firms between 1975 and
1988 and concluded that they created significant wealth gains for both bidders and
targets. Conn and Connell (1990) examined a sample of cross-border mergers
involving U.S. and U.K. firms 1971-1980 and reported abnormal returns of about 20
percent for the target firms and about 2 percent for the acquirers. Several other studies
have compared the gains to the target companies from foreign and domestic mergers.
For example, Harris and Ravenscraft (1991) examined a sample of 1114 U.S.
domestic mergers and 159 cross-border mergers between 1970 and 1987. They
concluded that foreign acquisitions of U.S. firms resulted in a 13.4 percent higher
abnormal return for the target firm. Swenson (1993) in a study of 477 domestic
acquisitions and 226 foreign acquisitions found that foreign acquisitions provided a
10.8 percent higher abnormal return than the domestic equivalents. Dewenter (1995)
compared 90 foreign acquisitions of U.S. firms in the chemical and retail industries
1979-1989 with 294 domestic acquisitions. In both cases the mean abnormal return to
the target shareholders was about 20 percent, but there was no significant difference
between the two samples. Doukas (1995) and Doukas and Travlos (1988) have
examined returns to bidders in cross-border acquisitions. The former looked at 463
international acquisitions by U.S. firms between 1975 and 1989 and found small
positive abnormal returns to the bidder, while the latter found no evidence of
abnormal returns. Finally, Fatemi and Furtado (1988) looked at a sample of 117 U.S.
bidders 1974-1979 and found negligible abnormal returns.
4. Covariances
If a cross-border merger simply involves a combination of the two firm’s cash flows
and the rates at which these flows are discounted are unchanged, then the covariance
structure of the stock returns should be unaffected. In other words, the sensitivity of
the returns of the merged firm’s stock to any factors, including national market
returns, should be the same as that of a portfolio of the stocks of the two separate
firms.
4
There are two principal reasons that the factor loadings may change as a result of the
merger. First, cross-border mergers may affect the aggregate cash flow stream to the
equity holders. For example,



the acquirer may divert production to its home country or to the country of the
target;
there may be synergies that are realized by the combined entity;
the merger may be accompanied by a change in the currency of the firm’s debt.
Such moves could be expected to induce a change in the exposure of the stock of the
combined firm to country factors, though the sign of the changes in the stock’s
exposure to these factors is ambiguous.
Second, if international equity markets are segmented and there are country factors in
expected returns, then the merger will result in a shift in investor clienteles and
thereby unambiguously cause the stock of the merged firm to move more closely with
the returns of the acquirer’s national market.
5. Hypotheses
The month of the merger is defined in terms of its announcement month Ta and the
completion month Tc. All variables are ordered in time. For economy in notation we
omit the time subscript.
We define variables as follows:
RA returns to acquirer
RT returns to target
RM post-merger returns to the pooled acquirer and target
We also define the derived variable:
RS = A RSA + (1-A )RST
where A is the market value at Tc of the acquirer’s stock as a proportion of the
aggregate value of the acquirer and target. Thus the variable RS is the return to the
combination of the acquirer and the target before the acquisition occurs. The
superscript ‘S’ refers to the fact that the variable refers to the period when the two
companies are separate.
We define the market environment by a set of variables:
RMA returns to the market of the acquirer
RMT returns to the market of the target
RW returns to the world market
We again use the superscripts ‘S’ and ‘M’ to denote whether the returns refer to the
period before the merger (‘S’) or after the merger(‘M’).
5
We define F as a vector of factors and  as a vector of factor loadings. Prior to the
merger the factor loadings are given by the regression:
RS =  S.F + eS
The return variable in this regression is the combination of the returns to the two
separate firms prior to the merger. After the merger the loadings are given by the
post-merger regression:
RM =  M.F + eM
Here the return variable is for the merged firm. The hypotheses we wish to test
concern the factor loadings, .
Hypothesis 0: Neutrality
The first proposition that we wish to test is that the stochastic structure of returns is
unaffected by the acquisition. This corresponds to the view that the acquisition does
no more than pool the stochastic properties of the two separate companies, without
altering them in any way:
H0 :  M =  S
If neutrality is rejected, there is a variety of alternate hypotheses that could be tested.
Some concern the factor loadings with respect to currency variables. For instance, we
could test whether hedging lowers the exposure with respect to the currency of the
target firm’s home currency. The focus of this paper is, however, on the factor
loadings with respect to the home stock markets of the acquirer and the target. The
alternate hypotheses discussed below concentrate on this dimension of the impact of
the merger on risk.
Hypothesis 1: Operational Effects
The most obvious alternative to the neutrality hypothesis is that the operational risks
of the target, the acquirer, or both are affected by the acquisition. If the future
operating cash flows of either the target or the acquirer are expected to change, then
the stochastic behaviour of the present value of these flows will change. Some
possible sources of this impact are:




The acquirer is expected to make efficiency gains in the acquiree
The acquisition signals the intent of the acquirer to expand overseas
There are synergies to the acquisition that reduce common fixed costs
The acquisition signals increased growth opportunities in general for the
acquirer
6
If the gains are efficiency gains to the target, or if the acquisition signals the intent of
the acquirer to make more acquisitions, then we would expect the beta in the target
country to rise and the beta with respect to the acquirer market to fall:
H1A(increased foreign activity):  TM   TS and  AM   AS
If there are reduced common costs to the merged firm, then we would expect the betas
with respect to both the acquirer’s and the target’s home markets to fall as operating
leverage is reduced:
H1B(cost cutting):  TM   TS and  AM   AS
If the acquisition signals increased growth opportunities in general for the acquirer,
then it should increase the betas with respect to both markets:
H1C(increased growth opportunities):  TM   TS and  AM   AS
Hypothesis 2: Purchaser Domicile Matters
While operating effects are the most obvious source of change in betas, the literature
on location of trade effects means that there is another hypothesis. This is that the
result of the merger will be that the beta with respect to the market of the acquirer will
rise and the beta with respect to the target will fall because the trading of the merged
entity will be in the acquirer’s home market:
H2:  TM   TS;  AM   AS
An operating effect that might also generate this pattern of betas would be that the
merger signals for the acquirer a substitution of costs in the target market for costs in
its home market beyond that included in the purchase of the target. If costs are
positively related to the market in which they are generated, then the betas would
behave as in H2. We note that the ‘purchaser domicile’ effect may simply be due to
this type of operating effect and discuss below how to distinguish between them.
Table 1 summarises the four alternate hypotheses in terms of their implications for the
betas of the pooled company relative to the markets of the acquirer and the target.
The null hypothesis of neutrality implies no change in the betas.
Table 1: Implications of the hypotheses for the change in betas.
Hypothesis
H1A: Inc. foreign activity
H1B: Cost cutting
H1C: Increased growth
H2:Purchaser domicile
Beta with respect to Beta with respect to
target’s home market
acquirer’s home market
UP
DOWN
UP
DOWN
DOWN
DOWN
UP
UP
7
6. Data
IFR Securities Data provided us with a record of all cross-border mergers involving
two public companies between 1987 and 1992.3 This amounted to 570 mergers. For
each merger we required the country of domicile of the acquirer and target We also
required the first announcement date of the merger, the effective date and the
proportion of the target owned immediately before the announcement and after the
effective date.
We used Datastream to extract monthly returns for the two firms prior to the merger
and for the combined firm afterwards. Datastream also provided the market
capitalisation of their equity on the merger announcement date. There were a number
of cases of missing data for target companies and as a result our final sample size
consisted of 74 mergers.
For each month we also collected from Datastream monthly data for the national
stock market index of both the acquirer and target and for the exchange rate between
the currencies of the acquirer and the target. For each merger we also compute a
“world index” return series, which is an equally weighted average of the returns on N
national market indexes. The world index excludes the national market indexes of the
acquiring and target firms.
The first merger announcement in our sample occurred in February 1987 and the last
occurred in October 1992. As Table 2 shows, there is a broad distribution of mergers
by year.
Table 2 Distribution of mergers by year of announcement
No. of mergers
Year
Working sample
1987
6
1988
22
1989
20
1990
12
1991
8
1992
6
Total
74
The acquiring firms come from a total of 15 countries and the targets come from 10.
As Table 3 shows, Anglo-Saxon countries account for a preponderance of the target
firms and a somewhat smaller proportion of the acquirers. Other countries such as
Japan, Switzerland and Sweden feature heavily as the homes of acquirers but not of
targets.
3
The record of cross-border mergers is not fully complete for 1987 and 1992.
8
Table 3 The countries of origin for the acquiring and
target firms (number of firms)
USA
Canada
UK
Ireland
Scandinavia
Switzerland
France
Other Europe
Japan
Australasia
South Africa
Acquirers
Targets
9
8
32
6
18
5
4
5
6
6
7
6
1
26
0
1
2
0
4
0
3
0
The target firms were much smaller than the acquirers and the mean value of the
acquired stock as a proportion of the stock of the acquirer was only 15.3 percent. We
discuss below the implications of this for methodology.
Monthly returns for each stock were calculated as ln(Pjt + Djt) - ln(Pjt-1), where Pjt is
the stock price at the end of month t and Djt is the dividend paid in month t, expressed
in terms of the currency of the acquiring firm.4 National and world market returns
were also expressed in the currency of the acquiring firm.
7. Methodology
We wish to compare the covariance structure of stocks before the merger with the
structure after merger. We measure prior returns relative to the merger announcement
date Ta. Subsequent returns are measured relative to completion date, Tc. The mean
time between the announcement and completion dates is 92 days.
Our null hypothesis is that the factor loadings of the combined firm on the national
market indexes are unaffected by the merger. The alternative hypothesis which is
consistent with segmented capital markets is that the loading on the national market of
the acquirer increases while that on the market of the target declines.
To test whether the factor loadings change, we form for each merger a capitalizationweighted portfolio of the two stocks and measure its returns RSAT for the months
4
The choice of currency is largely arbitrary. Our results are essentially unaffected if returns are stated
in the local currency.
9
January 1983 to Ta - 1.5 The portfolio weights are the market capitalizations at Ta 1of the acquirer’s stock and of the shares in the target firm that are acquired. We wish
to compare the factor loadings of this portfolio with the loadings of the merged firm.
A simple way to test the effect of each merger on the factor loadings is to undertake
an OLS regression of the postmerger returns on the returns of the two national
markets, where the variables are ordered by time:
RMAT = M0 + MA RMA + MT RMT + eM
(1)
A similar regression could then be undertaken for the returns of the combination of
the two firms before the merger and a comparison made of the estimated regression
coefficients. One problem with these regressions is that the returns on different
national markets are collinear. We can reduce the problem of colinearity in the
premerger period and potentially reduce the estimate errors by imposing the prior that
before the merger the returns on the two firms load only on their national markets with
a coefficient of unity. In this case the null hypothesis is that the coefficients after the
merger, MA and MT, are simply equal to the relative market capitalizations of the two
firms. We, therefore, need to estimate equation (1) only for the postmerger period (Ta
to Ta+61) and compare the estimated coefficients with their expected values of MA =
a, and MT = 1 - a. Our first set of tests are based on equation (1).
Since many of the firms that engage in cross-border mergers are multinationals with
exposure to a number of national markets, equation (1) is liable to suffer from an
omitted variables problem. Therefore, it is helpful to include in our regression the
return (rW) on an equally weighted portfolio of the national markets other than those
of the acquirer and target. We refer to this portfolio as the “world” portfolio.
As noted above, the returns on different national markets are correlated. To reduce
the problem of colinearity, we first regress the returns on the acquirer’s national
market and the target’s national market on the world market returns, and we calculate
the residuals (RMA* and RMT*) from these regressions. We take these residuals as
measures of the national market factors, purged of any world market influence.
Thus in our second set of tests we estimate the loadings of the combined company on
the country and world factors for the premerger months (January 1983 to Ta-1) by the
following expanded OLS regression:
RSAT = S0 + SA RMA* + ST RMT* + SW RW + eS
(2)
Similarly, we estimate the loadings of the merged company on the country and world
factors by the second OLS regression:
RMAT = M0 + MA RMA* + MT RMT* + MW RW + eM
(3)
5
For N firms data is available for at least 24 months but less than 60 months. In these cases we used
the maximum available data.
10
Under the null hypothesis the loadings on the national market indexes are the same
before and after the merger. Under the alternative hypothesis that the domicile of the
purchaser matters, we expect the first inequality to show that the beta against the
target market falls after the acquisition (MTST) and the second inequality to show
that the beta against the acquirer’s market rises (MASA).
Where the target is very small relative to the acquirer, the likely change in the
regression coefficient is likely to be negligible relative to the estimate error. For this
reason, tests based on an unweighted average of the estimated coefficients are liable to
be inefficient. After the merger we can observe only the betas of the combined firms,
but we would like to be able to break out the betas of the target component. The
segmented market hypothesis predicts that the regression coefficients of the acquiring
firms are unaffected by the merger. We assume that this is so and estimate the beta of
the target component in the merged firm as T = (ATM - aAS)/(1 - a), where the
prefix denotes that the regression coefficient refers to the acquirer, the target, or the
combined firm. This allows us to derive betas for the target component of the merged
firm both against the target’s index and the acquirer’s index. We also compute the
error variance of the estimated target betas as V(T) = [V(ATM) + a2 V(AS)]/[1 a]2. Finally, we compute the differences between the premerger target betas and the
postmerger target betas and the standard error of these differences. We calculate a
weighted mean of these differences, where the weights are the inverse of the standard
errors.
8. Results
8.1 Tests on averages
For our first test we estimate equation (1) for the post-merger period and compare the
estimated coefficients against the relative market capitalizations of the two firms. The
results are summarised in Table 4.
Table 4 Difference between coefficients from a regression of the returns from
the combined firm on the two national market indexes (equation (1)) and the
relative market capitalizations of the two firms.
Mean difference
 MT - a
-.04
(se = .06)
MA - (1 - a)
+.12
(se = .05)
 MT - a
MA - (1 - a)
% -ve at .05 sig.
12
% +ve at .05 sig.
19
Since the mean value of the target as a proportion of the combined firm was a = .15,
under the null hypothesis the expected mean values of MT and MA are respectively
.15 and .85. The actual mean of the 74 estimated values for MT is .11, while the
mean for MA is .97. These estimates are respectively .62 and 2.35 standard errors
from the expected values under the null. In 53 percent of the cases the regression
coefficient on the target index was lower than the target’s relative market
capitalisation and in 12 percent of the cases it was significantly lower at the .05
11
significance level. In 59 percent of the cases the coefficient on the acquirer’s index
was higher than acquirer’s relative market capitalisation and in 19 percent of the cases
it was significantly higher.
In our second set of tests we estimate equations (2) and (3) and examine the changes
in the loadings on the national market factors. We first employ a non-parametric test
in which we look at the estimated coefficients on the two home markets of the
merging firms and we count the incidence of the inequalities:
MTST and MASA
Under the null hypothesis we expect each of the four possibilities to have a frequency
of one quarter.
The results are shown in Table 5. In 39 percent of the cases the regression coefficient
of the combined firms’ returns on the market of the acquirer increases while the
coefficient on the market of the target declines. The null hypothesis that the
observations are equally distributed across the four cells can be rejected at the 5
percent significance level. The segmented market hypothesis implies that there
should be an excess of observations in the bottom left-hand cell. On a one-tailed test
we can reject the hypothesis that the proportion of observations in this cell is less than
or equal to 25 percent at the 1 percent significance level.
Table 5 Percentage of firms with a specified change in the regression coefficients on
the national market returns (equations (2) and (3))
 TST
MTST
M
N = 74
MASA
24
39
H0: 25% of obs. in each cell
H0: 25% of obs. in bottom-left cell
MASA
16
20
2 = 8.9, df = 3
2 = 7.9, df = 1
In a number of cases the value of the target company is very small relative to the
acquiring firm. In this case the changes in the estimated regression coefficients are
likely to consist largely of noise. To reduce the weight placed on these small mergers,
we arbitrarily weight each occurrence by the relative size of the target and again
summed the number of observations in each cell. Table 6 shows that the conclusions
are even stronger even though the effective sample size is somewhat reduced.
Table 6 Percentage of firms (value weighted) with a specified change in the
regression coefficients on the national market returns (equations (2) and (3))
MTST
MTST
MASA
15
58
H0: 25% of obs. in each cell
MASA
9
18
2 = 45, df = 3
12
H0: 25% of obs. in bottom-left cell
2 = 43, df = 1
A few of the mergers involve the purchase of relatively small fractions of the target’s
shares. Table 7 shows the results of excluding those mergers where less than 50% of
the target’s shares are acquired in the merger transaction. Again, the conclusions are
largely unaffected.
Table 7 Percentage of firms with a specified change in the regression coefficients on
the national market returns (equations (2) and (3)) excluding acquisitions of  50%.
MASA
26
42
MTST
MTST
N = 65
H0: 25% of obs. in each cell
H0: 25% of obs. in bottom-left cell
MASA
14
18
2 =12, df = 3
2 =10, df = 1
Our second parametric test involves estimating both pre- and postmerger the
regression coefficients of the combined firm on a world index and the national
indexes (purged of any correlation with the world index (equations 2 and 3). The
mean betas against the acquirer’s and target’s markets are shown in Table 8 for both
the pre-merger and post-merger periods. Since the target firms are smaller than the
acquirers, the returns of the combined firm have a relatively low loading on the target
firm’s national market. As predicted, after the merger the mean coefficient of the
combined firm on the target market declines, while that on the acquirer’s market rises.
The change is both statistically and economically significant.
Table 8 Coefficients of the returns from the combined firm on the returns of the
world market and of the two national market factors (equations (2) and (3))
MT -ST
Mean coefficients
ST
 MT
SA
 MA
.21
.09
.76
.93
MA -SA
Mean
% -ve at
Mean
change
.05 sig.
change
-.12
15
+.17
(se = .07)
(se = .06)
% +ve at
.05 sig.
16
In our final test using sample averages we assume that the regression coefficients of
the acquiring firm are unaffected by the merger and use this assumption to break out
the beta of the target component of the merged firm. The results are summarised in
Table 9. The estimates for the implied target are weighted by precision, as some have
very large standard errors. The loadings on each of the three indexes post-merger are
similar to those of the acquirer pre-merger. This implies that the target component
has become less sensitive to changes in the target’s market index and more sensitive
to changes in the acquirer’s index. Indeed, the combined beta with respect to the
13
target’s market is insignificantly different from zero after the merger, even though it
was significant before the merger.
Table 9 Mean estimated coefficients of the returns from the separate firms on the
returns of the world market and of the two national market factors
T
.12 (.05)
.87 (.08)
.21 (.05)
.09 (.06)
-.18 (.15)
Regressor:
Acquirer pre-merger
Target pre-merger
Combined pre-merger
Combined post-merger
Implied target post-merger*
A
.86 (.04)
.26 (.09)
.76 (.04)
.93 (.05)
.97 (.16)
( ) = se’s
W
.80 (.05)
1.02 (.05)
.81 (.05)
.78 (.04)
.79
* Post-merger coefficients for acquirer assumed unchanged
8.2 The effect of merger type
If the cause of the change in betas is operating effects, we might expect mergers
within industries to exhibit different effects to cross-industry mergers. Our sample
contains 35 mergers within the same industry (defined by 2-digit SIC code) and 39
mergers across industries. Table 10 shows the result of splitting the sample into these
two groups.
Table 10 Percentage of firms with a specified change in the regression coefficients on
the national market returns. Same industry first, cross industry second.
MTST
MTST
MASA
23,26
37,41
MASA
17,15
23,18
N = 35,39
Table 11 Change in betas for mergers within industries and
between industries. (t-values in parentheses)
Same industry
bT2-bT1
bA2-bA1
Different industry
bT2-bT1
bA2-bA1
-0.097 (0.784)
0.185 (2.098)
-0.136 (1.63)
0.158 (1.82)
The results in Table 10 show that there is no difference between the two groups in the
effect of the merger on betas. This is confirmed by the changes in betas shown in
14
Table 11. For both types of merger, the beta with respect to the target country falls
and the beta with respect to the acquirer’s country rises. The sizes of the effects are
the same for the two types of merger.
8.3 Cross-sectional relationships
In an attempt to gain more insight into the structure of the changes in risk, we now
estimate cross-sectional relationships in the impact of cross-border merger on risk.
We cannot observe the separate betas of the merging companies once they have
merged. So separating the changes in betas between impacts on the acquirer, the
target, or synergy gains is difficult. Initially, therefore, we estimate the standard
predictive beta regression on the pooled betas:
Mj = constant + coefficient * Sj + error
(j = T,A,W)
The coefficients of this regression tell us how t predict the betas of the merged
company from the pooled betas of the pre-merger companies.
Table 12 shows the results of this regression. The first row shows that there is no
persistence of the beta with respect to the target market for the merged company.
Furthermore, the constant term with respect to the target market is insignificantly
different from zero. The second row shows that there is also little persistence in the
beta with respect to the acquirer’s market. So the merger appears to reduce the
amount of beta predictability that we would normally expect to see. The constant in
the regression with respect to the acquirer’s market is, however, large. So the best
prediction of the merged beta with respect to the acquirer’s market is largely
independent of the pre-merger betas. The regression of the betas with respect to the
world index shows more normal behaviour. There is persistence of beta from premerger to post-merger, although the coefficient is not as high as one would normally
see.
Table 12: Cross-sectional regressions of post-merger betas on pooled pre-merger
betas.
Index
constant SE
coeff.
SE
R2
obs.
T
A
W
0.09
0.84
0.52
0.07
0.12
0.09
0.04
0.12
0.31
0.14
0.14
0.10
0.00
0.01
0.12
74
74
74
The cross-sectional regressions of pooled betas suggest that the optimal forecast of the
post-merger betas with respect to the target and acquirer markets are approximately
zero and one respectively. If these effects are primarily driven by changes in
operations, we would expect the size of the change in the beta to be related to features
of the particular merger such as the size of the synergy, the size of the target, and the
difference between target and acquirer premerger betas. To test this, we develop in
the Appendix a model of the relationship between premerger and post merger betas.
The model is based on three assumptions:
15
A1: There is no change in the betas of the acquirer’s existing business
A2: The betas of the target company’s business are affected by the merger. The size
of the effect is  . So the target company’s betas move towards the acquirer’s
betas by the factor  .
A3: The betas of the synergy are weighted averages of the betas of the acquirer and
the target. The weight of the acquirer is  . If  is zero, the synergy beta is the
value-weighted beta of the companies. If  is one, the synergy beta is the beta of
the acquirer. So  measures the extent to which the synergy beta is more heavily
influenced by the beta of the acquirer.
We estimate the parameters by taking the residuals from the cross-sectional regression
of the pooled betas and regressing them on two variables that reflect the impact of the
acquirer on the target’s beta and the synergy beta:
e ji  constant   j X 1 ji   j X 2 ji  u ji
where:
eji is the residual from the cross-sectional beta regression

 

X1 ji   Ti ( Aji   Tji ) for firm i
X 2 ji
Si

(  jiA  (ai  jiA  (1  ai )  jiT )) for firm i.
uji is an error term
The variable X1 is the proportion of the value of the merged entity represented by the
target multiplied by the difference between the target beta and the acquirer beta. X2 is
the proportion of the value of the merged entity represented by synergy multiplied by
the difference between the acquirer’s beta and the pooled beta of the two companies.
 j then measure the degree to which the target beta and
The coefficients  j and 
synergy beta with respect to index j are pulled towards the acquirer’s beta.
The correlations between X1 and X2 are -0.38 for betas with respect to the target
market and 0.33 for betas with respect to the acquirer’s market, so they are measuring
different effects. Table 13 shows the result of running this cross-sectional regression.
In Panel A the results for the betas with respect to the target’s market are given. None
of the coefficients, including the constant, are significant. This suggests that the best
conditional forecast of the beta with respect to the target’s market is that the beta after
the merger is still approximately zero, even once the particular features of the merger
are allowed for. In Panel B the results for the betas with respect to the acquirer’s
market are given. Once again, all the coefficients in this cross-sectional regressions
are insignificant, implying that there is no relationship between the change in betas
and the characteristics of the merger. So the impact of the merger on betas does not
appear to have the characteristics that one might expect if it were the result of
operating changes.
16
Table 13: Cross-sectional estimation of the Risk effect.
Panel A: With respect to target market
Coefficient
0.004
0.022
-1.143
Constant


SE
0.063
0.266
1.285
2
R 2 = 0.01; R = - 0.01; Observations = 74
Panel B: With respect to acquirer market
Coefficient
-0.011
0.194
-2.626
Constant


SE
0.060
0.367
2.090
2
R 2 = 0.02; R  - 0.01; Observations = 74
9. Abnormal Announcement Returns
Most previous studies of the wealth effects of cross-border mergers have been limited
by data availability to acquisitions by, or of, U.S. companies. Thus, while our main
focus in this paper is on the covariance structure of the merging firms, our sample also
allows us to provide some further evidence on the abnormal returns in the months
surrounding the announcement date of a cross-border merger. These abnormal returns
are shown in Table 14. The abnormal returns for the target and acquirer are broadly
consistent with other studies of both domestic and cross-border mergers. The target
shareholders realise high percentage returns, while the acquirer receives a statistically
insignificant percentage gain. While the target is typically much smaller, even in
money terms the target appears to realise almost six times the gain of the acquirer.
Table 14 Mean cumulative abnormal returns* over a 5-day
period centered on the merger announcement date
Target
Acquirer
Combined
Merger gain as % of target
Abnormal return %
+24.5 (4.0)
+.9 (2.0)
+3.6 (2.0)
+26.4 (8.2)
( ) = se’s
* Defined as return in excess of the national index return
17
Although studies of merger gains commonly report simple means of abnormal returns
to acquirer and target, it seems highly likely that the impact of an acquisition on the
acquirer is a function of the size of the acquisition. In this case averaging the gains to
the acquirer across large and negligibly small acquisitions will be sample-dependent
and misleading. We therefore calculate the aggregate money abnormal return to the
target and acquirer and express this as a proportion of the value of the target. There
appears to be little or no relation between this proportionate gain from merger and the
merger’s relative size (Spearman’s rank correlation is .06) However, if the acquirer is
relatively large, the sampling error in measuring the acquirer’s abnormal return will
show up as a large error in our measure of the proportionate gain. To estimate the
standard error of the proportionate gain we divide our sample into 4 size groups and
calculate the standard error of the mean proportionate gain for each size group. The
standard error declines monotically from 31.8 percent for the smallest acquisitions to
.09 percent for the largest acquisitions.
To calculate the grand mean of the proportionate gains, we weight the means for the
different size buckets by the inverse of their error variances. Thus we estimate that on
average cross-border acquisitions result in a total gain amounting to 26.5 percent of
the value of the acquired firm.
10. Summary and Conclusions
In this paper we have examined the effect of cross-border mergers on the covariance
structure of the stock returns. The principal finding is that the effect of the merger is
to cause the returns to load more heavily on the market of the acquiring firm and less
heavily on the market of the target. While our sample is relatively small, the effect is
sufficiently strong that the result is highly significant. It is also robust to changes in
the way that we measure the influence of the national market factors.
There are two reasons that one might expect a cross-border merger to change the
covariance structure. One is that the market expects changes in the structure of the
underlying cash flows. Since, for example, production could be shifted to or from the
target’s country, the effect of operational changes on the covariance structure is
ambiguous. Moreover, we might expect operational effects to be more marked in the
case of within-industry mergers and we might expect the change in the loadings on the
two market factors to be a function of the loadings for the two firms before the
merger. The data do not support either conjecture.
The alternative explanation, which we believe is more consistent with the data, is that
the covariance structure is a function of the country in which the firm’s stock is traded
and thus the change in domicile that results from the merger also changes the
covariances. The consequence is that the structure after the merger is effectively
independent of the loadings of the target company on the national market factors.
This apparent importance of firm domicile suggests (a) that the comovement between
stocks may be driven as much by common changes in the discount rate as by common
influences on the cash flows, and (b) that international capital markets are segmented
so that discount rates in different countries are imperfectly linked.
18
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21
Appendix: Cross-sectional estimation of the risk effect.
In this section we present a model of the effect of cross-border merger on risk. We
use the following notation:
VA
VT
VM
VS
Value of acquirer ex merger prospects
Value of target ex merger prospects
Post-merger value (adjusted for market movements)
Value of synergy
The synergy value is defined by:
VS  VM  VT  VA
We define the value proportions of the merged firm by:
 A  VA / VM ;  T  VT / VM ;  S  VS / VM
and the value proportions of the unmerged firm by:
a = VA/(VA+VT)
The betas we observe are:
Before the merger:
 Aj beta of VA with respect to market j
 Tj beta of VT with respect to market j
Market j can be the world portfolio, the acquirer’s market or the target’s market.
After the merger:
M
j betas of merged value with respect to markets j.
We do not observe the separate betas of the acquirer, target and synergy after the
merger. These are denoted by:
 Aj ,  Tj , Sj
,
To generate parametric predictions about the behaviour of the post-merger betas of the
merged firm, we need a model about its constituent betas. We assume the following:
A1: There is no change in the betas of the acquirer’s existing business
22
 Aj   Aj
A2: The betas of the target company’s business are affected by the merger. The size
of the effect is  . So the target company’s betas move towards the acquirer’s
betas by the factor  :

 Tj  (1  ) Tj   Aj   Tj    Aj   Tj

So  measures the extent to which the post-merger beta of the target is moved
towards the acquirer beta.
A3: The betas of the synergy are weighted averages of the betas of the acquirer and
the target:
Sj  (1   )[(1  a) jT  a jA ]   jA  (1   )(1  a) jT  (1   )a jA   jA
If  is zero, the synergy beta is the value-weighted beta of the companies. If  is
one, the synergy beta is the beta of the acquirer. So  measures the extent to which
the synergy beta is more heavily influenced by the beta of the acquirer.
The betas we observe after the merger are those for the merged entity:
M

j   A  Aj   T Tj   S Sj
Substituting the expressions for  Aj ,  Tj , Sj
gives:
 in the expression for  M
j
    
   (   (a
 jM 
A
S
A
j
A
j
T
T
j
A
j
 
  S (a jA  (1  a )  jT )   T (  jA   jT )
 (1  a )  jT ))


The first term in this expression is the beta for the merged entity if the merger has no
impact on operations (the ‘neutral’ beta). The second and third terms measure the
regression of the target beta to the acquirer and the third is the impact on the beta of
synergy. The terms inside the square brackets are observable.
We estimate the parameters by a two-stage procedure. First, to allow for the fact that
the betas are estimated with error, so we cannot directly observe the ‘neutral’ beta, we
run a standard cross-sectional predictive beta regression:
 jM  constant +  j jiM  e ji
23
where:
M
ji is the observed merged beta for firm i against index j


jiM   Ai  jiA  Ti  jiT   Si (ai  jiA  (1  ai )  jiT ) for firm i
eji is an error term
We interpret the predictive part of this equation as our estimate of the ‘neutral’ beta.
We then estimate  j and  j by running the regression:
e ji  constant   j X 1 ji   j X 2 ji  u ji
where:

 

X1 ji   Ti ( Aji   Tji ) for firm i
X 2 ji
Si

(  jiA  (ai  jiA  (1  ai )  jiT )) for firm i.
uji is an error term
 j then measure the degree to which the target beta and
The coefficients  j and 
synergy beta with respect to index j are pulled towards the acquirer’s beta.
24
Other studies of the wealth effect of cross-border mergers include Parhizgari, A.M. and De Boyrie,
M.E. (1995).
[**Check Chang et al (1993) and Diwan et al (1993)]
[**Urias preliminary. Cite only if published]
Unfortunately, while we can keep the weights of the portfolio constant before the
merger, we cannot do so afterwards, for, once the merger has been completed, the
relative values of the two components are unobservable but not constant. If, for
example, there was a systematic increase in the value of firms located in the acquirer’s
country relative to that of firms in the target’s country, then the loading of the merged
firm’s stock on the acquirer’s local market would be expected to increase. To control
for such effects, we multiplied the pre-merger market capitalization of each firms’
stock by the cumulative returns of its national market from Ta - 1 to Tc + 61. We then
recalculated the weights a and (1 - a) using these estimates of the market capitalization
of the two components at Tc + 61. In practice the choice of weights made a negligible
difference to our findings and we report results only for the weights calculated from
the premerger values.
Brewer, H.L., “Investor Benefits from Corporate International Diversification,”
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referred to
Chang, E., Eun, C. and Kodolny, R., “International Diversification through ClosedEnd Country Funds,” working paper, University of Maryland, 1993.
Diwan, I., Errunza, V. and Senbet, L., “National Index Funds:
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Empirical
Fatemi, A.M., “Shareholders’ Benefits from Corporate International Diversification,”
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Kaplanis, E. and Schaefer, S.M., “!
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International Economics, 33: 41-56 (1992). **not referred to
Parhizgari, A.M. and De Boyrie, M.E., “Return to Shareholders of U.S. targeted
Companies Acquired by Foreign Corporations,” Applied Financial Economics, 5:
265-272 (1995). **not referred to
Senschak, A. and Beedles, W.L., “Is Indirect Diversification Desirable,” Journal of
Portfolio Management, 49-57 (1980). **not referred to
25
Servaes, H. and Zenner, M., “Taxes and the Returns to Foreign Acquisitions in the
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6intmerg
26