Long-Run Post Merger Stock Performance of UK Acquiring Firms a Stochastic Dominance Perspective

7/28/2019 Long-Run Post Merger Stock Performance of UK Acquiring Firms a Stochastic Dominance Perspective

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Long-run Post Merger Stock Performanceof UK Acquiring Firms: A Stochastic

Dominance Perspective

Abhay AbhyankarSchool of Management and Economics, University of Edinburgh, Edinburgh EH8 9JY, UK

Keng-Yu HoDepartment of Finance, National Central University, Taoyuan 320, Taiwan

Huainan Zhao*

Faculty of Finance, Cass Business School, London EC1Y 8TZ, UK

September 2006

Abstract

We study, using the idea of stochastic dominance, the long-run post merger stock

performance of UK acquiring firms. We compare performance by using the entire

distribution of returns rather than only the mean as in traditional event studies. Our

main results are as follows: First, we find that, in general, acquiring firms do not

significantly underperform in three years after merger since we observe no evidence of

first- or second-order stochastic dominance relation between acquirer and benchmark

portfolios. Second, we find that acquirers paying excessively large premiums arestochastically dominated by their benchmark portfolio implying that overpayment is a

possible reason for post merger underperformance. We find, in consistent with previous

studies, cash financed mergers outperform stock financed ones. Finally, we do not

observe any evidence that glamour acquirers underperform value ones as no stochastic

dominance relations between the two. In general, our results underline the importance

of examining long-run post merger stock performance from alternative perspectives.

JEL Classification: D81, G11, G14, G34.

Keywords: Stochastic Dominance, Mergers and Acquisitions, Corporate Takeovers,

Abnormal Returns, Market Efficiency.

*Corresponding author: Tel: +44-(0)20-7040-5253; fax: +44-(0)20-7040-8881.

Email address: [email protected]


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1. Introduction

A general conclusion, based on studies of long-run (up to five years) stock performance

following mergers, is that there is evidence of significant underperformance1. This

finding, in contrast to the prediction of the efficient market hypothesis, is both

interesting and puzzling because it presents an efficient market anomaly in general and

a puzzle for merger activity in particular. Fundamentally, the question is whether

mergers, on average, destroy firms value in the long run, or whether these findings are

merely a result of flaws of methodologies used. This question is important to the

formation of merger policies as well as to shareholders and managers.

The evaluation of post-merger stock price performance has relied almost exclusively on

the metric of long-run abnormal returns using an event study approach. However this

methodology is fraught with many econometric difficulties including the choice of a

benchmark, the method used to compute abnormal returns, value- versus equal-

weighting of event firm portfolios, cross-sectional correlations in event time, non-

normality of the abnormal returns, and the choice of model for risk corrections (see, for

example, Lyon, Barber, and Tsai, 1999 and Mitchell and Stafford, 2000). Moreover,

some recent research (see for example, Schultz, 2004, Dahlquist and De Jong, 2003, and

Viswanathan and Wei, 2004) questions whether the issue of long-run underperformance

could be resolved using an event study framework. For example, Viswanathan and Wei

1 A non-exhaustive list of US studies includes for example: Asquith (1983), Malatesta (1983), Jensen and

Ruback (1983), Magenheim and Mueller (1988), Agrawal, Jaffe, and Mandelker (1992), Loderer and

Martin (1992), Anderson and Mandelker (1993), Loughran and Vijh (1997), Rau and Vermaelen (1998),

and Agrawal and Jaffe (2000). Examples using UK data are: Firth (1979), Franks and Harris (1989),

Limmick (1991), Limmack and McGregor (1995), Kennedy and Limmick (1996), Gregory (1997),Chatterjee (2000), Aw and Chatterjee (2004).


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(2004) show that expected long-run abnormal returns using an event study methodology

are negative in any fixed sample. Furthermore, they show that the statistical tests used

in long-horizon event studies have low power when endogenous variation in the number

of events is correctly accounted for. Given these findings it is of great interest to study

long-run abnormal performance using an alternate methodology for comparing

acquiring firm returns to commonly used benchmark portfolio returns.

Our main purpose in this paper is to explore, using the idea of stochastic dominance,2

whether investors with specific preferences would choose to invest in a merger portfolio

of acquiring firms relative to a size and book-to-market matched benchmark portfolios

commonly used in the literature. We therefore compare whether the cumulative

distribution of the returns (or payoffs) to a merger portfolio stochastically dominate that

of a benchmark portfolio. There are several advantages to using the idea of stochastic

dominance for this exercise. First, we can compare the entire return distributions of the

event and benchmark portfolios instead of just the mean portfolio return in order to

mitigate the non-normality problem of long-horizon abnormal returns. More

importantly, we do not need to specify an asset pricing model to estimate expected

returns. Finally, we can allow for simple assumptions about investor preferences in the

comparison of acquiring firm portfolios versus benchmark portfolios; for example, non-

satiation in the case of first-order stochastic dominance (FOSD) and risk aversion in the

2 Most standard finance textbooks (e.g., Huang and Litzenberger, 1988) include sections on the concept of

stochastic dominance. However, we see few empirical applications in recent finance literature. Some

early exceptions include Porter and Gaumnitz (1972), Porter (1973), Joy and Porter (1974), Tehrenian

(1980), and more recently Post (2001) among others. We note that the methodology in Post (2001)

focuses on portfolio diversification issues by comparing a given portfolio to a set of assets. In our paper,

we only compare two return distributions and therefore do not use the linear programming method in Post

(2001). Comparisons of income, wealth and earning distributions using tests for stochastic dominance are

however common in empirical economics (see for example Anderson, 1996 and Davidson and Duclos,2000).


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case of second-order stochastic dominance (SOSD). This is important since the view of

investors towards various benchmarks depends crucially on their risk preferences and

investment goals.

Our main results are as follows. First, we find no evidence of any first- or second-order

stochastic dominance relation between the merger portfolios and the benchmark

portfolios matched on both size and book-to-market ratios. This result is consistent

with the efficient market hypothesis and gives no support for the anomaly of long-run

post merger underperformance. Second, we show that the benchmark portfolio

stochastically dominates the merger portfolio that paid the highest merger premiums to

the targets. This finding suggests that overpayment is a possible reason for the long-run

underperformance of some acquiring firms. Third, we find that the cash-financed

merger portfolio clearly dominates the benchmark portfolio while there is no evidence

of stochastic dominance between stock-financed merger portfolio and the benchmark.

This finding is consistent with previous evidence that cash-financed mergers outperform

the stock-financed ones. Finally, in contrast to some recently evidence, we do not find

the stylized value/glamour effect in mergers since we do not observe stochastic

dominance relations between the value/glamour portfolio and the benchmark portfolio.

The rest of the paper is organized as follows. In Section 2 we briefly review prior

research on long-run post merger performance that relies exclusively on the event study

methodology. In Section 3, we describe our data before outlining the methodology used

in our tests for stochastic dominance. In Section 4 we present the empirical results and

Section 5 concludes the paper.


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2. Review of Previous Studies

We provide here a brief and selective review of prior research on long-run post merger

underperformance3. A more comprehensive review is available in Agrawal and Jaffe

(2000).

We begin with studies that use US market data. For example, Langetieg (1978) reports

significant CARs (cumulative abnormal returns) between 2.23% and 2.62% over a

six-year period after a merger. Asquith (1983) finds that acquiring firms CAR

decreases by 7.2% in one-year following the completion of mergers. Malatesta (1983)

finds a statistically significant CAR of 7.6% one-year after the merger announcement.

Jensen and Ruback (1983), who survey seven studies, report an average CAR of 5.5%

one-year after the merger. Magenheim and Mueller (1988) report a significant CAR of

-2.4% in three-year after the merger. Lahey and Conn (1990) find a significant three-

year CAR of 10.2% and 38.57% respectively relative to two benchmarks. Agrawal,

Jaffe and Mandelker (1992), in a comprehensive analysis of the post-merger stock

performance use a large sample of mergers over a 30-year period. They find that

acquiring firms suffer a statistically significant wealth loss of about -10% over a five-

year post-merger period. Anderson and Mandelker (1993) also find significant five-year

CARs of 9.6% and 9.3% under a size and a size and book-to-market adjustment

model respectively. Loughran and Vijh (1997) report a statistically significant five-year

BHAR (buy-and-hold abnormal return) of 15.9% following mergers relative to a size

and book-to-market adjusted benchmark. Finally, Rau and Vermaelen (1998) use the

3

We note here that other studies, for example, Bradley and Jarrell (1988), Franks, Harris and Titman(1991) do not find significant underperformance up to three years after the merger.


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size and book-to-market adjustment method and report a statistical significant three-year

CAR of 4%. Most recently, in a review paper, Agrawal and Jaffe (2000) conclude that

the long-run post merger stock performance is significantly negative.

Since we use UK merger data we also provide a brief review of prior research on post-

merger stock price performance of UK firms. Barnes (1984) and Dodds and Quek

(1985), for example, report CARs of 6.3% and 6.8% respectively over the five-year

period following a merger announcement. Franks and Harris (1989), use a large

comprehensive sample of 1,800 UK mergers between 1955-1985 and find that acquiring

firms suffer significant wealth loss in two- year (CAR = -12.6%) after the merger

completion. Limmack (1991), uses three benchmarks, also finds that all benchmarks

produce significant negative CARs in the two-year period after the mergers, with an

average CAR of 9%. Further, Limmack and McGregor (1995) also find a significant

negative two-year CAR of 14.1%. Gregory (1997), uses six benchmarks, and finds that

the two-year CARs are significant and between 11.8% to 18%. Chatterjee (2000) and

Aw and Chatterjee (2004), use the market model, and find significant negative three-

and two-year CARs of 35.3% and 10.44% respectively. A general conclusion from

these studies using UK data, is similar to that seen for the US market; there is

statistically significant underperformance following the merger when using traditional

event study metrics.

We sum up this review by noting that the study of long-horizon abnormal returns has

almost exclusively use measures of performance and statistical tests of these that rely on

the standard event study methodology. However, as Lyon, Barber, and Tsai (1999) note,


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the measurement problems in this approach are treacherous and the econometric

problems make inference difficult. In addition, recent work by Schultz (2004) and

Viswanathan and Wei (2004), casts doubt on whether event study methodology would

be able to resolve the issue of long-term abnormal performance. In this paper therefore

we turn to an alternative approach and use stochastic dominance to study the relative

performance, over long horizons, between merger portfolios and benchmark portfolios.

3. Data and Methodology

3.1. Data

We study a sample 305 successful public mergers by UK firms from 1985 to 20004.

Our sample is drawn from the Securities Data Corporation (SDC) using the following

criteria: (1) All acquiring and target firms are UK public firms; (2) The deal value of the

merger is over one million US dollars5; (3) Excluding financial and utility firms; (4)

Bidders acquire at least 50% of the targets in obtaining an absolute control. In addition,

acquiring firms monthly stock prices, size (market value), and book-to-market ratios

are obtained from Thomson Financial Datastream. Following Lyon, Barber, and Tsai

(1999), sample firms with negative book value of equity are excluded. We also obtain

data regarding the method of payment and the one-month merger premium6

from the

4 1985 is the earliest year for which UK M&A data is available in the Securities Data Corporation (SDC)

database.5

We follow the literature (see for example Fuller, Netter, and Stegemoller, 2002 and Moeller,

Schlingemann and Stulz, 2004 a,b) and use a one million dollars cut-off point so as to exclude very small

deals.6

Evidence shows that target firm share prices only change significantly at merger announcement date and

the day before. Thus the use of one-month merger premium can reflect the true difference between offer

price and targets normal price. See, for example, Dodd (1980), Asquith (1983), Dennis and McConnell(1986), Huang and Walkling (1987), and Bradley and Jarrell (1988). The one-month merger premium


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SDC. Our final sample consists of 305 UK public acquiring firms selected from the

intersection of the above databases. We then sort the sample firms into portfolios based

on the following criteria: the one-month merger premium, the method of payment, and

value/glamour acquirers.

We construct the merger and benchmark portfolios as follows. For a given acquirer, we

find a single control firm that has the closest size and book-to-market ratio as the

acquiring firm. For a given merger, we calculate the acquiring and benchmark firms

three-year buy-and-hold returns (BHRs) from the month immediately after the merger

completion. After obtaining all the BHRs for our sample and benchmark firms, we then

partition these into portfolios based on the following criteria: one-month merger

premium, the method of payment, and value/glamour acquirers.

We present in Tables 1 and 2 some descriptive statistics of our sample. We report, in

Table 1, the number of mergers, the proportion of firms under different method of

payment, and the average merger premiums at each calendar year from 1985 to 2000.

We note that that cash payment (44%) is the major financing method with evenly

divided stock and mixed payment. Further, the one-month merger premiums range from

30% to 56% with an average of 43%. In Table 2, we report sample statistics for the

sample firms BHR, control firms BHR, and sample-control BHAR. As can be seen,

the mean, median, and standard deviations are quite similar between the sample firm

BHR and the control firm BHR.

equals to the difference between the initial bid price and target market price four weeks before the initialmerger announcement divided by the same target price four-week prior to the announcement.


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3.2. Methodology

We first begin with a brief description of stochastic dominance relations as applied in

our specific context to compare the buy-and-hold return distributions from a merger

portfolio and a size and book-to-market matched benchmark portfolio. Next we describe

the statistical tests used in our empirical work.

Decision making under uncertainty concerns the choice between random payoffs and is

an important topic in economics and finance. The idea of stochastic dominance offers a

general decision rule provided the utility functions share certain properties.

Specifically, we study whether, given preferences like non-satiation or risk aversion,7

one random variable (in our case buy-and-hold return distribution of a merger portfolio)

dominates a benchmark portfolio that are commonly used in assessing post-merger

performance. In other words we investigate whether an investor with specific

preferences prefers a portfolio of acquiring firms relative to an investment in a

benchmark portfolio. We do this by comparing whether the buy-and-hold return

distribution of payoff to the merger portfolio stochastically dominates the buy-and-hold

return distribution of a benchmark portfolio.

We compare the buy-and-hold return distributions of our two candidate portfolios using

the first two orders of stochastic dominance. These are defined as follows. A cumulative

distribution function G is said to first- and second-order stochastically dominate a

cumulative distribution function distribution Fif:

7 The stochastic dominance approach allows for a more general framework than one that uses only the

mean and the variance as measures of comparative risk since these imply that the utility function isquadratic or the distribution of payoffs is normal.


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);();( 11 FzGz , (1)

);();( 22 FzGz , (2)

wherez is the joint ordered data points of the two samples and where:

)();(1 zFFz = , (3)

==zz

dtFtdttFFz0

10

2 );()();( , (4)

I1(z;G) and I2(z;G) are analogues of Equation (3) and (4) in the case of the cumulative

distribution function G.

We rely, in this paper, on tests for detecting stochastic dominance described in Barrett

and Donald (2003). The test compares the two candidate cumulative distribution

functions distributions at all points in the sample.8

The null hypothesis, in these tests, is

that cumulative distribution function G stochastically dominates cumulative distribution

function F for the jth order (this hypothesis also includes the case where the two

distributions are equal everywhere) while the alternative is that stochastic dominance

fails at some points. These hypotheses can be more compactly written as:

H0: zFzGz jj allfor);();( , (5)

H1: zFzGz jj somefor);();( > . (6)

The Barrett and Donald (2003) test statistic is:

));();((sup NjMjz

j FzGzMN

MNS

+

= , (7)

where the operator Ij are given by:

8Several tests proposed earlier (for example Anderson, 1996 and Davidson and Duclos, 2000) compare

the distribution functions only at a fixed number of arbitrarily chosen points. In general, comparisons

using only a small number of arbitrarily chosen points will have low power if there is a violation of theinequality in the null hypothesis on some subinterval lying between the evaluation points used in the test.


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=

=

==

N

i

j

ii

N

i

XjNj XzzXjN

zN

Fzi

1

1

1

))((1)!1(

11)1;(

1);( , (8)

=

=

==

M

i

j

ii

M

iYjMj YzzYjMzMGz i 1

1

1 ))((1)!1(

11

)1;(

1

)

;( . (9)

The test statistics for stochastic dominance beyond the first order (e.g. second-order

stochastic dominance) do not have closed-form limiting distributions. As a result, p-

values need to be obtained by simulation (see also McFadden, 1989). Barrett and

Donald (2003) propose two methods to obtain simulated p-values; by simulation and by

bootstrapping.

The first test statistic (KS1) using simulation to obtain the exact p-values is:

));((sup * Njz

F

j FzS = . (10)

The second test statistic (KS2) is:

));();(1(sup **, NjMjz

GF

j FzGzS = , (11)

where )( MNN += .

In both cases, the probability that a test statistic using random variables exceeds that

using the empirical sample is computed using simulation. The approximate p-values and

the decision rules for rejecting the null hypotheses are:

RejectH0 if =

R

r

j

F

rj

F

j SSR

p1

, )(1

1 , (12)

RejectH0 if =

R

r

j

GF

rj

GF

jSS

Rp

1

,

,

,)(1

1 , (13)


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where R is the number of replications used in the simulation, and is the specified

significance level.

The other method to obtain exact p-values used by Barrett and Donald (2003) tests is

the bootstrap. An advantage of the bootstrap relative to the multiplier method is that we

now do not necessarily need to characterize the distribution. We follow Barrett and

Donald (2003) and use three different bootstrapped-based simulation methods. The first

test statistic (KSB1) using the bootstrap is:

));();((sup *, NjNjz

F

bjFzFzNS = , (14)

where );( *Nj Fz is the analogue of Equation (8) for a random sample of size N drawn

from },...,{ 1 NXX= .

The second test statistic (KSB2) using the bootstrap is:

));();((sup**,

1, NjMjz

GF

bj FzGzMN

MNS

+

= , (15)

where );( *Mj Gz is the analogue of Equation (9) for a random sample of size Mdrawn

from the combined sample },...,,,...,{ 11 MN YYXX= , and );( *Nj Fz is the analogue of

Equation (8) for a random sample of size N drawn from the combined sample

},...,,,...,{ 11 MN YYXX= .

Finally, the third test statistic (KSB3) using the bootstrap is:

)));();(());();(((sup**,

2, NjNjMjMj

z

GF

bjFzFzGzGz

MN

MNS

+

= , (16)


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where );( *Mj Gz is the analogue of Equation (9) for a random sample of size Mdrawn

from the sample },...,{ 1 MYY= , and );( *Nj Fz analogue of Equation (8) for a random

sample of sizeNdrawn from the sample },...,{ 1 NXX= . In this case the two draws are

independent.

In each of the three bootstrap-based simulation methods described above, we are

interested in computing the probability that the test statistic using random variables

exceeds the value of the test statistic using the empirical sample. The exact p-values

and the decision rules for rejecting the null hypotheses in the case of KSB1, KSB2, and

KSB3 respectively are:

RejectH0 if =

R

r

j

F

rbj

F

bj SSR

p1

,,, )(1

1~, (17)

RejectH0 if

=

R

rj

GF

rbj

GF

bj SSRp 1

,

,1,

,

1, )

(1

1~ , (18)

RejectH0 if =

R

r

j

GF

rbj

GF

bj SSR

p1

,

,2,

,

2, )(1

1~ , (19)

where R is the number of replications used in the bootstrap simulation, and is the

specified significance level. To sum up, we use two test statistics using simulation and

three that use bootstrapping to obtain the p-values used to test for various orders of

stochastic dominance. In the case of first-order stochastic dominance since an analytic

solution is available we are not required to use either simulation or bootstrapping to

obtain the exact p-value9.

9

Ho (2003) is a recent example of an application of stochastic dominance tests to evaluate IPO long-runperformance.


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4. Empirical Results

We now turn to the results of our statistical tests for stochastic dominance relations

between the merger portfolios and a set of benchmark portfolios using three-year buy-

and-hold returns (BHRs). We report results based on the comparison of the entire

distribution of three-year BHRs of portfolios of merger firms relative to benchmark

firms using equally weighted portfolios following the literature reviewed earlier. In the

Tables, we do not report p-values but instead report only the qualitative conclusion for

ease of interpretation using a 5% level of significance.

4.1. Results of the Full Sample and Subsample Periods

We present, in Table 3, results of our tests for first- and second-order stochastic

dominance between the merger portfolio and benchmark portfolio returns. Our tests

follow a two-step procedure: First, we test the null hypothesis as to whether the

benchmark portfolio stochastically dominates the merger portfolio return. Second, we

report p-values of tests for the converse hypothesis, i.e. whether the merger portfolio

stochastically dominates the benchmark portfolio. The results of our statistical tests can

be interpreted as follows: If we fail to reject the null in the first step that the benchmark

portfolio stochastically dominates the merger portfolio but reject the null in the second

step that the merger portfolio stochastically dominates the benchmark portfolio, we can

then conclude that the benchmark portfolio stochastically dominates a merger

portfolio.10

However if we reject or fail to reject both steps of the test, we can only

10

Alternately, if we fail to reject in the second step that the merger portfolio stochastically dominates thebenchmark portfolio but can reject in the first step that the benchmark portfolio stochastically dominates


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conclude that there is no stochastic dominance relation between the two portfolio

returns.

We begin with our results of tests for first-order stochastic dominance (FOSD). We

find that, during the full sample period (1985-2000), there is no evidence of a FOSD

relation between the merger portfolio and a size and book-to-market matched

benchmark portfolio. During the two sub-sample periods (1985-1990 and 1991-2000),

we also find similar results. These results imply that investors, who prefer more wealth

to less (i.e., for the case of FOSD), would be indifferent between a merger portfolio and

a size and book-to-market matched portfolio. These results contrast with those obtained

comparing only the mean portfolio returns as in a standard event study methodology.

Comparison of the mean alone may not fully reflect the difference between the merger

portfolio and the benchmark portfolio for investors who prefer more wealth to less.

We next report results of our tests for second-order stochastic dominance (SOSD)

between the merger portfolios and the benchmark portfolios. As Table 3 shows we find

no evidence of any second order stochastic dominance relation between the merger

portfolios and benchmark portfolios for both the full sample and two subsamples. Our

results imply that an investor, who is risk-averse, would still be indifferent between a

merger portfolio and a benchmark portfolio.

We find, in contrast to most previous studies, that there is no evidence that acquiring

firms significantly underperform in three years after mergers based on tests of both first-

the merger portfolio, we conclude that there is a stochastic dominance relation of merger portfolio overthe benchmark portfolio.


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and second-order stochastic dominance. Our findings are in line with some of most

recent studies11

that argue that the conventional event study method may not reliable

due to a bad model problem (see for example, Fama, 1998) and possible

misspecification of the conventional test statistic used in long-run event studies that too

often leads to the over-rejections of the null hypothesis.

4.2. Test of Overpayment Hypothesis

A popular explanation of the widely documented long-run post merger

underperformance of acquiring firms is that it represents a delayed market reaction to

overpaid mergers. In other words, acquirers may have paid higher premiums than

warranted for targets, leading to a delayed price correction in their post merger period.

There are several possible reasons for such overpayment in mergers. For example, the

conflict of interest hypothesis states that acquirer management will engage in

activities that benefit themselves even if they reduce shareholders wealth (Jensen and

Meckling, 1976; Fama, 1980; Fama and Jensen, 1983; and Jensen, 1986). It predicts

that acquirer management will knowingly overpay for target firms. On the other hand,

the hubris hypothesis states that managers of acquiring firms might be too optimistic

about their past performance and the perceived synergy of the merger. These managers

believe that they could improve the performance of the acquired firms sufficiently to

recoup the higher premiums offered (Roll, 1986). Relying on this literature, we test

the following hypothesis: If overpayment is responsible for acquirers long run

11

See for example, Barber and Lyon (1997), Kothari and Warner (1997), Fama (1998), Lyon et al (1999),and Viswanathan and Wei (2004).


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underperformance, the higher the premium paid the worse would be the acquirers

long-run post merger performance.

We now report, in Table 4, results of our tests for first-order and second-order stochastic

dominance relation between each of the benchmark portfolios and merger portfolios

sorted by one-month merger premiums. As can be seen from the Table, there is no

FOSD relation between the low- and medium-premium merger portfolios and their

respective size and book-to-market matched benchmark portfolios. This finding

suggests that investors who only prefer more wealth to less would be indifferent

between the low- and medium merger portfolios and the respective benchmarks.

However, we find that the benchmark portfolio marginally stochastically dominates the

high-premium merger portfolio. This result implies that investors who only prefer more

wealth to less would prefer a size and book-to-market matched benchmark portfolio

than the high-premium (i.e., the excessively overpaid) merger portfolio.

Our results for tests of second-order stochastic dominance (SOSD) relation mirror the

findings of first-order stochastic dominance (FOSD). Our results suggest that risk-

averse investors are indifferent between the low- and medium-premium merger

portfolios and the respective benchmarks, while they would prefer a size and book-to-

market matched benchmark relative to a high-premium merger portfolio. Thus, it is

clear that, under both FOSD and SOSD, acquiring firms that have paid highest

premiums to the targets underperform their size and book-to-market matched peers.

Our results suggest therefore that overpayment may be a possible reason for the

documented long-run post merger underperformance of acquiring firms.


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4.3. Test of Method of Payment Hypothesis

The method of payment in mergers has various valuation effects and can signal

important information of the true value of acquiring firm under asymmetric information.

Myers and Majluf (1984), for example, show that the use of equity can convey

unfavourable information. The intuition for this idea is that if managers are better

informed than the market, they will tend to pay mergers by stock if they believe their

stock is overvalued and use cash otherwise. Further, cash offer allows the acquiring

firms current shareholders to retain all of the future (positive) returns. Stock offer, on

the other hand, shifts part of the (possibly negative) future returns to the new

shareholders. Thus, cash payment is widely interpreted as a release of positive

information while stock payment as negative information. Further, Jensen (1986)

argues that the use of cash in financing takeovers can increase firms value by obviating

potential agency problems associate with excessive retention of free cash flows. 12

Hence, on average, the long-run performance of stock-financed mergers will be

negative and positive to cash-finance mergers13

under the prediction of either

information signalling or agency costs.

We now present, in Table 5, results of our tests for first-order and second-order

stochastic dominance relation between merger portfolios sorted by method of payment

and each of the benchmark portfolios. We find that a cash-financed merger portfolio

12 Hansen (1987), Fishman (1989), and Eckbo, Giammarino, and Heinkel (1988) also argue that the

choice of method of payment in takeovers may be partially driven by agency cost considerations.13

See for example, Wansley, Lane, and Yang (1983), Travlos (1987), Huang and Walkling (1987), Eckbo

(1988), Eckbo, Giammarino, and Heinkel (1988), Asquith, Brunner, and Mullins (1988), Franks, Harris,

and Mayer (1988), Limmack and McGregor (1995), Loughran and Vijh (1997), and Rau and Vermaelen(1998).


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marginally stochastically dominates the benchmark portfolio in the first order. In other

words investors, who only prefer more wealth to less, would favour a portfolio of cash-

financed acquirers than the benchmark portfolio. However, we find no FOSD relation

between a stock-financed portfolio and the benchmark portfolio. This implies that

investors, who only prefer more wealth to less, would be indifferent between a portfolio

of stock-financed acquirers and the benchmark. Our results for tests of second-order

stochastic dominance (SOSD) relation remain unchanged for these two sets of

portfolios. Our overall results suggest that risk-averse investors would prefer a cash-

financed merger portfolio than a benchmark portfolio but are indifferent between a

stock-financed merger portfolio and the benchmark. Thus we find that, under both

FOSD and SOSD, acquiring firms using cash payment outperform their size and book-

to-market matched peers in three years after the merger. This evidence is consistent

with the results reported in previous studies. However, contrary to past evidence, we do

not find that stock-financed acquirers underperform the benchmark firms in the long

run.

4.4. Test of Performance Extrapolation Hypothesis

The performance extrapolation hypothesis (e.g., Rau and Vermaelen, 1998) posits that

the market over extrapolates the past performance of acquirer when it assesses the value

of a merger. Thus, glamour acquirers (low book-to-market ratios) are more likely to be

infected by hubris (Roll, 1986) and tend to be overconfident about their ability to

manage a merger. Indeed, Lakonishok, Shleifer, and Vishny (1994) find that glamour

firms are typically with high past stock returns and high past growth in cash flows and


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earnings, which will presumably strengthen the managements belief about their ability

to handle the merger. On the other hand, value acquirers (high book-to-market ratios)

are more prudent for a merger and hence more likely to create value for shareholders.

Rau and Vermaelen (1998) find that value acquirers outperform glamour acquirers in the

three years period after the merger with value acquirers earn significant positive

abnormal returns of 8% while glamour acquirers lost a significant 17% in three years

after the merger. It is therefore interesting to look at the performance extrapolation

hypothesis from a stochastic dominance perspective.

We present, in Table 6, results of our tests for first-order and second-order stochastic

dominance relation between merger portfolios sorted by book-to-market ratios (i.e.,

value or glamour acquirers) and each of the benchmark portfolios. We find no evidence

of any FOSD relation between the glamour acquirer portfolio and the benchmark

portfolio. Our results suggest that investors, who only prefer more wealth to less, would

be indifferent between a portfolio of glamour acquirers and the benchmark. The same

result also holds true for value acquirer portfolio. Our results for tests of second-order

stochastic dominance (SOSD) relation are similar to the tests for the first-order

stochastic dominance (FOSD). These results suggest that risk-averse investors would

be indifferent between the glamour acquirer portfolio and the benchmark, and between

the value acquirer portfolio and the benchmark. Thus in contrast to Rau and Vermaelen

(1998), our results based on both FOSD and SOSD show that, glamour acquirers do not

significantly underperform their size and book-to-market matched peers. We therefore

give little support to the performance extrapolation hypothesis based on our tests of

stochastic dominance.


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5. Summary and Conclusions

In this paper, we re-visit the long-run post merger performance of acquiring firms by

using stochastic dominance to compare portfolio return distributions. Specifically, we

compare whether an investor, given an ex ante choice of a benchmark portfolio, prefers

the benchmark portfolio to a portfolio of acquiring firms on the basis of statistical tests

for stochastic dominance.

Our main results are as follows. First, we observe no evidence of a first- or second-

order stochastic dominance relation between the merger portfolios (for both the full

sample and two subsamples) and their respective benchmark portfolios matched on both

size and book-to-market ratios. Our result contrast with previous research, that relies on

an event study approach to measure relative performance and reports long-run

underperformance following mergers. Our results are consistent with the evidence of

no underperformance reported by Bradley and Jarrell (1988), and Franks, Harris, and

Titman (1991). Second, we find that the benchmark portfolio stochastically dominates

the merger portfolio of acquirers that paid highest merger premiums to the target firms.

This result suggests that overpayment may be a possible reason for the long-run

underperformance of some acquiring firms. Third, we find that cash-financed merger

portfolio clearly dominates the benchmark portfolio while there is no evidence of a

stochastic dominance relation between the stock-financed merger portfolio and our

benchmark portfolio. Our result is consistent with previous evidence that cash-financed

mergers outperform the stock-financed ones. Finally, in contrast to some recently

evidence (e.g., Rau and Vermaelen 1998), we do not find the stylized value/glamour


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effect in mergers since we find no evidence of a stochastic dominance relation between

value/glamour portfolio and the benchmark portfolio.

We note, in conclusion that many issues make the measurement of abnormal

performance over long horizons difficult. These include, identification of benchmark

models, the use of buy-and-hold versus cumulative abnormal returns, the use of value-

versus equal-weighted portfolios, corrections for cross-correlations in event time

returns, accounting for time variation in risk over the event window and the non-

normality of long-run abnormal returns. Recently doubts have been expressed over

whether event studies can be a useful tool to measure long-run performance in respect

of events like mergers that are endogenous. Our paper is a first step to use an alternate

methodology that throws some more light on evaluating post-merger performance over

the long run. Further research is needed therefore to develop methods that control for

the various biases in measuring long-run abnormal performance so that we can

understand whether post-merger underperformance is really a puzzle or merely a

statistical artifact of the data. We leave these interesting and important issues for future

research.


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Table 1. Descriptive statistics for mergers between 1985-2000

The sample consists 305 U.K. public acquiring firms with a deal value of one million dollars ormore. There are three methods of payment for the merger: pure cash, pure stock, and mixed.The mixed payment subset includes all mergers in which the method of payment is neither purecash nor pure stock. The merger premium is defined as the four-week pre-announcementpremium. It equals to the difference between the initial bid price and target market price fourweeks before the initial merger announcement divided by the same target price four-week priorto the announcement. It shows the number of mergers, the proportion of different method ofpayment, and the average merger premium in each calendar year.

Table 2. Descriptive statistics for sample and matching portfolios three-year stock returns

The sample consists 305 U.K. public acquiring firms with a deal value of one million dollars ormore. Sample firms are matched with control firms with the closest size and book-to-marketratio. BHR is the three-year buy-and-hold return. BHAR is the three-year buy-and-holdabnormal return. We present for each portfolio the mean, median, standard deviation (SD),

minimum, and maximum of its BHRs.

Mean Median SD Min Max

Sample FirmBHR

0.9616 0.8543 0.7460 -0.3251 5.3789

Matching FirmBHR

0.9571 0.8741 0.6859 -0.3675 4.0712

Sample-MatchingBHAR

0.0045 -0.0198 0.1120 0.0424 1.3077

Year Firms Cash (%) Stock (%) Mixed (%) Premium (%)

1985 4 50 25 25 N/A

1986 3 33 67 0 43

1987 19 21 53 26 40

1988 29 66 17 17 36

1989 34 35 35 29 401990 16 50 25 25 56

1991 18 44 22 28 44

1992 9 22 22 56 30

1993 12 25 25 42 55

1994 14 43 29 29 46

1995 24 46 25 29 34

1996 14 36 36 29 31

1997 20 40 45 15 53

1998 32 50 25 22 38

1999 37 41 22 38 40

2000 20 40 30 30 50

All 305 44 29 27 43


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Table 5. The Stochastic Dominance Relation between Merger Portfolios and BenchmarkPortfolios Sorted by Method of Payment

In this table we report the first-order and second-order stochastic dominance relation betweenthe equal-weighted three-year buy-and-hold merger portfolios sorted by method of payment and

each of the size and book-to-market matched benchmark portfolios. There are three methods ofpayment for the merger: pure cash, pure stock, and mixed. The mixed payment subset includesall mergers in which the method of payment is neither pure cash nor pure stock. B meansbenchmark portfolio stochastically dominates merger portfolio; M means merger portfoliostochastically dominates benchmark portfolio; N means no stochastic dominance relationbetween the two portfolios. We use a 5% significance level as a criterion. BD Test is theBarrett and Donald test.

Cash Stock Mixed

BD Test

First-Order M (Marginal) N B

Second-Order M N N

Table 6. The Stochastic Dominance Relation between Merger Portfolios and BenchmarkPortfolios Sorted by Book-to-Market Ratio

In this table we report the first-order and second-order stochastic dominance relation betweenthe three-year buy-and-hold merger portfolios sorted by book-to-market ratio and each of thesize and book-to-market matched benchmark portfolios. Acquiring firms are ranked againsttheir book-to-market ratio and partitioned into three portfolios according to their rankings. Lowbook-to-market portfolio (glamour portfolio) consists the low 30% firms. Medium book-to-market portfolio consists the middle 40% firms. High book-to-market portfolio (valueportfolio) consists the high 30% firms. B means benchmark portfolio stochasticallydominates merger portfolio; M means merger portfolio stochastically dominates benchmarkportfolio; N means no stochastic dominance relation between the two portfolios. We use a5% significance level as a criterion. BD Test is the Barrett and Donald test.

Low B/M(Glamour) Medium B/M

High B/M(Value)

BD Test

First-Order N N NSecond-Order N N N

Long-Run Post Merger Stock Performance of UK Acquiring Firms a Stochastic Dominance Perspective

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