Download A Study on the Relationship between Relative Bargaining Power

A Study on the Relationship between Relative Bargaining Power and
Market’s Expected Synergy in Equity Exchange M&A Cases
Shean-bii Chiu *
Tsung-kang Chen **
Hsien-hsing Liao ***
ABSTRACT
Methods to measure synergies of mergers and acquisition activities have been
proposed in many literatures. However, few literatures discuss the influencing
variables or conditions on the M&A activities’ synergy from the perspective of each
participant’s relative bargaining powers. Regarding synergy measurement, most
studies focus on the indictors of operating performance for quite a long time after
M&A activities. In this study, with an efficient market assumption, we develop an
indictor—“Market’s Expected Synergy” to evaluate synergy of M&A activities within
a short term after the occurrence of an M&A activity. It is constructed based upon
the market reaction to the acquiring firm’s stock price rather than traditional
measurements mostly relying upon acquiring firm’s financial performance after the
M&A. Furthermore, we develop a “Market’s expected Net Synergy Model” that can
integrate the market expected synergy and merger premium in an analytical
framework in terms of a plane with “firm value” and “exchange ratio” as horizontal
axis and vertical axis respectively. This model can firstly make a direct linkage
between expected synergy and merger premium. It not only provides a measurement
tool to do time varying observation of market reaction after an M&A but also supplies
an indicator for measuring the rate of return of an M&A from the perspective of the
acquiring company. In addition, we introduce variables of relative bargaining
powers in the explanation for market expected net synergy. We find that their
explanations are quite significant in several time points during an M&A process
(announcement date, completion date, and the month following completion).
Therefore, the variables of relative bargaining powers provide a foundation for
evaluating the potential net synergy generated by an equity exchange M&A activity.
*
Professor, Department of finance, National Taiwan University, Taiwan, [email protected]
Ph.D. Student, Department of finance, National Taiwan University, Taiwan, [email protected]
***
Associate Professor, Department of finance, National Taiwan University, Taiwan, [email protected]
**
I.
Introduction
Methods to measure synergies of mergers and acquisition activities have been
proposed in many literatures.
However, few literatures discuss the influencing
variables or conditions on the M&A activities’ synergy1 from the standpoint of each
participant’s relative bargaining powers.
Regarding synergy measurement, most
studies focus on the indictors of operating performance2 for quite a long time after the
M&A activities so that it is very difficult to compare synergy with merger premium
within a short term after the occurrence of an M&A activity. Nevertheless, merger
premium represents the costs of the merger and synergy stands for incremental cash
inflow after the merger.
As a result, it is very important to establish the analytical
relationship between synergy and merger premium and then can we measure the net
synergy efficiently.
In this paper, with an efficient market assumption, we develop a “Market’s
expected Net Synergy Model”(MNS model) that can integrate the market expected
synergy3 and merger premium in an analytical framework in terms of a plane with
“firm value” and “exchange ratio” as horizontal axis and vertical axis respectively4.
1
Generally speaking, synergy can be divided into three parts: operation synergy, synergy from the
changes of risk structure, and financial synergy (Lubatkin, 1983; Singh & Montgomery, 1987, Fowler
& Schmidt, 1989). However, these three kinds of synergy can be achieved by different M&A types
including horizontal, vertical, conglomerate and diversification types. Additionally, Kitching (1967)
discovers that financial synergy occurs more often and operating synergy will be difficult to be
appeared.
2
In the past researches related to “the proxies for synergy”, most scholars used financial or market
variables as the measures of synergy. Ansoff, Portner, Brandenburg, and Radosevich (1971) bring up
the twelve financial variables as the proxies for synergy, including sales revenue, total assets, PE ratio,
EPS and so on. Kim and McConnel (1977) use the creditor’s return rate and debt ratio as the
measures of creditor’s CAR and leverage ratio. Beattie(1980) measures the M&A’s synergy as
unsystematic risk and stock return (from the view of risk structure). Hoshino (1982) selects seven
variables as the proxies for performance indictors, including book value to total debt ratio, book value
to total asset ratio, current ratio, debt to equity ratio, turnover ratio, net income to total debt ratio and
net income to total assets ratio. Muller (1985) mentions the market share as the judgment indictor to
evaluate the changes of performance before and after M&A activities.
3
Normally, there are many measures for synergy. But in this paper, synergy will be defined as “added
value of combined firm” at the time after M&A’s announcement. This is because market’s response for
M&A’s activity can be rapidly reflected on the added value and the purpose of this research is to explore
the relation between the market’s responses and merger premium.
4
In the past, Larson & Gonedes (1969) mention an effective method (also called L-G model) to decide
whether the negotiated exchange ratio is reasonable or not in equity exchange M&A activities. In the
original L-G Model, they form their research framework on a plane of exchange ratio(ER) and
price-to-earning per share (PE). Although L-G model has been testified in several empirical tests in
the U.S. and U.K. markets (Conn & Nieisen, 1977; Cooke, Gregory and Pearson, 1994), L-G Model
has some theoretical assumptions which don’t conform to the real world, especially the assumptions
1
This model can firstly make a direct linkage between expected synergy and merger
premium while the reasonable exchange ratio computed by this model can reflect
market’s opinion by combined valued of two firms (V12).
Besides, merger premium
can be compared with synergy in several time points during an M&A process within
the same framework and we can further discover the market expected net synergy5.
Within the framework of synergy and merger premium, we can observe both
markets’ and acquiring firm’s opinions6 for M&A activities through the plane of MNS
model.
We define a new variable “the Return Rate of Market’s Expected Net
Synergy” (RMNS) to be a proxy for the relationship between expected synergy and
premium as equation (1):
Synergyte − premiumo
RMNSt =
premium0
（1）
According to equation (1), RMNS is not only the concept of investment’s return
rate but also a leading indicator to determine whether an M&A activity is reasonable
or not.
With the assumption of efficient market, we can expect that investors will
have rapid and exact reactions on the merger case based on publicly available
information (it also refers to investors’ expected synergy, Synergy te )7.
We can then
realize market’s opinions during the three observed time periods.
On the other hand, some scholars paid attention to the relationship among merger
that “the sum of participants’ earnings is immediately equal to the combination of earnings during the
assessment periods (combined earnings is fixed)” and “no consideration of synergy”. Moreover, L-G
model doesn’t consider synergy so that the merger premium would not directly conform to market’s
opinion about the future.
5
In the past literatures, most scholars (e.g. Conn & Nielsen, 1977; Firth, 1979; Cooke, Gregory and
Pearson, 1994) paid attention to the following three time points: announcement date, completion date,
and the month following completion date.
6
In MNS model, the market’s opinion represents for the market expected synergy and the acquiring
firm’s opinion stands for the merger premium in the M&A activities.
7
Theoretically, in an efficient market, all the relevant information concerning the takeover should be
impounded in the share price on completion of the takeover, since that is the point at which acquisition
has become a certain rather than a probable event. However, Conn &Nielsen and several event
studies (e.g. Firth, 1979) investigate price behavior one month after merger and we follow this to allow
our results to compared with those of other results.
2
premiums and its influencing factors, including the acquiring firm’s financial
variables 8 , operating variables and other institutional variables 9 .
But in these
researches, they primarily focus on the acquiring firm and its performance for quite a
long time after the M&A activities; however, they seldom care about the target firm’s
relative status to acquiring firm.
Hence, we introduce the variables of relative bargaining powers in the
explanation for market expected net synergy and explore the relationships among
them.
The variables of relative bargaining powers indicate those variables that
influence an M&A’s equity exchange ratio. These variables usually relate to the
relative capital size, relative earning generating ability, and relative stock prices
between the acquiring company and the target company.
Therefore, this study use
three ratios between acquiring and target companies to proxy these variables,
including relative capital ratio, relative earnings per share ratio and relative stock
price ratio. Through the empirical results, we find that their explanations are quite
significant in several time points during an M&A process (announcement date,
completion date, and the month following completion).
Therefore, the variables of
relative bargaining powers provide a foundation for evaluating the potential net
synergy generated by an equity exchange M&A activity.
The rest of the paper is divided into four sections：First, we construct a “Market’s
Expected Net Synergy Model”, including a discussion on the model’s assumptions,
the model’s inference and the model’s meanings; Second, we present the data and
8
Shawver, T.J.(2000) used both simple linear regression and multiple-linear regression to discover the
relationships between the independent variables and the dependent variable which is represented by
merger premiums for banks. There is a positive and significant relationship between the dependent
variable: merger premium, and the independent variables: the deal value, acquirers assets, target return
on assets, target return on equity, and accounting method for the merger as a pooling of interests.
9
On the part of the institutional relationship between the firm’s performance and the probability of
CEO turnover, a negative relation between the likelihood of non-routine CEO turnover and firm
performance is documented in studies by Coughlan and Schmidt (1985), Warner et al. (1988),
Weisbach (1988), Gibbons and Murphy (1990), Murphy and Zimmerman (1993), Blackwell et al.
(1994), and Kang and Shivdasani (1995). In addition, Zhao and Lehn (2003) discover two important
findings. One is a strong inverse relation between the returns to acquiring firms and the likelihood
that their CEOs are subsequently fired. The other is that no significant relation exists between the
probability that bad bidders get fired and various corporate governance characteristics, including the
size and structure of boards.
3
methodology;
Third, we empirically examine the effectiveness of our model and
explore the relationships between RMNS and the variables of relative bargaining
power.
In the last section we conclude this study.
II. Market Expected Net Synergy Model
In this section we are going to introduce the construction of the “Market’s
expected Net Synergy Model”. We will first present the assumptions of this model
and then provides a detailed derivation of it.
Besides, we also make a discussion on
the implication of each quadrants classified by the MNS model in M&A activities.
II－1. Model Assumptions:
1.
This model only applies in the equity exchange M&A cases.
2.
Since this research focus only in equity exchange M&A cases, we assume that
the total debt value after combination is equal to the sum of participants’ debt
values and it will keeps unchanged during our research period.
It can be
expressed as equation (2).
D12t = D120 = D10 + D20
3.
（2）
The goal is to pursue maximization of wealth both for corporate management and
stockholders.
4.
The acquiring firm and the target firm are both traded in the exchanges or OTC.
Figure 1 represents a graphical determination of the exchange ratio in which
subscripts 1, 2, and 12 refer to acquiring, acquired and combined firms, respectively,
and:
ER: exchange ratio, number of acquiring firm’s shares exchanged for each share of
acquired firm’s equity.
AER: actual exchange ratio
S1: acquiring firm’s pre-combination shares outstanding
S2: acquired firm’s pre-combination shares outstanding
E1, P1: the total earnings and stock price per share of acquiring firm
4
E2, P2: the total earnings and stock price per share of acquired firm
P12, PE12: expected price per share and PE ratio of the combined entity
V12: the market value of the combined entity
ER1: the maximum exchange ratio which is acceptable to acquiring firm’s stockholders.
ER2: the minimum exchange ratio which is acceptable to the acquired firm’s
stockholders.
Furthermore, we can observe the “Return Rate of Market’s Expected Net Synergy”
(RMNS) through the relationship between market expected net synergy and
participants’ expected synergy10.
First, let define V12 = Equity value + Debt value = ( PE12 ⋅ E12 ) + D12 , E12 is a
random variable with time-varying including the consideration of market’s expected
synergy so that equity value can reflect market’s attitude for this M&A event.
D12
stands for combine firm’s total debt and is a constant according assumption 1.
Second, make inferences in the following:
z
When P12 = P1 (acquiring firm’s wealth position is unchanged):
P12 = PE 12 ⋅ EPS 12 =
V12 − D12
E12
V12 − D12
⋅
⇒ P1 =
E12
S 1 + S 2 ⋅ ER
S 1 + S 2 ⋅ ER
V − D12 − P1 ⋅ S 1
⇒ ER 1 = 12
P1 ⋅ S 2
z
When P12 = P2 ⋅
（3）
1
(acquired firm’s wealth position is unchanged):
ER
P12 = PE 12 ⋅ EPS 12 =
V12 − D12
E12
P
V12 − D12
⋅
⇒ 2 =
E12
S 1 + S 2 ⋅ ER
ER S 1 + ER ⋅ S 2
P2 ⋅ S 1
⇒ ER 2 =
V12 − D12 − P2 ⋅ S 2
（4）
ER, V12 are random variables and form the new plane of MNS model.
Therefore, when participants’ wealth position are unchanged:
10
Participants’ expected synergy refer to the merger premium willing to pay by acquiring firm or
acquired firm..
5
P12 = P1 = P2 ⋅
P
1
⇒∴ ER = 2
ER
P1
V12 = D12 + ( P1 ⋅ S 1 + P1 ⋅ ER ⋅ S 2 ) = D12 + ( P1 ⋅ S 1 + P1 ⋅ (
P2
) ⋅ S2 )
P1
（5）
= D12 + ( P1 ⋅ S 1 + P2 ⋅ S 2 ) = V120
According to the all above, we illustrate the new plane of the MNS model as figure
111.
V120 is combined firm’s value on announcement (assume no synergy)
V AER is participants’ anticipated value including synergy.
V AER − V120 is participants’ expected synergy; that is merger premium
VT is combined firm’s value at the time after announcement(including market
expected synergy )
ER
ER1
Ⅱ
AER
Ⅲ
Ⅰ
P2
P1
ER2
Ⅳ
V12
VAER
VT
Figure 1. Expected synergy of the market and the participants in MNS model
11
The plane constructed by MNS model is similar to the one generated by L-G model (Larson &
Gonedes, 1969). However, different from L-G model, MNS incorporates the concept of synergy,
relaxes the limitation of one-period wealth and further establishes an analytical framework that can
integrate the market’s expected synergy and merger premium. In addition, we create a new
measure—RMNS (the return rate of market expected net synergy) and then it can be taken as an
indictor for assessing market’s reaction toward an M&A. All these advantages are not found in the
L-G model. A detailed introduction of L-G model pleases refer to footnote 4.
6
II－2. Model Implications:
In this study, the primary purpose of MNS model is to decide the RMNS and then
we can measure the reaction degree of market on the M&A activities; The other
objective is to find ER1 and ER2 that will make participants of mergers could maintain
at least their original firm’s values. Therefore, we can judge that participants’ wealth
positions are expropriated or not12 at the related three time points in M&A process
from figure 1.
The actual quadrant status (I, II, III, IV) of the merger is determined by the
relation of the AER to the exchange ratios implied by the ex post combined firm value.
The following criteria define the realized quadrant of any given merger:
z
If ER1>AER>ER2, then Quadrant I.
It implies that the wealth of acquiring company and acquired company are both
not expropriated.
z
If AER>ER1 & ER2, then QuadrantⅡ
It implies that the wealth of acquiring company is expropriated but the acquired
company is not.
z
If ER2>AER>ER1, then Quadrant Ⅲ.
It implies that the wealth of acquiring company and acquired company are both
expropriated.
z
If AER<ER1 & ER2, then Quadrant Ⅳ.
It implies that the wealth of acquired company is expropriated but the acquiring
company is not.
According to the above criteria, we can summarize that it is a good M&A deal
for acquiring company when the negotiated exchange ratio (AER) locates in quadrant
12
In MNS model, each participant’s wealth position would maintain at least original level if AER falls
behind Quadrant I. However, how to decide which Quadrant AER falls behind? In this study, we
introduce the concept of methods mentioned by Larson and Gonedes (1969) as follows: First,
computing the combined firm’s value (V12 ) at any observed time points. Second, let V12 into formulas
of ER1 and ER2 and get the range for judgments. Besides, in order to avoid pre-announcement effects,
the P1 and P2 variables were calculated using an average of monthly high and low share prices during
the period three to five months prior to the month of merger announcement.
7
I or IV and it is a bad M&A deal for acquiring company when AER locates in
quadrant II or III.
III. Data and Methodology
I. Data
The data required for this study are described as follows:
The sample of 251 U.S. equity exchange mergers for the period 1997 to 2001 is
obtained from Bloomberg database. It includes merger type, exchange ratio, merger
premium, announcement date, completion date, and some details about the mergers.
The variables of relative bargaining powers include relative price ratio, relative capital
ratio, and relative EPS ratio13 for merger participants (acquiring firm and acquired
firm).
These related financial data are obtained from COMPUSTAT and CRSP.
II. Methodology
In this study, two major inquiries are investigated. We state the methodologies
for each inquiry respectively as follows.
Inquiry 1. Can MNS model effectively differentiate good M&A deals and bad
M&A deals?
To examine this inquiry, we will compute the cumulative abnormal returns
(CAR) of each group (quadrant) for the following year from announcement or
completion date.
And under this inquiry, we will expect that good deals will have
fairly positive cumulative abnormal returns and bad deals will have negative
cumulative abnormal returns (no consider the deal size). That is to say, we expect
that the groups both of quadrant I and quadrant IV should have positive cumulative
abnormal returns and the groups both of quadrant II and quadrant III should have
negative cumulative abnormal returns for acquiring company.
13
Bargaining power of each participant plays an important role in the negotiation process. However,
it also primarily depends on each wealth status and each future growth of merger participants. As a
result, we employ these three relative ratio as the proxies for each participant’s bargaining power.
8
Inquiry 2. Can the variables of relative bargaining power explain the market’s
expected net synergy?
To examine this inquiry, a regression analysis is used for the cases with
positive merger premiums14.
RMNS t = α + β 1 ⋅ (
P1
S
EPS1
) + β2 ⋅ ( 1 ) + β3 ⋅ (
) + β 4 ⋅ AER + β 5 ⋅ premium + β 6 ⋅ Quadrantt + ε t
P2
S2
EPS 2
Where RMNSt is the return rate of market’s expected net synergy at time t; (P1/P2),
(S1/S2), and (EPS1/EPS2) are all the proxies for relative participants’ bargaining power.
And the other variables are taken as control variables, AER is the negotiated exchange
ratio; premium is the merger premium of each case, and Quadrantt is the decided area
of the “MNS model”.
In order to proceed the above regression model, we have to calculate the return
rate of market’s expected net synergy15 by the definition of equation (1). In addition,
we will make regressions on the three related time points of M&A process
(announcement date, completion date, and the month following completion) and
observe the regression results on the three related time points.
Before proceeding the investigation of the above two inquiries, we firstly have to
classify these M&A cases into the four quadrants defined by MNS model.
With the
information of the quadrant that each sample case belongs, we can differentiate good
M&A deals and bad M&A deals.
14
In this study, we focus on the cases with positive premium. In negative premium cases, the acquiring
firm will be beneficiary when gain the compensation from the acquired firm. It reveals that the
acquired firm is bad in essence so that it is no meaning to exploring the relative wealth status; that is to
say, acquired firm has no bargaining power on the negotiating process!
15
In this study, we introduce the concept of efficient market assumption and we can reasonably think
that investors will have some expectations on what happened. Application on mergers’ announcement,
investors will not only consider merger premium but also expected synergy from the mergers.
9
IV. Empirical Results
Empirical results in this section are divided into three sub-sections. First, we
present the classification results of the sample M&A cases by the MNS model.
Based on these results, the empirical examination results of the MNS effectiveness are
shown in the next sub-section. In the third part of the section, we demonstrate the
empirical exploration results of the second inquiry, that is the explaining ability of the
variables of relative bargaining powers for market’s expected net synergy.
IV-1 Classification Results of the sample M&A cases
Table 1 shows the result that how many cases are reasonable or not out of 251
equity exchange M&A cases in U.S. As table 1 illustrates, 148 of the 251 proposed
mergers produced sufficient combined value to maintain each participant’s on
quadrant I status at the time of merger announcement. Alternatively, the
announcement effects produced wealth losses for one or both firms in 103(70+25+8)
of the mergers. For the merger period examined (i.e., from announcement to the
month following completion), at least 40% do not locate on the rationality quadrant of
the MNS model. In addition, figure 2 provides how many merger cases on quadrant
I, II, III, and IV for the announcement and completion time periods.
The incidence of wealth loss was much greater for acquiring firms than acquired.
Quadrants II and III represent realized wealth declines for the acquiring firms. In the
periods of merger announcement and completion, 95 and 104 acquiring firms
respectively, out of 251, incurred decreases in stockholder wealth. On the other hand,
stockholders in only 33 and 45 acquired firms in the two time periods failed to
maintain an equivalent wealth position (quadrants III and IV). Similarly, rarely did
the acquiring firm’s stockholder gain while the acquired firm’s stockholders lost.
Quadrant IV contains only 8 and 5 merger cases for the two time periods respectively.
10
Table 1. Numbers of mergers by ex post quadrant and changes in quadrant status from
month of announcement to month following merger completion.
From Period of
To Period of
Month Following
Announcement
Ⅰ
Ⅰ
Ⅱ
Ⅳ
148
Ⅱ
Announcement
Ⅲ
Completion
70
Ⅲ
Ⅰ
Ⅱ
Ⅲ
Ⅳ
Ⅰ
Ⅱ
Ⅲ
Ⅳ
116
21
10
1
110
22
15
1
19
40
11
0
19
38
13
0
5
3
17
0
7
5
13
0
2
0
2
4
2
0
1
5
128
9
4
1
5
52
7
0
4
4
31
1
1
0
0
4
25
Ⅳ
8
Ⅰ
142
Ⅱ
Completion
Completion
64
Ⅲ
40
Ⅳ
5
Ⅰ
Month
138
Ⅱ
Following
65
Ⅲ
Completion
42
Ⅳ
6
ER
ER1
70a, 64c
Ⅱ
25a, 40c
148a, 142c
Ⅲ
Ⅰ
ER2
8a, 5c
Ⅳ
V12
Figure 2. Number of mergers by quadrants of realized combined value in month
of announcement(a) and completion(c) for 251 mergers sampled
11
In table 1, stock market is apparently capable of reassessing the merger prospects
during the period from the announcement to the month following completion. But
we can’t exactly know that investors are optimistic or pessimistic at announcement
date. It is mainly because that information is time-varying and new information may
significantly change investors’ expectations16. However, from the date to a month
following completion, we still find a trend skewing toward quadrant I. That is to say,
most investors tend to think that the mergers are reasonable.
IV-2 Effectiveness of the MNS model in differentiating good and bad M&A deals
After categorizing merger cases into different quadrants by MNS model, we
perform further tests for MNS model’s effectiveness by using CAR17 during the
period from announcement or completion to the following year. It will show as in
Figure 3, 4.
CAR
Performance From Announcement Date
0.3
0.2
0.1
18
1
19
1
20
1
21
1
22
1
23
1
24
1
91
10
1
11
1
12
1
13
1
14
1
15
1
16
1
17
1
71
81
61
51
41
31
21
1
11
-9
-1
9
-2
9
0
Time
-0.1
-0.2
Quadrant I
Quadrant II
Quadrant III
Quadrant IV
-0.3
Figure 3. Acquiring company’s performance for the period from the prior one month
to the following one year
16
Due to the evidence of semi-strong efficient market, publicly available information will exactly and
rapidly react on the changes of stock prices. Applying on the three time periods (announcement,
completion, one month after completion), there will be different information set, that is to say,
investors’ expected synergy will vary with time.
17
Benchmarks: All stocks traded in NYSE/AMEX/NASADQ exchanges
12
CAR
Performance After Completion Date
0.25
0.2
0.15
0.1
0.05
23
5
24
4
21
7
22
6
19
9
20
8
18
1
19
0
16
3
17
2
14
5
15
4
12
7
13
6
10
9
11
8
91
10
0
82
73
64
55
46
37
19
28
1
10
0
Time
-0.05
-0.1
-0.15
Quadrant I
Quadrant II
Quadrant III
Quadrant IV
Figure 4. Acquiring company’s performance for the period from completion to the
following one year
From figure 3, we discover that there are some evidences supportive of the MNS
model. Acquiring firms in quadrants I or IV will perform well in the following one
year from the announcement date; in contrast, acquiring firms in quadrants II or III
will perform badly in the following one year. As a result, the hypothesis III will
match up with the empirical results judged by the MNS model.
We therefore can say
that MNS model is useful for judging mergers are good deals or not.
However, there is little different result in figure 4.
As figure 4 illustrates,
acquiring firms in quadrant I will keep constant CAR (about 5%) in the following one
year from completion date, in quadrant II will keep decreasing trend, in quadrant IV
will have large CAR, but in quadrant III unexpectedly perform from bad state to good
state in the future. It is a confusing problem! Consequently, in order to explore
what causes, we will divide three groups for quadrant III by deal size. The result
will shows as figure 5.
13
Performance After Completion Date~Quadrant III
CAR
0.2
0.15
0.1
0.05
99
10
8
11
7
12
6
13
5
14
4
15
3
16
2
17
1
18
0
18
9
19
8
20
7
21
6
22
5
23
4
24
3
90
81
63
72
54
45
36
27
9
18
0
0
Time
-0.05
-0.1
-0.15
below 0.5 billions
0.5~2.0 billions
above 2.0 billions
-0.2
Figure 5. Completion effect for Quadrant III
From figure 5, we can see that small deal will have positive CAR in the
following one year from the completion date.
This is because that small deals
influences slightly on the combined firm whether the merger case is successful or not.
Hence, if we exclude small deals from sample, acquiring firms in quadrant III will
perform expectedly bad in the future.
Therefore, we can conclude that the MNS model is exactly effective to judge
which merger case is reasonable or not.
IV-3 Relationship between the return rate of market’s expected net synergy and
the relative bargaining powers’ variables of participants (regression analysis from
1997 to 2001on the positive premium cases)
Table 2 shows the results of testing whether the variables of relative bargaining
power have significant impacts on the return rate of market’s expected net synergy on
announcement date. The tests include three parts, one is for the whole sample with
positive premium, the other is the sub-sample with smaller deal size (which is below
14
0.2 billions), and another is the sub-sample with larger deal size (which is above 1.0
billions). In the same way, we perform similar tests on completion date and the
following one month after completion. These results will show in table 3 and 4
respectively.
In other words, we perform the regression model during the three
important time periods. One is for the period of the announcement, the other is for
the period of the completion, and another is for the period of the following month
after the completion. The purpose of observing during the three periods is to explore
what relative bargaining powers’ variables still significantly influence market’s
reaction in the future.
From tables 2, 3 and 4, the results show that in all three periods, the relative price
ratio and the relative capital ratio have positive and significantly influence on the
return rate of market’s expected net synergy in the whole sample.
A positive
relationship between the relative price ratio and the return rate of market’s expected
net synergy means that participants’ stock prices are considered by the market. It
also implied that the higher the relative price ratio is, the higher the negotiated ability
of acquiring firm is. In this situation, acquiring firm hardly encounter harm from
unreasonable or higher premium.
Therefore, market will give the positive reaction
on the mergers. The similar explanation can be applied to the relative capital ratio.
That is, the higher relative capital ratio means strong bargaining power of the
acquiring firm, too.
Moreover, these two significant variables also influence the
return rate of market’s expected net synergy in the same direction during the three
periods.
Further, we select two sub-samples for the smaller and the larger deal size out of
the whole sample. On the part of the smaller deal size sub-sample, the relative price
ratio and capital ratio are significantly positive-related to the return rate of market’s
expected net synergy; the relative EPS ratio and premium are significantly
negative-related to the return rate of market’s expected net synergy. It implies that
the higher the acquired firm’s EPS is in smaller deals, the higher the return rate of
market’s expected net synergy is. That is to say, the merger case is valuable when
15
acquired firm operated well even though it is a small deal. As for premium, the
lower the compensation for the acquired firm is and then the higher return rate of
market’s expected net synergy is.
On the other part of the larger deal size sub-sample, the relative capital ratio is
significantly positive-related to the return rate of market’s expected net synergy; the
quadrant variable is significantly negative-related to the return rate of market’s
expected net synergy. It reveals that relative capital ratio plays a very important role
in participants’ negotiating process when it will be a larger deal size.
This is
primarily because the relative capital ratio is a good proxy for relative negotiating
ability. And when the merger case locates in quadrant I, the return rate of market’s
expected net synergy will be higher. Besides, there is something to notice that the
relative price ratio is not significant in larger deal. It may be that the relative capital
ratio represents the bargaining power better than the relative price ratio in larger deals.
Moreover, according to the tables 2, 3 and 4, we discover that their explanations
for market expected net synergy are quite significantly in several time points during
an M&A process (announcement date, completion date, and the month following
completion) 18 and the explanation power of regression model increase from
announcement date to completion date19. It revels that the uncertainty of the M&A
activities will decrease by approaching the completion date and then the variables of
relative bargaining power can explain more for the return rate of market expected net
synergy20.
18
It is meaningless to compare the return rate of market’s expected synergy in different time points of
M&A process. This is because information set is not fixed. Due to information randomly enters into the
market, we can’t directly compare the return of the announcement to the return of the completion.
19
For total cases with positive premium, the adjusted R-square changes from 0.1264 to 0.2541 during
the period between announcement date and completion date.
20
It reflects on the decreasing of uncertainty, namely the error term (unexplainable parts). In addition,
16
Table 2. Multiple-linear regression of the return rate of market’s expected net synergy
against the variables of relative bargaining powers for the period of announcement.
(Positive premium)
RMNS t = α + β 1 ⋅ (
P1
S
EPS1
) + β2 ⋅( 1 ) + β3 ⋅(
) + β 4 ⋅ AER + β 5 ⋅ premium + β 6 ⋅ Quadrant t + ε t
P2
S2
EPS 2
RMNS for the Announcement Period (N=214)
Regression model R-square
0.1264
Adj R-square
ANOVA F-value
6.1345***
Regression coefficients results
Coefficients
β1
β2
β3
β4
β5
β6
Estimates
79.4068
2.0186
-0.7162
36.8500
0.0006
-104.351
t-value
2.9679***
4.0896***
-0.4100
0.5192
0.0537
-1.1385
Sub-sample test—deal size is below 0.2 billions (N=74)
Regression model R-square
0.3969
Adj R-square
ANOVA F-value
9.0058***
Regression coefficients results
Coefficients
β1
β2
β3
β4
β5
β6
Estimates
349.5948
3.1399
-380.0210
268.9537
-12.8377
-134.973
t-value
5.0319***
3.2207***
-4.9739***
0.9280
-1.9435*
-0.5543
Sub-sample test—deal size is above 1.0 billions (N=68)
Regression model R-square
0.1438
Adj R-square
ANOVA F-value
2.8762**
Regression coefficients results
Coefficients
β1
β2
β3
β4
β5
β6
Estimates
2.5595
0.2285
-0.0391
-1.3466
-0.0001
-18.5629
t-value
0.5989
2.3348**
-0.6029
-0.4185
-0.1894
-3.2641***
*** Significant under 99% confidence
** Significant under 95% confidence
* Significant under 90% confidence
17
Table 3. Multiple-linear regression of the return rate of market’s expected net synergy
against the variables of relative bargaining powers for the period of
completion. (Positive premium)
RMNS for the Completion Period
Regression model R-square
0.2541
Adj R-square
ANOVA F-value
13.0940***
Regression coefficients results
Coefficients
β1
β2
β3
β4
β5
β6
Estimates
45.4881
2.1638
-0.4976
6.0820
0.0007
-85.0572
t-value
2.7402***
7.0654***
-0.4591
0.1381
0.0957
-1.4956
Sub-sample test—deal size is below 0.2 billions (N=74)
Regression model R-square
0.5455
Adj R-square
ANOVA F-value
15.6006***
Regression coefficients results
Coefficients
β1
β2
β3
β4
β5
β6
Estimates
208.4182
3.1719
-243.2666
94.8565
-8.9729
-91.5763
t-value
5.2216***
5.6631***
-5.5421***
0.5697
-2.3644**
-0.6546
Sub-sample test—deal size is above 1.0 billions (N=68)
Regression model R-square
0.2216
Adj R-square
ANOVA F-value
4.1798***
Regression coefficients results
Coefficients
β1
β2
β3
β4
β5
β6
Estimates
2.3450
0.7863
-0.0441
-4.5426
-0.0002
-23.3913
t-value
0.2923
4.2804***
-0.3623
-0.7520
-0.1980
-2.1909**
*** Significant under 99% confidence
** Significant under 95% confidence
* Significant under 90% confidence
18
Table 4. Multiple-linear regression of the return rate of market’s expected net synergy
against the variables of relative bargaining powers for the period of the
following month after completion. (Positive premium)
RMNS for the following month after Completion Period
Regression model R-square
0.1986
Adj R-square
ANOVA F-value
9.7966***
Regression coefficients results
Coefficients
β1
β2
β3
β4
β5
β6
Estimates
90.8900
2.6828
-0.7958
32.4323
0.0007
-98.1908
t-value
3.4243***
5.4787***
-0.4592
0.4606
0.0623
-1.0798
Sub-sample test—deal size is below 0.2 billions (N=74)
Regression model R-square
0.5036
Adj R-square
ANOVA F-value
13.3436***
Regression coefficients results
Coefficients
β1
β2
β3
β4
β5
β6
Estimates
368.0020
4.1270
-398.4181
234.9363
-14.4316
-100.2510
t-value
5.6580***
4.5219***
-5.5703***
0.8659
-2.3337**
-0.4398
Sub-sample test—deal size is above 1.0 billions (N=68)
Regression model R-square
0.2590
Adj R-square
ANOVA F-value
4.9022***
Regression coefficients results
Coefficients
β1
β2
β3
β4
β5
β6
Estimates
2.9166
0.6315
-0.0356
-3.1069
-0.0002
-16.6402
t-value
0.4929
4.6616***
-0.3969
-0.6974
-0.2949
-2.1133**
*** Significant under 99% confidence
** Significant under 95% confidence
* Significant under 90% confidence
19
In summary, our inquiries are supported by empirical results. In inquiry 1, the
four quadrants decided by the MNS model exactly perform differently by the trend of
the cumulative abnormal returns since the date of the announcement or completion.
Good deals (quadrant I or IV) for acquiring company will have positive CAR and bad
deals (quadrant II or III) for acquiring company will have negative CAR. In inquiry
2, some relative bargaining powers’ variables, which are the relative price ratio and
the relative capital ratio, significantly influence the return rate of market’s expected
net synergy in the whole sample. Moreover, their explanations are quite significant
in all three observed time points in M&A process and increase with approaching the
completion date. In particular, the relationships between the return rate of market’s
expected net synergy and all the variables of relative bargaining power are the same in
all three time points. Therefore, the variables of relative bargaining powers provide
a foundation for evaluating the potential net synergy generated by an equity exchange
M&A activity.
V. Limitation
In summary of the above, there are some theoretical limitations of the MNS
model.
First, since not every merger participant is traded in exchanges, the
information of the market values of participants may be restrictive21. Second, the
equity exchange type is part of mergers so that the MNS is limited in this merger type.
Third, the MNS model also neglects the possible change in the risk-return profile of
the merger participants22. If a merger results in reduction of unsystematic risk that is
unobtainable for existing stockholders, their risk-return position may actually improve
even if return declines. As a result, the criteria of at least maintaining price per share
in the MNS model may substantially restrict the “rationality” quadrant.
21
In practice, not every merger participant is traded in exchanges; in other words, not every merger
participant’s value can be described as stock value.
22
Please refer to “An Empirical Test of the Larson-Gonedes Exchange Ratio Determination Model”,
1977
20
VI. Conclusions
The main contributions of this study are threefold. It introduces the concept of
expected synergy, investigates the relationship between merger premium and expected
synergy in MNS model, and empirically examines the influence of the relative
bargaining powers related variables on the return rate of market’s expected net
synergy for merger cases.
We develop the MNS model that much better match the
real world (semi-strong form efficient market) without the constraint of the single
period wealth of traditional model. In addition, the concept of efficient market also
provides explanations for time-varying information set and changes of the return rate
of market’s expected net synergy during the three time periods (announcement,
completion, one month after completion).
With regard to the relationship between the relative bargaining powers and the
market expected net synergy, empirical results shows that the former explain the latter
quite significantly in several time points during an M&A process (announcement date,
completion date, and the month following completion). Therefore, the variables of
relative bargaining powers provide a foundation for evaluating the potential net
synergy generated by an equity exchange M&A activity.
Further study could be extended to include relative institutional factors’ ratios of
participants to provide more explanations for the market expected net synergy.
21
Reference
1.
Anonymous, 1992, “Stock Price Premiums: A Market Phenomenon”, Mergers
and Acquisitions Philadelphia Volume: 26 Issue: 4 56-61.
2.
Ansoff, H. Igor., Brandenburg, Richard G., Portner, Fred E., and Radosevich,
Raymond, 1971, “Acquisition Behavior of U.S. Manufacturing Firms”,
1946-1965, Vanclerbilt University Press.
3.
Beattie, D. L., 1980, “Conglomerate Diversification and Performance: A Survey
and Time Series Analysis”, Applied Economics”, pp.251-273.
4.
Blackwell, David W., James A. Brickley and Michael S. Weisbach, 1994,
“Accounting Information and Internal Evaluation: Evidence from Texan Banks”,
Journal of Accounting and Economics 17, pp.331-359.
5.
Brealey and Myers, 2004, “Principles of Corporate Finance”, 7th edition,
McGraw-Hill /Irwin Company, pp.344-371.
6.
Conn, R. L. and J. F. Nieisen, 1977, “An Empirical Test of the Larson-Gonedes
Exchange Ratio Determination Model”, The Journal of Finance, 32(3),
pp.749-759.16
7.
Cooke Terry, Gregory Alan and Pearson Bernard, 1994, “A UK Empirical Test of
the Larson-Gonedes Exchange Ratio Model”, Accounting and Business Research,
Vol. 24, No. 94, pp.133-147.
8.
Coughlan, Anne T. and Ronald M. Schmidt, 1985, “Executive Compensation,
Management Turnover, and Firm Performance: An Empirical Investigation”,
Journal of Accounting and Economics 7, pp.43-66.
9.
Firth, M., 1979, “The Relationship between Stock Market Returns and Rates of
Inflation, The Journal of Finance 34, pp.743-749.
10. Fowler L.K, and R.D. Schmidt, 1988, “Tender Offers, Acquisition, and
Subsequent Performance in Manufacturing Firm,” Academy of Management
Journal, 31(4), pp.41~57.
22
11. Gibbons, Robert S. and Kevin J. Murphy, 1990, “Relative Performance
Evaluation for Chief Executive Officers”, Industrial and Labor Relations Review
43, 30S-51S.
12. Hoshino Yasuo, 1982, “The Performance of Corporate Mergers in Japan”,
Journal of Business Finance and Accounting, 9, pp.145-163.
13. Kang, Jun-Koo and Anil Shivdasani, 1995, “Firm Performance, Corporate
Governance, and Top Executive Turnover in Japan”, Journal of Financial
Economics 38, pp.29-58.
14. Kim, E. Han, and McConnell, John, J., 1977, “Corporate Mergers and the
Co-Insurance of Corporate Debt.” The Journal of Finance, pp.93-119.
15. Kitching, J, 1967, “Why Merger Miscarry?”, Harvard Business Review, 45 ,
pp.84~ 101.
16. Larson, K.D. and N.J. Gonedes, 1969, “Business Combinations: An Exchange
Ratio Determination Model”, The Accounting Review, pp.721-728.
17. Lubatkin Michael., 1983, ”Mergers and the Performance of the Acquiring Firm.”,
Academy of Management Review , 8, pp.218-225
18. Mueller, Dennis C., 1985, “Mergers and Market Share.”, The Review of
Economics and Statistics, 67, pp.259-267.
19. Murphy, Kevin J. and Jerold L. Zimmerman, 1993, “Financial Performance
Surrounding CEO Turnover”, Journal of Accounting and Economics 16,
pp.273-315.
20. Penn, Thomas A. 1981, “Premiums: What Do They Really Measure?” Mergers
and Acquisitions Philadelphia Volume: 16 Issue: 3, 30-34.
21. Shawver, T.J., 2000, “Determinants of bank merger premiums.” Bank,
Accounting & Finance, 15(6), 26-29.
22. Shay, Scott A., 1981,”Setting the 'Right' Premium in an Efficient Market”,
Mergers and Acquisitions Philadelphia, Volume: 16 Issue: 1 23-28.
23
23. Singh, Habir and Cynththia A. Montgomery, 1987, “Corporate Acquisition
Strategies and Economic Performance”, Strategic Management Journal, 8(4),
pp.337~ 386.
24. Warner, Jerold B., Ross L. Watts, and Karen H. Wruck, 1988, “Stock Prices and
Top Management Changes”, Journal of Financial Economics 20, pp.461-492.
25. Weisbach, Michael S., 1988, “Outside Directors and CEO Turnover”, Journal of
Financial Economics 20, pp.431-460.
26. Zhao Mengxin and Lehn Kenneth, 2003, “CEO Turnover After Acquisitions: Do
Bad Bidders Get Fired? , Working paper.
24