The Determinants of Merger Waves
Klaus Gugler
Dennis C. Mueller
B. Burcin Yurtoglu
University of Vienna
Department of Economics
Vienna, Austria
1
Stylized facts on mergers
• Mergers come in waves
• Waves are positively correlated with share
prices and price/earnings ratios
2
Mergers and Average P/E ratio
45
40
35
30
25
20
15
10
5
0
1895 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
Average P/E
Mergers/Population
3
•
•
•
An enormous number of hypotheses
have been advanced to explain why
mergers take place.
Most of the hypotheses have been
advanced to explain specific kinds of
mergers.
Many of them are plausible explanations
for some mergers, but they do not offer
convincing explanations for waves in
aggregate merger activity.
4
• Example: Vertical mergers
– to increase market power by increasing
barriers to entry (Comanor, 1967)
– to increase efficiency by reducing transaction
costs (Williamson, 1975).
• However, it is difficult to imagine why
– the conditions necessary to make such
mergers profitable would appear across a
sufficient number of industries at a particular
point in time to generate a wave in aggregate
merger activity, and
– why this point in time should correspond to a
stock market rally.
5
•
•
•
For a merger wave to occur some sorts of
mergers must greatly increase in frequency at
particular points in time.
We want to determine which hypotheses are
likely to predict such variations in the frequency
of mergers over time.
We examine four hypotheses that have been
put forward specifically as explanations of
merger waves
1)
2)
3)
4)
the q-theory
the industry shocks hypothesis
the overvaluation hypothesis
The managerial discretion hypothesis
6
• The first two are neoclassical in that they
assume that
(1) managers maximize shareholders’ wealth
(2) mergers are wealth creating
(3) capital market efficiency
• The other two may be classified as
behavioral, because they drop the
assumption of capital market efficiency
and/or that managers maximize their
shareholders’ wealth.
7
Neoclassical hypotheses
The q-theory of mergers
– Jovanovic and Rousseau (2002)
The industry shocks hypothesis
– Mitchell and Mulherin (1996)
– Harford (2004)
8
Behavioral hypotheses
The overvaluation hypothesis of mergers
Shleifer and Vishny (2003)
Rhodes-Kropf and Viswanathan (2003)
Rhodes-Kropf, Robinson and Viswanathan (2003)
The managerial discretion hypothesis of
mergers
– Marris (1964)
– Mueller (1969)
9
• The theories differ with respect to their
predictions about
– the determinants of mergers, (DM)
– the determinants of tender offers versus
friendly mergers, (TO vs. FM)
– the characteristics of target firms
– the post-merger share performance of
acquiring firms (SP)
10
The q-Theory of Mergers
• Underlying logic:
– Firms with qs > 1 can profitably expand by
acquiring assets
• Critique:
– If managers are maximizing shareholders’ wealth,
and they have just become more talented, then the
mergers must benefit the acquirers’ shareholders.
– Three options: new plant and equipment, used plant
and equipment, purchase another company. Why
only latter two? (Jovanovic and Rousseau, 2002);
Premia rise in wave!
11
Table 1: Number of Acquirers and Targets in Friendly Mergers (FM) and Tender Offers (TO) and Mean Tobin’s qs
Acquirers
Year
FM
TO
%TO
Targets
FM
TO
FM
FM
TO
TO
MV / TA
MV / TA
MV / TA
DV / TA
MV / TA
DV / TA
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
205
311
486
478
166
156
177
181
273
318
346
513
607
726
817
960
1001
599
588
550
453
339
14
23
23
29
41
56
47
60
55
26
19
16
25
33
57
55
73
72
63
63
47
37
6.39%
6.89%
4.52%
5.72%
19.81%
26.42%
20.98%
24.90%
16.77%
7.56%
5.21%
3.02%
3.96%
4.35%
6.52%
5.42%
6.80%
10.73%
9.68%
10.28%
9.40%
9.84%
1.275
1.216
1.377
1.411
1.154
1.245
1.380
1.298
1.327
1.532
1.459
1.873
1.681
1.644
1.623
1.803
1.902
2.004
2.218
2.708
1.962
1.705
0.664
0.906
0.781
0.921
0.902
1.001
1.118
1.316
0.998
1.356
1.282
2.034
1.557
1.732
1.570
1.581
1.652
1.732
1.860
1.646
2.416
2.006
1.011
0.846
1.052
1.218
1.085
1.234
1.204
1.384
1.306
1.341
1.397
1.343
1.384
1.238
1.147
1.490
1.213
1.590
1.687
2.012
1.490
0.862
0.756
0.829
1.018
1.097
1.465
1.654
1.564
2.048
1.588
1.435
1.857
2.123
2.096
2.060
2.292
2.819
2.295
3.095
3.109
2.340
2.281
1.000
1.066
0.758
0.797
1.073
1.075
1.232
1.140
1.046
1.266
1.253
1.274
1.133
1.706
1.259
1.503
1.200
1.057
1.274
1.498
1.886
1.091
1.017
0.787
0.711
0.804
0.897
1.754
1.815
1.662
1.757
2.008
1.694
2.144
1.720
2.548
2.556
2.951
2.525
2.340
2.602
2.216
2.076
1.865
1.468
Total
10250
934
8.35%
1.742
1.489
1.298
1.976
1.118
1.854
Wave
4515
383
7.82%
1.988
1.683
1.433
2.611
1.358
2.471
Nonwave
5735
551
8.77%
1.548
1.347
1.216
1.589
1.117
1.618
12
The Industry Shocks Hypothesis
• Underlying logic:
– Shocks to industry (e.g., technological innovations and
deregulation) make mergers profitable and lead to
industry merger waves
– several industries must enter a wave at the same time
– However, these shocks are not enough. There must be
sufficient capital liquidity to accommodate the asset
reallocation. (macroeconomic liquidity)
• Critique:
– Ignores association of wave and stock market boom
(Harford, 2004)
– Given its neoclassical nature, the role of liquidity is
problematic
13
Implications of the ISH
• Why does liquidity play an important role?
• This can be reconciled with the efficient
capital market assumption, if one assumes
that the firms making acquisitions are
undervalued, and thus cannot profitably
finance an acquisition by issuing shares.
• This interpretation of the ISH leads to a
testable prediction:
– firms undertaking acquisitions during a
merger wave will be undervalued.
14
• A second implication of the ISH
– There should be a relative expansion of the
amount of assets acquired by issuing debt
during a merger wave, because it is the fall in
borrowing costs that precipitates mergers in
industries experiencing shocks.
– Table 2 presents the sources of finance for
mergers over our sample period.
– During the merger wave years (1995-2000), the
relative importance of debt actually fell.
15
Table 2: Sources of Finance for Acquisitions: Total Amounts of
Assets Financed by the Various Sources
Year
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Non-Wave
Wave (1995-2000)
All years
Equity
14.54
22.18
16.57
18.25
16.85
22.33
25.69
14.62
22.23
29.88
31.00
27.97
30.45
35.33
35.86
29.52
18.40
20.11
31.75
24.22
Cash
78.06
73.38
78.70
74.89
76.36
68.79
63.88
73.97
68.17
63.63
62.89
65.55
63.19
59.49
60.00
63.96
74.28
72.22
62.46
68.77
Other
7.45
4.73
5.12
7.09
6.96
9.30
10.81
10.21
9.64
7.04
6.57
7.00
6.85
5.72
4.53
7.21
7.77
7.85
6.29
7.30
16
• A third implication of the ISH is that acquirers’
shareholders benefit from the mergers.
– An industry shock creates profitable merger
opportunities, and shareholder-wealth-maximizing
managers seize these opportunities.
– The assumption of capital market efficiency implies that
all wealth gains from mergers are registered in share
price movements at their announcements, and thus that
the shares of acquirers exhibit positive abnormal returns
at the announcements.
– Over longer time spans following the mergers share
performance should be indistinguishable from nonmerging firms.
– These predictions also differ from those of both the
managerial discretion and overvaluation hypotheses and
thus constitute tests to discriminate between the two sets
of hypotheses.
17
Summary
• Both the q - and industry shocks theories suffer as
explanations of merger waves, because important
implications of them are not supported by the data.
– Target firms do not become relatively inexpensive during a merger
wave as predicted by the q-theory, they become relatively more
expensive than capital equipment.
– Debt-financed mergers do not become relatively more important, as
predicted by the industry shocks hypothesis, they become less
important.
– An additional reason for rejecting these two theories is that they fail to
incorporate the most salient characteristic of a merger wave into their
explanation for it -the market’s over-optimism.
– Since this over-optimism plays a central role in both behavioral
theories of merger waves, we now discuss the psychology of stock
market booms.
18
The Psychology of Stock Markets
Today’s share price should have a
definite relationship to a firm’s
future earnings and dividends.
Shiller (1981): swings in stock prices
in the USA over the 20th century
were far greater than could be
accounted for by subsequent
swings in earnings and dividend
payments.
1920s optimistic
1930s pessimistic
Assuming an average rate of growth
of gi from now to infinity, (1)
becomes

 it
t 0
(1  ki )
Vi 0  
t
 io (1  gi )t
 io
Vi 0  

t
(1

k
)
ki  g i
t 0
i
(1)

(2)
19
• if ki > gi, which implies that the price/earnings ratio of firm i should
equal 1/(ki - gi).
• In the 1990s stock market boom, the S&P price/earnings ratio
topped 40.
• Assuming an average ki of 0.12, roughly the average return on
stocks over the period 1928-2004, then a P/E of 40 implies an
expected, perpetual growth rate of 0.095, which is more than four
times the average growth rate over the same period.
• Why should share prices rise to unprecedented levels?
• a “new era” ?
• 1920s, mergers in the steel industry, railroads
– community of interest: mergers will avoid “much economic waste” and
effect “various economies coincident to consolidation.”
• 1960s conglomerate merger wave, conglomerate mergers
– “P/E magic”
• 1990s, Harford (2005) industry shocks
20
Industry
Reason / Shock
The insurance industry 1998
“big is safer, leading to
consolidation, especially in
reinsurers.”
The medical equipment
industry - 1998
(1) acquisitions in core areas
to grow
(2) acquisitions outside core
areas to offer broad products
to increasingly consolidated
customers (hospitals).”
21
The Managerial-Discretion Hypothesis
• Underlying logic:
– Managers sacrifice profits for size and growth;
constraints less binding in waves due to increased
optimism, high share prices, and cash flows
• Critique:
– The MDH departs from most neoclassical
economics by assuming that managers
pursue growth and not shareholder wealth,
and that stock market psychology influences
managers’ decisions
22
•
managers’ utility can be expressed as a function of the growth of their
firms, g, and the threat of takeover, which is inversely related to q
U  U  g, q
U g  0
 U g  0
2
2
U q  0
2
U
0

q

2
g  g (M )
(U / g )(g / M )  (U / q)(q / M )
23
Figure 2: The Managerial Trade-off
(A)
(B)
 u q 


 q M  N
u g
,
g M
u q

q M
 u q 


 q M  B
q
B
1.0
u g
N
g M
MN
MB
M
M
24
Tests:
• Degree of speculation, Shiller (2000):
(P/Et)
• MDH-DM:
M t
M t
M t
M t
M t
 0,
 0,
 0,
 0,
0
CFt 1
 ( P / E )t
qt 1
 (qt 1  CFt 1 )
St 1
• MDH-TO:
– The MDH-DM receives less support
for tender offers, TOit, than for
friendly mergers, FMit.
25
• MDH-SP1:
– The shares of acquiring firms earn large
negative abnormal returns over long time
spans following the mergers, but not
immediately when they are announced.
• MDH-SP2:
– The post-merger performance of acquirers’
shares is worse for mergers undertaken
during merger waves.
26
The Overvalued Shares Hypothesis
• Underlying logic:
– Shleifer and Vishny (2003); managers
exchange their overvalued shares for real
assets of another company
• Critique:
– managers of overvalued firm maximize
welfare of current shareholders at the
expense of new ones;
– why pay high premia? (Any other asset
would work)
27
How to measure the overvaluation
•
•
•
Logically difficult
Others’ (Verter, 2002; Ang and Cheng, 2003; Dong, Hirshleifer, Richardson
and Teoh, 2005; and RKRV, 2005) measures typically involve the ratio of
market to book value of equity or its reciprocal.
all firms in an industry have the same costs of capital and expected growth
rates and use equation 2 to estimate 1/( ki - gi) for a typical firm by
regressing the market values of all firms in the industry on their profits for a
period of time when, based on the aggregate P/E ratio for the S&P index,
shares in aggregate do not appear to be overpriced. Call this estimate of
1/( ki - gi), .
Vit   it
Oit  Vit  Vit
(4)
(5)
28
Tests:
• OVH-DM1:
– The assets acquired through mergers are
positively related to Oit.
• OVH-DM2:
– The assets acquired through mergers are
positively related to dOit and Ot, and both
variables have identical coefficients
(dOit = Oit - Ot)
29
• OVH-TO:
– The OVH is better supported for friendly mergers than
for tender offers.
• OVH-TC:
– The probability that firm i is acquired in t is a
positive function of VSit and Oit.
• OVH-SP1:
– The shares of acquiring firms earn large negative
abnormal returns over long time spans following the
mergers, but not immediately when they are announced.
• OVH-SP2:
– The post-merger performance of acquirers’ shares is
worse for mergers undertaken during merger waves.
30
Previous Tests of the Two Hypotheses
• MDH
– Schwartz (1984)
– Harford (1999)
(1) cash rich companies are more likely to undertake
acquisitions,
(2) their acquisitions are more likely to be
diversifying acquisitions,
(3) the abnormal share price reaction of bidders is
negative and lower than for bidders which are not
cash rich, and
(4) operating performance deteriorates after
acquisitions by cash rich companies.
31
• OVH
– Dong et al. (2002) focus mainly on the choice
of payment in mergers, and the pattern of
post merger returns.
– Both Ang and Cheng and RKRV find a
positive relationship between the likelihood
that a firm becomes an acquirer and
measures of overvaluation.
– Ang and Cheng (2003, Table 3) include size in
their logit regression to predict the identities of
acquirers. It picks up a positive coefficient
and is by far the most significant variable in
the equation.
32
Returns to acquiring and target firm
shareholders
Three categories:
1)
2)
3)
very short windows, acquirers experience zero or slightly positive
returns
very short windows, acquirers experience negative returns, and
conclude that some non-neoclassical hypothesis must explain
mergers
event windows spanning two, three or more years, none
estimated positive returns to acquirers over long windows
33
Table 2a: Summary statistics, mean values
All
Acquisitions
Tender
Offers
Friendly
Mergers
Acquirer characteristics:
Tobin's q
Overvaluation (% of Total assets)
Cash flow/Total assets
Total assets (Mn 1985 USD)
1.71
69.7
0.064
4828.1
1.48
55.3
0.094
8296.6
1.74
71.6
0.060
4461.0
Target characteristics:
Tobin's q
Overvaluation (% of Total assets)
Cash flow/Total assets
Deal Value (Mn 1985 USD)
1.28
43.7
0.037
307.32
1.18
34.6
0.079
474.76
1.33
48.6
0.021
283.91
Mit
0.121
Tobin's q
Overvaluation (% of Total assets)
Cash flow/Total assets
Total assets (Mn 1985 USD)
0.168
Non-merging firms:
1.50
37.6
0.014
503.6
0.116
34
Table 4
Eq.
Hypothesis
Type
qit-1
Explaining the Amounts of Assets Acquired
1
MDH
FM
0.027
16.69
2
MDH
TO
0.0058
1.13
Oit
3
OVH
FM
4
OVH
TO
0.078
23.56
0.031
3.66
dOit
0.062
18.82
0.42
29.79
Ot
P/Et
CFit-1
qit CF,it-1
Kt-1
Kt-12
Kt-13
Industry dummies
N
R2
Consistent
with Hypothesis
5
OVH
FM
0.012
40.53
0.21
7.89
0.022
3.7
9.3*10-6
17.83
-5.5*10-11
13.39
0.012
14.62
1.05
9.21
-0.078
3.81
2.0*10-5
14.60
-1.2*10-10
10.62
7.1*10-17
1.6*10-16
11.22
9.52
Yes
No
Yes
No
Yes
89182
0.137
82724
0.102
50238
0.073
45974
0.002
50238
0.11
Yes
Yes
Yes
Yes
No
35
Discriminating between the MDH and OVH
• Predicting the Probability of Being
Acquired (PAQt)
NW: PAQt = 3.99*10-7 Oit - 0.00115 VSit, n=20,378, R2=0.0009
0.14
1.22
W: PAQt = -8.10*10-6 Oit - 0.00191 VSit, n=7,826, R2=0.0005
1.22
0.63
36
• Predicting the Means of Finance
SFt = 63.82 + 5.19 O/MVt 141.33CF/MVt 2.87FFt +10.58TMV/MVt
28.51 4.49
10.72
9.46
4.17
N = 3840, R2= 0.071
SFt, the fraction of assets acquired by a firm in year t through the issuance of
new shares
O/MVt, the ratio of the dollar amount by which an acquiring firm is overvalued to
its market value in year t,
CF/MVt, the ratio of the acquiring firm’s cash flow to its market value in
year t,
FFt, the federal funds rate in year t, and
TMV/MVt, the ratio of the target’s market value to the acquirer’s market
value in year t
37
Abnormal Returns
• We measure the AR‘s of acquiring companies
(A) using
AR
A
t n
R
A
t n
R
NA
t n
• The benchmark is the mean return on a portfolio
of non-acquiring (NA) companies, which are in
the same size decile as the acquiring company.
• Datastream, using the changes in the total return
index, which is adjusted for dividend payments
and share splits.
38
Table 5: The Returns to Acquiring Firms
Window
Month of
Acquisition
Period of
Acquisition
Friendly Mergers
N
Non-Wave
1624
Wave
1396
Difference
One Year after
Acquisition
N
-0.32
180
0.37
(1.42)
0.00
165
-0.35
(1.00)
-0.32
Mean
0.92
(1.39)
Median
N
-0.00
1804
2.23
(2.56)
1.01
1561
-1.31
(1.09)
-1.01
Mean
0.11
(0.21)
Median
-0.29
0.56
(0.25)
0.13
-0.45
(0.33)
-0.42
1645
-5.21
(5.85)
-8.43
184
-2.78
(2.61)
-6.09
1829
-4.97
(0.84)
-8.31
Wave
1524
-7.95
(7.36)
-10.83
171
-10.88
(3.05)
-12.43
1695
-8.24
(1.02)
-10.97
2.73b
(1.95)
2.40a
8.09b
(4.00)
6.34b
3.27b
(1.32)
2.66a
Non-Wave
1636
-15.37
(10.18)
-22.17
183
-2.63
(4.73)
-13.98
1819
-14.09
(1.44)
-21.18
Wave
1513
-20.75
(12.35)
-27.11
169
-31.19
(4.42)
-37.36
1682
-21.80
(1.58)
-28.35
5.37b
(2.39)
4.94a
28.55a
(6.50)
23.38a
7.70a
(2.13)
7.17a
Difference
Three Years after
Acquisition
Median
All Acquisitions
Non-Wave
Difference
Two Years after
Acquisition
Mean
0.021
(0.09)
Tender Offers
Non-Wave
1625
-23.38
(11.24)
-34.63
183
-12.09
(6.37)
-23.82
1808
-22.23
(1.98)
-33.47
Wave
1480
-31.20
(13.93)
-42.62
167
-45.85
(6.14)
-58.55
1647
-32.68
(2.11)
-44.58
7.81a
(2.55)
7.99a
33.75a
(8.89)
34.73a
10.45a
(2.89)
11.11a
Difference
39
Table 6: Predicting the Post-merger Returns of Acquirers
Window
Period
1 Month
2
Constant
Tt
Ot
dOit
Tt*Oit
N
R
Whole
0.016
27.98
-0.031
-8.82
-0.020
18.46
0.001
2.32
0.007
4.92
17015
0.021
1 Month
Wave
0.011
6.25
-0.046
-8.81
-0.012
-5.18
0.001
0.45
0.009
5.16
12462
0.018
1 Month
NW
0.018
23.11
-0.017
-3.77
-0.025
-13.81
-0.001
-4.07
0.005
1.87
4553
0.019
1 Year
Whole
0.268
29.78
-0.467
-8.65
-0.247
-14.28
0.002
0.77
0.112
5.38
16682
0.017
1 Year
Wave
0.188
6.42
-0.695
-8.29
-0.115
-2.78
0.006
1.55
0.136
4.91
4464
0.017
1 Year
NW
0.311
25.85
-0.303
-4.19
-0.366
-12.83
-0.002
-0.47
0.106
2.82
12218
0.014
2 Years
Whole
0.491
32.16
-0.823
-9.16
-0.437
-14.75
-0.010
-2.4
0.171
4.99
14891
0.021
2 Years
Wave
0.221
4.99
-0.958
-7.18
-0.104
-1.75
0.004
0.68
0.144
3.49
3619
0.014
2 Years
NW
0.508
24.16
-0.705
-5.81
-0.456
-9.11
-0.029
-4.14
0.260
4.15
11272
0.011
3 Years
Whole
0.767
31.18
-1.209
-9.78
-0.743
-14.51
-0.032
-4.23
0.214
4.33
13372
0.026
3 Years
Wave
0.408
4.84
-0.988
-5.64
-0.419
-3.28
0.006
0.5
0.154
2.66
2943
0.017
3 Years
NW
0.644
21.23
-1.186
-7.03
-0.333
-4.6
-0.057
-5.77
0.205
2.36
10429
0.010
Tt: The size of the target at the announcement, Ot : The average overvaluation in the market at the time of the acquisition
dOit : The acquirer’s deviation from the average overvaluation in the market at the time of the acquisition
40
Conclusions
• Support for Behavioral Theories stronger
than for Neoclassical
• Some evidence against overvaluation
hypothesis
• Overvaluation hypothesis cannot explain
increase in mergers financed by debt or
cash flows
• Managerial discretion hypothesis – first
decide whether to make an acquisition or
not, second how to finance it.
41
Based on our empirical results we offer the following
account of merger waves:
– At some points in time, shareholder optimism begins to rise.
– This optimism in the market allows managers to undertake wealthdestroying acquisitions, and not have their announcements met by
immediate declines in their companies’ share prices.
– The number of wealth-destroying mergers increases dramatically
during a stock market boom creating a merger wave.
– As the market learns about the mergers, it realizes that they will not
produce synergies, and that the theories behind them were false.
– The market’s optimism disappears and the share prices of acquiring
firms fall relative to those of other companies. Because of the premia
paid for the targets and the transaction costs of integrating separate
companies, the losses to shareholders of companies making
acquisitions are greater than one expects, simply because the
42
acquiring companies were overvalued.