Acquisition Performance:
Experience or Competence?
Steven E. Phelan
Tomas Mantecon
University of Nevada Las Vegas
1
Background
• Phelan
 Research Questions
• Entrepreneurial competence
• Alliances
• Acquisitions
 Methods
• Agent-based models
• Experimental game theory
• Event studies
2
Central Questions in Strategy
• Do some firms perform better than others?
 Do some firms (consistently) create more shareholder
value than others?
• Sustainable competitive advantage – the holy grail
• There is a deeply held belief (bias?) that this is true
• Why do some firms perform better than others?
 Most research focuses on this question
• Can I make this specific firm perform better than
others?
 Little on this
3
Acquisition Research in
Strategy
• Same questions
 Do some firms perform consistently better on
acquisitions than others?
 Why is this the case?
 What should a specific firm do to increase its
acquisition performance?
• General perception that…
 …acquirers (bidders) lose value in
acquisitions and that the targets capture most
of the value created
4
The Resource-Based View of Strategy
• A General Theory for Why Firm A
Outperforms Firm B
 Firm A possesses a value-creating resource
(asset) that Firm B does not, or
 Firm A uses a resource in a way Firm B does not
(it possesses a competence or capability that
Firm B finds difficult to imitate)
 If Firm A can acquire its resources for a lower
cost than Firm B (due to information asymmetry
or luck) than they will also have a competitive
advantage
5
Resource-Based View of Acquisitions
• Firm can acquire valuable resources through
acquisitions
 Emphasis on creating a synergy between old and
new resources (1+1=3)
• Porter has two tests:
 Is firm better off?
• Is additional value being created in the merger?
 Cost of entry
• Is the acquisition premium you are paying less than the value
created (and preferably much less)
• Links to the synergy trap or winner’s curse
6
Recent theory
• Hitt, Hoskisson, and Ireland
 Firms may develop a competency in
identifying, negotiating, and/or integrating
acquisitions that can lead to a competitive
advantage
 Classic examples: Cisco, GE – who make
dozens of acquisitions each year
 Not much empirical evidence for an
acquisition competence
 Those with an acquisition competence should
have a higher performance
7
The Role of Experience
• Simple enough
 The more acquisitions you do the better you
should get at acquisitions
• Easy to study
 Simply count how many acquisitions a firm
makes and see if performance increases with
experience
8
Measuring performance
• Market efficiency
 If you believe that markets are reasonably
efficient then the deviation in a firm’s stock
price following the acquisition announcement
will reflect the market’s judgment on the
wisdom of the acquisition (after adjusting for
normal daily market movements)
• Window
 We use a 3-day day window that includes
movements one day before and after the
announcement.
9
Previous Studies
• Kusewitt (1985)
 Returns decline if firms do more than one acquisition
per year
• 138 companies, 3500 events, 1967-76
• Fowler (1989), Bruton (1994)
 Small positive relationship between experience and
performance
• Only 41 and 52 events respectively
• Lahey and Conn (1990)
 No difference in performance between firms making
single or multiple acquisitions in a six year window
• 91 events over $10m
10
Previous Studies
• Haleblian & Finkelstein (1999)
 Reported U-shaped relationship between experience
and performance using 449 events >$10m
 Convoluted logic to explain effect
• Hayward (2002)
 535 acquisitions by 100 firms
 No relationship between experience and performance
 Time between acquisitions was significant
• Inverted U-shape
• Zollo & Reuer (2003)
 51 banks, 577 events
 No relationship between experience and performance
11
Meta-Analysis
• King et al (2004) meta analysis
 Compared 7 studies, 1300 events
 Different performance measures ranging from
days to months to years
 No relationship between experience and any
performance measure
• We hypothesize no relationship between
experience and performance for our
sample
12
Competence
• So what is making GE and Cisco so good
at acquisition if not experience?
 Perhaps raw experience is not a good proxy
for competence
 Chambliss (1999) found that Olympics
swimmers were qualitatively different from
amateurs not just quantitatively different (they
have differential technique)
• “superlative performance is really a confluence of
dozens of small skills and activities, each one
learned or stumbled upon”
13
Differential Learning
 There may also be an interaction between
experience and competence
• competent companies may learn faster (perhaps
masking an experience main effect)
 Hypotheses:
• Qualitative competence will be associated with
performance
• There will be an interaction between competence
and experience
14
Sample
• All reported acquisitions in SDC database
between 1991 and 2002
 Dropped firms without CRSP data
 Dropped recaps, spinoffs, LBOs, contaminated events
(i.e. earnings announcement at same time)
 Dropped outliers (|CAR|>0.5) – only 50 cases
 Final sample 10,574 events
• 5734 private targets
• 1465 public targets
• 3375 subsidiary targets
15
Design
• Sample was divided into 2 time periods
 1991-1996 & 1997-2002
(although other divisions were tested)
• We operationalized ‘competence’ as the
average (mean or median) performance in
the first six years
 Two measures CAR and residual CAR
 Considered 1, 3, 5 qualifying events
16
Results
• Controls:
 Event year, relative acquisition size, acquirer
performance, contested bids, business
similarity, method of payment, use of advisor
• Raw correlations
 Positive correlation between competence and
performance,
 Negative correlation between experience and
performance
17
Results
Competence as…
CAR
Residual CAR
Model 1
Model 2
Model 3
Model 4
Mean
Median
Mean
Median
Firm Performance
1.74
1.68
1.94
1.9
Cash
0.49
0.53
0.83
0.8
Stock
-9.58**
-9.72**
-9.64**
-9.86**
Contested Bid
-15.49
-15.33
-15.51
-15.14
Business Similarity
-6.2*
-6.14*
-6.1*
-5.83*
Relative Acquisition Size
4.53*
4.51*
4.48*
4.35*
Use of Advisor
-5.13*
-5.01*
-4.67
-4.5
-2.98***
-2.93***
-3.37***
-3.53***
Past experience (log)
-4.73
-5.18
-5.26
-5.1
Past experience2
0.93
1.00
0.97
0.90
52.22**
50.83**
43.64*
43.24*
Competence * Experience
-29.43
-7.68
17.10
37.81
R2
0.035
0.035
0.035
0.036
Bidder size
Acquisition competence
18
Results by Target Status
Model 1
Model 2
Model 3
Private
Public
Subsidiary
Firm Performance
-0.22
6.39*
1.83
Cash
-3.12
20.51*
-11.78*
Stock
-0.67
-11.88
-22.8*
Contested Bid
-0.17
-6.85
24.11
Business Similarity
1.12
-4.26
-9.49*
Relative Acquisition Size
9.21***
-1.54
11.68**
Use of Advisor
9.98*
-15.53*
5.5
Bidder size
-2.14*
-1.86
-3.72**
Past experience (log)
-7.49
-9.85
5.29
Experience squared
0.87
2.45
-0.31
Acquisition competence
72.62**
-33.68
56.49*
Competence * Experience
-34.87
-99.82
-4.12
R2
0.043
0.064
0.058
19
Discussion
• Experience has no relationship with
performance
 Confirms meta-study
 We also found no U-shaped relationship on
normalized data
• Artifact of extreme measures?
• Past performance predicts future
performance
 Arguably an unobserved competence
20
Discussion
• Competence relationship:
 Strongly significant for private firms
 Marginally significant for subsidiaries
 Not significant for public acquisitions
• Suggests an informational component
 Private market is less competitive than public market
 Perhaps, competent firms have lower search costs
• No interaction between experience and
competence
 Competent firms did not leverage experience better
21
Extension I. Firm Effects
Variable
DF
SS
MS
F
p
Firm Size
1
0.16698
0.16698
46.79
<.0001
Relative Acq Size
1
0.00057
0.00057
0.16
0.6894
11
0.04553
0.00414
1.16
0.3109
2
0.11561
0.05781
16.2
<.0001
58
0.35904
0.00619
1.73
0.0007
Cash
1
0.00246
0.00246
0.69
0.4065
Stock
1
7.5E-05
7.5E-05
0.02
0.8848
Contested
1
0.00869
0.00869
2.44
0.1189
Similarity
1
0.00177
0.00177
0.5
0.4815
Advisor
1
0.00234
0.00234
0.66
0.4178
Firm
149
0.84942
0.0057
1.6
<.0001
Firm*Year
738
3.13511
0.00425
1.19
0.0037
Year
Target Status
Target Industry
N=2224
22
Extension II. Cross Border
Acquisitions
Variable
DF
SS
F
p
19
0.057
1.019
0.4345
Target Status
5
0.032
2.194
0.0522
Bidder Size
1
0.007
2.466
0.1164
Competing Bids
1
0.000
0.079
0.7781
Relative Acq Size
1
0.028
9.409
0.0022
Prior Holdings
1
0.007
2.463
0.1166
Advisors
1
0.001
0.338
0.5612
Industry
64
0.187
0.992
0.4952
Business Similarity
1
0.011
3.616
0.0573
Cash
1
0.048
16.235
<.0001
Cultural Distance
1
0.034
11.414
0.0007
Year
R2=0.035
N=4682
23
Extension III. Recursive Partitioning
Analysis
All Rows
Count
Mean
Std Dev
59456
0.0049712
0.0561442
Log Rel Acq Size<2.60667683
Count
Mean
Std Dev
39483
-0.009572
0.0467052
Cash?(No)
Count
Mean
Std Dev
Advisors?(No)
Count
Mean
Std Dev
20979
-0.020158
0.0391379
R2=.198
Log Rel Acq Size>=2.60667683
Count
Mean
Std Dev
Cash?(Yes)
22648
-0.01961
0.0397567
Count
Mean
Std Dev
Advisors?(Yes)
Count
Mean
Std Dev
1669
-0.012723
0.0463063
19973
0.0337201
0.0618611
T_Status(Public)
16835
0.0039322
0.0516895
Count
Mean
Std Dev
Advisors?(Yes)
Count
Mean
Std Dev
1811
-0.015257
0.0753235
T_Status(Govt.,J.V.,Mutual,Sub.,Priv.,Unk.)
2682
-0.004401
0.0735244
Count
Mean
Std Dev
Advisors?(No)
Count
Mean
Std Dev
871
0.0181717
0.064027
17291
0.039633
0.057635
Cash?(Yes)
Count
Mean
Std Dev
4937
0.0244832
0.0737465
Cash?(No)
Count
Mean
Std Dev
12354
0.0456873
0.0484547
24