A Risk Management Model
for Merger and Acquisition
B. S. Chui
Sage International Group Limited, Hong Kong
Abstract In this paper, a merger and acquisition risk
management model is proposed for considering risk
factors in the merger and acquisition activities. The
proposed model aims to maximize the probability of
success in merger and acquisition activities by managing
and reducing the associated risks. The modeling of the
proposed merger and acquisition risk management
model is described and illustrated in this paper. The
illustration result shows that the proposed model can
help to screen the best target company with minimum
associated risks in the merger and acquisition activity.
Keywords Merger and Acquisition, Risk Analysis, Risk
Management
1. Introduction
Driven by globalization, international business looks for a
bigger market to achieve the scale of economy, so as to
overcome the economic barriers. Many organizations
adopt merger and acquisition (M&A) as a part of their
corporate strategy to achieve their business objectives.
M&A is a kind of transaction that can upgrade and
optimize the capital structure of companies with a
transfer of ownership and property rights. M&A has
already been developed with one century of history in the
world and give rise to merger and acquisition waves over
the world sixth times since the 1990s. Benefits of M&A
include increased economies of scale, increased market
share, enhanced efficient resource allocations, expanded a
larger asset base, increased reputation or added name
recognition, and instantly adopted expert talent lacking
in one to the other organization. Moreover, organizations
use M&A to penetrate into new markets and new
geographic regions, gain technical/management expertise
and knowledge, or allocate capital. Even though business
organizations often utilize corporate M&A strategy to
expand their business, many poorly understood and
managed M&A result in failure.
Systematic corporate M&A research can help to
understand the M&A activities. However, Sirower (1997)
emphasized the lack of clear understanding of how to
maximize the probability of success in M&A despite a
decade of empirical research. Financial economics and
strategic management literature are the two main streams
of literature framework to understand the M&A activities
(Datta, D., et al., 1992). Financial economists view M&A
as contests between competing management teams for
the control of corporate entities (Datta, D., et al., 1992).
Strategic management researchers use a different
approach by analyzing M&A via examining management
controlled factors, such as: diversification strategies (i.e.,
related vs. unrelated diversification), different types of
acquisitions (i.e., merger vs. tender offer), or different
types of payments (i.e., cash vs. stock). Nonetheless,
neither of these disciplines provides sufficient
explanation for the failure of M&A. Therefore, in order to
minimize the failure in M&A, a risk management
perspective is proposed in this paper, i.e. an approach
that attempts to maximize the probability of success in
M&A by managing and reducing the risks that associated
in the M&A activities. For this reason, this paper attempts
to propose a risk management model for the M&A
activities, and is organized as follows. Section 2 discusses
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International Journal of Engineering Business Management,
Vol. 3, No. 2 (2011) ISSN 1847-9790, pp 37-44
the current theories and models of M&A activities in the
literature. Section 3 proposes a theoretical framework for
the M&A risk management model while section 4
illustrates the mechanism of the proposed model with
data. Finally, a conclusion is given in section 5.
2. Theories and Models of M&A
There has been a long history for M&A activities, and
different types of models and theories have been
developed. In most M&A studies in the literature, they
are about the acquisition of all of a target organization
while the remainders are about either partial acquisitions
of a target, or completing acquisitions where part of the
target was previously owned by the acquirer.
The M&A research findings from the earlier studies
showed that most targets significantly increase in value
whereas acquirers typically experience small declines in
values. These transactions have been extensively
examined from perspectives such as auction theory and
the “winner’s curse” is an established part of the
literature. Subsequent studies have added refinement to
the understanding of the earlier studies in M&A
(Andrade, G., et al., 2001). Other studies in M&A include
analyzing the economic monetary effects in addition to
the percentage abnormal returns (Bradley, M., Desai, A.
& Kim, E., 1988). The significance of the type of
consideration in M&A was noted and has been
extensively examined (Heron, R. & Lie, E., 2002). The
extensive series of method-of-payment in M&A studies
are succinctly summarized by Bharadwaj and Shivdansi
(2003). They carried out empirical examination in
determining the distribution of gains between bidders
and targets in M&A. Theoretical work pertaining to M&A
has also been studied, including the findings of average
negative returns to acquirers led to theories pertaining to
agency considerations and free cash flow (Jensen, M.,
1986), and the role of hubris (Roll, R., 1986). Behavioral
finance theories have also been put forward to structure
the examination of M&A in which acquisitions are
motivated by stock market conditions (Schleifer, A. &
Vishney, R., 2003).
Other M&A studies have explained the activity by the
“tariff-jumping” argument that investing via M&A is an
alternative mode to enter other markets or expand the
market. These ideas have been formalized in theoretical
models in terms of trade costs (Carr et, D., et al., 2001;
Markusen, J., 2002; Blonigen, B., et al., 2003). Another
strand of literature has investigated the determinants of
international M&A activities from a more industrial
organization oriented background. For example, Horn
and Persson (2001), Bjorvatn (2004) and Norback and
Persson (2004) provided theoretical models where foreign
organizations may acquire domestic acquisition targets,
with the acquisition price being determined
endogenously in a bargaining process. In these models,
contrary to the tariff-jumping argument, high trade costs
do not necessarily induce cross-border M&A. High trade
costs not only encourage tariff-jumping mergers, but also
increase the incentives for domestic mergers as they
reduce the degree of competition in the domestic market,
thereby increasing the acquisition price domestic
acquirers are prepared to pay for domestic targets.
Furthermore, Neary (2007) developed a model of mergers
in a two-country oligopoly in general equilibrium.
Moreover, a number of studies have examined the
consequences cross-border M&A activities. Doukas and
Travlos (1988) found generally insignificant valuation
consequences for acquirers with the exception of when
the acquisition was the first major step of the acquirer
into the target’s country. Lin, Madura, and Picou (1994)
found material variation in acquirer valuation reactions
according to the domicile of the target. Kang (1993)
examined matched-pairs of acquisitions in cross border
M&A activities. He found statistically significant wealth
gains for both organizations. As is appropriate for crossborder acquisitions, he placed his analysis in the context
of Direct Foreign Investment (DFI). The M&A studies in,
Barros and Cabral (1994), Head and Ries (1997), Kabiraj
and Chaudhuri (1999), and Horn and Levinshon (2001)
analyzed the welfare effects of M&A and derive policy
implications. The positive issue of equilibrium market
structure via M&A in an international context has been
analyzed by Horn and Persson (2001), NorbKack and
Persson (2004).
For the analysis of M&A performance, Beena (2000)
analyzed the significance of mergers and its
characteristics, and reported that the most mergers were
dominated by mergers between companies belonging to
the same business group or house with similar product
lines. The study also revealed that mergers between
unrelated companies were gaining ground but mergers
contributed significantly to asset growth in only one-fifth
of the sampled companies studied. Langhe and Ooghe
(2001) examined the M&A performance of smaller
companies involved in the takeover, and their findings
showed that following the takeover, profitability,
solvency and liquidity of most of the merged companies
declined. Pawaskar (2001) analyzed the post-merger
operating performance of the acquiring companies and
attempted to identify the sources of merger-induced
changes. The study reported that as indicated by all the
profitability measure, mergers had a negative impact on
the acquirers’ performance. Moreover, no significant
improvement in profitability was found when comparing
the profitability difference between the pre and post
M&A activity. Sharma and Ho (2002) reported a decline
in operating performance after M&A activity when
carried out studies on the operating performance of 36
Australian companies involved in mergers. Rahman and
Limmack (2004) analyzed control-adjusted operating cash
flow performance using a sample of Malaysian
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companies involved in M&A activity. Their study
examined whether shareholders’ wealth increased as a
result of takeover. The study results suggested that
acquisitions led to improvements in the long-run
operating cash flow performance. Powell and Stark (2005)
compared post-takeover performance with combined pretakeover performance and suggested that there were no
significant improvements in operating performance.
The M&A studies in the literature are mostly from the
financial and economics perspectives, which leading to
the design and development of the existing M&A models
and theories are based on the cost objective function. The
cost-orientated perspectives can be good for projecting
the potential gain/profit from the M&A, however, the
potential lost or risks result from the M&A activities
cannot be reflected. Therefore, it is worth taking to
propose a M&A model and theory from a project risk
management perspective, such that the probability of
success in M&A can be maximized by managing and
reducing the risks that associated in the M&A activities.
3. M&A Risk Management Model
The M&A risk management model is used to identify and
manage the risks arising from the M&A processes so as to
maximize the probability of success in M&A by
managing and reducing the risks that associated in the
M&A activities. The approach of the M&A risk
management model is divided into two steps, i.e. Risk
Identification and Risk Quantization
3.1 Risk identification with fish bone method
Risk identification is to identify the risk factors that exist
in the M&A activity. In the risk identification of the M&A
risk management model, the fish bone method is adopted
to identify possible risks. The fishbone diagram can
identify many possible causes for an effect or problem. It
can be used to structure a brainstorming session, and
immediately sorts ideas into useful categories.
The fishbone diagram is a tool for analyzing process
dispersion. The diagram illustrates the main causes and
sub-causes leading to an effect (symptom). It is a team
brainstorming tool used to identify potential root causes
to problems. Because of its function it may be referred to
as a cause-and-effect diagram. In a typical fishbone
diagram, the effect is usually a problem needs to be
resolved, and is placed at the “fish head”. The causes of
the effect are then laid out along the “bones”, and
classified into different types along the branches. Further
causes can be laid out alongside further side branches. So
the general structure of a fishbone diagram is presented
in Fig. 1.
The main goal of the fishbone diagram is to illustrate in a
graphical way the relationship between a given outcome
and all the factors that influence this outcome. The steps
for constructing and analyzing a Cause-and-Effect
Diagram are outlined below:
Step 1 – Identify and clearly define the outcome or effect
to be analyzed.
Step 2 – Use a chart pack positioned so that everyone can
see it, draw the spine and create the effect box.
Step 3 – Identify the main causes contributing to the effect
being studied. These are the labels for the major branches
of the diagram and become categories under which to list
the many causes related to those categories.
Step 4 – For each major branch, identify other specific
factors which may be the causes of the effect
Figure 1. General structure of a fishbone diagram
Step 5 – Identify increasingly more detailed levels of
causes and continue organizing them under related
causes or categories.
Step 6 – Analyze the diagram to identify causes that
warrant further investigation.
3.2 Risk quantification with Fuzzy-AHP method
The Analytic Hierarchy Process (AHP) is adopted in the
M&A risk management model to quantify the identified
risk factors. AHP was developed by Tomas L. Saaty in the
1970s (Saaty, T., 1979) and has been extensively studies
and refine since then. It is very powerful systematic
analysis technique by which the relative factors can be
ranked and the importance of them can also be figured
out. AHP links the quantitative and qualitative method
together, and it helps to solve decision problem by
weighting selection factors and analyzing the data
collected for the factors. In this way, the complex problem
of choosing the right strategies can be resolved.
In the AHP, it is needed to identify the goal, criteria and
strategy of evaluation, form a multilevel tree structure. In
the multilevel tree structure, the first level is the goal of
the overall evaluation; the second layer is the criterion
level or risk factors level that is a concrete manifestation
of the overall goal or specific guidelines; the third level is
strategic one that is used to select the suitable partner.
After identify the multilevel structure, a quantitative scale
to judge is needed to establish, it is used to compare each
factors over another to determine the relative importance
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between the scale factors. A sample of a quantitative scale
in AHP is illustrated in Table 1.
Based on the AHP, fuzzy comprehensive evaluation is
further integrated to select the strategies by linking
weightings to the relative criterions. In the fuzzy
comprehensive evaluation, the triangular fuzzy numbers
Scale Definition Explanation
1 Equal importance Two activities contribute
equally to the objective
3 Weak important of
one over another
Experience and judgment
slightly favor one activity
over another
5 Essential or strong
importance
Experience and judgment
strongly favor one activity
over another
7 Demonstrated
importance
One activity is strongly
favored and its dominance
is demonstrated in practice
9 Absolute
importance
The evidence favoring one
activity over another is of
the possible order of
affirmation
2 4 6 8 Intermediate values
between the two
adjacent judgments
When compromise is
needed
Table 1. A quantitative scale in AHP
to are used to represent the pair-wise synthetic
weightings criteria in order to capture the vagueness and
uncertainty of the domain knowledge. A fuzzy number is
a special fuzzy set , where x takes its
values on the real line R: -∞ < x < +∞ and is a
continuous mapping from R to the closed interval [0, 1].
A triangular fuzzy number denoted as where
a ≤ b ≤ c under the following triangular-type membership
function:
Alternatively, by defining the confidence level α, the
triangular fuzzy number can be characterized as:
The triangular fuzzy numbers to are utilized to
capture the vagueness and uncertainty of domain
knowledge. The eigenvalue of the pair-wise comparisons
matrix in the membership function can provide the
weightings for the criteria. The computational procedures
for the synthetic weightings are summarized as follows:
Step 1: Compare the score of the criteria. The triangular
fuzzy numbers are used to indicate the
relative strength of each pair of elements in the same
hierarchical structure of the modeling framework.
Step 2: Construct the fuzzy pair-wise comparison matrix
using the triangular fuzzy, and the fuzzy judgment
matrix as follows:
Step 3: Solve the fuzzy eigenvalues. A fuzzy eigenvalue
is a fuzzy number solution to , where is a n x
n fuzzy matrix containing fuzzy number and is a
non-zero n x 1 fuzzy vector containing fuzzy number .
To perform fuzzy multiplications and additions using the
interval arithmetic and α-cut, can be further
elaborated as:
for 0 < α ≤ 1, i, j = 1, 2, … n,
where , , ,
,
Degree of satisfaction with the judgment matrix is
estimated by the index of optimism μ. The larger value of
the index μ indicates a higher degree of optimism. The
index of optimism is a linear convex combination, and is
defined as:
While α is fixed, the matrix of optimism can be obtained
in order to estimate the degree of satisfaction, and the
eigenvector/ maximal eigenvector can also be determined
by fixing the μ value.
Step 4: Determine the synthetic weightings. By
synthesizing the priorities over the optimism matrix and
by varying the α-value, the synthetic weightings for the
performance parameters and its criteria/ sub-criteria can
be obtained.
The Fuzzy-AHP is an approach to decision making that
involves structuring multiple choice criteria into a
hierarchy, assessing the relative importance of these
criteria, comparing alternatives for each criterion, and
determine an overall ranking of the alternatives for each
criterion, and determining an overall raking of the
alternatives. It helps capture both subjective and objective
evaluation measures, providing a useful mechanism for
checking the consistency of the evaluation measures and
alternatives suggested by the team thus reducing bias in
decision making. In addition, the Fuzzy-AHP method
allows organizations to minimize common pitfalls of
decision making process, such as lack of focus, planning,
participation or ownership, which ultimately are costly
distractions that can prevent teams from making the right
choice (Yung, K. & Li, J., 2004).
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4. Model Illustration
Risk identification is the fundamental process in the risk
analysis of M&A activity. In the illustration of the M&A
risk management model, risks are generally divided into
three types under the project management approach with
fish bone analysis, and the fish bone risk identification
analysis of M&A activity is illustrated in Fig. 2.
For the identified risks, the AHP is adopted to determine
the weighs of each risk factor, and the fuzzy
comprehensive evaluation method is to do with the
comprehensive evaluation. The M&A risk management
model can be developed to analyze the risks and select
the suitable target company in the M&A activity that
have minimized risks, i.e. the risk management of the
M&A activity is focused on the evaluation of targeted
company that is intended to merge or acquire by
weighting the various risk factors associated with
alternative target company. Fig. 3 illustrated the hieratical
approach of the proposed M&A risk management model.
M&A Activity
Cost Risk
Quality Risk Time Risk
Operation Cost
Process Sequence
Operation Cost
Project Delay
M&A Result
Compatibility
Integration
Figure 2. Fish bone analysis for M&A activity
M&A
Activity
B1 B2 B3
C1 C2 C3 C4 C5 C6 C7
P1 P2 P3 P4
Level 1 (Goal)
Level 2 (Criteria)
Level 3 (Risk Factors)
Level 4 (Target Company)
Figure 3. The hieratical approach of the proposed M&A risk
management model
As shown in Fig. 3, it shows the relationship between the
different risk factors, and their relationship between the
risk factors and target companies. Level one is the goal of
the M&A activity; level two shows the criteria that will
affect the performance of the M&A activity, which are
identified by the fishbone analysis, i.e. B1: Time Risk, B2:
Cost Risk, and B3: Quality Risk; level three shows the
sub-risk factors that is related to B1, B2, B3 It includes the
sub-factors of the criteria, i.e.
B1: C1: M&A activity processes sequencing risk
B1: C2 M&A project delay risk
B2: C3: Operation cost risk
B2: C4: M&A processes cost risk
B3: C5: M&A result risk
B3: C6: M&A compatible risk
B3: C7: M&A integration risk
With the above criteria and sub-factors in the hieratical
approach of Fuzzy-AHP, the computation of the M&A
risk management model can be carried out to determine
the importance of different risk factors and the relative
degree of M&A merger or acquire preferences towards
different target companies. The consolidation of formula
of Fuzzy-AHP for M&A activity follows four steps, i.e.
Step one: Create the fuzzy set of relevant factors
Assume there are 4 target companies namely P1, P2, P3,
and P4. The set of Target Company then is {P1, P2, P3,
P4}, and the set of M&A comments is designed as
excellent, good, medium, bad, very bad, which means the
corresponding Comment Set is {5, 4, 3, 2, 1}.
Step two: Determine the weighs of expert
Assume there are 5 experts who are going to weigh the
different risk factors, and the expert set then is {R1, R2,
R3, R4, R5}, and the ranking of the experts and their
relative ranking are assumed as shown in Table 2.
Through the calculation of the evaluation matrix of
expert, the relative weight of experts, eigenvector r =
{0.419, 0.263,
Expert R1 R2 R3 R4 R5
R1 1 2 3 4 5
R2 0.5 1 2 3 4
R3 0.333 0.5 1 2 3
R4 0.25 0.333 0.5 1 2
R5 0.2 0.25 0.333 0.5 1
Table 2. The evaluation matrix of experts
0.160, 0.097, 0.062}, eigenvalue λmax = 5.067. The order of
the evaluation matrix is 5, CI = 0.0169, CR = 0.0151 < 0.1.
This means that the evaluation matrix of experts is
acceptable.
Step three: Identify the weight of factors in level 2 and
level 3
In identifying the weight of factors in level 2, each expert
will give weighting on the three factors, and an example
of the assumed evaluation matrix determined by the
experts is shown in Table 3.
Factors in Level 2 B1 B2 B3
B1 1 1/5 1/7
B2 5 1 1/3
B3 7 3 1
Table 3. The assumed evaluation matrix determined by expert R1
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Through the calculation of the evaluation matrix of R1
expert, the eigenvector is wB1 = {0.072, 0.279, 0.649}T,
eigenvalue λmax = 3.065. The order of the evaluation
matrix is 3, CI = 0.0325, CR = 0.056 < 0.1. This means that
the evaluation matrix of experts is acceptable.
Similarily, the remaining evaluation matrix in level 2 are
shown as follows:
wB2 = {0.105, 0.258, 0.637}T, eigenvalue λmax = 3.039. CI =
0.0195, CR = 0.0336 < 0.1. This means that the evaluation
matrix of experts is acceptable.
wB3 = {0.071, 0.178, 0.751}T, eigenvalue λmax = 3.029. CI =
0.0145, CR = 0.025 < 0.1. This means that the evaluation
matrix of experts is acceptable.
wB4 = {0.097, 0.333, 0.570}T, eigenvalue λmax = 3.025. CI =
0.0125, CR = 0.022 < 0.1. This means that the evaluation
matrix of experts is acceptable.
wB4 = {0.105, 0.637, 0.258}T, eigenvalue λmax = 3.0385. CI
= 0.0125, CR = 0.019 < 0.1. This means that the evaluation
matrix of experts is acceptable.
Based on the above five eigenvectors, the relative weight
between factors in the level 2 and factors in level 3 would be

In identifying the relative weight of factors in the level 3
over level 2, the eigenvector according to each evaluation
matrix are for C1, C2, C3, C4, and C5 are wC1 = (0.125,
0.875)T; wC2 = (0.167, 0.833)T; wC3 = (0.750, 0.250)T; wC4 =
(0.250, 0.750)T; wC5 = (0.167, 0.833)T.
The weight of C1 over C2 from experts are assumed as
1/7, 1/6, 1.3, 1/5, 1/7. Thus, the weight of C1 over C2 in
eigenvector is

Simliarily, the weights of C3 over C4 from the five experts
are assumed as 1/7, 1/5, 3, 1/3, 1/5, then the weight of C3
over C4 in eigenvector is .
The relative weights of C5, C6, and C7 from the five
experts are shown in Table 4.
Factors C5 C6 C7
C5 1 1/5, 1/3, 1/7, 3, 1/5 1/3, 1/5, 1/5, 3, 1/7
C6 5, 3, 7, 1/3, 5 1 3, 1/3, 4, 1, 1/3
C7 3, 5, 5, 1/3, 7 1/3, 3, 1/4, 1, 3 1
Table 4. The relative weight of C5, C6, and C7
Then the weight of C3 over C4 in eigenvector is
.
Based on the above results, the weightings of each factor
are summarized in Table 5. As from the table, it shows
the relative importance of different risk factors, where the
higher the weight, the more importance of the factor is.
Step four: Establish the fuzzy evaluation matrix
This step is about the calculation of the evaluation matrix
level 2 and level 3 as well as the final fuzzy evaluation of
risk factors in level 3 and gets the weight of alternative
target company. The assumed risk evaluation matrix for
P1, P2, P3, and P4 is illustrated in Table 6.
As from the data in Table 10, the evaluation matrix is
determined as:

By developing the evaluation matrix of S1, S2, and S3, the
final evaluation of risk factors in level 3 as well as the
weighting of each target company can then be
determined as follow:
Level
One
Level
Two
Level
Three
Level
Four
M&A
Activity
Factor Weight Factor Weight Target
Company:
P1, P2, P3,
P4
B1 0.085 C1 0.250
C2 0.750
B2 0.285 C3 0.153
C4 0.847
B3 0.630 C5 0.145
C6 0.481
Table 5. The summary of weighting results
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Target Excellent Good Medium Bad Very Bad
C1 P1 0 0.6 0.4 0 0
P2 0 0.6 0.4 0 0
P3 0 0.4 0.6 0 0
P4 0 0.4 0.6 0 0
C2 P1 0 0.2 0.4 0.2 0.2
P2 0 0.2 0.6 0.2 0
P3 0.2 0.6 0.2 0 0
P4 0.2 0.4 0.4 0 0
C3 P1 0 0.4 0.6 0 0
P2 0 0 0.4 0.2 0.2
P3 0 0.4 0.2 0.2 0
P4 0 0.6 0.4 0 0
C4 P1 0.2 0.6 0.2 0 0
P2 0.2 0.6 0.2 0 0
P3 0 0 0.6 0.2 0.2
P4 0 0.2 0.6 0.2 0
C5 P1 0.2 0.6 0.2 0 0
P2 0 0.4 0.6 0 0
P3 0 0.2 0.6 0.2 0
P4 0 0 0.6 0.2 0.2
C6 P1 0 0.4 0.6 0 0
P2 0 0.4 0.6 0 0
P3 0.4 0.4 0.2 0 0
P4 0 0.6 0.4 0 0
C7 P1 0 0.4 0.4 0.2 0
P2 0 0 0.4 0.4 0.2
P3 0 0 0.4 0.4 0.2
P4 0 0.6 0.4 0 0
Table 6. Risk evaluation matrix for P1, P2, P3, and P4

Similarly,

Then, the comprehensive evaluations are:
According to the above analysis, the sequence of
performance of target companies P1, P2, P3, and P4 is:
5. Conclusion
In conducting the M&A activity, it is necessary to work
on how to minimize the risk of implementation, people,
cultures, different legacy systems, business disruption,
etc. are taken as the complex matters which need to be
considered. The important things that the M&A project
manager should concern about are whether the project
can be done with the objectives of the M&A activity. The
implementation of M&A project is not something that
should be approached without a great deal of careful
planning. Some obstacles should overcome at the path of
implementation as well. The successful factors that
should pay attention to are time, quality and cost of the
project associated with real situation in the M&A
processes.
As from the illustration in this paper, the best target
company that is more preferred to undergo merger or
acquisition can be screened by the proposed M&A risk
management model, so as to initially figure out which
company is the best that can reduce most risks in the
M&A activity, i.e. the partner selection can reduce most
possible risks from the M&A process. Moreover, the
model can also identify the most important factors that
can affect the M&A activity. However, it needs to note
that one of the major deficiencies of the model is that it is
highly depends on the experts’ initial weighting on the
factors, and the weightings also needed to be controlled
by the project team, and find the most appropriated
strategies to cope with.
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