Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
Impact of Mergers & Acquisitions on Technical Efficiency:
A Stochastic Distance Function Approach
Priya Bhalla1
It is of importance to study efficiency in financial entities, particularly in emerging economies
as these countries are struggling with the decision of allocating scarce financial capital. While
the theory suggests that consolidations could result in increase in efficiency, but, a review of
the empirical literature is mostly ambiguous. Furthermore, there are hardly any studies in the
context of Indian financial entities. Accordingly, the present study attempts to examine their
technical efficiency of Indian financial sector entities using stochastic distance function (SDF)
approach. This is one of the first analytical studies to provide such a vast and comprehensive
coverage of the entire gamut of mergers and acquisitions (M&A) transactions that arise during
1995-2011 amongst all kinds of entities (banks and non-bank financial entities) belonging to
financial sector of India. The study finds no significant technical efficiency differences among
acquirers and targets in the pre M&A period. However, in the post M&A period, acquirers
have significantly higher efficiency compared to targets suggesting that M&A could have
positive effect on technical efficiency of financial entities. These financial entities can rapidly
gain size, efficiency and hence market power becoming financial superpowers (mostly global
in operations) by undertaking such M&A. The large sized financial entities pose a threat to
financial stability of an economy, calling for effective regulation of these entities.
1. Introduction
The importance of efficiency in financial entities, particularly in emerging economies
stems from the need to efficiently allocate scarce financial resources. While the theory
suggests that consolidations could result in increase in efficiency, the empirical
literature on the benefits from mergers and acquisitions (M&A) has remained mostly
ambiguous. Furthermore, there are hardly any such studies in the context of financial
entities in India that attempt to examine the impact of M&A on technical efficiency. This
is one of the first analytical studies to provide such a vast and comprehensive
coverage of the entire gamut of M&A transactions that arise amongst the entities
belonging to financial sector of India.
The study goes beyond the existing studies and utilises econometric technique of
Stochastic Distance Function (SDF) that is particularly suited to assess efficiency for
multi-product firms like financial entities. The study finds that there are insignificant
efficiency differences among acquirers and targets in the pre M&A period. However,
the acquirers are found to be more efficient compared to targets in the post M&A
period.
The present study is structured as follows. Section 2 reviews the related empirical
literature. Section 3 presents the analytical framework. Section 4 and 5 outline the
database and methodology adopted in the study. Section 6 reports the empirical
results along with policy implications of the findings. Section 7 concludes with a
summary of main findings.
1
Assistant Professor (Economics), Motilal Nehru College (E), University of Delhi, New Delhi, India, Tel:
919868970936, 919899618885, priyabhalla01@gmail.com
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Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
2. Literature Review
In spite of a large number of studies, the efficiency implications of M&A remain largely
ambiguous in the literature. While some studies found that mergers have no
significant effect on efficiency, others found improvement in efficiency (Focarelli and
Panetta, 2003) and some others found evidence of efficiency reduction (Berger and
Humphrey, 1994; Amel et al., 2004). Further, the effects of M&A on efficiency of firms
have been widely studied in the advanced economies and to a lesser extent in
emerging economies.
Gourlay et al. (2006) analyze the post merger technical efficiency benefits among 11
commercial banks from 1991-2005. The study found that banks in the post-merger
period achieve significant gains in efficiency. In yet another small sample based study,
Kaur and Kaur (2010) analyze the impact of M&A on cost efficiency of commercial
banks in India. The study found that 6 of the 11 banks analyzed experienced gain in
cost efficiency. However, both these studies in India have focused only on banks and
consequently based on an extremely small sample, as very little M&A activity has
taken place in the banking segment.
3.1 Variables
Dependent Variable: The dependent variable in the models is pre and post technical
efficiency.
Explanatory Variables: A firm’s efficiency is assumed to depend on a set of firm level
factors such size of the firms (SIZE), expenditure incurred by financial entities in
marketing as measured by ratio of marketing expenses to total expenses (MEXP),
proportion of information technology to total expenses (ITEXP), type of firm such as
nascent ventures (NEW), type of financial intermediation (BANK), listing classification
(LISTED), status in M&A (ACQUIRER or TARGET) ownership such as state
(GOVERNMENT) or foreign (FOREIGN) owned. Additionally, in the post M&A period,
to understand the performance implications of different type of M&A such as induced
by regulation (REGULATORY), involvement of related firms (RELATEDFIRMS),
involvement of more than two firms (MULTIPLEFIRMS) and firms repeatedly involved
in M&A (REPEATED) are included (Table 1).
Ownership: Two variables have been considered to account for two different aspects
of ownership: foreign (FOREIGN) and state (STATE) owned entities. It is hypothesized
that all other things being equal, foreign (domestic) and privately owned (state) entities
are likely to perform more (less) efficiently.
Size of the Entities: It is expected that all other things being equal, the large (small)
financial entities are likely to be more (less) efficient.
Selling Efforts: All other things being equal, higher (lower) ratio of marketing
expenses to total expenses (MEXP) is likely to have a positive (negative) impact on
efficiency of firms.
Information Technology: All other things being equal, the entities with higher (lower)
information technology expenses are likely to have higher (lower) technical efficiency.
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Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
Financial Intermediation: A dummy variable, BANK, assumes value 1 if the
acquiring firm is a bank and 0 if non-bank. In the M&A literature little is known about
the efficiency differentials of these different types of intermediation activities.
Accordingly, there is no prior directional expectation on the sign of this variable.
Participation in M&A: The theory and empirical studies suggests that prior to M&A,
acquirers are likely to be more efficient relative to non-participants. Accordingly, in the
pre M&A period, it is hypothesized that all other things being equal, the acquirers
(acquired) are likely to be more (less) efficient prior to M&A.
In the post M&A period, the efficiency of acquirer is likely to be higher due to reduction
in costs that could arise from sharing infrastructure such as branch-offices, IT and
distribution systems and information on potential and existing customers and crossselling of different types of financial products. Nevertheless, it is hypothesized that all
other things being equal, the acquirers are likely to be more efficient consequent to
M&A.
Cross Border M&A: The integration of firms across borders is likely to improve
efficiency due to access to superior raw materials, human resource and technology. In
the present study, it is hypothesized that all other things being equal, cross border
(domestic) M&A are likely to provide higher (lower) benefits to acquirers.
Regulatory Induced: As indicated earlier, many mergers in the financial sector,
particularly among banks, are likely to be induced by the state in order to maintain
financial stability. Consequently, they may not enhance efficiency. All other things
being equal, the regulatory induced mergers are less likely to augment efficiency in
acquirers.
Multiple Acquisitions: The firms that are repeatedly involved in M&A activity are
more likely to better handle M&A related issues (such as integration, legal etc.) as a
result of learning economies resulting in higher efficiency (Kwoka and Pollitt, 2010). All
other things being equal, the acquirers that are repeatedly involved in acquisitions are
more likely to achieve larger gains in efficiency
Finally, in order to account for other changes over time a trend variable (TREND) is
included to control for set of factors which are not included in the model but are likely to
affect the frontier, particularly technology. All these explanatory variables are
summarized below (Table 1).
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Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
Table 1: Explanatory Variables
Variable
Notation
Measurement
Expected
Sign
Acquirer in M&A
ACQUIRER
ACQUIRER=1 if firm is an acquirer
during 1995-2012
+
Target in M&A
TARGET
TARGET=1 if firm is a target during
1995-2012
-
Nationality
FOREIGN
FOREIGN=1 if the financial entity is
foreign owned
+
Ownership
GOVERNMENT
GOVERNMENT=1
controlled by state
-
Size of the Entity
SIZE
Natural Logarithm of Total Assets
+
Sales and Marketing
Expenses
MEXP
Sales and Marketing Expenses divided
by Total Expenses
+
Information and
Technology Expenses
ITEXP
Information and Technology expenses
divided by total expenses
+
Type of Firm (Listed
Firms)
LISTED
LISTED=1 if firm is listed in either BSE
or NSE stock exchange
+
Type of Firms (Banking
Firms)
BANK
BANK=1 if firm is a bank and 0
otherwise
+/-
New Entrants
NEW
NEW=1 if firm is incorporated in or after
1991 and 0 if before 1991
+/-
Involvement in Multiple
M&A deals
REPEATED
REPEATED=1 if firm is repeatedly
involved in M&A and 0 otherwise
+
Type of Firms Involved
DIVERSIFICATION
DIVERSIFICATION=1 if M&A takes
place amongst different firms such as
banks and NBFC and 0 otherwise
+/-
Nationality of Firms
CROSSBORDER
CROSSBORDER=1 if deals are across
national borders and 0 otherwise
+
Inducement behind M&A
REGULATORY
REGULATORY=1
if
M&A
are
government induced and 0 otherwise
-
No. of Firms Involved in
M&A deal
MULTIPLEFIRMS
MULTIPLEFIRMS = 1 if more than two
firms are involved in the deal and 0 if
only two firms are involved in M&A
+/-
Relatedness of Firms
RELATEDFIRMS
RELATEDFIRMS = 1 if firms belong to
same group or are subsidiaries and 0
otherwise
+
if
owned
and
3.2 Model Specification
The analysis is separated into two periods: pre and post M&A. Accordingly, two separate
models are estimated.
Model I: Pre-M&A Technical Efficiency (TE)
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Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
Specifically, it seeks to answer the following question: What are the efficiency levels in
the firms that are going to participate in M&A either as acquirers or targets?
Model II: Post M&A Technical Efficiency (TE)
Model II seeks to explore the efficiency of acquirers in relation to other firms after the
event of M&A? In other words, if the acquirers select the targets that may be superior
performers, then what can be said about the technical efficiency post M&A?
4. Data
Surprisingly, there is no comprehensive database available on M&A in India.
Accordingly, the data for the study is mainly compiled from the several available sources
of information on M&A in India such as Mergers & Acquisitions database compiled by
Centre for Monitoring Indian Economy (CMIE); Prowess database compiled by CMIE;
Company News and Notes (CNN), a publication by Department of Company Affairs
(DCA) and Securities and Exchange Board of India (SEBI) to create an exclusive
dataset on M&A in Indian financial sector during 1995-96 to 2011-12.
The present study includes both merger and acquisition transactions that take place
within the financial sector. An acquisition has been considered as a partial purchase of
target firm’s equity stake while a merger may be seen as a complete acquisition of
target firm’s equity. It is possible that acquiring a part of target’s equity (as done in
acquisitions) may indicate future intention of completely acquiring (or merging) the
target. A number of such firms have adopted such a strategic course, wherein, the
acquirer has initially acquired a part of the equity stake in the target through the
process of acquisition and then later completely acquired the target. Given the fact
that acquisition decision may be linked to the decision to merge at some later time
period, it is pertinent to recognize that the two may not always be independent.
Therefore, to gain greater insights on various aspects such as the nature, motives and
type of M&A and financial entities involved in the financial sector, a variety of
additional information such as mode of acquisition, i.e. merger, substantial or minority
acquisition; geographical borders, i.e. cross border or domestic transactions; relation
among the firms, i.e. affiliated or non-related firms; motive of merger, i.e. forced or
voluntary; type of combination, i.e. product diversifying or consolidating; nature of
firms such as age, activity, listing classification etc. has also been assembled from the
above-mentioned data sources.
All participating entities for which financial data is available in Prowess are included.
Specifically, there are 208 M&A deals in the sample involving 312 firms for which
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Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
financial information is available. Some financial entities have been dynamic
participants in M&A activity. This includes firms that are repeatedly engaged as
acquirers (almost 13%) as well as those that are engaged as both acquirers and
targets (almost 11%) in mergers and acquisitions. On the basis of M&A participation,
the latter set of firms could not be classified as either acquirer or target. Since these
firms could pose difficulty in identification of the characteristics of acquirers and
targets, these have not been considered in the empirical analysis (36). There are 271
participating firms in these M&A transactions that are finally selected. Further, a
control sample of 542 firms (twice the number of participant firms) that have not
participated in M&A has been included.
However, the analysis in the present study is based on a subset of these firms. All the
financial entities (acquirers, targets and non-participants) for which non-zero values of
input and output are available are considered. This exercise makes the panel
unbalanced.
Model I: Pre M&A
The analysis is based on the years prior to the event of M&A. This yields a pooled
data on 250 observations (72 acquirers, 130 targets and 48 non-participants) for the
period 1992-2011.
Model II: Post M&A
To investigate the impact of M&A on efficiency of acquirers, the analysis is carried on all
observations in the years subsequent to M&A. This yields a pooled data consisting of
378 observations (158 acquirers, 114 targets and 106 non-participants) for the period
1998-2011.
5. Methodology
The present study estimates efficiency in a single stage as the two-stage approach
has been criticized due to various reasons such as correlation of input and outputs
used in estimating efficiency with the explanatory variables in the second stage;
correlation of DEA scores violating the assumption of within sample independence
required in second stage regression; inconsistencies in the distributional assumptions
of inefficiency that are contradicted in the second stage etc. (Coelli et al., 2005).
Specifically, to account for multiple outputs, recent studies have utilized advanced
techniques such as stochastic distance function (Greene, 2005; Kumbhakar and
Wang, 2007; Yamori and Hamaya, 2010).
The distance functions are especially suitable in modelling the production process of
multiple input and multiple output firms like banks or other financial entities. The
distance functions are based on the logic of radial expansion of outputs (output
orientation) or contraction of inputs (input orientation). The explanation of input
distance function below is drawn from the work of Coelli et al., 2003, 2005;
Karagiannis, 2004; Greene, 2005; Kumbhakar and Wang, 2007; and Berg and Lin,
2008. An input distance function can be defined as the maximum amount by which
input usage can be reduced to bring output to frontier isoquant. In addition, to estimate
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Proceedings of 9th Annual London Business Research Conference
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inefficiency (u) an appropriate form of production function has to be assumed that
captures the relationship among inputs and outputs.
In estimation of efficiency using stochastic approaches, the Cobb Douglas production
function has several advantages such as it is simple and linear in natural logarithm of
the variables, its coefficients can be interpreted as output elasticity and has universally
smooth and convex isoquants (Greene, 2005). The translog function is simple, flexible
and satisfies the homogeneity condition. Therefore, in the present study both Cobb
Douglas and translog production function have been estimated using 2 inputs and 2
outputs. A modified translogarthmic input distance function of the following form is
used:
∑
∑
∑
∑
∑
∑
∑
∑
∑
∑
where subscripts i and j refer to firms and time period respectively.
The homogeneity condition may be imposed by dividing all input quantities with the
input used as numeraire. Formally,
where,
, and x1 is arbitrarily chosen as a numeraire. After normalizing all
inputs equation (3) may be expressed as
∑
∑∑
∑∑
∑
∑∑
∑
∑
Furthermore, since
and appending the stochastic error term v, to
account for statistical noise, the input distance function can be rewritten as
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Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
∑
∑
∑∑
∑∑
∑∑
∑
∑
Approaches to Select Inputs and Outputs: Due to the widely varying and
overlapping financial intermediation activities undertaken by these entities, it would be
appropriate to classify the inputs and outputs according to the two broad components
of financial income: interest and non-interest. Specifically, the present study
incorporates proxies for interest (fund-based) and non-interest (fee-based) expenses
and income. The asset or fund based activities include banking, hire purchase,
consumer credit, housing finance, insurance, mutual funds etc. and fee based
services include merchant banking, credit rating, stock broking and advisory services.
Thus, in the analysis that follows, production process is modelled using input oriented
specification2 on both Cobb-Douglas and Translog functional forms in a manner that
fee based expenses and fund based expenses are incurred by a financial entity to
generate fee based income and fund based income.
6. Empirical Analysis
6.1 Descriptive Statistics
Overall, the analysis is based on 1105 observations consisting of 71 banks and 1034
non-banks. The descriptive statistics of input and output and explanatory variables are
reported in the subsequent table (Table 3). In our sample of firms during 1992-2011,
the fee based and fund based income account for 93% of total income generated
among financial entities.
2
In preliminary investigations, both input and output distance functions were estimated. However, the output
distance fails to converge in translog specification in pre M&A period and in Cobb Douglas specification in post
M&A period. Therefore, the parameters of output distance are not reported.
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Proceedings of 9th Annual London Business Research Conference
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Table 3: Descriptive statistics, 1992-2011
Variables
Obs.
Mean
Std. Dev.
Min.
Max.
Inputs
Fund Based Financial Services Expenses (x1)
1105
2030.59
10827.13
0.1
193293.8
Fee Based Financial Services Expenses (x2)
1105
88.73
300.45
0.1
4285.6
Outputs
Fund Based Financial Services Income (y1)
1105
2975.9
15609.21
0.1
344355.6
Fee Based Financial Services Income (y2)
1105
451.28
1633.89
0.1
31232.8
Environmental Variables
Size of the Entity (SIZE)
1105
7.48
2.36
0.96
15.22
Sales and Marketing Expenses (MEXP)
770
0.03
0.07
0.00003
0.81
Information and Technology Expenses (ITEXP)
716
0.02
0.03
0.00005
0.36
It is evident that banks earn a major proportion (more than 90%) of income from fund
based services. The fee based financial services constitute a small proportion of their
income (almost 6%), although it is increasing over time. In contrast, the non-banks
have a much higher proportion of income from fee based services.
6.2 Stochastic Distance Function Results
The technical efficiency is estimated using Stochastic Distance Function (SDF)
separately in pre and post M&A period. The results are reported in Table 4 and 5
respectively.
Table 4: Parameter Estimates of the Input Distance Function, 3 Years prior to M&A
Production Function
Cobb Douglas
Translog
Production Function Parameters
Log (x2/x1)
Log y1
Logy2
(Log y1*Log y1)/2
0.41***
0.64***
9.62
4.15
-0.58***
-0.95 ***
-8.48
-4.27
-0.39***
-1.23***
-6.71
-5.54
-
-0.16***
-5.99
(Log y1*Log y2)/2
-
0.42***
6.58
(Log y2*Log y2)/2
-
-0.12***
-4.03
Log (x2/x1)* Log y1
-
-0.05***
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Proceedings of 9th Annual London Business Research Conference
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6.58
Log (x2/x1)* log y2
-
-0.06***
-3.9
t
t*t
0.09
0.11
0.17
0.6
0.001
-
0.17
(t*t)/2
-
0.01
0.95
t*log (x2/x1 )
-
0.03***
3.04
t*log y1
-
-0.003
-0.23
t*log y2
-
0.003
0.22
Constant
Inefficiency Function
Parameters
ACQUIRER
TARGET
BANK
NEW
FOREIGN
GOVERNMENT
SIZE
MEXP
ITEXP
t
0.28
4.21***
0.28
2.98
-0.14
-1.03***
-0.78
-4.37
Cobb Douglas
Translog
0.82
-0.51
0.65
-0.72
0.41
-0.69
0.35
-0.89
-3.24
-1.98
-1.38
-1
-0.89
-0.14
-1.1
-0.23
0.07
1.29
0.05
0.82
-0.19
0.61
-0.13
0.72
0.13
-0.62***
0.55
-2.71
-13.06
-9.7
-1.36
-1.57
-17.07
-6.52
-0.66
-0.63
0.37**
0.45***
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1.92
3.3
-2.31*
-0.56
-1.61
-0.69
-5.44
-0.14
-1.38
-0.07
No. of Iterations
23
11
No. of Observations
123
123
Log Likelihood
325.13
-150.77
Wald Chi Square
703.29
848.02
0
0
LISTED
Constant
p value
LR Chi Square (8)
p value
55.55
0
Source: Author’s calculations
Note: Z values are reported below the coefficient values; *** indicates significance at 1%, ** indicates
significance at 5%, and * indicates significance at 10%
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Proceedings of 9th Annual London Business Research Conference
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Table 5: Parameter Estimates of the Input Distance Function, Post M&A
Production Function
Cobb Douglas
Translog
0.49***
0.43***
17.77
2.23
-0.55***
-0.84***
-13.60
-3.24
-0.23***
0.10
-6.77
0.50
-
-0.19***
-
-5.13
-
0.16***
-
3.06
-
-0.08***
-
-2.75
-
-0.05***
-
-4.25
-
-0.02*
-
-1.82
0.02
0.28
0.57
1.40
-
-0.02*
-
-1.68
-
0.03***
-
2.59
-
0.05***
-
3.46
-
-0.03***
-
-2.54
0.20
-1.05
0.46
-0.60
-0.21**
-0.47***
-1.98
-4.37
Cobb Douglas
Translog
-5.62***
-3.09*
Production Function Parameters
Log (x2/x1)
Log y1
Log y2
(Log y1*Log y1)/2
(Log y1*Log y2)/2
(Log y2*Log y2)/2
Log (x2/x1)* Log y1
Log (x2/x1)* Log y2
T
(t*t)/2
t*Log (x2/x1 )
t*Log y1
t*Log y2
Constant
Inefficiency Function Parameters
ACQUIRER
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Proceedings of 9th Annual London Business Research Conference
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-2.49
-1.81
0.77
-0.84
0.49
-0.76
3.52*
0.93
1.88
0.70
-12.52***
-28.54
-2.68
-0.02
2.07***
0.54
2.56
1.41
-1.74
-0.56
-0.58
-0.50
7.99
-2.08
1.19
-0.42
-25.51
-32.22
-0.68
-1.26
5.92**
1.03
2.27
0.75
-0.57*
4.67**
-0.15
2.03
-7.34***
-3.17***
-2.55
-1.93
-17.10*
-2.90
-2.35
-0.75
No. of Iterations
13
20
No. of Observations
201
201
Log Likelihood
-269.65
-244.85
Wald Chi Square
1429.25
1803.47
0
0
REPEATED
MERGER
BANK
SIZE
LISTED
MEXP
ITEXP
CROSSBORDER
MULTIPLEFIRMS
RELATEDFIRMS
Constant
p value
LR Chi square (9)
p value
49.60
0
Source: Author’s calculations
Note: Z values are reported below the coefficient values; *** indicates significance at 1%, ** indicates
significance at 5%, and * indicates significance at 10%
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Proceedings of 9th Annual London Business Research Conference
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The coefficients of the distance function are significant suggesting that production function
based on Cobb Douglas and translog are good approximation of the frontier. However,
the generalized LR test rejects the Cobb Douglas in favour of more flexible translog
specification. Overall the results show that input distance functions in all estimations
are well behaved, i.e., non-decreasing in inputs and decreasing in outputs. It is the
parameters of the inefficiency model that are of prime interest in the analysis. These
are represented in the lower panel of Table 4 and 5. In the pre M&A period, it is found
that size is negatively associated with inefficiency or positively with efficiency. This
suggests that the large financial entities are likely to be more efficient. This is in
conformity with our hypothesis. This could be either due to economies of scale or
higher market power associated with size (Table 4).
Further, it is observed that there are no significant differences among acquirers and
targets (both ACQUIRER and TARGET are insignificant in Table 4) in the pre M&A
period. The type of entity (NEW, LISTED and BANK) as well as expenses incurred by
financial entity are insignificant in explaining efficiency in the pre M&A period.
Furthermore, ownership of an entity (FOREIGN and GOVERNMENT) is likely to be of
lesser importance in determining efficiency. The opening up of economy and the rising
competition could have narrowed the distinction among state and privately owned
banks as well as foreign and domestic entities. As a result, there are no significant
efficiency differentials observed across these entities. Similar finding has been
observed in Figueira et al., 2006 on African banks and Kumar et al., 2011 on Indian
banks. Further, Sarkar et al. (1998) based on Indian banks during 1993-95 found little
difference between state and private banks, although, the study found foreign banks
to be more efficient than domestic banks.
In the post M&A period, acquirers are likely to have higher efficiency (Table 5). All other
things remaining constant, the process of M&A, thus, is likely to confer some positive
technical efficiency benefits to acquirers. An evidence of efficiency improvement post
M&A has been observed in several studies on financial entities such as Worthington
(2001). In contrast, no significant efficiency improvement could be seen in frequent
acquirers (REPEATED).
The results suggest that efficiency is expected to be higher when financial entities
merge with entities that belong to the same group (RELATEDFIRMS). This is as
hypothesized. The mergers among firms with pre-existing relations are likely to result in
lower integration costs due to lower possibility of cultural clashes. This could have
translated into higher efficiency.
In addition, when more than two firms join together (such instances were observed in
merger) the efficiency is likely to be higher (MULTIPLEFIRMS). Further, contrary to
our expectations, efficiency was found to be insignificantly associated with information
technology (ITEXP) and marketing expenses (MEXP). In addition, no significant
efficiency implications can be observed in firms involved in cross border M&A deals.
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Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
7. Conclusion
Globally, the studies on M&A have been limited in coverage, focusing either on
commercial banks, cooperative banks or insurance firms. Therefore, very little is
known about the M&A activity in the non-banking segment. Further, there is
insufficient research that captures M&A activity across different types of entities such
as banks and insurance or banks and non-banks. This is one of the first analytical
studies to provide such a vast and comprehensive coverage of the entire gamut of
M&A transactions that arise amongst the entities belonging to financial sector of India.
Determining the effects of participation in M&A activity in the financial sector is an
important issue. However, the literature remains inconclusive on impact of M&A on
efficiency of firms. The differences in findings of these studies may be due to the
difference in methodology, sample and time period analyzed. Furthermore, in certain
M&A non-economic factors such as managerial self interest and agency problems
may be dominant. These reasons make it difficult to assess the implications of M&A.
Our analysis indicates that in the pre M&A period, no significant efficiency differences
among acquirers and targets can be discerned. However, in the post M&A period, the
acquirers demonstrate higher efficiency relative to other firms. Thus, the analysis
provides evidence that acquirers could derive positive benefits in technical efficiency
from M&A. The improvement in technical efficiency explains the rise in consolidation
activity among the financial sector entities. These financial entities can rapidly gain
size, efficiency and hence market power becoming financial superpowers (mostly
global in operations) by undertaking such M&A. The consolidation activity among
these global financial giants needs to be effectively supervised by the relevant
authorities such as Competition Commission of India (CCI) and Reserve Bank of India
(RBI) as they might misuse the economic and political power by mis-allocating the
funds to certain sectors or restricting credit to others.
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Proceedings of 9th Annual London Business Research Conference
4 - 5 August 2014, Imperial College, London, UK, ISBN: 978-1-922069-56-6
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