Merger & Acquisition and Cost Efficiency: A DEA Approach
D. SILAMBARASAN
Ph. D Research Scholar
Department of Commerce
Kanchi Mamunivar Centre for Postgraduate Studies
(Autonomous Centre with Potential for Excellence by UGC – Phase II)
[Reaccredited with “A” Grade by NAAC]
(Government of Puducherry)
Puducherry605 008, India
Phone: + (91)8695958104
EMail: dsarasansss@gmail.com
and
Dr. R. AZHAGAIAH
Associate Professor of Commerce
Associate Professor of Commerce
Kanchi Mamunivar Centre for Postgraduate Studies
(Autonomous Centre with Potential for Excellence by UGC)
(Government of Puducherry) Pondicherry University
Puducherry – 605 008, India
Phone: + (91) 9952474095
Fax: +91(413)  2251613
E. Mail: drrazhagaia@yahoo.co.in
Merger & Acquisition and Cost Efficiency: A DEA Approach
D. SILAMBARASAN^{#1}, Dr. R. AZHAGAIAH^{#2}
^{#1}
Ph.D Research Scholar, Kanchi Mamunivar Centre for Postgraduate Studies, Puducherry, India,
dsarasansss@gmail.com
,
Mobile: 8695958104
^{#2}
Associate Professor of Commerce, Kanchi Mamunivar Centre for Postgraduate Studies, Puducherry,
drrazhagaia@yahoo.co.in
, Mobile: 9952474095
Abstract
Efficiency, in general, defines the relationship between production and some desirable objective functions such as cost minimization or revenue and profit
maximization at a given level of technology. The study investigates the cost efficiency of banking sector based on secondary data collected from CMIE
Prowess Package over the period of 20102015. The present study has chosen Axis Bank, Kotak Mahindra Bank, ICICI Bank, Dhanalaxhmi Bank, Syndicate Bank and
State Bank of India as sample units. The statistical methods used are descriptive statistics, Data Envelopment Analysis (DEA), Constant Returns to Scale
(CRS) and Variable Returns to Scale (VRS). Based on the results, the study rejects hypotheses that there is no significant difference in the cost
efficiency of sample banks based on the selected methods. Hence, the study suggests for acceptable overall level of efficiency during the testing period,
with an average efficiency ranging from 0.94 to 1 and from 0.75 to 1 for CRS and VRS respectively. The results prove that reduced investment in equity
followed by more loan & advance creation is the most effective method for improving the operational efficiency of inefficient banking firms.
Key words:
Mergers and acquisitions, Data Envelopment Analysis, Cost efficiency, Constant returns to scale model, Variable returns to scale model.
JEL:
G34, D61.
Introduction
Cost efficiency (CE) is a measure to show how far the input and output of the best practice of bank cost in producing in the same environmental conditions.
One can ensure cost efficiency of a bank employing either nonparametric or parametric approaches. The nonparametric (nonstochastic) cost efficiency is
calculated by employing linear mathematical programming techniques while the parametric (stochastic) cost efficiency is derived from a cost function in
which variable costs depend on the input prices, quantities of variable outputs, random error, and inefficiency.
The firm, generally, faces a degree of competitiveness in input and output markets, and it’s rationale economic behavior that aims to maximize the
production by choosing either optimal input mix under cost minimization or optimal output under the revenue maximization objective.
Data Envelopment Analysis Approach
(DEA)
Data Envelopment Analysis (DEA) constitutes one of the productivity measurement methods and performance evaluation of one firm’s activity using
nonparametric approach, which is basically a linier programming based technique. It was Charnes et al. (1978), who first proposed this method.
The term Decision Making Unit (DMU) can be used for various units, such as banks, hospitals, retail stores, and whatever unit which has the similarity to
the operational characteristics. Further, comparison between input and output will result in efficiency value. According to DEA method, efficiency
constitutes a relative value instead of absolute value achieved by a unit. The DMU with the best performance will reach 100% efficiency, however other DMUs
below this performance will have varying efficiency, i. e. 0  100%.
Review of Literature
Maudos
and Pastor (2003)^{1}, in a study titled “Cost, revenue, and profit efficiency in the Spanish Banking Sector (19851996): A nonparametric approach” analysed the efficiency in costs and
profits of 98 Spanish banking sector (SBS) in the period 198596 using a nonparametric approach. The data were collected from balance sheets and profit
and loss accounts of the commercial and savings banks. The study used descriptive statistics, and correlation coefficients as research tools. The results
referring to 1996 indicated that the return on assets (ROA) and return on equity (ROE) of the Spanish Banking sector was increased by 2.4 and 24.4
respectively for ROA and ROE, eliminating the combined inefficiency in costs and revenues.
Mohammed et al.
(2008)^{2}, in a study titled “ Cost, revenue, and profit efficiency of Islamic versus Conventional Banks: International evidence using Data Envelopment Analysis” examined 43
Islamic and 37 Conventional banks during the period from 1990 to 2005. The study used descriptive statistics and KruskalWallis (H) test for analysis
besides using Data Envelopment Analysis (DEA) method to measure the cost, revenue, and profit efficiency of Islamic versus Conventional banks. The findings
of the study suggested that there were no significant differences between the overall efficiency results of Islamic banks versus Conventional banks in
respect of cost, revenue and profit efficiency.
Pardeep
and Gian (2010)^{3}, in a study titled “Impact of mergers on the cost efficiency of Indian commercial banks” analyzed cost efficiency
of Indian commercial banks using a nonparametric Data Envelopment Analysis (DEA) technique. The study was based on unbalanced panel data over the period
from 199091 to 200708, which tested the efficiency differences between public and private sector banks using both parametric & nonparametric tests.
The study showed that over the study period, average cost efficiency of public sector banks was found to be 73.4 and that of for the private sector was
76.3 %.
Liargovas
and Repousis (2011)^{4}, in a study titled “The impact of mergers and acquisitions on the performance of the Greek Banking sector: An event study approach” examined the impact of Greek
mergers and acquisitions on the performance of the Greek banking sector during the period 19962009. The study was based on 20 financial accounting ratios
viz., return on equity (ROE), return on assets (ROA), net profit margin (NPM) net income margin (NIM), etc. The study stated that the bank mergers and
acquisitions have no impact on the operating performance of Greek Banking sector.
Rossazana Collins et al.
(2012)^{5}, in a study titled “The cost efficiency effects of involuntary bank mergers: Evidence from the Malaysian banking industry”
attempted to quantify the impact of the involuntary merger and acquisition on the cost efficiency gains over the period 19902005. The study used Data
Envelopment Analysis (DEA) method to measure the cost efficiency of Malaysian banking sector. The study assessed the cost, allocative, technical, pure
technical and scale efficiencies of Malaysian banking industry as the result of the merger and acquisition. The used variables (size, economic growth,
market concentration, risk and the government ownership) are regressed on each type of the cost efficiency using the Tobit regression model approach, a
bootstrapping technique and used several tests (ttest, Wilcoxon rankSum, KruskalWallis, MannWhitney and KolmogorovSmirnov). The study showed that the
enforcement of the bank merger policy has resulted in to an improvement of bank efficiency level.
Devarajappa
(2012)^{6}, in a study titled “Mergers in Indian banks: A study on mergers of HDFC Bank Ltd and Centurion Bank of Punjab Ltd” compared pre
and postmerger financial performance of merged banks considering financial parameters viz., gross profit margin (GPM), net profit margin (NPM), operating
profit margin (OPM), return on capital employed (ROCE), return on equity (ROE), and debt equity ratio (DER). The data were collected from Indian banking
industry and banks’ annual reports. The premerger (three years’ prior) and postmerger (three years after) performance was studied using financial ratios.
The study showed that the banks were positively affected by the event of merger and acquisition.
Adekule
and Ayorinde (2012)^{7}, in a study titled “Effects of merger and acquisition on the performance of selected commercial banks in Nigeria” used secondary data collected through compilation and
extracts from published data including published audited financial accounts of sampled banks from 2001 to 2010. The selected variables viz., gross
earnings, profit after tax and deposit profile of seven Nigerian commercial banks were used. The study showed that, in the post merger and
acquisition period, the banks have significantly improved their financial performance when compared to the premerger and acquisition period.
Sharma
(2012)^{8}, in a study titled “Impact of mergers & acquisition on financial performance: With special reference to Tata Group” examined
some pre and post merger and acquisition financial ratios, with the sample firms chosen for the period from 20032004 to 20072008, which includes three
years’ data of premerger and of postmerger acquisition periods respectively of the 24 firms of Tata group which involved in the merger and acquisition.
The study has taken mean premerger and acquisition and mean postmerger and acquisition ratios. The results on financial performance of the pre and
postmerger and acquisition periods of the Tata group firms reveals that there was no significant change (increase / decrease) in the financial ratios i.e.
the merger and acquisition has not significantly affected the financial performance of Tata group firms either way.
Kanahalli
and Jayaram (2014)^{9}, in a study titled “Financial performance of Tata Motors Firm Ltd: A postmerger analysis” explored the
potentialities and capabilities of the firm by studying pre and post merger and acquisition performance. The study was based on secondary data. In order to
evaluate financial performance, ratio analysis, and standard deviation and tTest were used as tools of analysis. The study showed that the merger and
acquisition of the selected firm has resulted in to significant difference between pre and postmerger financial performance.
The review of the past literature drew attention to the difference of mergers & acquisition on cost efficiency in Indian banking sector. However, the
studies related to previous literature Pardeep and Gian (2010) and Rossazana Collins et al. (2012) shows the difference
between Separate and Common frontier for public and private banks. Further, mergers & acquisition on cost efficiency in banking sector is good.
Majority of the studies include many input variables used (advance, noninterest income and spread) with output variables used (labour, loanable funds and
physical capital) and also it included the variables of assets, equity, employees, total income and loans & advances, Constant Returns to Scale and
Variable Returns to Scale, which was not considered in the previous studies.
Objectives
· To analyse the cost efficiency of Axis Bank Ltd, Kotak Mahindra bank, ICICI bank, Dhanalaxhmi Bank, Syndicate Bank and State Bank of India in respect of
Constant Returns to Scale.
· To analyse the cost efficiency of Axis Bank Ltd, Kotak Mahindra Bank, ICICI Bank, Dhanalaxhmi Bank, Syndicate Bank and State Bank of India in respect of
Variable Returns to Scale.
Hypotheses of the Study
H_{0}^{1}:
“There is no significant difference in the cost efficiency of Axis Bank Ltd, Kotak Mahindra
Bank, ICICI Bank, Dhanalaxmi Bank, Syndicate Bank and State Bank of India in respect
of Constant Returns to Scale”.
H_{0}^{2}:
“There is no significant difference in the cost efficiency of Axis Bank Ltd, Kotak Mahindra
Bank, ICICI Bank, Dhanalaxmi Bank, Syndicate Bank and State Bank of India in respect
of Variable Returns to Scale”.
Research Methodology
The study is based on secondary data which are collected from the Centre for Monitoring Indian Economy (CMIE) Pvt. Ltd., academic research journals, and
other published sources.
Sampling Design
In order to test the stated hypotheses and to address the objectives of the study, the present study has chosen 6 sample bank units from merged banks in
India during the period from 2010 to 2015.
Research Methods
The study used descriptive statistics viz., mean, standard deviation, and cost efficiency index using Data Envelopment Analysis (DEA), constant returns to scale (CRS) and variable returns to scale (VRS) models.
Variables used for Analysis
The following input and output variables are used for analysis;
(a)
Input variables
X_{1}= Assets
X_{2}= Equity
X_{3}= Employees
(b)
Output variables
Y_{1}= Total income
Y_{2}= Loans and advances
Ratios used for Analysis
a.
Return on Assets Ratio (ROA)
It is an indicator of how profitable a firm is relative to its total assets. Return on assets (ROA) gives an idea as to how efficient management it is in
using its assets to generate earnings. Calculated by dividing a firm’s annual earnings by its total assets, ROA is expressed as a percentage. Sometimes
this is referred to as return on investment (ROI) also.
Return on Assets =
b.
Return on Equity Ratio (ROE)
The amount of net income returned as a percentage of shareholders’ equity. Return on equity measures a firm’s profitability by revealing how much profit a
firm generates with the money shareholders have invested.
Return on Equity= X100
Cost Efficiency
The nonparametric methodology for calculating cost efficiency, let us suppose consider that there exists N firms (i=1,….,N) that produce a vector
of q OUTPUTS y_{i}=( y_{i}1,…., y_{i}q) Rq++ that they sell at prices r_{i}=( r_{i}_{1},…., r_{iq}) Rq++ using a vector of P inputs x_{i}=( x_{i1,…….,} x_{ip}) Rp++ for which they pay prices w_{i}=( w_{i1,….,} w_{ip}) Rp++. The cost
efficiency for the case of firm j can be calculated by solving the following problem of linear programming:
Min _{Pj }x_{ Pj}
s.t.
∑ λ_{i }y_{iq} ≥ y_{iq }
_{ }
∑ λ_{i } ≤ y_{iq }x_{jp}_{}
_{ }
∑ λ_{i } =1; λ_{i} ≥ 0; I = 1,…….,N
The solution to which, x_{ j} = (x^{*}_{ j1,….,} x^{*}_{ jp}) corresponds
to the input demand vector which minimizes the costs with the given prices of inputs, and is obtained from a linear combination of firms that produces at
least as much as of each of the outputs using the same or less amount of inputs. If this hypothetical firm had the same input price vector as firm j it would have a cost
C
^{*}
_{j}
=
_{Pj }
x^{*}
_{ Pj}
Which, by definition, will be less than or equal to that of firm j (C_{j} = _{Pj }x_{ Pj}).
Having obtained the solution to the problem the cost efficiency for firm j (CE_{j}) can be calculated by:
Where CE_{j} ≤ 1 represents the ratio between the minimum costs ( ) – associated with the use of the input vector ( ) that minimizes costs –
and the observed costs (C_{j}) for firm j.
Analysis and Discussion
The study has used descriptive statistics of input and output variables of banks and the results are presented in table 1 followed by two important models
for cost efficiency viz., constant returns to scale (CRS) and variable returns to scale (VRS) and the results are shown in the tables from 2 to 7. The
table 1 reveals that the input variables viz., total assets, total equity, and total employees ranges from 861829.4 to 100286602.3; 7362.3 to 69317.2;
1315995 to 13844 with mean 29218682.25, 34338.26, 365787 respectively for X_{1}, X_{2 }and X_{3}. However, the standard deviation
records at 14990312.52, 8585.03, and 196923.87 respectively for X_{1}, X_{2 }and X_{3}._{ } The table further reveals that
the output variables viz., total income (Y_{1}) and loans and advances (Y_{2}) ranges from 82327.2 to 8658209.4 and from 0 to 498179.4 with
mean 2587920.7 and 98474.25 and standard deviation 1287683.21 and 81031.69 respectively for Y_{1 }and Y_{2 }for the study period.
Table 1
Descriptive Statistics for Inputs and Outputs
Input / output

Variables

Min

Max

Mean

S.D

Input
Variables

X_{1}

861829.4

100286602.3

29218682.25

14990312.52

X_{2}

7362.3

69317.2

34338.26

8585.03

X_{3}

1315995

13844

365787

196923.87

Output Variables

Y_{1}

82327.2

8658209.4

2587920.7

1287683.21

Y_{2}

0

498179.4

98474.25

81031.69

Source:
Computed results based on compiled data collected from CMIE Prowess Package.
Note: Input variables: X_{1}= Total Assets, X_{2}= Total Equity, X_{3}=Total Employees;
Output variables: Y_{1}=Total Income, Y_{2}=Loans and Advances.
Table 2
Average Financial Ratios (ROA and ROE) per Bank for the period from 2010 to 2015
Sl. No.

Bank

ROA (%)

ROE (%)

1

Axis Bank

0.09

0.98

2

Kotak Mahindra Bank

0.11

0.42

3

ICICI Bank

0.09

0.36

4

Dhanalaxhmi Bank

0.09

0.91

5

Syndicate Bank

0.08

0.98

6

SBI Bank

0.09

0.98

Source:
Computed results based on compiled data collected from CMIE Prowess Package.
Note: ROA = Return on Assets; ROE= return on Equity
An attempt has been made to study the financial ratios viz., return on assets (ROA) and return on equity (ROE) of the selected banks and the results are
presented in table 2 followed by figure A. The results show that banks Kotak Mahindra Bank has more efficiency in respect of (0.11 %) ROA followed by other
banking units. However, with respect to ROE, Axis Bank, Syndicate Bank and SBI Bank have more efficiency (0.98 %) followed by other banking units
Dhanalaxmi Bank (0.91%); Kotak Mahindra Bank (0.42%); and ICICI Bank (0.36%).
Figure A
Average Financial Ratios (ROA and ROE) per Bank for the period from 2010 to 2015
Source:
Computed results based on compiled data collected from CMIE Prowess Package.
Note: ROA = Return on Assets; ROE= Return on Equity
Table 3
Results of Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) Models
DMU No.

Bank Name

Efficient Input Target

Efficient Output Target

Assets
(
X_{1})

Equity (
X_{2})

Employees
(
X_{3})

Total Income
(
Y_{1})

Loans and Advances (
Y_{2})

1

Axis Bank

21762261.10

27097.40

222954.00

2074313.30

5951.50

2

Kotak Mahindra Bank

4999909.29

22696.00

89170.00

557511.80

0.00

3

ICICI Bank

30586724.98

55761.35

354084.51

3002295.40

6897.32

4

Dhanalaxhmi Bank

861829.40

7362.30

13844.00

82327.20

3424.40

5

Syndicate Bank

14122611.80

37258.90

167509.00

1152867.10

83290.20

6

SBI Bank

100286602.29

42297.80

1315994.99

8658209.40

498179.40

Source:
Computed results based on compiled data collected from CMIE Prowess Package.
Note: CRS  Constant Returns to Scale; DMU  Decision Making Unit.
Note: Input variables: X_{1}= Total Assets, X_{2}= Total Equity, X_{3}=Total Employees;
Output variables: Y_{1}=Total Income, Y_{2}=Loans and Advances.
The results of the constant returns to scale (CRS) and variable returns to scale (VRS) models of efficient input target and efficient output target of
banks are presented in table 3. The table reveals the efficient input target variables viz., total assets (X_{1}), total equity(X_{2}), and total employees (X_{3}) and the efficient output target variables viz., total income (Y _{1}) and loans and advances (Y_{2}). It is inferred that variable, equity (X_{2}) has the
minimum input value as 27097.40 and maximum input assets (X_{1}) value as 21762261.10 for Axis Bank, while the output minimum loans
and advances (Y_{2}) value as 5951.50 and maximum total income (Y_{1}) value as 2074313.30.
Kotak Mahindra Bank equity (X_{2}) has the minimum input value as 22696.00 and the maximum assets (X_{1}) value
of input as 4999909.29, while the output minimum loans and advances (Y_{2}) value as 0 and maximum total income (Y_{1}) value as 557511.80. The ICICI Bank has the minimum equity (X_{2}) value as 55761.35 and maximum assets (X_{1} ) value as 30586724.98, while the output minimum loans and advances (Y_{2}) value as 6897.32 and maximum income value as
3002295.40. The Dhanalaxmi Bank equity (X_{2}) has minimum value as 7362.30 and maximum assets (X_{1}) value as
861829.40, while the output minimum loans and advances (Y_{2}) value as 3424.40 and maximum total income (Y_{1}) value as 82327.20. The Syndicate Bank has the input minimum equity (X_{2}) value as 37258.90 and maximum assets value (X_{1}) as 14122611.80, while the output minimum loans and advances (Y_{2}) value as 83290.20 and maximum total
income (Y_{1}) value as 1152867.10. The SBI Bank equity (X_{2}) has input minimum value as 42297.80 and maximum
assets value (X_{1}) as 100286602.29, while the output minimum loans and advances (Y_{2}) value as 498179.40
and maximum total income value (Y_{1}) as 8658209.40.
Table 4
VRS Model Slack Variable Analysis for the period from 2010 to 2015
DMU No.

Bank Name

Input Slacks

Output Slacks

Assets
(
X_{1})

Equity
(
X_{2})

Employees
(
X_{3})

Total Income
(
Y_{1})

Loans and Advances
(
Y_{2})

1

Axis Bank

0

0

0

0

0

2

Kotak Mahindra Bank

0

0

0

0

0

3

ICICI Bank

0

39142.47

3039.05

0

75329.67

4

Dhanalaxhmi Bank

0

0

0

0

0

5

Syndicate Bank

0

0

0

0

0

6

SBI Bank

0

0

0

0

0

Source:
Computed results based on compiled data collected from CMIE Prowess Package.
Note: Input variables: X_{1}= Total Assets, X_{2}= Total Equity, X_{3}=Total Employees;
Output variables: Y_{1}=Total Income, Y_{2}=Loans and Advances.
The results of the analysis of variable returns to scale (VRS) model of efficient input slacks and efficient output slacks of banks are presented in table
4. The ICICI bank exhibits an excess of `39142.47 in equity with a shortage of `75329.67 in loans and advances. However, use of DEACRS and DEAVRS with an
outputoriented assumption allows us to estimate the target for measuring and explaining the determinants of each firm’s cost efficiency.
Table 5
Average Efficiency during the period from 2010 to 2015
DMU
No.

Bank Name

CRS Efficiency

VRS Efficiency

Scale Efficiency

RTS

1

Axis Bank

1.00

1.00

1.00

Constant

2

Kotak Mahindra Bank

1.00

1.00

1.00

Constant

3

ICICI Bank

0.91

0.98

0.92

Decreasing

4

Dhanalaxhmi Bank

1.00

1.00

1.00

Constant

5

Syndicate Bank

1.00

1.00

1.00

Constant

6

SBI Bank

1.00

1.00

1.00

Constant

Source:
Computed results based on compiled data collected from CMIE Prowess Package.
Note: CRS  Constant Returns to Scale, VRS – Variable Returns to Scale, RTS – Returns to Scale: DMU  Decision Making Unit.
The results of the average efficiency of CRS, VRS and Scale efficiency of banks during the period from 2010 to 2015 are presented in table 5. The average
of CRS, VRS, and scale efficiency scores of Axis, Kotak, Dhanalaxmi, Syndicate and SBI reached 1, which indicates that they are at an optimal level of
efficiency, however the ICICI is still an inefficient, although its’ average CRS, VRS, and scale efficiency are close to 1.
Table 6
CRS Model Slack Variable Analysis for the Period from 2010 to 2015
DMU No.

Bank Name

Input Slacks

Output Slacks

Assets
(
X_{1})

Equity
(
X_{2})

Employees
(
X_{3})

Total Income
(
Y_{1})

Loans and Advances
(
Y_{2})

1

Axis Bank

0

0

0

0

0

2

Kotak Mahindra Bank

0

0

0

0

0

3

ICICI Bank

0

7948.30

0

0

6897.32

4

Dhanalaxhmi Bank

0

0

0

0

0

5

Syndicate Bank

0

0

0

0

0

6

SBI Bank

0

0

0

0

0

Source:
Computed results based on compiled data collected from CMIE Prowess Package.
Note: Input variables: X_{1}= Total Assets, X_{2}= Total Equity, X_{3}=Total Employees;
Output variables: Y_{1}=Total Income, Y_{2}=Loans and Advances.
The results of the CRS model slack variable analysis of input slacks and output slacks for banks for the period from 2010 to 2015 are presented in
table 6. The ICICI bank exhibits an excess of `7948.30 in equity with a shortage of `6897.32 in loans and advances. However, use of DEACRS and DEAVRS
with an outputoriented assumption allows us to estimate the target for measuring and explaining the determinants of each firm’s cost efficiency.
Findings
All firms surveyed, using the DEA approach, have an acceptable level of efficiency, with CRS scores ranging from 0.94 to 1.00, whereas VRS efficiency
scores range from 0.75 to 1.00. the sum of lambdas scores range from 1.00 to 2.23. The average of CRS, VRS, and scale efficiency scores of Axis bank, Kotak
bank, Dhanalaxhmi bank, Syndicate bank and SBI bank reached 1, which indicates that they are at an optimal level of efficiency, however the ICICI bank is
still an inefficient, although it’s average CRS, VRS, and scale efficiency are close to 1. This implies that most of the large size DMUs and their small
counterparts are operating at a suboptimal level of efficiency. Therefore, necessary measures should be taken to improve their operational performance for
cost efficiency. The results from the study suggest that inefficient firm needs improvement. For instance, the ICICI bank exhibits an excess of `7948.30 in
equity with a shortage of `6897.32 in loans and advances. However, use of DEACRS and DEAFindings
All firms surveyed, using the DEA approach, have an acceptable level of
efficiency, with CRS scores ranging from 0.94 to 1.00, whereas VRS efficiency
scores range from 0.75 to 1.00. the sum of lambdas scores range from 1.00 to
2.23. The average of CRS, VRS, and scale efficiency scores of Axis bank, Kotak
bank, Dhanalaxhmi bank, Syndicate bank and SBI bank reached 1, which indicates
that they are at an optimal level of efficiency, however the ICICI bank is still
an inefficient, although it’s average CRS, VRS, and scale efficiency are close
to 1. This implies that most of the large size DMUs and their small counterparts
are operating at a suboptimal level of efficiency. Therefore, necessary measures
should be taken to improve their operational performance for cost efficiency.
The results from the study suggest that inefficient firm needs improvement. For
instance, the ICICI bank exhibits an excess of `7948.30 in equity with a
shortage of `6897.32 in loans and advances. However, use of DEACRS and DEAVRS
with an outputoriented assumption allows us to estimate the targets for
measuring and explaining the determinants of each firm’s performance and
costefficiency.
Conclusionview
resource utilization for improving their operational performance and costefficiency.
Limitations and Scope for Further Studies
Ø In the present study, a sample of six firms of banking sector has been considered for analysis. In future, researchers can consider inclusion of more
number of banking firms by referring to the other data sources and take up a study with large sample units to explore further results.
Ø In the present study, descriptive statistics, Data Envelopment Analysis, parametric and nonparametric test, Constant Returns to Scale (CRS) and Variable
Returns to Scale (VRS) models are only used for analysis; therefore future researches may appropriate advanced models, which may bring a differing
inference.
References
Adekule, A. R., and O. Ayorinde. 2012. Effects of merger and acquisition on the performance of selected commercial banks in Nigeria. International Journal of Business and Social Research (IJBSR) 2(7): 14857.
Devarajappa, S. 2012. Merger in Indian banks: A study on mergers of HDFC Bank Ltd., and Centurion Bank of Punjab Ltd. International Journal of Marketing, Financial Services & Management Research 1(9): 3342.
Kanahalli, M. M., and S. Jayaram. 2014. Financial performance of Tata Motors Firm Ltd: A post merger analysis. International Journal of Marketing, Financial Services & Management Research (IJMFSMR) 3(10): 2130.
Liargovas, P., and S. Repousis.
References ef. 2008. Cost, revenue, and profit efficiency of Islamic versus Conventional Banks: International evidence using
data envelopment analysis. Islamic Economic Studies 15(2): 2476.
Pardeep, K., and K. Gian. 2010. Impact of mergers on the cost efficiency of Indian commercial banks. Eurasian Journal of Business and Economics 3(5): 2750.
Rossazana, A. R., N. Ghani, S. Ramlee and F. Ahmad. 2012. The cost efficiency effects of involuntary bank mergers: Evidence from the Malaysian banking
industry. Thammasat Economic Journal 30(1): 13060.
Sharma, R. 2012. Impact of mergers & acquisitions on financial performance: With special reference to Tata Group. International Journal of Research in Commerce & Management 3(7): 1403.
Websites
http://www.businessdictionary.com/definition/costefficiency.html
http://www.entrepreneurship.org/resourcecenter/mergersandacquisitionsanintroduction.aspx
http://whatis.techtarget.com/definition/mergersandacquisitionsMA
http://whatis.techtarget.com/definition/mergersandacquisitionsMA
