Financial
Performance Evaluation of Commercial Banks by AHP: An Evidence from India
Pawan
Kumar
Research Scholar, School of Management, The NorthCap University, Gurugram, India, Email- pawan18msd004@ncuindia.edu
Dr.
Deergha Sharma
Assistant Professor, School of Management, The NorthCap University, Gurugram, India, Email- deerghasharma@ncuindia.edu
Dr.
Ramkaran Singh
Professor, Department of Civil & Environmental Engineering,
The NorthCap University, Gurugram, India, Email- ramkkaransingh@ncuindia.edu
Abstract
In present transforming phase of the
Indian banks, the study has evaluated the financial health of ten commercial
banks working in India by employing Analytical Hierarchy Process” (AHP). For
assessment, six financial criteria such as Assets Quality, Capital Adequacy
ratio, Liquidity, Earning, Management Efficiency, and Profitability has been
utilized for a period from 2009 to 2018. For in depth analysis, these criteria
are further categorized into twenty-four sub-criteria. A paired comparison
methodology is employed to rationalize relative weights of identified criteria
and sub-criteria and AHP is used to rank the banks accordingly. The results of
the study shows that capital adequacy ratio is most significant criteria among
six criteria opted for the analysis. Besides, the ranking of banks shows that the
weightage of the financial ratios plays a critical role rather than size of bank in their financial
performance. The study would provide valuable inputs to policy makers,
academicians and researchers to evaluate the ongoing strategies of bank
management. It would also
suggest some useful strategies to inefficient banks to improve their business
performance.
Keywords: AHP,
Financial Performance, Indian Commercial Bank, Criteria, Sub- criteria.
Introduction
Presently Indian banking sector is witnessing
a wide range of structural changes in policies and regulation which affects
every facet of Indian banking system ranging from financial position to
customer reliance (Gayval and Bajaj, 2016).As banks are imperative for industrial
development, economic growth and prosperity of a country, an assessment of bank
financial performance is the key concern for all the stakeholders including the
regulator, bank management, and the general public (Tran, 2019). It is essential
to introduce the precise and useful modern method for evaluating financial
performance of various commercial banks working in India (Sharma, 2014).
The financial performance of banks is
evaluated by various researchers. Various traditional techniques based on ROE,
ROA, CAMEL approach was utilized to determine the financial performance of
banking sector. Dhanabhakyam, and Kavitha (2012), evaluated
the financial performance of Indian public sector banks by Ratio Analysis,
Correlation, and Regression. Kumar and Sharma (2014) utilized rating based on
CAMEL approach to rank the Indian Banks Subsequently, approach
of data envelopment analysis (DEA) analyzed the relative efficiency of banks by
financial ratios as multiple inputs and outputs (Vegesna and Dash ,2014). Mishra
and Sahoo (2012) used the Panel data regression to evaluate the financial
performance of Indian banks. Although, existing studies did not provide weightage
to the financial parameters based on their relative importance (Frei and
Harker, 1999).Moreover, the bank managers were unable to prioritize their operational
strategies on the basis of the findings of existing studies. Therefore, evaluation
of financial performance based on the weightage of the financial ratios of different
aspects such as asset quality, capital adequacy ratio, liquidity, earning,
management efficiency, and profitability is required to differentiate the
efficient banks from inefficient ones.
The present study is an effort to
analyze the financial performance of ten banks by using multi criteria for a
period from 2009 to 2018.The ten banks were selected on the basis of branch
offices present in India. Further, the AHP model (Saaty, 1988) is used to assign
relative weightage to selected criteria and rank the banks. The study would
highlight the indicators of efficient performance and contribute to existing
literature by providing a new approach for comprehensive assessment of
commercial banks working in India.
Further, section 2 has reviewed the
existing literature of methodologies used to assess financial performance of
banking system. Section 3 describes the research objectives. Section 4 outlines
the research methodology of present study. Section 5 discussed data analysis
and model synthesis. Section 6 deals with results and discussion. Section 7
includes the conclusion and scope of future studies.
Review of Literature
Evaluation of Indian
Banks financial performance
There are numerous techniques used
by various researchers to assess the banks' financial performance; conventionally,
bank financial performance was evaluated by some financial return ratios likewise
Returns on assets (ROA) and Returns on Equity (ROE).Eventually, other
methodologies were introduced to analyze the financial performance of banks in
comprehensive manner, such as econometric models, CAMEL approach and DEA
methodology(Kumar et al. ,2012; Bansal and Mohanty, 2013; and Kaur et al., 2015;
Dash and Gosh, 2009; Sharma et al. 2012). Therefore, a multi-criteria decision-making
technique such as AHP is proposed to evaluate the bank performance based on the
weightage of every criterion used in the analysis of the performance. Table 2.1
shows the existing studies and different methodologies used for performance
evaluation of Indian banks.
Table:
2.1. An overview of existing literature on Indian banks' financial performance
evaluation
|
Author |
Method |
||||
|
Kumar and Gulati (2008) |
DEA CCR model was
employed to evaluate the technical efficiency of public sector banks in
India. |
||||
|
Debnath and Shankar(2008) |
DEA-BCC model was
employed for financial analysis of Indian banks. |
||||
|
DEA technique was applied
for the assessment of the efficiencies of the Indian banks using different
financial ratios. |
||||
|
Das and Ghosh (2009). |
DEA methodology was utilized
for evaluating the cost and profit efficiency of banks working in India |
||||
|
Das (2010, January). |
The Stochastic Frontier
Approach was employed for the analysis and comparison of cost efficiency of
different Indian banking groups. |
||||
|
Malhotra et al. (2011) |
A Panel data approach has
been employed on various financial ratios for the evaluation of Indian Banks.
|
||||
|
Dhanabhakyam and Kavitha(2012). |
Ratio Analysis,
Correlation, and Regression were used for the financial performance evaluation
of selected public sector banks. |
||||
|
Kaur (2012). |
|||||
|
Sharma et al. (2012). |
DEA technique was
utilized for evaluating relative efficiency of Indian banks, in which
expenses and deposits have taken as input variables, whereas income, loans,
and advances have taken as output variables, further TOBIT regression is used
to identify the association of bank-specific factors in their performance. |
||||
|
Mishra and Sahoo (2012). |
The panel data regression
method has been applied to selected financial ratios to evaluate the
financial performance of Indian banks. |
||||
|
Bapat (2012). |
DEA technique applied for
evaluating the efficiency of Indian Public and private sector banks by taking
expenses as an input variable and income as outputs variable. |
||||
|
Mishra
and Sahoo(2012). |
“Two-stage least squares (2SLS)” method of estimation
has been applied to the panel dataset of Indian banks to determine the
interrelationship among the financial performance, banks’ conduct, and the structure
of the market. |
||||
|
Kumar
et al. (2012). Bansal
and Mohanty (2013). |
Banks were ranked on the basis of different ratios used
in CAMEL rating methodology. |
||||
|
Kumar and Sharma (2014). Kaur et al. (2015). Shukla (2015). |
|||||
|
|
|||||
|
Goyal (2013). |
Multiple regression was utilized
to study the impact of the capital structure on the profitability of Indian
banks. |
||||
|
Rao and Kumar (2013). |
Linear regression model was
applied on various financial ratios to evaluate the performance of the Indian
banks before and after the merger. |
||||
|
Haque (2014). |
ANOVA was used to analyze variance
in the performance of different banking groups in terms of financial ratios such
as ROA, ROE, and NIM. |
||||
|
Aspal and Dhawan(2014). |
CAMELS model was used for rating old
private sector banks in India. |
||||
|
Jayaraman and Srinivasan (2014). |
Evaluated financial performance of
banks using different DEA models, i.e. cost, revenue, and profit model, and a
combined efficiency index was developed from the efficiencies of cost,
revenue, and profit model of DEA using
Shannon entropy method to rank the banks in a more meaningful way. |
||||
|
Paul and Das (2015). |
DEA- CCR output-oriented model was
adopted for analyzing relative efficiency of the Indian banks' wherein, Non-interest
income, interest spread, net worth, borrowings of the banks, operating expense
and number of employees was considered as input variables, while deposits,
net profits, and advances were considered as an output variable. |
||||
|
Goncharuk(2016). |
Highlighted the key area of
research in banking including performance efficiency, ranking of banks based
on efficiency, etc. |
Bank Performance
Evaluation using AHP
AHP proposed by Saaty (1980) is a fundamental multi-criteria
decision-making (MCDM) method widely used across the industry wherein the
decision is of multi-criterion nature (Stankeviciene and Mencaite, 2012; Onder
et al.,2013; Tran,2019). When the problem is complex, and decision-making is complicated,
this method organize the problem into a hierarchical structure which consists
of goal at the top of the hierarchy and the criteria, sub-criteria, and the
alternatives at the subsequent level (Akhisar and Karpak, 2010; Bhandari and
Nakarmi, 2014). The other important aspect of the AHP method is the weightage of
criteria and sub-criteria positioned on the same hierarchy structure level (Frei
and Harker, 1999; Cehulic et al., 2011). Despite the extensive use of AHP
methodology in business and industry, the application of this method was scarce
in banking system. Globally, several studies have used MCDM tools for
performance evaluation of different entities (Bhattarai and Yadav, 2009; Sipahi
and Timor, 2010).However, in Indian banking context, very limited studies have
used AHP for decision making and ranking various commercial banks working in
India.
In literature, the application of
AHP in bank performance evaluation appeared in the nineties, and the most substantial
use of AHP found after 1998. Frei and Harker (1999). described that all the
parameters do not have equal importance for an institution's efficiency. Some
parameters have more weightage than others. The study used AHP to weigh the
various performance parameters based on their relative importance and synthesis
was done to rank the selected institution. Hunjak et al.(2001) applied a combined
approach of DEA and AHP on selected financial ratio to evaluate Croatian bank's
performance. Bhattarai and Yadav (2009) reviewed the number of articles on the application
of AHP in the finance sector and concluded that the use of the AHP specifically
in the banking sector is scarce and requires in-depth academic research for decision
making. Bhattarai et al.(2009) emphasized on holistic decision-making approach
by putting the qualitative and quantitative information in a single framework
using the AHP for Nepalese financial institutions. Rakocevic and Dragasevic (2009)
introduced MCDM method for comparing and ranking of Montenegrin banks based on
several criteria. They used AHP methodology for evaluating the quantitative and
qualitative parameters related to the performance and supervision of Montenegrin
banks. Akhisar and Karpak (2010) employed AHP to place the selected financial
ratio of Turkish banks in a hierarchy structure for determining the financial
performance score and established the relationship between bank failure and
their financial performance. Cehulic et al. (2011)proposed AHP model to compare
and analyze the financial ratios categorized into four groups, namely Income
Statement Ratios, Market Ratios, Balance Sheet Ratios, and Profitability Ratios
and several subgroups for evaluating the performance of Croatian banks. The
study of Stankeviciene and Mencaite (2012) suggested AHP
model for assessment of Lithuanian banks and ranked them based on their
performance score.Lu et al.(2013) identified various
bank’s operation risk items and prioritized these risk items using AHP. Bhandari
and Nakarmi (2014) recommended AHP model to evaluate the financial performance
of Nepalese commercial banks by employing several financial ratios. Sharma
(2014) developed an Analytic Hierarchy Process model that comprised of both
financial and human aspects and the relative importance of both aspects was
measured by experts' opinion. The analysis revealed that the human aspect is
more important than the financial aspect. Dinner, (2015) developed a hybrid
model based on the Fuzzy Analytic Hierarchy Process (FAHP) and DEA to assess
overall efficiency level of banks quoted in BIST 100 Index with the interest
and non-interest based income parameters. Gayval and Bajaj (2016) employed a combined
approach of DEA and AHP to determine efficiency scores of Indian banks. Ghasempour
and Salami (2016) developed a decision model with six criteria selected based
on the CAMELS approach to evaluate the performance of the Iranian banks. Tran
(2019) explored the utility of the AHP in selecting and calculating the
relative weight of the important criteria for proposing a suitable model for
partner evaluation and choosing strategic banking alliances in Vietnam.
Objectives of the study
The study has formulated following
objectives:
• To identify the criteria and sub-criteria
for financial performance evaluation of Indian banks.
·
To establish the priorities of the six
criteria’s including Assets Quality, Capital Adequacy, Liquidity, Earning,
Management Efficiency, and Profitability, which further categorized into 24
sub-criteria measured in terms of financial ratios.
•
To propose a hierarchy structure of the AHP model for the comparison of the bank's
financial ratios.
•
To use the AHP application for the comparison of selected Criteria and
sub-criteria measured by financial ratios and its validation.
•
To analyze the results of banks comparison supported by the proposed model and
to give them ranking.
Research Methodology
Proposed Hierarchy
model for the study
In this study, a MCDM technique
popularly known as AHP is utilized to assess the financial performance of 10
largest Indian commercial banks (Annexure-1)based on their number of branch
offices. An average of financial ratios for ten years from 2009 to 2018is
employed for the evaluation.
The selection of main criteria and
sub-criteria is based on available past literature. A set of AHP questionnaire is
constructed and based on opinion of bank experts, the pair-wise comparison of
the criteria and sub-criteria are done at a given level of hierarchy to
determine their relative weights. Further the weights of all the sub-criteria
are used to determine the ranking of the banks using AHP.
Proposed AHP Model for Indian
Bank’s Financial Performance Evaluation
The proposed AHP model is explained
in the following steps:
Step
1:
The hierarchical model for bank’s financial performance evaluation developed in
such a way that the goal positioned at the top (1st level), (i.e.,
Evaluation of financial performance) with criteria (6 criteria) at 2nd level
and sub-criteria (24 sub-criteria) at 3rd levels and finally the
alternatives (10 largest Commercial Banks) at the bottom (4th level)
of the model. Figure 4.1 has depicted the proposed AHP model.
Step 2:
For determining the relative importance of criteria and sub-criteria, pairwise
comparisons of each of the six criteria and 24 sub-criteria has been done at
all possible pairs of criteria and sub-criteria. Table 4.1 has shown all the
opted criteria and sub-criteria. The squared pairwise comparison matrixes are
constructed in which all the sets of elements are compared with each other. The
expert's opinion regarding preference of criteria are expressed in terms of
verbally described scale of importance and corresponding numeric values based
on the fundamental pair-wise comparison scale
of AHP
preferences depicted in Table 4.2 are filled in the matrices. The total number of comparisons at each
hierarchy level is N(N-1)/2. There are seven pairwise comparison matrix
constructed, one pairwise comparison matrix is formed for criteria level, and
six pairwise comparison matrix is created for the sub-criteria level. These
pairwise comparison matrixes described how one attribute (i.e., criteria and
sub-criteria measured in terms of financial ratio) preferred over others. The
pairwise comparison matrixes are obtained by keeping one criterion as a
reference and pairing it with all other criteria. A typical pairwise comparison
matrix for criteria level filled by one respondent is demonstrated for
reference in Table 4.3.
Figure
4.1: Proposed AHP Model
Table 4.1: Selected
criteria and sub-criteria of the model
Note: - RWA- Risk-weighted Asset.
NPA- Non-performing Asset.
CRAR Capital to Risk-weighted Asset
Ratio.
Table 4.2: Fundamental
Pair-wise comparison scale for AHP preferences
A similar response is obtained for
each sub-criteria level as well. Table 4.3 demonstrates a pairwise comparison
matrix of criteria for reference; where the respondent believes that asset
quality is slightly more important than capital adequacy ratio, which means a
rating of 2 from table 4.2, so the numeric value 2 is mentioned in the cell
where asset quality criterion in row and capital adequacy ratio criterion in
column and simultaneously the numeric value 1/2 (The reciprocal response) is
mentioned in the cell where capital adequacy ratio in row and asset quality
criterion in column. Similarly, all the paired comparison has been made for the
remaining criteria in the matrix. The diagonal value of the matrix is one as
every criterion compared with itself would have equal importance. This process
would be repeated for all
the criteria and sub-criteria. Further, all fractional values are converted to
a decimal value.
Table 4.3: The Pairwise
Comparison Matrix for the selected Criteria
Step 3: To
make selected criteria and sub-criteria comparable, normalized pairwise
comparison matrixes are calculated using the vector normalization technique. Firstly,
the beneficial and non- beneficial criteria are identified then the vector
distance is calculated by adding squared value and taking the square root of
the values filled by the respondent in each column of the pairwise comparison
matrixes. Secondly, for beneficial criteria, each response value divided by
vector distance and for non- beneficial criteria one minus the response value
is divided by vector distance. Weight of each criterion and sub-criteria is then
calculated by taking the average of the normalized value of each row of the
respective criteria.
Step 4: This step deals with the calculation of eigen
value (λ) and
maximum eigen value (λmax).
For that the weight of every criterion is multiplied with each value in the
column of pairwise comparison matrixes to calculate weighted pairwise
comparison matrixes. To obtain weighted sum value of all the criteria and
sub-criteria, the addition of all the elements in each row of the corresponding
criteria and sub-criteria is done. Then the weighted sum value of corresponding
criteria and sub-criteria is divided with each value in the row of weighted
pairwise comparison matrixes. On taking the sum of all values in each of row of
the corresponding criteria and sub criteria, the value of λ (lambda) is determined. The average value of λ (lambda)
represents λ max.
Step 5:
In
this step, consistency index (CI) is determined for every paired comparison
matrix of criteria and sub-criteria by the formula given below,
CI = (λmax-n)/n-1
Where n presents the number of
criteria used in each paired comparison matrix.
Step 6:
To check the consistent behavior of responses
filled by the respondent’s consistency ratio (CR) is determined
by dividing the consistency index (CI) value of corresponding criteria and
sub-criteria with the random index (RI) value depicted in table 4.4
CR= CI/RI
Table 4.4: The Random
Index table
Step
7:
Saaty
(1980), suggested that the value of CR
is considered acceptable up to 0.10 or 10%.Value higher than 10% shows that response
obtained from the respondent is inconsistent, and it needs to reviewed and
improved to get the consistent matrixes. In present case, the value of consistency
ratio for all the criteria and sub-criteria is less than 0.01. For the
reference result of the pairwise comparison matrix for criteria level filled by
one respondent is depicted in table 4.5.Similarly consistency results have been
obtained for each sub-criteria level as well.
Table 4.5: Consistency
result of the pairwise comparison matrix for selected criteria
|
Criteria |
Weight |
λ
max, CI, RI |
CR |
|
Asset Quality |
0.136429646 |
|
|
|
Capital Adequacy |
0.192055012 |
λ max = 6.074 |
CR = 0.012 |
|
Liquidity |
0.170434783 |
CI = 0.014 |
|
|
Earning |
0.156895566 |
RI = 1.24 |
|
|
Management Efficiency |
0.167098233 |
||
|
Profitability |
0.177086759 |
|
|
Step 8: The steps from 3-7is repeated for all the criteria and sub-criteria in
the hierarchical levels to calculate local weights of each criterion and
sub-criteria. Subsequently, the global weightis determined by multiplying the
criteria weight with the sub-criteria weight.
Data Analysis and Model
Synthesis
To apply the proposed model for
evaluating financial performance of commercial banks working in India, data of
6 criteria, measured by 24 sub-criteria in terms of financial ratio of 10
largest commercial bank’s is collected from the RBI statistical reports. The
average time-series data for the period of 2009 to 2018 is taken for the
analysis. The average of these ratios is used in the form of the decision
matrix depicted in table 5.1.
In all the selected sub-criteria
(Financial ratios), some sub-criteria are beneficial, of which higher value is desired,
and some sub-criteria are non- beneficial of which lower value is desired. So
to make all the sub-criteria comparable and to bring uniformity, the normalized
decision matrix is calculated using linear normalization technique. For
normalization, first of all, the beneficial and non- beneficial sub-criteria
are identified. Then the maximum performance value of beneficial sub-criteria
and minimum performance value of non- beneficial sub-criteria is taken, and
then all the performance value of corresponding beneficial sub-criteria is
divided by maximum performance value of that sub-criteria and for non-
beneficial criteria the minimum performance value of corresponding
non-beneficial sub-criteria is divided by performance value in each cell of
that sub-criteria, as described below
For Beneficial Criteria = Xij/Xj Max
For Non- Beneficial Criteria = XjMin/Xij
Where Xij, represent the values in
each cell, Xj Max, represents the maximum value, and XjMin represents the
minimum value.
On solving normalized decision
matrix is obtained, and based on the response given by the experts the global
weight of all the sub-criteria has been calculated as described earlier in the
paper (Table 5.3). Then the global weight of each sub-criterion is multiplied
with the corresponding value of the financial ratio in each cell of
corresponding sub-criteria of the normalized decision matrix to get the
weighted normalized decision matrix. From the weighted normalized decision
matrix, the preference score is obtained by summing up the performance value of
all the sub-criteria in the weighted normalized decision matrix correspond to
each bank. Based on the preference score, the ranking of banks is obtained. As
illustrated in table 5.4
Table: 5.1: Average of
10-years financial ratios of selected criteria
Table: 5.2: Weight of
identified criteria and sub-criteria
Table: 5.3: Global
weight of selected sub-criteria
Table: 5.4: Preference
score and ranking of commercial selected banks
Figure: 5.1: Graphical representation
of preference score and ranking of Selected commercial banks
Results &
Discussions
The data of 24 financial ratios of
ten largest Indian commercial banks for the time period of 2009 to 2018 is extracted
from the RBI statistical reports and the weightage of selected criteria and
sub-criteria is calculated by employing paired comparison method based on the
response of banking experts using AHP. The capital adequacy ratio has highest
weightage (0.192055012) among all the criteria followed by profitability
(0177086759), liquidity (0.170434783), management efficiency (0.167098233),
earning (0.156895566) and asset quality (0.136429646) which has least weightage.
Similarly, the capital adequacy ratio tier-I has highest weightage among all
the sub-criteria (0.352191019), and return on advance has been assigned the
least weight (0.188306358) (table 5.2). Then the global weight is calculated by
multiplying the weight of all the criteria with the weight of the respective
sub- criteria (table 5.3.) shows that the global weight of capital adequacy
ratio tier-I has highest weightage (0.06764005) among all the sub criteria and
the provision coverage ratio has least weightage (0.028763414), the ranking of
all the sub- criteria based on their weightage is obtained (table5.3). Further,
the average of 24 financial ratios of ten largest Indian commercial banks for
the period of 2009 to 2018 is taken as decision matrix (table5.1). To make all
the financial ratios comparable normalization is done because all the financial
ratios have different units of measurement. So linear normalization technique
is used to get the normalized decision matrix. Subsequently, global weight of
all financial ratios is multiplied with the respective value of these normalized
financial ratios, and a weighted normalized decision matrix is obtained. The
value in each cell of the weighted normalized decision matrix is taken as the
performance value, and the sum of the performance value of all the financial
ratios representing 24 sub-criteria of the respective bank is calculated to
obtain the overall preference score (table 5.4). Based on the preference score,
the ranking is obtained, higher the overall preference score higher
is the ranking (table-5.4, fig 5.1). The overall preference score shows that
HDFC Bank Ltd. is having highest preference score(0.887420275) followed by
ICICI Bank Ltd (0.816487122), Canara Bank (0.653474296), Bank of Baroda
(0.649406372), State Bank of India (0.647864331), Union Bank of India (0.643209577),
Punjab National Bank (0.634668742), Syndicate Bank (0.610560365), Bank of India
(0.609132742), whereas the Central bank has got least preference score
(0.555516108). Further the result suggest that the banks which are lower in
ranking need to improve their certain key financial ratios such as capital
adequacy ratio, profit per employee, cash to deposit ratio, deposit to
liability and ratio of interest income to total assets etc., these are some of
the financial ratios which are having highest weightage and these banks need to
improve these ratios in order to improve their financial performance.
Conclusion
The study has ranked the financial
performance of ten commercial banks working in India based on selected
criteria. AHP method is utilized for providing relative weight to set criteria
and appropriate ranking has been assigned to banks on the basis of their
evaluation. The result of the study has demonstrated that the private sector
bank HDFC has attained highest rank among ten commercial banks under study
followed by ICICI. State bank of India, which is the largest commercial bank in
India, has been ranked on fifth position. It is evident from the study the size
of the bank is not associated with the financial performance of the banks. Further,
this paper gives priority ranking to the different key financial ratio based on
their weightage which enables the bank management to take a decision for improvement
of the key financial ratios and their financial results thereof.
As the study is confined to ten
commercial banks working in India, the findings cannot be generalized for all
the Indian commercial banks. In future researches, larger sample size can be
considered for study. Further, the ranking can be obtained for each year
separately to understand the change in performance every year. Moreover, different
sets of criteria can be explored to validate the results of existing studies.
Annexure: 1: List of
Selected Banks and their ranks based on their number of branch offices present
in India.
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