The evaluation of bank performance using a multicriteria decision making model: a case study on Lithuanian commercial banks.
Stankeviciene, Jelena ; Mencaite, Evelina
1. Introduction
The evaluation of banks and their performance attracts significant
attention from public and financial regulators as banks are critical
institutions in most economies. Their ability to attract financial
resources and provide various credit operations and different financial
services activate financial flows that influence the growth and economic
development of a nation. Moreover, the banking sector is considered as a
vital segment of modern economy, the existence economy of which cannot
implement its role and carry out specific functions. There is no doubt
that in order to ensure a healthy financial system together with
efficient economy, banks must be evaluated and analyzed using the most
accurate and modern evaluation techniques and, additionally, compared
between each other in order to distinguish those leading and standing
behind.
Recent financial turmoil has drawn even more discussions regarding
the topic of banks and their performance evaluation as bank runs became
more apparent and disturbing trends towards bank performance were
observed. The need to evaluate banks in a more efficient way was
identified and enhanced not only by supervising institutions, regulators
and bank management bodies but also by clients, as their concern about
the stability and sustainability of these financial institutions has
grown significantly. This influenced to rethink the current performance
evaluation techniques and models along with their applicability and
improvement.
The novelty of this paper is reflected by creating a new model for
evaluating banks and their performance. The model is based on a
multicriteria approach, brings together financial and non-financial
evaluation measures and consists of applying the multiciteria decision
making technique such as Analytical Hierarchy Process (AHP). The
adaptation of the AHP model helps with evaluating banks and their
performance by combining different advantages of multicriteria decision
making with a single balanced system. The model also can be considered
as the universal one because it can be applied and used by both
professional analysts and the clients of a bank as it requires
information available in public sources.
The theory and practice have provided a number of valuation models
but hardly any balanced methodology could be found. Also, an obvious
point is that even the failure of one bank can have significant outcomes
for the whole banking sector in the country and the performance of banks
must be followed in a structured and efficient manner.
2. Previous researches on the performance evaluation of banks
This section of the paper will represent a review of previous
researches made in the field evaluating bank performance. The analysis
of different approaches and ideas will help with identifying the most
valuable measures and methodologies and see the linkages to the
evaluation model provided in this paper.
In an attempt to monitor and evaluate the performance of banks,
most financial economists and analysts have been using financial ratios
(Ayadi et al. 1998). The most popular and commonly used ones are return
on equity (ROE) and return on assets (ROA). Badreldin (2009) agreed on
insights provided by Lindblom and Von Koch (2002) and Wilcox (1984), who
were asserting that return on equity as a financial measure could be
discovered in the majority of research analyzing the performance of
banks and concern analyst reports or company financial results. It is
also important to highlight, that in the banking sector, ROA and ROE
measures largely correlate with each other and both of them provide
particularly the same indication of performance associated with the
tendency and movement of financial performance (Karr 2005) (Table 1).
Although these measures are used widely, they are criticized and
have significant shortcomings that are proposed by different financial
analysts and scientists. The main limitations of ROE are as follows: 1)
can induce inaccurate and incorrect results because of a different size
of companies and in terms of credit risk (Lindblom, Von Koch 2002), 2)
face the problems connected with the allocation of assets, equity and
net income in case of branch level (Avkiran 1997), 3) the cost of equity
is not taken into account in its calculation, thus it can be seen that
bank performance is good when the value to its shareholders is
diminishing. Moreover, when talking about ROA, as it was proposed by
Chapman et al. (2007), this measure deals with some technical issues
like: what assets should be included in the denominator, how to count
income used in the numerator and the impact of different valuation rules
such as replacement cost accounting.
Another block of literature concerning the measurement of bank
performance concentrates on the sets of ratios or entire systems of
measures. Most of these systems with minor adjustments and replacements
are created on the basis of the Du Pont System for Financial Analysis
(Badreldin 2009; Tamosaitiene 2011). One of these systems is
Schierenbeck's Basic ROE Scheme. This model has three important
advantages: the ability to conform an overall view of bank performance,
the ease of gathering and obtaining information necessary for making
calculations of its ratios and includes ROA in its analysis that helps
with combining the advantages of both measures (Kalhoefer, Salem 2008).
The return on equity model analyzed by Collier and McGowan (2010)
is also based on the Du Pont system, though in contrast to
Schierenbeck's Basic ROE Scheme separates performance into three
elements that determine return on equity: 1) net profit margin, 2) total
asset turnover and 3) equity multiplier. This system allows evaluating
the income statement and its components by using profit margin measure,
meanwhile, the total asset turnover and equity multiplier helps with
evaluating the left-hand side of the balance sheet composed of asset
accounts and the right-hand side of the balance sheet that comprises
liabilities and owner's equity.
European Central Bank (2010) suggests using the system of financial
ratios. Traditional measures of performance comprise such ratios as ROA,
ROE, cost-to-income ratio and net interest margin. Meanwhile, economic
measures include EVA (economic value added) and RAROC (risk-adjusted
return on capital). Finally, market-based measures of performance
characterize the activity of a company valued by capital markets
compared with the estimated accounting or economic value. The most
commonly used metrics include total share return (TSR), price-earnings
ratio (P/E), price-to-book value (P/B) and credit default swap (CDS).
If we have a look at the evaluation of bank performance from the
point of rating agencies, they follow a more holistic approach. As
rating agencies make an overall assessment of banks and assigning
grades, they consider all types of prudential returns such as capital,
asset quality and liquidity that are integrated in measuring the
performance of a bank. Besides, they take a more dynamic approach and
more attention is paid to changes in revenue composition and cost
elements. Rating agencies also try to add market-based indicators while
analyzing the performance of banks (European Central Bank 2010).
For the evaluation of bank performance, Cicea and Hincu (2009) used
an approach to investment activities and introduced several methods: 1)
financial indicators, 2) quantitative models (Sharp model,
Treynor's measure of performance, Jensen's measure of
performance). Researchers also analyzed the possibility of applying
these quantitative models and made the conclusion that Sharp model was
considered the most appropriate in terms of data availability in
commercial banks.
However, the usage of financial ratios and their systems is
criticized by other scientists. Yeh (1996) notes that dependence on
benchmark ratios, i.e. reliance on comparable norms and standards is the
most significant shortage of financial ratios approach that does not
allow to composite an overall score on the entire bank soundness.
Moreover, Sherman and Gold (1985) point out those financial ratios
mainly reflect short-term rather than long-term measures of performance
that is more relevant. According to them, financial ratios combine many
aspects of performance, including operations, marketing and financing.
Thus, such an approach is not appropriate.
As a result, CAMEL and DEA approaches were introduced in literature
on evaluating bank performance. The acronym CAMEL is derived from the
five main segments of bank operation: Capital adequacy, Asset quality,
Management quality, Earnings ability and Liquidity (CAMEL). Moreover,
Hays et al. (2009) presented the enhancement of the CAMEL model by
federal banking regulators in order to assess the overall performance of
commercial banks. Regulators created an additional measure, sensitivity,
to evaluate market risk associated with changing interest rates and
other factors.
DEA (Data Envelopment Analysis) was developed by Charnes in 1978
and considered mathematical programming technique calculating the
relative efficiency of objects on multiple criteria (Banker, Morey
1986). Mathematically, as Step 1, DEA identifies an "efficient
frontier" comprised of analyzing the outputs and inputs of a
certain set of objects that will be evaluated and called decision making
units (DMUs) (Weber 1996). According to Ayadi et al. (1998), the DEA
approach should be considered as a relative measure of efficiency as it
identifies the 'best practice' firm in a group according to
the observed outputs and inputs. Then, each firm in the group is
evaluated relatively to the 'best' firm.
Chen, T. and Chen, C. (2008) began a discussion that,
traditionally, many performance measures and their schemes have been
based on financial aspects, whereas non-financial aspects were ignored,
although their role in performance evaluation is important. They also
propose that the evaluation of bank performance usually employs
financial indices, thus providing a simple description about the
financial performance of the bank in comparison to the previous periods
and focusing only on financial aspects. However, this is not enough to
keep in line with a changing environment in business.
In 1996, Kaplan and Norton introduced the concept of a Balanced
Scorecard (BSC), which was proposed as background for a strategic
management system (Kaplan, Norton 2004). The main feature of this
approach is that financial and non-financial aspects were included and
allowed incorporating business strategies into management systems.
Manandhar and Tang (2002) concluded that BSC was not only a system for
measuring performance but also a system of strategy analysis. However,
one aspect has been considered as a disadvantage of the BSC system;
therefore, it is difficult to make comparisons within and across
companies on such a basis (Chen, T., Chen, C. 2008).
Abdelgawad, Fayek (2010) developed an approach provided by Meyer
and Markiewicz who identified critical success factors in banking
performance and grouped them into eight main categories: 1)
profitability, 2) efficiency and productivity, 3) human resource
management, 4) risk management, 5) sales effectiveness, 6) service
quality, 7) capital management and 8) competitive positioning. Financial
and non-financial measures are combined together into the balanced
system, which takes into account different fields of banking activity.
It can be proposed that such approach helps with evaluating bank
performance in a more comprehensive manner.
Finally, the study made by Spathis et al. (2002) presented strong
linkages between bank size (non-financial measure) and performance
efficiency were identified. In order to identify the differences of
profitability and operation related with the size of banks, the
multicriteria methods of M. H. DIS and UTADIS were applied. Seven
financial ratios were selected for examination: return on equity, return
on assets, net interest margin, the ratio of loans to deposits, the
ratio of current assets to total loans, total assets to the total equity
ratio and the ratio of total equity to total assets. The results
indicated that large banks were more efficient than the small ones that
can be characterized by high capital yield (ROE), high interest rate
yield (MARG), high financial leverage (TA/TE) and high capital adequacy
(TE/TA). Meanwhile, large banks can be considered as having high asset
yield (ROA) and low capital and interest rate yield.
To sum up, it can be proposed that observations and literature
analysis according to the measurement of bank performance clearly show
that different techniques and methods can be applied in this field. The
main purpose of creating the evaluation process is to choose those
ratios and methods, including financial and non-financial measures that
would reflect the most accurate view of banking activities and would
help with solving a specific problem of evaluation.
3. Development of the model
This section of the paper is designated for highlighting the main
aspects of developing the AHP model applied for evaluating the
performance of banks. It will cover such topics as an overview of
applying the AHP model, the structure of the model and, finally, a
detailed sequence and steps necessary to apply this technique.
The AHP model was chosen based on several reasons. First, it allows
considering financial and non-financial measures in the evaluation
process, which is very important because the business of banking is very
complex, and therefore it is not enough to take into account only
financial measures. It helps with revealing bank ranking and recognizing
better performing banks and those that need more attention either from
supervisory institutions or management in order to improve the current
performance. This model also includes external judgment that could help
with building a more specific evaluation framework.
The AHP model can be considered as a new approach that got full
attention in the recent years and found its applicability in different
fields. Zavadskas and Kaklauskas developed a method of a multiple
criteria complex proportional assessment of projects for determining the
priority and utility degree of alternatives. Lithuanian and foreign
scientists applied the original or expanded method for solving different
engineering and management multi-attribute problems in the period of
1996-2011 (Zavadskas et al. 2008, 2009a, 2009b; Podvezko et al. 2010;
Tupenaite et al. 2010; Sivilevicius 2011a, 2011b; Lashgari et al. 2011).
Haq and Kannan (2006) used this approach for selecting a vendor, Kaya
and Kahraman (2011)--for developing e-banking website quality
assessment, Hsu (2006)--for developing a new model to select public
relation firms in high-tech industry, Wu et al. (2010)--for evaluating
business performance of wealth management banks. These examples show
that AHP can be considered as a beneficial approach and appreciated by
many researchers.
The analysis of researches and case studies provided above
indicates that AHP consists of four main fragments depicted in Figure 1.
[FIGURE 1 OMITTED]
It can be noticed that the application of the model begins from a
definition of qualitative and quantitative criteria. Further, the AHP
technique is applied to find out relative weights between the chosen
criteria. The outcome of the applied model would be ranking banks, which
will allow not only evaluating banks but also identifying better bank
performance considering the banks that could be considered as showing a
lower performance level (Gnanasekaran et al. 2008).
As proposed by Hsu (2006), the model can be divided into two major
parts: in the first one, the AHP approach is applied and in the
second--GRA application is involved. These two major parts comprises
smaller and more detailed elements and procedures provided below. The
interpretation of these elements and procedures is based on research
made by Hsu (2006), Wu et al. (2010), Farhan, Fwa (2010).
Part 1 involves the application of AHP for defining relative
criteria weightings. This part comprises six following steps (based on
Hsu 2006; Wu et al. 2010):
1) the definition evaluating criteria and sub-criteria and both
qualitative and quantitative measures;
2) the establishment of a hierarchical structure breaking the
problem of evaluating bank performance into the hierarchy of
interconnected decision elements containing a) the ultimate goal, b)
criteria and c) sub-criteria;
3) the establishment of a pair-wise comparison matrix where a
pair-wise comparison of decision elements is made by an expert and
relative scores are assigned;
4) to calculate the eigenvector of each matrix of the pair-wise
comparison;
5) this step consists of testing the consistency of each comparison
matrix;
6) to estimate the relative weights of the elements at each level;
7) the definition of criteria and data treatment;
8) the normalization of individual values of the criteria before
calculating relational grades, in case of variances between individual
criteria units;
9) to calculate the difference series;
10) to compute relational coefficients;
11) to compute a relational grade;
12) to reveal the ranking of the chosen banks to determine the best
performing banks and those falling behind.
To sum up this section, the AHP methods provide a comprehensive
tool for conducting analysis in different fields, including business
performance evaluation, the selection of suppliers, projector
environment evaluation, etc. The main advantage of researches using this
model is that both qualitative and quantitative criteria can be taken
into consideration. The AHP method could be easily applied for finding a
solution to the problem. Moreover, Gnanasekaran et al. (2008), state
that another major advantage of this integrated model is flexibility as
the employment of this model makes it easy to include any new subject
(supplier, bank, project, etc.) in the evaluation process.
4. The application of the proposed model: a case study on
Lithuanian commercial banks
This section describes the application of the AHP model and
investigates the performance of Lithuanian commercial banks. The
application process of the AHP model begins from the establishment of an
evaluation framework comprised of different evaluation criteria,
including quantitative and qualitative measures.
The evaluation framework of this research is based particularly on
observations made by several scientists who were dealing with evaluating
the business and performance of banks (Wu et al. 2010) and on adding
additional criteria. Overall, the evaluation model consists of 22
different criteria (Fig. 2).
The problem of the evaluation of bank performance was decomposed or
split into a hierarchy of interconnected decision components containing:
a) the ultimate goal, b) criteria and c) sub-criteria, i.e. the problem
of identifying the best performing commercial banks and those that fall
behind according to the chosen evaluation framework can be solved by
evaluating customer perspective criteria, financial perspective criteria
and qualitative evaluation criteria.
In Step 3, a pair-wise comparison procedure should be applied. The
procedure is established with the help of an expert review of decision
elements (both criteria and sub-criteria) and the assignment of relative
scores, i.e. a pair-wise comparison shows the importance of one element
over the element it is compared with. The pair-wise comparison matrix is
established using Formula (1):
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (1)
[FIGURE 2 OMITTED]
The structure of the matrix is comprised of the compared elements
and relative weights. The diagonal cells of the matrix always have the
meaning of 1, as the same elements have no importance or difference
between each other.
The comparison matrix of the main criteria (C1 - customer
perspective; C2--financial perspective; C3--qualitative evaluation) is
provided below in Table 2.
The pair-wise comparison matrix of the main criteria showed that
financial perspective was dominant over customer perspective and
qualitative evaluation. On the other hand, customer perspective is
characterized as less important than qualitative evaluation.
Moreover, the comparison matrixes of importance were established
for sub-criteria that are falling under the main categories.
Step 4 can be considered as the step of calculating the eigenvector
of each pair-wise comparison matrix.
First, for eigenvector calculation, Sivilevicius and Maskeliunaite
(2010), Podvezko et al. (2010) suggest adding the columns of each
pair-wise comparison matrix. Then, the values of the matrix should be
normalized by dividing each element in the column from the total score
of column values. When the matrix with normalized values is established,
the mean of every row is provided with an eigenvector. The calculations
of the first eigenvector were implemented taking the main criteria. The
table of the eigenvectors of the main criteria indicates that financial
perspective is the most important criteria (0.7014) comparing with the
values of the eigenvectors of customer perspective (0.0853) and
qualitative evaluation (0.2132) criteria (Table 3).
The difference between the eigenvector of financial and customer
perspective criteria is very obvious. Moreover, the eigenvectors of each
element at sub-criteria level were calculated.
Step 5 of the AHP model provides an ability to test the consistency
of each comparison matrix using formulas (2) and (3):
CI = [[lambda].sub.max]-n / n-1; (2)
CR = CI/RI. (3)
Although in some researches this step is not included, it can be
considered to be of key importance a sit helps with detecting
inconsistencies in the evaluations and rankings of the preferences
estimated by experts or evaluators.
The summary of the consistency test and calculations is provided in
Table 4.
If the matrix consists of less than 5 criteria, the requirement for
consistency ratio (CR) is CR < 0.05. The table above indicates that
the matrixes of the main criteria, C1 and C3 sub-criteria are consistent
because their CR scores are 0.046, 0.034 and 0.048 respectively. On the
other hand, if the matrix consists of more than 5 criteria, CR should
already be less than 0.1. As the C2 sub-criteria matrix consists even of
15 measures and its CR equals 0.058, it can be also considered as
consistent.
Thus, it could be concluded that preferences indicated in the
evaluation are correct and further steps of the proposed model can be
applied.
The last step in the first part of model application is Step 6
estimating the relative or global weights of the elements at each level.
The weights of the criteria within a certain group have already been
estimated and this step is dedicated exclusively for calculating the
overall weight of the criteria in the entire evaluation system.
In order to estimate the overall weights, the eigenvector of the
criterion is multiplied by the relative weight of the group it belongs
to. The obtained results showed that there were four top ratios
recognized based on an expert review. These include significance
(0.105798), liquidity ratio (0.105235), capital adequacy ratio
(0.099951) and proportion between net cash flow from operating
activities and total cash flow (0.092347).
Step 7 requires estimates, the definition of the criteria and data
treatment. As there are even 22 criteria included in the evaluation
process, the table below has been established to enable data analysis
quicker and easier (Table 5).
Next, Step 9 requires proceeding with model application considering
the normalization of individual criteria values prior to the calculation
of relational grades, in case of the variances between individual
criteria units. Data normalization is implemented in order to convert
the values of criteria into series from 0 to 1 and continue
calculations. This procedure helps with a comparison of how banks are
performing in terms of a single criterion.
Step 10 can be considered as the one calculating difference series
determined deducting the actual values of normalized individual criteria
from referential series.
The next step calculates grey relational coefficients in order to
express the relationship and connection between the best (reference) and
the actual normalized values. These coefficients must be estimated prior
the relational grade is obtained and fluctuate within the range from 0
to 1. According to Jangraa et al. (2011), the relational grade can be
interpreted as a weighting-sum of relational coefficients. The
relational grades of Lithuanian commercial banks are shown in Table 6
indicating the grades assigned for each commercial bank in terms of a
single criterion. The grades marked in bold are considered as 'the
best' ones while the scores marked in grey are the lowest within
the group. The provided information suggests that the majority of banks
have gathered from 4 to 6 'best' scores and that the lowest
scores are concentrated in predominantly three commercial banks.
According to the theory, the highest relational grade shows the
best alternative. The final score is calculated by simply adding all
relational grades for a certain bank. As all calculations have already
proceeded, the final ranking of Lithuanian commercial banks can be
revealed.
The results provided in Table 7 propose that the best performing
bank is bank X3 having score 0.676. The second and third places are
assigned to banks X1 and X4 with scores 0.635 and 0.621 respectively.
What is more, no significant gap between the above evaluated commercial
banks can be noticed. The lowest performance results were identified in
banks X7 and X9 that got quite similar scores making 0.484 and 0.468.
The conducted investigation has revealed that the difference in
performance can be influenced by the size of a bank. The best performing
banks belonging to small or medium bank categories are those X6 and X9.
In conclusion of this section of the paper, the AHP model can be
successfully applied for Lithuanian banking market. Depending on the
priorities of evaluators, different weights for criteria can be
assigned, however, as the final result, ranking banks can be established
and leading banks can be distinguished from those falling behind.
5. Conclusions
Banks can be considered as extraordinary business entities that
perform particularly specific functions such as the attraction of
financial resources, the provision of various credit operations and
different financial services and the activation of financial flows that
have a significant impact on the growth and development of a nation.
These financial institutions manage a portentous proportion of assets in
the entire financial system and their performance together with an
overall condition have a major influence on the stability and soundness
of national economy.
Moreover, banking as a branch of industry has gone through a path
of significant changes during last decades. Business models and
processes have become more multifaceted and the number of services and
areas of activity has increased several times. All these factors have
lead to the situation indicating that the measurement of bank
performance has became more sophisticated and as a result the need for a
methodology fully reflecting different aspects of performance and
satisfying different interests of clients, investors, supervising
authorities and management of the bank have come to the front.
The first attempts to monitor and evaluate the performance of banks
were mainly based on the analysis of various distinct financial
indicators (return on assets, return on equity, capital adequacy,
profitability, etc.) that later went through the process of grouping and
were combined into entire financial systems of evaluation. Although
financial ratios together with mathematical programming techniques were
widely applied, a lack of non-financial evaluation was identified. There
still was a necessity to create a solid and flexible evaluation
technique or model.
The proposed model was introduced in order to eliminate the
shortcomings of the previous methodologies and include considerable
non-financial aspects to the evaluation framework. This model allows
splitting the problem of evaluating the performance of banks into four
major stages such as the definition of qualitative and quantitative
criteria, the establishment of the evaluation framework, the acquisition
of the relative weights of criteria and sub criteria using AHP, the
determination of the weight business performance in banks and the final
business evaluation score.
Following the analysis of literature on the performance of banks,
three main pillars of financial and non-financial evaluation were
identified and the application process of AHP was conducted. The first
pillar of measures was evaluating the performance of banks from
customer's perspective and consisted of such indicators as customer
increasing rate, accessibility for a customer, the number of provided
services and products, the quality of the Internet page. The second
pillar covered financial ratios. Finally, the third pillar of measures,
including support, significance, management and quality maturity of a
bank was used for qualitative evaluation. Overall, the performance of
banks was investigated using 22 criteria.
It should be noted, that the performance of banks can be influenced
by their size. The interaction between these two aspects was identified
in the carried out research. Smaller banks were falling behind the banks
that were managing solid assets and controlling a significant part of
the market. Banks X6 and X8 were performing best with the final scores
of 0.566 and 0.552 respectively in the category of small or medium
banks.
The scope of applying the model proposed in this paper can be very
wide as it can be employed by customers, investors, supervising
authorities, the management of the bank and other shareholders. For the
evaluation of commercial banks, the AHP model used publicly available
information and does not require particularly extraordinary knowledge
that is a great advantage for ordinary clients of the bank.
When looking to the prospects of the model, new criteria could be
added and proportion between quantitative and qualitative measures
modified in order to satisfy the needs of different evaluators by
bringing out the most important aspects of performance. Finally, this
model can be characterized as universal as it can be applied not only
for Lithuanian market, but also in foreign countries.
doi: 10.3846/20294913.2012.668373
Received 18 December 2010; accepted 24 October 2011
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Jelena Stankeviciene (1), Evelina Mencaite (2)
Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223
Vilnius, Lithuania
E-mails: (1) jelena.stankeviciene@vgtu.lt (corresponding author);
(2) e.mencaite@gmail.com
Jelena STANKEVICIENE. Assoc. Prof., PhD at the Department of
Finance Engineering, Vilnius Gediminas Technical University (Lithuania),
the Dean of the Faculty of Business Management. Research interests:
assets and liability management, regulation of financial institution,
financial management of value creation, value engineering.
Evelina MENCAITE. MSc in Business from Vilnius Gediminas Technical
University (Lithuania), the Faculty of Business Management. Research
interests: management of financial institutions, valuation models, fund
rising.
Table 1. An overview of previous researches on
the performance evaluation of banks
Authors Method
Wilcox (1984) Emphasized the importance of
Karr (2005) ROA and ROE measures for
Badreldin (2009) evalu-ating bank performance.
These measures were considered
as the main performance
indicators.
Sherman, Gold (1985) Criticized the usage of
Yeh (1996) benchmark ratios for the
performance evaluation of
banks.
Banker, Morey (1986) For bank evaluation, the DEA
Weber (1996) approach-a mathematical
program-ming technique was
used.
Avkiran (1997) Provided the main shortcomings
Lindblom, Von Koch (2002) of ROE and ROA measures.
Chapman et al. (2007)
Meyer, Markiewicz (1997) Have identified critical
success factors of banking
performance and grouped them
into eight main categories: 1)
profitability, 2) ef-ficiency
and productivity, 3) human
resource management, 4) risk
management, 5) sales
effectiveness, 6) service
quality, 7) capital
management, and 8) competitive
positioning.
Ayadi et al. (1998) Five main segments of bank
Hays et al. (2009) operation are analyzed:
capital adequa-cy, asset
quality, management quality,
earnings ability and liquidity
(CAMEL). Sensitivity is added
and CAMEL is model introduced.
Kalhoefer, Salem (2008) Looked at the evaluation of
Badreldin (2009) banks through the usage of the
Collier, McGowan (2010) Du Pont System for Financial
Analysis. The evaluation of
performance was separated into
three elements: 1) net profit
margin, 2) total asset
turnover and 3) the equity
multiplier.
Spathis et al. (2002) Observed strong linkages
between bank size (non-
financial mea-sure) and
performance. Multicriteria
methods of M.H.DIS and UTADIS
were applied.
Manandhar, Tang (2002) Were analyzing the application
Chen, T. and Chen, C. (2008) of BSC to evaluate performance
not only by financial measures
but also by incorporating the
non-financial approach.
Chen, T., Chen, C. (2008) Emphasized the role of non-
financial measures to evaluate
banks.
Cicea, Hincu (2009) For bank performance
evaluation, used the approach
of investment activities and
introduced several methods: 1)
financial indicators (such as
safety margin vis-a-vis
investment risks, the rate of
em-ploying resources in
investment, the rate of
employing deposits in
investment and investment
profitability), 2)
quantitative models (such as
Sharp' model, Treanor's
measure of performance,
Jensen's measure of
performance).
European Central Bank (2010) Suggests using a system of
financial ratios combined of
three catego-ries: 1)
traditional measures of
performance, 2) economic
measures of performance and 3)
market based measures of
performance. Rating agencies
consider all types of
prudential returns such as
capital, asset quality,
liquidity that are integrated
in measuring the performance
of a bank.
Table 2. The pair-wise comparison matrix of the main criteria
C1 C2 C3
C1 1 1/7 1/3
C2 7 1 4
C3 3 1/4 1
Total 11 1.393 5.333
Table 3. The values of the eigenvectors of the main criteria
C1 C2 C3 Eigenvector
C1 0.091 0.103 0.063 0.0853
C2 0.636 0.718 0.750 0.7014
C3 0.273 0.179 0.188 0.2132
Table 4. A summary of the consistency test
Main criteria C1 C2 C3
RI 0.580 0.580 1.590 0.900
[[lambda] 3.052 3.039 16.290 4.130
.sub.max]
CI 0.026 0.019 0.092 0.043
CR 0.045 0.034 0.057 0.048
n 3.000 3.000 15.000 4.000
Table 5. The rates of data series
X1 X2 X3 X4 X5 X6 X7
SC12 1.000 1.501 1.390 0.663 0.575 0.008 0.213
SC13 1.000 0.667 0.333 0.333 0.667 0.333 0.667
SC14 0.909 0.818 1.000 1.000 0.909 0.727 0.818
SC201 0.593 0.815 1.037 0.593 0.519 0.963 0.444
SC202 0.785 0.308 0.439 1.000 0.537 0.131 0.463
SC203 0.773 2.021 0.290 1.000 0.008 0.201 0.021
SC204 1.001 1.328 1.217 1.000 0.915 1.957 1.283
SC205 0.982 1.088 1.316 1.000 2.281 1.754 1.158
SC206 0.006 0.365 0.029 1.000 0.000 0.000 0.088
SC207 1.000 0.338 0.167 0.581 0.451 0.013 0.431
SC208 1.000 0.772 0.830 1.030 0.355 0.567 0.364
SC209 1.000 0.500 3.500 7.000 14.500 0.500 5.500
SC210 1.000 0.615 5.154 8.385 34.538 0.923 9.538
SC211 1.000 0.600 4.400 7.600 28.400 0.200 13.400
SC212 0.902 0.974 0.718 1.000 0.455 0.642 0.669
SC213 1.222 1.556 1.667 1.000 2.222 5.556 1.667
SC214 1.087 2.131 1.403 1.000 0.849 1.393 1.296
SC215 1.259 0.813 1.585 1.000 1.420 1.064 1.077
SC31 1.000 0.667 1.000 1.000 1.000 0.667 0.333
SC32 1.000 0.667 1.000 1.000 0.333 0.333 0.333
SC33 1.000 1.000 0.667 1.000 0.333 0.333 1.000
SC34 0.333 0.667 0.667 0.667 0.333 0.333 0.667
X8 X9
SC12 0.696 0.147
SC13 1.000 0.667
SC14 1.000 1.000
SC201 1.000 0.407
SC202 0.271 0.346
SC203 1.704 0.976
SC204 1.376 1.513
SC205 2.246 0.930
SC206 1.465 2.235
SC207 8.801 0.550
SC208 0.518 0.372
SC209 5.500 18.500
SC210 6.769 32.385
SC211 10.800 16.200
SC212 0.920 0.650
SC213 2.000 2.111
SC214 1.940 1.399
SC215 0.964 1.244
SC31 0.333 0.333
SC32 0.667 0.333
SC33 0.667 0.667
SC34 1.000 0.667
Table 6. Relational grades of Lithuanian commercial banks
X1 X2 X3 X4 X5 X6 X7
SC12 0.029 0.049 0.043 0.023 0.022 0.016 0.018
SC13 0.029 0.015 0.015 0.015 0.015 0.010 0.015
SC14 0.004 0.003 0.007 0.007 0.004 0.002 0.003
SC201 0.026 0.038 0.065 0.027 0.024 0.053 0.023
SC202 0.036 0.021 0.024 0.055 0.026 0.018 0.024
SC203 0.031 0.092 0.041 0.053 0.038 0.040 0.038
SC204 0.037 0.048 0.043 0.037 0.035 0.105 0.046
SC205 0.019 0.016 0.013 0.018 0.007 0.009 0.015
SC206 0.020 0.016 0.020 0.011 0.021 0.021 0.019
SC207 0.006 0.006 0.006 0.006 0.006 0.006 0.006
SC208 0.011 0.007 0.008 0.012 0.004 0.005 0.004
SC209 0.020 0.022 0.016 0.012 0.009 0.020 0.014
SC210 0.021 0.023 0.018 0.016 0.008 0.022 0.015
SC211 0.016 0.018 0.013 0.011 0.006 0.017 0.009
SC212 0.021 0.019 0.028 0.018 0.055 0.033 0.031
SC213 0.014 0.015 0.015 0.014 0.017 0.042 0.016
SC214 0.038 0.017 0.028 0.042 0.052 0.028 0.031
SC215 0.054 0.033 0.100 0.040 0.070 0.043 0.043
SC31 0.027 0.013 0.027 0.027 0.027 0.013 0.009
SC32 0.106 0.053 0.106 0.106 0.035 0.035 0.035
SC33 0.061 0.061 0.031 0.061 0.020 0.020 0.061
SC34 0.007 0.010 0.010 0.010 0.007 0.007 0.010
X8 X9
SC12 0.024 0.017
SC13 0.029 0.015
SC14 0.007 0.007
SC201 0.061 0.022
SC202 0.020 0.022
SC203 0.075 0.053
SC204 0.050 0.057
SC205 0.007 0.020
SC206 0.009 0.007
SC207 0.018 0.006
SC208 0.005 0.004
SC209 0.014 0.007
SC210 0.016 0.008
SC211 0.010 0.008
SC212 0.020 0.032
SC213 0.016 0.017
SC214 0.019 0.028
SC215 0.038 0.053
SC31 0.009 0.009
SC32 0.053 0.035
SC33 0.031 0.031
SC34 0.020 0.010
Table 7. Ranking Lithuanian commercial banks in terms of performance
Relational grade Ranking
Bank X1 0.635 2
Bank X2 0.597 4
Bank X3 0.676 1
Bank X4 0.621 3
Bank X5 0.508 7
Bank X6 0.566 5
Bank X7 0.484 8
Bank X8 0.552 6
Bank X9 0.468 9
