Determination of Camels model on bank's performance

Abstract

Banks need a way to evaluate performance and consider some important financial ratios and find the strengths and weaknesses. "CAMELS" model is a way to calculate and evaluate banks and financial institutions' performance. Capital adequacy, Asset quality, Management soundness, Earnings and profitability, and Liquidity and Sensitivity are the focus points of this rating. In this study the effects of each category of CAMELS are studied on performance. Q-Tobin's ratio is put as performance indicator. And also, data which is used in this study is gathered from annual financial reports of an Iranian bank and at the end, the model is extracted from analyses. With CAMELS studies, banks can focus on risk and some important ratios and try to manage and control some possible crisis.

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Volume: 2, Issue: 10, 652-664
Oct 2015
www.allsubjectjournal.com
e-ISSN: 2349-4182
p-ISSN: 2349-5979
Impact Factor: 5.742
Malihe Rostami
Department of management,
university of Grenoble,
France.
Correspondence
Malihe Rostami
Department of management,
university of Grenoble,
France.
Determination of Camels model on bank's performance
Malihe Rostami
Abstract
Banks need a way to evaluate performance and consider some important financial ratios and find the
strengths and weaknesses. "CAMELS" model is a way to calculate and evaluate banks and financial
institutions' performance. Capital adequacy, Asset quality, Management soundness, Earnings and
profitability, and Liquidity and Sensitivity are the focus points of this rating.
In this study the effects of each category of CAMELS are studied on performance. Q-Tobin's ratio is put as
performance indicator. And also, data which is used in this study is gathered from annual financial reports
of an Iranian bank and at the end, the model is extracted from analyses. With CAMELS studies, banks can
focus on risk and some important ratios and try to manage and control some possible crisis.
Keywords: CAMELS model, Bank's Performance, Ratios
1. Introduction
The banking sector occupies a very important position in the country's economy, acting as an
intermediary to all industries, ranging from agriculture, construction, textile, manufacturing, and
so on. The banking industry thus contributes directly to national income and its overall growth
(Dash & Das, 2009) and although the banking sector is the backbone of any economy and this
gains more importance for a country (Chandani et al., 2014). As the banking sector has a major
impact on the economy as a whole, evaluation, analysis, and monitoring of its performance is
very important (Dash & Das, 2009). Overall, financial markets are defined as a system which
brings economic actors together that need funds and have overmuch sources. In this structure, it
is necessary for the third parties called as financial intermediates to reduce the risks and to
provide effective fund transfer (Dincer et al, 2011)
Bank is very old institution that is contributing toward the development of any economy and it's
treated as an important service industry in modern world. Nowdays the function of bank is not
limited to within the same geographical limit of any country. It is an important source of
financing for most businesses. The common assumption, which underpins much of the financial
performance research and discussions, is that increasing financial performance will lead to
improved functions and activities of the organizations (Nimalathasan, 2008)
Simply stated much of the current bank performance literature describes the objective of
financial organizations as that of earning acceptable returns and minimizing the risks taken to
earn this return (Hempel et al., 1996).
Generally, the financial performance of banks and other financial institutions has been measured
using a combination of financial ratios analysis, benchmarking, measuring performance against
budget or a mix of these methodologies (Avkiran, 1995).
Bank performance is one of the vital issues for the healthy functioning of economy. Although
the measures evaluating bank performance are ample in amounts (Oztorul, 2011). In order to
ensure a healthy, solid and stable banking sector, the banks must be analyzed and evaluated in a
way that will allow the smooth correction and removal of the potential vulnerabilities (Roman &
Sargu, 2013) because bank play crucial role in the economic life of the nation (Dincer et al,
2011) and bank have a prominent role in the financial and business environment (Doumpos &
Zopounidis, 2010). Even if banks seem to create no new wealth, the transactions such as
borrowing and lending actually expedite the process of production, distribution, exchange and
consumption of wealth (Dincer et al, 2011).
2. CAMELS' framework
In recent years one of the most used models for the estimation of a bank performances and
soundness is represented by the CAMELS framework (Roman & Sargu, 2013). Actually, the
analytical framework is based on the CAMELS rating system, a device created by federal
banking regulators to assess the overall performance of banks (Rose & Hudgins, 2010).
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In order to evaluate banks’ overall financial condition,
CAMELS supervisory rating system is built and introduced
first in USA for onside monitoring. Now, it is used both on-
site and off-site monitoring purposes (Kaya, 2001).
The central banks that are responsible for supervising the
banks in each country use rating system to assess the
soundness of the banks (Doumpos & Zopounidis, 2010). Due
to lack of sufficient historical data about bank defaults, bank
rating system are usually based on empirical assessment
techniques (Sahajwala & Van den bergh, 2000). Creadit
agencies, auditors and bank regulators have traditionally
relied on the CAMELS model (Pasiouras et al., 2006)
Actually the most effective way to enforce financial rules and
regulations in the financial supervisory system is to conduct
financial examinations (Kao & Liu, 2004). The most popular
approach is based on the CAMELS framework, which involes
the consideration of six major factors (Doumpos &
Zopounidis, 2010). The CAMEL acronym stands for Capital
adequacy, Asset quality, Management, Earning and Liquidity.
Regulators created an additional measure, Sensitivity, to
evaluate market risk associated with changing interest rates
and other factors (H. Hays et al., 2009), especially in the
financial crisis (Kandrac, 2014).
CAMELS rating are calculated in order to show financial
performance of the banks in different respects (Oztorul,
2011). This system is a natural object of analysis, as it is not
only a widespread supervisory tool, but also one of the few
generally accepted quantifiers of the otherwise soft notion of
bank safety (Derviz & Podpiera, 2008). CAMELS ratio model
is very suitable and accurate to be used as a performance
evaluator banking industries and to predict the failure rate
(Salhuteru & Wattimena, 2015).
This system relies on various financial ratios obtained from
periodic reports of the entities under their jurisdiction. The
ratios are also aggregated into performance indices based on
various weighting or scoring schemes. The aggregation of the
ratios can be a complicated process involving subjective
judgment. The changing economic conditions have made such
aggregations even more difficult, increasing the need for a
more reliable way to express a bank's financial condition (Kao
& Liu, 2004).
By concentrating on the top line and bottom line, banks across
the board have improved their profit while reducing their
operational costs and more number of banks has improved
their financial performance by using the concept of mergers
and acquisitions. CAMEL rating is used by most banks across
the world as a performance evaluation technique (Raiyani,
2010)
Elizabeth and Ellot (2004) indicated that all financial
performance measure as interest margin, return on assets, and
capital adequacy are positively correlated with customer
service quality. To assess the performance of the bank is
necessary to report the financial reports usually consists of a
balance sheet, income statement, cash flow statement,
statement of changes in equity and notes to the financial
statement (Salhuteru & Wattimena, 2015). Some data from
financial ratios are often compared to CAMELS in correctly
identifying or predicting crisis events but sometimes, the
relevant factors behind future failures or rating downgrades
are satisfactorily captured by judiciously constructed risk-
sensitive summary statistics of conventional balance sheet
data (Derviz & Podpiera, 2008).

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3. Some important indicators in prior studies of CAMELS model
Table 1: Some important indicators
Author Title of study Year Capital Asset quality Management quality Earnings Liquidity Risk
Dincer, H.,
Gencer, G., Orhan,
N., & Sahinbas, K.
A Performance Evaluation of
the Turkish Banking Sector
after the Global Crisis via
CAMELS Ratios
2011
Equity to (Loan
+ Market +
Principle
Amount Subject
to Operational
Risk) / Equity to
Total Assets/
Equity to
(Deposit + Non-
deposit Sources)
Financial Assets to Assets/ Loans
and Receivables to Assets
/Permanent Assets to Assets
Interest expenses to total
expenses/interest incomes
to total incomes/total
incomes to total expenses
Net Profit to Total
Assets/Net Profit to
Equity
liquid assets to
Assets/liquid assets
to short term
liabilities/liquid
assets to deposit and
non-deposit sources
Total Assets to
Sector Assets/
(Loans and
Receivables) to
(Sector Loans and
Receivables)/
Deposits to Sector
Deposits
Roman, A., &
Şargu, A. C.
Analysing the Financial
Soundness of the
Commercial Banks in
Romania: An Approach
Based on the Camels
Framework
2013 CAR/equity to
total asset
Impaired loans to gross loans /loan
loss provision to net interest
revenue /total loans to asset
Operating expenses to
asset/interest expenses to
Deposits
ROA/ROE/cost to
income ratio
liquid assets to
(deposit and short
term funding)/Net
loans to (deposit and
short term funding)
The ratio of its
assets to the
assets
Chandani, A.,
Mehta, M., &
Chandrasekaran,
K. B.
A Working Paper on the
Impact of Gender of Leader
on the Financial Performance
of the Bank: A Case of ICICI
Bank (india)
2014
CAR/ proportion
of debt to
capital/Debt to
assets/bond
investments to
assets
Noncurrent receivables gross to
debt/ Noncurrent debt to debt/Loans
to assets/Noncurrent net debt to
loans
Debt to deposits/ Returns
per employee
Operating profit to
average capital
turnover rate/ margin
or net profit to assets/
interest income to
income
Securities to
assets/Assets to
deposits
-
Rodica-Oana, I. The evolution of Romania's
financial and banking system 2014 Solvability ratio/
Equity ratio
Risk ratio/Interbank loans and
investments to assets/Loans to
Assets/Net overdue and doubtful
loans to Loans/Net overdue and
doubtful claims to Assets/Net
overdue and doubtful claims to
Attracted and borrowed funds/
NPL/Total amounts due and
overdue/Debtors and overdue
debtors number/ Number of loans
State banks and with state
major ownership / Private
banks and with private
ownership/Banks legal
persons/Branches of
foreign banks
Total provision Loss
category
Effective liquidity to
Required liquidity
Loans granted
and commitments
assumed by bank
in some currency
Kao, C., & Liu, S.
T.
Predicting bank performance
with “financial forecasts:A
case of Taiwan commercial
banks
2004 Deposits/ Interest expenses to Noninterest expenses/Loans /Interest income to non-interest income
Kandrac, J.
Modelling the causes and
manifestation of bank
stress:an example from the
financial crisis
2014 Stress indices bank
Derviz, A., &
Podpiera, J.
Predicting bank CAMELS
and S&P ratings: the case of
the Czech Republic CAR/ assets/ Loans to Assets
Salhuteru, F., &
Wattimena, F. (
Bank Performance with
CAMELS Ratios towards
earnings management
practices In State Banks and
2015 CAR/ Profit before tax to assets/ ROA/ Net profit margin/ Loan to Deposit

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Private Banks
Hays, F. H., De
Lurgio, S. A., &
Gilbert, A. H.
Efficiency Ratios and
Community Bank
Performance
2009 Capital to assets - Salaries and benefits to
average assets ROA Liquidity ratio
The relationship
between interest
rate sensitive
assets and
liabilities
sensitive to
interest rate/ GDP
Soni, R.
Applicability of CAMELS
Rating for Supervisory
Regulation of the Indian
Banking
2012
CAR/ Debt to
capital/ Debt to
assets/
Investment
securities to
assets
Non-current receivables to total
receivables/Noncurrent debt to
assets/Investments to assets/percent
changes in non-current receivables
Total debt to total
deposits/Per capita profit
per employee/ROE/
Earnings per employee
Operating profit to
average working
capital/ margin to total
assets/Net profit to
assets/Interest income
to total income/Non-
interest income to total
income
Liquid assets to total
deposits/Securities to
assets
-
Gunsel, N.
Financial ratios and the
probabilistic prediction of
bank failure in North Cyprus
2007
Total capital to
assets/Loans to
assets
Loan to assets
Operating costs to
assets/Interest expense to
total deposits
Net income to total
assets/Interest income
to assets
Liquid assets to total
deposits/ Deposits to
total loans
Assets to assets of
the banking
system
Iqbal, M. J.
Banking sector's performance
in Bangladesh-An application
of selected CAMELS ratio
2012 CAR NPL -
Rozzani, N., &
Rahman, R. A.
Camels and performance
evaluation of banks in
Malaysia: conventional
versus Islamic
2013 Earning to assets NPL Staff costs to assets ROA/ROE
Net loans to (deposits
and short-term
financing)/Short-term
liquid assets to
deposits and
financing
Risk sharia
Dash, M., & Das,
A.
A CAMELS Analysis of the
Indian Banking Industry 2009 CAR
(Gross noncurrent receivables, net
of non-current receivables, net of
noncurrent receivables) to loans
Investment to assets/ Loans
to deposits/Per capita
revenue /per capita income
Operating profit to
average working
capital/ Net profit to
assets/ROE
Securities to
investment/Securities
to assets
EBTA
Venkatesh, D., &
Suresh, C.
Comparative Performance
Evaluation of Selected
Commercial Banks in
Kingdom of Bahrain Using
CAMELS Method.Chithra
2014
CAR/Equity to
assets/ Net
capital to
facilities/Capital
to short-term
funding/ Capital
to debt
Loan loss reserve to gross loans/
Loan loss provisions to net interest
revenue/ loan loss reserve to
impaired loans /Net charge offs to
average gross loans/ impaired loans
to equity
Noncurrent loans to
equity/Non operational
items to net income/Equity
to asset/Operating profit to
total risk weighted asset
Rate margin/ cost of
assets minus interest
income divided by
average assets/ other
operating income to
assets/ ROA/Equity
ratio of operating
expenses to operating
income/ Noninterest
expenses to assets
Receivables from
other banks divided
by debt to other
banks/ Assets to
loans/ Net loans to
short-term deposits/
Net loans to total
deposits/ Cash to
short-term deposits/
Cash to deposits
The risk of
interest rate/
exchange rate
risk/ risk stocks

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3. Research methodology
3.1. Research type
The type of this study is applied in terms of aim to find the
relation between bank's performance and any cluster of
CAMELS ratios. It is Cross-correlation to define relative
indicators of CAMELS for bank.
3.2. Research Variables
3.2.1. Dependent variable
Performance can be shown with some ratios and tools. Banks
need to find a system to evaluate performance. One of ratio
that can be useful and practical to evaluate and decide is
Tobin’s Q ratio (Vafeas & Theodorou, 1998 and Rostami,
2015). In this study, performance of bank is dependent
variable which is presented with Tobin’s Q ratio. This ratio is
calculated by the sum of market value and book value debts to
book value assets. This ratio is devised by James Tobin of
Yale University (Mehrani et al., 2013 and Sadeghi et al.,
2009)
3.2.2. Independent variable
In this study, six categories of ratios according to CAMELS
categories are applied and summarized to define CAMELS
model in each group of ratios. Those categories as Gunsel, N.,
(2005) & Nimalathasan, B., (2008) & Peterson, (2006) and
Sarker (2005) pointed, are:
 Capital (C) The first variable group is the indicators of
capital and relevant indicators those present capital, the
ratio of capital to assets and show organization strengths.
 Asset Quality (A) Asset quality ratios are one of the
main risks that banks face. As loans have the highest
default risk, an increasing number of non-performing
loans shows a deterioration of asset quality.
 Management Quality (M) As management is a
qualitative issue, such as the ability for risk taking, it is
usually difficult to measure the quality of management.
The management quality of a bank can be measured by
some important ratios those are used in CAMELS model.
 Earning Ability (E) Earning is the most important
performance measurement of banks. The ratios of earning
and relative financial ratios are calculated in this study.
 Liquidity (L) Liquidity risk measures an institution’s
ability to meet unanticipated funds that are claimed by
depositors. Liquidity ratios are expected to be both
positively and negatively related to the likelihood of
failure those are set in model.
 Sensitivity (S) Sensitivity ratios those are related to risk
and covering power of organization that are defined and
calculated to finalize bank's performance model because
risk indicators is very important and highlighted in
CAMELS model.
In figure 1, CAMELS model is shown to clarify six
categories.
Fig 1: CAMELS model
In each category, 5 indicators are chosen and analyzed in
CAMELS model to find best model for forecasting future
trend and destination. All ratios those are used in bank's
performance model based on CAMELS framework as
independent variables are defined as follow in table 2.
Table 2: Research indicators
Ratios Indicators Definition
Capital
TIER1 Total shareholders 'equity/Total risk-weighted assets
TIER2 Total complementary capital/Total risk-weighted assets
CAR Total capital base/ Total complementary capital
TLTE Liabilities/ Equity
TDTE Deposits/Equity
Assets quality
EATA Rate base assets /Total assets
TBPA Bank shares of income/Total assets
DA Deposits/Total assets
TAEA Fix assets/Equity
FIX Fix assets/Total assets
Manageme
nt quality
TPTB Net profit/Number of branches
TATB Total assets/Number of branches
TLTB Total liabilities/Number of branches
TDTB Total deposits/Number of branches

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TFTB Total loans/Number of branches
Earnings
FTI Fees and commissions/Total Income
PF Loan income/Loans
PD Deposit cost/Deposit
PP Loan income/Deposit cost
OI Cost/Income
Liquidity
INTA (Investment/Total assets)
VTC Current liquidity/Deposits
OTA Security/Total assets
VD Current liquidity/(Demand deposits)
LI Liquidity/Assets
Sensitivity and
risk
NPL Doubtful debts/Loans
PROV Provisions of loan/Loans
NPL2 (Bad debts + Overdue)/Loans
OPER Long term deposits/Deposits
DD Demand deposits/Deposit
3.3. Data collection
Data which is used in this study is gathered from annual
financial reports of an Iranian bank.
3.4. Research analysis
The data are analyzed with Eviews and Microsoft Excel
software.
3.5. Results and discussion
3.5.1. Performance figure
The direction of "Bank Performance" is shown by figure 2.
Fig 2: Q-Tobin direction
3.5.2. Estimation of model
According to extracted results, the model of "Bank
Performance" based on Q-Tobin indicator as it is noticed, is
estimated. In the model as dependent variable equal to some
coefficient multiple independent variables plus a fix amount.
This model is finalized at bank based on information in 2005 -
2014. This model can predict the relationship between
CAMELS's categories and Tobin’s Q ratio in bank. This is
clear that Prob (F-statistic) is less than 0.05, so, this model is
accepted and there is a logical relationship between dependent
and independent variables and also, all coefficients are
significant. According to results, R-squared amount is shown
that estimation can explain variables well and the changes of
independent variable were presented with independent
variables completely. If the model considers degree of
freedom, Adjusted R-squared is close to one and is logical.
Amount of these R-squared and Adjusted R-squared show that
the specified model makes the certainty properly for deciding
and other analyses. Durbin-Watson statistic is suitable to
distinguish autocorrelation disturbance components in
regression model.
3.5.3. Normality test
According to normality test result, Jarque–Bera test and
probability more than 0.05, it is concluded that the normality
of distribution of residual sentences and also the skewness and
kurtosis of model are probed final model of "Bank
Performance".
3.5.4. Autocorrelation test
Autocorrelation test (Breusch-Godfrey Serial Correlation LM
Test) is shown the Prob (F-statistic) is more than 0.05 and this
point is reviewed there is not autocorrelation from higher than
one in the finalized model of "Bank Performance".
3.5.5. Heteroskedasticity Test
One of important hypothesis test is Heteroskedasticity Test for
finding homogeneity of variance. If there is not the same
variance in terms of disruption will be accrued the anisotropy
of variance in model. In this model the results of
Heteroskedasticity Test based on Breusch-Pagan-Godfrey are
shown that the Prob (F-statistic) is more than 0.05 and this
point probe that the model does not have problem with
variance.
The estimation of model, Normality test, Autocorrelation test
and Heteroskedasticity test of each category are calculated as
follows:
0.40
0.50
0.60
0.70
0.80
0.90
1.00
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

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a. Capital ratios
a. 1. Estimation of model
R-squared 0.884336 Mean dependent var 0.810000
Adjusted R-squared 0.739755 S.D. dependent var 0.146818
S.E. of regression 0.074898 Akaike info criterion -2.061669
Sum squared resid 0.022439 Schwarz criterion -1.880118
Log likelihood 16.30834 Hannan-Quinn criter. -2.260830
F-statistic 6.116569 Durbin-Watson stat 1.455022
Prob(F-statistic) 0.051909
a. 2. Research model (Substituted Coefficients)
Q = 0.27920 + 1.75178*TIER1 + 1.82387*TIER2 - 1.74595*CAR - 0.0330261725468*TLTE + 0.10054*TDTE
a. 3. Normality test
a. 4. Autocorrelation (Breusch-Godfrey Serial Correlation LM Test)
F-statistic 6.889154 Prob. F(2,2) 0.1268
Obs*R-squared 8.732437 Prob. Chi-Square(2) 0.0127
R-squared 0.873244 Mean dependent var -4.81E-15
Adjusted R-squared 0.429597 S.D. dependent var 0.049932
S.E. of regression 0.037711 Akaike info criterion -3.727157
Sum squared resid 0.002844 Schwarz criterion -3.485089
Log likelihood 26.63579 Hannan-Quinn criter. -3.992706
F-statistic 1.968330 Durbin-Watson stat 2.736558
Prob(F-statistic) 0.377737
a. 5. Heteroskedasticity (Breusch-Pagan-Godfrey)
F-statistic 0.306062 Prob. F(5,4) 0.8869
Obs*R-squared 2.767134 Prob. Chi-Square(5) 0.7358
Scaled explained SS 0.502794 Prob. Chi-Square(5) 0.9920
R-squared 0.276713 Mean dependent var 0.002244
Adjusted R-squared -0.627395 S.D. dependent var 0.003565
S.E. of regression 0.004547 Akaike info criterion -7.664821
Sum squared resid 8.27E-05 Schwarz criterion -7.483270
Log likelihood 44.32411 Hannan-Quinn criter. -7.863982
F-statistic 0.306062 Durbin-Watson stat 1.949254
Prob(F-statistic) 0.886889
b. Asset quality management ratios
b. 1. Estimation of model
R-squared 0.999266 Mean dependent var 0.810000
Adjusted R-squared 0.998349 S.D. dependent var 0.146818
S.E. of regression 0.005965 Akaike info criterion -7.122205
Sum squared resid 0.000142 Schwarz criterion -6.940654
Log likelihood 41.61102 Hannan-Quinn criter. -7.321366
F-statistic 1089.770 Durbin-Watson stat 2.903328
Prob(F-statistic) 0.000002
0
1
2
3
4
-0.10 -0.05 -0.00 0.05
Series: Residuals
Sample 1 10
Observations 10
Mean -4.81e-15
Median 0.010512
Maximum 0.055918
Minimum -0.107875
Std. Dev. 0.049932
Skewness -1.049445
Kurtosis 3.271278
Jarque-Bera 1.866220
Probability 0.393328

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b. 2. Research model (Substituted Coefficients)
Q = 0.27438 - 0.00036*EATA - 0.00337*TBPA + 0.0074*DA + 0.06119*TAEA + 0.01225*FIX
b. 3. Normality test
b. 4. Autocorrelation (Breusch-Godfrey Serial Correlation LM Test)
F-statistic 4.894830 Prob. F(2,2) 0.1696
Obs*R-squared 8.303598 Prob. Chi-Square(2) 0.0157
R-squared 0.830360 Mean dependent var 6.85E-17
Adjusted R-squared 0.236619 S.D. dependent var 0.003976
S.E. of regression 0.003474 Akaike info criterion -8.496280
Sum squared resid 2.41E-05 Schwarz criterion -8.254212
Log likelihood 50.48140 Hannan-Quinn criter. -8.761828
F-statistic 1.398523 Durbin-Watson stat 2.100185
Prob(F-statistic) 0.478286
b. 5. Heteroskedasticity (Breusch-Pagan-Godfrey)
F-statistic 0.757971 Prob. F(5,4) 0.6230
Obs*R-squared 4.865118 Prob. Chi-Square(5) 0.4326
Scaled explained SS 0.359558 Prob. Chi-Square(5) 0.9964
R-squared 0.486512 Mean dependent var 1.42E-05
Adjusted R-squared -0.155349 S.D. dependent var 1.44E-05
S.E. of regression 1.55E-05 Akaike info criterion -19.02806
Sum squared resid 9.61E-10 Schwarz criterion -18.84650
Log likelihood 101.1403 Hannan-Quinn criter. -19.22722
F-statistic 0.757971 Durbin-Watson stat 2.985282
Prob(F-statistic) 0.622970
c. Management quality ratios
c. 1. Estimation of model
R-squared 0.973681 Mean dependent var 0.810000
Adjusted R-squared 0.940781 S.D. dependent var 0.146818
S.E. of regression 0.035728 Akaike info criterion -3.542052
Sum squared resid 0.005106 Schwarz criterion -3.360501
Log likelihood 23.71026 Hannan-Quinn criter. -3.741213
F-statistic 29.59578 Durbin-Watson stat 2.314662
Prob(F-statistic) 0.002951
c. 2. Research model (Substituted Coefficients)
Q = 0.82324 + 2.986634*TPTB - 3.21095*TATB + 2.71155*TLTB + 2.14742*TDTB + 1.08386*TFTB
0
1
2
3
-0.005 0.000 0.005
Series: Residuals
Sample 1 10
Observations 10
Mean 6.85e-17
Median -0.000180
Maximum 0.006509
Minimum -0.005243
Std. Dev. 0.003976
Skewness 0.302808
Kurtosis 1.923817
Jarque-Bera 0.635392
Probability 0.727824

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c. 3. Normality test
c. 4. Autocorrelation (Breusch-Godfrey Serial Correlation LM Test)
F-statistic 1.176343 Prob. F(2,2) 0.4595
Obs*R-squared 5.405137 Prob. Chi-Square(2) 0.0670
R-squared 0.540514 Mean dependent var -3.96E-16
Adjusted R-squared -1.067688 S.D. dependent var 0.023819
S.E. of regression 0.034250 Akaike info criterion -3.919698
Sum squared resid 0.002346 Schwarz criterion -3.677630
Log likelihood 27.59849 Hannan-Quinn criter. -4.185247
F-statistic 0.336098 Durbin-Watson stat 2.352481
Prob(F-statistic) 0.883902
c. 5. Heteroskedasticity (Breusch-Pagan-Godfrey)
F-statistic 0.379299 Prob. F(5,4) 0.8418
Obs*R-squared 3.216307 Prob. Chi-Square(5) 0.6667
Scaled explained SS 0.322088 Prob. Chi-Square(5) 0.9972
R-squared 0.321631 Mean dependent var 0.000511
Adjusted R-squared -0.526331 S.D. dependent var 0.000602
S.E. of regression 0.000744 Akaike info criterion -11.28548
Sum squared resid 2.21E-06 Schwarz criterion -11.10393
Log likelihood 62.42740 Hannan-Quinn criter. -11.48464
F-statistic 0.379299 Durbin-Watson stat 3.113264
Prob(F-statistic) 0.841838
d. Earnings ratios
d. 1. Estimation of model
R-squared 0.986354 Mean dependent var 0.810000
Adjusted R-squared 0.969297 S.D. dependent var 0.146818
S.E. of regression 0.025726 Akaike info criterion -4.198940
Sum squared resid 0.002647 Schwarz criterion -4.017389
Log likelihood 26.99470 Hannan-Quinn criter. -4.398101
F-statistic 57.82674 Durbin-Watson stat 2.794836
Prob(F-statistic) 0.000804
d. 2. Research model (Substituted Coefficients)
Q = 0.27869 - 0.007836*FTI + 0.05364*PF - 0.01829*PD + 0.00300*PP - 0.00210*OI
0
1
2
3
4
-0.02 0.00 0.02 0.04
Series: Residuals
Sample 1 10
Observations 10
Mean -3.96e-16
Median -0. 007024
Maximum 0.042743
Minimum -0.026361
Std. Dev. 0.023819
Skewness 0.732857
Kurtosis 2.251778
Jarque-Bera 1.128397
Probability 0.568816

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d. 3. Normality test
d. 4. Autocorrelation (Breusch-Godfrey Serial Correlation LM Test)
F-statistic 14.73936 Prob. F(2,2) 0.0635
Obs*R-squared 9.364650 Prob. Chi-Square(2) 0.0093
R-squared 0.936465 Mean dependent var 1.30E-16
Adjusted R-squared 0.714092 S.D. dependent var 0.017150
S.E. of regression 0.009170 Akaike info criterion -6.555104
Sum squared resid 0.000168 Schwarz criterion -6.313036
Log likelihood 40.77552 Hannan-Quinn criter. -6.820652
F-statistic 4.211244 Durbin-Watson stat 2.881341
Prob(F-statistic) 0.205269
d. 5. Heteroskedasticity (Breusch-Pagan-Godfrey)
F-statistic 1.296775 Prob. F(5,4) 0.4123
Obs*R-squared 6.184618 Prob. Chi-Square(5) 0.2887
Scaled explained SS 0.556297 Prob. Chi-Square(5) 0.9899
R-squared 0.618462 Mean dependent var 0.000265
Adjusted R-squared 0.141539 S.D. dependent var 0.000296
S.E. of regression 0.000274 Akaike info criterion -13.28209
Sum squared resid 3.01E-07 Schwarz criterion -13.10054
Log likelihood 72.41045 Hannan-Quinn criter. -13.48125
F-statistic 1.296775 Durbin-Watson stat 2.644404
Prob(F-statistic) 0.412277
d. Liquidity ratios
e. 1. Estimation of model
R-squared 0.914309 Mean dependent var 0.810000
Adjusted R-squared 0.807196 S.D. dependent var 0.146818
S.E. of regression 0.064467 Akaike info criterion -2.361618
Sum squared resid 0.016624 Schwarz criterion -2.180067
Log likelihood 17.80809 Hannan-Quinn criter. -2.560779
F-statistic 8.535919 Durbin-Watson stat 2.433555
Prob(F-statistic) 0.029417
e. 2. Research model (Substituted Coefficients)
Q = 1.00681 - 0.01414*INTA - 0.00428*VTC - 0.00418*OTA + 0.00018*VD - 0.00387*LI
0
1
2
3
-0.03 -0.02 -0.01 0.00 0.01 0.02 0.03
Series: Residuals
Sample 1 10
Observations 10
Mean 1.30e-16
Median 0.000121
Maximum 0.025889
Minimum -0.027098
Std. Dev. 0.017150
Skewness 0.012329
Kurtosis 2.124357
Jarque-Bera 0.319733
Probability 0.852258

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e. 3. Normality test
e.4. Autocorrelation (Breusch-Godfrey Serial Correlation LM Test)
F-statistic 1.460105 Prob. F(2,2) 0.4065
Obs*R-squared 5.935133 Prob. Chi-Square(2) 0.0514
R-squared 0.593513 Mean dependent var 2.10E-16
Adjusted R-squared -0.829190 S.D. dependent var 0.042978
S.E. of regression 0.058127 Akaike info criterion -2.861822
Sum squared resid 0.006757 Schwarz criterion -2.619754
Log likelihood 22.30911 Hannan-Quinn criter. -3.127370
F-statistic 0.417173 Durbin-Watson stat 2.305035
Prob(F-statistic) 0.838933
e. 5. Heteroskedasticity (Breusch-Pagan-Godfrey)
F-statistic 0.369927 Prob. F(5,4) 0.8477
Obs*R-squared 3.161965 Prob. Chi-Square(5) 0.6750
Scaled explained SS 0.746348 Prob. Chi-Square(5) 0.9803
R-squared 0.316197 Mean dependent var 0.001662
Adjusted R-squared -0.538558 S.D. dependent var 0.003010
S.E. of regression 0.003734 Akaike info criterion -8.059226
Sum squared resid 5.58E-05 Schwarz criterion -7.877675
Log likelihood 46.29613 Hannan-Quinn criter. -8.258387
F-statistic 0.369927 Durbin-Watson stat 2.987897
Prob(F-statistic) 0.847671
f. Risk ratios
f. 1. Estimation of model
R-squared 0.969350 Mean dependent var 0.810000
Adjusted R-squared 0.931037 S.D. dependent var 0.146818
S.E. of regression 0.038556 Akaike info criterion -3.389720
Sum squared resid 0.005946 Schwarz criterion -3.208169
Log likelihood 22.94860 Hannan-Quinn criter. -3.588881
F-statistic 25.30094 Durbin-Watson stat 3.076973
Prob(F-statistic) 0.003985
f. 2. Research model (Substituted Coefficients)
Q = 1.20859 + 0.00315*NPL - 0.06968*PROV + 0.01488*NPL2 - 0.00145*OPER - 0.01744*DD
0
1
2
3
4
5
-0.05 0.00 0.05 0.10
Series: Residuals
Sample 1 10
Observations 10
Mean 2.10e-16
Median -0.011588
Maximum 0.100038
Minimum -0.049582
Std. Dev. 0.042978
Skewness 1.228166
Kurtosis 3.950491
Jarque-Bera 2.890415
Probability 0.235697

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f. 3. Normality test
f. 4. Autocorrelation (Breusch-Godfrey Serial Correlation LM Test)
F-statistic 4.058780 Prob. F(2,2) 0.1977
Obs*R-squared 8.023239 Prob. Chi-Square(2) 0.0181
R-squared 0.802324 Mean dependent var 1.46E-16
Adjusted R-squared 0.110457 S.D. dependent var 0.025704
S.E. of regression 0.024243 Akaike info criterion -4.610845
Sum squared resid 0.001175 Schwarz criterion -4.368777
Log likelihood 31.05423 Hannan-Quinn criter. -4.876393
F-statistic 1.159651 Durbin-Watson stat 2.364843
Prob(F-statistic) 0.537380
f. 5. Heteroskedasticity (Breusch-Pagan-Godfrey)
F-statistic 0.734401 Prob. F(5,4) 0.6349
Obs*R-squared 4.786238 Prob. Chi-Square(5) 0.4425
Scaled explained SS 0.453179 Prob. Chi-Square(5) 0.9937
R-squared 0.478624 Mean dependent var 0.000595
Adjusted R-squared -0.173096 S.D. dependent var 0.000682
S.E. of regression 0.000739 Akaike info criterion -11.30008
Sum squared resid 2.18E-06 Schwarz criterion -11.11853
Log likelihood 62.50042 Hannan-Quinn criter. -11.49924
F-statistic 0.734401 Durbin-Watson stat 3.229892
Prob(F-statistic) 0.634941
4. Conclusion
"CAMELS" rating is a common phenomenon for all banking
system all over the world. It is mainly used to measure a
ranking position of a bank on the basis of few criteria (Datta,
2012). Bank's performance or rather solvency or insolvency
has been given much attention both at the local and
international level. Financial ratios are often used to measure
the overall financial soundness of a bank and the quality of its
management (Wirnkar & Tanko, 2008).
Traditional method of applying financial ratios to evaluate
bank's state of performance has been long practiced, with
practitioners using CAMELS rating to measure their banks'
performance. CAMELS bank rating is used by bank's
management to evaluate financial health and performance
(Rozanni & A. Rahman, 2013).
In this study the effect of each category of CAMELS model
on performance are analyzed and interpreted. Actually there
are significant relation between each category and Q-Tobin's
ratio as bank's performance ratio. The important factor to
analyze this model is to find and concentrate on effective
indicators and elements in each category. In fact, choosing
indicators can be different in each industry, so, it can be
challengeable and interpreting.
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0
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