CAMEL framework is an indicator used for evaluating soundness of Financial Institutions. It was developed by regulatory authorities of the U.S banks. Monetary authorities in most of the countries are using CAMEL parameters to check up health of financial institutions. There are a number of studies which were carried out on the use of CAMEL model to assess performance of banks.
Several studies provide explanations for choice of CAMEL measures: Lane et al. (1986), Looney et al. (1989), Elliot et al. (1991), Eccher et al. (1996), Gilbert et al. (2000), Lacewell (2001), Barr et al. (2002), Godlewski (2003) and Derviz et al. (2004). For examples;
Baral (2005) study the performance of joint ventures banks in Nepal by applying the CAMEL Model. His study was mainly based on secondary data drawn from the annual reports published by joint venture banks. His report analyzed the financial health of joint ventures banks in the CAMEL parameters. His findings of the study revealed that the financial health of joint ventures is more effective than that of commercial banks. Moreover, the components of CAMEL showed that the financial health of joint venture banks was not difficult to manage the possible impact to their balance sheet on a large scale basis without any constraints inflicted to the financial health
Bodla & Verma (2006) examined the performance of SBI and ICICI through CAMEL model. Data set for the period of 2000-01 to 2004-05 were used for the purpose of the study. With the reference to the Capital Adequacy, it concluded that SBI has an advantage over ICICI. Regarding to assets quality, earning quality and management quality, it can be said that ICICI has an edge upon SBI. Therefore the liquidity position of both banks was sound and did not differ much
Gupta and Kaur (2008) conducted a research on the sole aim of examining the performance of Indian private Sector banks by using CAMEL model and by assigning rating to the top five and bottom five banks. They rated 20 old and 10 new private sector banks based on CAMEL framework. The study covered financial data for the period of 5 years i.e. from 2003-07. The research as determined by CAMEL Model revealed that HDFC was at its higher position of all private sectors banks in India succeeded by the Karur Vyasa and the Tamilnad Mercantile Bank. However the Gobal Trust Bank and the Nedungradi Banks was considered as bad management The findings summarized that new private sector of banks have attained the higher position due to core banking, aggressive marketing strategies and high level of technology. To attain perfection banks should always concentrate on new financial assets, excellent service and customer loyalty.
Wirnkar and Tanko (2008) analyzed the adequacy of CAMEL in evaluating the performance of bank. This empirical research was implemented to find out the ampleness of CAMEL in examining the overall performance of bank, to find out the importance of each component in CAMEL and finally to look out for best ratios that bank regulators can adopt in assessing the efficiency of banks. The analysis was performed from a sample of eleven commercial banks operating in Nigeria. The study covered data from annual reports over a period of nine years (1997-2005). The analysis disclosed the inability of each component in CAMEL to congregate the full performance of a bank. Moreover the best ratios in each CAMEL parameter were determined.
Cinko & Avci (2008) noticed that globally all the banking supervisory authorities are using CAMEL rating system for many years. In this synthesis financial ratios were applied to calculate components of CAMEL ratings for the period of 1996-2000. The financial ratios were also employed to anticipate the delegation of commercial banks in 2001 to the SDIF by adopting discriminant analysis, logistic regression and neural network models. However the conclusion revealed that it was impossible to predict the transfer of a bank to SDIF by mode of CAMEL ratios.
Hays, Lurgio & Arthur (2009) have utilized CAMEL model to examine the performance of low efficiency vs. high efficiency community banks in conjunction with the logistical regression analysis. The analysis used data which are based on quarterly reports by commercial banks. The discriminant model derived from the CAMEL parameters is tested among data for 2006, 2007, 2008. Its results concluded that the model accuracy floats from approximately 88% to 96% for both original and cross-validations data sets.
Dash & Das (2009) have analyzed the Indian Banking Industry under CAMELS framework. The thesis compares the performance of public sector banks with that of private/ foreign banks. The analysis was performed from a sample of 58 banks operating in India of which 29 were public sector banks and 29 were private/foreign sector. The data used were from the audited financial statement for the financial years 2003-2008. The findings concluded that private/foreign banks have an edge over the public sector banks. The two factors of the CAMEL parameters that contribute to the best performance of the private banking/foreign were the Management Soundness and Earnings and profitability.
Agarwal & Sihna (2010) have analyzed the financial performance and thereby the sustainability of micro finance institutions (MFIs) in India by employing the CAMEL model.
Kaur (2010) have made an analysis of commercial banks operating in India with reference to CAMEL approach. In his article he has categorized the banks into Public sector Bank, Private sector Banks and Foreign Banks. He used the CAMEL analysis technique with the purpose of ranking the banks. Each component of CAMEL has been interpreted using two ratios and a final composite index has been established. The data tools which were used was a sample of 28 public sector, 26 private sector and 28 Foreign banks and the data used was in secondary nature which was collected from statistical tables related to the Banks in India in the financial year 200-01 to 2006-07. The experiment revealed that the best bank from the public sector has been awarded to Andhra Bank and State Bank of Patiala. In the category of private sector banks, Jammu and Kashmir Bank has been assigned the first rank succeeded by HDFC Bank. Among the foreign sector banks, Antwerp has bagged the first rank followed by JP Morgan Chase Bank.
Sangmi & Nazir (2010) has evaluated the financial performance of 2 top major banks in the northern India representing the biggest nationalized bank (i.e. Punjab national Bank, PNB) and the biggest private sector bank (i.e. Jamuna and Kashmir Bank, JKB) by applying CAMEL model. These 2 banks were selected in view their role and involvement in shaping the economic conditions of the northern India, specifically in terms of advances, deposits, man power employment, branch network etc. The research was mainly conducted on secondary data from annual reports of the respective banks. And the data used is related to five financial years (i.e. 2001-2005). The results highlighted that the position of the banks under study is sound and satisfactory as far as their capital adequacy, asset quality management capability and liquidity is implicated.
CAMEL FRAMEWORK
CAMEL is an acronym that comes from the key areas of financial institution's safety and soundness examination. It is a collection of the first letter of the five components evaluated. It stands for:
"C" for Capital Adequacy
The quality of banking capital and their availability to offset unexpected losses.
"A" for Asset Quality
Level, trend and comparison of non-accrual and renegotiated loans.
"M" for Management factors
Technical competence, leadership, skill, talent, experience of middle and senior management and their compliance with banking laws and regulations.
"E" for Earnings
Returns on assets, income and expenses as compared to other firms averages.
"L" for Liquidity
Adequacy of liquid sources that is availability of assets readily convertible into cash without undue loss, to cater for present and future needs.
Capital Adequacy
Capital base of financial institutions help depositors in establishing their risk perception about the institutions. It is also a key factor for financial managers to adhere adequate levels of capitalization. It reflects the leverage the banks enjoy as it must benefit from lucrative investment opportunities that may arise in future as well to resist unexpected adversity. Capital adequacy ultimately determines how well financial institutions can cope with shocks to their balance sheets.
Capital to Risk Asset Ratio (CRAR)
Capital Adequacy Ratio is a measure of banks capital. It is expressed as a percentage of bank's risk weighted credit exposures. It is also known as "Total Qualifying Capital to Risk Weighted Assets" Ratio (CRAR). Capital adequacy is measured by the ratio of capital to risk-weighted assets (CRAR). A sound capital base strengthens confidence of depositors. .This ratio is used to protect depositors and promote the stability and efficiency of financial systems around the world.
Total Qualifying capital (TCQ) includes Tier-I capital and Tier-II capital. Tier 1 capital provides the most permanent and readily available support to a bank against unforeseen losses. It consists of paid up capital, share premium statutory reserve, surplus arising from sale of fixed assets, general reserves, other disclosed free reserves created by appropriations from post-tax retained earnings and undistributed balance in profit and loss account attributable to previous years. Accumulated losses current year's interim losses, goodwill and other intangible assets will be deducted from Tier 1 capital. However Tier 2 capital also known as supplementary capital is composed of undisclosed reserves, fixed assets revaluation reserves, general provisions/general loan loss reserves, subordinated debt. The Tier 2 capital is mainly comprised of revaluation reserve and general loan loss reserve. Bank of Mauritius requires the banks to maintain a minimum capital risk weighted asset ratio (CRAR) of 10% against 8% decreed in the Basel documents. The higher the CRAR the stronger is considered a bank, as it ensures high safety against bankruptcy.
Risk weighted assets is a measure of the amount of a bank's assets, adjusted for risk. Different assets on a bank's balance sheet carry different risks. This ratio Compares the amount of capital a bank has with the amount of its assets gives a measure of how able the bank is to absorb losses.
Formula
Capital to Risk Asset Ratio = Total Qualifying Capital (TCQ) / Risk Weighted Asset
Total Qualifying Capital /Off Balance Sheet Items
Off Balance Sheet Items are obligations that are contingent liabilities of a bank, and thus do not appear on its balance sheet. In general, off-balance sheet items include the following: credit guarantees acceptances and endorsements, inwards bills for collection, outwards bills for collection and outward cheque receivable. The ratio calculates the percentage of off balance sheet items over Total Qualifying capital.
This ratio shows how much TQC are available for assuring the bank against credit risk. That is, will the TQC be sufficient to support the bank from unforeseen losses arising from off balance sheet item.
Formula
Total Qualifying Capital / Off balance Sheet Items
Non-Performing Loans / Core Capital
A non-performing loan is a loan that is in default or close to being in default. Many loans become non-performing after being in default for 3 months, but this can depend on the contract terms.
"A loan is nonperforming when payments of interest and principal are past due by 90 days or more, or at least 90 days of interest payments have been capitalized, refinanced or delayed by agreement, or payments are less than 90 days overdue, but there are other good reasons to doubt that payments will be made in full" (IMF)
This ratio revealed the amount of core capital in contrast to NPLs available to compensate then bank if any NPLs losses occur.
Formula:
Non-performing Loans / Core Capital
Asset Quality
Asset quality determines the robustness of financial institutions against loss of value in the assets. A comprehensive evaluation of asset quality is one of the most important components in assessing the current condition and future viability of the bank (Kaur 2010). The deteriorating value of assets, being prime source of banking problems, directly pour into other areas, as losses are eventually written-off against capital, which ultimately menaced the earning capacity of the institution. Poor asset quality also reflects upon management competence.
Non-performing Loan / Gross Loan (Average
Bank nonperforming loans to total gross loans are the value of nonperforming loans divided by the total value of the loan portfolio (including nonperforming loans before the deduction of specific loan-loss provisions). The loan amount recorded as nonperforming should be the gross value of the loan as recorded on the balance sheet, not just the amount that is overdue. Averages is better to used for gross loans because a loan made on the first day of the year generates 365 days of interest income, while a loan made on the last day of the year only generates 1 day of interest income
Formula
Gross Loans (Average) = Opening Loan + Closing Loan
2
Non-performing Loans / Gross Loans (Average)
Earning Assets / Total Assets
Earning assets are assets that generate income to the banks. Examples of earning assets for banks are mainly investments and other securities, loans and advances, government and other securities, etc.
Total Net Assets is equal to total assets minus total liabilities.
Earning Assets / Total net Assets
Fixed Assets / Core capital
Fixed assets are long-term, tangible assets held for business use and not expected to be converted into cash in the current or upcoming year. They include equipments, building and property.
The ratio indicates how much banks have invested on fixed assets based upon available core capital.
Fixed Assets / Core Capital
Management
This involves a subjective analysis for measuring the efficiency of the management
(Kaur 2010). Therefore the management factor is based on on-site inspection and other corporate governance issues.
Earnings
Earnings and profitability, the main source of increase in capital base is discussed as regards interest rate policies and the adequacy of provisions. Moreover, it also contributes in supporting current and future institutional operations.
Return on Average Assets
Return on average assets (ROAA) is a measure of profits relative to size that is most commonly used in analyzing banks and finance companies. It is calculated by taking the profit before interest and tax (PBIT) over average assets. This parameter identifies how assets are used to generate profits for banks.
Formula
Average Assets= Opening Asset + Closing Asset
2
