Pakistan is living in a highly integrated world and a major turmoil of this magnitude and would definitely create certain implications for Pakistan’s economy. Pakistan already reeling from high food and fuel prices could face adverse consequences of the global financial crisis. The country’s economy is already confronted with worst kind of macroeconomic imbalances and obviously need financing desperately. Pakistan’s economic growth has slowed down and the ripple effects of this financial crisis may or may not hit with same intensity or severity as it is doing to the developed world, but still there are various channels through which the crisis may hit Pakistan economy. The crisis affected area, United States and Europe, hold a fundamental value for Pakistan’s economy. The financial turmoil is more than likely to affect Europe, Japan and North American countries with full intensity. Pakistan’s external sector comprised of trade, foreign investment, remittances, and capital flows is interwoven with these countries. All these indicators of external sector have more than 50 per cent of the stake in this region. The growth model being followed in Pakistan over the years is highly dependent on foreign capital inflows, mainly from these countries. More than one-half of Pakistan’s external trade is dependent on these countries. The country could be hurt if demands for its export products dropped significantly, foreign investment declines substantially and if the terms of trade are affected. Pakistan has a very inelastic import structure and if exports are hit by a crisis than the current account deficit is likely to go beyond the sustainable limits. There is an agreement among analysts that countries with heavy external financing needs are potentially more vulnerable to a credit crunch. The major area of the economy of any country is its financial sector, in recent times financial sector has received renewed focus in the world. And within the broad domain of the financial sector, it is the banking industry that has been the center of attraction for the government and policymakers, particularly in the landscape of the Universal Banking Model. Banking is one of the most sensitive businesses all over the world. Banks plays very important role in the economy of the country and Pakistan is no exception. Banks are not only the custodian of the assets of the general masses but also act as a major financial intermediary of the country. The banking sector influences many different but integrated economic activities like mobilization of resources, collection & distribution of public finance. Pakistan’s financial sector consists of Scheduled Commercial Banks which include nationalized, foreign, and private banks; and Non-banking Financial Institutions (NBFIs) which include Development Finance Institutions (DFIs), Investment Banks, leasing companies, modarabas, and housing finance companies. Scheduled Banks and NBFIs (excluding modaraba and leasing companies) are both regulated by the State Bank of Pakistan’s Prudential Regulations, and are subject to different SBP regulatory requirements such as capital and liquidity reserve requirements. The banking sector in Pakistan has been going through a comprehensive but complex and painful process of restructuring since 1997. It is aimed at making these institutions financially sound and forging their links firmly with the real sector for promotion of savings, investment and growth. Although a complete turnaround in banking sector performance is not expected till the completion of reforms, signs of improvement are visible. The almost simultaneous nature of various factors makes it difficult to disentangle signs of improvement and deterioration.
Pakistan’s Banking Sector
After witnessing a strong growth till 2008, the banking industry started showing signs of slowdown, as deposits, assets, investment and profitability of banking sector is on decline while credit risk, market risk, interest risk, NPLs and advances are widening. According to the assessment of the State Bank of Pakistan’s Quarterly Performance Review of the Banking System (July-September 2008), due to deteriorating macroeconomic factors the performance of the banking system on asset quality and earnings has slightly declined. Banking industry deposit component witnessed a significant decline of Rs 124 billion or 3 percent during the third quarter of 2008. Therefore, the share of deposits in overall funding structure declined to 73.8 percent from 76 percent in last quarter, the report said. The SBP revealed that profitability of the banking system remained steady during the quarter though return indicators that slightly declined due to higher provisioning and operating expenses. The banking system posted a before tax profit of Rs 20.7 billion during the third quarter, translating into year to-date profit of Rs 82.1 billion whereas after tax profit stood at Rs 54.9 billion in September 2008. The credit risk has somewhat increased since the previous quarter. As of end-2008, data from the banking sector confirms a slowdown (after a multi-year growth pattern). In the meanwhile, the SBP has jacked up economy-wide rates of interest (the 3-month treasury bill auction has seen a jump from 9.09 percent in January 2008 to 14 percent as of January 2009 and bank lending rates are as high as 20 percent). Overall, Pakistan’s banking sector hasn’t been as prone to external shocks as have been banks in Europe. To be certain, liquidity is tight but that has little to do with the Global Financial Crisis and more to do with heavy government borrowing from the banking sector and thus tight liquidity and the ‘crowding out’ of the private sector. Several banks have established outstanding record of growth, value creation and innovation. While Mckinsey report, Banking Industry (2010) identifies four important opportunities and challenges in local Banking sector highlighting Market is falling in discontinues growth with new products, services, fee based income and Investment Banking. Windfall gains with the decrease in Interest rates would not be enjoyed. The increase in competition and added interest in Foreign Banks will intensify. With changes in the demographic factors, the requirements of service and institutional capabilities will also increase. The Mckinsey report, 2010 also highlights the facts that Foreign Banks will begin the Mergers & Acquisition in recent years buying out old sector and new sector banks as a result new private banks and foreign banks will grow at a greater rate as compare to public banking sector. Pakistan has majority of public sector banking system monitored and or supervised by State Bank of Pakistan, and has performed best in stable way during worst times in the world financial system with less developments in innovation and inclusion.
An Overview of Last Few Years
As of end-2008, data from the banking sector confirms a slowdown (after a multi-year growth pattern). As of October 2008, total deposits fell from Rs3.77 trillion in September to Rs3.67 trillion. Provisions for losses over the same period went up from Rs173 billion in September to Rs178.9 billion in October. In the meanwhile, the SBP has jacked up economy-wide rates of interest (the 3-month treasury bill auction has seen a jump from 9.09 percent in January 2008 to 14 percent as of January 2009 and bank lending rates are as high as 20 percent). Overall, Pakistan’s banking sector hasn’t been as prone to external shocks as have been banks in Europe. To be certain, liquidity is tight but that has little to do with the Global Financial Crisis and more to do with heavy government borrowing from the banking sector and thus tight liquidity and the ‘crowding out’ of the private sector. Increased competition in the banking sector will force smaller banks to either sell out to other larger banks or merge. A small capital base will also restrict branch expansion of smaller banks, forcing them to focus on relatively smaller retail clients. Hence, it is foreseen that a major merger/acquisition potential in the banking sector. Competition would also spill over to other customer services such as provision of ATM machines and better banking facilities. Again, only the larger banks would be able to invest in automation technology and branch expansion necessary to improve efficiencies and mobilize cheaper funds.
List of Banks in Pakistan
BANKS IN PAKISTAN Public Sector Banks First Women Bank Limited The Bank of Khyber National Bank of Pakistan The Bank of Punjab Sindh Bank Islamic Banks AlBaraka Bank (Pakistan) Limited BankIslami Pakistan Limited Burj Bank Limited Meezan Bank Limited Dubai Islamic Bank Pakistan Limited Private Banks Allied Bank Limited Askari Bank Limited Bank Alfalah Limited Bank Al Habib Limited Faysal Bank Limited Habib Bank Limited Habib Metropolitan Bank Limited JS Bank Limited KASB Bank Limited MCB Bank Limited NIB Bank Limited Samba Bank Limited SILKBANK Limited Soneri Bank Limited Summit Bank Limited United Bank Limited Foreign Banks Barclays Bank PLC Citibank N.A. – Pakistan Operations Deutsche Bank AG – Pakistan Operations HSBC Bank Middle East Limited – Pakistan Operations Industrial and Commercial Bank of China Limited – Pakistan Branches Oman International Bank S.A.O.G – Pakistan Operations The Bank of Tokyo-Mitsubishi UFJ Limited – Pakistan Operations Development Financial Institutions House Building Finance Corporation Pak Brunei investment Company Limited Pak – China Investment Company Limited PAIR Investment Company Limited Pakistan Kuwait Investment Company Limited Pak Libya Holding Company Limited Pak Oman Investment Company Limited Saudi Pak Industrial & Agricultural Investment Company Limited Specialized Banks Industrial Development Bank of Pakistan The Punjab Provincial Cooperative Bank Ltd SME Bank Limited Zarai Taraqiati Bank Limited Micro Finance Banks / Institutions KASHF Microfinance Bank Limited Khushhali Bank Limited Apna Microfinance Bank Limited NRSP Microfinance Bank Limited Pak Oman Microfinance Bank Limited Rozgar Microfinance Bank Limited Tameer Micro Finance Bank Limited The First Micro Finance Bank Limited
The Problem Statement
Global financial crisis hit all the financial institutions around the world. Bank’s performance or rather solvency or insolvency has been given much attention both at the local and international level. This aim of this research is to analyse the financial performance of Pakistani banks, the major reasons of their decline/incline nowadays, problems faced by them in recent time and the Initiatives that should be taken to bolster bank operations in Pakistan.
Chapter 2 – Literature Review
The Banking profits have gained significant importance in recent years as banks are the institutions, which contribute for overall economic activities that are happening in any country. Post 1990’s, due to financial liberalization and deregulation of Banks, there has been entry of foreign banks and some large private sector banks with the huge capital and man power has played a key role in Pakistani economy. Even public sector has not lagged behind as they have constantly changed and adapted to the new technological innovations. Banks traditional mode of getting funds at a low cost and the spread between getting funds and providing loans and advances has reduced. Thus, traditional banking activities yielded low profits and banks started looking for new avenues for increasing their bottom-line (Chowdhury & Chowdhury, 2010). According to Chowdhury, Banking conventions usually suggests that with the increase in fee based income, risks can be diversified. Thus, Pakistani banking sector has to focus on fee based income like other developed nations. Thus it becomes important to understand the factors play in total profit, total income, interest income and non- interest income in order to provide stability to business of banking. Few Studies have revealed that the impact of privatization on banks performance and efficiency shown that privatized banks have performed better than fully public sector banks and they are catching up with the banks in the private sector (Sathye, 2005). The major factors affecting the profitability and efficiency of the banks were directed investments, directed credit, growth in assets, growth in advances and increased proportion of other income in total income of the banks (Bhaumik and Dimova, 2004). The Banks liquidity position was severely affected due to increasing mismatches in deposits and credit growth rates, apart from several structural components such as huge gaps in maturity of assets and liabilities due to increasing exposure in infrastructure projects, which are long term in nature. The Banking Stability, when compared to previous period depicted relative movements in risk parameters of the banking system over a period of time, which indicated marginal rise in the risks with reference to liquidity compared to the previous year. However, the Banking Stability Indicator, showed overall improvements in stability compared to the previous reporting period (State Bank of Pakistan, 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 quality of its management. Banks’ regulators, for example, use financial ratios to help evaluate a banks’ performance as part of the CAMEL system (YUE, 1992). Empirical evidence on the use of ratios for banks’ performance appraisal include; Beaver (1996), Altman (1968), Maishanu (2004), Mous (2005). The camel framework was originally intended to determine when to schedule on-site examination of a bank (Thomson, 1991; Whalen and Thomson, 1988). The five CAMEL factors, viz. Capital adequacy, Asset quality, Management soundness, Earnings and profitability, and Liquidity, indicate the increased likelihood of bank failure when any of these five factors prove inadequate. The choice of the five CAMEL factors is based on the idea that each represents a major element in a bank’s financial statements. Several studies provide explanations for choice of CAMEL measures: Lane et al. (1986), Looney et al. (1989), Elliott et al (1991), Eccher et al. (1996), and Thomson (1991). For example, Waldron et al (2006) suggested that one of these threats represented in CAMEL exists in the loss of assets (A); similarly, short-term liquid assets (L) aid in covering loan payment defaults and offset the threat of losses or large withdrawals that might occur. The CAMELS framework extends the CAMEL framework, considering six major aspects of banking: Capital adequacy, Asset quality, Management soundness, Earnings and profitability, Liquidity, and Sensitivity to market risk. Beaver (1966) was the first person to use financial ratios for predicting bankruptcy his study was limited to looking at only one ratio at a time. Altman (1968) changed this by using a multiple discriminate analysis (MDA). His analysis combined the information from several financial ratios in a single prediction model. Altman’s z- score model was the result of this multiple discriminate analysis and has been popular for a number of decades as it was easy to use and highly accurate. But there was critique on the MDA model. Altman treated businesses from different sectors as the same, ignoring the fact that there should be different values for a healthy indication by the financial ratios of the different kinds of businesses. Maishanu (2004) identified eight financial ratios that could serve in informing financial analysts on the financial state of a bank. As such, he put forth a univariate model for predicting failure in commercial banks. In comparing two bankruptcies predicting models using financial ratios, (Mous, 2005) found that the decision tree approach performed better than the multiple discriminant analysis (MDA) with decision tree correctly classifying 89% of bankrupt banks within two years while multiple discriminant analysis (MDA) got 81%. The financial ratios used had variables; profitability, liquidity, leverage, turnover and total assets. Cole et al. (1995) conducted a study on “A CAMEL Rating’s Shelf Life” and their findings suggest that, if a bank has not been examined for more than two quarters, off-site monitoring systems usually provide a more accurate indication of survivability than its CAMEL rating. Godlewski (2003) tested the validity of the CAMEL rating typology for bank’s default modelisation in emerging markets. He focused explicitly on using a logical model applied to a database of defaulted banks in emerging markets. Said and Saucier (2003) examined the liquidity, solvency and efficiency of Japanese Banks using CAMEL rating methodology, for a representative sample of Japanese banks for the period 1993- 1999, they evaluated capital adequacy, assets and management quality, earnings ability and liquidity position. Prasuna (2003) analyzed the performance of Indian banks by adopting the CAMEL Model. The performance of 65 banks was studied for the period 2003-04. The author concluded that the competition was tough and consumers benefited from better services quality, innovative products and better bargains. Cole and Gunther (1998) investigated on the comparison of on-site monitoring and off-site monitoring and selected a sample of 9,880 insured commercial banks analyzed, 2,008 had camel ratings at year-end 1987 based on financial data from 1986 or earlier. If these banks are incorporated in 2008, and the entire sample was analyzed from 9,880 banks, the precision of the monitoring system off-line in the camel ratings were even higher. If the fault is 10 percent of banks can be expected from the worst criticism, the camel rating, only 74 percent of the incidents took place to identify, and the results of the identification of off-site surveillance system, 88 percent used outside of the control system of reference by means of accounting information available to the public. Their results suggest that if the bank no more than two semesters, are considered off-measurement systems are usually more for the survival of their assessment camel. The accuracy of forecasts have lower camel ratings, the older, two bedrooms or more, because the precision of the camel rating, off-measuring systems. More accurate forecasts are at valid, the off-site update of the score for each bank in each district and accuracy of financial data on which they rest. Cole and Gunther (1998) claimed to the conclusion that the systems off-site monitoring role of the monitoring process continues to play as a complement to onsite inspections. Dar and Presley (2000) have discussed and analyzed the third area of CAMEL model i.e. Management and control of internal governance of banks and financial companies. The Islamic banks and financial companies of Muslim world are taken into consideration. They have found that the an absence of correct balance between management and control rights is the major cause of lack of profit and loss sharing in the Islamic finance structures. Bhayani (2006) analyzed the performance of new private sector banks through the help of the CAMEL model. Four leading private sector banks – Industrial Credit & Investment Corporation of India, Housing Development Finance Corporation, Unit Trust of India and Industrial Development Bank of India – had been taken as a sample. Gupta and Kaur (2008) conducted the study with the main objective to assess the performance of Indian Private Sector Banks on the basis of Camel Model and gave rating to top five and bottom five banks. They ranked 20 old and 10 new private sector banks on the basis of CAMEL model. They considered the financial data for the period of five years i.e., from 2003-07. R. Alton Gilbert, Andrew P. Meyer and Mark D. Vaughan (2000), “The federal reserve bank of St. Louis” stated in his research that his work examines the potential contribution to bank supervision of a model designed to predict which banks will have their supervisory ratings downgraded in future periods. Bank supervisors rely on various tools of off-site surveillance to track the condition of banks under their jurisdiction between on-site examinations, including econometric models. One of the models that the Federal Reserve System uses for surveillance was estimated to predict bank failures. Because bank failures have been so rare during the last decade, the coefficients on this model have been “frozen” since 1991. Each quarter the surveillance staff at the Board of Governors provides the supervision staff in the Reserve Banks the probabilities of failure by the banks subject to Fed supervision, based on the coefficients of this bank failure model and the latest call report data for each bank. Inscribed on the National Archives Building in Washington, D.C. is the prophetic phrase “The Past Is Prologue”, words borrowed from William Shakespeare’s Hamlet in the early 17th Century. Later 20th Century philosopher George Santayana expressed a similar concern when he said “Those who cannot remember the past are condemned to repeat it”. Unfortunately some bankers failed to learn the lessons of the Banking and S&L Crisis of the late 1980’s and early 1990’s despite repeated admonitions from banking regulators. In some ways the Financial Crisis of 2008 was distinctly different than the earlier crisis. The earlier crisis had origins in rapidly rising energy prices that led to concentrations of loans to energy related companies for oil exploration and distribution. As discussed earlier, the 2008 crisis was precipitated by the bursting of asset bubbles related to subprime lending. As the crisis deepened and U.S. economy plummeted, the real estate crisis extended into commercial land development and construction lending. These loans are more difficult to securitize and are therefore more likely to remain on commercial bank balance sheets. Excessive concentrations of these types of loans further accentuate losses, deplete capital and threaten the financial viability of commercial banks. (See Bair, 2010) Hays, Fred & Gail, Sidne investigated three periods which are: The End of the Boom (2006.4); Market Collapse (2008.4); and the Road to Recovery? (2010.1). the discriminate model correctly classifies approximately 81-84% of cases in both the original and the validation groups. Following the Banking and S&L Crisis, banks fundamentally changed their business models. Rather than originate loans with the intent to hold them on their balance sheets, banks utilized the securitization process to adopt an “originate and place” strategy. (See Acharya and Richardson for further discussion). By generating new loan prospects and performing the initial credit evaluation, banks were able to focus on origination fees as a primary source of revenue along with servicing fees for passing payments through to third party holders of the loans. Since loan margins are relatively low, banks compensated by increasing their loan volumes to generate additional revenues. During the Banking and S&L Crisis many savings and loan associations failed. S&Ls had historically been key lenders for residential mortgages. After the crisis, banks seized market share in residential mortgages previously claimed by savings and loan associations prior to their demise.. Banks also expanded their commercial real estate business as well. This was prompted in part by a decline in commercial and industrial lending to major corporations that turned to the developing commercial paper market to meet their short term credit requirements. Wirnkar and Tanko (2008) identified and ranked the best ratios in each of the CAMELS quantitative components apart from the “S” component (Sensitivity to market risk) which cannot be easily quantified. They brought forth a new acronym for CAMEL known as CLEAM in order to reflect the magnitude and ability of each component to capture the performance of a bank in descending order. The usage of the CAMEL(S) framework in banking studies in emerging economies is limited. Wirnkar and Tanko (2008) studied banking performance of major Nigerian banks using the CAMEL framework. Very recently, Sangmi and Nazir (2010) have studied banking performance of two Indian banks using the CAMEL framework. Also, Agarwal and Sinha (2010) have studied the performance of microfinance institutions in India using the CAMEL framework. A case study of commercial banks efficiency in Tanzania by Aikaeli (2008) was made to investigate their efficiency using non parametric data envelopment analysis for the period 1998-2004. The result showed that commercial banks in Tanzania is not disappointing to financial sector reforms as the data envelopment analysis DEA efficiency scores was high, 96%. The usage of the CAMEL(S) framework in banking studies in emerging economies is limited. Banking sector’s literature show that there are many researches on evaluation of financial performance of banks except camels there are many tools PEARL, DEA etc. Najjar (2008) analyzed of the bank of Palestine and Jordanahli bank. The main objectives of this study were to investigate into the performance of Jordanahli bank and Palestine, and used the CAMEL analysis to ensure equitable distribution to shareholders depends on fundamental analysis. Wirnkar & Tanko (2008) considered banking performance of major Nigerian banks using the camel framework. Negu & Mesfin (n.d) has measured financial performance and efficiency of commercial banks in sub Saharan African with DEA model. Ali (2009) has worked on a project on camels framework, investigated the strengths of using camels framework as a tool of performance evaluation for banking institutions of Kathmandu. Dash and Das (2010) has analyzed the banking sector of India using camels model the analysis was performed for a sample of fifty-eight banks operating in India, of which twenty-nine were public sector banks, and twenty-nine were private sector/foreign banks. The study covered the financial years 2003-04, 2004-05, 2005-06, 2006-07, and 2007-08 (i.e. Prior to the global financial crisis). The data for the study consisted of financial variables and financial ratios based on the CAMELS framework, obtained from the capitaline database. The results show that private banks / foreign banks are better than in the public sector, the factors that most studies to reduce the camels. These two factors in order is to improve the performance of private banks / foreign-run and accurate and profitability. The results of the study suggest that public sector banks have to adapt quickly to changing market conditions, in order to compete with private/foreign banks. This is particularly due to the wide difference in their credit policy, customer service, ease of access and adoption of it services in their banking system. Public sector banks must improve their credit lending policies so as to improve asset quality and profitability. K.V.N. Prasad (2012), in his research “A Camel Model Analysis of Nationalized Banks in India” stated that banking sector is one of the fastest growing sectors in India. Today’s banking sector becoming more complex. Evaluating Indian banking sector is not an easy task. There are so many factors, which need to be taken care while differentiating good banks from bad ones. To evaluate the performance of banking sector we have chosen the CAMEL model which measures the performance of banks from each of the important parameter like Capital Adequacy, Assets Quality, Management Efficiency, Earning Quality and Liquidity. After deciding the model we have chosen twenty nationalized banks. According to the importance of study each parameter is given equal weights. Results shown that on an average Andhra bank was at the top most position followed by bank of Baroda and Punjab & Sindh Bank. It is also observed that Central Bank of India was at the bottom most position. In the process of continuous evaluation of the bank’s financial performance both in public sector and private sector, the academicians, scholars and administrators have made several studies on the CAMEL model but in different perspectives and in different periods. Derviz et al. (2008) investigated the determinants of the movements in the long term Standard & Poor’s and CAMEL bank ratings in the Czech Republic during the period when the three biggest bank s, representing approximately 60% of the Czech banking sector’s total assets, were privatized (i.e., the time span 1998-2001). Mohi-ud-Din Sangmi (2010), ” Analyzing Financial Performance of Commercial Banks in India: Application of CAMEL Model” stated in his research that Sound financial health of a bank is the guarantee not only to its depositors but is equally significant for the shareholders, employees and whole economy as well. As a sequel to this maxim, efforts have been made from time to time, to measure the financial position of each bank and manage it efficiently and effectively. In this paper, an effort has been made to evaluate the financial performance of the two major banks operating in northern India. This evaluation has been done by using CAMEL Parameters, the latest model of financial analysis. Through this model, it is highlighted that the position of the banks under study is sound and satisfactory so far as their capital adequacy, asset quality, Management capability and liquidity is concerned. Fred Hays & Sidne Gail Ward (2010), in their research “Fantasyland revisited? Bank construction and development lending and the financial crisis” stated that multivariate discriminate techniques to analyze the financial performance of commercial banks with total assets less than or equal to $10 billion. These banks are divided into two groups. In each group are approximately 1,500 banks. The first group contains banks with the highest concentrations of commercial construction and land development loans. These loans are among the riskiest assets currently held by commercial banks and are major contributors to the financial difficulties of almost 800 “problem” banks. The other group contains banks with the lowest concentrations of the same type loans. This group represents very conservative lenders. The model utilizes the CAMELS rating framework popularized by banking regulators and researchers. Included in the model are proxy variables for capital adequacy, asset quality, management, earnings, liquidity and sensitivity to market risk.
In 2010 K.V.N. Prasad and G. Ravinder did a research named, “A CAMEL model analysis of nationalized banks in india” which was published in International Journal of Trade and Commerce-Iiartc. In this research they mentioned that banking sector is one of the fastest growing sectors in India. Today’s banking sector becoming more complex. Evaluating Indian banking sector is not an easy task. There are so many factors, which need to be taken care while differentiating good banks from bad ones. To evaluate the performance of banking sector we have chosen the CAMEL model which measures the performance of banks from each of the important parameter like Capital Adequacy, Assets Quality, Management Efficiency, Earning Quality and Liquidity. After deciding the model we have chosen twenty nationalized banks. According to the importance of study each parameter is given equal weights. Results shown that on an average Andhra bank was at the top most position followed by bank of Baroda and Punjab & Sindh Bank. It is also observed that Central Bank of India was at the bottom most position. K.V.N. Prasad and G. Ravinder (2010) also stated that CAMEL is basically ratio based model for evaluating the performance of banks. It is a management tool that measures capital adequacy, assets quality, and efficiency of management, earnings’ quality and liquidity of financial institutions. The period for evaluating performance through CAMEL in this study ranges from 2005-06 to 2009-10, i.e., for 5 years. The absolute data for twenty nationalized banks on capital adequacy, asset quality, management efficiency, earning quality and liquidity ratios is collected from various sources such as annual reports of the banks, Prowess, Ace Analyzer, Analyst journal and average of each ratio calculated for the period 2006- 10. All the banks were first individually ranked based on the sub-parameters of each parameter. The sum of these ranks was then taken to arrive at the group average of individual banks for each parameter. Finally the composite rankings for the banks were arrived at after computing the average of these group averages. Banks were ranked in the ascending/descending order based on the individual sub-parameter. The conclusion of the research done by K.V.N. Prasad and G. Ravinder (2010) states that economic development of any country is mainly influenced by the growth of the banking industry in that country. The current study has been conducted to examine the economic sustainability of a sample of thirty nine banks in India using CAMEL model during the period 2006-10. The study revealed that: Canara Bank stood at top position in terms of capital adequacy, In front of asset quality, Andhra Bank& Bank of Baroda was at top most position, In context of management efficiency, Punjab & Sindh bank positioned at first, In terms of earnings quality Indian Bank sustained the top position, Bank of Baroda rated top in case of liquidity position, Overall performance table shows that, Andhra Bank is ranked first followed by Bank of Baroda, Punjab & Sindh Bank, Indian bank , Corporation Bank, In bottom five, Central Bank of India was on the last position, following the other banks i.e. Bank of Maharashtra, UCO Bank, United Bank of India, and Vijaya Bank. Mohi-ud-din sangmi (2010), “analyzing financial performance of commercial banks in india: application of CAMEL model” stated that sound financial health of a bank is the guarantee not only to its depositors but is equally significant for the shareholders, employees and whole economy as well. As a sequel to this maxim, efforts have been made from time to time, to measure the financial position of each bank and manage it efficiently and effectively. In this paper, an effort has been made to evaluate the financial performance of the two major banks operating in northern India .This evaluation has been done by using CAMEL Parameters, the latest model of financial analysis. Through this model, it is highlighted that the position of the banks under study is sound and satisfactory so far as their capital adequacy, asset quality, Management capability and liquidity is concerned. Methodology describes the research route to be followed, the instruments to be used, universe and sample of the study for the data to be collected, the tools of analysis used and pattern of deducing conclusions. For the purpose of the present study, the research instrument used is the CAMEL Model which is the recent innovation in the area of financial performance evaluation of banks. The model is explained as under: Parameters defined by Mohi-ud-din sangmi (2010) states that, this system was adopted in India since 1995 at the suggestion of Mr. Padmanabhan, Governor RBI. Under this system the rating of individual banks is done along five key parameters- Capital adequacy, Asset quality, Management capability, Earnings capacity, and Liquidity (yielding the rating systems acronym – CAMEL). Each of the five dimensions of performance is rated on a scale of 1 to 5, varying from fundamentally strong bank to fundamentally weak bank. This model has been applied in the following select banks. Sample of the study by Mohi-ud-din sangmi (2010), The present study seeks to evaluate the financial performance of the two top banks based in northern India, representing the biggest nationalized bank (i.e Punjab National Bank, PNB) and the biggest private sector bank (i.e Jammu and Kashmir Bank, JKB). These two banks were purposely selected for the study, keeping in view their role and involvement in shaping the economic conditions of northern India, specifically in terms of advances, deposits, manpower employment, branch network etc. Data and tools as defined by Mohi-ud-din sangmi (2010) states that the study is mainly based on secondary data drawn from the annual reports of the respective banks. This data is related to 5 years (2001-2005). For analysis of the data, two important statistical tools viz. mean and standard deviation has been used to arrive at conclusions in a scientific way. The analysis and the discussion in the proceeding pages reveals that both the banks are financially viable as both have adopted prudent policies of financial management. Both the banks have managed their capital adequacy ratio well above the minimum standard of 10% fixed by RBI. The average leverage ratio in case of PNB is more (1.746) compare to JKB (0.828). So far as Asset quality is concerned both the banks have shown significant performance. The PNB has been able to maintain the ratio of Net NPAs to Net advances at 3.42%. The JKB bank has been more efficient by maintaining the average ratio of Net NPAs to Net advances at 1.760%. Similarly, the average loan loss cover maintained by JKB (9.52%) is more than that of PNB (8.288%). The business (Advances +Deposits) of the PNB and the JKB have registered a compound growth rate of 14% & 16% respectively. However, the compound growth rate of operating profit has been 24% in PNB and 5% in JKB. The PNB has succeeded in diversifying its business from fund based to fee based activities and registered an average income of 14.95% while as JKB has generated 12.25% from this activity. The JKB, in view of the squeezing of spread scenario needs to add more fee based products and services in its portfolio. However, the productivity ratios like earnings per employee and expenditure per employee are more in case of JKB compare to the PNB. The PNB has generated an average Net Interest margin of 0.034 compare to 0.028 generated by JKB. However, return on assets is more (1.498%) in case of JKB compare to PNB (0.936%). The spread management shows that PNB has received more interest on advances viz-a-viz interest paid on deposits, the average spread ratio being 0.350. With average spread ratio of 0.320, the JKB has not been as successful as PNB in the management of its spread (interest received-interest paid). The liquidity in a bank is what blood is in a human body. The bank should be in a position to meet its liability holders as an when demand arises. Thus the appropriate mixture of liquid and non liquid asset is maintained. For this an appropriate strategy of liability and assets management is designed. “Fantasyland revisited? Bank construction and development lending and the financial crisis” by Fred Hays & Sidne Gail (2009); states that the current study utilizes multivariate discriminate techniques to analyze the financial performance of commercial banks with total assets less than or equal to $10 billion. These banks are divided into two groups. In each group are approximately 1,500 banks. The first group contains banks with the highest concentrations of commercial construction and land development loans. These loans are among the riskiest assets currently held by commercial banks and are major contributors to the financial difficulties of almost 800 “problem” banks. The other group contains banks with the lowest concentrations of the same type loans. This group represents very conservative lenders. The model utilizes the CAMELS rating framework popularized by banking regulators and researchers. Included in the model are proxy variables for capital adequacy, asset quality, management, earnings, liquidity and sensitivity to market risk. Three periods are investigated: The End of the Boom (2006.4); Market Collapse (2008.4); and the Road to Recovery? (2010.1) – the latest available data. The discriminate model correctly classifies approximately 81-84% of cases in both the original and the validation groups. This study examines the financial performance of banks with high versus low concentrations of commercial construction and land development loans at year-end 2006 and 2008 and in the first quarter of 2010, the latest data currently available. Data were obtained through subscription to SNL Unlimited-Financial Services from SNL Corporation in Charlottesville, VA for almost 7,000 U.S. commercial banks with less than or equal to $10 billion in total assets that were established on or before January 1, 2000. The latter requirement eliminates the special complications of de novo or newly charted institutions. Both operating and defunct institutions were included to avoid “survivorship bias”. (Brown, Goetzmann, Ibbotson, & Ross, 1992) Subsequent failed institutions are reflected as missing values. These banks were sorted from high to low values based on the ratio of commercial construction and development loans to total loans for year-end 2009. Fifty six banks were removed from the study. These banks were largely trust operations or special purpose institutions for which commercial lending data were not applicable. From the remaining institutions, 1,500 banks with the highest and 1,500 banks with the lowest ratios of commercial construction and development loans to total loans were retained for further study. This “polar extremes” approach is discussed in greater detail in (Hair, Black, Babin, & Anderson, 2010). The banks outside the two groups of 1,500 were not analyzed further. It is possible that a future study could examine this “intermediate” group using either a three or four group multiple discriminate analysis Data were subsequently imported into Excel 2007 using an SNL add-in and then exported to IBM SPSS Statistics 18.0 for further analysis. Any missing variables in the study were replaced with mean values. This study finds evidence that statistically significant differences exist between banks with heavy concentrations in commercial real estate loans that focus on construction and land development and those that avoided excessive concentration in that loan category. This is consistent with the experience of banking regulators as they deal with increasingly large numbers of problem banks and, in many instances, bank failures. The conclusion of this study is quite simple and yet quite alarming. Some banks paid attention to regulatory warnings and are doing quite well. Those that did not may not survive. They may succumb to their own decisions. For them the recent signing into law of financial reform in the form of the Dodd Frank Wall Street Reform and Consumer Protection Act is perhaps too little, too late. They may not be around when the enabling regulations are written. On numerous occasions since the Banking and S&L Crisis in the late 1980’s and early 1990’s, bankers have been warned by regulators that they should avoid excessive concentrations in a single asset category such as real estate and in particular subcategories such as commercial real estate lending that does not easily lend itself to risk transfer techniques through asset securitization. There were autopsy studies done and published, regulatory guidance provided and warnings issued. Still the lessons of the past went unlearned. Instead, many bankers used the opportunity of rising real estate prices combined with low interest rates to take on disproportionately large concentrations of commercial real estate loans, especially to land developers and construction companies. Most bankers did not anticipate the financial tsunami presented by the Financial Crisis of 2008. Few individuals, including those in Congress and banking regulatory agencies as well as bankers and their boards of directors could imagine a crisis of such depth and breadth. Unfortunately, it is these unimaginable events that pose enormous systemic danger to financial institutions. The S&L Crisis two decades ago provided a glimpse of what might occur and provided advance warning of the consequences for commercial banks. Autopsy studies were conducted to investigate what went wrong and why. Directives were written providing bankers with guidance to avoid future crises. Some bankers preferred to continue living in Fantasyland where prices only rise, deals are abundant, profits are plentiful, shareholders and directors are content and where all loans are paid back in full and on time. And, for a time, it appeared that such a world might actually existA¢â‚¬A¦at least until 2006. Storm clouds gathered on the horizon and the forecast appeared to worsen. It wasn’t until 2008 that reality set in. By that time, it was too late. Major institutions one after another succumbed to financial pressures as panic set in and financial markets froze. It was too late for bankers to extricate themselves from the trap they had created. As real estate prices plunged, commercial deals no longer made economic sense. As economic growth turned negative, unemployment grew and with it came growing loan delinquencies. Bank examiners upon investigating loan records demanded that banks recognize their commercial real estate losses. The same loans that looked so solid a year or two before now looked hopelessly uncollectible. With new loan volume declining, there was little hope of new infusions of capital as banks burned quickly through their existing capital as losses were absorbed. It’s a sad tale, but true. If only some bankers had pursued reality rather than fantasy. “Camels(s) and banks performance evaluation: the way forward” by Wirnkar A.D. & Tanko M. (2008), Despite the continuous use of financial ratios analysis on banks performance evaluation by banks’ regulators, opposition to it skill thrive with opponents coming up with new tools capable of flagging the over-all performance ( efficiency) of a bank. This research paper was carried out; to find the adequacy of CAMEL in capturing the overall performance of a bank; to find the relative weights of importance in all the factors in CAMEL; and lastly to inform on the best ratios to always adopt by banks regulators in evaluating banks’ efficiency. The data for the research work is secondary and was collected from the annual reports of eleven commercial banks in Nigeria over a period of nine years (1997 – 2005). The purposive sampling technique was used. The presentation of data was in tables and analyzed via the Efficiency Measurement System (EMS) 1.30 software of Holger School and independent T-test equation. The findings revealed the inability of each factor in CAMEL to capture the holistic performance of a bank. Also revealed, was the relative weight of importance of the factors in CAMEL which resulted to a call for a change in the acronym of CAMEL to CLEAM. In addition, the best ratios in each of the factors in CAMEL were identified. For example, the best ratio for Capital Adequacy was found to be the ratio of total shareholders’ fund to total risk weighted assets. The paper concluded that no one factor in CAMEL suffices to depict the overall performance of a bank. Among other recommendations, banks’ regulators are called upon to revert to the best identified ratios in CAMEL when evaluating banks performance. Wirnkar Alphonsius & Dzeawuni (2010), “Camel based derived w-score function for banks performance evaluation: an urgent necessity” While the consequences of the credit crunch appear all too apparent, the intricacies of the complex financial instruments involved, combined with the vast sweep of the global financial system, seem to defy explanation. Attempts to accuse negligent regulators, fraudulent brokers and greedy borrowers cast much blame but little light on the causes of the crises. This paper has as its primary objective not to advise on the measures to be taken to combat the present economic state but to derive an integral CAMEL based function that can be used by banks regulators and managements to check, monitor, identify and correct emerging problems at short notice on a daily, weekly, monthly or annul basis before they become out-of bounds or unbearable. The data for this research work is secondary. The study builds on the coefficients of the identified best ratios for CAMEL now CLEAM as in Wirnkar and Tanko (2008). Statistical techniques are used to further manipulate the data towards arriving at the objective of the paper. The paper derived a function known as the W-Score. That is W-Score =0.20886579C- 0.197883635L+0.197834192L-0.197730975A-0.197685405M. The respective W-scores for best performing bank, average performing bank and least performing bank were ascertained. Margins of safety for each component in CAMEL were also computed. As one of the recommendations of this paper, bank’ regulators and those in academia are implored to test the efficacy of the CAMEL (CLEAM) derived function and certify its application in the banking industry. The data was already in ratio form. The data is the collection of the coefficients of the identified best ratios for CAMEL now CLEAM as in Wirnkar and Tanko (2008). These coefficients are converted to normal distribution or continuous variables of exhaustive approximation. As these continuous variables are negative, in order to change them to positive variables with the same relative weights, complement variables are calculated using a technique similar to the Bayes’ theorem. See Crawshaw and Chambers (1990:115). Followed, is the computation of weight proportions among these complements. Lastly, these weight proportions are accordingly bridged into a CLEAM derived function with due attention to the performance of a bank with dynamics to each of the identified best ratios in CAMEL. This derived function will be known as the W-Score. The paper concludes with a CAMEL based derived function known as the W-Score function. The W-Score function is derived from the best ratios in each of the CAMEL components. This function can capture the wholistic performance of a bank. The W-Score function (W-S (fn) = 0.20886579C- 0.197883635L+0.197834192L-0.197730975A-0.197685405M. We conclude that a W-Score of 5.53 and above shows that a bank is among the best performing banks in all respect. On the other hand, an average performing bank will have a W-Score of 1.218 while a very weak bank or a bank experiencing distress symptoms will have a W-Score of -.014. We conclude that any bank with decreasing W-Score signifies alarming problems. The margins of safety for each component in CAMEL were also computed. For instance, the margin of safety for C is from 26.5386 – 0.9788; for L is from 0.3865 – 0.7410; for E is from 0.3727 – 0.1162; for A is 0.0272 – 0.3850 and lastly for M is from 0.0042 – 0.0965. These margins of safety per component are from best performing to least performing bank respectively. Any bank with a component value far outside the range calls for thorough examination for any window dressing or accounting gimmickry in its accounts. Recommendations that were made states that bank’ regulators and those in academia to test the efficacy of the CAMEL (CLEAM) derived function (W-Score (fn) and certify its application in the banking industry, if this derived function is found effective, a software compiler be designed and manufactured so that the performance state of a bank can be easily and quickly ascertained at a point in time, more research work needs to be carried-out especially at this time of global financial meltdown to find better ways of identifying emerging problems in the banking industry for corrective measures. Mihir dash & Annesha das (2009), “A camels analysis of the Indian banking industry” The banking sector occupies a very important place in the country’s economy, acting as an intermediary to all industries, ranging from agriculture, construction, textile, manufacturing, and so on. The banking sector thus contributes directly to national income and its overall growth. As the banking sector has a major impact on the economy as a whole, evaluation, analysis, and monitoring of its performance is very important. Many methods are employed to analyze banking performance. One of the popular methods is the CAMELS framework, developed in the early 1970’s by federal regulators in the USA. The CAMELS rating system is based upon an evaluation of six critical elements of a financial institution’s operations: Capital adequacy, Asset quality, Management soundness, Earnings and profitability, Liquidity, and Sensitivity to market risk. Under this bank is required to enhance capital adequacy, strengthen asset quality, improve management, increase earnings, maintain liquidity, and reduce sensitivity to various financial risks. The analysis was performed for a sample of fifty-eight banks operating in India, of which twenty-nine were public sector banks, and twenty-nine were private sector/foreign banks. The study covered the financial years 2003-04, 2004-05, 2005-06, 2006-07, and 2007-08 (i.e. prior to the global financial crisis). The data for the study consisted of financial variables and financial ratios based on the CAMELS framework, obtained from the Capitalize database. The variables used in the analysis were: Tier-I Capital, Tier-II Capital, and Capital Adequacy Ratio (for Capital Adequacy); Gross Non-performing Assets, Net Non-performing Assets, and Net Non-performing Assets to Total Advances Ratio (for Asset Quality); Total Investments to Total Assets Ratio, Total Advances to Total Deposits Ratio, Sales per Employee, and Profit After Tax per Employee (for Management Soundness); Return on Net Worth, Operating Profit to Average Working Fund Ratio, Profit After Tax to Total Assets Ratio (for Earnings and profitability); Government Securities to Total Investments Ratio and Government Securities to Total Assets Ratio (for Liquidity); and Beta (for Sensitivity to Market Risk). In order to calculate the CAMELS ratings for the banks, the ratios corresponding to each CAMELS factor were considered: viz. Capital Adequacy Ratio, Net Non-performing Assets to Total Advances Ratio, Total Investments to Total Assets Ratio, Total Advances to Total Deposits Ratio, Sales per Employee, Profit After Tax per Employee, Return on Net Worth, Operating Profit to Average Working Fund Ratio, Government Securities to Total Investments Ratio, and Beta (two ratios, viz. Profit After Tax to Total Assets Ratio and Government Securities to Total Investments Ratio were removed). The variables were normalized using the formula: z = x – l / u – l, where u represents the upper bound, and l the lower bound; the ratings were assigned as follows: 1 = 0.0 – 0.2, 2 = 0.2 – 0.4, 3 = 0.4 – 0.6, 4 = 0.6 – 0.8, and 5 = 0.8 – 1.0 (except for non-performing assets and beta, for which the ratings were reversed). The CAMELS rating was obtained as the total of the individual variable ratings. The results of the study show that private/foreign banks fared better than public sector banks on most of the CAMELS factors in the study period. The two contributing factors for the better performance of private/foreign banks were Management Soundness and Earnings and Profitability. The results of the study suggest that public sector banks have to adapt quickly to changing market conditions, in order to compete with private/foreign banks. This is particularly due to the wide difference in their credit policy, customer service, ease of access and adoption of IT services in their banking system. Public sector banks must improve their credit lending policies so as to improve asset quality and profitability. They need to continuously monitor the health and profitability of bank borrowers, so that the risk of non-performing assets decreases. They also must improve their marketing and distribution strategies in order to attract customers and provide better customer service. They also must take steps to improve employee motivation and productivity. There are some limitations inherent in the present study. The sample size used for the study is limited. Further, the study period was limited due to the limited availability of data. Another limitation was in the nature of the overall CAMELS rating used: the rating gives undue importance to the factors of management soundness and earnings. Further, the CAMELS framework is not a comprehensive framework; for example, it does not take into consideration other forms of risk (such as credit risk). Further studies can incorporate other risk factors into the framework to provide a more comprehensive measure of banking performance. B. Nimalathasan (2009), “A comparative study of financial performance of banking sector in Bangladesh – an application of CAMELS rating system” The Banking sector in Bangladesh is different from the banking as seen in other developed countries. This is one of the Major Service sectors in Bangladesh economy, which divided into four categories of scheduled Banks. These are Nationalized Commercial Banks (NCBs), Government Owned Development Financial Institutions (DFIs), Private Commercial Banks (PCBs), and Foreign Commercial Banks (FCBs). Performance of financial Institution is generally measured by applying quantitative techniques of financial measurement. It is a post – mortem examination techniques of achievement of a bank. Many Studies are conducted in different countries to judge the performance of their banking system, using different statistical methods such as Data Envelopment Analysis (DEA) and Stochastic Frontier Approach (SFA). The present Study is initiated a Comparative Study of Financial Performance of Banking Sector in Bangladesh using CAMELS rating system with 6562 Branches of 48 Banks in Bangladesh from Financial year 1999-2006. CAMELS rating system basically quantitative technique, is widely used for measuring performance of banks in Bangladesh. Accordingly CAMELS rating system shows that 3 banks was 01 or Strong, 31 banks were rated 02 or satisfactory, rating of 07 banks was 03 or Fair, 5 banks were rated 4 or Marginal and 2 banks got 05 or unsatisfactorily rating. 1 NCB had unsatisfactorily rating and other 3 NCBs had marginal rating. Secondary data were used for the present study. The annual data for all banks during the financial years of 1999-2006 are used for rating the performance of the banks. In addition another source of data was through references to the library and the review of different articles, papers, and relevant previous studies. The sample for this studies all branches of the banks in Bangladesh. The Banking sector in Bangladesh is different from the banking sector as seen in developed countries. This is one of the major service sectors in Bangladesh economy and can be divided mainly into four categories Nationalized Commercial Banks (NCBs), Government Owned development finance Institutions (DFIs), Private Commercial Banks (PCBs), and Foreign Commercials Banks (FCBs). At present there are 48 Scheduled banks operating in Bangladesh of these 4 are nationalized, 5 are development finance institutions, 30 are local private commercial and 9 are foreign commercial banks. All branches of the banks are taken for the present study. In the preceding analysis, it has been that the performance measurement of a bank under traditional measures as CAMELS rating techniques. CAMELS rating system basically quantitative technique, is widely used for measuring performance of banks in Bangladesh. Accordingly CAMELS rating system shows that 3 banks was 01 or Strong, 31 banks were rated 02 or satisfactory, rating of 7 banks was 03 or fair, 5 banks were rated 04 or Marginal and 2 banks got 05 or unsatisfactorily rating. 1 NCB had unsatisfactorily rating and other 3 NCBs had marginal rating. Zohra Jabeen (2010), “Study of the efficiency measures in the banking sector in Pakistan (2006-2010) quantitative analysis with qualitative inferences” Achievement of Efficiency is considered to be an important factor for all entities, yet it is a tricky one, primarily because it is measured in relative and comparative terms. For the financial sector, it has tremendous importance, having material benefits and losses too. Therefore it becomes an important benchmark of achievement. This study is first part of a series of studies to be continued in the efficiency measurement in the financial sector in Pakistan. The current study measures efficiency of fourteen select banks in the financial sector of Pakistan and addresses the interpretation of efficiency. It uses the parametric OLS technique, using the definition of efficiency and the set of variables chosen from the CAMEL rating system of the regulators of financial institutions. It further applies the non parametric Data Envelopment Analysis Approach to the sample and assesses their relative efficiency in terms of inputs and outputs of the intermediation approach. It discusses the results in the context of the background of the variables of assessment and their relationship to efficiency of banks. The study aims at finding a better view of performance in the financial sector for more reliable results. The paper is part of an ongoing study regarding efficiency assessment in the banking sector in Pakistan. At its initial step, it provides important clues to the efficiency assessment measures for financial institutions in Pakistan. It followed the established research methodology for the OLS technique and the DEA analysis. We can deduce from the results of the OLS method that the CAMEL ratios do attempt to gauge the efficiency ratios of the sample under considerations. Within the five independent factors, the CAR and Ern have significant predictability. The results show that the CAR and Ern ratios are the most significant contributors to the ER, and these conform to previous studies. However, if we simply find out the efficiency ratio for the sample banks, the ones which have a good efficiency ratio do not necessarily have the same standing in CAR and Ern ratios. The results of the DEA technique are not giving any conclusive answers, as to which bank is less efficient, except Bank Islami which appears to be overall inefficient. It is showing nine out of the sample of fourteen banks to be efficient. Therefore there is need for further work on the application of the DEA Model, before being conclusive on the results. The main point to ponder seems to be whether the large majority of the sample banks are actually efficient. Whether, these high values of efficiency have anything to do with the high interest rate spread in the market, or not? It is suggested that the study be expanded to include more variables that may be considered to be important in measurement of efficiency. However, (it is seen from the preliminary review of literature that) the DEA approach in the financial sector, has lack of consensus or consistency in choosing variables as inputs and outputs (Kabir, (2006), Elisa and Luca (2007) and others). Similarly, with different choices, the results are very different and it is feared that these quantitative tools can be misunderstood or misleading in results. The study was limited to a five year data (2006-2010). This can be expanded to include more years beyond 2005 backwards and perhaps more banks and Development Financial Institutions to get a better view of the financial sector in Pakistan. In this study, the GAP analysis was not conducted. It was limited to CAMEL and not CAMELS. This is the S part of the CAMELS assessment. GAP ratio is commonly termed as a sensitivity ratio showing the exposure of the financia