The Financial Performance of Banks in Pakistan Finance Essay

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Pakistan is included in the countries that are heavily affected by each other, and any sort of chaos of any scale would surely harm Pakistan’s economy. A country like Pakistan that is already fighting for high food and fuel prices could pay horrible cost of global financial crisis. Pakistan’s economy is dealing with most horrible sort of macroeconomic discrepancies and surely need financing. Pakistan’s financial development has slowed down and the flow of those financial crisis perhaps or might not strike with similar amount or strictness as those financial are doing to the urbanized world, but still there are a range of sources through which the crisis may harm Pakistan’s economy. The areas which are affected through these financial crisis such as United States of America & Europe, grasp a primary worth for Pakistan’s financials. The monetary chaos is expected to influence Japan, Europe and North American countries with occupied strength. Pakistan’s outer division encompasses foreign investment, trade, transmittals and resources are interlinked with above economies. All these pointers of exterior sector have more than 50 per cent of the stake in this region. Fifty percent of foreign business of Pakistan is reliant on above mentioned countries. A country surely hit by bad financial conditions if foreign investment declines significantly, the demand for its exports drops and the conditions of doing business changes. Our country has a sort of inflexible trade in composition in such conditions if exports face any catastrophe, than current account shortfall will surely run farther than the bearable restrictions. 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. In most sensitive businesses banking is also included. In any economy banks are very vital for its development and Pakistan is also included among those countries. Banks doesn’t only play the role of guardian of assets but also operate as chief monetary mediator of any economy. Banking division controls numerous but incorporated monetary activities which may include enlistment of wealth, compilation and allocation of community business. Pakistan’s monetary division consists of listed Banks which comprises public sector, overseas and personal banks. Non-banking monetary Institutions comprise of home finance corporation, modarabas, Development Finance organizations, Investment Banks and leasing companies. Financial division of Pakistan is facing a complete however difficult & agonizing procedure of reforms since 1997. The aim behind is to make banking sector monetarily strong and copy their associations with genuine division intended for endorsement of Investment, savings and growth. An absolute rotation in banking sector presentation isn’t predictable until the conclusion of restructuring. The roughly concurrent scenery of a variety of issues makes it hard to separate symbols of development & worsening.

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Pakistan’s Banking Division

After witnessing a strapping growth till 2008, banking industry started presenting symbols of decelerate, as assets, investment, deposits and profitability of banking sector is on decline whereas market risk, advances, credit risk and interest risk are broaden. As per the evaluation of the State Bank of Pakistan’s periodical Performance evaluation of the Banking System, because of worsening macroeconomic conditions the performance of the banking structure on asset class & profits has somewhat reduced. Banking sector deposit section observed a major turn down of Rs 124 billion throughout the third section of 2008. So, the allocation of deposits in the whole financial support construction reduced to 73.8 percent from 76 percent in last quarter. State bank discovered that productivity of banking structure remained firm throughout the quarter although return pointer shows somewhat decline due to advanced provisioning and working expenditures. In general Pakistani banking division has not been as flat to exterior distresses as it happened with banks in Europe. Liquidity is tense but it has small concern about Global Financial Crisis and further concern with profound government borrowing from banking division. 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

At the end of 2008 statistics from the banking division witness decelerate. In October 2008, deposits reduced from Rs 3.77 trillion to Rs 3.67 trillion. Supplies for sufferers in same phase depart from Rs 173 billion to Rs 178.9 billion. For the time being, State bank has pushed economy – extensive charges of interest. Amplified struggle in the banking division 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 – 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. Banking division’s performance, insolvency or solvency has been specified a great deal of consideration at domestic as well as at global 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.

Objectives of the Study

Eventual purpose of this research is to gauge the financial performance of banks under study. It can be broken down as follows: To examine the monetary performance of banks To carry out the aspects which have directed to existing financial conditions Propose procedures for the banks on the basis of results of the research, which will help banks to improve financial performance

Scope of the Study

This study or analysis of the banks will help in gauging the performance of banks in Pakistan. It includes detailed study of renowned banks operating in Pakistan for the period undertaken.

Limitations of the Study

Results of the research are merely based upon the information given by institutions and other secondary sources. The key issues which can obstruct the current & expected performance of banks are monetary circumstances and government strategies. This research is limited to the study of banks operating in Pakistan.

Basic Assumptions

For this research I have taken the following assumption: The data provided by the banks for research analysis is completely true The banks under consideration had some consequence of financial crisis

Key terms

CAMEL model, Non – banking Financial Institutions, Development Finance Institutions, Financial Crisis, Financial Performance, Banking, Performance Evaluation.

Chapter 2 – Literature Review

Introduction

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 non – interest income, interest income, total profit and total income in order to provide stability to business of banking. Few Studies have revealed that the consequence of privatization on bank’s effectiveness & efficiency revealed that privatized banks have executed better than completely public division banks & they are transmittable with banks in private division (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).

Historical Perspective

To compute the monetary reliability of banks & excellence of management, financial ratios are used. Say for example, controller of banks uses financial ratios to calculate effectiveness of banks (YUE, 1992). Beaver (1996), Altman (1968), Maishanu (2004), Mous (2005) used financial ratios to evaluate the performance appraisal of banks, experiential proof by them are present. Basically Camel framework was proposed to conclude at what time to program on-site assessment of any bank (Thomson, 1991; Whalen and Thomson, 1988). The factors of CAMEL model show increased probability of bank breakdown while whichever of factor demonstrate insufficient, and they are capital adequacy, asset quality, management soundness, earnings & profitability and Liquidity. Agenda behind selection of CAMEL factors is that every factor signifies key component of bank’s financial statements. There are a number of studies which supply clarification of choosing CAMEL model; they are Lane et al. (1986), Looney et al. (1989), Thomson (1991), Elliott et al. (1991) and Eccher et al. (1996). The first being to employ financial ratios to forecast bankruptcy was Beaver (1966), though his study was limited for looking one ratio at a time. Altman (1968) altered the concept of Beaver by means of using numerous categorized studies. The study joined data of various financial ratios in a solitary forecasting model. The z-score model of Altman’s was output of his numerous categorized studies and it was famous for a lot of decades because his model was simple correct. Maishanu (2004) recognized 8 financial ratios which can provide information to the analysts of finance. He also introduced another model which was used to predict breakdown of commercial banks. Cole et al. (1995) conducted a study on “A CAMEL Rating’s Shelf Life” and the findings of them proposed that a bank should be examined in a time period of maximum two quarters. Godlewski (2003) tested soundness of CAMEL model to determine the default of a bank in rising economies. Said and Saucier (2003) examined the liquidity, solvency and efficiency of Japanese Banks with CAMEL rating tactic for the period of 1993 – 1999 of a delegate sample, the focus of their study was capital adequacy, assets quality, management capability, earnings ability and liquidity of Japanese banks. 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. Bhayani (2006) analyzed the performance of new private sector banks through the help of the CAMEL model. The sample was four leading private sector banks. 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. 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. After deciding the model we have chosen twenty nationalized banks. According to the importance of study each parameter is given equal weights. 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.

Current Perspective

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. After deciding the model we have chosen twenty nationalized banks. According to the importance of study each parameter is given equal weights. 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 was 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. Mihir das & Annesha das (2009), analysed Indian banking industry using CAMEL model, they states that Indian banking sector holds a key position in Indian economy, banks act as mediator between all businesses. In this way Indian banking division plays a vital role in state profits. So the analysis & evaluation of banking sector is very important. They analyzed 58 banks and the data used was before global financial crisis. The study concludes that private banks are improved as compare to public banks for the period data was collected. The main reasons of better performance of private banks were the quality of management and profitability of banks. Public sector banks need to have quick response to altering market circumstances to fight with private banks. The reason of difference between their performances is due to the credit policy they have adopted, service provided to customers and shaking hand with changing IT environment in the banking industry. To improve the performance public division banks change their credit lending policy that will also result in improved quality of assets and better profitability. Banks should monitor the profitability and strength of borrowers on a continuous basis because it will decrease the hazard of non – performing assets. Banks should advance the marketing tactics and alter their distribution phenomenon to magnetize more clients and then supply them with best customer service. Banks should also progress in employee motivation & efficiency. 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 financial entity to interest rate risk. The Current year Cumulative Yield/Interest Risk Sensitivity Gap minus previous year Cumulative Yield/Interest Risk Sensitivity Gap gives up the GAP. However, with our ratios form in the CAMEL, a ratio like figure was required. More insight into the GAP working is needed in further study in this area. Rehana K. & Irum S. (2010), “gauging the financial performance of banking sector using CAMEL model: comparison of conventional, mixed and pure Islamic banks in Pakistan” The study is a comparison based on performance of Pure Islamic banks, mixed banks (we use this word for all those banks that have their Islamic as well conventional branches) and conventional banks using CAMEL model. It is an appropriate and simple model to evaluate the financial and managerial assessment of institutions. The ratios defined by CAMEL method are analyzed by using ANOVA to investigate any significant difference. The data analysis is done using SPSS. Based on our analysis, we found that Islamic banks have adequate capital and have good asset quality when compared to Islamic branches of conventional banks and conventional banks. Moreover, Islamic banks in general have good management competency in comparison to conventional banks. The earnings of Islamic branches of conventional banks are greater than full-fledge Islamic banks and conventional banks. Finally, it can be concluded that Islamic banks have a developing setup. As study is related to the performance assessment of banking sector based on the CAMEL model so following section explained the variables of study. Based on CAMEL, there are five categories of variables. These categories are Capital Adequacy, Asset Quality, Management Capability, Earnings, and Liquidity. Statistical findings reveal that there are significant differences in the mean CAMEL ratios of three bank types. The performance measurements of Islamic banking in Pakistan are different in comparison to the results drawn from the similar studies done in other parts of the world. For example it is argued that UAE Islamic banks are relatively more profitable, less liquid, less risky, and more efficient as compared to the UAE conventional banks. Samad & Hassan (2000) revealed in their study that BIMB (Bank Islam Malaysia Berhad) is less profitable, relatively less risky and more solvent as compared to conventional banks of Malaysia. The difference in results is largely due to the fact that Islamic banking has longer history in these countries as compared to Pakistan where full-fledged Islamic banking started merely few years back. Moreover, conventional banking has a longer history, deeper roots, vast experience of learning from the financial markets mechanisms, and larger share in the Pakistan financial sector. Considering these facts of the matter, we don’t find the results of our study surprising. However, the way Islamic banking sector is improving and growing in Pakistan. Islamic Banking Department was established on 15th September, 2003 and has been entrusted with the task of promoting and developing the Shariah Compliant Islamic Banking as a parallel and compatible banking system in the country. Islamic Banking is one of the emerging field in global financial market, having tremendous potential and growing at a very fast pace all around the world. In January 2002, Meezan Bank Limited was granted first Islamic Banking License by State Bank of Pakistan. The progress of Islamic Banking in Pakistan has also been commendable during the last Five years. Currently there are six licensed full-fledged Islamic Banks and twelve conventional banks with independent Islamic Branches.

The Evaluation Model – CAMEL

CAMEL is basically ratio based model for evaluating the performance of banks. It is a management tool that measures capital adequacy, asset quality, efficiency of management, quality of earnings and liquidity of financial institutions.

Variables

There are five categories of variables in which multiple ratios are calculated, these categories are Capital Adequacy, Asset Quality, Management Capability, Earnings and Liquidity of the institutions, all are specifically explained below:

Capital Adequacy

In bank’s perspective it is important to preserve depositors’ assurance and avoiding the bank from getting bankrupt. This factor imitates the whole financial situation of bank and also the capability of management to congregate the need of added funds. Below mentioned ratios determine capital adequacy: Capital Adequacy Ratio (CAR): The capital adequacy ratio is developed to make sure that banks can soak up rational level of losses occurred due to operational losses and determine the capacity of the bank in meeting the losses Debt-Equity Ratio (D/E): The extent of leverage of a bank is specified by this ratio. This ratio point out the level of a bank’s business funded by debt & the level of equity Advance to Assets Ratio (Adv/Ast): This is the ratio which point out bank’s assertiveness in lending whose eventual outcome showed in improved profitability Government Securities to Total Investments (G-sec/Inv): It is an important indicator showing the risk-taking ability of the bank. It is a bank’s strategy to have high profits, high risk or low profits, low risk.

Asset Quality

The excellence of all assets is significant factor to measure the potency of any bank. Key dictum of gauging assets quality is to determine the part of non-performing assets as a percentage of the total assets. Ratios which are calculated to review the asset quality are: Net NPAs to Total Assets (NNPAs/TA): This ratio discloses the efficiency of bank in assessing the credit risk and, to an extent, recovering the debts Net NPAs to Net Advances (NNPAs/NA): It is the mainly usual calculation of asset quality which measures net non-performing assets as percentage to net advances Total Investments to Total Assets (TI/TA): It indicates the extent of deployment of assets in investment as against advances Percentage Change in NPAs: This measure tracks the movement in Net NPAs over previous year. The higher the reduction in the Net NPA level, the better it for the bank

Management Efficiency

CAMEL model’s another vital component is Management efficiency. Ratios in this section include subjective study to compute the competence and efficacy of management. The ratios used to evaluate management efficiency are described as: Total Advances to Total Deposits (TA/TD): A bank’s management’s effectiveness is measured through this ratio. It measures the capability of bank’s supervision in changing the funds deposited with the bank excluding other funds like equity capital, etc. into high earning advances Profit per Employee (PPE): Surplus generated per employee is revealed through this ratio. Total number of employees is placed in denominator and income after tax generated by the bank is placed in numerator, the answer is calculated by dividing the friction. Business per Employee (BPE): Output of a bank produced through human force is calculated through this ratio. To determine the effectiveness of human force which generates business for the bank business per employee ratio is a vital tool to deploy Return on Net worth (RONW): It is the calculation of prosperity of bank. Here, PAT is expressed as a percentage of Average Net Worth.

Earning Quality

The quality of earnings is a very important criterion that determines the ability of a bank to earn consistently. It basically determines the profitability of bank and explains its sustainability and growth in earnings in future. The following ratios explain the quality of income generation. Operating Profit to Average Working Funds (OP/AWF): This ratio indicates how much a bank can earn profit from its operations for every rupee spent in the form of working fund Percentage Growth in Net Profit (PAT Growth): It is the percentage change in net profit over the previous year Net Profit to Average Assets (PAT/AA): This ratio measures return on assets employed or the efficiency in utilization of assets

Liquidity

Risk of liquidity is curse to the image of bank. Bank has to take a proper care to hedge the liquidity risk; at the same time ensuring good percentage of funds are invested in high return generating securities, so that it is in a position to generate profit with provision liquidity to the depositors. The following ratios are used to measure the liquidity: Liquid Assets to Demand Deposits (LA/DD): A bank meets demands from depositors in a specified time period is due to its ability; this ratio computes that capability of the bank. To offer higher liquidity for them, bank has to invest these funds in highly liquid form Liquid Assets to Total Deposits (LA/TD): Liquidity available for the whole deposits of a bank is calculated through this ratio Liquid Assets to Total Assets (LA/TA): It measures the overall liquidity position of the bank. The liquid asset includes cash in hand and wealth at call & short notice. The entire assets include the revaluation of all the assets G-Sec to Total Assets (G-Sec/TA): It measures the risk involved in the assets. This ratio measures the Government securities as proportionate to total assets Approved Securities to Total Assets (AS/TA): This is arrived by dividing the total amount invested in Approved securities by Total Assets

CHapter 3 – Research Methodology

Research Strategy

The research strategy selected is Archival Research because it formulates the utilization of administrative proceedings and credentials as the primary resource of information. Though the word archival has chronological associations, it may refer to current as well as chronological statistics.

Sampling Technique

I have selected non probability sampling (or non-random sampling) which endow with a variety of procedures to pick sample on the basis of subjective judgment. In such type of research plans my objectives, research question and choice of research strategy utter non-probability sampling.

Sampling Method

The method selected is Purposive or judgmental sampling. This form of sample is frequently used while functioning with very small samples and when you aspire to choose cases that are mainly informative. It enables me to judge & select cases that best permit me to respond my research query and to meet my objectives.

Sample size

Matter of sample size is indefinite and unlike probability sampling, there are no regulations. Rather the rational association among sample selection method & the reason and focus of study is vital. Consequently, sample size is reliant on study question and objectives, what will be useful, what will have credibility, what I need to find out and what can be achieved through accessible resources.

Data Type

The data type can be classified into three types that can be implemented in quantitative analysis of financial problems they are time series data, panel data and Cross sectional data. However, for our research we will consider Panel Data. Thus panel data have both the dimensions of time series and cross sectional data and the judgment of panel regression is considered to be developing and interest area in econometrics.

Research Model

This research is based on the econometric analysis using Regression Model to get the results by plugging in the different variables or more specifically financial ratios and or the values which is reflected in the balance sheet of the banks. Before we could look into the various variables used in the model and their description we have to throw a light on the importance and usefulness of econometric analysis and regression model used in this study.

Econometric Analysis

Econometrics is ‘Measurement in economics’. As the origination of the econometrics is derived from the economics and can be seen from the first four letters ‘Econ’ (Brooks, 2005). It can also be said as ‘The combination of the probability, sampling and economic models results in an econometric model that will connect a specified sampling process to data. The adjective of the econometric arises from the realization, identification and incorporation of an economic component into the formation and interpretation of the model. Econometric model signifies our knowledge on sampling of economic values in the form of random variables that can be interpreted, have dependence structure and have joint probability distribution (Mittelhammer & Miller, 2000). To certain degree the econometric model can be represented as accurate measure of true data sampling. The model thus highlights an overview of analyst knowledge of group of economic outcomes and gives the indication about what is assum ed and what is left to be discovered in the process of research (Mittelhammer & Miller, 2000).

Regression Model

Regression analysis or model is the considered to be the most important tool in the hands of econometrician. Regression model enables to describe & evaluate the association among known variable & one or more other variables (Brooks, 2005). Thus, regression clarifies the ups and downs in variable with reference to ups and downs in one or more other variables. Regression considered as more powerful and flexible than correlation. In regression both dependent variable (y) and independent variables (x) are considered different. Variable y is said as stochastic (random) that is to encompass a probability distribution. The x variable is said to be non-stochastic (fixed) value in repetitive samples. Linear Regression model equation for a straight line can be shown as below y= a+bx+u where, y=Dependant variable, a=coefficient, b=coefficient, x=given observations or samples, u= Residual or u ~ N(0,1) However, we also use the below equation which is extension of the above equation.

Y=a+bx1+cx2+dx3+ex4+fx5+gx6+hx7+ix8+jx9+kx10+lx11+mx12+nx13+ox14+px15…..+ut

Where X1, X2, X3A¢â‚¬A¦..Xn states the given observations or samples in panel data The below are the variables used in our panel data to find out the random variables that can be interpreted. Those are Dividend payout ratio, Return on Capital, Return on Assets, Income growth rate, Profit per Employee, Total debt to Capital, Long term debt, GDP Growth rate, Market Share, Capital Adequacy-Basel II, Credit -Deposit Ratio, Investment-Deposit Ratio, Net NPA to Net Advances, Business per Employee and Dummy Variable. These variables are explained below in detail along with their computation methodology.

Dividend Pay-out Ratio

Dividend payout ratio is one of the variables used in our econometric model. It comes under the profitability ratios of the banks or companies. The D/P ratio which calculates the association among income belonging to usual shareholders and dividend rewarded to those shareholders. It can be said as Dividend payout ratio shows percentage share of total income after tax and preference dividend is given as dividend to equity share holder (Khan & Jain, 2005). Below equation shows how the dividend payout ratio is calculated. Dividend pay-out ratio = Total cash dividend to equity holders / Total net profit belonging to equity holders X 100 This ratio is widely used ratio across the world and it can be compared with so many years trend and to highlight the comparison of adequacy of the inter-firm or intra-industry. Thus, in the econometric model this variable is used to highlight the adequacy of the banks operating in Pakistan.

Return on Capital

It is also known as Return on Capital Employed and similar to ROA except that earnings are associated with entire wealth in use. Meaning of entire wealth in use is long-term funds given to owners or lenders of the bank or company. ROC can be calculated in two ways i. ROC can be equal to long term liabilities + Owner’s equity, ii. ROC equals to net working capital + fixed assets. This ratio highlights how efficiently and effectively the long term funds of owners and lenders of the bank or company has been utilized and also reflects higher the ratio it is recommended (Khan & Jain, 2005). It can be calculated in many ways important being shown below ROC = Net profit after taxes + Interest A¢Ë†’ Tax advantage on Interest / Average total capital employed A¢Ë†’ Average intangible assets X 100

Income Growth Rate

Income Growth Rate (IGR) is the maximum rate at which bank or any company can grow in terms of sales or assets without any external financing. In simple words IGR is the percentage of total income of any individual bank out of the group (namely, public, private or foreign bank). There are certain assumptions to evaluate IGR they are i. Assets increase in proportion to the sales, ii. The Earnings after Tax is directly proportionate to sales, iii. The bank can have target retention ratio which it can maintain, iv. Bank does not raise external funds to finance assets (Khan & Jain, 2005). IGR = ROA X Retention Ratio / 1 A¢Ë†’ (ROA X Retention Ratio)

Return on Assets

Return on Assets is one of the important profitability ratios and calculated in provision of association among assets and net profits. It is also known as profit-to-asset ratio. ROA is Earnings after tax available to owners and interest to creditors. Assets are financed by both owners and creditors. Below is the method to compute the ROA. ROA = Net Profit after tax + Interest / Average total assets X 100

Profit per Employee

It is known as the return on intangibles. In the recent past the profit per employee (PPE) has generated incomes to the several banks in the form of intangibles income and Profit per employee is one and return on invested capital (ROIC) is other which modern companies or Banks adopt. The very measure of Profit per employee gives emphasis on return on talent. The approach will lead the managers to increase profits relative to people employed in a company (Bryan, 2007). In short a net profit is divided per head of employed staff or employee is a recent way to assess the profits of the bank or a company.

Total Debt to Capital

The Total Debt to Capital is a leverage ratio and it’s the relationship between creditor’s funds and owner’s capital. Total capitalization of a bank or a company is related to liabilities from outside and not merely from equity of shareholders only. The total debt of bank consists of Long term debts + current liabilities and total assets encompass of stable capital + current liabilities. There are several approaches to find out or compute debt to capital ratio one of the important being Total Debt to Capital Ratio = Total debt / Total Assets

Long Term Debt

The Long term debts are obligations in the form of loans and advances which exceeds more than one year or the maturity of the loans such as bonds, notes and commercial papers. It is in the ability of the bank/firm to pay off the long term debts to show its solvency. If it is unable to pay of its long term debt may lead to insolvency or winding up of the bank or a firm.

GDP Growth Rate

The Gross Domestic Product (GDP) growth rate is the sell price of all the commodities and services produced during a period of one year in a country. There are two types of GDP they are real GDP and nominal GDP. The economic growth rate or real GDP growth rate is percentage change in variable (Mishkin, 2010). This can be computed as GDP Growth rate = A°A‘A¥A°A‘A¡A¢Ë†’A°A‘A¥A°A‘A¡A¢Ë†’1 / A°A‘A¥A°A‘A¡A¢Ë†’1 X 100 Where, t indicates today and t-1 a year earlier.

Market Share

Market Share means the position or the place in which the bank or a company is placed within the similar group of companies taking into consideration any one of the component for comparison out of the other banks in a group or sector or industry. Thus, in our case we have taken into consideration as Advances out of each group total advances disbursed in a year versus the individual banks advance disbursement in a year. Market Share (%) by Group-wise = Individual Bank Advance / Total Advances of Group X 100

Capital Adequacy-Basel II

The Capital to Risk-Weighted Assets Ratio is measure be maintained by all the scheduled commercial banks operating in India as Capital Adequacy norms prescribed by Basel II. As per The Committee on Banking Regulations and Supervisory Practices (Basel Committee) Capital Adequacy Ratio or CRAR: It is ratio of capital fund to risk weighted assets expressed in percentage terms with minimum capital requirements under Tier I, Tier II. This has to be followed very stringently by all the banks without failing as per the Basel committee.

Credit-Deposit Ratio

The Credit-deposit ratio means amount of loan-assets created by banks from deposits collected from customers. The concept of CDR was first implemented by RBI in 1980 to Public sector banks to maintain 60% in semi urban and rural areas in order to encourage the decrease of inter-regional imbalances and convince the banks to lend in semi urban and rural areas where they mobilized the deposits.

Investment-Deposit Ratio

In the similar way to CDR, Investment- Deposit Ratio means the sum of both long term and short term Investments on other banks, advances and loans, share market etc. divided by the sum of the amount collected by bank on various accounts such as Savings Bank Account, Recurring deposit account, current account and fixed account.

Net NPA to Net Advances

Non-Performing Assets are one which challenges the banks stability and profitability by the way of loss of interest income, waving off the principal and so on. Net Non-Performing assets: Can be computed by deducting accounting items like payment received partially, interest due but not recovered. Thus, Net NPA to Net Advances is a ratio to measure on how much advances issued has turned into unrealized or bad debt. Thus, giving signal to banks if it crosses the limits to which the bank cannot sustain.

Business per Employee

Business per Employee means the overall business generated by each employee who is working in any organization or bank. This can be done by dividing overall business generated divided by per head of the employees working in each of the banks forming business per employee.

Dummy Variable

In economic and financial modeling, it’s frequent that one or more residuals will cause to the rejection for the assumption of normality. Thus, leading to observations would appear in the tails of the distribution and would therefore lead u, which enters into the definition of kurtosis since it’s very large. Those data which does not fit with the pattern of data are said to be outliers. In this case to increase the normality curves is to use dummy variables. The dummy variables are simply used like other variables in the regression model. Yt = AŽA²1 + AŽA²2 x2t + AŽA²3 x3t + AŽA²4DUMMYt + ut The dummy variable considers the value one for single observation from whole of the sample panel data by making the residual of that panel observation to zero. It is also known as the sensible way to use dummy variables to the regression model if both the statistical need and theoretical justification for the inclusion (Brooks, 2005).

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The Financial Performance Of Banks In Pakistan Finance Essay. (2017, Jun 26). Retrieved November 26, 2022 , from
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