Banking industry of Pakistan
Data Collection and Methodology:
The industry focused in the study is banking industry of Pakistan. The total population of the industry operating in the country is 39 of which thirty three are pakistani banks and the remaining six are foreign banks. The actual target was to cover the complete population in the industry but relying on the availability of the financial data, the initial sample consisted of 30 banks. As all of the banks were not rated by PACRA as per the requirement of study thus in order to keep the number of observations viable, the analysis was needed to be done over the number of years. Therefore, the analysis was moved towards collecting the sample of the banks which were being regularly rated by PACRA. This resulted to extend the analysis over the four year period, which was available for the 11 banks of the industry.
The finanical data for all of these banks was collected for last four years i.e. from 2005 till the most recent one 2008 through the annual reports of these banks. The credit risk rating was taken from the Pakistan Credit Risk Rating Agency (PACRA) website. In Pakistan, the financial year of banks ends on December 31st of the respected year and normally the ratings for the banks are announced after six months of the year end. Therefore, the financials of a particular year of a bank is compared with the next following rating announced by PACRA for that bank.
The technique employed in the study is Mulltiple Discriminant Analysis (MDA) due to its popularity and use in understanding the power of financial ratios in discriminating the distinguishing groups which is rating in the case of present study. (Altman, 1968), (Deakin, 1972), (Ohlson, 1980) used dicriminant analysis to predict bankruptcy in their respective studies. (Pinches & Mingo, 1973) employed discriminant analysis in predicting industrial bond ratings whereas (Sinkey, 1975) made the effective use of MDA is distinguishing between the problem and non-problem banks.
Variables used in the study:
The data for variables is extracted from the publicly available information of banks in Pakistan which includes financial statemnts of the banks which is available in printed format on annual basis in the market as well as the website of State Bank of Pakistan. The Credit Risk Rating of the banks assigned by Pakistan Credit Rating Agency (PACRA) was obtained from the PACRA web site which is taken as dependent variable in the study.
The independent variables are the financial ratios calculated using the information from financial statements of the bank. The following information has been derived from the financial statement in order to be used in calculating financial ratios:
Liquid assets include the cash, balances with other banks, lendings to other fiancial institutions and governent securities. The basic idea of liquid assets is to reflect the fastest available cash or cashable assets of the banks. Cash itself represents the amount available with bank in local currency in hands. Balances with other banks is the amount of money of the bank in other bank account(s). The head of lendings to other financial institutions shows the amount a bank has lended to other banks or financial institutions for various purposes. Government securities are part of investments which a bank makes into various securities or certificates or its own subsidiaries or stocks etc in order to keep on earning for short or long-term on the money available with it in excess of needed in a normal situation.
Gross Advances include the loans, cash credits, running finances, investment in finance lease, bills discounted and purchased and other credit facilities bank provides to its customers against some interest. Excluding the provisions (allowed) by state bank would lead to the number of Net Advances.
Total Assets of the bank are calculated by adding up cash, balances with other banks, lendings to other financial institutions, investments, advances, operating fixed assets, deffered tax assets and other assets in the balance sheet.
Deposits are basically the liability part of a balance sheet which shows the sum of amount a bank has in its holding from various customers either consumers or corporates or could also be another financial institution up on which the depositors are paid various interest amount (if any applicable) for keeping their money with the bank.
Total Equityof the bank is represented by adding up the share capital (i.e. issued, subscribes and paid up capital), reserves, unappropriated profits (if any) and amount (surplus or deficit) on revaluation of securities (if any).
The loans or advances given by the banks against which no interest and principle is being recovered are considered under non- performing status and knowns as Non-performing loans or NPLs.
Independent Variables (financial ratios) used:
(Beaver, 1966), (Altman, 1968), (Libby, 1975) used the liquidity ratios in order to predict the bankruptcy and found significance impact of liquidity ratios to the subject. Adams, Burton And Hardwick (2003) found that higher level of liquidity leads to higher A.M. Best and S&P ratings. (Yeh, 1996) found that the banks with higher Data Envelopment Analysis (DEA) scores have lowers liqidity ratios than those with lower DEA scores. (Kumar & Arora, 1995) were able to correctly classify the major percentage of sample into failed or safe banks through incorporating liquidity as one of the variables in the study. (Sinkey, 1975), (Meyer & Pifer, 1970) used liquidty ratio in order to distinguish between problem and non problem banks and a significant difference was noticed between the liquidity of both the groups. This can be noted that from the prediction powers to the impact powers liquidity ratios has been playing an important role in the literature, therefore, liquidity ratio will be incorporated in the present study playing an important independent variables in finding out its relationship with credit risk rating in Pakistani banks. Hence the hypothesis cosidered here would be estimating that the credit risk rating is directly proportional to liquidity of the banks.
Liquid Assets / Deposits
Liquid Assets/ Total Assets (Kumar & Arora, 1995); (Sinkey, 1975); (Meyer & Pifer, 1970)
Net Advances/ Total Deposits (Kumar & Arora, 1995)
Liquid Assets/ Total Liabilities (Kumar & Arora, 1995)
Capital adequacy ratio basically reflects the banks equity or capital against its risk containing assets and determines a bank’s ability to meet its liabilities and risks. (Yeh, 1996) found that firms with higher DEA score had a higher capital adequacy ratio showing a better utilization of its assets. (Sensarma & Jayadev, 2009) found Capital Adequacy Ratio (CAR) as the only significant ratio in determining the stock returns. (Kumar & Arora, 1995) found capital adequacy playing an important role in predicting failed and safe banks while developing a risk rating scheme for banks. (Sinkey, 1975) noticed an inadequate and deteriorating capital position of the average problem bank as compare to the average non problem bank. Thus, capital adequacy would also be incorporated as an important independent variable in this study hypothesising that banks with weak capital positions are associated with lower credit risk rating.
Total Equity/ Total Assets (Kumar & Arora, 1995)
Loans/[Capital + Reserves] (Sinkey, 1975)
NPLs/ Capital (Sarkar & Sriram, 2001)
(Beaver, 1966), (Altman, 1968), (Ohlson, 1980), Adams, Burton And Hardwick (2003), (Pinches & Mingo, 1973), (Libby, 1975) in their analysis concluded that profitability/ income ratios play a very important role in prediction of bankcruptcy. Gray, Mirkovic & Ragunathan (2006) found profitability ratios to have a significant impact on credit risk rating of firms. (Yeh, 1996) also found a positive impact of profitability on theDEA scores calculated while distinguishing efficient and inefficient banks where higher DEA scored banks had a higher rate of profitability ratios. An important role of profitabilitywas also noticed when a study conducted by (Kumar & Arora, 1995) successfully predicted failed and safe banks. The simple growth trend in net income would be identified here expecting as a hypothesis that positivegrowth in income is directly proportional to credit risk rating and positive growth in admin expenses is inversel y proportional to credit risk rating.
Net Income Growth
Admin Expenses Growth
(Kumar & Arora, 1995) found a significant role of asset quality while developing the risk schemefor banks and analyzing the predictive ability of ratios in classifying failed and safe banks. (Thomson, 1991) observed asset quality gaining importance as predictor of failure with the increase of time. Asset quality will aslo be an important part of independent variables in this study with ahypothesis that it is directly proportional to credit risk rating. As the ratio calculated under this head represents the non performing advances as a percentage of total assets, so lower the value observed, better the asset quality would be and credit risk rating would be expected tobe higher with the decreasing value of the ratio.
NPLs/ Total Assets (Kumar & Arora, 1995); (Sarkar & Sriram, 2001)
The total number of reporting scheduled banks as per State Bank of Pakistan (SBP) on June 30, 2008 are 45 out of which 6 are the Indian banks vested in the custodian of enemy property since September 1965 and are non-operative.