A CAMELS ANALYSIS OF THE INDIAN BANKING INDUSTRY

A CAMELS ANALYSIS OF THE INDIAN BANKING INDUSTRY MIHIR DASH 1 ANNYESHA DAS INTRODUCTION 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 analyse 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. LITERATURE REVIEW The analysis of banking performance has received a great deal of attention in the banking literature. A popular framework used by regulators is the CAMELS framework, which uses some financial ratios to help evaluate a bank s performance (Yue, 1992). Several studies involve the use of ratios for banks performance appraisal, including Beaver (1966), Altman (1968), Maishanu (2004), and Mous (2005). Beaver (1966) initiated the use of financial ratios for predicting bankruptcy, considering only one ratio at a time. Altman (1968) went further, using a multiple discriminant analysis (MDA) for the same purpose, combining several financial ratios in a single prediction model called the Altman s z-score model. However, Altman s model ignored the industry-specificity of healthy indications by the financial ratios. Maishanu (2004) studied financial health of banks, and suggested eight financial ratios to diagnose the financial state of a bank. Mous (2005) studied bankruptcy prediction models of banks using financial ratios of profitability, liquidity, leverage, turnover and total assets in decision tree models and multiple discriminant models, and found that the decision tree approach performed better. The CAMEL framework was originally intended to determine when to schedule onsite 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 1 The first author is a senior faculty at Alliance Business School, No. 2 & 3, 2 nd Cross, 36 th Main, BTM Layout, I Stage, Bangalore-560068, and can be contacted by phone on +91-9945182465, or by email at mihirda@rediffmail.com. The other author is a research scholar at the same institution. 1 Electronic copy available at: http://ssrn.com/abstract=1666900

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. 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. The present study analyses and compares the performance of public and private/foreign banks in India using the CAMELS framework. DATA AND METHODOLOGY 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 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 Nonperforming 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 Nonperforming 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:, 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. 2 Electronic copy available at: http://ssrn.com/abstract=1666900

ANALYSIS AND INTERPRETATION CAPITAL ADEQUACY: Table 1 shows the Tier-I Capital, Tier-II Capital, and Capital Adequacy Ratio of public and private/foreign banks. It was found that private/foreign banks had higher Tier-I Capital than public sector banks, while public sector banks had higher Tier-II Capital than private/foreign banks. It was also found that private/foreign banks had higher Capital Adequacy Ratio than public sector banks. In particular, these differences were statistically significant in 2008. ASSET QUALITY: Table 2 shows the Gross Non-performing Assets, Net Nonperforming Assets, and Net Non-performing Assets to Total Advances Ratio of public and private/foreign banks. It was found that public sector banks had higher Gross Non-performing Assets and Net Non-performing Assets than private/foreign banks, and that these differences were statistically significant. On the other hand, there was no significant difference in the Net Non-performing Assets to Total Advances Ratio of public and private/foreign banks. MANAGEMENT SOUNDNESS: Table 3 shows the Total Investments to Total Assets Ratio, Total Advances to Total Deposits Ratio, Sales per Employee, and Profit After Tax per Employee of public and private/foreign banks. It was found that private/foreign banks had higher Total Investments to Total Assets Ratio than public sector banks, while public sector banks had higher Total Advances to Total Deposits Ratio than private/foreign banks; however, these differences were not statistically significant. It was found that private/foreign banks had higher Sales per Employee than public sector banks, and that these differences were statistically significant. It was also found that private/foreign banks had higher Profit After Tax per Employee than public sector banks, but that these differences were not statistically significant. EARNINGS AND PROFITABILITY: Table 4 shows the Return on Net Worth, Operating Profit to Average Working Fund Ratio, Profit After Tax to Total Assets Ratio of public and private/foreign banks. It was found that public sector banks had higher Return on Net Worth than private/foreign banks, and that these differences were statistically significant. On the other hand, it was found that private/foreign banks had higher Operating Profit to Average Working Fund Ratio and Profit After Tax to Total Assets Ratio than public sector banks, though the differences were not statistically significant. LIQUIDITY: Table 5 shows the Government Securities to Total Investments Ratio and Government Securities to Total Assets Ratio of public and private/foreign banks. It was found that public sector banks had higher Government Securities to Total Investments Ratio and Government Securities to Total Assets Ratio than private/foreign banks (except in 2008), but the differences were not statistically significant. SENSITIVITY TO MARKET RISK: Table 6 shows the Beta of public and private/foreign banks. It was found that public sector banks had higher Beta than private/foreign banks, and the difference was statistically significant. OVERALL CAMELS RATINGS: Table 7 shows the overall CAMELS ratings for all the sample banks in the study period. It was found that Barclays Bank was the best performing bank in the years 2003-04, 2004-05, and 2005-06, while Bank of America was the best performing bank in the years 2006-07 and 2007-08. Table 8 shows the overall CAMELS ratings of public and private/foreign banks. There was found to be no significant difference in the overall CAMELS ratings of 3

public and private/foreign banks. Moreover, there was a trend improvement in the overall CAMELS ratings of private/foreign banks over that of public sector banks. DISCUSSION 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. BIBLIOGRAPHY Agarwal, P.K. and Sinha, S.K. (2010), Financial Performance of Microfinance Institutions of India, Delhi Business Review, 11(2). Altman, I.E. (1968), Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy, Journal of Finance, September 1968, New York University. Eccher, E. A., Ramesh K., and Thiagarajan S. R. (1996), Fair value disclosures by bank holding companies, Journal of Accounting and Economics, 22(1). Elliott, J. A., Douglas, H. L. J., and Shaw, W. H. (1991), The Evaluation by the Financial Markets of Changes in Bank Loan Loss Reserve Levels, The Accounting Review, 66(4). Lane, W. R., Looney, S. W., and Wansley J. W. (1986), An Application of the Cox Proportional Hazards Model to Bank Failure, Journal of Banking and Finance, 10(4). Looney, S. W., Wansley, J. W., and Lane, W. R. (1989), An Examination of Misclassifications with Bank Failure Prediction Models, Journal of Economics and Business, 41(4). Maishanu, M.M. (2004), A Univariate Approach to Predicting failure in the Commercial Banking Sub-Sector, Nigerian Journal of Accounting Research, Vol. 1, No. 1. 4

Mous, L. (2005), Predicting bankruptcy with discriminant analysis and decision tree using financial ratios, Working Paper Series, University of Rotterdam. Sangmi, M. and Nazir, T. (2010), Analyzing Financial Performance of Commercial Banks in India: Application of CAMEL Model, Pak. J. Commer. Soc. Sci., 4(1) Thomson, J. B. (1991), Predicting Bank Failures in the 1980s, Federal Reserve Bank of Cleveland Economic Review, 27. Waldron, M., Jordan, C., and MacGregor, A. (2006), the Information Content of Loan Default Disclosure in the Prediction of Bank Failure, Journal of Business & Economic Research, 4(9). Whalen, G. and Thomson, J. B. (1988), Using Financial Data to Identify Changes in Bank Conditioning. Federal Reserve Bank of Cleveland, Economic Review, 24(1), 17-26. Wirnkar, A.D. and Tanko, M. (2008), CAMELS and Banks Performance Evaluation: The Way Forward, Working Paper Series, SSRN: http://ssrn.com/abstract=1150968 Yue, P. (1992), Data Envelopment Analysis and Commercial Bank Performance: A Primer with Applications to Missouri Banks, Working Papers, IC 2 Institute, University of Texas at Austin. 5

Tier I Capital Tier II Capital Capital Adequacy Ratio Table 1: Capital Adequacy mean 13.5043 9.8710 12.9090 9.0603 13.2128 10.0245 11.9670 8.8720 12.9999 7.4134 std. dev. 8.1287 6.5372 10.8474 6.3911 11.8815 5.0085 7.6960 3.8540 8.6535 2.2510 F-statistic 3.4700 2.7100 1.7730 3.7490 11.3160 p-value 0.0678 0.1050 0.1880 0.0580 0.0010 mean 3.9157 4.6717 3.1341 4.5121 2.7790 3.1648 2.4824 4.0307 2.2703 4.3148 std. dev. 2.3999 1.3222 1.4922 1.5782 1.9754 1.1115 1.8280 1.4965 1.7239 1.4608 F-statistic 2.1903 11.6720 0.8400 12.4560 23.7420 p-value 0.1446 0.0010 0.3630 0.0010 0.0000 mean 16.4231 14.5241 16.0431 13.5724 15.7955 13.1893 14.4490 12.9028 15.2693 11.7283 std. dev. 8.0232 5.5702 10.7070 5.9343 11.2442 4.3927 6.7998 2.9257 7.9247 2.4937 F-statistic 1.0960 1.1810 1.3520 1.2650 5.2690 p-value 0.3000 0.2820 0.2500 0.2650 0.0250 Gross Nonperforming Assets Net Nonperforming Assets Net Nonperforming Assets: Total Advances Table 2: Asset Quality mean 287.3079 1770.2390 281.9297 1663.5238 243.1379 1420.7266 326.7738 1356.8621 470.4955 1409.5845 std. dev. 553.9922 2435.2389 507.3847 2307.9851 421.4886 1782.7094 760.6410 1837.4099 1389.6714 2328.8894 F-statistic 10.2250 9.9130 11.9840 7.7810 3.4770 p-value 0.0020 0.0030 0.0010 0.0070 0.0670 mean 69.4252 642.1021 129.7760 585.7270 104.1886 502.4679 145.8483 530.5334 206.8386 614.0869 std. dev. 70.3939 1049.5997 276.3352 991.0549 202.7454 894.0809 371.3168 954.5044 641.0819 1350.9161 F-statistic 8.5950 5.6950 5.4730 4.0910 2.1510 p-value 0.0050 0.0200 0.0230 0.0480 0.1480 mean 2.3745 2.6279 2.4066 1.8617 1.0200 1.2028 0.7521 0.8879 0.6414 0.7259 std. dev. 2.3914 2.3650 4.4495 1.6081 1.0940 0.7646 0.7459 0.5230 0.5918 0.4786 F-statistic 0.1650 0.3850 0.5440 0.6450 0.3570 p-value 0.6860 0.5380 0.4640 0.4250 0.5520 6

Total Investments: Total Assets Total Advances: Total Deposits Sales per Employee Profit After Tax per Employee Table 3: Management Soundness mean 33.9520 39.9900 34.0070 36.0970 30.0930 29.8450 29.7030 26.3860 28.4069 24.0517 std. dev. 13.8621 10.3075 8.9716 9.4176 8.0381 8.1042 7.7604 6.9939 13.3129 7.8020 F-statistic 3.5430 0.7490 0.0140 2.9240 2.3100 p-value 0.0650 0.3910 0.9070 0.0930 0.1340 mean 63.2424 105.0652 73.2493 117.5234 77.0934 2040.2352 84.7807 1285.3172 77.8710 580.3107 std. dev. 42.5020 185.0132 49.6188 217.6143 43.2790 10549.0729 63.4981 6484.2471 46.3586 2694.3073 F-statistic 1.4080 1.1410 1.0040 0.9940 1.0080 p-value 0.2400 0.2900 0.3210 0.3230 0.3200 mean 5.7541 2.2328 6.2979 3.1010 6.8490 3.8903 7.3938 4.6790 8.9931 5.9145 std. dev. 4.0709 0.9473 4.1143 2.3069 4.3031 2.8337 4.4179 2.3429 5.9585 3.0223 F-statistic 20.5840 13.3210 9.5630 8.5470 6.1570 p-value 0.0000 0.0010 0.0030 0.0050 0.0160 mean 0.1752 0.0800 0.1466 0.0755 0.1862 0.0762 0.1286 0.0845 0.1548 0.0897 std. dev. 0.3995 0.2241 0.3342 0.2459 0.5104 0.2474 0.1929 0.2566 0.2529 0.2718 F-statistic 1.2520 0.8500 1.0910 0.5480 0.8940 p-value 0.2680 0.3600 0.3010 0.4620 0.3490 Return on Net Worth Operating Profit: Average Working Fund Profit After Tax: Total Assets Table 4: Earnings and Profitability mean 15.8445 25.3186 9.6024 18.2507 11.0345 15.2852 12.7783 17.6931 12.8828 19.2259 std. dev. 11.1593 10.4188 7.8660 9.2394 6.4684 7.2117 7.3289 5.7299 6.9565 5.9922 F-statistic 11.1680 14.7310 5.5830 8.0940 13.8410 p-value 0.0010 0.0000 0.0220 0.0060 0.0000 mean 3.2338 3.0772 2.0593 2.3969 2.8607 2.0186 2.9145 1.9734 3.0662 1.7824 std. dev. 2.9614 0.7279 1.4878 0.7739 3.0354 0.3934 1.7458 0.3383 1.8654 0.5503 F-statistic 0.0760 1.1750 2.1950 8.1210 12.6360 p-value 0.7830 0.2830 0.1440 0.0060 0.0010 mean 1.3676 1.3348 0.6969 0.9907 1.3597 0.9110 1.4172 0.9879 1.4214 0.9731 std. dev. 1.1553 0.4765 1.2869 0.4988 1.9140 0.4114 1.0914 0.2657 0.9207 0.3269 F-statistic 0.0200 1.3140 1.5230 4.2360 6.1050 p-value 0.8880 0.2570 0.2220 0.0440 0.0170 7

Government Securities: Total Investments Government Securities: Total Assets Table 5: Liquidity mean 72.2450 78.7110 74.4170 79.3930 75.8070 81.6790 71.9720 81.2340 72.4690 78.7034 std. dev. 23.0563 15.4482 13.4782 20.0318 10.3587 11.0560 17.9599 10.5502 22.8196 18.6039 F-statistic 1.5740 11.6720 4.3570 5.7340 1.3000 p-value 0.2150 0.0010 0.0410 0.0200 0.2590 mean 26.0970 32.0450 25.4720 28.8790 22.4520 24.8280 21.0030 21.8340 22.0862 20.2034 std. dev. 11.6054 10.1892 9.2848 10.5742 4.1967 7.3647 3.3962 6.2052 9.2968 6.8011 F-statistic 4.3020 1.7000 2.2780 0.4000 0.7750 p-value 0.0430 0.1980 0.1370 0.5300 0.3830 Table 6: Sensitivity to Market Risk Beta mean 0.4148 0.8921 0.4207 0.8645 0.4490 0.6862 0.4331 0.7224 0.4897 0.6397 std. dev. 0.5262 0.7518 0.5107 0.7322 0.5807 0.5056 0.4751 0.5360 0.5338 0.4428 F-statistic 7.8430 7.1660 2.7530 4.7310 1.357 p-value 0.0070 0.0100 0.1030 0.0340 0.249 8

Table7: Overall CAMELS Ratings Bank CAMELS 2008 CAMELS 2007 CAMELS 2006 CAMELS 2005 CAMELS 2004 Allahabad Bank 29 30 32 36 34 Andhra Bank 32 31 29 34 34 Bank of Baroda 29 27 25 32 31 Bank of India 33 27 25 26 29 Bank of Maharastra 29 27 27 32 33 Canara Bank 30 29 29 33 31 Central Bank 25 26 26 32 30 Corporation Bank 32 29 29 33 33 Dena Bank 30 25 26 30 29 EXIM Bank 34 31 27 34 26 IDBI Bank 27 26 27 31 31 Indian Bank 34 34 31 33 31 Indian Overseas Bank 32 34 33 35 32 NABARD 21 23 22 31 32 Oriental Bank 28 29 29 36 34 Punjab National Bank 31 27 27 30 32 Punjad Sind Bank 33 31 27 26 25 State Bank of Indore 29 28 27 33 36 State Bank of Mysore 33 31 32 38 34 State Bank of Patiala 30 29 30 34 36 State Bank of Bikaner and Jaipur 30 30 28 36 34 State Bank of Hyderabad 31 32 33 34 35 State Bank of Travancore 23 32 29 36 35 State Bank of India 25 26 29 35 32 Syndicate Bank 30 31 32 35 33 United Bank of India 26 29 29 36 33 UCO Bank 24 25 26 32 32 Union Bank 34 29 25 33 31 Vijaya Bank 27 30 28 36 36 ABN Amro Bank 34 36 31 35 32 American Express Bank 20 30 30 32 25 AXIS Bank 31 30 29 32 32 Bank of America 46 39 31 35 33 Bank of Rajasthan 29 29 22 29 32 Barclays Bank 32 36 40 42 45 BNP Paribas 39 35 28 30 30 Celyon Bank 44 38 35 33 31 Development Credit Bank 28 27 22 25 28 Deutshe Bank 39 31 27 32 39 Dhanalakshmi Bank 28 25 24 27 29 HDFC Bank 34 32 30 33 31 9

HSBC Bank 32 33 29 33 34 ICICI Bank 29 28 29 32 32 IndusInd Bank 23 26 27 35 34 ING Vysya Bank 27 27 24 27 28 Jammu & Kashmir Bank 28 26 26 28 31 Karnataka Bank 30 25 28 33 30 Karur Vysya Bank 33 33 28 31 33 Kotak Mahindra Bank 30 28 29 30 33 Lakshmi Vilas Bank 25 25 26 29 28 Mizuho Corporate Bank 35 31 25 38 31 Nainital Bank 20 27 27 30 18 Ratanakar Bank 31 25 22 23 28 Standard Chartered Bank 36 36 31 34 34 Societe Generale Bank 38 33 34 41 35 South Indian Bank 28 29 26 30 33 TamilNad Merchantile Bank 32 32 27 33 30 Yes Bank 34 29 27 26 17 Table 8: Overall CAMELS ratings CAMELS mean 30.8966 32.2069 31.6552 33.17241 28.0690 28.2414 30.3793 28.8966 31.5517 29.3448 std. dev. 5.2328 2.6777 4.2951 2.8166 3.9364 2.6546 4.1440 2.6905 6.0979 3.4566 F-statistic 1.4411 2.5305 0.0382 2.6118 2.8747 p-value 0.2350 0.1173 0.8457 0.1117 0.0955 10