A JOURNAL OF COMPOSITION THEORY ISSN : 0731-6755

The Financial Performance of Selected Public Sector Banks in India: Empirical Evidence

Meenakshi Research Scholar Department of Economics Banasthali Vidyapith, Jaipur [email protected]

Dr. Qamar Alam Assistant Professor Department of Economics Banasthali Vidyapith, Jaipur [email protected]

Abstract

This study analyses the financial performance of selected public sector banks in India using CAMEL Approach. In this study, the financial performance of 13 nationalized banks measured from the period 2005 to 2017. Under CAMEL analysis, there are several parameters, namely, Capital Adequacy Ratio, Net NPA Ratio, Credit Deposit Ratio, Return on Asset and Cash Deposit Ratio are taken into consideration. The present study is based on secondary data drawn from the annual report of Reserve Bank of India. The study used the panel data analysis, pooled OLS, fixed and random effects. However, based on Hausman test fixed effect is rejected in the analysis. The finding of the study revealed that Bank of Baroda, United Bank of India, Syndicate Bank, Punjab National Bank and Indian overseas bank stood on the top. While, Bank of Baroda, United bank of India stood the least. Results show that capital adequacy ratio, net NPA ratio is significant.

Key Words: CAMEL Model, Panel Data, Pooled OLS, (Fixed & Random Effects), Hausman Test, Dummy Variables etc.

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Introduction Banking sector plays very important role in the economy of each country. It acts as a backbone of the national financial system. Indian banking sector plays a crucial role in the mobilization of savings and promotion of economic development. As the real economy is dynamic, it is imperative that the banking system is adaptive and competitive enough to cope with multiple demands made on it by various constituents of the economy. In 1991, the Indian economy went through a procedure of economic liberalization, which follows up by the initiation of fundamental reforms in the banking sector in 1992. The banking reforms are based upon the recommendation of the Narasimham Committee-I (1991) and Narasimham Committee-II (1998). Both committees advocate a more market-oriented banking system, which would operate in an environment of prudential regulation and transparent accounting. The primary aim behind this drive is to introduce an element of market discipline into the regulatory process that would reinforce the supervisory efforts of the Reserve Bank of India (RBI).

Mainly there are two forms i.e. direct and indirect in which financial institutions impacts the real economy. The direct impact of financial institutions on the real economy is minor. The indirect impact of financial markets and institutions in economic development is extraordinarily important.

The financial sector mobilizes savings and distributes credit across space and time. It provides not only payment services, but also enable firms and households to cope with economic uncertainties by hedging, pooling, sharing and pricing risks. An capable financial sector cut down the cost and risk of producing and trading goods and services and thus makes an essential contribution to boost the standard of living. How an economy would perform without a financial sector and then go ahead to introduce a simplified financial sector with direct financial transactions between savers and investors. Financial intermediaries work in the saving- investment cycle of an economy by serving as conduits to finance between the borrowers and the lenders. Thus, the financial sector can improve both the quantity and quality of real investment and thereby increase income per capita.

Rating system based upon CAMEL framework has been first developed in the 1970s by the three Federal Banking Supervisors (Federal Reserve, Federal deposit insurance corporation, Office of

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the Comptroller of the currency) and other financial supervisory agencies to provide a convenient summary of bank conditions at the time of investigation.

The main objective of this study is to analyse the performance of 13 public sector banks of India using CAMEL Approach. The scope of the study delimited to 13 public nationalised banks, which includes Allahabad bank, Bank of Baroda, Bank of India, Bank of Maharashtra, Central bank of India, Dena bank, Indian Overseas bank, Punjab and Sind bank, Punjab National bank, Syndicate bank, Union bank of India, United bank of India, Vijaya bank. The time-period of the study is from 2005 and 2017. The financial performance of the banks is measured using CAMEL model.

Theoretical Framework: CAMEL Model The acronym “CAMEL” refers to the five components of a bank’s conditions that are assessed: Capital Adequacy, Asset Quality, Management, Earning, and Liquidity. A sixth component, a Bank’s Sensitivity to Market Risk was added in 1997; hence the acronym has changed to CAMELS.

CAMEL is a ratio-based model for evaluating the performance of banks. It is a tool that measures various financial aspects of banks like capital adequacy, asset quality, and efficiency of management, quality of earnings and liquidity. A bank’s CAMEL rating is directly known only by the bank’s senior management and the proper supervisory staff. A CAMEL rating is never released by supervisory agencies, even on a lagged basis. This system was adopted in India since 1995 on the suggestion of Shri S. Padmanabhan, Governor, RBI. Under this system the rating of individual bank is done along five key parameters – Capital Adequacy, Asset Quality, Management Capability, Earning Capacity, and Liquidity. To make the rating system the more risk focused, a sixth component relating to Sensitivity to Market Risk was added to the CAMEL rating, making it CAMELS. The components of CAMEL and a final CAMEL rating, representing the composite total of the part CAMEL scores as a measure of the Bank’s overall condition.

Various components of the financial Aspect of CAMEL Model have been discussed below in CAMEL. In CAMEL “C” stands for Capital Adequacy, “A” stands for Asset Quality, “M”

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stands for Management Soundness, “E” stands for Earning and Profitability, “L” stands for Liquidity and each component has been explained below;

Capital Adequacy: It replicates the overall financial condition of banks and the ability of management to meet the need of added capital. There are various indicators of Capital Adequacy viz. Capital Adequacy Ratio, Debt Equity Ratio, Total Advance to Total Asset Ratio, Government securities to total investment. Out of these ratios the present study uses Capital Adequacy Ratio for analysis point of view.

Capital Adequacy Ratio: Capital Adequacy Ratio has been used by banks to determine the adequacy of their capital keeping in view their risk exposures. A Capital Adequacy Ratio measures the amount of a bank’s core capital expressed as a percentage of its risk-weighted asset.

Asset Quality: The quality of assets is a substantial parameter to measure the strength of bank. The prime motto behind measuring the asset quality is to find the component of non-performing assets as a percentage of the total assets. There are various indicators of Asset Quality viz. Gross NPA Ratio and Net NPA Ratio. Out of these ratios the present study uses Net NPA Ratio for the analysis

Net NPA Ratio: Net NPA Ratio reflects the performance of banks. An elevated level of NPAs suggests a high probability of many credit defaults that affect the profitability and Net-worth of banks and wear down the value of the asset.

Management Capability: Sound management is one of the most principal factors behind financial institutions performance. This ratio measures the efficiency and effectiveness of management. There are various indicators of management soundness viz. Credit Deposit Ratio, Business per Employee and Profit per Employee. Out of these ratios this study uses Credit Deposit Ratio for analysing the management soundness aspect of banking sector.

Credit Deposit Ratio: It is the ratio of how much a bank lends out of the deposits has mobilise. The bank must repay deposits on request, so having a ratio that’s too high puts the bank at substantial risk.

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Earning and Profitability: It fundamentally determines the profitability of the bank and explains its sustainability and growth in earnings in future. Strong earning and profitability profile of banks reflects the ability to support present and future operation. There are various indicators of Earning & Profitability viz: Dividend Payout Ratio, Return on Asset, Operating Profits to Total Assets, Interest Income to Total Income and Other Income to Total Income. Out of these ratios this study uses Return on Asset ratio for analysing the Earning and Profitability aspect of the banking sector.

Return on Asset (ROA): Net profit to total asset shows the efficiency the efficiency of the banks using their assets in generating profits. A higher ratio shows the better income generating ability of the assets and better efficiency of management in the future.

Liquidity: The excellent liquidity position of the bank favourable impact the financial performance of the banks. In this category of ratios, the ability of banks to meet its obligations is assessed. There are various indicators of Liquidity viz. Liquidity Ratio, Government Securities to Total Asset, Approved Securities to Total Asset, Liquidity Assets to demand deposit and Cash Deposit Ratio. Out of these ratios this study uses Cash Deposit Ratio for analysing the liquidity aspect of banking sector.

Cash Deposit Ratio: It specifies how much of a bank’s funds are being used for lending the main banking activity. Banks’s total of cash in hand and balances with RBI divided by total deposits.

Review of Literature Some studies related to the analysis and evaluation of banks’ financial performances based upon the CAMEL model have been reviewed.

Baral (2005) studied the performance of joint ventures banks in Nepal by applying the CAMEL Model. The study was based on secondary data drawn from the annual reports published by joint venture banks. The findings of the study revealed that the financial health of joint ventures is more effective than that of commercial banks. Moreover, the financial health of joint venture

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banks was not difficult to manage the possible impact to their balance sheet on a large-scale basis without any constraints inflicted to the financial health.

Bodla and Verma (2006) examined the performance of SBI and ICICI through the CAMEL model. Data set for the period of 2000-01 to 2004-05 were used for the study. With the reference to the Capital Adequacy, it concluded that SBI has an advantage over ICICI. Regarding to Assets Quality, Earning Quality and Management Quality, it can be said that ICICI has an edge up on SBI. Therefore, the liquidity position of both Banks was sound and did not differ much.

Gupta and Kaur (2008) conducted a research on the sole aim of examining the performance of Indian private sector banks by using the CAMEL model and by assigning a rating of the leading five and lowest five banks. Twenty old and ten new private sector banks ranked based on CAMEL Approach. The study covered financial data for the period of 5 years i.e. from 2003-07. The findings summarized that new private sector of banks have attained the higher position due to core banking, aggressive marketing strategies and prominent level of technology.

Dash and Das (2009) analyse the Indian banking industry under CAMEL framework. The thesis compares the performance of public sector banks with that of private/ foreign banks. The analysis was performed from a sample of 58 banks operating in India of which 29 were public sector banks and 29 were private/foreign sector. The data used to be from the audited financial statement for the financial years 2003-2008. The findings concluded that private/foreign banks have an edge over the public-sector banks.

Kaur (2010) categorized banks into public sector banks, private sector banks and foreign banks and uses a CAMEL analysis technique with the purpose of ranking the banks. The data used to be in secondary nature which was collected from statistical tables related to the banks in India from financial year 2001 to 2007. The experiment revealed that the best Bank from the public sector has been awarded to Andhra Bank and State Bank of Patiala. In the category of Private Sector Banks, Jammu and Kashmir Bank has been assigned the first rank succeeded by HDFC Bank. Among the Foreign Sector Banks, Antwerp has bagged the first rank followed by JP Morgan Chase Bank.

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Sangmi and Nazir (2010) evaluated the financial performance of top two major banks in the northern India representing the biggest nationalized bank (i.e. Punjab National Bank) and the biggest private sector bank (i.e. Jammu and Kashmir Bank). The research was conducted on secondary data from annual reports of the respective banks. And the data used was related to five financial years (i.e. 2001-2005). The results highlighted that the position of the banks under study is sound and satisfactory as far as their capital adequacy, asset quality management capability and liquidity is implicated.

Khurana and Goyal (2011) analysed the financial performance of public sector banks and commercial banks in India from the period 2001 to 2007, they examined the productivity and efficiency using the trend of operating cost / total cost, cost to income, labour/non-labour cost, Net interest income, NPA and capital to risk weighted asset ratio. The study observed that there is a need for increased absorption of enhanced technological capability by several banks to a further argument yield of the banking sector and this would call for changes in processes and improvement in human resource skills.

Zafar et .al (2012) concluded that in capital adequacy CANARA Bank performed better than other banks and in asset quality ICICI Bank has better performed, Management Efficiency ICICI and IDBI Bank Better performed, in Earning Efficiency CANARA Bank has performed better than another bank. Last one, in the Liquidity ICICI Bank has performed better than other selected banks. Performance in the year 2010 has been much better as against their performance during the previous year ended 2009.

Makkar (2013) considered a sample of 37 banks (22 public sector banks and 15 private sector banks) for the period from 2006-07 to 2010-11. The results of the’t’ - test disclosed that there is a significant difference in the Capital Adequacy, Asset Quality and Earning Capacity of public and private sector banks in India, while there is no significant difference in the Management, Liquidity Position and Sensitivity to market risk of the two different bank groups. The study terminated that there is no statistically significant difference in the financial performance of the public and private sector Banks in India.

Khatik and Nag (2014) conducted a study of the financial soundness of 5 nationalized banks by using camel model. The study concluded that Bank of Baroda has been ranked in the top position

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with a composite average of 1. The Union Bank of India and Dena Bank secured the 2nd position with 2 each. The State Bank of India, which secured the 4th position with the composite average of 3.6 and in the last position, was the UCO Bank, which secured the 5th rank with the composite average of 4.4.

Prasad and Shreenath (2016) evaluated the performance level of three public sector banks (Vijay Bank, United Bank of India, Bank of Maharashtra) using camel model. Data were being used from 2010 to 2012. The study concluded all the 3 banks have succeeded in maintaining CRAR at a higher level than the prescribed level 9 percent. Business per Employee and Profit per Employee was more in Vijaya Bank.

Data Sources and Methodology

The present study is based on the secondary data and data span is from 2005-2006 to 2016-2017. Data have been collected from the Reserve Bank of India, Financial Development and Structure Dataset (WDI, 2007), Indian Bank’s Association (IBA) bulletin. In this study, the descriptive statistics tools used to rate the overall performance of the banks, panel regression model was used to measure the impact of CAMEL Approach elements on bank performance. In this analysis, the dependent variable is Return on Asset (ROA) and the independent variables include Capital Adequacy Ratio, Net NPA, Credit Deposit Ratio and Cash Deposit Ratio.

Panel data refers to a multidimensional data collected over a period. In panel regressions pooled OLS, fixed and random effect, Hausman test have been estimated to look the relationship among dependent and independent variables. In a Fixed Effect model, the parameters of the model are fixed alternatively, the group means are fixed. The fixed effect model can be predicted with the help of dummy variables. Random Effect Model is also known as the variance components model. Random effect model also allows for heterogeneity and is also time invariant, but the individual specific effect is uncorrelated with the independent variables.

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Table 1: RANKING OF PUBLIC SECTOR BANKS BASED ON CAMEL MODEL RANK Name of Bank Capital Asset Management Earning and Liquidity Adequacy Quality Capability Profitability (CADR) (CAD) (NNPA) (CDR) (ROA) Allahabad Bank 6 9 8 11 10 Bank of Baroda 1 13 4 7 13 Bank of India 3 3 9 4 7 Bank of Maharashtra 9 8 3 3 3 Central Bank of India 5 6 6 2 2 Dena Bank 12 10 5 9 4 Indian overseas Bank 7 2 11 6 1 Punjab and Sind Bank 10 7 10 8 8 Punjab National Bank 2 4 7 1 11 Syndicate Bank 13 11 1 12 6 Union Bank of India 4 5 2 10 9 United Bank of India 8 1 13 13 5 Vijaya Bank 11 13 12 5 12

Table-1 depicts the results of 13 nationalised public sector banks, Bank of Baroda ranked first for capital adequacy, United bank of India ranked first for asset quality, Syndicate bank ranked first for management capability, Punjab national bank ranked first for Earning and profitability and Indian overseas bank ranked first for Liquidity. On the other hand, Syndicate bank, Bank of Baroda, United bank of India is the last place for all five indicators of CAMEL Model.

Model Specification This study has used a sort of panel data regression model to analyse the collected data. Panel data is a bunch of cross section and time series observations. The following equation indicates the fixed effect model with respect to profitability indicator ROA.

ROAit = β1i + β2CADit+ β3NNPAit+ β4CDRit+ β5CADRit+Wit

Where, β1i notice that subscript i on the intercept i.e. β1 to suggest that the intercept of the

thirteen banks may be different; β2, β3, β4 and β5 are coefficient of the explanatory variables; i shows the cross sectional units or sampled public sector banks and t indicates the time period

from 2005 to 2017; Wit = εi+μi, consists of two components, εi, which is cross sectional, and μi which is the combined time series and cross sectional error component. Table-2 shows the variables, notations and measures used in the study.

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Table 2: Definition and Measurement of Study Variables Variables Measures Indications Dependent ROA Net Income / Average total It reflects the ability of a bank’s assets management to generate profits from the bank assets. Capital CAD Capital funds/Risk-weighted CAD also known as capital-to-risk weighted Adequacy assets of the banks) *100 asset ratio to protect depositors and promote the stability and efficiency of the financial system around the world. Asset Quality NNPA Net NPA /total loan Net NPA Ratio reflects the performance of banks. An elevated level of NPAs suggests high a probability of many credit defaults that affect the profitability and Net-worth of banks and wear down the value of the asset. Management CDR Loans/Deposits The loan to deposit ratio is used to calculate Capability a lending bank’s ability to cover withdrawals made by its customers. Earning Quality ROA Net Income /Average Total Net profit to total asset shows the efficiency Assets the efficiency of the banks using their assets in generating profits. A higher ratio shows the better income generating ability of the assets and better efficiency of management in the future. Liquidity CADR Liquid asset/total Deposit It specifies how much of a bank’s funds are being used for lending the main banking activity. Bank’s total of cash in hand and balances with RBI divided by total deposits.

Panel Model Regression

The term panel data refer to the pooling of observation on a cross sectional of nationalized banks over several time periods. This study has only one dependent variable, i.e. ROA. To choose the pooled regression model, fixed or random effect of panel data and Hausman test has been estimated.

Table 3: Panel Data Estimation Results based on Pooled OLS Regression Model Variable type ROA Coef. Std. Err. t p>t

Capital Adequacy CAD 0.089342 0.020752 4.305315 0.0000* Asset Quality NNPA -0.151113 0.009921 -15.23105 0.0000* Management Capability CDR 0.000682 0.003183 0.214187 0.8307 Liquidity CADR 0.007644 0.009352 0.817349 0.4149 Constant -0.181931 0.392568 -0.463439 0.6437

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*, and ** = significant at 1%, and 5% confidence level No. of observations=169 Overall R2 =0.723882 Source: Author’s Calculation based on RBI data

As shown in the table 3, results of the pooled OLS Regression model. A regression model was applied to test that how far the independent variable impact on the dependent variable. Indicator of Capital adequacy and Asset Quality i.e. Capital Adequacy Ratio (CAD) and Net NPA have a statistically significant variables means (P < 0.05) explain ROA. Management and liquidity indicators are not significant for ROA.Coefficient of determination-R2 is the measure of the proportion of the variance of the dependent variable about its mean that is explained by the independent variable. (R2=0.7238; P>0.05). In this study a modified function of fixed effect by using 12 dummy variables among the set of explanatory variables are used.

ROAit = β1i + β2CADit+ β3NNPAit+ β4CDRit+ β5CADRit+β6D1t +β7D2t +β8D3t +β9D4t +β10D5t+

β11D6t + β12D7t + β13D8t + β14D9t +β15D10t + β16D11t + β17D12t+ Wit

Where,

DI=1 for, Allahabad bank = for others

D2=1 for, Bank of Baroda =0 for others

D3=1 for, Bank of Maharashtra =0 for others

D4=1 for, Central Bank of India =0 for others

D5=1 for, Dena Bank =0 for others

D6=1 for, Indian Overseas Bank = 0 for others

D7=1 for, Punjab and Sind Bank =0 for others

D8=1 for, Punjab National Bank

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=0 for others

D9=1 for, Syndicate Bank =0 for others

D10=1 for, Allahabad bank =0 for others

D11=1 for, Union Bank of India =0 for others

D12=1 for, United Bank of India =0 for others

Table 4: Results based on Fixed Effect or LSDV Model Variable type ROA Coef. St. Err. t P>t Capital Adequacy CAD 0.076915 0.020769 3.703334 0.0003* Asset Quality NNPA -0.154118 0.009395 -16.40496 0.0000* Management Capability CDR -0.005523 0.003372 -1.638182 0.1035 Liquidity CADR 0.008846 0.009381 0.942937 0.3472 Constant 0.396511 0.394994 1.003840 0.3171 *, and ** = significant at 1% and 5% confidence level No of observations = 169 Over all R2 = 0.789096 Source: Author’s Calculation based on RBI data

According to table-4, the results of Fixed Effect Model or LSDV Model, the parameters of the model are fixed alternatively, the group means are fixed. The fixed effect model estimated with the aid of dummy variables. Indicator of Capital adequacy and Asset Quality i.e. Capital Adequacy Ratio (CAD) and Net NPA have a statistically significant variables means (P < 0.05) explain ROA.Management and liquidity indicators are not significant for ROA. Coefficient of determination-R2 is the measure of proportion of the variance of dependent variable about its mean that is explained by the independent variable. (R2=0.7890; P>0.05). The following equation indicates the fixed effect model with respect to profitability indicator ROA.

ROAit = β1 + β2CADit+ β3NNPAit+ β4CDRit+ β5CADRit+Wit

Where, β1 indicates the random variable; β2, β3, β4 and β5 are coefficient of the explanatory variables; i shows the cross sectional units or sampled public sector banks and t indicates the

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time period from 2005 to 2017; Wit = εi+μi, consists of two components, εi, which is cross

sectional, and μi which is the combined time series and cross sectional error component.

Table 5: Results based on Random Effect Model

Variable type ROA Coef. St. Err. t P>t Capital Adequacy CAD 0.081389 0.020109 4. 047410 0.0001* Asset Quality NNPA -0.153178 0.009271 -16.52181 0.0000* Management CDR -0.003240 0.003201 -1.012206 0.3129 Capability Liquidity CADR 0.008551 0.009071 0.942678 0.3472 Constant 0.184352 0.382988 0.481353 0.6309 Hausman (prob> chi-sq.) 0.1273 *, and ** = significant at 1% and 5% confidence level No of observations = 169 Over all R2 = 0.746930 Source: Author’s Calculation based on RBI data

Table-5 depicts the results of Random Effect Model. Asset Quality and Capital Adequacy indicators, i.e. CAD and NNPA are significant (P< 0.05). Variables of management and liquidity are not significant for ROA. Coefficient of determination is (R2; 0.74>P). The Hausman test shows that random effect model is accepted i.e. (P>0.05).

Conclusion and Suggestions

The Indian banking sector is the backbone of the Indian Economy. It plays a crucial role in the mobilization of savings and promotion of economic development. This paper envelops bank- wise financial performance through the CAMEL Model i.e. the various ratios relating to capital adequacy, asset quality, management, earning capacity and liquidity. The finding of the study revealed that Bank of Baroda, United Bank of India, Syndicate Bank, Punjab National Bank and Indian overseas bank stood on the top. While, Bank of Baroda, United bank of India stood the least. Pooled regression results suggest that capital adequacy ratio and net NPA explanatory variables are significant in determining the profitability indicator i.e. ROA. In fixed and random panel data same variable are found significant in determining the profitability. Hausman test accept the null hypothesis, i.e. Random effect model is appropriate and reject the alternative hypothesis. The study revealed that capital adequacy and Net NPA were the key drivers of profitability of

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nationalized public sector banks in India. Therefore, the bank manager is advised to give due attention to these variables to improve profitability.

References

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