bank specific, industry specific and macro economic ...

Int. J. Agricult. Stat. Sci. Vol. 13, No. 2, pp. 655-662, 2017

ISSN : 0973-1903

ORIGINAL ARTICLE

BANK SPECIFIC, INDUSTRY SPECIFIC AND MACRO ECONOMIC DETERMINANTS OF PROFITABILITY OF PUBLIC SECTOR BANKS IN INDIA : 2010-2016 – A PANEL DATA APPROACH A. Subbarayan* and J. Jothikumar1 Department of Computer Applications, Faculty of Sci. and Humanities, S.R.M. Univ., Kattankulathur - 603 203, India. 1 Faculty of Science and Humanities, S.R.M. University, Kattankulathur - 603 203, India. E-mail: [email protected] Abstract : India’s banking sector performance over the past eight years since 2008-2009 global financial crisis reflects a contrasting picture between three types of commercial banks viz., public sector banks, private sector banks and foreign banks. The private sector banks and foreign banks have exhibited profitability improvements whereas the public sector banks shown declining earnings growth, narrowing profit margins and significant deterioration in asset quality. In this study, we intend to examine the impact of bank specific, industry specific and macro economic factors influencing the profitability of public sector banks in a dynamic model frame work. The panel data consist of public sector banks for the period 2010-2016. We have used pooled ordinary least squares method for investigating the impact of factors on Return on Assets. The empirical results clearly demonstrate that the profit is not only determined by its own characters, but also by industry specific and macro economic factors. The results of the study are of value to both academics and policy makers. Key words : Profitability, Return on Assets, Panel data, Macro Economic Variables, Least Square Method.

1. Introduction Banks in India are considered to be the life line of the economy. The Indian Banking sector has shown strong progress over the last decades and has supported the country’s economic growth. The services provided by the banks have expanded in a rapid manner. The banks provide a wide range of financial services like insurance, investment and asset management etc., in addition to the traditional activities viz., ‘savings and loans’ and increased their role in the economy. The Reserve Bank of India (RBI) of the country, regulating operations of banks, managing money supply and discharging myriad responsibilities that are usually associated with a central bank. The Indian banking sector consists of 27 public sector, 26 private sector, 46 foreign, 56 regional rural banks, 1574 urban cooperative banks and 93,133 cooperative banks in addition to cooperative credit institutions. Public sector banks control more than 70% of the assets of the banking system and balance assets are shared by other banks. *Author for correspondence

Received May 27, 2017

Initiation of financial liberalization in the early 1990’s has lead for the introduction of multiple phases of reforms in the Indian Banking sector. These reforms were introduced with the objective of creating an efficient, competitive and profitable banking sector for the economic growth of the country. The Narasimham (1998) committee highlighted the urgent need to improve asset quality and recommended for bringing down nonperforming assets. Indian banks have successfully adopted the Based II norms and majority of the banks have already met Based III capital norms. The banking system reforms resulted in changes in market structure, pattern of ownership and financial operations of the commercial banks in India. Government of India has allowed foreign banks to operate in India in the Union budget 2002-2003. Eichengreen and Gupta (2013) have noted that during the financial crisis in 2007-2008, Indian Banks with substantial portion of deposits flight from small/ medium private domestic and foreign banks to large Revised October 14, 2017

Accepted October 31, 2017

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A. Subbarayan and J. Jothikumar

public sector banks. This has resulted with increased inefficiency and affected the stability and performance of the above small/medium private sector and foreign sector banks. Das (2013) has remarked that the global financial crisis had limited impact on the interest margin of the banks in India. The report committee on Financial sector assessment also commented that the Indian Banks were sound and broadly compliant with international norms. It is important to note that catastrophic effects of the financial crisis have not significantly changed structure of the Indian banking industry. In the subsequent periods, the Government out India and Reserve Bank of India has brought out a variety of liquidity-easing and confidence building measures to infuse liquidity in the banking system. During 20072014 Government of India infused the capital mounting 717 billion rupees in the public sector banks. The financial analysts in India are of the strong opinion that the future of banking in India looks not only exciting but also transformative. The paper is structured as follows: Section 2 contains a brief overview of operations and performance of Scheduled Commercial Banks during 201-16. In Section 3, we have given an overview of the existing literature on related studies. Section 4 describes the empirical methodology used in the study and description of the data. Section 5 presents and discusses the details of empirical results. The conclusions based on the study are presented in Section 6.

2. An Overview of Operations and Performance of Scheduled Commercial Banks during 2015-16 The consolidated balance sheets of the banking sector have continued to grow during 2015-16 with assets/liabilities expanding at 7.7% compared to 9.7% in 2014-15. The growth in loans and advances of public sector banks decelerated to 2% in 2015-16 from 7.4% in 2014-15. Private sector and Foreign sector banks witnessed higher growth in current savings account as compared to public sector banks. Public sector banks reported losses to the tune of rupees 180 billion with net profits declining by 148% over the previous years. Return on Assets (ROA) and Return on Equity (ROE) showed a substantial decline as compared to 2014-15, reflecting the impact of sharp decline in net profits. Public Sector Banks reported negative ROA (Table 1). From the above facts, we observe that Return on Assets (ROA) and Return on Equity (ROE) of the

banks have lowered down in 2015-16. This makes the study on the determinants of ROA of public sector banks very significant particularly, when the banking system in India is predominantly led by public sector banks.

3. A Brief Review of Literature related to Bank Profitability Molyneux and Thornton (1992) for the first time studied the determinants of bank profitability across eighteen European countries between 1986 and 1989 and found a significant positive association with the return on equity and the level of interest rates, bank concentration and government ownership. Demirguc and Huizinga (1999) examined the determinants of bank interest margins and profitability on a sample of bank level data for 80 countries with respect to individual bank characteristics as well as macro economic conditions. The results indicated that larger ratio of bank assets to Gross Domestic Product (GDP) and a lower market concentration led to lower margins and profits. Abreu and Mendes (2002) studied determinants of bank interest margins and profitability for some European countries and reported that well-capitalized banks had better profitability. Athanasoglou et al. (2008) examined the factors affecting profitability of Greek banks and concluded that profitability was pro-cyclical and the effect of business cycle was asymmetric. Bodla and Verma (2007) have attempted to study the impact of financial variables on the profitability among the public sector banks in India. The study revealed that the variables viz., non-interest income, operating expenses, provisions and contingencies and spread have a significant relationship with net project. It is to be noted that among the variables mentioned above, two variables viz., provisions and contingencies and operating expenses have negative relationship with net profit. Singh and Chaudhary (2009) studied the determinants of profitability in public sector, private sector and foreign sector banks in India and shown that investments had significant impact on the profitability for all the banks among all the three sectors. Singh (2010) examined the bank specific and macro economic variables on the performance and macroeconomic determinants of bank profitability of 35 banks for the period 2003-04 to 2006-09. Siva Reddy (2011) identified the deter minants of profitability of commercial banks in for the unbalanced data relating to 87 banks for the period 1992-2000. The study

Bank Specific, Industry Specific and Macro Economic Determinants of Profitability of Public Sector Banks

revealed that the ratio of capital to assets, non-interest income to asset and bank concentration is positively associated with the profitability of banks. The study also revealed that inflation and rate of interest are negatively associated with profitability of banks. Bhatia et al. (2012) investigated the determinants of profitability of private sector banks in India for the period 2001-07 to 2009-10. It is shown that Spread ratio, Provisions and contingencies, Non interest income, Operating expenses ratio, Profit per employee, Investment/Deposit ratio and Non-performing assets are significant variables affecting the profitability of private sector banks. Karimzadeh et al. (2013) used a balanced panel set drawn from Indian banking industry and investigated the nature of relationship between the profitability and the factors that determine profitability of banks in India for the period 2003-2011. The study revealed that the size of the bank makes more contribution to their profitability and market concentration affects the profitability of banks. Seenaiah et al. (2015) identified the major determinants that have influenced the profitability of Indian banking sector in the post-reform period for the period 1995 to 2012 based on a panel data model. The study has indicated that the operating profits, wage bills, non-performing assets and net interest margin affect the profitability of banks. The priority sector lending has not shown any impact on profitability and net interest margin has significantly reduced profitability. Sinha and Sharma (2016) studied the impact of bank specific, industry specific and macroeconomic factors affecting profitability of Indian Banks in a dynamic model framework. In this study, they concluded that the Indian Banks in the last decade have been moving towards efficiency and dynamism. Gulati and Kumar (2016) have assessed the impact of the global financial crisis on the profit efficiency of Indian Banks. They have found that the global financial crisis has not resulted with any adverse effect on the profit efficiency of the banking sector in India due to the adoption of accommodative macro policies aiming at injecting sufficient liquidity in the system. Ahamed (2017) investigated whether a shift in non-interest income activities has improved the profitability of Indian banks. He has shown that higher share of non-interest income yielded higher profits and risk adjusted profits when banks are involved in more trading activities. He has further stated that private foreign banks earn more risk-

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adjusted profits compared to public sector and private sector banks.

4. Methodological Aspects and Description of Data 4.1 Data structure The bank specific variables and industry specific variables used in the study are taken from the Report on Trend and Progress of Banking in India and Statistical Tables relating to Banks published periodically by the Reserve Bank of India. Macro Economic Variables are taken from Hand book of Statistics on Indian Economy for various years. It may be noted that each variable exactly stands for the factors for which, it has been used as a proxy. Due to this fact that we may assume that, there is no ambiguity about the variables used in this study. Descriptions of the variables used in the study are presented in the Table 2. This study is based on panel data set covering 26 commercial banks of India (20 Nationalized Banks and 6 State Bank of India and its associates) over the period 2010-11 to 2015-16.

4.2 Description of Variables under Study 4.2.1 Dependent Variable Return on Assets (ROA) is the commonly used indicator of profitability and it is defined as the ratio of profit after taxes to the total of average assets of a bank. Return on Assets (ROA) is taken as the banks real independent variable as it reflects how well a bank’s management is using the banks real investment resources to generate profits. Most of the researchers have considered ROA as a key ratio for measuring profitability of banks. 4.2.2 Independent Variables Bank Specific Variables Bank Size (LNTA) The logarithm of total assets is considered as a proxy of size to capture possible cost advantages associated with size (economies of scale) Logarithm of total assets (LNTA) may save a possible effect on bank’s profitability if there are significant economies of scale. In case of banks increased diversification leading to higher risks, the variable may exhibit negative effects. Capital Adequacy Ratio (CAR) The ratio of equity to total assets is one of the important basic ratios for the capital strength. It is

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expected that the higher the CAR, the lower the need for external funding and higher the profitability of the bank. CAR indicates the ability of the bank to absorb losses and handle risk exposure with share holder. The ratio is expected to have a positive relation with ROA.

demand for loans and deposits. This variable varies with time but not among banking industry. The coefficient of this variable indicates a positive relationship between GDP growth and profitability.

Net Interest Income to Total Assets (NIITA)

Inflation rate is another important macroeconomic condition possibly affects the level of overhead costs and the net interest revenues. It is included in the analysis to account for economic uncertainty. Researcher’s have noted that the expected effect of this variable on profitability is ambiguous.

Net Interest Income is the difference between the revenue that generated from a bank’s assets and expenses associated with paying out it liabilities. The higher the ratio of net interest income to total assets, the better a bank is at managing its assets and interest requirements. The ratio is a proxy for non-traditional income of a bank and will exhibit the magnitude of diversification of banks. This variable is expected to exhibit positive relationship with bank profitability. Reserves and Surplus (RS) Reserves are amount taken from the profit and loss account to meet the sudden losses due to natural calamities or to pay the premium to the share holders when profits are insufficient. The excess of income over expenditure is known as Surplus. The relationship of Reserves and Surplus to Return on Assets will be either positive or negative. Operating Expenses to Total Assets (OEXTA) The ratio of operating expenses to total assets will provide information on non-interest expenses of a bank. The operating expenses consist of total wages and salaries of employees and cost of running office facilities. The ratio of these expenses to total assets is expected to be negatively related to profitability, since improved management of these expenses will increase efficiency and therefore raise profits. Capital (CAP) Capital is the value of the bank’s assets extending its liabilities or debts. Assets include cash, loan and securities, while liabilities cover customer’s deposits and money owed to other banks and bond holders. It represents the net worth of bank on its value to investors. This variable will be positively related to ROA. Macro Economic Variables Gross Domestic Product Growth (GDP) The Gross Domestic product is one of the most macroeconomic indicators for measuring an economy’s total economic activity. This measure is expected to influence numerous factors related to supply and

Inflation Rate (INFR)

Industry Specific Variables Hirschman-Herfindahl Index (HHI) Hirschman-Herfindahl Index (HHI) is introduced in this study as a proxy variable for the market concentration and its impact on bank profitability. Most of the researchers used this measure for market concentration. The higher concentration means lower competition and vice-versa. It is calculated as the sum of squares of market-share. [HHI = s2i, where si is the share of the total industry assets of each bank]. A higher concentration may lead to a positive impact on profitability. 4.3 The Model The general model to be estimated is of the following linear form K

Yit  C 



k k X it

(1)

  it

k 1

it = i + uit where, Yit is the profitability of bank i at time t, with i = 1, 2, ..., N; t = 1, 2, ..., T, C is a constant term, Xit’s are k independent variable and it is unobserved bank-specific effect and uit is the idiosyncratic error. It is to be noted that the above model is a one-way error component regression model, where i ~ IIN (0, 2í) and independent of uit ~ IIN (0, 2u). As it has been noted earlier, the independent variables are grouped into bank specific, macroeconomic and industry specific variables. The above model is separated into these three groups as J

Yit  C 

 j 1

L

 j X iti 

 l 1

M

l X it1 



m m X it

it (2)

m 1

where, the Xit’s with subscripts j, l and m, denote


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Table 1 : Scheduled Commercial Banks Return on Assets and Return on Equity (Bank Group wise). (Percent) S. No.

Bank Group

1. 2. 3. 4.

Public Sector Banks Private Sector Banks Foreign Banks All Scheduled Commercial Banks

Return on Assets 2014-15 0.46 1.68 1.84 0.81

2015-16 1.50 1.45 0.31

Return on Equity 2014-15 7.76 15.74 10.24 10.42

2015-16 13.81 8.00 3.59

(Source: Bank’s annual accounts). Table 2 : Description of the variables used in the Regression Model. Variable

Measure

Notation

Hypothesized relationship with Profitability

Net Profits/ Total assets.

ROA

—

Size

ln of Total Assets

LNTA

Negative

Capital Adequacy Ratio

Equity / Total Assets

CAR

Positive

Net Interest Income

Net Interest Income/Total Assets

NIITA

Positive

Reserves and Surplus

Reserves and Surplus

RS

Positive/ Negative

Operating Expenses

Operating Expenses/Total Assets

OETA

Negative

Capital

Capital

CAP

Positive

Gross Domestic Product

Gross Domestic Product

GDP

Positive

Inflation Rate

Current period Inflation Rate

INFR

Positive/ Negative

Market Concentration

Herfindahl-Hirschman Index

HHI

Positive

Dependent Variable Profitability

Return on Assets

Independent variables

bank-specific, macroeconomic and industry-specific determinants respectively. 4.3.1 Correlation Matrix In Table 3, we have presented the correlation matrix for the variables in our study. The correlation matrix shows that in general the correlation between independent variables are not strong, thus suggesting that multicollinearity problems are not severe.

5. Empirical Results Descriptive statistics for all the variables used in the analysis are presented in Table 4. All the variables have a positive mean value. ROA has a mean of 0.5451% and a standard deviation of 0.4846%. The variables Reserves and Surplus and Capital present larger standard deviations compared with other variables. This fact clearly reveals that Reserves and Surplus and Capital have wide significant variance than other variables. The difference between maximum and

minimum for Reserves and Surplus is also high and this suggests the heterogeneity among public sector banks in respect of this variable. In Table 5, we have presented tolerance and variance inflation factor test. This test is performed for the detecting multicollinearity (variance inflation factor) in the model and to support the validity of the regression result. It is reported that if the value of variance inflation factor is below 10 and tolerance nearer to zero, it will then suggest no multicollinearity. The values of variance inflation factor for the independent variables in the model ranges between 1.1294 to 5.5847 and the value of tolerance from 0.1791 to 0.8854 for Gross Domestic Product to Capital suggesting the absence of multicollinearity among the variables of the model. We have presented the empirical results based on panel data regression model using ROA as dependent variables in Table 6.

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Table 3 : Correlation Matrix. (Pearson Correlation Coefficients) [Prob > |r| under H0 : =0] ROA

LNTA

CAR

NIITA

RS

OETA

CAP

GDP

INFR

HHI

ROA

1

LNTA

– 0.1379 (0.1330)

1

CAR

0.4805 (< 0.001)

– 0.1042 (0.2574)

1

NIITA

0.2900 (0.0013)

– 0.0430 (0.6411)

– 0.6382 1 (< 0.0001)

RS

– 0.0809 (0.3800)

0.9260 – 0.0443 (< 0.0001) (0.6310)

OETA

0.1781 (0.0517)

– 0.1382 (0.1323)

0.7922 0.7308 – 0.1016 (< 0.0001) (< 0.0001) (0.2694)

CAP

– 0.2415 (0.0079)

– 0.0770 (0.4033)

0.0075 (0.9354)

GDP

– 0.7602 0.2969 (< 0.0001) (0.0010)

– 0.6427 – 0.1888 (< 0.0001) (0.0390)

0.2615 (0.0039)

– 0.3805 – 0.0573 (< 0.0001) (0.5378)

1

INFR

0.6197 – 0.2190 (< 0.0001) (0.0163)

0.5540 0.0779 (< 0.0001) (0.3977)

– 0.2038 (0.0256)

0.3519 0.0651 (< 0.0001) (0.4797)

– 0.7849 1 (< 0.0001)

HHI

– 0.5719 0.2696 (< 0.0001) (0.0029)

– 0.3812 0.1841 (< 0.0001) (0.0442)

0.2377 (0.0089)

– 0.1596 (0.0817)

0.8192 – 0.7733 1 (< 0.0001) (< 0.0001)

0.0286 (0.7568)

– 0.0420 (0.6488)

1 1

– 0.2068 0.1437 (0.0234) (0.1175)

1

– 0.0802 (0.3837)

Table 4 : Descriptive Statistics for Dependent and Independent variables. S.No. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Variable Return on Assets (ROA) Log of Total Assets (LNTA) Capital Adequacy Ratio (CAR) Net Interest Income to Total Assets (NIITA) Reserves and Surplus (RS) Operating Expenses to Total Assets (OETA) Capital (CAP) Gross Domestic Product (GDP) Inflation rate (INFR) HH Index (HHI)

Mean

Standard deviation

Minimum

Maximum

0.5451 21.4026 11.2548 2.6599 156691214 0.0139 6970908 108390 8.4667 0.0867

0.4845 0.7466 3.3231 1.1266 208071754 0.0046 6392750 17832 1.9172 0.0039

1.0200 20.0700 0 0 28008129 3.02072E-7 207500 87360 5.6000 0.0825

1.5300 23.8408 15.3800 5.7505 1434981583 0.0221 40468251 135761 10.4000 0.0940

Table 5 : Tolerance and Variance Inflation Factor. S.No. 1. 2. 3. 4. 5. 6. 7. 8. 9.

Variable Log of Total Assets (LNTA) Capital Adequacy Ratio (CAR) Net Interest Income to Total Assets (NIITA) Reserves and Surplus (RS) Operating Expenses to Total Assets (OETA) Capital (CAP) Gross Domestic Product (GDP) Inflation rate (INFR) HH Index (HHI)

Tolerance 4.3515 4.0595 2.5547 4.0444 3.7522 1.1294 5.5847 3.5968 5.5471

Variance Inflation Factor 0.2298 0.2463 0.3914 0.2473 0.2665 0.8854 0.1791 0.2780 0.1802


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Table 6 : Determinants of Return on Assets (ROA) and Related Results. S. No. Variable Intercept 1. Log of Total Assets (LNTA) 2. Capital Adequacy Ratio (CAR) 3. Net Interest Income to Total Assets (NIITA) 4. Reserves and Surplus (RS) 5. Operating Expenses to Total Assets (OETA) 6. Capital (CAP) 7. Gross Domestic Product (GDP) 8. Inflation rate (INFR) 9. HH Index (HHI) 10. R2 11. Adjusted R2 12. F 13. Durbin - Watson

Parameter Estimate Standard Error 0.9950 1.6883 0.0489 0.0593 0.0038 0.0129 0.2089 0.0301 4.34685E-10 2.05089E-10 37.4260 8.8511 1.96585E-8 3.527441E-9 0.00002080 0.000028 0.0733 0.0210 26.6613 12.6936 0.7200 0.7028 41.7200 (< 0.0001) 2.0540

There exists positive relationship between ROA and the bank specific variables viz., Ratio of Net Interest Income to Total Assets and Reserves and Surplus. Ratio of Net Interest Income to Total Assets is significant at 1% level of significance. The Reserve and Surplus is significant at 5% level of significance. We find negative relationship between ROA and size of the bank and Capital Adequacy Ratio. These two bank specific variables are not significant in the analysis carried out. The bank specific variables viz., Operating expenses to total assets and Capital are negatively related to ROA and the two variables are significant at 1% level of significance. The macroeconomic variable viz., Gross Domestic Product is negatively related to ROA and the same is significant at 1% level of significance. The macro specific variable viz., Inflation rate is positively related to ROA and it is significant at 1% level of significance. The industry specific variable viz., HH Index is positively related to ROA and the same is significant at 5% level of significance. The value of R2 is 0.72 which clearly reveals 72% of the variation in the dependent variable is explained by the independent variables. The value of F-statistic is 41.72 and is significant and the same specifies the validity of the model. Additionally, to test the assumption of independent errors (auto-correlation) the Durbin-Watson Statistic is computed and this value is found to be 2.0540. This value lies between 1.5 and 2.5. This clearly indicates that the assumed model is valid and reliable one in our study.

t 0.5900 0.8300 0.300 6.9400 2.1200 4.2600 5.5800 7.4000 3.4900 2.1000

Pr.(t) 0.5566 0.4105 0.7679