Dr. Ritu Wadhwa
Assistant Professor
Department of Finance
Amity Business School
Amity University, Noida
Banks are the critical elements of the Indian Economy, and they have significant control over the supply of money in the Economy. In the last few years, India's financial markets and banks have seen a dramatic change with a lot of mergers taking place like united bank of India and oriental bank of commerce merged with PNB. Both external and internal factors can affect the performance of the banks. This study aims to determine whether the ROA and NPA’s of Indian banks get affected due to changes in the macro and micro- economic variables. The study was conducted on a six-year data of (public and private) highly profitable and low-income banks in 2020. Descriptive statistics, Correlation Analysis and Multiple Regression Analysis techniques were used to perform the analysis.
Keywords: Non-Performing Assets (NPAs), Profitability, Micro and Macro variables
Banking industry has contributed considerably in exponential expansion of Indian Economy since independence.
The holistic expansion of any financial structure relies primarily on its banking sector which must have three objectives to offer: maximum profit, to provide quality services to customers and to have good amount of credit services for debtors. Therefore, the gains made by bank can be determined at low and major levels. An effort is put by banks to obtain and achieve business profits, especially when the financial markets are so attractive. To maintain financial stability, profit making banks should take in unwanted surprise.
A study of gains made by banks plays pivotal part in expansion of economy. Nonetheless, in developing countries like India, the importance of gains made by SCBs is not as prevalent as it is in developed countries like the United States. Understanding the principles tells about the banks’ gains is vital to economic stability because they are vital to economic permanence. To analyze the various consequence of microeconomic factors for different banks various studies have been performed. However, the question that often arises is whether the macro-economic variables impact banks' profitability or not. The present study attempts to address the above question by analyzing the outcome of distinct macro and micro factors.
In the year 1921, India commenced its banking service by creating the Presidential Bank leading to foundation of the Imperial Bank of India to carry out banking services. In 1934, RBI was set up and later nationalized in the year 1949, to oversee the work of other banks. After acquiring the Imperial Bank of India, it was retitled as State Bank of India by RBI in 1955. To widen the reach and strength of banks, another 14 banks were established and nationalized by the Government of India in 1969. As a result of which six more public banks got nationalized in the year 1980.
In India banking industry which comprises of co-operative and commercial banks, has seen a considerable growth as an outcome of recent regulations and the problem of Non-Performing Assets also decreased considerably.
Various changes have been suggested by the Narasimham Committee (1992) to strengthen the banking system in India and also to ensure its resilience and stability. Furthermore, under the Banking Regulation Act, 1993, private sector banks were granted access to the Indian banking system. The changes made in the banking system were important because they included:(1) monetary restructuring;(2) interest rate deregulation;(3) the implementation of market basked exchange rate system;(4) new regulatory requirement;
(5) new asset classification and asset liability norms and(6) new risk management provisions and standard.
The RBI was instrumental in improving the performant of the financial market. The cash reserve ratio (CRR) was not given enough importance, and open market operations (OMOs) were applied for liquidity management mechanism as a result of alterations in monetary policy regime. Deregulation of interest rates; flexibility in the banks license policies; escalation of capital structure; and independence of functionality in public sector banks were all introduced to ensure stability. Implementation of these policies aided the economy’s growth.
The expansion of banking industry is occurring at a rapid rate, with deposits increasing at a CAGR of 21%. The bank's performance, i.e., profitability, can be affected by micro and macroeconomic variables like economic growth, unemployment rate, productivity, international trade, money supply, budget deficit, business cycle, and general price level of inflation as well as exchange rates. The variables specific to banks might differ from bank to bank, whereas macroeconomic variables are not under its control.
Loans offered by the bank are regarded as its assets. As result, if the lender (bank) cannot afford the principle or interest, or both the loan would be listed as a NPA (Non- Performing Asset). Any asset that ceases to give return to its investors for a certain duration is classified as an NPA. Generally, in most countries, the time limit is set as 90 days by various lending institutions. Also, as per terms of contract the lending establishments and the borrower can differ.
Random development of corporate housing during the development phase and loans taken at lower rates over time are offered higher prices, therefore, leading to NPAs. The high level of the NPA undermines the trust of lenders, investors etc. This may also give rise to financial mismanagement, which can make debt consolidation more difficult. Loan defaults have an effect not only on future credit availability, but also on the banks’ financial stability.
Profitability: NPAs may lead to adverse repercussion on profits as banks stop earning money. Asset (credit) contraction: Increased Non-Performing Assets put burden on spending and deteriorate the capacity of banks to lend more and thus paving a path to diminished interest rates which could further lead to recession.
Liability Management: ‘To retain NIM, banks are forced to cut deposit interest rates and charge high rate of lending on loans due to high NPAs. This will have an adverse result on bank system’s smooth operation, which in turn can affect the economy’s growth.
Capital Adequacy: Banks are expected to retain sufficient resources on risk- weighted assets on continuous
basis as per Basel norms. A rise in nonperforming assets raises risk weighted assets which authorize the banks to shore up their capital base even further.
Shareholders’ confidence: Often, shareholders seek to maximize the value of their assets through higher returns and market savings, which may be possible only if the bank produces proceeds from transactions. An increase in NPA rate may have a damaging outcome for banking business and profits where shareholders do not receive market returns on their capital and might damage their investment value. According to the available guidelines, before announcing the dividend, it becomes mandatory for the banks with an NPA rate of 5% and above to obtain consent from RBI.
Public confidence: The high level of non-performing assets has a direct effect on the financial system's reputation because it shakes the public's trust in the banking system. As a result, a rise NPA affects the smooth functioning of banks also the growth of economy. In short, high NPA events have a detrimental effect on all the important bank financing rates, Net Interest Margin, return on assets, Profits, Dividend Pay-out, etc., can alter the investors’ value, Borrowers, Employees, and the general public.
As banks perform arbitration functions in the real sector, they are open to business cycle conditions that greatly control the total health of the real sector. As monetary conditions decline during the financial slowdown, the risk of intermediation might rise. Banks are susceptible to negative choice and moral risk behaviour of their borrowers. Furthermore, since lower interest rates on inflation lead to the decline of bank lending rates, the economic downturn may have a bleak outcome on gains of banks. In addition, dropping stock prices and an inadequate mergers and procurements can reduce revenues. Overall, bank risk is negatively associated with the business cycle, rising at periods when economic activity is decreasing.
The theoretical impact of fluctuations in the exchange rate on a bank's risk depends on the interaction between cash flows and international bank exposure. The depreciation of domestic currency can be expected to hurt banks with foreign currency debt which exceeds their assets in foreign currencies. However, the outcome of exchange rate is influenced by the operation of banks’ borrowers as its main effect on bank profitability, i.e., they give more significance to the connection between currency rate and credit risk rather than risking such money. Taken together, depreciation of domestic currency will increase the credit risk for bank loans offered to foreign buyers and lessen the risk of debt in the foreign trade. The bank's full exposure to exporting or importing corporate purchaser will determine changes in bank's overall risk profile. Currency price can affect banks with various types of exposure in different ways.
Changes in commercial terms affect banking risks by influencing the profitability of bank borrowers, i.e., they also significantly affect credit risk. The decline in trade occurs when imports are more expensive compared to exports, limiting imports. The collapse of trade regulations can increase the risk of bank debt.
For banks, interest rate fluctuations on the open market are a major source of market risk. If interest rates increase, borrowers would be enticed to take on riskier investments. Furthermore, increasing market interest rates would give rise to lending risk by resulting in higher bank returns on new or variable-interest loans. However, in view of the assumptions of distorted intelligence theories, high rates of lending tend to worsen the problem of poor selection – i.e. the choice of borrowers who are most likely to have inapt project outcomes or “major risks in the context of credit relationships.
Increased inflation lowers the real value of a bank's asset, resulting in credit rationing. As a result, nations having soaring inflation rates would see a drop in bank reserves, affecting current borrowers' earnings and, as a result, the profitability of previously extended loans. Increased inflation rates could cause banks to take less risk on their balance sheets if the impact of a credit rating appears to be high.
(B. Mohanty and S. Sarkar, S. 2020) in research titled “Impact of Bank-Specific and External Factors on Profitability: An Empirical Study of PSU Banks in India” investigated the effect of internal and external economic factors on banks profitability. Evidence suggests that the liquidity risk has a considerable adverse impact on the PSU banks’ profits. Monetary progress, bank size and return on assets adversely affect the profits; only one of the results was statistically important. The increase in NPA negatively affects profits. [1]
(J. Bawa, S. Basu et al 2018) in the research titled “An analysis of NPAs of Indian banks: Using a comprehensive framework of 31 financial ratios” investigated the outcome of fiscal ratios on bank NPAs using a frame-work of 31 variables under the intermediation approach. The research examined the cause of NPA in the India’s banking industry in India for SCBs for timeline from 2007 to 2014. The data was evaluated using a GMM model which fixes the data endogeneity issues. With an r-square of 85 percent, this model correctly predicted NPA. The intermediation Return on Investment, NPAs and cost ratio all had a considerable adverse relationship. [2]
(A. Makkar and Hardeep 2018) in the research titled “Key factors influencing profitability of Indian commercial banks” studied to determine the banks’ profit along with understanding of important feature contributing to the profitability of forty-six Indian commercial banks (twenty six government banks and twenty private banks) using the financial data of 15 year (2001) -02 to -2015-16). Research has found that efficiency, liquidity, size and solvency are the important factors contributing to the profit of Indian SCBs. The research concluded that there is consistency in the profits of PSU banks while there is a lack of resilience to private sector banking. [3]
(M. Kaur, and R. Kumar, 2018) in the research titled “Bank Specific and Macro Determinants Influencing Non-Performing Assets in Indian Public Sector Banks” examined various causes of NPAs in the bank system including the effect of specific variables causing NPAs. A set of samples was acquired from ten PSU banks based on sample and size by applying quartile deviation. Panel data was adopted to inspect the set, and it was discovered that both bank-specific and macro-determinants affect the amount of gross non- performing assets (NPAs) in PSU banks. [4]
(L. Memdani, 2017) in the research titled “Macroeconomic and Bank Specific Determinants of Non- Performing Loans (NPLs) in the Indian Banking Sector” looked at the various reasons of non-performing assets of banks in India, analysed them to check whether they varied between public, private and foreign banks. The econometric technique of Fixed Effects model and Random Effects model were applied for analysis purpose. The outcomes unveiled the macro-economic factors, like Inflation (INFN) and log of per capita income (LPCY) have major impact for NPAs in Public Sector Banks (PSBs) whereas in private, LPCY is highly important but bank specific variables such as size and total loan amount are not. [5]
(V. Singh, 2016) in the research titled “A Study of Non-Performing Assets of Commercial Banks and it’s recovery in India” explain the status and pattern of NPAs in the Indian Banking System for Scheduled commercial banks, the variables causing non- performing assets, the reasons for outcome of non-performing assets and resuscitation of NPAs through various methods. [6]
(J. Sheefeni, 2015) in the research titled “The Macroeconomic Determinants of Profitability among Commercial Banks in Namibia” studied to adjust the macro variables that influence the banks’ profit of banks. According to the assessment made, the macroeconomic variable, i.e. GDP, Rate of Interest and rate of Inflation does not have a considerable association with the commercial banks' profitability in Namibia. [7]
(K. Narwal, and S. Pathneja, 2015) in the research titled “Determinants of Productivity and Profitability of Indian Banking Sector” did a detailed study to obtain factors of profitability and productivity of banks in India based on pre (2003-04 to 2008- 09) and post (2008-09 to 2013-14) reformatory era by using DEA analysis. Technologies, the bank and market share size were observed to comprehend its outcome on the yield of banks. Different variables like ROAA (Return on Average Assets), Spread and Diversification were examined to understand the profitability. According to the result, the Private sector outperformed public sector banks in their productivity due to efficient technology use. Moreover, private banks are ahead of public banks paper when it comes to profitability. [8]
(I. Trenca, E. Corovei, et al 2015) in the research titled “Impact of Macroeconomic Variables upon the Banking System Liquidity” studied the result of macro-economic variables (inflation rate, unemployment rate, public deficit, GDP, Etc) on banks' liquidity for different countries. Total of 40 banks was analysed for a timeline beginning from 2005 to 2011 using regression model. The outputs indicated that GDP had the minimal effect on liquidity, whereas the inflation rate had the maximum impact. [9]
(C. Andoh, A. Alhassan, et al 2014) in their study-“Asset quality in a crisis period: An empirical examination of Ghanaian banks” used a database of 25 banks to research the criterion of asset quality degradation during the economic meltdown of 2005 to 2010. They discovered that, in addition to increasing debt, bank market structure, bank size, inflation, foreign currency’s real rate and growth of GDP were all important factors that were segment of banking assets in Ghana, using the Generalized Method of Moments' measure.[10]
(S. Kanwal, and M. Nadeem, 2013) in the research titled “The Impact of Macroeconomic Variables on the Profitability of Listed Commercial Banks in Pakistan” conducted a ten-year study to understand how macroeconomic variables (rate of interest, rate of inflation and GDP) influenced the Pakistan’s PSU banks’ profits. Banks in Pakistan use ROE, ROA and EM ratios as proxies for measuring the profitability. [11]
(K.R.M. Rao & T.B. Lakew, 2012) in the research titled “Determinants of Profitability of Commercial Banks in a Developing Country” examined factors affecting the profitability of commercial banks of Ethiopia. The assessment was executed with the help of data for the timeline from 1999/00- 2008/09. The outcome highlighted the profitability of banks were significantly affected due to change in macroeconomic factors. In the research, it was also highlighted that Inflation rate and real GDP growth rate are closely related to profitability of banks, however, they do not have much impact. [12]
(F. Sufian, and F. Kamarudin, 2012) in the research titled “Bank-specific and Macroeconomic Determinants of Profitability of Bangladesh’s Commercial Banks “identified banking related factors and significant economic downturns of the bank division in Bangladesh. The assessment was executed by taking the data of thirty-one banks for a timeline starting from 2000-2010. Various factors were identified using multiple regression analysis which had a substantial outcome influencing the banking division. The research showed the profitability of banks get significantly affected due to change in the macroeconomic factors. Although the coefficient of interest rate was high, but the connection in relation with economic growth and performance of bank was low. [13]
(D. Louzis, V. Metaxas et al 2012) in the research titled “Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios” it was analyzed that in the economic expansion phase, NPAs are much lower because both consumer and firm incomes are growing and thus are competent enough to pay their dues. During the downturn, however, lending institution are expected to give credit to low-income borrowers, resulting in a rise in bad debts. [14]
(S. Patidar, and A. Katatia, 2012) in the research titled “(An Analysis of NPA in priority sector lending: A comparative study between public Sector Banks and Private Sector Banks of India)” identified and contrasted the non-performing assets of SCBs in India. It is learnt that lending to the Priority Sector has a significant impact on Total NPA in PSU banks, while it has negligible bearing on Total NPA in private sector. [15]
(Aburime, U., 2008) in the research titled “Determinants of Bank Profitability: Company Level Evidence from Nigeria” studied the macroeconomic components of banking profits using 154 Nigerian banks covering the period from 1980 to 2006 and it was learnt that the gains earned by banks were considerably influenced due to fluctuation in the rate of interest and rate of exchange. [16]
(Athanasoglou, et al 2008) in the research titled “Bank-specific, industry-specific and macroeconomic determinants of bank profitability” the notable highlights were that the relationship between gains earned by banks and inflation rate has been an area of constant debate. [17]
(G. Wood and C. Staikouras, 2004) in the research titled “The Determinants of European Bank Profitability” evaluated the effect of micro and macro aspects on the rate of return, i.e. profit of the European Banks using the OLS model and fixed effects model and multiple regression models. The outcome indicates that the management-related decisions and changes in some external macroeconomic variables affected European Banks' profitability. [18]
(A. Pavković, 2004) in the research titled “Instruments for evaluating the performance of commercial banks”, banking sector profitability is derived net interest margin (NIM), return on average equity (ROAE) and the return on average assets (ROAA). Based on these profit criteria, suggestions are given to the boards of directors. The study follows the DuPont procedure of business activity estimation and express profitability with one value. [19]
(A. Prasad, S. Ghosh et al 2002) in the research titled “Banking sector reforms: A critical overview” explained that the India’s banking division is undergoing a phased approach to enhance its effectiveness and profitability. The deregulation of the banking sector was mainly for three reasons. Firstly, the system acts as a conduit for resource mobilisation. Furthermore, the conception of financial deregulation adopted worldwide with the extent of increasing financial crisis cases and thirdly reduced transaction cost because of the advent of information and communication technology. [20]
The banks have a significant part in the development of the economy and for smooth running of the gains made by banks is required just like any different division. The profits earned by banks helps in absorbing various losses that may arise due to change in policies, unforeseen circumstances that can contribute in the nations’ economic stability. Thus, it is essential to observe the internal and external variables that may impact the banks’ performance. India’s capital market has seen a dramatic fluctuation in incorporation and diversification over the past few years. In addition, the Indian banks profitability gets affected due to change in the global economic conditions. Thus, it is seen that credit system in India are exposed to various external factors which are rapidly changing. Therefore, it is necessary to ascertain how the bank’s profitability gets influenced by fluctuations in the macroeconomic and micro (bank specific) factors.
In economically advanced nations, gains made by banks has always been an area of main focus and many researches has been conducted on the same. However, in countries like India, Pakistan, and Bangladesh which are now an emerging economy, it is seen that not much research is carried out upon the profits of commercial bank. Various research was being conducted to comprehend the outcome of microeconomic components for numerous banks. However, the question that often arises is whether the macroeconomic variables impact banks' profitability or not. The present study aims to answer the above question by considering the effect of distinct macroeconomic variables.
Descriptive research method was followed to conduct the research. The data being used for the research is secondary data. The figures were acquired from the financial reports of some of SCBs and RBI website. By using correlation and regression analysis, the relationship and impact are calculated.
From 2014-2015 to 2019-2020, figures on inflation, interest rates, GDP growth rate, and bank ROA was gathered from the RBI website and balance sheets report of PSU banks and private banks that were taken into account for this assessment.
The assessment was executed using convenience sampling. Ten banks were considered for this research. The following banks were carefully chosen for this assessment:
[30]
Correlation Analysis: is used to check whether the macro and micro-economic variables are related to the NPA and ROA of the banks.
Multiple Regression: is deployed to see how the NPA and ROA of banks gets affected by various macro and micro-economic variables.
|
|
Mean |
Median |
Standard Deviation |
Kurtosis |
Skewness |
|
Axis Bank |
0.756666667 |
0.595 |
0.673013125 |
-1.823157284 |
0.49484955 |
|
HDFC Bank |
1.699382399 |
1.701818422 |
0.033663927 |
0.382188847 |
-0.8107833 |
|
ICICI Bank |
1.031444233 |
1.020392769 |
0.505267292 |
-1.069884811 |
0.04630261 |
|
IDBI Bank |
-2.253385131 |
-1.888489487 |
1.93870212 |
-1.478961593 |
-0.2146016 |
|
Yes Bank |
0.000175303 |
1.412328921 |
3.143502895 |
5.619608399 |
-2.3583438 |
|
SBI |
0.273837688 |
0.373720098 |
0.301158602 |
-0.410171455 |
-0.6687136 |
|
PNB |
-0.459154854 |
-0.277726966 |
0.849880216 |
-1.831523616 |
-0.3793076 |
|
BOI |
-0.549936481 |
-0.668593832 |
0.508520615 |
-0.329949178 |
0.86135035 |
|
BOB |
-0.061922195 |
0.047754609 |
0.448810359 |
0.594147776 |
-0.841388 |
|
UBI |
-0.211626987 |
-0.198678534 |
0.610137595 |
-1.684136938 |
-0.2925917 |
Table 4.1 Descriptive Statistics for ROA
The illustrative data for ROA of all the selected scheduled banks is shown in Table 4.1. Most of the banks are negatively skewed i.e. their mean is less than their median except Axis Bank, ICICI Bank and BOI that are positively skewed. HDFC, Bank of Baroda and Yes Bank have positive kurtosis indicating that they have leptokurtic distribution while all other banks have negative kurtosis indicating that they have platykurtic distribution.
|
|
Mean |
Median |
Standard Deviation |
Kurtosis |
Skewness |
|
Axis Bank |
11368.518 |
12609.13 |
8388.824428 |
-2.3723 |
-0.206248 |
|
HDFC Bank |
2236.4233 |
2222.505 |
1056.808041 |
-1.8755 |
-0.006253 |
|
ICICI Bank |
16065.042 |
13437.09 |
8667.619208 |
-1.5287 |
0.583513 |
|
IDBI Bank |
15797.297 |
14740.42 |
9589.285238 |
-1.6134 |
0.308683 |
|
Yes Bank |
2644.3067 |
1192.51 |
3331.396345 |
1.55198 |
1.492418 |
|
SBI |
61715.953 |
57042.2 |
27350.59877 |
2.75363 |
1.140522 |
|
PNB |
31577.005 |
31369.89 |
10880.11798 |
1.48176 |
0.172897 |
|
BOI |
21409.372 |
22212 |
6673.679453 |
-2.4763 |
-0.204003 |
|
BOB |
17704.147 |
18743.32 |
5452.346671 |
1.65827 |
-1.198489 |
|
UBI |
16956.48 |
18067.62 |
5978.112751 |
1.04873 |
-0.829966 |
Table 4.2Descriptive Statistics of NPA
The descriptive statistics for NPA of all the selected scheduled banks is shown in Table 4.2. Most of the banks are negatively skewed i.e. their mean is less than their median except IDBI, ICICI, Yes Bank, SBI and PNB which are positively skewed. Yes Bank, SBI, PNB, BPB and UBI have positive kurtosis indicating that they have leptokurtic distribution while all other banks have negative kurtosis indicating that they have platykurtic distribution.
|
|
Mean |
Median |
Standard Deviation |
Kurtosis |
Skewness |
|
GDP Growth Rate |
6.835 |
7.225 |
1.504709274 |
1.400371856 |
-1.2686496 |
|
Interest Rate |
6.125 |
6.375 |
1.126388033 |
3.38882283 |
-1.6531328 |
|
Inflation Rate |
5.233333333 |
5.26 |
1.683183492 |
1.731683779 |
-0.3858709 |
Table 4.3 Descriptive Statistics for Macro-Economic Variables
The descriptive statistics for Macro-Economic variables is shown in Table 4.3. It can be interpreted that all the macro-economic factors i.e. Interest rate, growth rate of GDP and rate of Inflation are negatively skewed meaning that their mean is less than their median. In case of Kurtosis, Growth rate of GDP, rate of Inflation and rate of Interest have positive kurtosis which means that they has leptokurtic distribution.
|
|
GDP Growth Rate |
Interest Rate |
Inflation Rate |
|
Axis Bank |
0.541331002 |
0.710352881 |
0.059404185 |
|
HDFC Bank |
0.006998118 |
0.194721576 |
0.215328373 |
|
ICICI Bank |
0.62436955 |
0.510401565 |
-0.46338749 |
|
IDBI Bank |
0.771415784 |
0.652664748 |
-0.472462117 |
|
Yes Bank |
0.916818974 |
0.922139663 |
-0.203157968 |
|
SBI |
0.176627385 |
0.176430543 |
-0.207968109 |
|
PNB |
0.054049925 |
-0.030385136 |
-0.352014954 |
|
BOI |
0.069789463 |
0.140764278 |
-0.184006604 |
|
BOB |
-0.176042936 |
0.013989438 |
0.070677887 |
|
UBI |
0.526916945 |
0.513683144 |
-0.26548943 |
Table 4.4 Correlation of Macro-Economic Variables and ROA of Banks
In Table 4.4, the connection between the macro-economic variables and ROA of bank has been shown. It is witnessed that the ROA of all the PSU banks have low negative correlation with the rate of inflation except BOB which has slightly positive correlation. Regarding private banks, most banks have moderate negative correlation with inflation rate. Axis and HDFC have a low positive correlation with inflation rate.
According to the table, this can be interpreted that ROA of all private banks have high positive correlation with GDP growth rate except HDFC which has a low positive correlation. In regard to PSU banks, UBI has a moderate positive correlation. While SBI, PNB and BOI have a low positive correlation and BOB has slight negative correlation with GDP growth rate.
In case of Interest Rate, it can be interpreted from the table that ROA of various public and private banks ` have a medium to high positive correlation with Interest Rate except PNB which have a slight negative
correlation with Interest Rate.
|
|
GDP Growth Rate |
Interest Rate |
Inflation Rate |
|
Axis Bank |
-0.77579649 |
-0.746375111 |
0.311364185 |
|
HDFC Bank |
-0.816111955 |
-0.770034471 |
0.35778949 |
|
ICICI Bank |
0.383645271 |
-0.019498182 |
-0.59991352 |
|
IDBI Bank |
0.51630783 |
0.175283502 |
-0.52586276 |
|
Yes Bank |
-0.954514038 |
-0.873952589 |
0.371424188 |
|
SBI |
0.008831075 |
-0.169890162 |
-0.096348223 |
|
PNB |
0.158299839 |
-0.148730628 |
-0.285576692 |
|
BOI |
0.608743072 |
0.24213859 |
-0.502100172 |
|
BOB |
-0.258891482 |
-0.633882292 |
-0.295574114 |
|
UBI |
-0.185346414 |
-0.394472206 |
-0.101673384 |
Table 4.5 Correlation of Macro-Economic Variables and NPA of Banks
In Table 4.5, the association between the macro-economic elements and NPA of bank is indicated. It observes that the NPA’s of all the PSU banks have a slight negative correlation with the rate of inflation except BOI which has an moderate negative correlation. In case of private banks, the majority of them have low to moderate negative correlation with inflation rate. Axis and HDFC have a slight positive correlation with inflation rate.
From the table, it can be interpreted that NPA of various private banks have high negative correlation with GDP growth rate except IDBI and ICICI which have a low to moderate positive correlation. For public banks, BOI has a moderate positive correlation. While SBI and PNB have a low positive correlation. UBI and BOB has low negative correlation with GDP growth rate.
In case of Interest Rate, it can be interpreted from the table that NPA of various public and private banks have a medium to high positive correlation with Interest Rate except ICICI, SBI and PNB which have a slight negative correlation with Interest Rate. The NPA’s of BOI and IDBI have slight positive correlation with Interest Rate.
|
|
Total Assets |
Total Deposits |
Total Advances |
|
Axis Bank |
0.955617321 |
0.934996244 |
0.960082871 |
|
HDFC Bank |
0.983320663 |
0.978454323 |
0.98047109 |
|
ICICI Bank |
0.021881987 |
0.024078944 |
-0.003465186 |
|
IDBI Bank |
0.427174191 |
0.359390676 |
0.138585518 |
|
Yes Bank |
0.489993133 |
0.065036995 |
0.535015758 |
|
SBI |
0.503540448 |
0.1002514 |
0.404228732 |
|
PNB |
0.427699869 |
0.383421982 |
0.311691918 |
|
BOI |
-0.64613419 |
-0.597588565 |
-0.609380003 |
|
BOB |
0.304013172 |
0.258896514 |
0.264645493 |
|
UBI |
0.689841487 |
0.739323287 |
0.676488653 |
Table 4.6Correlation of Micro-Economic Variables and NPA of Banks
According to Table 4.6, the correlation between micro-economic variables and NPA of bank has been shown. It is witnessed that the NPA’s of all the PSU banks have a low to moderate positive correlation with the Total advances except BOI which has a moderate negative correlation. In regard to private banks, most of them have moderate to high positive correlation with Total Advances. ICICI has a slight negative correlation with Total Advances.
According to the table, this can be interpreted that NPA of Axis and HDFC have high positive correlation with Total Assets whereas Yes Bank, IDBI and ICICI have low to moderate positive correlation. For PSUs, BOI has a moderate negative correlation. While SBI and UBI have a moderate positive correlation. PNB and BOB has slight positive correlation with Total Assets.
In case of Total Deposits, it can be interpreted from the table that NPA of various public and private banks
have an average to high positive correlation with Total Deposits except ICICI, SBI and PNB which have a slight negative correlation with Total Deposits. The NPA’s of BOI and IDBI have slight positive correlation with Total Deposits.
|
|
Total Assets |
Total Deposits |
Total Advances |
|
Axis Bank |
-0.79241886 |
-0.761133478 |
-0.808903137 |
|
HDFC Bank |
-0.34040582 |
-0.315797532 |
-0.296463702 |
|
ICICI Bank |
-0.86401205 |
-0.858369642 |
-0.881490056 |
|
IDBI Bank |
0.851368842 |
0.898464898 |
0.936219153 |
|
Yes Bank |
-0.17864377 |
0.264102735 |
-0.239832512 |
|
SBI |
-0.66818176 |
-0.434640282 |
-0.597177703 |
|
PNB |
-0.42616712 |
-0.414378867 |
-0.42102988 |
|
BOI |
0.267326749 |
0.553329686 |
0.917835594 |
|
BOB |
0.190493536 |
0.225285503 |
0.203780335 |
|
UBI |
-0.79292761 |
-0.819683746 |
-0.715825502 |
Table 4.7Correlation of Micro-Economic Variables and ROA of Banks
Table 4.7, the correlation between the micro-economic variables and ROA of bank has been shown. It is witnessed that the ROA of all the PSU banks have a low to moderate positive correlation with the Total advances except BOB and BOI which have low positive correlation and high positive correlation accordingly. In case of private sector banks, Axis and ICICI have high negative correlation with Total Advances. HDFC and Yes Bank have a slight negative correlation while IDBI has high positive correlation with Total Advances.
From the table, it can be interpreted that ROA of most private banks have high negative correlation with Total Assets except Yes Bank and HDFC which have a low negative correlation. For PSU banks, BOI and BOB have a low positive correlation. While SBI and UBI have a moderate negative correlation. PNB has slight negative correlation with Total Assets.
In case of Total Deposits, it can be interpreted from the table that ROA of Axis, ICICI and UBI have a high negative correlation while HDFC, SBI and PNB have low negative correlation with Total Deposits. Yes Bank, BOI and BOB have low positive correlation while IDBI has high positive correlation with Total Deposits.
|
GDP Growth Rate |
Interest Rate |
Inflation Rate |
||
|
Axis Bank |
R Square |
0.756237863 |
|
|
|
|
Coefficients |
0.2714583 |
0.770031993 |
0.054705885 |
|
|
P Value |
0.530221133 |
0.514389925 |
0.817412341 |
|
|
Significance F |
0.31109141 |
|
|
|
HDFC Bank |
R Square |
0.333806493 |
|
|
|
|
Coefficients |
0.008612354 |
0.052372381 |
0.001879215 |
|
|
P Value |
0.803244388 |
0.602950688 |
0.928909166 |
|
|
Significance F |
0.504806229 |
|
|
|
ICICI Bank |
R Square |
0.850498618 |
|
|
|
|
Coefficients |
0.13686885 |
0.700900398 |
-0.153439169 |
|
|
P Value |
0.594583156 |
0.394111773 |
0.409383081 |
|
|
Significance F |
0.244609589 |
|
|
|
IDBI Bank |
R Square |
0.600753939 |
|
|
|
|
Coefficients |
0.845375359 |
1.165113264 |
-0.334144962 |
|
|
P Value |
0.566559265 |
0.750278321 |
0.697063347 |
|
|
Significance F |
0.395458529 |
|
|
|
Yes Bank |
R Square |
0.754152026 |
|
|
|
|
Coefficients |
0.309910481 |
-0.295019831 |
-0.071072737 |
|
|
P Value |
0.372063356 |
0.688133212 |
0.675993868 |
|
|
Significance F |
0.312391602 |
|
|
Table 4.8 ROA-Macro Multiple Regression Statistics for Private Sector Banks
Table 4.8 indicate the multiple regression output for private sector banks in which the ROA of bank has been considered as the dependent variable while inflation rate, GDP growth rate, rate of interest are examined as an independent variables. From the above table, it is inferred that the H0 will be considered (p value > 0.05) for various private banks i.e. the impact of the three macro-economic variables on the ROA of private banks is insignificant.
|
GDP Growth Rate |
Interest Rate |
Inflation Rate |
|
||||||
|
SBI |
R Square |
0.89915328 |
|
|
|
||||
|
|
P Value |
0.593884982 |
0.179723868 |
0.247648439 |
|
||||
|
|
Significance F |
0.201314608 |
|
|
|
||||
|
PNB |
R Square |
0.999507431 |
|
|
|
||||
|
|
P Value |
0.019787232 |
0.009974427 |
0.010770469 |
|
||||
|
|
Significance F |
0.014128777 |
|
|
|
||||
|
BOI |
R Square |
0.43438172 |
|
|
|
||||
|
|
P Value |
0.384240204 |
0.274040914 |
0.315444712 |
|
||||
|
|
Significance F |
0.467250346 |
|
|
|
||||
|
BOB |
R Square |
0.406247348 |
|
|
|
||||
|
|
P Value |
0.265870742 |
0.254623256 |
0.295158002 |
|
||||
|
|
Significance F |
0.478127855 |
|
|
|
||||
|
UBI |
R Square |
0.648691903 |
|
|
|
||||
|
|
P Value |
0.916738511 |
0.362836395 |
0.470143492 |
|||||
|
|
Significance F |
0.37173401 |
|
|
|||||
Table 4.9 ROA-Macro Multiple Regression Statistics of Public Sector Banks
Table 4.9 indicate the multiple regression result for PSU banks in which the ROA of bank has been considered as the dependent variable while inflation rate, GDP growth rate, rate of interest are contemplated as the independent variables. With reference totable 4.9, it can be interpreted that the H0 will be considered (p value
> 0.05) for various PSU banks except PNB. The acceptance of null hypothesis means that the impact of 3 macro- economic variables on the ROA of banks is insignificant.
For PNB, the value of P indicates the rejection of null hypothesis (p value < 0.05), thus Alternate Hypothesis will be accepted i.e. the three macro-economic variables considered for this research have considerable effect on the ROA of banks.
|
|
|
GDP Growth Rate |
Interest Rate |
Inflation Rate |
|
Axis Bank |
P Value |
0.733921483 |
0.532510073 |
0.599801139 |
|
|
R Square |
0.245927709 |
|
|
|
|
Significance F |
0.416381065 |
|
|
|
HDFC Bank |
P Value |
0.630694549 |
0.424955313 |
0.478747009 |
|
|
R Square |
0.452025048 |
|
|
|
|
Significance F |
0.31004929 |
|
|
|
ICICI Bank |
P Value |
0.489580918 |
0.497341248 |
0.684273957 |
|
|
R Square |
-0.183270592 |
|
|
|
|
Significance F |
0.617761182 |
|
|
|
IDBI Bank |
P Value |
0.424675615 |
0.462975624 |
0.581412018 |
|
|
R Square |
-0.144294174 |
|
|
|
|
Significance F |
0.60066426 |
|
|
|
Yes Bank |
P Value |
0.659381433 |
0.697053994 |
0.893909737 |
|
|
R Square |
0.848774913 |
|
|
|
|
Significance F |
0.089348754 |
|
|
Table 4.10 NPA-Macro Multiple Regression Statistics of Private Sector Banks
Table 4.10 indicate the multiple regression output for private sector banks in which the NPA of bank has been considered as the dependent variable while inflation rate, GDP growth rate, rate of interest are taken into
account as the independent variables. From the above table, it is inferred that H1 will be considered (p value
> 0.05) for various private banks i.e. the impact of the three macro-economic variables on the NPA of private banks is insignificant.
|
|
|
GDP Growth Rate |
Interest Rate |
Inflation Rate |
|
SBI |
P Value |
0.374002238 |
0.361764223 |
0.404967663 |
|
|
R Square |
-0.464371511 |
|
|
|
|
Significance F |
0.733378007 |
|
|
|
PNB |
P Value |
0.179607778 |
0.175598775 |
0.221752098 |
|
|
R Square |
0.264501472 |
|
|
|
|
Significance F |
0.40704324 |
|
|
|
BOI |
P Value |
0.041991018 |
0.049032865 |
0.065738323 |
|
|
R Square |
0.86060923 |
|
|
|
|
Significance F |
0.082457608 |
|
|
|
BOB |
P Value |
0.065889499 |
0.050763411 |
0.087287676 |
|
|
R Square |
0.832371911 |
|
|
|
|
Significance F |
0.098871572 |
|
|
|
UBI |
P Value |
0.389423512 |
0.34894311 |
0.421254891 |
|
|
R Square |
-0.312751831 |
|
|
|
|
Significance F |
0.672732974 |
|
|
Table 4.11NPA-Macro Multiple Regression Statistics of Public Sector Banks
Table 4.11 indicate the multiple regression output for public sector banks in which the NPA of bank has been considered as the dependent variable while inflation rate, GDP growth rate, rate of interest are examined as independent variables. From the above table, it is inferred that the H1 Hypothesis will be considered (p value > 0.05) for various PSU banks i.e. the impact of the three macro-economic variables on the NPA of public banks is insignificant.
|
|
|
Total Assets |
Total Deposits |
Total Advances |
|
SBI |
P Value |
0.131861657 |
0.628358391 |
0.151142866 |
|
|
R Square |
0.493971511 |
|
|
|
|
Significance F |
0.287691035 |
|
|
|
PNB |
P Value |
0.511423536 |
0.785142574 |
0.570225144 |
|
|
R Square |
-0.510024713 |
|
|
|
|
Significance F |
0.750812349 |
|
|
|
BOI |
P Value |
0.273230971 |
0.454603403 |
0.221317945 |
|
|
R Square |
0.428858015 |
|
|
|
|
Significance F |
0.322295488 |
|
|
|
BOB |
P Value |
0.119222352 |
0.142557305 |
0.163218159 |
|
|
R Square |
0.478727324 |
|
|
|
|
Significance F |
0.295843953 |
|
|
|
UBI |
P Value |
0.235135296 |
0.152927747 |
0.747160438 |
|
|
R Square |
0.630649694 |
|
|
|
|
Significance F |
0.213211389 |
|
|
Table 4.12 NPA-Micro Multiple Regression Statistics of Public Sector Banks
Table 4.12 indicate the multiple regression output for public sector banks in which the NPA of bank has been considered as dependent variable while Total assets, Deposits and Advances are examined as an independent variable. According to the table No.12, it is inferred that the Null Hypothesis will be considered (p value > 0.05) for various PSU banks i.e. the effect of three micro-economic variables on non-performing assets of public banks is insignificant.
|
|
|
Total Assets |
Total Deposits |
Total Advances |
|
Axis Bank |
P Value |
0.641031323 |
0.529993211 |
0.831870895 |
|
|
R Square |
0.855723508 |
|
|
|
|
Significance F |
0.085304672 |
|
|
|
HDFC Bank |
P Value |
0.142981119 |
0.170162656 |
0.547498017 |
|
|
R Square |
0.974334747 |
|
|
|
|
Significance F |
0.015359561 |
|
|
|
ICICI Bank |
P Value |
0.953027504 |
0.787816727 |
0.648753065 |
|
|
R Square |
0.124998023 |
|
|
|
|
Significance F |
0.955806875 |
|
|
|
IDBI Bank |
P Value |
0.032309708 |
0.732991649 |
0.022148424 |
|
|
R Square |
0.914323697 |
|
|
|
|
Significance F |
0.050962808 |
|
|
|
Yes Bank |
P Value |
0.102789013 |
0.007910952 |
0.485275684 |
|
|
R Square |
0.98292279 |
|
|
|
|
Significance F |
0.010228808 |
|
|
Table 4.13 NPA-Micro Multiple Regression Statistics of Private Sector Banks
Table 4.13 indicate the multiple regression output for private sector banks in which the NPA of bank has been considered as the dependent variable while Total assets, Deposits and Advances are examined as the independent variables. From the above table, it is presumed that the Null Hypothesis will be considered (p value > 0.05) for various private banks excluding HDFC and Yes Bank. The acceptance of null hypothesis means, the impact of three micro-economic variables on the NPA of banks is insignificant.
For HDFC and Yes Bank, the value of p points towards the rejection of null hypothesis (p value < 0.05), thus Alternate Hypothesis will be accepted i.e. the three micro-economic variables considered for this research have considerable impact on NPA of banks.
|
|
|
Total Assets |
Total Deposits |
Total Advances |
|
Axis Bank |
P Value |
0.759133358 |
0.991230109 |
0.627200715 |
|
|
R Square |
0.371727039 |
|
|
|
|
Significance F |
0.352180885 |
|
|
|
HDFC Bank |
P Value |
0.0827463 |
0.150656579 |
0.286308044 |
|
|
R Square |
0.680839424 |
|
|
|
|
Significance F |
0.185247839 |
|
|
|
ICICI Bank |
P Value |
0.880951025 |
0.528308058 |
0.379893683 |
|
|
R Square |
0.616958138 |
|
|
|
|
Significance F |
0.220783048 |
|
|
|
IDBI Bank |
P Value |
0.35749097 |
0.424079789 |
0.206485327 |
|
|
R Square |
0.823519577 |
|
|
|
|
Significance F |
0.103996944 |
|
|
|
Yes Bank |
P Value |
0.489558055 |
0.000850639 |
0.032106749 |
|
|
R Square |
0.998317911 |
|
|
|
|
Significance F |
0.001009083 |
|
|
Table 4.14 ROA-Micro Multiple Regression Statistics of Private Sector Banks
Table 4.14 indicate the multiple regression output for private sector banks in which the ROA of bank has been considered as the dependent variable while Total assets, Deposits and Advances are examined as independent variables. From the above table, it is inferred that the Null Hypothesis will be considered (p value > 0.05) for various private banks except Yes Bank. The acceptance of null hypothesis means, the impact of three micro- economic variables on ROA of banks is insignificant.
For Yes Bank, the value of p indicates the rejection of null hypothesis (p value < 0.05), thus Alternate Hypothesis will be accepted i.e. the three micro-economic variables considered for this research have considerable effect on ROA of Yes Bank.
|
Table
4.15ROA-Micro Multiple Regression Statistics of Public Sector Banks
Table 4.15 indicate the multiple regression output for public sector banks in which the ROA of bank has been considered as the dependent variable while Total assets, Deposits and Advances are evaluated as an independent variable. From the above table, it is presumed that the Null Hypothesis will be considered (p value > 0.05) for various PSU banks except UBI. The acceptance of null hypothesis means, the impact of 3 micro- economic variables on the ROA of banks is insignificant.
For UBI, the value of p indicates the rejection of null hypothesis (p value < 0.05), thus Alternate Hypothesis will be accepted i.e. the three micro-economic variables considered for this research have considerable effect on ROA of UBI.
All selected banks are negatively skewed, except for Axis, ICICI, and BOI, which are positively skewed, according to the descriptive statistics of ROA. The kurtosis of HDFC, BOB, and Yes Bank is positive, while the kurtosis of others is negative. All selected banks are negatively skewed, except IDBI, ICICI, Yes Bank, SBI, and PNB, which are positively skewed, according to the descriptive statistics of ROA. Positive kurtosis is found in Yes Bank, SBI, PNB, BPB, and UBI, whereas negative kurtosis is found in other banks.
From the descriptive statistics of Macro-Economic variables, it is seen all the macro- economic factors are negatively skewed and have positive kurtosis. All banks, except for BOB, have a positive correlation between GDP growth and ROA. Except for PNB, all banks have a positive correlation between to rate of interest and return on assets. Except for Axis, HDFC, and BOB, all banks have a negative correlation between inflation and return on assets. The correlation in macro variables and NPA is negative for all with few exceptions. The correlation between micro-economic variables and NPA is positive for all with few exceptions.
The correlation between micro-economic variables and ROA is positive for all with few exceptions. The impact of macro variables on return of assets of private banks is insignificant. The impact of macro variables on return of assets of public banks is insignificant except in case of PNB where the three macro-economic variables observed have significant impact on ROA of banks. The impact of the three macro-economic elements on the NPA of all banks is insignificant. The impact of micro-economic variables on non-performing assets of public banks is insignificant. The impact of micro-economic variables on non-performing assets of private banks is insignificant excluding HDFC and Yes Bank where micro-economic variables observed have significant impact on the NPA of banks. The impact of micro-economic variables on return of assets of private banks is not significant excluding Yes Bank where micro-economic variables considered have significant impact on ROA of bank. The impact of micro-economic variables on the return of assets of public banks is insignificant excluding UBI where micro-economic variables considered have significant impact on ROA of bank.
A few suggestions through which a check can be kept on the NPA and Profitability of Banks:
the required degree, and some problems remain that must be addressed immediately.
In the last many years, India's financial markets and banks have seen a dramatic change with a lot of mergers taking place like united bank of India and oriental bank of commerce merged with PNB. In addition, the Indian banks profitability gets influenced due to alterations in the conditions of international economies as well as any change inthe guidelines or policies of the home country. Thus, it can be highlighted that the Indian banking industry is exposed to various external factors which are rapidly changing. So, the banks need to be agile and require taking immediate measures so that it can safeguard their profitability. Also, according to the research it can be seen that the only these limited set of variables alone cannot be measure of analyzing the profitability and NPA’s of the banks. Future research also can be performed by considering more variables both external as well as internal factors and study how they impact the bank’s profitability and NPA.
Pacific Business. https://doi.org/10.1080/10599231.2020.1783983
International Journal of Academic Research And Development, 3(1), 373-378.
Annual Research Journal Of SCMS.
Journal of Banking & Finance, 36(4), 1012-1027. https://doi.org/10.1016/j.jbankfin.2011.10.012
Available from: http://www.ssrn.com/ abstract=1231064
Retrieved from https://hrcak.srce.hr/26203
Retrieved 13 January 2021, from https://www.businesstoday.in/current/corporate/icici-bank-fy20-results-profit-jumps-135- to-rs-7931- crore-asset-quality-improves/story/403334.html
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