IMPACT OF NON PERFORMING ASSETS ON STRATEGIC BANKING VARIABLES IN SELECTED PUBLIC SECTOR BANKS IN INDIA

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IMPACT OF NON PERFORMING

ASSETS ON STRATEGIC BANKING

VARIABLES IN SELECTED PUBLIC

SECTOR BANKS IN INDIA

GOUR BANDYOPADHYAY

The Heritage Academy, Chowbaga Road, Anandapur Kolkata, West Bengal, India

[email protected]

Abstract :

The study uses correlation and regression analysis to examine the impact of Non Performing Assets (NPA) on selected banking variables in two Public Sector Banks (PSBs) in India. Initially to examine degree of association between the strategic banking variables identified, simple correlation co-efficients have been computed and their significance examined. For the purpose of examining impact of NPA on the profitability and other strategic banking variables including time variable, simple linear regression and multiple regression (as appropriate) have been attempted. To diagnose the problem of multi co-linearity in multiple regressions, the value of tolerance factor (TOL) along with variance inflating factor (VIF) have also been computed and compared with standard. The study reveals that NPA has statistically significant negative impact on profitability and statistically significant impact on few strategic banking variables in respect of two selected PSBs.

Keywords: Non Performing Assets; Public Sector Banks; Pair wise correlation Analysis. Linear regression, Multiple regression.

1. Introduction

The evolution of Indian banking system, since the mid-eighties in general, and the launching of the economic policy in 1991, in particular, has been characterized by profound transformation as a result of prolonged and effective reform initiatives by Reserve Bank of India (RBI). The fundamental philosophy of the development process in Indian banking has shifted from highly regulated regime to free market economy as an outcome of macro policy initiatives of liberalisation, deregulation and globalisation of the economy.

Commercial banks in India in general with Public Sector Banks (PSBs) in particular have witnessed unprecedented growth in terms of deposit mobilization and disbursement of credit. Loans and advances figures in all the PSBs in India reveals existence of continuous upward trends over the last two decades, which indicate increasing credit appetite in the various segments of the society and thereby justifying the commonly held impression regarding ‘growth story’ in the Indian economy. Along with spectacular rise in total advances, Non Performing Asset (NPA) figures exhibit upward trends in the last three years (though prior to that period there was a declining trend in the NPA values over a period of six years in most of PSBs).

Bad Loans or NPAs are loan assets, which cease to generate income to the bank. These assets have well defined credit weakness that jeopardize the liquidation of debts and may be characterized by distinct possibilities that bank will sustain some losses. NPAs have dampening effect on banking system since long, though they were not in the public domain till early 90s (Khasnobis, 2006)[1]. By this time huge amount of advances involving uncertainty regarding repayment have piled up, resulting in question about the health of Indian banking system and their ability to honour their deposit commitments (Banerjee, Cole, & Duflo, 2004)[2]. One of the major challenges that the banking industry is facing today is the credit risks, exposing this fast growing industry to mounting NPAs. Though macro environmental factors including global recession, economic slowdown, rising inflation and inadequate legal frame work contributed heavily towards growth of distress asset in the banking sector, micro banking factors, including poor appraisal and follow-up, defect in the mindset of the borrower and lender are no less important.

2. Statement of the Problem

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de-motivates the staff and creates investor apathy and shakes the customer’s loyalty. As a result, productivity and other strategic banking variables also receive serious setback.

3. Review of Literature

In view of the seriousness of the problem, numerous research studies have been conducted on different issues concerning credit risks in banks including NPAs. However, empirical works on NPA problems in PSBs are inadequate. Sharma (2005)[3] makes an attempt to study the problem of NPAs in PSBs and also its impact on the performance of these banks. Impact of NPAs on the profitability of the banks is analyzed by applying multiple regression. Impact of NPA on productivity, represented by Business per Employee (BPE) and Operating Profit per Employee (OPE) and few other strategic banking variables like capital adequacy, credit deployment and deposit mobilization along with some areas of macroeconomic variable are also examined with the help of simple regression model. The issues of auto correlation and multi co-linearity have been diagnosed with the help of Durbin Watson Statistics (DWS) and Variance Inflation Factor (VIF) respectively. She stresses that NPAs not only affect performance of the banks, but also cause tremendous damage to the entire economy by dampening the very foundation of the credit system. The study suggests different preventive and curative measures to control NPAs in PSBs and urges that efficient legal framework, improvement in credit appraisal and monitoring skills of banks backed by strong political will can enable the Indian banks to tackle the burning issue.

Gopalakrishnan (2006)[4] examines the impact of NPAs on various micro and macro economic variables, with the help of simple regression analysis. It is observed that there is significant relation between NPAs and strategic variables stated above with very high value of R2. Simple regression models generated are highly significant with F test result with significance of less than .05 and moderate to very high R2 value ranging from 0.525 in case of Net Profit to 0.920 in case of Interest Rate on Advances. A multiple regression model has also been used to explain impact of NPA along with a few other strategic banking variables on net profit of PSBs.

Bodla and Verma (2006)[5] examine with the help multiple regression technique, the impact of a few banking variables including NPA on profitability in PSBs in India. The study has brought out that the explanatory power of some variables like, Spread, Net interest income, Provision and contingencies and Operating expenses are significantly high while others like Business per employee, Credit deposit ratio, NPAs as percentage of Net advances are found with low explanatory power. Durbin Watson Test (DWT) has been employed to examine the problem of auto-correlation. Moreover, to bring out the explanatory powers of each of the independent variables under study, the square of partial correlation coefficient has been worked out. The study concludes that variables such as Provision and contingencies, Non interest income, Spread and Operating expenses have significant relationship with net profit and therefore are major areas of concern in PSBs in India

Singla (2008)[6] examines empirically the financial performance in terms of profitability of sixteen selected banks for a period of five years (2000-01 to 2006-2007). The study reveals that the profitability position was reasonable during the period of study when compared with the previous years. With the help of statistical measures like correlation analysis and multiple regression analysis, the study examines the determinants of profitability of selected banks. During the study period, it was observed that the return on net worth had a negative correlation with the debt equity ratio. Interest income to working funds also had a negative association with Interest coverage ratio and the NPA to net advances was negatively correlated with Interest coverage ratio.

Agnani (2010)[7] examines relationship between NPAs and financial health of banks by analyzing the impact of NPA on profitability of banks. Two PrSBs and three PSBs have been identified for the study where structured questionnaires were used to find out the preference of the respondent with respect to his choosing a particular bank for investment. Spearman’s Rank Correlation has been used for identification of best bank. It is inferred in the study that lower profitability or higher NPA taken in isolation do not reflect the investor’s preference in deciding performance and future direction of success or failure of a bank in real and absolute term. In fact, an investor gives higher weight to goodwill and cutomer service of individual bank than NPAs or profitability.

Thiagarajan, Ayyappan, Ramachandran and Sakthivadivel (2011)[8] evaluate the determinants of profitability in the public and private sector commercial banks in India. Correlation Analysis, Multiple Regression Analysis and Factor Analysis have been used to estimate the contribution of select bank specific variables towards profitability. The correlation co efficient of the selected independent variables with the bank’s profitability has been worked out in order to identify the most significant variable that has strong relationship with the dependent variable. The study also examines the impact of several independent variable on profitability for which multiple regression analysis has been undertaken. The study also uses Factor Analysis to examine the value for the co efficient for regression when the variables are regressed upon the factors. In factor analysis the Varimax Rotation has been used. The study reveals that credit risk represented by NPA has a significant negative influence on the profitability on both private and public sector banks.

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Other Income, Interest Expenses, Operating Expenses, Net NPA and Spread are identified. With a database covering a period of 2004-07, the study employs Multiple Correlation and Multiple Regression Technique to examine the relation between above named variables with profitability. The study concludes that Interest Expenses is the only good predictor for profitability in a bank for the given dataset.

Yadav (2011)[10] examines the concept of NPA in PSBs, its magnitude and impact of NPA on (i) profitability, (ii) profitability with other variables and (iii) efficiency and productivity. Simple linear regression model is used to analyse the impact on profitability represented by profit as a percentage of total asset and on productivity represented by business per employee and profit per employee of public sector banks. The study also finds that NPA has a negative impact on profitability and is statistically significant and also has negative relationship with business per employee (a productivity variable) and magnitude of relationship is statistically significant. The profitability of all PSBs is affected to a great extent when NPAs working with other banking strategic variables also affect productivity and efficiency. High R2 value in the Regression Model explains variability of high order in the productivity and efficiency parameter of PSBs.

However empirical work on individual bank-wise impact analysis on NPA in PSB has seldom been attempted. It is against this backdrop that the present study is undertaken to fill up this gap and make a modest contribution in the field of management of NPA in banks in India. Accordingly, examination of impact of NPA on strategic banking variables along with time variable in selected PSBs, with the help of simple as well as multiple regression analysis have been undertaken.

4. Objective of the Study

In view of the relative importance of NPAs in banking sector in India in general with PSBs in particular, it is perceived that a comprehensive study in this area should be made. The present study is a humble endeavour to examine impact of NPAs in selected PSBs. The specific objectives embodied under the research are as follows:

(1) To examine the status of inter relationship existing among the set of relevant banking variables including NPA,

(2) To examine the impact of NPAs on profitability and other strategic banking variables. (i) On strategic banking variables, and

(ii) On strategic banking variables along with time variable

5. Methodology

For the purpose of examining degree of association and relationship between variables identified, simple pair wise correlation co-efficient has been computed and appropriate tests have been employed to judge the significance status of these co-efficient. This has enabled us to select the appropriate variables to be considered for further impact study. In this we have computed Pearson Correlation Coefficient and its corresponding p value (Sig. 2 tailed).

For the purpose of examining impact of NPA (GNPA/TA) over the statistically significant banking variables like Profitability (PBT/TA) and Productivity (OPE) in addition to variables like NIM/TA Ratio, Credit-Deposit Ratio, Ratio of Credit Growth etc have been regressed. There are two principal assumptions which justify the use of linear regression models for the purpose of examining the impact of independent variable(s) on dependent variable. They are independence of the errors and normality of the error distribution for which Durbin Watson (DW) test and Shapiro-Wilk (SW) test have been employed.

First of all, impacts of NPAs on the profitability of banks have been attempted with the help of simple linear regression. For few banks, which could not qualify linearity requirement as stated above, we have examined alternate conventional nonlinear models like Quadratic, Cubic, Logarithmic, Exponential and Growth curves and applied the model which suits the most with the given dataset. To examine the impact of time along with NPA, multiple regression model is invoked and diagnosed for auto correlation and multi collinearity. Same process has been followed for other variables as stated above.

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diagnose the problem of the multi co-linearity in multiple regressions, the value of tolerance factor (TOL) along with variance inflating factor (VIF) have also been computed and compared with standard.

6. Scope of the Study

Banking industry in India comprise of the PSBs and the Private Sector Banks (PrSBs) and Foreign Banks (FBs). PrSBs and FBs are particularly excluded from the study, as they are not strictly comparable with PSBs and they account for less than 16% and 5.30% of Gross NPAs of banking system in India respectively, as on March 2012.

Though the concept of NPA was first introduced in 1992, NPA data of individual PSBs are available from 1995-96. This provides justification for 1995-96 as the starting year in the present study. Further, the study covers the period up to 2011-12, i,e the latest year till the dataset is available. For the purpose of the study, we have taken one large sized PSB {State Bank of India (SBI)}, and one small sized PSB {UCO Bank (UCO)}.

7. Results & Discussions

7.1. Examination of interrelationship among the selected variables

7.1.1. Selection of statistically significant strategic banking variable having association with NPA

A pair wise correlation between NPA represented by Gross Non Performing Asset to Average Total Asset (GNPA/TA) Ratio and profitability represented by Profit before Tax to Average Total Asset (PBT/TA) Ratio, productivity represented by Operating Profit per Employee (OPE) Ratio others including Net Interest Margin to Average Total Asset (NIM/TA) Ratio, Credit Deposit Ratio (CDR) and Ratio of Credit Growth (CRDGROWTH) for 2 selected PSBs have been calculated and presented below.

Table 1. Correlation between NPA (GNPA/TA) and selected banking variables

SBI UCO

GNPA/TA GNPA/TA PBT/TA Pearson Correlation -0.447 -.890**

Sig. (2-tailed) 0.072 0 OPE Pearson Correlation -.751**_-.691**

Sig. (2-tailed) 0.001 0.002

NIM/TA Pearson Correlation -0.368 -0.348 Sig. (2-tailed) 0.146 0.171

CDR Pearson Correlation -.668** -.918** Sig. (2-tailed) 0.003 0 CRDGROWTH Pearson Correlation -0.413 -.537*

Sig. (2-tailed) 0.099 0.026 *. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

Statistical Interpretation:

Profitability (PBT/TA) of UCO has strong negative linear correlations over NPA (GNPA/TA). We also get a support in favour of our statement by a 2-tailed significance test at 95% confidence level, because all p values<0.05. The profitability (PBT/TA) of the other bank (SBI) also has negative linear correlations over NPA (GNPA/TA). But, we find statistical significance at 90% confidence level to nullify null hypothesis. So, we can say that the relationship between the two variables of SBI is may not be due to by chance at 90% confidence level.

Productivity (OPE) of the banks SBI and UCO have strong negative linear correlations over NPA (GNPA/TA). We also get a support in favour of our statement by a 2-tailed significance test at 95% confidence level, because all p values<0.05, sufficient to reject the null hypothesis.

NIM/TA of the banks SBI and UCO have negative linear correlations over NPA (GNPA/TA). But, we found no statistical significance even at 90% confidence level to nullify null hypothesis. So, we can say that the relationship between these two variables of the two banks is may be due to by chance (valid for this sample only).

CDR of the banks SBI and UCO has strong negative linear correlations over NPA (GNPA/TA). We also get a support in favour of our statement by a 2-tailed significance test at 95% confidence level, because all p values<0.05, sufficient to reject the null hypothesis.

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From the above correlation analysis, following statistically significant strategic banking variables having association with NPA (GNPA/TA) have been finally selected.

Table 2. Selection of Statistically Significant Strategic Banking Variables having Association with NPA (GNPA/TA)

Bank Statistically significant banking variable at 5% level of significance SBI OPE, CDR

UCO PBT/TA, OPE, CDR, CRDGROWTH

7.1.2. Selection of statistically significant strategic banking variables having association with Profitability A pair wise correlation between profitability represented by Profit before Tax to Average Total Asset (PBT/TA) Ratio and NPA represented by Gross Non Performing Asset to Average Total Asset (GNPA/TA) Ratio and productivity represented by Operating Profit per Employee (OPE) Ratio others including Difference between Net Interest Margin and Burden to Average Total Asset {(NIM-Burden)/TA} Ratio, Credit Deposit Ratio (CDR), Ratio of Credit Growth (CRDGROWTH), and Priority Sector Advances to Average Total Asset (PSADV/TADV)] for 2 selected PSBs have been calculated and presented below.

Table 3. Correlation between Profitability (PBT/TA) and Selected Banking Variables

SBI UCO

PBT/TA PBT/TA GNPA/TA Pearson Correlation -0.447 -.890**

Sig. (2-tailed) 0.072 0 OPE Pearson Correlation 0.37 .528*

Sig. (2-tailed) 0.143 0.029 (NIM-BURDEN)/TA Pearson Correlation 0.913 0.999

Sig. (2-tailed) 0 0 CDR Pearson Correlation 0.319 .691**

Sig. (2-tailed) 0.212 0.002 CRDGROWTH Pearson Correlation -0.019 .675**

Sig. (2-tailed) 0.944 0.003 PSADV/TADV Pearson Correlation .535*_-.331**

Sig. (2-tailed) 0.027 0.195 *. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

Statistical Interpretation:

NPA (GNPA/TA) of UCO has strong negative linear correlations over profitability (PBT/TA). We also get a support in favour of our statement by a 2-tailed significance test at 95% confidence level, because all p values<0.05. The profitability (PBT/TA) of the other bank (SBI) also has negative linear correlations over NPA (GNPA/TA). But, we find statistical significance at 90% confidence level to nullify null hypothesis. So, we can say that the relationship between the two variables of the bank SBI is may not be due to by chance at 90% confidence level.

Productivity (OPE) of UCO has positive linear correlations over profitability (PBT/TA). We also get a support in favour of our statement by a 2-tailed significance test at 95% confidence level, because all p values<0.05. The productivity (OPE) of the other bank (SBI) also has positive linear correlations over profitability (PBT/TA). But, we find no statistical significance even at 90% confidence level to nullify null hypothesis. So, we can say that the relationship between the two variables of SBI is may be due to by chance at 90% confidence level (valid for this sample only).

(NIM - BURDEN)/TA of both the banks has strong positive linear correlations over profitability (PBT/TA). We also get a support in favour of our statement by a 2-tailed significance test at 95% confidence level, because all p values<0.05.

CDR of UCO has strong positive linear correlations over profitability (PBT/TA). We also get a support in favour of our statement by a 2-tailed significance test at 95% confidence level, because all p values<0.05. The CDR of the other bank (SBI) has positive linear correlations over profitability (PBT/TA). But, we find no statistical significance even at 90% confidence level to nullify null hypothesis. So, we can say that the relationship between these two variables of SBI may be due to by chance (valid for this sample only).

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PSADV/TADV of SBI has strong positive linear correlations over profitability (PBT/TA). We also get a support in favour of our statement by a 2-tailed significance test at 95% confidence level, because p values<0.025. The PSADV/TADV of the other bank (UCO) is medium correlated over profitability (PBT/TA). But, we found no statistical significance even at 90% confidence level to nullify null hypothesis. So, we can say that the relationship between these two variables of the other banks may be due to by chance (valid for this sample only).

From the above correlation analysis, following statistically significant strategic banking variables having association with Profitability (PBT/TA) have been finally selected.

Table 4. Selection of Statistically Significant Strategic Banking Variables having Association with Profitability (PBT/TA)

Bank Statistically significant banking variable

SBI (NIM-BURDEN)/TA, PSADV/TADV

UCO GNPA /TA, OPE, (NIM-BURDEN)/TA, CDR, CRDGROWTH,

7.2.Examination of Impact of NPA on Strategic Banking Variables

We analyze the data for a simple linear regression models to examine impact of NPA on strategic banking variables.

7.2.1. Impact of NPA on profitability

To examine the impact of NPA (GNPA/TA) over profitability (PBT/TA), simple linear regression is tried.

Evaluating Model Fit and Verifying Assumptions

Table 5. DW and SW Statistics for Profitability (PBT/TA) and NPA (GNPA/TA)

Bank Durbin Watson Shapiro-Wilk Statistic Sig.

SBI 2.063 0.949 0.44

UCO 0.864 0.972 0.852

DW statistics value for SBI is higher than 2, while it is much less for UCO. This suggests that the assumption of independent (no serious serial correlation) errors has been met for SBI, but not for UCO. Sig of SW Statistic of both banks are above .05. Hence assumption of normality of error distribution is met for both models.

Redefining Model for UCO

Since the linear model is not suitable for UCO we look for an alternate model. We tried few conventional nonlinear models like Quadratic, Cubic, Logarithmic, Exponential and Growth. For UCO we found Quadratic model somehow suitable. Summary of DW and SW test statistics for quadratic model for UCO is given below which suggest both the requirement of normality and independence of error distribution:

Table 6. DW and SW Statistics for Profitability (PBT/TA) and NPA (GNPA/TA) for UCO

UCO 1.986 0.896 0.059

Model Summary for Profitability (PBT/TA) and NPA (GNPA/TA)

Table 7. Regression Model Summary for Profitability (PBT/TA) and NPA (GNPA/TA)

Bank R R2 _df _F _{Sig. of F}

SBI .447a 0.2 1 3.739 .072a UCO .968a_0.937₁ _103.436 _.000a

Dependent Variable: PBT/TA (Profitability)

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Model Co-efficient Analysis

Table 8. Summary of the Coefficients of the Models Selected Banks

Bank Unstandardized Coefficients Standardized

Coefficients

T Sig.

B Std. error Beta

SBI (Constant) 0.016 0.002 10.17 0 SBIGNPA/TA -0.065 0.034 -0.447

-1.934 0.07

UCO (Constant) 0.003 0.002 1.946 0.07 UCOGNPA/TA 0.205 0.07 1.004 2.929 0.01

(UCOGNPA/TA)2_-2.901 _0.515 _1.932 -5.637

0

For SBI the NPA (b=-0.065) is not significant (p=0.07), but only just so (as it is marginally above .05), and the coefficient is negative which would indicate that larger NPA is related to lower profitability, which is what we would expect. For UCO the effect of NPA is significant (p < .05) and its coefficient is negative indicating that larger NPA is related to lower profitability.

Impact Analysis of the Independent Variable

Our main objective of developing regression models is to examine the impact of NPA (GNPA/TA) over profitability (PBT/TA) which is summarized in model below.

Table 9. Model Equations on Profitability (PBT/TA) and NPA (GNPA/TA)

Bank Equation SBI SBIPBT/TA (Profitability) = .016-.065*( SBIGNPA/TA)

UCO UCOPBT/TA (Profitability) = .003+.205*( UCOGNPA/TA) -2.901*(UCOGNPA/TA)2

To examine the impact of NPA (GNPA/TA) along with time variable over profitability (PBT/TA) multiple regression is tried.

Table 10. Regression Model Assumptions Verification for Profitability (PBT/TA) and NPA (GNPA/TA) & Time (Year)

SBI 2.054 .949 .434

UCO 1.199 .965 .721

The value DW statistics computed are all higher than 1.19 and less than 2.06. The p values of the SW statistic all banks are higher than 0.05. This suggests that the assumption of independent (no serious serial correlation) errors and normality of the error distribution has been met for both the models.

Collinearity Analysis of the Models

Table 11. Regression Model collinearity statistics for Profitability (PBT/TA) and NPA (GNPA/TA) & Time (Year)

Bank Collinearity Statistics

Tolerance VIF

SBI (Constant)

SBIGNPA/TA 0.158 6.315

YEAR 0.158 6.315

UCO (Constant)

UCOGNPA/TA 0.156 6.406

YEAR 0.156 6.406

From the above table, we may observe that the tolerance for NPA and Time for the models SBI and UCO are above 0.1, but VIF is <10 and hence quite satisfactory.

Model Summary

Table 12. Regression Model Summary for Profitability (PBT/TA) and NPA (GNPA/TA) & Time (Year)

Bank R R2 _df _F _{Sig of F}

SBI .584a_{.341 2}_3.618 _.054a UCO .910a_{.828 2}_{33.688 .000}a

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R2 in the model summary is very low (.341) for SBI and pretty high (.828) for UCO. The sig of F-ratio are very smalls (for UCO less than .05 while for SBI it is marginally higher than .05). Hence, we can conclude the goodness of fit for UCO and SBI are well established at 95% confidence interval and at 90% confidence interval respectively.

Table 13. Regression Model Coefficients, t stat for Profitability (PBT/TA) and NPA (GNPA/TA) & Time (Year)

Bank Unstandardized

Coefficients

Standardized Coefficients

T Sig.

B Std. error Beta

SBI (Constant) 0.017 0.007 2.483 0.026 SBIGNPA/TA -0.08 0.087 -0.551 -0.919 0.374 YEAR -7.23E-05 .000 -0.114 -0.19 0.852 UCO (Constant) 0.023 0.007 3.35 0.005

UCOGNPA/TA -0.271 0.057 -1.327 -4.728 .000 YEAR .000 .000 -0.475 -1.692 0.113

In respect of SBI for both the variables NPA and Time, coefficient is negative which would indicate that larger NPA is related to lower profitability, but are not statistically significant (p is very high), and therefore we refrain from developing model for SBI. For UCO the effect of NPA is significant (p < .05) and its coefficient is negative indicating that larger NPA is related to lower profitability. For predictor Time, the co-efficient is .000, implying influence of Time for UCO is negligible.

Our main objective of developing regression models is to examine the impact of NPA (GNPA/TA) and Time (Year) over profitability (PBT/TA) which is summarized in model below.

Table 14. Model Equations on Profitability (PBT/TA) and NPA (GNPA/TA) & Time (Year)

Bank Equation UCO PBT/TA (Profitability)= 0.023-0.271*( GNPA/TA)-0.000*( YEAR)

To examine the impact of NPA (GNPA/TA) along with statistically significant variables over profitability (PBT/TA) multiple regression is tried.

Table 15. Regression Model Assumptions Verification Statistics for Profitability (PBT/TA) and statistically significant variables including NPA (GNPA/TA)

SBI 1.682 .942 .343

UCO 2.253 .931 .227

DW statistics for both banks are higher than 1.68 and less than 2.26. The p values of the SW Statistic of models of both banks are higher than 0.05 This suggests that the assumption of independent (no serious serial correlation) errors and normality of the error distribution has been met for both the models.

CollinearityAnalysis of the Models

Table 16. Multiple Regression Model Collinearity Statistics for Profitability (PBT/TA) & NPA (GNPA/TA), other Statistically Significant Variables

Bank Collinearity Stat

Tolerance VIF

SBI (Constant)

SBI(NIM_BURDEN)/TA .765 1.306

SBIPSADV/TADV .905 1.106

SBIGNPA/TA .776 1.288

UCO (Constant)

UCOGNPA/TA .026 38.144

UCOOPE .252 3.976

UCO(NIM_BURDEN)/TA .073 13.683

UCOCRD_GROWTH .432 2.313

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We interpret the tolerance for most of the variables for the models of UCO are less than 0.1, with VIF is >10. Accordingly, we examine the basis of redefining of these two models once again. The VIF for the other models are quite satisfactory.

Redefining Model for UCO

Since the multiple linear models with set of predictor variables are not suitable for UCO, we are in search of an alternate by reducing one or more variables from the models. The decision of elimination of variables is taken through correlation analysis. We find CDR is highly correlated with other variables. So, we remove CDR and rerun the multiple regression analysis and model fit summary statistic is given below.

Table 17. Regression Model Assumptions Verification for Profitability (PBT/TA) and NPA (GNPA/TA) along with Other Statistically Significant Variables

SBI 1.682 .942 .343

UCO 2.030 .940 .323

The above table suggests that the assumption of independent (no serious serial correlation) errors and normality of the error distribution has been met for both the models.

Table 18. Regression Model Collinearity Statistics for Profitability (PBT/TA) and NPA (GNPA/TA) & Other Statistically Significant Variables

Bank Collinearity Stat

Tolerance VIF

SBI (Constant)

UCO (Constant)

UCOOPE .415 2.409

UCOCRD_GROWTH .439 2.279

We interpret the tolerance for NPA and other variables for all the models are above 0.1 and VIF is <10. These clearly indicate that the multi collinearity is not cause of concern for all the models.

Model Summary

Table 19. Regression Model Summary for Profitability (PBT/TA) and NPA (GNPA/TA) & Other Statistically Significant Variables

SBI .960a_.921 _{3 50.615} _.000a UCO .999a_.999 ₄_2354.747 _.000a

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Table 20. Regression Model coefficients, t stat for Profitability (PBT/TA) and NPA (GNPA/TA) & Other Statistically Significant Variables

Bank Unstandardized

Coefficients

Standardized Coefficients

T Sig.

B Std. error Beta

SBI (Constant) -.015 .004 -3.573 .003

SBI(NIM_BURDEN)/TA 1.534 .164 .832 9.351 .000

SBIPSADV_TADV .053 .014 .308 3.762 .002

SBIGNPA/TA .001 .013 .009 .100 .922

UCO (Constant) .001 .000 1.126 .282 UCOGNPA/TA .001 .006 .004 .128 .900

UCOOPE -.007 .003 -.033 -2.086 .059 UCO(NIM_BURDEN)/TA 1.047 .028 1.029 37.781 .000

UCOCRD_GROWTH .000 .001 -.012 -.787 .446

It may be noted that p-values (marked) of two out of the three predictor variables for SBI and one out of the four predictor variables for UCO, are small, less than 0.05, and hence, we can conclude that these co-efficients are not zero for the related predictors only and these coefficients are not by chance at 95% confidence level and the other coefficients could be due to by chance (valid only for this sample).

Our main objective of developing regression models is to examine the impact of NPA (GNPA/TA) and other variables over profitability (PBT/TA) which is summarized in model below.

Table 21. Model Equations on Profitability (PBT/TA) and Strategic Banking Variables including NPA (GNPA/TA)

Bank Equation

SBI PBT/TA (Profitability)= -0.015+0.001*( GNPA/TA)+ 0.053*( PSADV/TADV)+ 1.534*(NIM - BURDEN)/TA

UCO PBT/TA (Profitability)= 0.001+0.001*( GNPA/TA)-0.007*( OPE)+ 1.047*(NIM - BURDEN)/TA

To examine the impact of NPA (GNPA/TA) along with statistically significant variables and time variable over profitability (PBT/TA) multiple regression is tried.

Table 22. Regression Model Assumptions Verification Statistics for profitability and statistically significant variables including NPA & Time

SBI 1.798 .956 .562

UCO 2.048 .966 .753

Table 23. Regression Model collinearity statistics for profitability (PBT/TA) and statistically significant variables including NPA & Time

Tolerance VIF

SBI (Constant)

YEAR .150 6.662

UCO (Constant)

UCOOPE .071 14.122

UCOCRD/GROWTH .419 2.385

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We interpret the tolerance for NPA (GNPA/TA) and other variables of SBI are above 0.1, but VIF is >10 which is less than 10 when Time (YEAR) was taken as predictor in the models. So, it is clear from the fact that for SBI regression model including Time (Year) is justified. For UCO, three out of five predictor in the models, TOL is less than 0.1 while VIF is <10. Hence we droping our idea of developing model for UCO.

Model Summary

Table 24. Regression Model Summary for Profitability (PBT/TA) and statistically significant variables including NPA & Time

SBI .961a_.924 _{4 36.460} _.000a

R2 value for SBI is pretty high and the significance of F-stat are very smalls, Hence, we can say that the goodness of fit for both the models are well established at 99% confidence interval. We can safely conclude that a high quality of models fit is exhibited. Therefore, the decision of multiple regression analysis including Time (Year) is justified for SBI as it improves the precision criterion.

Table 25. Regression Model Coefficients, t stat for profitability (PBT/TA) and NPA (GNPA/TA). Time (Year) & other statistically significant variables

Bank Unstandardized

Coefficients

Standardized Coefficients

T Sig.

B Std. error Beta

SBI (Constant) -.016 .005 -3.438 .005

SBI(NIM_BURDEN)/TA 1.557 .171 .845 9.098 .000

SBIPSADV/TADV .051 .014 .300 3.542 .004

SBIGNPA/TA .020 .031 .139 .648 .529

YEAR 0.00009 .000 .137 .669 .516

We notice that some p-values (marked) are small, less than 0.05, and hence, we can conclude that these co-efficients are not zero for the related predictors only and these coco-efficients are not by chance at 95% confidence level and the other coefficients could be due to by chance (valid only for this sample).

Our main objective of developing regression models is to examine the impact of NPA and other variables including time (Year) over profitability (PBT/TA) which is summarized in model below.

Table 26. Model Equations on Profitability (PBT/TA) and Strategic Banking Variables including NPA (GNPA/TA) & Time (Year)

Bank Equation SBI PBT/TA (Profitability)= -0.016+0.020*( GNPA/TA)+ 0.051*( PSADV/TADV)+

1.557*(NIM - BURDEN)/TA +0.000087*(YEAR)

7.2.2. Impact of NPA on productivity

To examine the impact of NPA over productivity, simple linear regression is tried.

Table 27. DW and SW test statistics for Productivity (OPE) and NPA (GNPA/TA)

SBI .334 .783 .001

UCO .367 .754 .001

DW statistics are all less than 0.4. The p values of the SW statistic of both model are lower than 0.05. This suggests that the assumption of independent (no serious serial correlation) errors and normality of the error distribution has not been met for both models.

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have tried for the conventional nonlinear models. But, we found no suitable model combination for the all the banks in our limited study area. So, we are dropping these models.

To examine the impact of NPA (GNPA/TA) along with time variable over productivity (OPE) multiple regression is tried.

Table 28. Regression Model Assumptions Verification for Productivity (OPE) and NPA (GNPA/TA) & Time (Year)

SBI 0.855 .954 0.516

UCO 0.926 .979 0.943

DW statistics of both models are lower than .93. This suggests that the assumption of independent (no serious serial correlation) errors has not been met for both the models. The p values of the SW Statistic for models of both banks are higher than 0.05. This suggests that the assumption of normality of the error distribution has been met for all the models.

Model Re-defined

Since the linear model is not suitable for SBI and UCO, we are in search of an alternate. We tried all the conventional nonlinear models like Quadratic, Multiple Quadratic, Cubic, Multiple Cubic, Logarithmic, Exponential and Growth curves. Finally we found multiple quadratic and multiple cubic models suitable SBI and UCO respectively which can incorporate time (Year).

Table 29. Assumptions Verification for Productivity (OPE) and NPA (GNPA/TA) & Time (Year) with Multiple Quadratic, Multiple Cubic Models

SBI 1.300 .959 .614

UCO 1.265 .941 .336

Collinearity Analysis of the Models

Table 30. Regression Model Collinearity statistics for Productivity (OPE) and NPA (GNPA/TA) & Time (Year)

Tolerance VIF

SBI (Constant)

(YEAR)2_.334_2.990

YEAR .145 6.905

(SBIGNPA/TA)2_.145 _6.905

UCO (Constant)

(UCOGNPA/TA)3 .708 1.412

(YEAR)3_.708_1.412

YEAR .078 12.893

PSBGNPA/TA .078 12.893

Tolerance for all predictors for the models SBI and two out of four for UCO are more than 0.1, but VIF is <10. Hence existence of multi collinearity can not be ruled out.

Model Summary for Productivity (OPE) and NPA (GNPA/TA) & Time (Year)

Table 31. Regression Model Summary for Productivity (OPE) and NPA (GNPA/TA) & Time (Year)

SBI .989a_.978₂ _315.753 _.000a UCO .967a_.936₂ _102.445 _.000a

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From the above table, we see R2 is very high (.978 for SBI and .936 for UCO). Significance F-stat are very smalls, all less than .05. Hence, we can conclude the goodness of fit for all models are well established.

Model Co-efficient Analysis for Productivity (OPE) and NPA (GNPA/TA) & Time (Year)

Table 32. Regression Model Coefficients, t stat for Productivity (OPE) and NPA (GNPA/TA) & Time (Year)

Bank Unstandardized

Coefficients

T Sig.

B Std. error

Beta

SBI (Constant) -.008 .008 -1.053 .310

SBIGNPA/TA .286 .124 .157 2.299 .037 (YEAR)2_.000_.000_1.113 _16.347_.000 YEAR_C .011 .001 1.565 10.744 .000 (SBGNPA/TA)2_1.092 _{.237 .671} _{4.607 .000}

UCO (Constant) .006 .005 1.201 .250 (UCOGNPA/TA)3_-3.811 _4.935 _-.062 _-.772 _.453 (YEAR)3_2.342E-5_.000_.933 _11.612_.000 (YEAR)2_.899_.223_.398 _4.034_.001 PNBGNPA/TA .001 .000 1.339 13.577 .000

We notice that p-values (marked) of all predictors {(except predictor (GNPA/TA)3} are small, less than 0.05, and hence, we can conclude that these co-efficients are not zero for the related predictors only and these coefficients are not by chance at 95% confidence level and the other coefficients of predictor (GNPA/TA)3 could be due to by chance (valid only for this sample).

Our main objective of developing regression models is to find the impact of NPA (GNPA/TA) and other variables over Productivity (OPE) which is summarized in model below.

Table 33. Model Equations on Productivity (OPE) and Strategic Banking Variables including NPA (GNPA/TA) & Time (Year)

Bank Equation

SBI Productivity (OPE)= -0.008+0.286*( GNPA/TA)+0.000*(YEAR)2 UCO Productivity (OPE)= 0.006-3.811*( GNPA/TA)3_{+0.00002342*(YEAR)}3

7.2.3. Impact of NPA on variable NIM/TA

The researchers have not found any statistically significant correlation between NPA (GNPA/TA) and NIM/TA. Hence, impact analysis of NPA on NIM/TA has not been conducted.

7.2.4. Impact of NPA on variable Credit Growth (CDR)

To examine the impact of NPA (GNPA/TA) over CDR, simple linear regression is tried.

Table 34. Regression Model Assumptions Verification for CDR and NPA (GNPA/TA)

Bank Durbin Watson Shapiro-Wilk Statistic Sig.

SBI .327 .948 .426

UCO .593 .936 .271

DW statistics for both banks are less than 0.6. This suggests that the assumption of independent (no serious serial correlation) errors has been violated seriously and models are highly affected by serial correlations. All the p values of the SW statistic are higher than 0.05. This suggests that the assumption of normality of the error distribution has been met for all the models.

Though a high negative linear correlation presents between the CDR and GNPA/TA, but due to the violation of linear model assumptions we feel developing a linear model concept would be nonsense idea. Since the linear models are not suitable for all the banks, we are in search of an alternate. We tried for the conventional nonlinear models. But, we found no suitable model combination for the selected PSBs in our limited study area. So, we are dropping these models.

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Table 35. Regression Model Assumptions Verification for CDR and NPA (GNPA/TA) & Time (Year)

SBI .510 .962 .670

UCO 1.102 .862 .017

DW statistics of UCO is higher than 1.1, and for SBI below .52. This suggests that the assumption of independent (no serious serial correlation) errors has not been met for SBI. The p values of the SW Statistic of SBI higher than 0.05, while for UCO the same is less than 0.05. This suggests that the assumption of normality of the error distribution has not met for UCO. Since the linear models are not suitable for both banks, we are in search of an alternate. We have tried for the conventional nonlinear models. But, we found no suitable model combination for the selected two PSBs in our limited study area. So, we are dropping the idea of modeling for both the banks for the given dataset.

7.2.5. Impact of NPA on variable Credit growth

To examine the impact of NPA (GNPA/TA) over Credit Growth, simple linear regression is tried.

Table 36. DW and SW test statistics for Credit Growth and NPA (GNPA/TA)

UCO 1.442 .956 .555

DW statistics is computed to evaluate independence of errors are 1.44. The p values of the SW statistic are all higher than 0.05. This suggests that the assumption of independent errors and normality of the error distribution has been met for UCO. So, we keep the model and analyze further.

Model Summary

Table 37. Regression Model Summary for Credit Growth and NPA (GNPA/TA)

Bank R R2 _df _F _{Sig of F} UCO .537 .288 1 6.074 0.026

Dependent Variable: Credit Growth

From the above table, R2 of UCO is .288. We can say an less than average quality of model fit for all banks are exhibited here. So, the Credit Growth is not explained properly by NPA. Hence, multiple regression analysis would be other alternative to get better models. Significance of F-stat is very smalls, less than .05. Hence, we can conclude the goodness of fit for UCO is well established at 95% confidence level.

Table 38. Regression Model Coefficients, t stat for Credit Growth and NPA (GNPA/TA)

Bank Unstandardized Coefficients

T Sig.

B Std. error

Beta

UCO (Constant) .313 .050 6.245 .000 UCOGNPA/TA -1.950 .791 -.537 -2.465 .026

We notice that p-values are small, less than 0.05, and hence, we can conclude that these co-efficients are not zero for the related predictors only and these coefficients are not by chance at 95% confidence level.

Our main objective of developing regression models is to find the impact of NPA (GNPA/TA) and other variables over Credit Growth. From the above output table we found our regression equations given below to get the impact of predictors in the models, which are summarized in model below.

Table 39. Model Equations on Credit Growth and NPA (GNPA/TA)

Bank Equation

UCO Credit Growth = 0.313-1.950*(GNPA/TA)

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Table 40. Regression Model Assumption Verification for Credit Growth and GNPA/TA & Time (Year)

Bank Durbin Watson Shapiro-Wilk Statistic Sig.

UCO 1.827 .850 .011

DW statistics is 1.827. This suggests that the assumption of independent (no serious serial correlation) errors has been met for UCO. The p value of the SW statistic is less than 0.05. This suggests that the assumption of normality of the error distribution has not met for UCO. We have tried for the conventional nonlinear models. But, we found no suitable model combination for UCO in our limited study area. So, we are dropping the idea of modeling for UCO for the given dataset.

7.3.Important Findings Collated (As Emerged from Analysis)

The main objective of the research work undertaken is to obtain a quantitative assessment of the relationship between NPA and selected strategic banking variables including profitability, productivity and few others and to examine the impact of NPA on such dependent variables. It is observed from above detailed study that NPA has different degree of association and impact on selected variables among six selected PSBs.

Correlation Analysis given in Table 1 exhibits the following relationship

(1) Profitability represented by PBT/TA of UCO has been found to have statistically significant (p value less than 0.05) negative relationship with NPA (GNPA/TA). Profitability of SBI also has negative relationship with NPA with p value marginally higher than 0.05.

(2) Productivity represented by OPE of both SBI and UCO, have been found to have statistically significant (p value less than 0.05) negative relationship with NPA (GNPA/TA), though at varying degree.

(3) The relationship between NIM/TA and NPA (GNPA/TA) of SBI and UCO are not statistically significant and hence left out from the scope of our study.

(4) CDR of both PSBs, have been found to have statistically significant (p value less than 0.05) negative relationship with NPA (GNPA/TA).

(5) CRDGROWTH of UCO has been found to have statistically significant (p value less than 0.05) negative relationship with NPA (GNPA/TA). CRDGROWTH of SBI though is having negative relationship with NPA (GNPA/TA), are not statistically significant and hence left out from the scope of our study. Impact Analysis examined with the help of simple as well as multiple regression reveal

(1) The study clearly reveals that NPA has negative impact on profitability for both the selected banks both in respect of simple regression with NPA as predictor and multiple regressions where along with NPA selected strategic banking variables are predictors. The multiple regressions have also improved the precision of the modeling as evident from the tremendous increase in the R2 value of the models in comparison to simple regression. It has been also observed that with time and NPA as predictors SBI and UCO are found to have statistically significant impact on profitability. However, with the inclusion of other statistically significant strategic variables in predictor only SBI is found to have statistically significant impact on profitability.

(2) In case of productivity (OPE) we observe that though it has strong negative correlation with NPA for both the banks but simple linear model cannot be established. However, with the inclusion of time in the predictor, models with very high R2 have been established for both the banks.

(3) In case of CDR we observe that though it has strong negative correlation with NPA for both the banks but regression models cannot be established.

(4) Since Credit Growth has negative correlation for UCO, statistically significant models have been established for UCO though R2 value is not good. With the inclusion of time in the predictor, model for both the banks cannot be developed.

8. Conclusion

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