Performance Analysis of Selected Public and Private Sector ...

www.theinternationaljournal.org > RJEBS: Volume: 05, Number: 08, June 2016 5

Performance Analysis of Selected Public and Private Sector Banks Operating in India

Asha Singh

Department of Business Management, Mewar University, Chittorgarh, Rajasthan, India Email:[email protected]

Abstract Commercial Banks play a vital role in the economic development of the country. With a view to have balanced growth in all the activities of the economic field our banking system should function in such a way that investors as well as bank customers should attract and contribute to the maximum possible extent in the smoothness of the economic progress. In the present scenario most of the commercial banks are facing the problems of overdue, recovery, nonperforming assets and other problems. Therefore, it is necessary to study performance of commercial banks in India. This paper attempts to examine the performance of public and private sector banks in India during the period 2002-03 to 2012-2013. The performance analysis for this study is based on selected 20 banks (10 public and 10 private sector banks).The performance analysis of 20 banks has been analyzed through regression model and also correlation and linear regression test. The study finds and that performance private sector banks are better than public sector banks in India. The required data for this study were collected from the various sources like monthly bulletins published by RBI, Govt. of India, Reports published by National Institute of Bank Management, Annual reports of various banks, Reports published by Indian Bank Association(IBA) etc. To analyze the performance of the bank multiple regression statistics is applied which helps to examine the relationship between various variables. Researcher has taken return on asset as dependent variable and capital adequacy ratio and non-performing assets as an independent variable. Keywords: Commercial banks, private sector banks, public sector banks, performance, non-performing assets, capital adequacy ratio, return on assets. 1. Introduction Any business is subject to fluctuations and at sometimes full of risk also. Banking is no exception to it. Banking assets in the form of loans, information, documents, investments, fixed and movable assets, etc., need to be managed to avoid losses/keep it at the barest minimum level. The banking has become the foundation of modern economic development. Banks have been playing a crucial role in the betterment of the economy and also the social life in the country. Banks are not just the store house of the country’s wealth but are reservoirs of resources necessary for economic development. An effective banking system is required to provide financial opportunities for a rising level of economic activity. Thus banking institutions occupy a key position in a modern economy. Indian banking system is unique and perhaps has no parallel in the banking history of any country in the world. The banking system has made a remarkable progress since independence. It has seen a number of phases till now. These phases can be divided into following three distinct categories:

I. First phase : Phase of banking consolidation (1948-1991) II. Second phase : Phase of innovating banking (1968-1991) III. Third phase : Phase of prudential banking (1991-onwards)

Financial sector reforms introduced in India in the early 1990 directed towards more competitive path for the banking sector. Considering the growing erosion in the productivity and profitability of banking sector, the Government decided to restructure the banking sector in order to infuse greater competition and efficiency in their working to increase their profitability. The broad directions of the financial sector reforms initiated in 1991were improvement in the overall monetary policy framework, strengthening financial institutions and gradual integration of the domestic financial system into the global economy. Within these broad goals of policy, the banking reforms measures fall into the following categories:

www.theinternationaljournal.org > RJEBS: Volume: 05, Number: 08, June 2016 Page 16

Ø Measures aimed at removing the external constraints bearing on the profitability of the banks; Ø Measures aimed at improving the financial health of the banks by the introduction of

appropriate prudential norms; Ø Measures aimed at building financial infrastructure relating to supervision, audit and

technology. Ø Measures aimed at up gradation of the level of managerial competence and the quality of

quality of human resources, and Ø Measures aimed at institutional strengthening including improving the competitiveness of the

system. The banking reforms based on these specific tasks have two aspects, macro-level policy changes and micro-level policy reforms. Therefore in this study, an attempt has been made to compare the performance of private sector and public sector banks. The comparative analysis of private sector and public sector banks has been done on the basis of ROA, Net NPAs and CAR. Statement of Research Problem In the present scenario, the activities of the commercial banks have increased many folds. The increase in financial transaction by the commercial banks have not only been the increase in volume but also provided greater scope of the risks. Earlier banks were only considered as means of depositing money but now the total scenario has changed. Today more and more private banks are being introduced for providing a number of financial and non-financial services. The modern banking is placed in a very complex and intricate environment so its proper functioning is very essential for the growth of an economy. This study is an attempt to have a comparative study of Non Performing Assets (NPA), capital Adequacy Ratio (CAR), return on Assets (ROA) of Private and Public Sector Banks in India. Objectives of the study These are the following specific objectives.

Ø To examine the performance of selected public and private sector banks. Ø To analyse the relation between NPA, CAR and ROA through regression model.

Research hypothesis Ha: The performance of private sector banks has improved more than in terms of ROA, NPA and CAR in comparison to public sector banks. Ho: The performance of private sector banks has not improved more than in terms of ROA, NPA and CAR in comparison to public sector banks.

2. Literature Review Within the last few years, a number of studies have provided the discipline into the practice of risk management within the corporate and banking sector. An insight of related studies is as follows: Rajeev and Mahesh (2010) emphasized on management of non-performing assets in the perspective of the public sector banks in India under strict asset classification norms, use of latest technological platform based on Core Banking Solution, recovery procedures and other bank specific indicators in the context of stringent regulatory framework of the RBI. Non-performing Asset is an important parameter in the analysis of financial performance of a bank as it results in decreasing margin and higher provisioning requirement for doubtful debts. Various banks from different categories together provide advances to different sectors like agricultural, SSI, priority sector, public sector and others, which require pre-sanctioning appraisal and post-disbursement control to contain the increasing non-performing assets in the Indian Banking. The reduction of non-performing asset is necessary to improve profitability of banks and comply with the capital adequacy norms as per the Basel Accord. Thiagarajan, et al (2011) analysed the role of market discipline on the behaviour of commercial banks with respect to their capital adequacy. The study showed that the Capital Adequacy Ratio (CAR) in the Indian Commercial Banking sector shows that the commercial banks are well capitalized and the ratio is well over the regulatory minimum requirement. The private sector banks show a higher percentage of Tier-I capital over the public sector banks. However the public sector banks show a higher level of Tier-II capital. Although the full implementation of Basel II accord by the regulatory authority (RBI)


may have influenced the level of capital adequacy in the banking sector. The study indicates that market forces influence the banks behaviour to keep their capital adequacy well above the regulatory norms. The Non-Performing Assets significantly influenced the cost of deposits for both public and private sector banks. The return on equity had a significant positive influence on the cost of deposits for private sector banks. The public sector banks can reduce the cost of deposits by increasing their tier I capital. Takang and Tenguh(2008) analysed that in measuring the profitability of a bank, bank regulators and analysts have used Return On Assets (ROA) and return on equity (ROE) to assess industry performance and forecast trends in market structure as inputs in statistical models to predict bank failures and mergers and for a variety of other purposes where a measure of profitability is desired. Prashanth K Reddy (2002) in his thesis titled, “A comparative study of Non Performing Assets in India in the Global context - similarities and dissimilarities, remedial measures” highlighted that financial sector reform in India has progressed rapidly on aspects like interest rate deregulation, reduction in reserve requirements, barriers to entry, prudential norms and risk-based supervision. But progress on the structural-institutional aspects has been much slower and is a cause for concern. The sheltering of weak institutions while liberalizing operational rules of the game is making implementation of operational changes difficult and ineffective. Changes required to tackle the NPA problem would have to span the entire gamut of judiciary, polity and the bureaucracy to be truly effective. The study deals with the experiences of other Asian countries in handling of NPAs. It further looks into the effect of the reforms on the level of NPAs and suggests mechanisms to handle the problem by drawing on experiences from other countries. Bidani (2002) in his book titled, "Managing Non-Performing Assets in Banks," highlighted that banks are concerned with their heavy NPA portfolio which was impairing their profitability and are taking all possible steps to contain the same. Banks have achieved a reasonable degree of success to bring down their existing NPAs but due to heavy slippage of standard accounts to NPA category the overall position continued to deteriorate. The main reasons responsible for such a situation include - slow economic and industrial growth, slump in capital market, financial indiscipline, wilful defaults by the borrowers, overburdened and slow judiciary, competition faced by local industries from the multi-nationals, lack of support to the borrowers from the banks at the time of the need, etc. In this book, the author has made an effort to deal with the practical aspects of the problem of management of NPAs right from identification stage till recovery of the dues including other aspects connected with the subject like asset classification, assessment of provision, pre-sanction appraisal and post-sanction appraisal and post sanction supervision, monitoring system for existing and likely NPAs, capital adequacy, reduction of NPAs, rehabilitation of sick non-performing units etc. G.V. Bhavani Prasad and Verma (2011) analysed that decade of nineties in last century brought revolution in Indian banking sector. Banks were made free from the clutches of hefty regulations and allowed to decide their own fate. Author suggested that Indian banks especially public sector banks will have to learn to live up with competitive environment. They must make persistent efforts to improve their profitability. On the revenue side, they should increase non-interest income by diversifying their operation into Para banking activities on the lines of new private banks. On the expenditure side, they must bring efficiency in their operations to minimize cost and strive hard to control the booming NPAs. Saggar (2005) in her research book titled, " commercial Banks in India", stated that it has been found over the years that the performance of banking sector has been a mixed one i.e. strong in widening the business coverage but weak in terms of sustainability and viability. Overtime, the viability particularly of a number of public sector banks has become a matter of great concern. According to author, profitability of banks is influenced by a combination of factors such as quality of asset-liability management, productivity levels, operating costs, organisational culture and most critical issue in present context, i.e., the non-performing assets (NPAs). She concluded that the public sector banks should move from deposit orientation to profit orientation. Profit plans should be developed to help them in recasting their cost estimates for their activities.


Khasnobis (2005) in his article, “NPAs Emerging Challenges in India” studied that the Indian banking sector has played a commendable role in fuelling and sustaining growth in the economy. In the recent past a large part of the banking sector’s growth has been on the back of financing consumption, as reflected in the growth of retail banking. While the progress on this front is likely to continue, sustaining this growth in the coming years may require focus on the supply side – capacity building. A growth driver in this phase would involve financing the emerging Small & Medium Enterprises (SMEs) sector of the economy. As such, banks would have to gear up for the challenges of managing growth and consequent risks in the SME sector financing. Addressing this issue and putting in place a suitable risk mitigation mechanism is going to be a fairly daunting challenge. One way of ensuring focus would be to free up capital – both financial and human – and make them available for sustaining the growth in assets and profitability. Farming out the banks, Non- Performing Assets (NPAs) portfolio to asset-recovery companies, which specialize in this segment of the financial sector, could be an option worth evaluating. Krishna (2008) studied that the Indian banking sector faced a serious problem of NPAs. The extent of NPAs has comparatively higher in public sectors banks. To improve the efficiency and profitability, the NPAs have to be scheduled. Various steps have been taken by government to reduce the NPAs. It is highly impossible to have zero percentage NPAs. But at least Indian banks can try competing with foreign banks to maintain international standard. Joshi Hemlata (2005) has attempted to investigate whether new private sector banks were serving properly to different segments of the economic sectors of India specially to economically weaker sector of the society or not and were the employees of these banks satisfied. Some other important parameters such as assets size, level of NPAs, interest and other incomes etc. were selected to make comparison between new private sector banks and public sector banks. Impact of economic reforms on banking sector has also been examined in the study. He concluded that public sector banks were coming up fastly to meet the challenges of open competition in financial markets in India. They were adopting latest banking technologies day by day and providing quality services to their respective customers at lower cost. Das and Ghosh (2003) observed that there has been a spirit in the lending activity of banks, in the recent past. This is due to two factors, viz. availability of huge surplus funds with the banks and the losses suffered by the banks in investment and treasury activities. While credit growth is needed for survival, it is imperative to ensure that the credit growth does not result in non-performing advances later. For this banks have to resort to effective pre-disbursement as well as post-disbursement monitoring. The authors concluded that negligence in monitoring a loan was less excusable than an error at the appraisal stage. Malyadri P. (2003) stated that the public sector banks have emerged strong across all key indicators as the global financial turmoil and slowing domestic economy put the banking sector on a test. The public banks have not only reduced the lending rates but have also managed to record higher average net profit and lower NPAs level than their private sector counterparts. According to the study the public sector banks (PSBs) have shown impressive performance across all the significant banking parameters. While they have reduced their prime lending rates, by private banks, their credit growth has far surpassed the latter registered by private banks. Despite lower lending rates, the net interest income growth of the PSBs was much higher than the private banks which fuelled the bottom line growth of public banks. Progressing upon bringing a significant shift in their conventional image, the public sector banks have considerably improved their quality of assets. The rise in Net NPAs of the private banks was far higher than the public sector banks. Carey (2001) stated that in commercial lending, commercial bank plays a dominant role. He analysed that in many countries, commercial banks routinely perform investment banking activities by providing new debt to their customers. The credit creation process works smoothly when funds are transferred from ultimate savers to borrower. There are many potential sources of risk, including liquidity risk, credit risk, interest rate risk, market risk, foreign exchange risk and political risks. He analysed that credit risk is the biggest risk faced by banks and financial intermediaries. The indicators of credit risk include the level of bad loans (Non- performing loans), problem loans or provision for


loan losses. Credit risk is the risk that a loan which has been granted by a bank will not be either partially repaid on time or fully and where there is a risk of customer or counterparty default. 3. Analysis of secondary data The researcher has used secondary data in study. The researcher has used multiple regression analysis which is used to test whether one or more independent variables (predicates) influence a dependent variable (outcome variable) and this effect is positive or negative. But before rushing towards data analysis, the researcher made a diagnostic test. Researcher has collected data of ROA, Net NPAs and CAR of Public and Private Sector banks from 2002-03 to 2012-13. 3.1. Multiple regression analysis of public sector banks (PSBs) Secondary data was used for the study. The data was analyzed using regression statistical tool run using SPSS program. The output of multiple regression analysis by using SPSS software has given below: Diagnostic tests

Table 1: Variables Entered/ Removeda

Model Variables Entered Variables Removed Method

2 CAR, Net NPAb - Enter

a. Dependent Variable: Return on Asset(ROA) b. All requested variables entered.

Table 1 displays the variables entered column there are two independent variables entered on the study, those are capital adequacy ratio (CAR) and net non- performing asset (Net NPA). Since there was no variable removed from the study, the 3rd removed /column is free. Does the model fits?

Table 2: ANOVAa

Model Sum of Squares df Mean Square F Sig.

2 Regression Residual Total

0.097 0.107 0.204

1 9 10

0.097 0.012

8.187 0.019b

a. Dependent variable: ROA b. Predictors:(constant), Capital Adequacy Ratio, Net NPA

From the above table we know that value of F-statistics is 8.187 of public sector banks and level of significance is 0.019 which is less than 5% level of significance. This indicates that the null hypothesis (H0) is rejected and alternate hypothesis (Ha) is accepted. Hence it can be concluded that CAR, Net NPA have significant impact on internal financial performance of public sector banks measured by ROA. How much the model is good?

Table 3: Model Summaryb

Model R R Square Adjusted R

Square Std. Error of the

Estimate Durbin Watson

2 0.690a 0.476 0.418 0.10883

1.593

a. Predictor:(constant), CAR, Net NPA b. Dependent variable: ROA


As the table 2 indicates the independent variable explained the dependent variable by 47%. A large R2

does not necessarily mean high predictability, nor does a low R2 necessarily mean poor predictability. The value of Adjusted R2 shows that here model is fit by 41%. There is a correlation of 0.690 between variables in public sector banks. The value of Durbin Watson shows that there is no multicollinearity. Correlation test

Table 4: Correlations ROA Net NPA CAR ROA Pearson Correlation sign.(2-tailed) N

1 11

0.387 0.240 11

0.690

0.019 11

Net NPA Pearson Correlation sign.(2-tailed) N

0.387 0.240 11

1 11

0.022 0.949 11

CAR Pearson Correlation sign.(2-tailed) N

0.690 0.019 11

0.022 0.949 11

1 11

*Correlation is significant at the 0.05 level (2-tailed)

Table 4 shows that the correlations between ROA and Net NPA and CAR are 0.387 and 0.690 respectively. Although these relations are in the low-to-moderate range, the p values associated with them (0.240 and 0.019) tell us that we might get correlations this high simply by chance when the two variables are actually unrelated in the overall population. The correlation between Net NPAs and CAR is 0.022, which has an associated probability of 0.949. This r is statistically not significant. The performance of banks that has less NPA is much better. Collinearity (Multi-colinearity) test

Table 5: Coefficientsa (Multi-Colinearity Test)

Unstandardised Coefficients

Standardised Coefficients

Collinearity Statistics

Model b Std. Error Beta t sig. Tolerance VIF 2 (constant) -2.490 1.074 -2.318 0.049 Net NPA 0.048 0.029 0.372 1.693 0.129 1.000 1.000 CAR 0.262 0.084 0.682 3.106 0.015 1.000 1.000

a. Dependent Variable: ROA

In Table 5 the value of VIF (variance inflation factor) and tolerance shows lack of multicollinearity among independent variables.


Collinearity Diagnostics Test

Table 6: Collinearity Diagnosticsa

Model Dimension Eigenvalue Condition Index Variance Proportions

(constant) Net NPA CAR

2 1 2 3

2.822 0.178 0.000

1.000 3.987 85.395

0.00 0.00 1.00

0.03 0.97 0.00

0.00 0.00 1.00

a. Dependent variable :ROA

The Table 6 display the values of Eigenvalue and Condition Index. Eigenvalues are really variances. Condition index is a ratio of variances. Eigenvalue shows that there is multicollinearity. Conditional Index value scored around 1, 4 and 85, it also shows there is multicollinearity among independent variables. Residual Statistics Test

Table 7: Residual Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value Residual Std. Predicted Value Std. Residual

0.7944 - 0.11209 - 1.396 - 1.030

1.0809 0.21037 1.513 1.933

0.9318 0.00000 0.000 0.000

0.09847 0.10325 1.000 0.949

11 11 11 11

a. Dependent Variable: ROA

Table 7, tells about the residual and predicted value. For each case, the predicted value is the value predicted by the regression model and for each case; the residual is the difference between the observed value of the dependent variable and the value predicted by the model. Residuals are estimate of the true errors in the model, if the model is appropriate for the data, the residuals should follow a normal distribution. Test for Normality of Residuals

Fig. 1 Regression Standardised Residual of PSBs


Fig. 1shows that the residuals from this regression appear to conform to the assumption of not being normally distributed. It has slight positive skewness.

Fig.2 Normal P-P Plot of Regression Standardised Residual of PSBs If the residuals are normally distributed, data point of the normal P-P plot will fall along the straight line through the origin. Due to slight positive skewness, there is slight deviation of data point of the normal P-P plot from straight line. Fig.3 Relationship between Regression Standardised Residual and Predicted Value of PSBs


From the above Fig. 3 shows no clear relationship between the residuals and the predicted values which is consistent with the assumption of linearity. The dispersion of residuals over the predicted value range between -1 and 1 looks constant, for predicted values below -1 there is too few points to provide evidence against a change in variability. Test of Heteroscedasticity

Fig. 4 Regression Standardised Residual of PSBs by Heteroscedasticity Fig. 4 shows that the variance of residuals from this regression is not constant. Therefore it shows heteroscedasticity. It has slight positive skewness.

Fig. 5 Normal P-P Plot of Regression Standardised Residual of PSBs by Heteroscedasticity


Due to slight positive skewness, there is slight deviation of data point of the normal P-P plot from straight line. This indicates heteroscedasticity.

Fig.6 Relationship between Regression Standardised Residual and Predicted Value of PSBs by Heteroscedasticity

From the scatterplot, it can be seen that most of the pattern of data points is fall below the line of zero standard residual value which indicates slight heteroscedasticity. 3.2. Multiple regression analysis of Private Sector Banks (PvtSBs) Diagnostic tests Below the researcher uses regression command for administration of regression. This is followed by the output of these SPSS commands.

Table 8: Variables Entered or Removeda

Model Variables Entered Variables Removed Method

2 CAR, Net NPAb

- Enter

a. Dependent Variable: Return on Asset(ROA) b. All requested variables entered.

Table 6.10 simply states the variables in the model and the selection method chosen. Does the model fits?

Table 9: ANOVAa

Model Sum of Squares df Mean Square F Sig.

2 Regression Residual

total

0.454 0.882 1.336

2 8 10

0.227 0.110

2.057 0.015b

a. Dependent variable: Return on Asset(ROA) b. Predictors:(constant), CAR, Net NPA


From the above table 9 we know that value of F-statistics is 2.057 of private sector banks and level of significance is 0.015 which is less than 5% level of significance. This indicates that the null hypothesis (H0) is rejected and alternate hypothesis (Ha) is accepted. Hence it can be concluded that CAR, Net NPA have significant impact on internal financial performance of private sector banks measured by ROA. How much the model is good?

Table 10: Model Summaryb Model

R R Square Adjusted R

Square Std. Error of the Estimate

Durbin Watson

2 0.583a 0.340 0.175 0.33211 1.512

a. Predictors:(constant), CAR, Net NPA b. Dependent variable: ROA

Table 10 indicates the independent variable explained the dependent variable by 34% and value of Adjusted R2 shows that the model is fit by 17%. There is correlation of 0.583 between variables in private sector banks. The value of Durwin Watson is 1.593. So there is no multicollinearity. Correlation test

Table 11: correlations ROA Net NPA CAR ROA Pearson Correlation Sign.(2-tailed) N

1 11

-0.555 0.076 11

0.391 0.234 11

Net NPA Pearson Correlation Sign.(2-tailed) N

-0.555 0.076 11

1 11

-0.412 0.208 11

CAR Pearson Correlation Sign.(2-tailed) N

0.391 0.234 11

-0.412 0.208 11

1 11

The above table 11 displays the correlations between ROA and Net NPAs and CAR are -0.555 and 0.391 respectively. The correlation between Net NPAs and CAR is -0.412, which has an associated probability of 0.208. This r is statistically not significant. Banks who has more NPA is not much better. Collinearity (Multi-colinearity) test

Table 12: Coefficientsa (Multi-Colinearity Test)

Model

Unstandardised coefficients

Standardised coefficient

Collinearity statistics

b Std. Error Beta t sig. Tolerance VIF 2 (constant) 0.612 0.969 0.632 0.545 Net NPA -0.126 0.084 -0.474 -1.504 0.171 0.831 1.204 CAR 0.041 0.066 0.196 0.621 0.552 0.831 1.204

From Table 12 we conclude that the value of VIF and Tolerance shows the lack of multicollinearity among independent variables.


Collinearity Diagnostics Test Table 13: Collinearity Diagnosticsa

Model Dimension Eigenvalue Condition

Index Variance Proportions (constant) Net NPA CAR

1 2 1 2 3

2.669 0.326 0.006

1.000 2.862 21.638

0.00 0.00 0.99

0.04 0.74 0.23

0.00 0.01 0.99

a. Dependent variable :ROA Table 13 displays that value of Eigenvalue which shows no multicollinearity. Since Conditional Index value scored around 1, 3 and 22 so there is no multicollinearity among independent variables. Residual Statistics

Table 14: Residual Statisticsa

Minimum Maximam Mean Std. Deviation N Predicted Value Residual Std. Predicted Value Std. Residual

0.5144 -0.65483 -2.152 -1.972

1.2004 0.54891 1.069 1.653

0.9727 0.00000 0.000 0.000

0.21304 0.29705 1.000 0.894

11 11 11 11

a. Dependent Variable: ROA Table 6.16, tells about the residual and predicted value. Residuals are estimate of the true errors in the model, if the model is appropriate for the data, the residuals should follow a normal distribution. Test of Normality of Residual

Fig. 7 Regression Standardised Residual of PvtSBs Fig. 7 shows that that the residuals from this regression appear to conform to the assumption of being normally distributed. It has no skewness.


Fig. 8 Normal P-P plot of Regression Standardised Residual of PvtSBs If the residuals are normally distributed, data point of the normal P-P plot will fall along the straight line through the origin. From the normal P-P plot, it can be seen that data points uniformly distributed along the straight line through the origin.


Fig. 9 Relationship between Regression Standardised Residual and Predicted Value of PvtSBs The above scatter plot of standardised residuals against predicted values should be a random pattern centered around the line of zero standard residual value. The points should have the same dispersion about this line over the predicted value range. From the above we can see relationship between the residuals and the predicted values which is consistent with the assumption of linearity.


Test of Heteroscedasticity Fig.10

Regression Standardised Residual of PvtSBs by Heteroscedasticity Fig. 10 shows that the variance of residuals from this regression is constant. Therefore there is no heteroscedasticity. Fig. 11 Normal P-P Plot of Regression Standardised Residual of PvtSBs by Heteroscedasticity.


From normal P-P plot, it can be seen that data points of the normal P-P plot uniformly distributed along the straight line through origin due to constant of variance of residual. This indicates no heterscedasticity. Fig. 12 Relationship between Regression Standardised Residual and Predicted Value of PvtSBs by Heteroscedasticity From the scatterplot, it can be seen that almost of the pattern of data points is fall along the line of zero standard residual value which indicates no heteroscedasticity. 4. Conclusion The method of our study is quantitative. We used regression model to analyze the collected data. Based on the regression outputs we conduct the analyses and answer our research hypothesis. The analyses are presented by using descriptive approach. We only describe the regression results without providing further explanation on the issues. (i) Diagnostic tests of public sector banks the value of R2 is 0.476 reveals that the independent variable explained the dependent variable by 47%.Adjusted R2 attempts to correct R square to more closely reflect the goodness of fit of the model in the population. From the report observed that value of adjusted R2 is 0.418 and conclude that the model is 41% fit/good. Where as in Diagnostic tests of private sector banks the value of R2 is 0.340 reveals that the independent variable explained the dependent variable by 34%. The value of adjusted R2 is .175 and concludes that the model is 17% fit/good. (ii) The value of Durbin Watson of public sector bank is 1.593 and 1.512 of private sector banks. The test statistic can vary between 0 and 4 with a value of 2 meaning that the residuals are uncorrelated. A value greater than 2 indicates a negative correlation between adjacent residuals, whereas a value below 2 indicates a positive correlation. It means there is no multicollinearity problem in both banks. (iii) There is a correlation of 0.690 between variables in public sector banks and there is correlation of 0.583 between variables in private sector banks. (iv) In correlation test of public sector banks, the correlations between ROA and Net NPA and CAR are 0.387 and 0.690 respectively. Although these relations are in the moderate-to-low range, the p values associated with them (0.240 and 0.019) tell us that we might get correlations this high simply by chance when the two variables are actually unrelated in the overall population. The correlation between Net NPA and CAR is 0.022, which has an associated probability of 0.949. This r is


statistically not significant. The performance of banks that has less NPA is much better in public sector banks and in private sector banks the correlations between ROA and Net NPA and CAR are -0.555 and 0.391 respectively. Although these relations are in the low-to-moderate range, the p values associated with them (0.076 and 0.234) tell us that we might get correlations this high simply by chance when the two variables are actually unrelated in the overall population. The correlation between Net NPA and CAR is -0.412, which has an associated probability of 0.208. This r is statistically not significant. Banks who has more NPA is not much better. (v) In collinearity test of public and private sector banks tolerance and VIF shows lack of multicollinearity among independent variables. (vi) In collinearity diagonistic test since conditional index value scored around 1, 4 and 85, from this ground there is mild multicollinearity among independent variables in public sector banks and in private sector banks conditional index value scored around 1, 3 and 22 from this ground there is no multicollinearity among independent variables. (vii) In collinearity diagonistic test eigenvalues of public sector banks are 2.822, 0.178, 0.000 and private sector banks are 2.669, 0.326, and 0.006. According to eigenvalue there is multicollinearity in public and private sector banks. (viii) Based on test of normality of residuals results, the residuals from this regression appear to conform to the assumption of being normally distributed. It has slight positive skewness in public sector banks but in private sector banks it has no skewness. (viii) Based on the result of heteroscedasticity in public sector banks, the variance of residuals from this regression is not constant. Therefore it shows heteroscedasticity. It has slight positive skewness. Graph indicates that one residual is somewhat distant from others at close to +2 standard deviations away from the mean but in private sector banks no skewness it means absence of heteroscedasticity. (ix) Normal P-P Plot of Regression Standardised Residual and relationship between Regression Standardised Residual and Predicted Value graphs of Public Sector Banks indicates Heteroscedasticity and in private sector banks indicates no Heteroscedasticity. (x) Scatterplot between regression standardized residual and regression standardized predicted value of public sector banks indicates heteroscedasticity and in private sector banks indicates no heteroscedasticity. References

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