Assessing the Impact of Non-Performing Assets on Profitability of Public Sector Banks in India

 

Sk Mujibar Rahaman

Assistant Professor, Department of Commerce, Memari College, West Bengal, India.

 

ABSTRACT:

The Indian banking system is considered as the heart of the financial system in the country. Banking organisations infuse money in the system resulting in additional purchasing power. In effect, banks mobilise funds from surplus units to deficit units, thereby pave the way for a sustainable economic development. Banks also play pivotal role in the socio-economic transformation efforts through supporting financial and economic policies of the government. Actually, by playing an all-round role as catalyst of development, commercial banks act as the back-bone of economic growth and prosperity of the country. A well-organised, efficient, planned and viable banking system is, therefore, a desired concomitant of an all-round development of the country. But all these requirements can be fulfilled only when banks are able to sustain a healthy bottom-line. But now-a-days, the mounting NPAs is the single most source of concern for the Indian banks; resulting in the Indian banks handicapped in performing their functions. In this backdrop, the present study attempts to assess the impact of NPAs on profitability of 26 public sector banks in India.

 

 

 

1. INTRODUCTION:

Nowadays, a substantial part of the financial system is occupied by the banking sector and in India also it has become almost synonymous with the financial system. Stability of the banking system in a country like India is considered sine qua non for rapid and sustained economic progress.  In fact, banking organisations play a pivotal role in the development process by way of channelising funds from surplus units to deficit units for productive purposes. Banks also play a significant role in the socio-economic transformation efforts through supporting financial and economic policies of the government. In order to accelerate the growth of an economy, a strong and sound financial system is a necessary precondition. The rate of economic growth, financial intermediation and capital formation are inextricably linked (Rahaman, 2022). Higher the rate of financial intermediation, higher the rate of capital formation and higher will be the economic growth rate and vice-versa. But all these requirements can be fulfilled only when banks are able to sustain a healthy bottom-line.

 

But in recent times, the mounting NPAs of public sector banks (PSBs) in India is the single largest source of concern for the Indian banks; resulting in the Indian banks handicapped in performing their core functions. The gross non-performing assets (NPAs) ratio for PSBs as a group was as high as 14.58 per cent while this ratio for all scheduled commercial banks in India was reported at 11.18 per cent in March 2018. High level of distressed assets coupled with wilful defaulters made significant reduction in profitability and depicted massive bleeding of balance sheets of Indian banks in recent past. As a result, regulators have been implementing strictest vigilance, follow-ups, recovery measures such as corporate debt restructuring, Bankruptcy laws 2016, high provisioning and heavy pecuniary penalties to the defaulters and so forth. In this backdrop, the present paper attempts to assess the impact of NPAs on profitability of 26 selected PSBs in India during the period 1999-2000 to 2016-17. The remainder of the paper is set out as follows. In the next section, the previous literature in this field is briefly reviewed and objectives of the study are stated. The methodology adopted in the study is discussed in Section 3. The results and discussion are presented in Section 4. In Section 5 concluding remarks are made.

 

2. LITERATURE REVIEW:

A good number of studies have been conducted on the profitability and NPAs of banks throughout the world. A brief review of some of the selective studies are presented as follows. Bansal, et al. (2018) made an effort to identify the major factors affecting profitability of Indian banking sector by using panel regression. The study observed that credit deposit ratio has negative influence on profitability whereas capital adequacy and advance to loan ratio have positive impact on the profitability of banks in India. Devanand and Prasad (2015) made a study on the performance of Indian public sector banks. The study used the technique of ratio analysis in assessing performance of banks. The researchers observed that in the post-reform period banks have shifted their focus towards increasing the productivity, profitability and improving operational efficiency. Spathis and Doumpos (2002), in their study, focused on the effectiveness of the Greek banks on the basis of size of assets of the bank. They used a multi-criteria methodology to classify the Greek banks on the basis of banks’ profitability and efficiency between small and large banks measured through return and operation factors of banks. Athma, Rao and Ibrahim (2018) conducted a study to identify the determinants of Indian banks’ profitability. The study used random effects model to assess the impact of macroeconomic and bank-specific factors based on the CAMEL framework. Barua et al. (2017) found a negative link between profitability and market concentration. They also observed that capitalization, credit risk, leverage and ownership structure are the most important elements of the viability of Indian banks. Ozili (2017) investigated the determinants of African bank profitability. Using the static and dynamic panel estimation techniques, the author concluded that bank size, total regulatory capital and loan loss provisions are the major elements of the ROA of listed banks in comparison to non-listed banks. Nachimuthu and Veni (2019) observed that non-performing assets have an adverse impact on the profitability of the scheduled commercial banks in India. They concluded that the non-performing assets or bad loans have been adversely affecting the world economy from time to time. Kumar and Ghani (2015) showed that there is  a high degree of negative correlation between NPA ratios with ROA. The multiple regression model revealed that the profitability of the banks can be enhanced if the NPAs follow a decreasing trend continuously. Chatterjee et al. (2012) found that NPAs have a negative effect on the achievement of capital adequacy level, funds mobilisation, banking system credibility and productivity on the overall economy. Their study also showed that private sector banks can protect themselves by adapting to the changing environment whereas public sector banks are facing enormous problems. Raju (2009), in his study, concluded that magnitude of NPA has a direct impact on the bank’s profitability as legally they are not allowed to book income and on the same time banks are forced to make provision on such assets as per RBI guidelines. Sharifi and Akhter (2016), in their study, observed that the NPA has a significant negative impact on bank's financial performance in the period under study. From the analysis of correlation coefficient, they found that there is a strong, negative and significant relationship between bank’s financial performance as measured by ROA, ROE and NIM and NPAs.

 

In most of the existing studies, researchers have made efforts to analyse profitability of banks with the help of ratio analysis and statistical tools such as mean, standard deviation, coefficient of variation and so on. Several studies have also been conducted to identify the significant factors responsible for profitability of banks by using panel data regression analysis with its different variants such fixed effects model, random effects model and with static and dynamic panel estimation techniques. Some of the studies have tried to assess the influence of NPAs on banks’ financial performance including profitability measured through individual ratios such as ROE, ROA, NIM etc. But no study has been found to assess the impact of assets non-performance on the profitability of banks measured through a composite profitability index of PSBs in India. Keeping this gap in consideration the researcher, therefore, in this study has made an effort to assess the impact of impaired loans on the composite profitability index of Indian PSBs during the study period.

 

3. METHODOLOGY OF THE STUDY:

The present study is based on 26 PSBs in India which were in operation throughout the entire study period 1999-2000 to the year 2016-17. In effect, we have constructed a balanced panel data set with 468 observations for each variable under study. The period under study extends over the initial two decades of the new millennium and also covers the implementation phases of the recommendations of the Narasimham Committee-II report. The data used in the present study have mainly been collected from secondary sources, i.e., the Report on Trend and Progress of Banking in India and the Statistical Tables Relating to Banks in India as available in the official website of the RBI, and the Capitaline Corporate Database, Capital Market Publishers (I) Ltd., Mumbai as available in the Department of Commerce, The University of Burdwan, India. In the present study, we have selected four indicators of profitability for deriving the composite profitability index, viz., return on assets, net profit to total funds ratio, return on equity and inverse of cost of deposits ratio. The definitions of these variables have been presented in Appendix 1. In the first place, in order to arrive at a profitability index, we have followed the data driven approach suggested by Rahaman (2022). The profitability indices of the selected banks have been presented in Appendix 2. Thereafter, in order to investigate the impact of NPAs on profitability we have employed the correlation analysis of profitability index with current gross NPA ratio and one period and two period lags of gross NPA ratio. Three types of correlation coefficients have been computed between gross NPA ratio and profitability index by taking into account their magnitudes (by Pearson’s simple correlation coefficient), rankings of their magnitudes (by Spearman’s rank correlation coefficient), and the nature of their associated changes (by Kendall’s correlation coefficient). In order to test whether these coefficients are statistically significant or not, the t-test has been applied. After testing the statistical significance of each of the correlation coefficients, we have used a statistical test, viz., the P_λ test, as suggested by R. A. Fisher, to test the global null hypothesis of no correlation between the variables under consideration. In this test, λ = −2∑logeP_i (where P_i be the P-value of test i), which follows a χ² distribution with degrees of freedom 2n, where n is the number of tests. If the observed value of λ (i.e., λ_o) lies between the upper and the lower limit of 95% confidence interval, the global null hypothesis is accepted else rejected. In other words, we can infer that there is no significant association between the variables concerned if λ_o falls within the acceptance region. However, if the value of λ_o is greater than the upper limit or lower than the lower limit of confidence interval, we cannot accept the null hypothesis implying that there is significant relation between the variables. We have three P_λ tests for three types of correlation coefficients used. At this point, it is important to mention that we have considered the association to be significant if and only if all the three λ tests are found to be statistically significant at the 0.01 level or at the 0.05 level of significance. Now, in order to ascertain the nature of association between the variables (whether negative or positive), we rely on the number of statistically significantly positive and negative correlation coefficients. If the number of statistically significantly negative correlation coefficient is greater than the number of statistically significantly positive correlation coefficient, we conclude that the overall association between the variables is significantly negative and vice-versa. In order to capture the impact of NPAs on profitability, further light has been shed on the matter by ascertaining pooled correlation coefficient using panel data of the 26 selected PSBs in India for the period 2000 to 2017.

 

4. RESULTS AND DISCUSSION:

In Table 1 an we have made an attempt to ascertain the influence of assets non-performance on the profitability of the PSBs in India during the study period. The assets non-performance has been measured in terms of gross NPA ratio, while the degree of profitability of the bank has been ascertained on the basis of profitability index which has been presented in Appendix 2. In the present study, we have analysed three types of correlation coefficients, viz. Pearson’s correlation coefficient, Kendall’s correlation coefficient and Spearman’s rank correlation coefficient. In order to judge whether these coefficients are statistically significant or not both

 

Table 1: Analysis of the Impact of NPAs on the Profitability of PSBs in India

Sl. No.	Public Sectors Banks	Correlation Coefficients between Profitability Index and Gross NPA Ratio
		Pearson	Kendall	Spearman
1	Allahabad Bank	-0.647*	-0.298	-0.472
2	Andhra Bank	-0.499*	-0.328	-0.538*
3	Bank of Baroda	-0.344	-0.463*	-0.568*
4	Bank of India	-0.568*	-0.491**	-0.565*
5	Bank of Maharashtra	-0.254	-0.037	-0.041
6	Canara Bank	-0.393*	-0.286	-0.372
7	Central Bank of India	-0.292	-0.262	-0.351
8	Corporation Bank	-0.432	0.029	0.155
9	Dena Bank	-0.500**	-0.536**	-0.673**
10	IDBI Bank	-0.663**	-0.136	-0.219
11	Indian Bank	-0.891**	-0.802**	-0.918**
12	Indian Overseas Bank	-0.704*	-0.464*	-0.519*
13	Oriental Bank of Commerce	-0.066	0.235	0.321
14	Punjab and Sind Bank	-0.661*	-0.658**	-0.793**
15	Punjab National Bank	-0.590*	-0.569**	-0.705**
16	State Bank of Bikaner and Jaipur	-0.126	0.046	0.171
17	State Bank of Hyderabad	-0.355	-0.165	-0.239
18	State Bank of India	-0.307	-0.278	-0.336
19	State Bank of Mysore	-0.520	-0.256	-0.378
20	State Bank of Patiala	-0.224	0.136	0.225
21	State Bank of Travancore	-0.440	-0.395*	-0.473
22	Syndicate Bank	-0.177	0.256	0.323
23	UCO Bank	-0.720**	-0.385*	-0.531*
24	Union Bank of India	-0.524*	-0.486**	-0.551*
25	United Bank of India	-0.539	-0.136	-0.382
26	Vijaya Bank	-0.141	-0.034	-0.011
Public Sectors Bank Group		-0.354	-0.031	-0.021
Fisher Test (λ)		163.335**	151.993**	162.405**
Note: Coefficients with superscript ** and * were found to be statistically significant at 0.01 and 0.05 levels respectively.
Source: Author’s own calculation

 

tailed t-test has been applied. Table 1 reveals that all the 26 Pearson’s correlation coefficients between gross NPA ratio and profitability index were negative, of which 12 coefficients were found to be statistically significant either at 0.01 or at 0.05 levels of significance. Similarly, 21 Kendall’s correlation coefficients were negative, of which 10 coefficients were found to be statistically significant either at 0.01 or at 0.05 levels of significance. Table 1 also discloses that out of 26 Spearman’s rank correlation coefficients between gross NPA ratio and profitability index, 21

coefficients were negative, of which 10 coefficients were found to be statistically significant either at 0.01 or at 0.05 levels of significance. Overall, it is observed that out of 78 correlation coefficients between gross NPA ratio and profitability index, 68 coefficients were negative, of which 32 coefficients were found to be statistically significant either at 0.01 or at 0.05 levels of significance. Table 1 also depicts that all the  three correlation coefficients between gross NPA ratio and profitability index of PSB group were negative but none of them was found to be statistically significant even at 0.05 level of significance. It is also observed from table 1 that the λ values in case of Pearson’s, Kendall’s and Spearman’s correlation coefficients between gross NPA ratio and profitability index were 163.335, 151.993 and 162.405 respectively. The λ in all the three cases were found to be statistically significant at the 0.01 level of significance. This finding provides evidence in support of significant association between the NPAs and the profitability of the PSBs under study. As there is no statistically significant positive correlation coefficient (all significant coefficients were found to be negative) in Table 1, we can infer that the relationship between the NPAs and the profitability is significantly negative. Therefore, the net outcome derived from the analysis of correlation made in this table signifies the negative impact of NPAs of the selected PSBs on their profitability during the period under study. In order to shed more light on the impact of NPAs on the profitability, the analysis of pooled correlation coefficient between gross NPA ratio and profitability index by using panel data has been made in Table 2.  This table shows that all the three pooled correlation coefficients were negative and significant at the 0.01 level of significance. Therefore, the net outcome derived from the analysis of correlation between gross NPA ratio and profitability index of PSBs in India provides strong evidence of significant negative impact of assets non-performance on the profitability of selected PSBs in India during the period under study.

 

Table 2: Analysis of the Impact of NPAs on the Profitability of PSBs in India Based on Panel Data

Correlation Measures	Pooled Correlation Coefficient between Gross NPA Ratio and Profitability Index
Pearson’s Simple Correlation Coefficient	-0.371**
Kendall’s Correlation Coefficient	-0.237**
Spearman’s Rank Correlation Coefficient	-0.338**
Note: Coefficients with superscript ** were found to be statistically significant at the 0.01.
Source: Author’s own calculation

 

It can be theoretically argued that the NPAs have significant impact on the banks’ performance in subsequent years also. Therefore, in order to capture the influence of NPAs on the profitability of banks in subsequent years, we have made the analysis of correlation of profitability index with one period lag and two period lag of gross NPA ratio in Table 3 and Table 4 respectively. It is observed from Table 3 that out of 78 correlation coefficients 52 coefficients were negative but only 9 coefficients of them were found to be statistically significant either at 0.01 or at 0.05 levels whereas 3 coefficients out of 26 positive coefficients were found to be significant either at 0.01 or at 0.05 levels of significance. Moreover, this table also discloses that all the 3 correlation coefficients between gross NPA ratio and one period lag of profitability index of PSB group were negative but none of them was found to be significant even at the 0.05 level of significance. In addition to that, the λ test in only two cases were found to be statistically significant one at 0.01 level and the other at 0.05 level of significance. Therefore, the findings of this table fail provide any strong evidence of positive or negative impact of NPAs of the selected PSBs on their profitability in the next year. Table 4 reveals that out of 78 correlation coefficients 17 were negative of which only 1 coefficient was found to be statistically significant at 0.05 level of significance while out of the remaining 61 positive correlation coefficients only 8 coefficients were found to be statistically significant either at 0.05 level or at 0.05 level of significance. Moreover, this table also depicts that all the three correlation coefficients between profitability index and two period lag of gross NPA ratio of PSB group, though positive, were found to be insignificant even at 0.05 level of significance. Table 4 also depicts that out of three λ tests only two of them were found to be statistically significant which implies that there is no sufficient evidence of rejecting the null hypothesis of no significant relation between two-period lag of NPAs and profitability index of the PSBs under study.  Therefore, the net result derived from the Table 4 fails to provide any strong evidence of positive or negative influence of NPAs of selected PSBs in India on their profitability in the year subsequent to the next year.

 

5. CONCLUSION:

The analysis of correlation coefficient between gross NPA ratio and profitability index reveals that out of 78 correlation coefficients, 68 coefficients were negative, of which 32 coefficients were found to be statistically significant. Moreover, all the three λ-tests were found to be statistically significant. Therefore, the net results derived from the analysis of correlation between gross NPA ratio and profitability index provide strong evidence of negative impact of NPAs of the selected PSBs on their profitability during the period under study. This finding of negative influence was also corroborated by the analysis of pooled correlation coefficient between gross NPA ratio and profitability index of the banks under study. Whatsoever, the present study fails to provide any strong evidence of negative impact of NPAs of the selected PSBs on their profitability in the next year and in the year subsequent to the next year. Further studies could be conducted to assess the long-term influence of NPAs on the profitability of banks by using advanced time series techniques and considering a longer time frame.

 

Table 3: Analysis of the Impact of NPAs on the Profitability of PSBs in India

Sl. No.	Public Sectors Banks	Correlation Coefficients Profitability Index and One Period Lag of Gross NPA Ratio
		Pearson	Kendall	Spearman
1	Allahabad Bank	-0.298	-0.085	-0.151
2	Andhra Bank	-0.277	-0.166	-0.282
3	Bank of Baroda	-0.077	-0.455*	-0.577*
4	Bank of India	-0.182	-0.135	-0.149
5	Bank of Maharashtra	0.116	0.140	0.163
6	Canara Bank	0.011	-0.082	-0.011
7	Central Bank of India	0.135	-0.021	-0.031
8	Corporation Bank	-0.083	0.184	0.379
9	Dena Bank	-0.156	-0.253	-0.425
10	IDBI Bank	-0.498	-0.051	-0.086
11	Indian Bank	-0.781**	-0.586**	-0.776**
12	Indian Overseas Bank	-0.305	-0.114	-0.046
13	Oriental Bank of Commerce	0.187	0.268	0.511*
14	Punjab and Sind Bank	-0.454*	-0.415*	-0.589*
15	Punjab National Bank	-0.241	-0.357*	-0.468
16	State Bank of Bikaner and Jaipur	0.282	-0.019	0.026
17	State Bank of Hyderabad	0.185	-0.021	-0.066
18	State Bank of India	-0.100	-0.085	-0.135
19	State Bank of Mysore	0.062	-0.118	-0.137
20	State Bank of Patiala	0.179	0.218	0.369
21	State Bank of Travancore	0.093	-0.105	-0.176
22	Syndicate Bank	0.263	0.536**	0.719**
23	UCO Bank	-0.434	-0.254	-0.310
24	Union Bank of India	-0.109	-0.168	-0.243
25	United Bank of India	-0.164	0.085	0.048
26	Vijaya Bank	0.281	0.269	0.481
Public Sectors Bank Group		-0.001	-0.051	-0.013
Fisher Test (λ)		63.347	79.772*	88.506**
Note: Coefficients with superscript ** and * were found to be statistically significant at 0.01 and 0.05 levels respectively.
Source: Author’s own calculation

 

Table 4: Analysis of the Impact of NPAs on the Profitability of PSBs in India

Sl. No.	Public Sectors Banks	Correlation Coefficients between Profitability Index and Two Period Lag of Gross NPA Ratio
		Pearson	Kendall	Spearman
1	Allahabad Bank	0.136	0.182	0.203
2	Andhra Bank	0.141	0.107	0.130
3	Bank of Baroda	0.220	-0.278	-0.376
4	Bank of India	0.308	0.125	0.220
5	Bank of Maharashtra	0.510*	0.466*	0.588*
6	Canara Bank	0.479	0.182	0.276
7	Central Bank of India	0.456	0.144	0.233
8	Corporation Bank	0.349	0.296	0.462
9	Dena Bank	0.242	-0.028	-0.055
10	IDBI Bank	-0.184	-0.068	0.006
11	Indian Bank	-0.532*	-0.336	-0.462
12	Indian Overseas Bank	0.281	0.144	0.408
13	Oriental Bank of Commerce	0.399	0.258	0.437
14	Punjab and Sind Bank	-0.136	-0.321	-0.408
15	Punjab National Bank	0.092	-0.119	-0.216
16	State Bank of Bikaner and Jaipur	0.516	-0.032	0.147
17	State Bank of Hyderabad	0.409	0.031	0.095
18	State Bank of India	0.339	0.031	0.052
19	State Bank of Mysore	0.494	0.163	0.273
20	State Bank of Patiala	0.480	0.239	0.463
21	State Bank of Travancore	0.343	0.049	0.044
22	Syndicate Bank	0.518*	0.584**	0.745**
23	UCO Bank	0.097	-0.163	-0.202
24	Union Bank of India	0.350	0.106	0.144
25	United Bank of India	0.276	0.315	0.415
26	Vijaya Bank	0.684**	0.353	0.565*
Public Sectors Bank Group		0.418	0.163	0.280
Fisher Test (λ)		83.752**	71.843	79.955*
Note: Coefficients with superscript ** and * were found to be statistically significant at 0.01 and 0.05 levels respectively.
Source: Author’s own calculation

 

REFERENCES:

1.      Athma, P., Rao, K. P. V. and Ibrahim, F. (2018). Profitability of Public Sector Banks in India: A Study of Determinants. International Journal of Advanced in Management, Technology and Engineering Sciences. 8(3), 1086-1097.

2.      Balasubramanian, S. K. (2007). Financial Performance of Private Sector Banks in India - An Evaluation (2007). Available at SSRN: https://ssrn.com/abstract=1044621 or http://dx.doi.org/10.2139/ssrn.1044621. Retrieved on April 16, 2022.

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4.      Barua, R., Roy, M. and Raychaudhuri, A. (2017). Structure, Conduct and Performance Analysis of Indian Commercial Bank. South Asian Journal of Macroeconomics and Public Finance. 5(2), 157-185.

5.      Chatterjee, C., Mukherjee, J. and Das, R. (2012). Management of nonperforming assets -a current scenario. International Journal of Social Science and Interdisciplinary Research, 2012(1):11.

6.      Devanand, H. N. and Prasad, T. R. (2015). Performance Analysis of Public Sector Banks in India. Shanlax International Journal of Economics. 3(2), 84-94.

7.      Diwan, H. and Mehta, V.H. (2013). Indian Banking Sector: Stability and Improved Credit are Necessities. The Financial Express, March, 2013.

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9.      Kumar, A. and Ghani, U. (2015). An analysis of non performing assets and profitability of schedule commercial banks in India. International Journal of Business and Administration Research Review, 1(12).

10.   Nachimuthu, Kavitha and Veni, M. (2019). Impact of non-performing assets on the profitability in Indian scheduled commercial banks. African Journal of Business Management. 13(4), 128-137.

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Appendix-1: Selected Profitability Indicators of Indian PSBs

 

Appendix 2: Profitability Index of PSBs in India: 2000-2017

Sl. No.	Public Sectors Banks	Years
		2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017
1	Allahabad Bank	-1.13	-1.37	-0.81	-0.27	1.56	1.35	0.82	0.29	0.41	0.19	0.72	0.64	0.50	-0.45	-0.41	-0.60	-1.44	-1.05
2	Andhra Bank	-0.33	-0.99	-0.10	1.33	1.67	1.47	0.24	0.13	-0.15	-0.17	0.45	0.46	0.06	-0.39	-1.34	-1.16	-1.18	-1.22
3	Bank of Baroda	-0.15	-0.84	-0.20	0.46	1.30	0.38	0.00	-0.05	0.02	0.50	0.62	0.94	0.66	-0.01	-0.34	-0.71	-2.55	-1.29
4	Bank of India	-0.84	-0.63	0.32	1.14	1.30	-0.31	0.07	0.44	0.88	1.20	0.01	0.17	-0.04	-0.15	-0.36	-0.88	-2.32	-1.38
5	Bank of Maharashtra	0.12	-0.88	0.73	1.40	1.63	0.02	-1.25	0.48	0.32	0.20	-0.03	-0.57	0.05	0.47	-0.84	-0.63	-1.22	-1.88
6	Canara Bank	-0.69	-0.53	0.57	1.01	1.61	0.68	0.60	0.15	-0.22	0.17	0.64	0.75	-0.22	-0.56	-0.85	-0.90	-2.21	-1.21
7	Central Bank of India	-0.70	-0.69	-0.08	0.55	1.90	1.09	0.32	0.63	0.17	-0.11	0.39	0.54	-0.32	-0.08	-1.47	-0.47	-1.66	-1.70
8	Corporation Bank of India	0.52	0.66	0.61	1.11	1.11	0.28	0.20	0.08	0.08	0.21	0.31	0.24	-0.14	-0.45	-1.33	-1.40	-2.10	-1.36
9	Dena Bank	-0.29	-1.74	-0.45	0.35	1.14	-0.04	0.21	0.54	0.74	0.60	0.57	0.66	0.66	0.33	-0.61	-0.89	-1.78	-1.52
10	IDBI Bank	0.67	0.34	0.28	0.72	1.51	-0.35	-0.26	-0.34	-0.26	-0.38	-0.19	0.25	0.14	0.20	-0.14	-0.33	-1.86	-1.63
11	Indian Bank	-1.98	-1.76	-1.05	-0.36	0.29	0.31	0.35	0.99	1.02	0.90	1.06	0.99	0.59	0.10	-0.34	-0.46	-0.65	-0.34
12	Indian Overseas Bank	-0.99	-0.63	0.05	0.58	1.07	1.22	1.10	0.82	0.83	0.68	-0.32	0.14	-0.21	-0.56	-0.66	-1.15	-1.97	-1.79
13	Oriental Bank of Commerce	0.23	-0.32	0.60	1.17	1.97	1.34	0.27	-0.09	-0.66	-0.40	-0.15	0.15	-0.60	-0.47	-0.60	-1.05	-1.38	-1.60
14	Punjab and Sind Bank	-0.23	-0.89	-0.53	-0.23	-0.75	-1.03	0.49	1.13	1.45	1.19	0.97	0.69	-0.13	-0.30	-0.58	-0.90	-0.33	-0.46
15	Punjab National Bank	-0.66	-0.68	-0.28	0.64	1.14	0.38	0.13	0.20	0.14	0.61	0.88	0.81	0.42	-0.09	-0.57	-0.78	-2.28	-1.12
16	State Bank of Bikaner and Jaipur	0.34	-0.53	0.66	0.84	2.35	0.46	-1.22	0.15	-0.46	-0.12	-0.25	-0.05	-0.05	-0.26	-0.65	-0.60	-0.61	-2.23
17	State Bank of Hyderabad	0.08	-0.21	0.74	1.19	1.66	-0.59	0.42	0.36	-0.34	-0.43	0.05	0.72	0.29	-0.36	-1.34	-0.84	-1.42	-2.44
18	State Bank of India	-0.41	-1.54	-0.27	0.60	1.09	1.35	0.67	0.07	0.46	0.60	-0.14	-0.15	0.55	0.18	-0.96	-0.70	-1.39	-1.29
19	State Bank of Mysore	-0.33	-1.32	-0.14	1.12	1.59	1.49	0.94	0.46	0.29	-0.23	0.24	0.29	-0.64	-0.58	-1.13	-0.89	-1.15	-2.46
20	State Bank of Patiala	1.46	0.30	0.63	0.81	1.08	0.27	-0.03	-0.02	-0.37	-0.35	-0.26	0.02	-0.13	-0.46	-0.75	-0.80	-1.40	-1.43
21	State Bank of Travancore	-0.62	-0.26	0.05	0.66	1.43	1.03	0.46	0.23	0.04	1.09	0.76	0.44	-0.59	-0.67	-1.43	-1.40	-1.22	-2.30
22	Syndicate Bank	-0.23	-0.40	-0.25	0.64	1.69	0.40	0.47	0.33	0.02	-0.08	-0.40	0.29	0.25	0.40	-0.15	-0.51	-2.47	-1.36
23	UCO Bank	-1.07	-1.13	-0.02	0.37	1.36	0.61	-0.13	-0.06	-0.22	-0.09	0.44	0.59	0.27	-0.13	0.80	0.34	-1.94	-1.17
24	Union Bank of India	-1.55	-1.28	-0.11	1.07	1.40	0.94	0.03	0.31	0.85	0.79	0.78	0.44	-0.03	-0.25	-1.01	-1.03	-1.36	-1.52
25	United Bank of India	-1.14	-1.10	-0.37	1.17	1.25	1.21	0.59	0.62	-0.04	-0.34	-0.20	0.45	0.47	0.02	-1.38	-0.16	-1.04	-0.43
26	Vijaya Bank	-0.76	-0.39	0.19	0.99	2.58	1.54	-0.16	0.44	-0.10	-0.45	-0.01	-0.14	-0.37	-0.56	-0.93	-0.95	-0.93	-0.64
Public Sectors Bank Group (Average)		-0.41	-0.72	0.03	0.73	1.38	0.59	0.20	0.32	0.19	0.22	0.27	0.38	0.06	-0.19	-0.75	-0.76	-1.53	-1.42
Source: Author’s own calculation.
Note: Profitability Index = Return on Assets x 0.29 + Net Profit to Total Funds x 0.32 + Return on Equity x 0.31 + Inverse of Cost of Deposit x 0.08

 

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