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EMPIRICAL STUDY OF NON-PERFORMING ASSETS

MANAGEMENT OF INDIAN PUBLIC SECTOR BANKS

________________________________________________________________

Ms. Kanika Goyal, Lecturer, Department of Commerce & Management, Hindu Girls College, Jagadhri (Haryana)

ABSTRACT

The Indian banking system has undergone significant transformation following financial

sector reforms. It is adopting international best practices with a vision to strengthen the

banking sector. The public sector banks dominate the Indian banking system with almost 82

percent market share in the total deposits and advances of the industry. Several prudential

and provisioning norms have been introduced, and these are pressurizing banks to improve

efficiency and trim down NPAs to improve the financial health in the banking system. In the

background of these developments, this study strives to examine the state of affair of the

NPAs of the public sector banks in India. The study is analytical in nature, and it is based on

the secondary retrieved from Report on Trend and Progress of Banking in India, Report on

Currency and Finance etc. The scope of the study is limited to the analysis of NPAs of the

public sector banks for the period 2002-03 to 2008-09. It examines trend of NPAs; quality of

assets; health of several loan assets; sector wise NPAs etc. The data has been analyzed by

statistical tools such as descriptive statistics, correlation, regression analysis, one-way

ANOVA, and post-hoc Tukey HSD procedure. The study observed increase in gross as well as

net NPAs in absolute terms and improved asset quality of banks. The public sector banks

have managed its assets proficiently; however, the study observes that increased NPA’s in the

agriculture sector is a matter of great concern.

Keywords: Transformation, Prudential norms, Provisioning norms, financial health, proficiently.

1. INTRODUCTION

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introducing prudential norms; allowing entry of new private sector banks and enhanced presence of foreign banks; permission to access the capital market, operational flexibility and functional autonomy to public sector banks; strengthening of corporate governance practices and disclosure standards. Banks have increasingly diversified into non-traditional activities, and as a result several conglomerates have emerged. Thus deregulation has opened up new avenues for banks to augment income; it has also exposed the sector to greater risk of non- performing assets. It is obvious as a result of banking sector reforms the opportunities and challenges in the banking sector has augmented.

2. LITERATURE REVIEW

Rituparna Das (2002) performed a research on Managing the Risk of Non Performing Assets in the Small Scale Industries in India. In this article the researcher tries to seek a solution to the problem of NPA in the small scale industries under the present circumstances of banking and insurance working together under the same roof. What is stressed in this article is the pressing need of the small-scale entrepreneur for becoming aware and educated in modern business management holding a professional attitude toward rational decision-making and banks have to facilitate that process as a part of the credit policy sold by them. Prashanth K. Reddy (2002) in his research paper on the topic, “A comparative study of Non Performing Assets in India in the Global context” examined the similarities and dissimilarities, remedial measures. 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. The study reveals that 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. This paper 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.

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except may be its scrap value. The purpose of this paper is to indicate the various considerations that one has to bear in mind before zeroing on a resolution strategy and provides a State - Resolution - Mapping (SRM) framework. However, the paper has not specifically discussed about the various resolution strategies that could be put in place for recovery from NPAs, and in particular, in which situation which type of strategy should be adopted.

Isaac K. Otchere (2005) conducted a study on the performance of privatized banks in middle- and low-income countries shows mixed results by “Competitive and Value Effects of Bank Privatization in Developed Countries”. The paper observed that private banks in developed countries have experienced significant improvements in operating performance. The improvement in performance remains significant after controlling for persistence in bank performance. A comparison of the performance of privatized banks in developed and developing countries suggests that privatization has encouraged excessive risk taking among privatized banks in developing countries, with the consequence that those banks carry large non-performing assets than their counterparts in the developed countries. They also observe that consistent with the competitive effects hypothesis, investors view privatization announcements as foreshadowing bad news for rival banks.

Dr. Amitabh Joshi (2003) conducted a survey on “Analysis of Non-Performing Assets of IFCI Ltd”. The study found that Profitability and Viability of Development Financial Institutions are directly affected by quality and performance of advances. The basic element of Sound NPA Management System is quick identification of Non-performing advances, their containment at minimum levels and ensuring that their impingement on the financials is at low level. Excessive reliance on Collaterals has led Institutions to long drawn litigations and hence it should not be sole criteria for sanction. Banks should manage their exposure limit to few borrower(s) and linkage should be placed with net owned funds for developing control over high leverages of borrower level. Study also revealed that exchange of credit information among banks would be immense help to them to avoid possible NPAs. Management Information system and Market intelligence should be utilized to their full potential

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these bad loans has not occurred in Russia at the levels one might expect. This has been due to both a relatively small amount of loans that under-perform as well as legal and regulatory impediments that have discouraged investors and lenders alike. The study has been conducted to examine the expansion of consumer credit in Russia and the circumstances under which it is occurring indicate that the level of non-performing loans is due to rapidly increase and as the rationale for maintaining the impediments that stand in the way of securitizing these loans is being re-examined, those impediments are being scaled back to make way for market participants to engage in such securitizations. Thus, this article anticipates a significant rise in the level of non-performing loans, which will be logically paired with an increased interest of Russian lenders in securitizing these assets.

Usha Arora, Bhavna Vashisht & Monica Bansal (2009) in the research on “An Analytical Study of Growth of Credit Schemes of Selected Banks” analyzed and compared the performance (in terms of loan disbursement and non- performing assets) of credit schemes of selected banks for the last five years. This paper is divided into two parts. In the first part, bank-wise as well as year-wise comparisons are done with the help of Compound Annual Growth Rate (CAGR), mean and standard deviation; and in the second part, a positive relationship is found between total loan disbursement and total NPA O/S of selected banks with the help of a correlation technique. The study found a positive relationship between total loan disbursement and total Non-Performing Assets Outstanding (NPA O/S) of selected banks.

3. NEED AND OBJECTIVE OF THE STUDY

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4. HYPOTHESES

1. Gross NPAs and Gross Advances of Public sector banks are not significantly associated.

2. Net NPAs and Net Advances of Public Sector Banks are not significantly associated. 3. Sub-standard assets, doubtful assets, and loss assets on an average not differ

significantly.

4. The average assets of various sectors not differ significantly.

5. The distribution on number of banks by Net NPA to Net Advances not differs significantly.

5. METHODOLOGY

The study is analytical in nature, and it is based on the secondary data. The information has been retrieved from Report on Trend and Progress of Banking in India, Report on Currency and Finance, Economic Surveys of India, various books and journals. The scope of the study is limited to the analysis of NPAs of all the public sector banks over the period of 2002-03 to 2008-09. It examines trend of Gross NPAs, Net NPAs; asset quality of assets; health of diverse categories of loan assets; sector wise NPAs etc. The data has been analyzed using percentage method, and selected statistical tools such as descriptive statistics, correlation and regression analysis, adjusted co-efficient of determination, one-way ANOVA, and post-hoc Tukey HSD procedure.

6. ANALYSIS AND INTERPRETATION: This analytical part of the study has been divided into following two sections. Section I deals with Analysis of trend and asset quality of gross advances and gross NPAs, trend and asset quality of net advances and net NPAs, analysis of classification of loan assets, sector wise analysis of NPAs , and evaluation of distribution of public sector banks by ratio of net NPAs to net advances. Section II deals with statistical analysis of data and tests the various hypotheses.

6.1.1 SECTION I:ANALYSIS OF TREND OF ADVANCES, NPAs AND ASSET

QUALITY

6.1.1. ANALYSIS OF TREND AND ASSET QUALITY OF GROSS ADVANCES

AND GROSS NON PERFORMING ASSETS: The study first of all examined the trend of gross advances, gross NPAs, ratio of gross NPAs to Gross Advances, and ratio of gross NPAs to total assets. It is apparent from table 1 that gross advances of the banks have shown a rising trend since 2003-04. Gross advances of the public sector banks in absolute term have

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increase of 295.19 percent in gross advances of the public sector banks during the study

period. The gross NPAs of the banks in absolute terms amounted to Rs 54090 crore in

2002-03, and Rs 51538 crore, Rs 48399 crore, Rs 41358 crore, Rs 38968 crore, Rs 40597 crore and Rs 45156 crore for the years 2002-03, 2003-04, 2004-05, 2005-06, 2006-07, 2007-08 and

2008-09 respectively. The gross NPAs in absolute terms have decreased by 16.5 percent in

the year 2008-09 over 2002-03. An in-depth analysis into gross NPAs shows that the gross

NPAs of the public sector banks have declined up to the year 2006-07, and increased in the

last two years of study i.e. 2007-08, and 2008-09. The study observed that the gross NPAs of

public sector banks have depicted a mixed trend over the period of study. It is found on the basis of analysis of data that the asset quality of public sector banks improved consistently in the past few years as reflected in the decline in the two ratios i.e. gross NPAs as percentage of

gross advances, and gross NPAs as percentage of total assets.

TABLE 1

GROSS NPAS OF PUBLIC SECTOR BANKS

YEARS Gross

Advances( Cr.)

GROSS NPAS

Amount ( Cr.) Per cent to

Gross Advances

Per cent to

Total Assets

2002-03 577813 54090 9.4 4.2

2003-04 66975 51538 7.8 3.5

2004-05 877825 48399 5.5 2.7

2005-06 1134724 41358 3.6 2.1

2006-07 1464493 38968 2.7 1.6

2007-08 1819074 40597 2.2 1.3

2008-09 2283473 45156 2 1.2

Source: Trends & progress of banking in India, RBI Publication

6.1.2. ANALYSIS OF TREND AND ASSET QUALITY OF NET ADVANCES AND

NET NON PERFORMING ASSETS: The study then investigated net advances, net NPAs, ratio of net NPAs to net Advances, and ratio of net NPAs to total assets (Table 2). The study found that the net advances of the public sector banks have increased in absolute term have increased by 311.42 percent in 2008-09 over 2002-03. Over the period of study, Net NPAs in

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decreased during the period of 2002-02 to 2005-06, thereafter, it has shown an increasing trend. It is observed that despite increase in gross nonperforming assets (NPAs) in absolute terms during the year, asset quality of public sector banks improved in the past few years as

reflected in the decline in these two ratios i.e. net NPAs as percentage of net advances, and

net NPAs as percentage of total assets. Hence, it can be stated that as a whole there is

improvement in the asset quality of public sector banks.

TABLE 2

NET ADVANCES AND NET NPAS OF PUBLIC SECTOR BANKS

Years

Net

Advances

(Cr)

Net NPAS

Amount

(Cr)

Per cent to

Net Advances

Per cent to

Total Assets

2002-03 549351 24877 4.5 1.9

2003-04 631383 19335 3.1 1.3

2004-05 848912 16904 2.0 1.0

2005-06 1106288 14566 1.3 0.7

2006-07 1440146 15146 1.1 0.6

2007-08 1797504 17839 1.0 0.6

2008-09 2260156 21033 0.9 0.6

Source: Trends & progress of banking in India, RBI Publication

6.1.3. ANALYSIS OF NPAs ON THE BASIS OF CLASSIFICATION OF LOAN

ASSETS: Loan assets of banks can be classified into three categories i.e. sub-standard assets, doubtful assets, and loss assets (Table 3).

TABLE 3

CLASSIFICATION OF LOAN ASSETS OF NPAS OF PUBLIC SECTOR BANKS

YEARS

Classification of Loan Assets (Amount in Rs. Crore)

Sub-standard Assets Doubtful Assets Loss Assets

Amount %age* Amount %age* Amount %age*

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YEARS

Classification of Loan Assets (Amount in Rs. Crore)

Sub-standard Assets Doubtful Assets Loss Assets

Amount %age* Amount %age* Amount %age*

2005-06 11453 1.0 25028 2.2 5636 0.5 2006-07 14275 1.0 19873 1.4 4826 0.3 2007-08 17298 1.0 19291 1.1 4018 0.2 2008-09 20603 0.9 21019 0.9 4296 0.2

* Loan asset as a percentage of total gross non- performing assets

Analysis of loan assets depicted that the substandard assets has shown a decline to 0.9 percent in 2008-09 from 2.6 percent in 2002-03. The doubtful assets have declined to 0.9 percent from 5.6 percent during the same period. The Loss assets have declined to 0.2 percent in 2008-09 from 1.2 percent in 2002-03. The decline in various categories of loan assets indicates recovering health of public sector banks. All the three categories of NPAs as a percentage of gross non- performing assets have registered a decline over the period of study.

6.1.4. ANALYSIS OF NPAs ON THE BASIS OF SECTOR WISE ANALYSIS: Sector

wise the NPAs have been classified into three sectors i.e. priority sector, public sector and non priority sector as depicted in table 4.

TABLE 4: SECTOR-WISE CLASSIFICATION OF NPAS OF PUBLIC SECTOR

BANKS (AMOUNT IN CRORES)

YEARS

PRIORITY SECTOR

Total

Agriculture Public Sector Non Priority

Sector

Amount %age Amount %age Amount %age Amount %age

2002-03 24937 47.23 1085 2.05 26783 50.72 52806 100 2003-04 23840 47.54 610 1.22 25698 51.24 50148 100 2004-05 23397 49.05 450 0.95 23849 50 47696 100 2005-06 22374 54.07 340 0.82 18664 45.11 41378 100 2006-07 22954 59.46 490 1.27 15158 39.27 38602 100 2007-08 25287 65.26 299 0.77 13163 33.97 38749 100 2008-09 24318 55.21 474 1.08 19250 43.71 44042 100

Source: Trends & progress of banking in India, RBI Publication

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percentage of NPAs. The NPAs of the agriculture sector has increased to 55.21 percent in 2008-09 from 47.23 percent in 2002-03. The public sector banks have managed to reduce NPAs in the non priority sector. In this sector, the NPAs have reduced to 43.41 percent in 2008-09 from 50.52 percent in 2002-03. In the priority sector also, the NPAs have reduced to 1.08 percent in 2008-09 from 2.05 percent in 2002-03.

6.1.5. ANALYSIS OF DISTRIBUTION OF PUBLIC SECTOR BANKS BY RATIO

OF NET NPAS TO NET ADVANCES: Table 5 depicts the number of public sector banks by ratio of net non performing assets to net advances. It is found on the basis of analysis that in 2002-03 there were 2 banks with net NPAs to net advances ratio of more than 10 percent.

However, none of the banks have net NPAs to net advances ratio of more than 10 percent

since 2003-04 up to the year 2008-09. The number of banks having net NPAs to net advances

ratio of 5-10 percent was 7, 3 and 2 for the years 2002-03, 2003-04 and 2004-05 respectively,

and none of the banks fall in this category during the years 2003-04 to 2008-09.

TABLE 5

DISTRIBUTION OF PUBLIC SECTOR BANKS BY RATIO OF NET NPA TO NET ADVANCES (NO. OF BANKS)

YEARS

Ratio of Net Non Performing Assets to Net Advances

Total No.

of Banks Upto 2 per cent

Above 2

and upto 5

per cent

Above 5

and upto 10

per cent

More

than 10

per cent

2002-03 4 14 7 2 27

2003-04 11 13 3 0 27

2004-05 19 7 2 0 28

2005-06 23 5 0 0 28

2006-07 27 1 0 0 28

2007-08 28 0 0 0 28

2008-09 27 0 0 0 27

Source: Trends & progress of banking in India, RBI Publication

Similarly, the number of banks with net NPAs to net advances ratio of 2-5 percent were 14,

13, 7, 5 and 1 for the years 2002-03, 2003-04, 2004-05, 2005-06, and 2006-07 respectively.

For the years 2007-08 and 2008-09 none of the banks falls in this category of net NPAs to

Net Advances ratio. Analysis reveals that most of the banks have shown an improvement so

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banks falling in the category of net NPAs to net advances ratio up to 2 per cent has increased

from 4 to 27 over the period of study. It can be stated that almost all the banks have been able

to reduce the ratio of net NPAs to net advances to the level of 2 percent. Hence, it can be

stated that public sector banks had efficiently managed Net NPAs to Net Advances over the

period of study.

6.2 SECTION II: STATISTICAL ANALYSIS OF DATA

6.2.1. ASSOCIATION BETWEEN GROSS NPAs AND GROSS ADVANCES: The study

tested the null hypothesis that there is no significant association between GNPAs & gross Advances of Public sector banks. The statistical values are depicted in table 6.

TABLE 6

SUMMARY OF STATISTICAL RESULTS OF RELATION BETWEEN GROSS NPAs AND GROSS ADVANCES

Results R R Square Adjusted

R Square ANOVA F-value

Values .684

( p-value=0.055) .468 .362

4.399 (p-value=0.090)

The Statistical test of Pearson Correlation shows that there is moderate degree of positive correlation between Gross Advances and Gross NPAs with R=0.684, P=0.055, and since P>0.05 the null hypothesis of insignificant association between Gross Advances and Gross NPAs is acceptable. In the test, R square is 0.468 and adjusted R square is 0.362. On the basis of Adjusted R square, it can be stated that only 36.2 percent of variation in Gross NPAs is explained by variation in Gross advances. Further, the one way ANOVA showed F to be insignificant with F = 4.399, at p = 0.09. Hence the null hypothesis of insignificant association between GNPAs & Gross Advances of Public Sector Banks is acceptable.

6.2.2. TESTING ASSOCIATION BETWEEN NET NPAS AND NET ADVANCES OF

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TABLE 6.1

SUMMARY OF STATISTICAL RESULTS OF RELATION BETWEEN NET NPAs AND NET ADVANCES

Results R R Square Adjusted

R Square ANOVA F-value

Values -0.162

( p-value=0.364) 0.026 0.168

0.135 (p-value=0.728)

The Statistical test of Pearson Correlation shows that there is low degree of positive correlation between Net Advances and Net NPAs with R=0.162, P=0.364; since P>0.05 the null hypothesis is accepted. In the test, R square is 0.026 and adjusted R square is 0.168. On the basis of Adjusted R square, it can be stated that 16.8 percent of variation in Net NPAs is explained by variation in Gross advances. Further, the values of one way ANOVA reveal that F = 0.135 with p=0.728. Since the p-value is more than 0.05, the null hypothesis of no significant association between Net NPAs and Net Advances of Public sector banks is acceptable. So, it has been observed that there is no significant association between Net NPAs and Net Advances of Public Sector Banks.

6.2.3. TESTING SIGNIFICANCE OF DIFFERENCE OF NPAS ON THE BASIS OF

CLASSIFICATION OF LOAN ASSETS: The paper examined the significance of difference between various classes of loan assets i.e. sub standard assets, doubtful assets, and loss assets. To evaluate the significance of difference between various classes of loan assets the study have formulated and tested the hypothesis that there is insignificance difference between average sub-standard assets, doubtful assets, and loss assets.

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TABLE 7

DESCRIPTIVE STATISTICS FOR CLASSIFICATION OF LOAN ASSETS

N Mean Std. Deviation

sub standard assets 7 15216.43 3380.497

doubtful assets 7 25298.00 5408.116

loss assets 7 5345.86 1006.099

Total 21 15286.76 9065.005

TABLE 8

ONE WAY ANOVA RESULTS FOR CLASSIFICATION OF LOAN ASSETS

Sum of Squares Df Mean Square F Sig.

Between

Groups 1.39E+09 2 6.97E+08 50.136 0

Within Groups 2.50E+08 18 1.39E+07 Total 1.64E+09 20

TABLE 9

POST HOC TUKEY HSD RESULTS OF MULTIPLE COMPARISONS FOR CLASSIFICATION OF LOAN ASSETS

(I) groups (J) groups

Mean Difference (I-J)

Std.

Error Sig. sub standard assets doubtful assets -10081.571* 1992.551 .000

loss assets 9870.571* 1992.551 .000

*. The mean difference is significant at the 0.05 level.

TABLE 10

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6.2.4. STATISTICAL ANALYSIS OF NPAs ON THE BASIS OF SECTOR WISE

ANALYSIS: This section deals with sector wise analysis of non-performing assets. The hypothesis formulated and tested the null hypothesis that there is no significant difference in the mean non -performing assets of various sectors. Analysis of table 11 of descriptive statistics shows that mean values for NPAs in Public Sector, Non Priority Sector, and Priority are Rs 535.43 crore, Rs 20509.43 crore, Rs 23872.43 crore respectively. It is clear that average NPAs in the Priority Sector is maximum, and it is lowest in the public sector. Sector wise analysis of mean values on the basis of ANOVA (table 12) shows that F=126.595, P=0.00. It is found that the p-value is less than 0.01 which indicates that there is highly significant difference between the mean values of sector wise distribution of nonperforming assets of the public sector banks. Tukey HSD procedure (Table 13) performed on various sectors of NPAs shows that average NPAs in the Priority Sector differ highly significantly from Public Sector (p-value 0.00) as well as Non Priority Sector (p-value 0.00). Tukey HSD Homogeneous Subsets (Table 14) shows that there is no significant difference in the average NPAs in Non- Priority Sector and Priority Sector, whereas these two differ significantly from average NPAs in the Public Sector.

Groups

N

Subset for alpha = 0.05

1 2 3

loss assets 7 5345.86

sub standard assets 7 15216.43

doubtful assets 7 25298.00

Sig. 1.000 1.000 1.000

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TABLE 11

DESCRIPTIVE STATISTICS FOR SECTOR WISE DISTRIBUTION OF NPAs

Sectors N Mean Std. Deviation

Priority Sector 7 23872.43 1052.675

Public Sector 7 535.43 262.937

Non Priority Sector 7 20509.43 5022.216

Total 21 14972.43 10923.522

TABLE 12

ANOVA RESULTS OF SECTOR WISE DISTRIBUTION OF NPAs

Sum of Squares Df Mean Square F Sig. Between Groups 2.228E9 2 1.114E9 126.595 .000 Within Groups 1.584E8 18 8799969.952

Total 2.386E9 20

TABLE 13

POST HOC TUKEY HSD MULTIPLE COMPARISONS OF SECTOR WISE DISTRIBUTION OF NPAS

(I) groups of sector wise NPAs

(J) groups of sector wise NPAs

Mean

Difference (I-J) Std. Error Sig. Priority Sector Public Sector 23337.000* 1585.647 .000

Non Priority Sector

3363.000 1585.647 .114

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TABLE 14

TUKEY HSD HOMOGENEOUS SUBSETS OF SECTOR WISE DISTRIBUTION OF NPAs

groups of sector wise

NPAs N

Subset for alpha = 0.05

1 2

Public Sector 7 535.43

Non Priority Sector 7 20509.43

Priority Sector 7 23872.43

Sig. 1 0.114

Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size = 7.000.

6.2.5. TESTING SIGNIFICANCE OF DIFFERENCE OF DISTRIBUTION OF

RATIOS OF NET NPAS TO NET ADVANCES OF PUBLIC SECTOR BANKS

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TABLE 15

DESCRIPTIVES FOR DISTRIBUTION OF NO. OF BANKS BY NET NPAs TO NET ADVANCES

N Mean Std. Deviation

upto 2 percent 7 19.86 9.209

Above 2 and upto 5 per cent 7 5.71 5.936 Above 5 and upto 10 per cent 7 1.71 2.628

More than 10 per cent 7 .29 .756

Total 28 6.89 9.515

TABLE 16

ANOVA RESULTS FOR DISTRIBUTION OF NO. OF BANKS BY NET NPAs TO NET ADVANCES

TABLE 17

POST HOC TUKEY HSD MULTIPLE COMPARISONS FOR DISTRIBUTION OF NUMBER OF BANKS BY NET NPAS TO NET ADVANCES

(I)

ratio_percent (J) ratio_percent

Mean Difference (I-J)

Std.

Error Sig. upto 2 percent Above 2 and upto 5 per cent 14.143* 3.018 .001 Above 5 and upto 10 per cent 18.143* 3.018 .000 More than 10 per cent 19.571* 3.018 .000

*. The mean difference is significant at the 0.05 level. Sum of

Squares Df Mean Square F Sig. Between Groups 1679.536 3 559.845 17.560 .000 Within Groups 765.143 24 31.881

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TABLE 18

TUKEY HSD HOMOGENEOUS SUBSETS FOR DISTRIBUTION OF NO. OF BANKS BY NET NPAs TO NET ADVANCES

The statistical results of Tukey HSD procedure shows the classification of banks which are insignificant in the Net NPAs to Net Advances Ratios within own sub-set, and significant in terms of comparison with other sub-set. Analysis of table 17 reveals that there is significant difference between average number of banks falling in the Net NPA to Net Advances ratio of up to 2 percent with banks falling in the category of above 2 percent and up to 5 percent (p-value 0.001), above 5 and up to 10 percent (p-(p-value 0.000), more than 10 percent (0.000). Table 18 reveals the results of Tukey HSD Homogeneous subsets for distribution of number of banks by Net NPAs to Net Advances. It is found on the basis of analysis that the banks with Net NPAs to Net Advances ratio of more than 10 percent, above 5 percent and up to 10 percent, above 2 percent and up to 5 percent comprise subset 1, these are homogeneous and not differ significantly from each other. However, banks falling in this subset 1 of Net NPAs to Net Advances ratio differ significantly from the banks falling in the subset 2 with Net NPAs to Net Advances ratio of up to 2 percent.

7. CONCLUSION

The study observed that there is increase in gross as well as net advances over the period of the study. However, the decline in ratio of gross NPAs to gross advances, gross NPAs to total assets, net NPAs to net advances and net NPAs to total assets indicate improvement in the asset quality of Indian public sector banks. All the three categories of loan assets i.e. sub-standard assets, doubtful assets and loss assets as a percentage of gross non- performing

ratio_percent N

Subset for alpha = 0.05

1 2

More than 10 per cent 7 .29

Above 5 and upto 10 per cent 7 1.71 Above 2 and upto 5 per cent 7 5.71

upto 2 percent 7 19.86

Sig. .298 1.000

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assets have registered a decline over the period of study. The decline in various categories of loan assets indicates recovering health of public sector banks. The public sector banks have managed to reduce NPAs in the public sector lending as non-priority sector, however, NPAs in the agriculture sector have increased over the period of study. Further, majority of banks have been able to reduce the ratio of net NPAs to net advances to the level of 2 percent.

Hence, it can be stated that public sector banks had efficiently managed Net NPAs to Net

Advances over the period of study. The statistical tests found insignificant association

between gross NPAs and gross Advances, and net NPAs and Net Advances. Statistical results

observed highly significant difference in mean sub standard assets, doubtful assets and loss assets. It is further observed highly significant difference between the sector wise NPAs on an average, with highest NPAs in the priority sector. It is found on the basis of analysis that there is significant improvement in the management of nonperforming assets of the public sector banks in India. The public sector banks have managed its assets proficiently; however, the study observes that increased NPA’s in the agriculture sector is a matter of great concern. The study finally observes that the prudential and provisioning norms and other initiatives taken by the regulatory bodies has pressurized banks to improve their performance, and consequently resulted into trim down of NPA as well as improvement in the financial health of the Indian banking system.

REFERENCES

1. Arora, Usha , Vashisht, Bhavna and Bansal, Monica, An Analytical Study of Growth of Credit Schemes of Selected Banks (March 26, 2009). The Icfai University Journal of Services Marketing, Vol. VII, No. 1, pp. 51-65, March 2009. Available at SSRN: http://ssrn.com/abstract=1368624

2. Das, Rituparna, Managing the Risk of Non Performing Assets in the Small Scale Industries in India (June 15, 2002). Available at SSRN: http://ssrn.com/abstract=1330798

3. Datta Chaudhuri, Tamal, Resolution Strategies for Maximising Value of Non-Performing Assets (NPAs) (December 19, 2005). Available at SSRN: http://ssrn.com/abstract=871038

4. Ferguson, Thomas P., Observations on the Securitization of Non-Performing Loans in Russia (September 1, 2007). Bucerius Law Journal, Bucerius Law School, Hamburg, Germany, March 2008. Available at SSRN: http://ssrn.com/abstract=1017288

5. Joshi, Dr. Amitabh, Analysis of Non-Performing Assets of IFCI Ltd (2003). Available at SSRN: http://ssrn.com/abstract=921860

6. Reddy, Prashanth K., A comparative study of Non Performing Assets in India in the Global context - similarities and dissimilarities, remedial measures (October 2002). Available at SSRN: http://ssrn.com/abstract=361322 or doi:10.2139/ssrn.361322 7. Report on Currency and Finance 2003-2008.

Figure

TABLE 4: SECTOR-WISE CLASSIFICATION OF NPAS OF PUBLIC SECTOR  BANKS (AMOUNT IN CRORES)

References

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I would like to thank Nicole Cook of Annenberg Center for the Performing Arts, Greg DeCandia of BCKSEET Productions, Nick Anselmo of Drexel University’s Theatre Program, David

Praise Band (Level 1) vocal, rhythm and, instrumental parts:.. Lead Sheet (SAT) – for the worship leader and vocal team Trumpet 1-2 Piano/Vocal (SATB) – for the pianist and