Rising Non-Performing Loans and Banks Profitability in Pakistan Banking Industry

Khadim Ali Shah Bukhari Institute of Technology A thesis on “Rising Non-performing Loans and Banks Profitability. Analytical study of of Credit Risk Assessment Assessment policies under SBP regulations. Bank AL Habib Habib Ltd as a case case study from 2005 2005 to date”

Submitted to: Faculty of Management Sciences In partial fulfillment of the requirements for the degree of MASTER OF BUSINESS ADMINISTRATION (MBA) Submitted By:

Salman Qadir ID: 4514 Major Subject: Finance

DEDICATION I am dedicating this research to my beloved parents who did not deprive me from benefiting and having the light of education.

I would like to express my indebtedness to my parent for their intangible sacrifices and uninterrupted inspiration during my research work.

ACKNOWLEDGEMENT This thesis has been the result of research conducted during 2011 within the division of the Department of Management Sciences at KASBIT, Karachi.

All the praise is for Allah, the most merciful and beneficent, who blessed me with the knowledge, gave me the courage and allowed me to accomplish this research. We gratefully acknowledge Mr. Hamza Khalil Chaudhry for his supervision, advice and crucial contribution which made him a backbone to this thesis. His involvement with his originality has triggered and nourished our intellectual maturity that we will benefit from, for a long time to come.

It is also my immense pleasure to express sincere gratitude to the staff of Bank AL Habib Ltd whose inspiring guidance, remarkable suggestion, keen interest and constructive criticism helped me to complete this research efficiently.

I found this research interesting, challenging and most of all rewarding. I hope the report is informative to anyone who refers to it.

BY

Salman Qadir ID No. 4514 Department of Finance, Faculty of Management Sciences, Khadim Ali Shah Bukhari Institute of Technology (KASBIT) Dated: May 14, 2012

ABSTRACT

This research approach provided us the role of NLP’s on banks profitability and credit risk assessment moreover it explains that how loan growth rate is a basic driver for the non performing loans. There has been alarming increase in the amount of non-performing loans (NPLs) during the last 30 months of the present government. According to the latest figures available in the Ministry of Finance the non-performing loan are upraised from 10% to 15%. This study indicates that what are the variables that are influenced loan growth rate as well as it will determines their impact on drivers of NPL’s. Using the threshold multi regression technique, we found some evidences that non-performing loans have non-linear effect on banks’ lending behavior. Each non-performing loan in the financial sector is viewed as an obverse mirror image of an ailing unprofitable enterprise. From this point of view, the eradication of non-performing loans is a necessary condition to improve the economic status. If the non-performing loans are kept existing and continuously rolled over, the resources are locked up in unprofitable sectors; thus, hindering the economic growth and impairing the economic efficiency.

TABLE OF CONTENTS Dedication

I

Acknowledgement

II

Abstract

III

CHAPTER # 01

1.

Descriptive Introduction to Research

1

1.1

Reason of Topic Selection

1

1.2

Introduction to NPLs

1

1.3

Introduction to Research Topic

2

1.4

Problem Statement

3

1.5

Objective of Research

3

1.6

List of Hypothesis

4

1.7

Alarming Increase of NPLs

5

1.8

Corporate NPLs

5

1.9

Rising NPLs Key Challenges

7

1.10

SBP Prudential Regulation about NPLs (Corporate, SME & Consumer Loans)

10

CHAPTER # 02

2.

Literature Review

35

2.1

International and National Studies

35

2.2

Literature Written by Mr. Ishrat Hussain (Governor SBP)

39

2.2.1

Dealing with NPLs of the Banks

39

2.2.2

Proactive Treatment of Stock of NPLs

43

2.2.3

Policy and Regulatory Environment

45

CHAPTER # 03

3.

Research Methodology and Research Design

47

3.1

Research Design

47

3.2

Conceptual Frame Work

47

3.3

3.2.1

Dependant Variable

47

3.2.2

Independent Variable

47

3.2.3

Moderate Variable

47

Theoretical Frame Work 3.3.1

3.4

48

NPLs in the Banking Industries

50

3.3.1.1

Introduction

50

3.3.1.2

Failure in Management

50

3.3.1.3

First Reason is Credit Management

51

3.3.1.4

Second Reason is Individual / Entities Capacity

51

3.3.1.5

Recommendation

51

3.3.1.6

Conclusion

53

Research Methodology

53

CHAPTER # 04

4. Statistical Reporting and Data Analysis

55

4.1

Finding and Interpretation of Results

56

4.2

Hypothesis Assessment Summary

61

CHAPTER # 05

5. Conclusion and Recommendations

67

5.1

Conclusion

67

5.2

Recommendation

67

5.3

Future Research

68

5.4

Limitations

68

5.5

Some Important Literature, Discussion and Observations

69

REFERENCES

81

BIBLIOGRAPHY

84

87

APPENDIX

Appendix-1

Bank AL Habib Ltd (BAHL) Data Summary

87

Appendix-2

Profit & Loss Account of BAHL for the year 2011

88

Appendix-3


89

Appendix-4


90

Appendix-5


91

Appendix-6


92

Appendix-7


93

Appendix-8

Balance Sheet of BAHL for the year 2011

94

Appendix-9

Balance Sheet of BAHL for the year 2009

95

Appendix-10 Balance Sheet of BAHL for the year 2007

96


97


98


99

LIST OF TABLES Table 1.1

Limit & Exposure of Corporate Portfolio

11

Table 1.2

Guidelines in matter of classification of Corporate Portfolio

16

Table 1.3

Limit & Exposure of SME Portfolio

18

Table 1.4

Guidelines in matter of classification of SME Portfolio

24

Table 1.5

Limit & Exposure of Consumer Portfolio

25

Table 1.6

Guidelines in matter of classification of Consumer Portfolio

28

Table 1.7

Guidelines in matter of classification of Credit Card Loan

29

Table 1.8

Guidelines in matter of classification of Auto Loan

31

Table 1.9

Guidelines in matter of classification of Home Mortgage Loan

32

Table 1.10

Guidelines in matter of classification of Personal Loan

34

Table 4.1

Bank AL Habib Data Summary

55

Table 4.2

Regression Analysis

56

Table 4.3

Coefficients

58

Table 4.4

Hypotheses Assessment Summary

61

LIST OF GRAPHS OF BANK AL HABIB LTD Graph # 1

Bar Graph Showing the Trend of Advances

63

Graph # 2

Bar Graph Showing the Trend of Deposits

63

Graph # 3

Bar Graph Showing the Trend of NPLs

64

Graph # 4

Bar Graph Showing the Trend of Profit Before Tax

64

Graph # 5

Bar Graph Showing the Trend of Profit After Tax

65

Graph # 6

Line Graph Showing the Trend of Profit Before Tax and NPLs

65

Graph # 7

Line Graph Showing the Trend of Profit After Tax and NPLs

66

Graph # 8

Line Graph Showing the % of NPLs to Advances Trend

66

Chapter # 01 DESCRIPTIVE INTRODUCTION TO RESEARCH 1.1 REASON OF TOPIC SELECTION:

As per the World Bank analysis provided in 2002 and 2010, the non-performing loans are continuously increasing debtor’s rate of default is elaborating a high degree of curve therefore the researcher came up with idea to evaluate empirically that what the relationship between SBP regulations and non-performing loans as well as researcher wants to elaborate the reasons of NPLs by formulating its relationship with the credit risk risk assessment policies

1.2 INTRODUCTION TO NPL’S

A Non-performing loan is a loan that is in default or close to being in default. Many loans become non-performing after being in default for 3 months, but this can depend on the contract co ntract terms. A loan is nonperforming when payments of interest and principal are past due by 90 days or more, or at least 90 days of interest payments have been capitalized, refinanced or delayed by agreement, or payments are less than 90 days overdue, but there are other good reasons to doubt that payments will be made in full.

1.3 INTRODUCTION TO RESEARCH TOPIC:

There has been alarming increase in the amount of non-performing loans (NPLs) during the last 30 months of the present government. According to the latest figures available in the Ministry of Finance the stock of NPLs to Rs. 290 billion by end 2001 from Rs. 207 billion in 2000 and Rs. 185 billion in the year 1999. According to the latest country report of the World Bank this amount may increase to Rs 384 billion by end of the current year. Recovery of stuck up loans was one of the prime objectives of General Musharraf, when he took over in October 1999, the then previous government. The recovery drive launched by the National Accountability Bureau (NAB) made wholesale arrests of the businessmen, even on circumstantial defaults, resulting in severe backlash and further loss of confidence. NAB was able to recover few billions rupee from the erring industrialists, but during the process many industries fell sick. The contracting economic activity in the country fuelled further failures, and more loans were categorized as non-performing. Despite the official claims of over Rs. 50 billion cash recoveries, the Finance Ministry figures show an increase of almost Rs. 105 billion in the NPLs during the present regime. Realizing its dismal performance on the recovery of defaulted loans the government took a fresh initiative in this regard. The then President Rafiq Tarar promulgated an ordinance to provide for expeditious legal remedies for the matters relating to non-performing assets. The ordinance also provided for legal remedies for the recovery of outstanding loans of the banks and other financial institutions to make them attractive for privatization and to promote the revitalization of national economy and rehabilitation and restructuring undertakings. The ordinance was expected to enable the government to expedite the process of recovery of outstanding loans as it provided for the high courts to set up special tribunals to deal with the loan defaulters. However like previous attempts and actions, this effort has also failed to bring about any significant improvement in the situation. According to the latest country report of

World Bank (2002) Pakistan Development Policy Review the non-Performing Loans (NPLs) of the Nationalized Commercial Banks (NCBs) have become a major problem because of political interference and directed credits to individuals and companies. The amount of NPLs have reached alarming proportions, that is, Rs. 384 billion in the current fiscal year. It is supposed to be one of the strangest

1.4 PROBLEM STATEMENT:

The non-performing loans are continuously increasing debtor’s rate of default is elaborating a high degree of curve therefore the researcher came up with idea to evaluate empirically that what the relationship between SBP regulations and non-performing loans as well as researcher wants to elaborate the reasons of NPLs by formulating its relationship with the credit risk assessment policies 1.5 OBJECTIVES OF RESEARCH: 9

To determine the reasons of non-performing loans on the basis of debtor’s evaluation that is based on the SBP’s Policies and his past credit history.

9

The role of KIBOR’s fluctuation on the credit risk assessment and to forecast its impact on the debtor’s credit historical evaluation.

9

To review and advise the modifications in the policies given by the collaboration of Banks and SBP to identify future credit risk and to prevent the future losses via NPL’s.

1.6

LIST OF HYPOTHESIS:

Hypothesis No. 1 Ho: There is no significant relationship with loan growth rate and d eposit growth rate. H1: There is significant relationship with loan growth rate and deposit growth rate.

Hypothesis No. 2

growth rate and capital growth rate. Ho: There is no significant relationship with loan growth H1: There is significant relationship between loan growth rate a nd capital growth rate.

Hypothesis No. 3 Ho: There is no positive relationship other asset growth rate and loan growth rate. H1: There is positive relationship other asset growth rate and loan growth rate.

Hypothesis No. 4 Ho: There is no positive relationship between bank profitability and inflation rate. H1: There is a positive relationship between bank ban k profitability and inflation rate.

Hypothesis No. 5 Ho: There is no positive relationship between loan growth rate and NPL’s. H1: There is positive relationship between loan growth rate and NPL’s.

1.7 ALARMING INCREASE IN NON-PERFORMING LOANS

The banking system of Pakistan remained distressed during the time period, July-Sept 2009 as expressed by the State Bank banking performance review for the said period. In the quarter under assessment, the non-performing loans (NPLs) ascended by 6 percent to Rs 452 billion in comparison to the previous quarter, April-June 2009 while, net NPLs to net loans ratio remained at 4.1 per cent. Rising non-performing loans are hampering the profitability of Pakistani’s banking sector. Many reasons are causing this alarming rise but, primarily high interest rate, economic slowdown and poor law and order situation in the country are the main factors. Non payments are increasing in all fields but mostly from the agriculture and government sector. Though, the interest rate has been reduced to 12.45 percent by SBP, yet the industrialists are unsatisfied by this decrease and find it difficult to repay their loans due to lower production and reduced repayment capacity. Currently, the law and order situation is hitting business activities badly which in result, is hindering the repayment potential.

1.8 CORPORATE NON-PERFORMING LOANS

The ailing textile sector consuming loans up to approximately Rs543.8 billion till October 19, 2009, can be a big hazard for banks. As the chances of recovery of these loans seem lesser now because the borrowers have either defaulted or are currently near default. defau lt. The risk of the banks is also high because, only 0.5 per cent of the total borrowers of the Pakistani banks with loan size of more than Rs10 million are consuming 71.7 per cent of total bank credit portfolio, a fact recently acknowledged by the State Bank. The ever-increasing terrorism terrorism threat is another factor hindering the business activities in the economy along with the persisting energy shortfall, which has also severely added to the disaster of the banking sector on the loan recovery front. front. Many banks were involved in consumer financing, now the bursting bubble of consumer financing has

caused an immense hike in default rates. On the other hand recovered collaterals have been perished or became worthless for banks. Thus, the consumer banking assortment has a major contribution in the upward trend of NPLs. Exporters of the country are now demanding demanding for extension in repayment as they have been stuck into the dilemma of lower demand at global level and a slowdown in production at local level due to poor law and order situation and electricity outages. However, State Bank of Pakistan is dealing with with the situation situation in a comprehensive manner through many tools like; improvement in coverage and reporting of NPLs, a proactive treatment of the existing stock of NPLs, stemming flow of new NPLs and improving the policy and regulatory environment. The bank is of the view that the stock of existing NPLs will always grow over time, even if all the new loans being granted are fully serviced. This will happen because the declared de clared amount consists of principal and mark up. By its very definition, de finition, if the loan is not being serviced and is overdue by 90 days then the unrealized markup will continue to be added up to the total amount of NPLs. Moreover, some of the commercial banks’ foreign branches had granted loans in foreign currency. Now, the overdue amount is escalating automatically with the increase in exchange rate of dollar. State Bank of Pakistan is striving seriously to tackle the problem, as a measure, exporters have been allowed an extension of one year for their loans repayment. It has decided to distinguish between willful defaulters and circumstantial defaulters. The cases of willful defaulters have been referred to the National Accountability Bureau (NAB) for action under the NAB Ordinance. On September 27, 2007 the NAB Punjab had filed a reference in an accountability court against 12 people including six officials of Bank of Punjab (BoP) and six others accused of Rs9 billion fraud. Total of Rs18 billion loans written-off from the government-owned banks during the first three years of the military government, will be also recovered soon.

Despite these positive steps, the interest rate which is causing lower capacity of repayment of industrialists, should also be decreased in order to guarantee the repayments of due amounts. Moreover, cost of production should be brought to the normal level which is nowadays, on hike due to high cost of electricity, gas and petroleum products. On the other hand banks should further tighten their credit assessment security policy and arrange appropriate monitoring procedures in order to keep an eye on NPLs. In the prevailing circumstances, the banks should avoid financing against high risk risk securities. It is a matter matter of fact that, non-performing loans are steadily causing lesser profitability of the banking sector. Thus, all the resolving measures for this dilemma must be on an urgent basis, as the spreads of the banks are shrinking due to the lower recovery of loans and decreasing yields on lending.

1.9 RISING NON-PERFORMING LOANS KEY CHALLENGES

KARACHI: The biggest challenge for Pakistani banks in 2011 would be the increase in non performing loans (NPLs) as higher lending rates and weak economy would give a double whammy to the borrowers’ payment capacity, analysts said. Heavy flooding in Pakistan during August 2010 caused a humanitarian disaster and altered macroeconomic outlook, which severely weakened banks’ operating environment. The direct impact on rated Pakistani banks has been modest, as the floods plagued largely un-banked rural areas. However, an international rating agency expects weaker economic growth and higher inflation in the short run, as the floods have led to food shortages, rising commodity prices and a renewed recourse of the government to deficit monetization.

“These adverse developments create a difficult operating environment for banks, which is a key driver of our negative system outlook,” wrote Moody’s analyst Christos Theofilou in its latest

report. We expect these near-term economic challenges to dissipate only gradually through the first half of 2012, when operating conditions will likely to return to more favorable trends. Recently, the rating agency revised the ratings of five top banks to negative, which includes Allied Bank Ltd, Habib Bank Ltd, MCB Bank Ltd, National Bank of Pakistan and the United Bank Ltd. However, some investors foresee an early recovery in the banking sector. Quarterly analysis shows even better results results as the banking sector fought well with the flood incident and now seems to be on the recovery stage, registering 4.7 percent quarter-on-quarter (Q-o-Q) growth in net advances, whereas deposits grew by five percent Q-o-Q,” an analyst at Invest Cap said. While investment by the scheduled banks showed 6.2 percent quarterly increase, we see the incremental impact to ease off as growth in advances realize on the back of recovery from the present spell,” the analyst said. Another analyst at JS Research said that the recent data for the sector has been encouraging, and with a sequential Q-o-Q rise in yields. However, we remain wary of increased provisioning expense, a trend associated with the final quarter,” the analyst added. The State Bank of Pakistan in its third quarterly report in 2010 on banking performance said that the heightened credit risk is reflected in a noticeable and persistent increase in the non performing loans, doubling over two years by the end of 2009. The growth in NPLs, which decelerated during the first two quarters of 2010, grew by 7.4 percent during the third quarter to reach Rs494 billion,” the SBP said. The central bank said that the increased credit risk will remain a key challenge for banks. There is a need for banks to devise ingenious strategies for dealing with the high level of non-performing loans so that promising businesses, facing transitory difficulties only due to a constrained macro environment, continue to contribute in the economic growth and service their the ir obligations in an orderly manner.

The usual inventory builds up, particularly by Kharif crop-based industries during the last calendar quarter, will create additional demand for bank credits. Although the banks are expected

to remain liquid, the heightened demand for credit from the public sector will mean that the banks’ ability to finance additional private sector loans loa ns will be predicated upon mobilization of o f fresh deposits and retirement of commodity finance by the government-owned agencies, which continues to be extremely high, the SBP said. “Banks will need to reduce their large portfolio of government paper and lending to the public sector agencies so as to reduce their sovereign exposure, as well as to make credit available to the private sector for maintaining economic growth and, thereby, enhance and diversify revenues of the banking system,” it added. However, the central bank said that the aggregate earnings of the system are expected to be satisfactory, although these will continue to be concentrated in banks endowed with a wide network and competitively better placed to raise stable and relatively cheap funds. In addition, persistent macro-environment issues will pose a stiff challenge for some banks to enhance their minimum capital requirement to Rs 8 billion by the end of 2011.

1.10 SBP- PRUDENTIAL REGULATION ABOUT NON-PERFORMING LOANS CORPORATE PRUDENTIAL REGULATIONS REGULATION R-1

(Limit On Exposure to a Single Person / Group)

The total outstanding exposure (fund based and non-fund based) by a bank/DFI to any single person shall not n ot at a t any a ny point in time exceed exce ed 30% of the bank’s/DFI’s equity as disclosed in the latest audited financial statements, subject to the condition that the maximum outstanding against fund based exposure does not exceed 20% of the bank’s/DFI’s equity.

The total outstanding exposure (fund based and non-fund based) by a bank/DFI to any group shall not exceed 50% of the bank’s/DFI’s equity as disclosed in the latest audited financial statements, subject to the condition that the maximum outstanding against fund based exposure does not exceed 35% of the bank’s/DFI’s equity.

Limit on exposure to a single person/Group effective from 31-12-2009 and onward would be as under:

Table 1.1

(Source: SBP Prudential Regulations ‐ www.sbp.org.pk)

The group will cover both corporate entities as well as SMEs, in cases where such entities are owned by the same group.

REGULATION R-8 (Classification and Provisioning for Assets Loans/Advances)

Banks/DFIs shall observe the prudential guidelines given by state bank of Pakistan in the matter of classification of their asset portfolio and provisioning there-against.

In addition to the time-based criteria prescribed in Annexure-IV, subjective evaluation of performing and non-performing credit portfolio shall be made for risk assessment and, where considered necessary, any account including the performing account will be classified, and the category of classification determined on the basis of time based criteria shall be further downgraded. Such evaluation shall be carried out on the basis of credit worthiness of the

borrower, its cash flow, operation in the account, adequacy of the security, inclusive of its realizable value and documentation covering the advances.

The rescheduling/restructuring of non-performing loans shall not change the status of classification of a loan/advance etc. unless the terms and conditions of rescheduling/restructuring are fully met for a period of at least one year (excluding grace period, if any) from the date of such rescheduling/restructuring and at least 10% of the outstanding amount is recovered in cash. However, the condition of one year retention period, prescribed for restructured/rescheduled loan account to remain in the classified category, will not apply in case the borrower has repaid or adjusted in cash at least 50% of the total restructured loan amount (principal + markup), either at the time of restructuring agreement or later-on during the grace period if any.

The unrealized mark-up on loans (declassified after rescheduling/restructuring) shall not be taken to income account unless at least 50% of the amount is realized in cash. However, any short recovery in this respect will not impact the declassification of this account if all other criteria (meeting the terms and conditions for at least one year and payment of at least 10% of outstanding amount by the borrower) are met. The banks/DFIs are further directed to ensure that status of classification, as well as provisioning, is not changed in relevant reports to the State Bank of Pakistan merely because a loan has been rescheduled or restructured.

However, while reporting to the Credit Information Bureau (CIB) of State Bank of Pakistan, such loans/advances may be shown as ‘rescheduled/restructured’ instead of ‘default’. Where a borrower subsequently defaults (either principal or mark-up) after the rescheduled/restructured loan has been declassified by the bank/DFI as per above guidelines, the loan will again be

classified in the same category it was in at the time of rescheduling/restructuring and the unrealized markup on such loans taken to income account shall also be reversed. However, banks/DFIs at their discretion may further downgrade the classification, taking into account the subjective criteria. At the time of rescheduling/restructuring, banks/DFIs shall consider and examine the requests for working capital strictly on merit, keeping in view the viability of the project/business and appropriately securing their interest etc. All fresh loans granted by the banks/DFIs to a party after rescheduling/ restructuring of its existing facilities may be monitored separately, and will be subject to classification under this Regulation on the strength of their own specific terms and conditions.

Banks/DFIs shall classify their loans/advances portfolio and make provisions in accordance with the criteria prescribed above, keeping in view the following:

Banks are allowed to take the benefit of 40% of Forced Sale value (FSV) of the pledged stocks and mortgaged residential, commercial and industrial properties (where building is constructed) held as collateral against NPLs for three years from the date of classification for calculating provisioning requirement. However, the banks/DFIs can avail the benefit of 40% of FSV of mortgaged residential, commercial and industrial land (open plot and where building is constructed separate valuation of land must be available) held as collateral against NPLs for four years from the date of classification for calculating provisioning requirement. This benefit would be available in such cases where FSV valuation v aluation of land is not more than four years old. For the purpose of determination of FSV, revised Annexure-V of PR for Corporate/Commercial Banking shall be followed.

Banks/DFIs may avail the above benefit of FSV subject to compliance with the following conditions:

The additional impact on profitability arising from availing the benefit of FSV against pledged stocks and mortgaged residential, commercial and industrial properties (land and building only) shall not be available for payment of cash or stock dividend.

Heads of Credit of respective banks/DFIs shall ensure that FSV used for taking benefit of provisioning is determined accurately as per guidelines contained in PRs and is reflective of market conditions under forced sale situations.

Party-wise details of all such cases where banks/DFIs have availed the benefit of FSV shall be maintained for verification by State Bank’s inspection teams during regular /special inspection.

Any misuse of FSV benefit detected during regular /special inspection of SBP shall attract strict punitive action under the relevant provisions of the Banking Companies Ordinance, 1962. Furthermore, SBP may also withdraw the benefit of FSV from banks/DFIs found involved in its misuse.

TIMING OF CREATING PROVISIONS:

Banks/DFIs shall review, at least on a quarterly basis, the collectability of their loans/advances portfolio and shall properly document the evaluations so made. Shortfall in provisioning, if any,

determined, as a result of quarterly assessment shall be provided for immediately in their books of accounts by the banks/DFIs on quarterly basis.

REVERSAL OF PROVISION:

In case of cash recovery, other than rescheduling/restructuring, banks/DFIs may reverse specific provision held against classified assets, subject to the following: following:

(a) In case of Loss account, reversal may be made to the extent that the remaining outstanding amount of the classified asset is covered by minimum 100% provision. (b) In case of Doubtful account, reversal may be made to the extent that the remaining outstanding amount of the classified asset is covered by minimum 50% provision.

(c) In case of Substandard account, reversal may be made to the extent that the remaining outstanding amount of the classified asset is covered by minimum 25% provision. While calculating the remaining provision required to be held after cash recovery and reversal of provision there-against, the banks/DFIs will enjoy the benefit of netting-off the amount of liquid assets from the outstanding amount, in the light of guidelines given in this regulation. However, the provision made on the advice of State Bank of Pakistan will not be reversed without prior approval of State Bank of Pakistan.

Table 1.2



SME (SMALL & MEDIUM ENTERPRISES)

PRUDENTIAL REGULATIONS

REGULATION R-6 (Per Party Exposure Limit)

The maximum exposure of a bank/DFI on a single SME shall not exceed Rs 75 million. The total facilities (including leased assets) availed by a single SME from the financial institutions should not exceed Rs 150 million provided that the facilities excluding leased assets shall not exceed Rs 100 million. It is expected that SMEs approaching this limit should have achieved certain sophistication as they migrate into larger firms and should be able to meet the requirements of Prudential Regulations for Corporate/Commercial Banking.

REGULATION R-7 (Aggregate Exposure of a Bank/DFI on SME Sector)

The aggregate exposure of a bank/DFI on SME sector shall not exceed the limits as specified below: Table 1.3


REGULATION R-11 (Classification and Provisioning for Assets Loans/Advances)

Banks/DFIs shall observe the prudential guidelines given at Annexure-III in the matter of classification of their SME asset portfolio and provisioning there-against. In addition to the time based criteria prescribed in Annexure-III, subjective evaluation of performing and non performing credit portfolio shall be made for risk assessment and, an d, where considered necessary, any account including the performing account will be classified, and the category of classification determined on the basis of time based criteria shall be further downgraded. Such evaluation shall be carried out on the basis of credit worthiness of the borrower, its cash flow, operation in the account, adequacy of the security, inclusive of its realizable value and documentation covering the advances. The rescheduling/restructuring of non-performing loans shall not change the status of classification of a loan/advance etc. unless the terms and conditions of rescheduling/restructuring are fully met for a period of at least one year (excluding grace period, if any) from the date of such rescheduling/restructuring and at least 10% of the outstanding amount is recovered in cash. However, the condition of one year retention period, prescribed for restructured/rescheduled loan account to remain in the classified category, will not apply in case the borrower has repaid or adjusted in cash at least 50% of the total restructured loan amount (principal + markup), either at the time of restructuring agreement or later-on during the grace period.

The unrealized mark-up on loans (declassified after rescheduling/restructuring) shall not be taken to income account unless at least 50% of the amount is realized in cash. However, any short recovery in this respect will not impact the declassification of this account if all other criteria (meeting the terms and conditions for at least one year and payment of at least 10% of outstanding amount by the borrower) are met. The banks/DFIs are further directed to ensure that status of classification, as well as provisioning, is not changed in relevant reports to the State

Bank of Pakistan merely because a loan has been rescheduled or restructured. However, while reporting to the Credit Information Bureau (CIB) of State Bank of Pakistan, such loans/advances may be shown as ‘rescheduled/restructured’ instead of default.

Where

a

borrower

subsequently

defaults

(either

principal

or

mark-up)

after

the

rescheduled/restructured loan has been declassified by the bank/DFI as per above guidelines, the loan will again be classified in the same category it was in at the time

of

rescheduling/restructuring and the unrealized markup on such loans taken to income account shall also be reversed. However, banks/DFIs at their discretion may further downgrade the classification,

taking

into

account

the

subjective

criteria.

At

the

time

of

rescheduling/restructuring, banks/DFIs shall consider and examine the requests for working capital strictly on merit, keeping in view the viability of the project/business and appropriately securing their interest etc.

All fresh loans granted by the banks/DFIs b anks/DFIs to a party after rescheduling/restructuring of its existing facilities may be monitored separately, and will be subject to classification under this Regulation on the strength of their own specific terms and conditions.

Banks/DFIs shall classify their loans and advances portfolio and make provisions in accordance with the criteria prescribed above, keeping in view the following:

Banks/DFIs are allowed to take the benefit of 40% of Forced Sale Value (FSV) of the pledged stocks and mortgaged residential, commercial and industrial properties (building only) held as collateral against NPLs for three years from the date of classification for calculating provisioning

requirement. However the banks/ DFIs can avail benefit of 40% of FSV of mortgaged residential, commercial and industrial land (open plot, and where building is constructed separate valuation of land must be available) held as collateral against NPLs for four years from the date of classification for calculating provisioning requirement. This benefit would be available in such cases where FSV valuation of land is not more than four years old. For the purpose of determination of FSV, Annexure-IV of PR for SME Financing shall be followed.

Banks/DFIs may avail the above benefit of FSV subject to compliance with the following conditions:

i) The additional impact on profitability arising from availing the benefit of FSV against the pledged stocks and mortgaged residential, commercial and industrial properties shall not be available for payment of cash or stock dividend.

ii) Heads of Credit of respective banks/DFIs shall ensure that FSV used for taking benefit of provisioning is determined accurately as per guidelines contained in PRs and is reflective of market conditions under forced sale situations

iii) Party-wise details of all such cases where banks/DFIs have availed the benefit of FSV shall be maintained for verification by State Bank’s inspection teams during regular/special inspection.

c) Any misuse of FSV benefit detected during regular/special inspection of State Bank shall attract strict punitive action under the relevant provisions of the Banking Companies Ordinance,

1962. Furthermore, State Bank may also Withdraw the benefit of FSV from banks/DFIs found involved in its misuse.

In order to provide relief to the borrowers of flood affected areas identified by the National Disaster Management Authority (NDMA), banks/DFIs are encouraged to reschedule/restructure Agriculture and SME loans/advances to such borrowers, as per existing Prudential Regulations (PRs) of Agriculture and SME Financing where the possibility of recovery exists. For all such rescheduled/restructured loans and advances, Banks/DFIs may defer loan provisioning up to 31st December 2011. However, classification of such loans shall be done as per criteria laid down in the relevant PRs. This relaxation is available for loans and advances which have become non performing since July 1, 2010 in the affected areas identified by NDMA. Loans/advances classified before this date shall not qualify for this relaxation.

TIMING OF CREATING PROVISIONS:

Banks/DFIs shall review, at least on a quarterly basis, the collectability of their loans/advances portfolio and shall properly document the evaluations so made. Shortfall in provisioning, if any, determined, as a result of quarterly assessment shall be provided for immediately in their books of accounts by the banks/DFIs on quarterly basis.

REVERSAL OF PROVISION:

In case of cash recovery, other than rescheduling/restructuring, banks/DFIs may reverse specific provision held against classified assets, subject to the following: following:

i) In case of Loss account, reversal may be made to the extent that the remaining outstanding amount of the classified asset is covered by minimum 100%provision. ii) In case of Doubtful account, reversal may be made to the extent that there main outstanding amount of the classified asset is covered by b y minimum 50% provision. iii) In case of substandard account, reversal may be made to the extent that the remaining outstanding amount of the classified asset is covered by minimum 25% provision.

While calculating the remaining provision required to be held after cash recovery and reversal of provision there-against, the banks/DFIs will enjoy the benefit of netting-off the amount of liquid from the outstanding amount, in the light of guidelines given in this regulation. Further, the provision made on the advice of State Bank of Pakistan will not be reversed without prior approval of State Bank of Pakistan.

Table 1.4


CONSUMER PRUDENTIAL REGULATIONS

REGULATION R-2 (Limit On Exposure Against Total Consumer Financing)

Banks/DFIs shall ensure that the aggregate exposure under all consumer financing facilities at the end of first year and second year of the start of their consumer financing does not exceed 2 times and 4 times of their equity respectively. For subsequent years, following limits are placed on the total consumer financing facilities:

Table 1.5


REGULATION R-3 (Total Financing Facilities to be Commensurate with the Income)

While extending financing facilities to their customers, the banks/DFIs should ensure that the total installment of the loans extended by the financial institutions is commensurate with monthly income and repayment capacity of the borrower. In this connection, while determining the credit worthiness and repayment capacity of the prospective borrower, the banks/DFIs shall ensure that the total monthly amortization payments of consumer loans should not exceed 50% of the net disposable income of the prospective borrower1. This measure would be in addition to

banks’/DFIs’ usual evaluations of each proposal concerning c oncerning credit worthiness of o f the borrowers, to ensure that the banks’/DFIs’ portfolio under consumer finance fulfills the prudential norms and instructions issued by the State Bank of Pakistan and does not impair the soundness and safety of the bank/DFI itself. Banks/DFIs may waive the requirement of 50% Debt Burden in case a Credit Card and Personal loan is properly secured through liquid assets (as defined in prudential regulations) with minimum 30% margin.

REGULATION R-5A Rescheduling / Restructuring of Non-Performing Consumer Loans:

a) Banks/DFIs should frame policy for rescheduling/ restructuring of non-performing consumer loans. The policy should be approved by the Board of Directors or by the Country Head/Executive/Management Committee in case of branches of foreign banks.

b) For the purpose of rescheduling/ restructuring, banks/DFIs may:

Club or consolidate outstanding amounts on account of personal loans and credit cards and create one loan. The new loan so created shall be placed in the lowest category of classification amongst the classifications of the loans clubbed. Convert revolving facility into an installment loan.

Change the tenure of the loan by maximum two years beyond any regulatory cap on maximum tenure.

c) Rescheduling/ restructuring should not be done just to avoid classification of loans /advances and provisioning requirements. In this connection, banks /DFIs shall ensure that consumer

financing facilities of any borrower should not be rescheduled/ restructured more than once within 12 months and three times during five year period.

d) While considering rescheduling/restructuring, banks/DFIs should, interlaid, take into account the repayment capacity of the borrower. The condition of 50% of Debt Burden Requirement (DBR) mentioned at Regulation R-3 of Prudential Regulations for Consumer Financing shall not be applicable to loan rescheduled/ restructured. However, any new consumer financing facility extended to a borrower who is availing any rescheduled/ restructured facility shall be subject to observance of minimum DBR prescribed in the Regulation R-3 of Prudential Regulations for Consumer Financing.

e) The status of classification of the non-performing loans shall not be changed because of rescheduling / restructuring unless borrower has paid at least 10% of the rescheduled / restructured amount or six installments as per terms & conditions of the rescheduling/ restructuring. However, for internal monitoring purpose, banks/DFIs may re-set the dpd (days past due) counter of the newly created loan to “0”.

f) Provisions already held against non-performing loan, to be rescheduled /restructured, will only be reversed if condition of 10% recovery or six installments is met.

g) If the borrower defaults (i.e. reaches 90 dpd) again within one year after declassification, the loan shall be classified as under:

Table 1.6

Type

of

Consumer Classification

Loan

Unsecured

Loss

Secured

Same category in which it was prior to rescheduling / restructuring. Banks

/

DFIs, however, at their discretion may further downgrade the classification based on their own internal policies.


CREDIT CARDS PRUDENTIAL REGULATIONS REGULATION O-1

The banks/DFIs should take reasonable steps to satisfy themselves that cardholders have received the cards, whether personally or by mail. The banks/DFIs should advise the card holders of the need to take reasonable steps to keep the card safe and the PIN secret so that frauds are avoided.

REGULATION O-2

Banks/DFIs shall provide to the credit card holders, the statement of account at monthly intervals, unless there has been no transaction or no outstanding balance on the account since last statement.

REGULATION O-3

Banks/DFIs shall be liable for all transactions not authorized by the credit card holders after they have been properly served with a notice that the card has been lost/stolen. However, the bank’s/DFI’s liability shall be limited to those amounts wrongly charged to the credit card holder’s account. In order to mitigate the risks in this respect, the banks/DFIs are encouraged to take insurance cover against wrongly charged amounts, frauds, etc.

The bank/DFI shall, however, not charge the borrowers’ account with any amount under the head of “insurance premium” (by what so ever name called) without obtaining consent of each existing & prospective customer in writing. In addition to obtaining consent in writing, the banks/DFIs may also use the following modes for obtaining prior consent of their customers provided proper record is maintained by banks/DFIs. REGULATION R-8 (CLASSIFICATION AND PROVISIONING)

The credit card advances advance s shall be classified and provided for in the following manner: Table 1.7


It is clarified that the lenders are allowed to follow more conservative policies. Further, provisioning may be created and maintained by the Bank / DFI on a portfolio basis provided p rovided that

the provision maintained by the Bank / DFI shall not be less that the level required under this Regulation.

AUTO LOANS PRUDENTIAL REGULATIONS REGULATION R-9

The vehicles to be utilized for commercial purposes shall not be covered under the Prudential Regulations for Consumer Financing. Any such financing shall ensure compliance with Prudential Regulations for Corporate/Commercial Banking or Prudential Regulations for SMEs Financing. These regulations shall only apply for financing vehicles for personal use including light commercial vehicles also used for personal purposes.

REGULATION R-10

The maximum tenure of the auto loan finance shall not exceed seven years.

REGULATION R-14

The auto loans shall be classified and provided for in the following manner:

Table 1.8


HOME MORTGAGE LOAN PRUDENTIAL REGULATIONS

REGULATION R-16

The housing finance facility shall be provided at a maximum debt-equity ratio of 85:15.

REGULATION R-22

The mortgage loans shall be classified and provided for in the following manner:

Table 1.9


PERSONAL LOAN PRUDENTIAL REGULATIONS (Regulations For Personal Loans Including Loans For The Purchase Of Consumer Durables)

REGULATION R-23 (Clean Limit Per Person for Personal Loans)

Banks/DFIs may assign personal loan limits to one person with a maximum unsecured limit not exceeding Rs 1,000,000/, subject to mandatory credit check & prescribed debt burden and condition that total unsecured personal loans limits availed by that person from all banks/DFIs does not exceed Rs. 1,000,000. Banks/DFIs may merge the clean limits to single person for Personal Loans and Credit Cards subject to the condition that total clean limit availed by him/her from all banks/DFIs does not exceed Rs. 2,000,000 at any point in time. It is re-emphasized that the aggregate clean limit of the borrower should not exceed Rs. 2,000,000.

Banks/DFIs shall ensure that overall personal loan limits and credit card limits, both on secured as well as on unsecured basis, availed by one person from all banks/DFIs in aggregate should not exceed Rs 5,000,000/-, at any point in time, subject to the condition that the overall unsecured/clean facilities on account of personal loan and credit card of that individual does not exceed Rs 2,000,000. The loan secured against against liquid securities securities shall, however, be exempted from this limit. The loans against the securities issued by Central Directorate of National Savings (CDNS) shall be subject to such limits as are prescribed by CDNS/Federal Government/State Bank of Pakistan from time to time.

REGULATION R-27

The personal loans shall be classified and provided for in the following manner: Table 1.10


Chapter # 02

LITERATURE REVIEW

2.1 INTERNATIONAL AND NATIONAL STUDIES

Corresponding author Department of Economics, of Economics, University of Birmingham of Birmingham Edgbaston Birmingham

The non-performing loan problem in commercial banks. Using the threshold regression technique, we found some evidences that non-performing loans have non-linear negative effect on bank lending behavior. On the contrary, when banks have non-performing loans lower than the threshold; they are less regressive in increasing lending as suggested by the estimated coefficients. However, when non-performing loan rates are under the threshold level, non performing loans have positive impacts banks’ lending behavior with a statistically significant positive coefficient 0.4909. 0. 4909. It suggests that banks may still increase their loans as the generation of non-performing loans is the natural result of lending, especially for banks in the expansionary stage. (Dermirgue-Kunt 1989, Barr and Siems 1994), and that failing banking.

A simple definition of non-performing is: is: A loan that is not earning income and: full payment of principal and interest is no longer anticipated, principal or interest is 90 days or more delinquent, or the maturity date has passed and payment in full has not been made. The issue of non performing loans (NPLs) has gained increasing attentions in the last few deca des. The immediate consequence of large amount of NPLs in the banking system is bank failure. Many researches on the cause of bank failures find that asset quality is a statistically significant predictor of insolvency (Dermirgue-Kunt 1989, Barr and Siems 1994), and that failing banking.

(Berger and Humphrey (1992), Barr and Siems (1994), DeYoung and Whalen (1994), Wheelock and Wilson (1994)),

Non-performing loans can lead to efficiency problem for banking bank ing sector. It is found by a number of economists that failing banks tend to be located far from the most-efficient frontier (Berger and Humphrey (1992), Barr and Siems (1994), DeYoung and Whalen (1994), Wheelock and Wilson (1994)), because banks don’t optimise their portfolio decisions by lending less than demanded. What’s more, there are evidences that even among banks that do not fail, there is a negative relationship between the non-performing loans and performance efficiency (Kwan and Eisenbeis (1994), Hughes and Moon (1995), Resti (1995)). United States Council of Economic Advisors (1991)

The phenomena that banks are reluctant to take new risks and commit new loans is described as the ‘”credit crunch” problem. According to the United States Council of Economic Advisors (1991), credit crunch is “a situation in which the supply of credit is restricted below the range usually identified with prevailing market interest rates and the profitability of investment projects

Krueger and Tornell (1999) and Agung et.al. (2001)

Krueger and Tornell (1999) support the credit crunch view and attribute the credit crunch in Mexico after the 1995 crisis partially to the bad loans. They point out that banks were burdened with credits of negative real value, thereby reducing the capacity of the banks in providing fresh fund for new projects. Agung et. al. (2001) using the macro and micro panel data analyses to study the existence of a credit crunch in Indonesia after the crisis. Both the macro and micro

evidences show that there was a credit crunch, characterized by an excess demand for loans, starting to emerge in August 1997, one month after the contagion effects of the exchange rate turmoil in Thailand spreading to Indonesia. The International Accounting Standard 39 revised in 2003

The International Accounting Standard 39 revised in 2003 focuses on recognition and measurement of financial instruments and, most importantly, defines and establishes the measurement and evaluation of impaired loans. As lenders usually make little or no loss provision for impaired loans, they are at a t risk to be suddenly forced to reclassify such loans as a loss and take a full write-down if the borrowers go bankrupt. The initiation of this standard is to prevent lenders from being caught off-guard. In addition, many global economists, rating agencies, and organizations such as the World Bank and the Asian Development Bank have begun to evaluate the effects of o f NPLs on GDP growth. They Th ey reduce growth estimates e stimates to reflect the time and cost of resolving large non-performing loan issues. Se-Hark Park (2003) “Financial Revival Laws-Based Debt Disclosure” in 1999.

There is no global standard to define non-performing loans at the practical level. Variations exist in terms of the classification system, the scope, and contents. Such problem potentially adds to disorder and uncertainty in the NPL issues. For example, as described by Se-Hark Park (2003), during 1990s, there were three different methods of defining non-performing loans in Japan: the 1993 method based on banking laws; the “Bank’s Self-Valuation” in March 1996; and the “Financial Revival Laws-Based Debt Disclosure” in 1999.

World Bank report October 1999

According to the latest country report of the World Bank this amount may increase to Rs 384 billion by end of the current year. Recovery of stuck up loans was one of the prime objectives ob jectives of General Musharraf, when he took over in October 1999, the then previous government. The recovery drive launched by the National Accountability Bureau (NAB) made wholesale arrests of the businessmen, even on circumstantial defaults, resulting in severe backlash and further loss of confidence. NAB was able to recover few billions rupee from the erring industrialists, but during the process many industries fell sick. Rafiq Tarar economy and rehabilitation 2009 Research article Financial Times

The contracting economic activity in the country fuelled further failures, and more loans were categorized as non-performing. Despite the official claims of over Rs. 50 billion cash recoveries, the Finance Ministry figures show an increase of almost Rs. 105 billion in the NPLs during the present regime. Realizing its dismal performance on the recovery of defaulted loans the government took a fresh initiative in this regard. The then President Rafiq Tarar promulgated an ordinance to provide for expeditious legal remedies for the matters relating to non-performing assets. The ordinance also provided for legal remedies for the recovery of outstanding loans of the banks and other financial institutions to make them attractive for privatization and to promote the revitalization of national economy and rehabilitation and restructuring undertakings. Report of World Bank (2002)

The ordinance was expected to enable the government to expedite the process of recovery of outstanding loans as it provided for the high courts to set up special tribunals to deal with the loan defaulters. However like previous attempts and actions, this effort has also failed to bring about any significant improvement in the situation. According to the latest country report of World Bank (2002) Pakistan Development Policy Review the non-Performing Loans (NPLs) of

the Nationalized Commercial Banks (NCBs) have become a major problem because of political interference and directed credits to individuals and companies. The amount of NPLs has reached alarming proportions, that is, Rs. 384 billion in the current fiscal year. It is supposed to be one of the strangest.

2.2 LITERATURE WRITTEN BY MR. ISHRAT HUSAIN (GOVERNOR STATE BANK OF PAKISTAN) April 8, 2009. 2.2.1 DEALING WITH NON-PERFORMING LOANS OF BANKS

A lot of confusion and misunderstanding has been created by several commentators on the issue of non-performing loans (NPLs) of banking system. They take the absolute amount of such loans at the current point of time and compare it with the quantum of such loans in October 1999 and make a hue and cry that the situation has deteriorated because the quantum of NPLs has gone up. Such a simplistic approach creates doubts in the minds of common people about the commitment of the government and the` State Bank of Pakistan towards recovery of these loans and distorts the true picture about this important issue. The aim of this article is to inform the public about the exact magnitude of the problem, the trend over time and the measures the State Bank of Pakistan is taking to tide over this problem. The State Bank of Pakistan (SBP) is dealing with the NPL issue in a comprehensive manner through (a) improvement in coverage and reporting of NPLs (b) a proactive treatment of the existing stock of NPLs (c) stemming flow of new NPLs and (d) improving the policy and regulatory environment. It should be realized that the stock of existing NPLs will always grow over time even if all the new loans being granted are fully serviced. This will happen because the declared amount consists of principal and mark up. By its very definition, if the loan is not being serviced and is overdue by 90 days then the unrealized

markup will continue to be added up to the total amount of NPLs. For example, if on October 1999 the principal amount due on a NPL was Rs 1 million and the contracted markup rate was 20 percent, then this amount will grow to Rs 1.2 million in October 2000, Rs 1.4 million in October 2001 and Rs 1.6 million in October 2002. So it can be seen that if the principal amounts overdue to the banking system in October 1999 were Rs 160 billion and these loans fell in the category of NPLs then three years later they will swell automatically to Rs 256 billion, assuming that the contracted markup rate was 20 percent per annum. Thus, one can expect that 60 percent increase will take place in the total quantum of NPLs after a three year period, even if every single new loan is performing well. The second complication arises if the NPL is denominated in foreign currency which is the case with 13 percent of all NPLs. These were granted by the foreign branches of NBP, HBL, UBL and Allied Bank. Assume that these loans were granted when the rupee-dollar exchange rate was Rs 46 to $ 1 and suppose the principal amount overdue was $ 1 million. At the time the loan was granted, its value on the books of the bank was Rs 46 million. As per Mr.Ishrat Mr.Ishrat literature in 2008 when the exchange rate is Rs 59, the same same NPL will be shown as Rs 59 billion i.e. 28 percent higher than the original value declared in October 1999. This excludes the mark-up overdue which will also move up and if this markup is included the same NPL will be at least 40 percent higher in value in October 2002 (as the dollar mark up rate has been lower than the rupee markup rate). Thus it should be seen that without any fault of the bank its aggregate value of NPLs (denominated (deno minated in foreign currency) has escalated by 40 percent. (a) Improvement in Coverage and Reporting. The SBP Inspectors have begun to apply more rigorous standards of classification. In September 2000, the SBP inspectors detected that some of the public sector specialized banks were reporting only default or overdue portion of their non performing loans instead of total outstanding amount of such loans. This led to an upward revision in the volume of NPLs reported by these banks and resulted in addition of Rs 47 billion

of loans classified as non-performing which were not shown as such in the period prior to September 2000. Thus overnight the total volume of declared NPLs rose by Rs 47 billion. The SBP has also revised the valuation method of collaterals underlying the classified loans and brought them in line with international practices. The banks b anks can ca n now no w take into account acco unt only the minimum realizable value of assets mortgaged or pledged for determining the provisions. The realizable value shall be the value that could currently be obtained by selling the mortgaged/pledged assets in a forced/distressed sale condition. The banks have been asked to earmark additional provisions against the revised valuations of collaterals. It can be seen from the above illustrations that the increase in absolute amounts of NPLs cannot be ipso facto attributed to any deterioration in the underlying quality of assets, but has occurred due to the stricter enforcement of regulatory, accounting, valuation and prudential rules. Despite the above factors i.e. addition of unrealized mark up for three years, currency revaluation due to depreciation of rupee and discovery of undisclosed NPLs, the overall quantum of NPLs by the end of June 2002 amounted to around Rs 259 billion – an increase of only Rs 47 billion in the last three years. This increase is equivalent to just the onetime adjustment made in September 2000 to the stock of NPLs due to improvement in the reporting methodology. While the nationalized commercial banks have brought down their non-performing loans, the largest single change has been in the category of specialized banks. Their NPLs have risen from Rs 19.3 billion to Rs 67 billion due to this onetime adjustment. To the banking regulator, it is not the absolute amount, but the ratios of NPLs to total advances which are the relevant indicators of the quality of assets and adequacy of capital of the banks. There are two ratios which ought to be monitored the gross NPLs/gross advances and net NPLs/net advances.

The reason for monitoring these ratios is straight forward and logical. As the banks grant new loans of good quality after careful appraisal and due diligence, these ratios are bound to decline over time and the overall quality of assets of the system will improve. More important, it is non provisioning of these NPLs which pose a systemic threat to the health of the banking system. The higher is the provisioning, the lower is the systemic risk. In June 1999, the ratio of gross NPLs/gross advances advan ces of the banks and DFIs was wa s 24 percent and is almost the same today. Had this under reported amount of Rs 47 billion been added to the portfolio of banks, particularly ADBP and IDBP, in June 1999 the ratio of gross NPLs / gross advances on comparable basis, would have been 29 percent. On this basis alone it can be seen that the ratio of gross NPLs to gross advances has declined by at least 5 percentage points over last three years. The more gratifying feature is that the ratio of net NPLs/net advances has declined from 15 percent to 11 percent as the banks and DFIs increased their holding of provisions to Rs 142 billion which covered 56 percent of their classified portfolios of both foreign and domestic loans. The situation will further improve in 2002 as the banks make more provisions against a declining portfolio of NPLs.

2.2.2 PROACTIVE TREATMENT OF THE STOCK OF NPLs

The State Bank is still not satisfied with this declining trend of these NPLs, as the spread between the deposit and lending rates is still high due to this drag. It has adopted a multi-pronged approach to resolve this issue. First, it has put pressure on the banks and DFIs to accelerate recovery. During the past three years, an amount of more than Rs 40 billion or 20 % of 1999 outstanding stock of NPLs has been recovered in cash. Second, it has decided to distinguish between willful defaulters and circumstantial defaulters. The cases of willful defaulters have been referred to NAB for action under the NAB Ordinance. NAB has helped in recovering

(including rescheduling) Rs 17.5 billion so far from these defaulters. Third, the Committee on Revival of Sick Units (CRSU) has been authorized to restructure the NPLs and revive the underlying sick units which are found to be financially and economically viable. Fourth, the Government has created an asset resolution framework in the form of Corporate and Industrial Restructuring Corporation (CIRC). This corporation acquires the bad loans from nationalized banks at a discount and auctions them publicly thus taking away the assets from the existing owners and repaying the proceeds to the banks. Fifth, as there are aged loans which can hardly be recovered due to passage of time and the diminution in their value, the SBP has developed general guidelines for the use of bank boards of Directors to write-off these loans particularly to help small and medium borrowers. Finally, 80 percent of these non-performing loans are concentrated in seven public owned banks and DFIs. NDFC has been merged with NBP. UBL has been privatized and HBL is in the process of privatization. IDBP and ADBP are being restructured and NBP shares are being floated. As this link between political loans and the public-owned banks will be b e severed the probability of huge accretion of bad b ad loans in the future will be minimized. The banks are in the business of risk taking and there are occasions when exogenous shocks or business cycles or frequent changes in government policies do turn some of their assets sour. Until and unless there is no personal motive of the bankers or any political pressure, the write off of loans and cleaning up of their balance sheets is the normal practice p ractice of o f the banks all over the world and Pakistani banks should not hesitate to take appropriate action on the basis of transparent criteria and policy guidelines.

The Board of Directors and the regulators should exercise oversight and make sure that the decisions taken by the bank management conform to the approved criteria and guidelines. Only

willful defaulters should be taken to task and made to repay their full liabilities. Legal action should be taken against them and their cases referred to NAB. (c) Stemming flow of new NPLs. The banking sector reforms implemented since 1997 have improved the quality of assets. The flow of NPLs has been significantly contained. The ratio of NPLs to total loans disbursed since 1997 has remained around 5 percent much lower than international norms. This will ensure that the future ratios of NPLs will look much better than the historical or current ratios. It has also been observed that the NPLs and their ratios to advances are much lower in case of domestic private banks and foreign banks. As the nationalized commercial banks (NCBs) and DFIs are privatized or liquidated or merged the incidence of new non-performing loans will be reduced significantly. There has been a perceptible change in the credit culture of the NCBs who now pay increasing attention to more rigorous credit appraisal, proper credit documentation, monitoring and follow up.

Their management have also developed mechanisms for better risk management and discontinued lending on political considerations. This new Credit Culture, of course, has some negative repercussions as credit officers have become more risk averse in recommending new loans and the potential borrowers have become more cautious in contracting new loans. Decline in private sector credit can be partially attributed to this risk aversion among the bank credit staff. The SBP is trying to mitigate this by asking the banks to diversify their portfolios and open up new lines of business – consumer financing, mortgage financing, SME lending, microfinance, agriculture credit and thus manage the aggregate risks better. This diversification and risk management strategy should help meet the broad credit demand of the various segments of the economy but also stem flow of nonperforming no nperforming loans.

2.2.3 POLICY AND REGULATORY ENVIRONMENT

The State Bank’s policies and regulatory environment have also been revamped to resolve this problem. Interest rates have declined significantly and low interest rates should help the borrowers in repaying their stuck up loans. Information on exposure to various individual companies and groups will now be available to the banks on-line to help them in making informed decisions on credit extension. The protracted and cumbersome legal processes and prolonged litigation for execution of decrees have been bee n a major stumbling block in the recovery of loans. An important recent development has been the enactment of a new recovery law in 2001 which enables the banks to repossess the collaterals without recourse to litigation. A new bankruptcy law is also in offing which will permit orderly resolution of debtor obligations under u nder distress. The SBP has also strengthened its supervisory capacity by shifting to risk based supervision and by assessing the strengths and weaknesses of internal controls, systems and risk management mechanisms within the financial institutions. The supervisors make a more prudent evaluation of the provisioning requirements and take corrective actions to enable the financial institutions to set aside the right amounts of provisions. The introduction of market based instruments, swap desks, development of a yield curve, allowing the banks to raise second tier capital through subordinated debt and matching their asset and liability maturities are some of the additional steps which have been taken by the SBP to facilitate the banks to manage their risks in a more prudent manner. To sum up, while significant progress has been made in dealing with the old stock of non-performing loans of the banks and DFIs, SBP is still not satisfied with the existing situation. The good news is that the proportion of NPLs among the new loans approved since 1997 is shrinking while a combination of policies aimed at cash recoveries, rescheduling, restructuring, sale of assets to third parties, execution of legal decrees, write off of

aged and irrecoverable loans are being pursued to reduce the quantum of old stock. For a variety of reasons legal, accounting, valuation, prudential and regulatory the stock may continue to Show an upward rise as mark-up is added over time to overdue principal, exchange rate revaluations are effected and more rigorous standards are applied by the SBP in classifying and reporting these loans. This rise should, therefore, be seen in the correct and overall perspective, as explained in this article.

Chapter # 03 RESEARCH METHODOLOGY METHODOLOGY & RESEARCH DESIGN

3.1 RESEARCH DESIGN

Research design is based on categories that include conceptual frame work and theoretical frame work this include the diagram that will explain the relationship between the variables and explains that what are the direct and moderate variable. As the word used moderated meant by that those variables that only influence the direct variables or independent variables.

3.2 CONCEPTUAL FRAME WORK

3.2.1 Dependent variables:Banking industry profitability

3.2.2 Independent Variables:Increasing non-performing loans Credit risk assessment policies 3.2.3 Moderate Variables:SBP regulations and restrictions

Sample size

Bank Al- Habib Ltd data will be consist of 12 years from 2000 to 2011

3.3 THEORETICAL FRAME WORK

Growing non-performing loans (NPLs) has become a threat for financial system of Pakistan which should be taken as a challenge by monetary authorities. SBP lowered interest rates in the last monetary policy may be due to this factor as economic growth is very slow which results in low demand and hence higher non-performing loans. NPLs have been continuing to grow since 2008 and now they have toughed unprecedented level of Rs 594.5 billion which should be a cause of concern for the economic managers trying to bring economy back on track, it said. Growing NPLs are result of unheard-of level of government borrowings which has choked the productive sector of the country due to crowding out effect , said Dr. Murtaza Mughal, President Pak economy watch. SBP has indicated an alarming growth in Non-Performing loans in banks and other financial institutions. Although the growth rate has remained low in the 2 nd quarter 2010, yet it has reached to the level of 473 billion rupees. For the last two years, especially after financial crises faced by the banks and financial institutions all over the world, the banks and

other financial institutions in Pakistan have also slowed down their loan / advances offering services to the private sector. Due to bad economic situation and very low development activities in Industrial sector, the banks and other institutions are either offering loans at very high interest rates or investing in government owned securities. This increase in NPL level can rightly be associated with the bad economic situation due to different problems like energy crises, loadshedding and prices hike in raw materials and other utilities; faced by industrialists. This increase in NPLs has not only created hesitation in banks in offering new loans but also urged them to play safe thru’ investing in government securities. State Minister for Finance, Revenue, Economic Affairs, Statistic and Planning and Development Hina Rabbani Khar January 2nd 2011 informed the National Assembly that Non-Performing Loans (NPLs) of banking sector has increased from 10 percent to 14 percent during the last two years. Replying to a Call Attention Notice raised by MNAs: Nuzhat Sadiq, Nisar Tanveer, Tahira Aurangzeb and others regarding unprecedented rise in NPLs of banking sector to Rs.494 billion. She said that high risks, security situation, energy shortage and overall economic recession are main reasons for NLPs increase. She said that 80 percent of the country’s banking is being run by private sector and the government has no role in writing off NPLs. The State Bank of Pakistan (SBP) being a regulator only formulates rules for banking sector, she added. The minister, besides local banks foreign banks waived off loans during the last years while private banks are being run by their Board of Directors.

3.3.1 NON-PERFORMING LOANS IN THE BANKING INDUSTRY

3.3.1.1 INTRODUCTION

Financial institutions such as banks are expected to maintain their credit management due to the increasing rate of non-performing loans. The increasing number of non-performing loans of different entities and individual creates a significant impact and negative values to the financial streams. In the long-run, this same impact will reach the entire economy and leads to increase the credit crisis. In this paper, there is an interest drawn by the researcher/s regarding the reason or several reasons that lead to non-performing loans. In the investigation, there will be appropriate analysis that can generate the recommendation on remedies to lessen the rate of non-performing loans. The focus of the paper is the situation in most of the developing countries, particularly in Kenya.

3.3.1.2 FAILURE IN MANAGEMENT

Banks are absolutely strong to hold the financial crisis. But in the recent years, this characteristic of bank changed due to the various economic changes and challenges offered by the

globalization. Added to this disadvantage is the growing numbers of non-performing loans that affects the financial stream and operations of the bank. The main objective of the bank in offering the financial credit and loans for the entities and small individuals can be viewed in the noble mission “to lessen the poverty”. The procedures and operations of the banks are tested through their model country. However, there are instances that the loan performance fails to follow its original plan and fail to produce the expected outcome because of the two aspects – the credit management of the financial institution and the failure of the individual or entities to wisely use the loans.

3.3.1.3 FIRST REASON IS CREDIT MANAGEMENT

From the simple transaction, different problems may arise. The failure of the customer to disclose any personal information during the application can be the greatest reason that might influence the overall performance of the banks. The fact that the information is insufficient may affect the loan’s fruitful expectations. This is also the representation of the bank’s lack of capacity to investigate and build strong transactions, as well as the debt collection.

3.3.1.4 SECOND REASON IS INDIVIDUALS / ENTITIES CAPACITY

The purpose of loans is to support the financial needs of the customers according to their proposed businesses or specific needs. If the bank doesn’t see any fruitful investment in the proposed plan, they will only simply reject it. However, due to the personal greediness, the individuals draw assumptions and deceive the banks. Where, on the other hand, the bank is incapacitated to extend its investigation to ensure that all the information they receive are true and legal.

3.3.1.5 RECOMMENDATIONS

The non-performing loans are great issues which includes the government and the economy. However, there are still suggestions that can be adopted to reduce and prevent the growing numbers of non-performing loans. First, the bank should include the two actions in their operations (Harrison, 2006): (a) estimate the non-performing loans and allocate it to the corresponding borrowers but consider how unpaid loans are recorded in the accounts in such a way as to increment principal outstanding; and (b) estimate the interest received, rather than the receivable, and on the interest payable so that the performing loans are not affected by the non performing loans.

Since the problem that constitutes in the non-performing loans is associated with the operation of the banks, there should be an aggressive debt collection policy. The lack of aggressiveness is popular in the developing countries and this is perceived as the ban ks specific factor that can also contribute to the non performing debt problem (Waweru, & Kalani, 2009). Banks should increase their competency and maintain it until they recover their position and have a normal operation. Because of the pursuance in the economic development of the country, the economic downturn in the internationals setting should be prevented to influence the domestic situation of Kenya. Since the banks have no control over the economic uncertainties, then, they must allow the government to have its action over the issues of inflation and monetary policies. p olicies.

In a highly competitive environment, the capabilities of the managers to handle the pressures and the increasing demand of the customers can be the problem that may arise in the workforce (Blaauw, 2009). Therefore, training and developmental options should be available to prevent the failure in assessing the capabilities of the individuals or entities to generate the interests in their loans.

3.3.1.6 CONCLUSION

By polishing the credit policies and establishing a strong capacity in the management, the bank can handle the non-performing loans, which can duly affect the progress of the economy. Thus, the involvement of the appropriate and updated credit management should be prioritized

3.4 RESEARCH METHODOLOGY

For a simple commercial bank balance sheet, assets are mainly composed of commercial loans and other earning assets; while on the liability side, deposits and capital are the main components. Thus, we can conjecture that the loan growth is affect by deposit growth, capital growth and other earning assets growth. In addition, we take the non-performing loan growth into consideration. The basic model is as follows:

LGR i , t

=

a0

+

a 1 DGR i , t

+

a 2 CGR i , t

+

a 3 OEAGR

i , t +

a 4 NPLGR

i , t −1

(1)

Where the index i is the index for individual banks and t is the index for time period. LGR i ,t is the loan growth rate, DGR i ,t is the deposit growth rate in each time period t , CGR i ,t is the capital growth rate, OEAGRi ,t is the other assets growth rate, and NPLGR i ,t 1 is non-performing −

loan growth rate of the previous year. ye ar.

As financial intermediations, commercial banks’ main function is to receive deposits and make loans to facilitate the flow of capitals. For most of the commercial banks, deposits are the main funding sources for commercial banks’ assets. And loans take up the biggest proportion in the asset portfolio. With the expansion of the asset size, banks will expand the volume of the loans to re-balance the asset portfolio. Under the normal situation, loan growth rate is expected to move in the same direction as the growth of deposits. The sign in front of DGR i ,t , thus, is expected to be positive.

Chapter # 04 STATISTICAL REPORTING & DATA ANALYSIS Table 4.1 Bank AL Habib Ltd Data from the year 2000 to 2011 Rs in Millions Year

Gross Advances

Deposits

Profit Before Tax

Profit After Tax

NPLs

% of NPLs to Advances

2000

14,772

17,822

403

373

117

0.79%

2001

16,004

24,697

551

246

185

1.16%

2002

23,994

34,240

619

290

482

2.01%

2003

35,544

46,178

1,513

1,012

646

1.82%

2004

47,537

62,171

1,039

541

206

0.43%

2005

55,526

75,796

2,022

1,464

383

0.69%

2006

71,036

91,420

2,689

1,761

388

0.55%

2007

79,447

114,819

3,052

2,211

217

0.27%

2008

101,402

144,390

3,579

2,425

863

0.85%

2009

108,373

189,280

4,512

2,856

2,068

1.91%

2010

129,083

249,774

5,656

3,602

2,944

2.28%

2011

120,003

302,098

7,155

4,533

3,204

2.67%

(Source: Bank AL Habib Ltd Annual Reports)

4.1 FINDINGS AND INTERPRETATION OF THE RESULTS

LGR i ,t

=

a0

+

a1 DGR i ,t + a 2 CGR i ,t + a 3 OEAGR

i ,t +

a 4 NPLGR

i ,t −1

Where the index i is the index for individual banks and t is the index for time period. LGR i ,t is the loan growth rate, DGR i ,t is the deposit growth rate in each time period t , CGR i ,t is the capital growth rate, OEAGRi ,t is the other assets growth rate, and NPLGR i ,t 1 is non-performing −

loan growth rate of the previous year. ye ar.

REGRESSION ANALYSIS: TABLE 4.2: Model Summary

ANOVA

Model

R Square

Adjusted R Square

1

.851

.796

Model

F

Sig.

Regression

24.097

.000

Residual Total

(Source: From this study)

INTERPRETATION:

This table shows the fitness of model. R Square of 0.851 indicates that the model is 85.1% fit for the analysis. This model shows that the independent variables have explained almost 85.1 percent of the influencing factors that affect the profitability of Banks. However, this also indicates that there is 20.4 percent unexplained variation, which shows that there are some significant variables missing in the model. This may call for a further study in the same area. This table also indicates that the regression is highly significant at 0.05 level of significance as the Sig. value of regression is less than 0.05. This defines that there is a positive impact of log of growth rate (NPL’s), DGR i ,t is the deposit growth rate in each time period, CGR i ,t is the capital growth rate, OEAGRi ,t is the other assets growth rate has a significant relationship with LGR loan growth rate. F statistics value of 24.097 significant at 0.000 suggests that the model calculation of R square is correct.

Table 4.3: Coefficients Model

Unstandardized Coefficients

B

Std. Error

.017

.010

DGR

.000

.000

CGR

-.001

OEAGR NPLGR

1 (Constant) LGR

Standardized Coefficients

Collinearity Statistics

t

Sig.

1.689

.113

.0228

.523

.609

.045

9.306

.000

-.0627

-1.284

.001

.569

1.498

-.011

.013

-.0107

-.836

.417

.231

1.146

.004

.002

.0340

1.867

.083

.431

2.318

.128

-.391

-2.346

.034

.379

15.947

Loan Loss -.300 Provision/Total Loans

Beta

Tolerance VIF


INTERPRETATION: ANALYSIS OF T-STATS

The statistical analytical report suggest that deposit growth rate has a significant relationship with loan growth rate as it reflects the value of 0.523 which stand with in non-critical region as well as capital growth rate has a significant relationship with the variable LGR as it reflects the value of t-stats of -1.284 which stands between the range of critical region. Other asset growth rate provides the t-stats of -0.836 which is still in the non-critical region and NPL growth rate

provides result of 1.867 which is still in the non-critical region therefore all the variables show the rejected values that means they have relationship with each other. ANALYSIS OF BETA

Deposit growth rate analysis (DGR) represents the value of 0.0228 that mean that if loan growth rate expends by 1% then deposit rate could be expected to gain by 2.28% whereas same is the situation with the capital growth rate (CGR) if loan growth rate enhance by 1% then CGR could be reduced by 6.27%. if loan growth rate increases by 1% then it would be possible that OEAGR will decline by 1.07% where as if NPL’s i.e. non-performing loans will increase by 3.4% if loan growth rate will increase by 1%. ANALYSIS OF MULTI-CO-LINEARITY

In some cases, multiple regression results may seem paradoxical. Even though the overall P value is very low, all of the individual P values are high. This means that the model fits the data well, even though none of the X variables has a statistically significant impact on predicting Y. How is this possible? When two X variables are highly correlated, they both convey essentially the same information. In this case, neither may contribute significantly to the model after the other one is included. But together they contribute a lot. If you removed both variables from the model, the fit would be much worse. So the overall model fits the data well, but neither X variable makes a significant contribution when it is added to your model last. When this happens, the X variables are collinear and the results show multicollinearity. To help you assess multicollinearity, In Stat tells you how well each independent (X) variable is predicted from the other X variables. The results are shown both as an individual R 2 value (distinct from the overall R 2 of the model) and a Variance Inflation Factor (VIF). When those R 2 and VIF values are high for any of the X variables, your fit is affected by multicollinearity. The best solution is to

understand the cause of multicollinearity and remove it. Multicollinearity occurs because two (or more) variables are related – they measure essentially the same thing. If one of the variables doesn’t seem logically essential to your model, removing it may reduce or eliminate multicollinearity. Or perhaps you can find a way to combine the variables. For example, if height and weight are collinear independent variables, perhaps it would make scientific sense to remove height and weight from the model, and use surface area (calculated from height and weight) instead. You can also reduce the impact of multicollinearity. One way to reduce the impact of collinearity is to increase sample size. You'll get narrower confidence intervals, despite multicollinearity, with more data. Even better, collect samples over a wider range of some of the X variables. If you include an interaction term (the product of two independent variables), you can also reduce multicollinearity by "centering" the variables. To do this, compute the mean of each independent variable, and then replace each value with the difference between it and the mean. In our research model we are viewing that the value of tolerance level is less than 0.5 and the value of VIF is also less than 10 degree that suggest that the model is not affected by the problem of multi-co-linearity.

4.2 HYPOTHESIS ASSESSMENT SUMMARY

Table 4.4

S.No 1

Hypothesis

t

Sig

Result

2.271

.029

Rejected

-.915

.366

Accepted

6.059

.000

Rejected

7.635

.000

Accepted

.547

.588

Rejected

Hypothesis 1:

There is no significant relationship with loan growth rate and deposit growth rate. 2

Hypothesis 2:

There is no significant relationship between loan growth rate and capital growth rate 3

Hypothesis 3:

There is no positive relationship other asset growth rate and loan growth rate 4

Hypothesis 4:

There is no positive relationship between bank profitability and inflation rate 5

Hypothesis 5:

There is no positive relationship between loan growth rate and NPL’s


FORMULA USED IN RESEARCH ANALYSIS

T-STATISTICS

F-STATISTICS

BANK AL-HABIB VARIOUS GRAPHS GRAPH # 1

(Source: Bank AL Habib Annual Reports ‐ For details see page number 87)

GRAPH # 2


GRAPH # 3


GRAPH # 4


GRAPH # 5


GRAPH # 6


GRAPH # 7


GRAPH # 8


Chapter # 05 CONCLUSION AND RECOMMENDATION RECOMMENDATION

5.1 CONCLUSION

This research approach provided us the role of NPL’s on banks profitability and credit risk assessment moreover it explains that how loan growth rate is a basic driver for the non performing loans. Statistical evaluation was based on the data charts of Bank AL-Habib Ltd which explains the rising trends in non-performing loans as per the diagrammed view mentioned above that explains that deposit rate have gain the high growth which explains the good capital adequacy ratio but still the profit after tax is showing the low trend value which means that there are less number of loans that are provided to the borrowers due to SBP strict regulations as well as it predicts that due to market risk Bank Al-Habib can suffer losses in future as it have high deposit rate but it can be predicted that financer behavior is converting towards bank deposits rather than to take a risk and enjoy good profit. Moreover it can be predicted based on BAHL data that most of the banking industry will suffer losses due to market risk situation and changing behavior of financers.

5.2 RECOMMENDATIONS

Based on our findings, it means that commercial banks should pay attention to several factors when providing loans in order to curtail the level of impaired loans. Specifically, commercial banks need to consider the international competitiveness of the domestic economy since this may impair the ability of borrowers from the key export oriented sectors to repay their loans which in turn would result in higher non-performing loans. These lending institutions should also take the performance of the real economy into account when extending loans given the reality that loan delinquencies are likely to be higher during periods of economic downturn. Finally, banks should

constantly review the interest rates on loans since loan delinquencies are higher for banks which increase their real interest rates.

5.3 FUTURE RESEARCH

Since our results for NPL’s are encouraging, the authors will replicate this study for with different banks in the Caribbean. In order to extend the literature on non-performing loans, the authors plan to incorporate corporate governance and the regulatory environment in our future research. This decision is motivated by two primary reasons: (i) the financial crisis in the Pakistan was blamed on deregulation and the weak regulatory framework in this country; and (ii) ethics seem to at the heart of the financial crisis in the Pakistan. While there are reasons to suspect that other financial crises occurred because of these factors, they were ignored in previous studies. 5.4 LIMITATIONS 9

This study has been conducted by a sample of one bank that is Bank Al Habib it will not reflects the actual picture of all banks.

9

Some important variables are missing which means the model is incomplete.

9

Due to time constraints researcher was unable to find a macro-economic impact of NPL’s

9

This research has been performed on data i.e. from 2001 to 2011 it could be also viewed further by adding the data of 2012

5.5 SOME IMPORTANT LITERATURE, DISCUSSION & OBSERVATIONS

Examining non-performing loans has expanded in line with the interest afforded to understanding the factors responsible for financial vulnerability. This situation may be attributed to the fact that impaired assets plays a critical role in financial vulnerability as evidenced by the strong association between NPLs and banking/financial crises in Argentina, East Asia and SubSaharan African Countries during the 1990s. In this section we review the existing literature so as to formulate a theoretical framework to investigate the determinants of non-performing loans in Guyana. Keeton and Morris (1987) present one of the earliest studies to examine the causes of loan losses. In the latter paper the authors examined the losses losses by 2,470 insured commercial commercial banks in the United States (US) over the 1979-85. Using NPLs net ne t of charge-offs ch arge-offs as the primary measure of loan losses Keeton and Morris (1987) shows that local economic conditions along with the poor performance of certain sectors explain the variation in loan losses recorded by the banks. The study also reports that commercial banks with greater risk appetite tend to record higher losses. Several studies which followed the publication of Keeton and Morris (1987) have since proposed similar and other explanations for problem loans in the US. Sinkey and Greenwalt (1991), for instance, investigate the loan loss-experience of large commercial banks in the US; they argue that both internal and external factors explain the loan-loss rate (defined as net loan charge offs plus NPLs divided by total loans plus net charge-offs) of these banks. These authors find a significant positive relationship between the loan-loss rate and internal factors such as high interest rates, excessive lending, and volatile funds. Similar to the previous study, Sinkey and Green walt (1991) report that depressed regional economic conditions also explain the loss-rate of the commercial banks. The study employs a simple log-linear regression model and data of large commercial banks in the United States from 1984 to 1987.

Keeton (1999) uses data from 1982 to 1996 and a vector auto regression model to analyse the impact of credit growth and loan delinquencies in the US. It reports evidence of a strong relationship between credit growth and impaired assets. Specifically, Keeton (1999) shows that rapid credit growth, which was associated with lower credit standards, contributed to higher loan losses in certain states in the US. In this study loan delinquency was defined as loans which are overdue for more than 90 days or does not accrue interest. Studies that examined other financial systems also provide similar results to those in the US. For instance, Bercoff et al (2002) examine the fragility of the Argentinean Banking system over the 1993-1996 period; they argue that NPLs are affected by both bank specific factors and macroeconomic factors. To separate the impact of bank specific and macroeconomic factors, the authors employ survival analysis. Using a dynamic model and a panel dataset covering the period 1985-1997 to investigate the determinants of problem loans of Spanish commercial and saving banks, Salas and Saurina (2002) reveal that real growth in GDP, rapid credit expansion, bank size, capital ratio and market power explain variation in NPLs.

Furthermore, Jimenez and Saurina (2005) examine the

Spanish banking sector from 1984 to 2003; they provide evidence that NPLs are determined by GDP growth, high real interest rates and lenient credit terms. This study attributes the latter to disaster myopia, herd behaviour and agency problems that may entice bank managers to lend excessively during boom periods. Meanwhile, Rajan and Dhal (2003) utilise panel regression analysis to report that favourable macroeconomic conditions (measured by GDP growth) and financial factors such as maturity, cost and terms of credit, banks size, and credit orientation impact significantly on the NPLs of commercial banks in India.

Using a pseudo panel-based model for several Sub-Saharan African countries, Fofack (2005) finds evidence that economic growth, real exchange rate appreciation, the real interest rate, net interest margins, and inter-bank loans are significant determinants of NPLs in these countries. The author attributes the strong association between the macroeconomic factors and non performing loans to the undiversified nature of some African economies. More recently Hu et al (2006) analyse the relationship between NPLs and ownership structure of commercial banks in Taiwan with a panel dataset covering the period 1996-1999. The study shows that banks with higher government ownership recorded lower non-performing loans. Hu et al (2006) also show that bank size is negatively related to NPLs while diversification may not be a determinant. ECONOMETRIC MODEL AND ESTIMATION PROCEDURE

Based on our review of the literature it is clear that there is extensive international evidence which suggests that NPLs may be explained by both macroeconomic and bank specific factors. In this study we employ a reduced form econometric model that is similar to Jimenez and Saurina (2005) to ascertain the determinants of NPLs in the Guyanese banking sector. The model is a simple linear regression function that links the ratio of NPLs to total loans and key macroeconomic and bank specific variables. The general regression equation is of the form:

lnNPL_Ai,t = β 0i 0i + β 1lnNPL_Ai,t-1 + β 2lnL_Ai,t + β 3SIZE i,t i,t + β 4 ∆ LOANS i,t i,t + β 5 ∆ LOANS i,,t-1 i,,t-1 + β 6 6 ∆ LOANS i,,t-2 i,,t-2 + β 7 ∆GDP t t + β 12 ∆GDP t-1 7lnRIR t + β 8lnRIRt-1 + β 9lnINF t t + β 10 10lnINF t-1 t-1 + β 11 11 ∆GDP 12 ∆GDP t-1+ β 13 13lnREERt + β 14 14lnREERt-1 + η + εi,t i = 1,…N, t= 1,…T

where: lnNPLi,t and lnNPLi,t represent the natural log of the ratio of NPLs to total loans for bank i in year t and t-1; ∆GDP ∆GDP t t and ∆GDP ∆GDP t-1 t-1 represent the annual growth in real GDP at time t and t-1

respectively; lnRIRt and lnRIRt-1 denote the natural log of the real interest rates (measured as the difference between the weighted average lending rate and the annual inflation rate) at time t and t-1; lnREERt and lnREERt-1 indicates the natural log of the real effective exchange rate at time t

and t-1; lnINF t t and lnINF t-1 t-1 indicate the natural log of the annual inflation rate at time t and t-1; SIZE i,t i,t is the ratio of the relative market share of each bank’s assets that capture the size of the

institution at time t ; lnL Ai,t is the natural log of the loans to total asset ratio for bank i in year t; ∆ LOANS i,t , ∆ LOANS i,,t-1 i,t i,,t-1 and ∆ LOANS i,,t-2 i,,t-2 represent the growth in loans for bank i in year t, t1,and t-2 respectively; and

εi,t

is the white noise error error term. In the model, the coefficient β 0i 0i

captures the idiosyncratic behaviour of commercial banks. The fixed effect coefficient allows for detecting those factors affecting NPLs that do not change over time. The model is estimated using pooled least squares with a fixed fixed effect estimator. estimator. Researchers who utilise this estimation technique argue that it is more efficient than the ordinary pooled least squares since it accounts for heterogeneity that is often present in panel datasets.1 In order to minimise the effect of heteroskedasticity, the White robust standard errors are computed. We also estimate our model with the dependent variable on the right-hand side with a lag of one year. This is done to overcome the persistence exhibited by the ratio of NPLs to total loans over our sample period (Figure 1). Additionally, we follow the general to specific approach to arrive at the parsimonious model. The rationale for considering each variable is provided in the ensuing section of the paper.

1 See Hu et al. (2006) and Wooldridge (2009).

MOTIVATION AND DESCRIPTION OF VARIABLES Macroeconomic variables

The existing literature provides evidence that suggests a strong association between NPLs and several macroeconomic factors. Several macroeconomic factors which the literature proposes as important determinants are: annual growth in GDP , credit growth, real interest rates, the annual inflation rate, real effective exchange rate ( REER REER), annual unemployment rate, broad money supply ( M2 M2) and GDP per capital etc. This study only considers the growth in real GDP (ΔGDP), annual inflation (INF) and the real effective exchange rate (REER). There is significant empirical evidence of a negative relationship between the growth in real GDP and NPLs (Salas and Suarina, 2002; Rajan & Dhal, 2003; Fofack, 2005; and Jimenez and Saurina, 2005). The explanation provided by the literature for this relationship is that strong positive growth in real GDP usually translates into more income which improves the debt servicing capacity of borrower which in turn contributes to lower non-performing loans. Conversely, when there is a slowdown in the economy (low or negative GDP growth) the level of NPLs should increase. The literature also provides evidence of a positive relationship between the inflation rate and non-performing loans. Fofack (2005), for instance, shows that inflationary pressures contribute to the high level of impaired loans in a number of Sub-Saharan African countries with flexible exchange rate regimes. According to this author, inflation is responsible for the rapid erosion of commercial banks’ equity and consequently higher credit risk in the banking sectors of these African countries.

There is also evidence in the literature of a positive association between NPLs and real effective exchange rate. Fofack (2005) reveals that changes in the real effective exchange rate have a positive impact on NPLs of commercial banks that operate in some Sub-Saharan African countries with fixed exchange rate regimes. The author argues that this result is due to the large concentration of loans to the export-oriented agriculture sector, which was adversely affected by the appreciation in the currency of these countries during the 80s and early 90s. The macroeconomic variables are included in our econometric model both contemporaneously and with one year lag since adverse shocks from the economy may not impact immediately on the loan portfolios of banks. Except for ΔGDP, the natural logarithms of the macro variables are used to estimate our model. We were unable to take the log of GDP since there were negative growth rates during our sample period. Additionally, while we allow the macroeconomic variables to vary over time they are the same across institutions. BANK SPECIFIC VARIABLES

Apart from macroeconomic variables, there is abundant empirical evidence that suggests that several bank specific factors (such as, size of the institution, profit margins, efficiency, the terms of credit (size, maturity and interest rate), risk profile of banks (measured by several proxies including total capital to asset ratio and loans to asset ratio) are important determinants of NPLs. This study only considers four bank specific variables owing to data availability. These are: real interest rate ( RIR), bank size (SIZE ), ), annual growth in loans ( ∆ LOAN ) and the ratio of loans to total asset ( L_A).

The impact of real interest rates on NPLs is extensively documented in the literature. In fact, several studies report that high real interest rate is positively related to this variable (see for example, Jimenez and Saurina, 2005 and Fofack, 2005). We construct this variable by subtracting the annual inflation rate from the weighted average lending rate of each bank. The variable is included contemporaneously ( RIR RIRi,t ) and with a lag of one year ( RIR RIRi,t-1). Excessive lending by commercial banks is often identified as an important determinant of NPLs (Salas and Saurina, 2002; and Jimenez and Saurina, 2005; Keeton and Morris, 1987; and Sinkey and Greenwalt, 1991; and Keeton, 1999). The variable to capture credit growth is constructed by finding the annual percentage change in the loan portfolio for each bank (ΔLOANS). This variable is introduced into our model contemporaneously and with up to two lags. Like the growth in real GDP, we were unable to take the natural logarithm of (ΔLOANS) since there were periods when some commercial c ommercial banks provided lower credit to the private sector. We expect this variable to have a significant positive relationship with NPLs since the literature shows that rapid credit growth is often associated with higher NPLs. The empirical evidence relating to the impact of bank size on NPLs appears to be mixed. For instance, some studies report a negative association between NPLs and bank size (see Rajan and Dhal, 2003; Salas and Saurina, 2002; Hu et al, 2006). According to these studies, the inverse relationship means that large banks have better risk management strategies that usually translate into more superior superior loan portfolios vis-à-vis their smaller counterparts. There are also studies which provide evidence of a positive association between NPLs and bank size (see Rajan and Dhal, 2003). In this study the SIZE variable is constructed by computing the relative market share of the asset of each commercial bank.

There is also evidence in the literature that shows a strong positive relationship between NPLs and the ratio of loans to asset ( L_A), which captures the risk appetite of banks (see Sinkey and Greenwalt, 1991). The supporting rationale is that banks that value profitability more than the cost of higher risk (represented by a high loan to asset ratio) are likely to incur higher levels of NPLs during periods of economic downturn. In this paper, SIZE and L_A variables are included contemporaneously. In addition, our bank specific variables vary with time and across institutions. In this study we use a panel dataset that consists of firm-level data for six commercial banks that operated during the 1994 to 2004 period.2 The dataset also includes macroeconomic variables such as the annual inflation rate, real effective exchange rate (REER), and annual growth in real GDP over the period of analysis. The firm-level data were obtained from the Annual Reports of Commercial Banks while the macroeconomic variables were obtained form the Bank of Guyana Annual Reports and the International Financial Statistics (IFS). The time period covered by our panel is selected for two primary reasons. Firstly, data for NPLs before 1994 were not available. Secondly, several local commercial banks (in collaboration with the government) embarked on an exercise to restructure their NPLs to the rice producing sector during 2005. This initiative saw a sharp contraction in the impaired assets of the banks and can therefore distort the econometric analysis. STYLIZED FACTS

2

The data for GNCB are excluded from the panel. In 2002 GNCB was merged with GUYBANK, where the former took over the non‐performing loans of the of the latter. The merger of these financial institutions therefore resulted in a significant growth in the aggregate NPLs of the banking sector which was not related to the performance of the of the domestic economy or credit policy of GNCB. of GNCB. Since the aim of this of this exercise is to determine the relationship between the growth in non‐performing loans and key macro‐economic and bank specific variables, GNCB was excluded to avoid the distortion that may be caused by the inclusion of this of this financial institution in our analysis.

The adoption of the Economic Recovery Programme (ERP) in 1989 saw the liberalisation of the Guyanese banking sector and the expansion of the real economy which in turn encouraged many commercial banks to extend credit rapidly to the private sector. The average real GDP growth rate during the 1991-97 was 7.1 percent. The ratio of credit to GDP rose from 19.8 percent in 1991 to approximately 50 percent in 1997. However, these trends were reversed after 1997 due to political instability, a slowdown in the real economy, and unfavourable external circumstances which contributed to a sharp sharp increase in NPLs. The total NPLs of the banking system which amounted to G$786 million in 1994 expanded to reach G$21 billion (or 45 percent of total loans and advances) at end-2001. The strong co-movement between the ratio of NPLs/total loans and our macroeconomic variables ( ΔGDP ΔGDP and REER) depicted in Figure 1 clearly reflects the sensitivity of impaired loans to the real economy and adverse external shocks. The widespread default on loans during the mid-1990s several commercial bank adopted a cautious lending stance. As a consequence, credit growth slowed significantly after 1998 as commercial banks shifted to safer investments, mainly treasury bills. These trends in the lending policies of local commercial banks ba nks are reflected in Figure 2, which shows a contraction con traction in credit growth and the ratio of loans to total assets. Based on Figure 2, credit growth reduced continually from 47 percent during 1995 to below 1 percent in 2004. The ratio of loans to asset also reduced from 45 percent to 31 percent over the corresponding period. period. The strong co-movements between NPLs and the various bank specific and macroeconomic macroecono mic factors are not only clearly clea rly visible from the Figures above but are confirmed by our correlation analysis reported in Table 2, which presents the correlation coefficient between NPLs/total loan ratio and the bank specific and macroeconomic variables for our panel dataset from 1994 to 2004.

In this study we employ a fixed effect panel model to identify the determinants of NPLs of local commercial banks. Tables 3 and 4 summarize the results of our regression model which is estimated using pooled least squares with a fixed effect estimator. Our model is estimated with a balanced panel dataset that consists of both macroeconomic and firm level data from 1994 to 2004. The variable L_Ai,t which represents the risk appetite of the commercial banks is positive and significant at the 15 percent and 5 percent levels of significance in our general and parsimonious models (see Tables 3 and 4). This means that banks which are high risk takers are likely to incur greater levels of NPLs(see Sinkey and Greenwalt, 1987). The variable SIZE i,t i,t (which represents the size of the bank) is positive but insignificant. This evidence which is inconsistent with previous studies (Rajan and Dhal, 2003; Salas and Saurina, 2002 and Hu et al, 2006) can be interpreted to mean that large banks are not necessarily more effective in screening loan customers when compared to their smaller counterparts. Similar to previous studies, however, we find a significant positive contemporaneous association between the real interest rate variable ( RIR RIRi,t ) and NPLs (see Sinkey and Greenwalt, 1991; Fofack, 2005; Jimenez and Saurina, 2005). This indicates that when a commercial bank increases its real interest rates this may translate immediately into higher non-performing loans. The variable which captures the relative credit growth of commercial banks ( ∆ LOANS i,t i,t ) is negative and significantly related to NPLsat time t , t-1 and t-2 respectively. Based on our results, it therefore follows that commercial banks which extend relatively higher levels of credit are likely to incur lower non-performing loans. It is important to note that our results are contrary to the international evidence which suggest a positive relationship between credit growth and NPLs (see Salas and Saurina, 2002 and Jimenez and Saurina, 2005).

Based on Tables 3 and 4, the real effective exchange rate (REER t) is positively and significantly related to NPLs suggesting that the international competitiveness of the domestic economy is an important determinant of credit risk. In other words, whenever there is the deterioration in the competitiveness in the local economy the level of NPLs emanating from the key export oriented economic sectors is likely to increase. This evidence is not only consistent in 2004 but in 2005 but highlights the high levels of NPLs that were reported for the agriculture sector as a result of lower commodity prices in the late 1990s. Consistent with previous studies we also find a significant negative contemporaneous relationship between

∆GDPt

and NPLs (see Salas and Suarina, 2002; Jajan and Dhal, 2003;

Fofack, 2005; and Jimenez and Saurina, 2005). Similar to these studies we interpret our findings to mean that an improvement in the real economy is likely to see an instantaneous reduction in the non-performing loan portfolios of commercial banks. Our results suggest a mixed relationship between inflation and non-performing loans. The variable has a negative relationship with NPLs at time t but a positive impact at time t-1. This means that high inflation in the current period should see a reduction in the level of NPLsin the banking sector. However, high inflation from the previous period causes commercial banks to incur higher non-performing loans. Apart from the mixed effects that inflation appears to exert on NPLsthe coefficients of the inflation variables v ariables are not statistically significant in our regression model.

REFERENCED BY EMPIRICAL STUDIES

REFERENCES 1. Andreeva, Olga (2004). “Aggregate bankruptcy probabilities and their role in explaining banks’ loan losses,” Working Paper 2004/2, Norges Bank, February.

2. Bercoff, Jose J., Julian di, Giovanni and Franque Grimard (2002). “Argentinean Banks, Credit Growth and the Tequila Crisis: A Duration Analysis” (Unpublished).

3. Fernandez de Lis, Santiago, Martinez, Jorge and Jesus Saurina (2000). “Credit Growth, Problem Loans and Credit Risk Provisioning in Spain.” Working Paper No. 0018, Banco de Espana, October.

4. Fofack, Hippolyte (2005). “Non-performing loans in sub-Saharan Africa: Causal Analysis and Macroeconomic Implications.” World Bank Policy Research Working Paper No. 3769, November.

5. Hasan, Ifetkhar and Larry D. Wall (2003). “Determinants of the loan loss allowance: some cross-country comparison.” Bank of Finland Discussion Papers Pap ers No. 33/2003.

6. Hoggarth, Glen (2003). “Assessing the strength of UK banks through macroeconomic stress tests.” Financial Stability Review, Bank of England, June.

7. Hoggarth Glen and John Whitley (2003). “Assessing the Strength of UK Banks through Macroeconomic Stress Tests.” Financial Stability Review, Bank of England, June, pp. 91-103.

8. Hu, Jin-Li, Yang Li and Yung-Ho, Chiu (2006). “Ownership and Non-performing Loans: Evidence from Taiwan’s Banks.” Developing Economies, (Forthcoming).

9. Jimenez, Gabriel and Jesus Saurina (2005). “Credit cycles, credit risk, and prudential regulation.” Banco de Espana, January.

10. Jordan, John S. (1998). “Problem Loans at New England Banks, 1989 to 1992: Evidence of Aggressive Loan Policies.” New England Economic Review, January/February, pp.2438.

11. Kearns, Allan (2004). “Loan Losses and the Macroeconomy: A Framework for Stress Testing Credit Institutions’ Financial Well-Being.” Financial Stability Report, CBFSAI, pp. 111-121.

12. Keeton, William R. (1999). “Does Faster Loan Growth Lead to Higher Loan Losses?” Federal Reserve Bank of Kansas City, Economic Review, Second Quarter 1999.

13. Keeton, William and Charles S. Morris (1987). “Why Do Banks’ Loan Losses Differ?” Federal Reserve Bank of Kansas City, Economic Review, May, pp. 3-21.

14. Pain, Darren (2003). “The Provisioning Experience of the Major UK Banks: a Small Panel Investigation.” Bank of England Working Paper No. 177, January.

15. Rajan, Rajiv and Sarat C. Dhal (2003). “Non-performing Loans and Terms of Credit of Public Sector Banks in India: An Empirical Assessment.” Occasional Papers, 24:3, pp. 81-121, Reserve Bank of India.

16. Salas, Vincente and Jesus Saurina (2002). “Credit Risk in Two Institutional Regimes: Spanish Commercial and Savings Banks.” Journal of Financial Services Research, 22:3, pp. 203-224.

17. Sinkey, Joseph. F. and Mary B. Greenwalt (1991). “Loan-Loss Experience and RiskTaking Behvior at Large Commercial Banks.” Journal of Financial Services Research, 5, pp.43-59.

18. Sorge, Marco (2004). “Stress-testing financial systems: an overview of current mythologies.” BIS Working Papers No. 165, December.

19. Sorge, Marco and Kimmo Virolainen (2006). “A comparative analysis of macro stresstesting methodologies with application to Finland.” Journal of Financial Stability, 2, pp.113-151.

20. Wooldridge, Jeffrey (2009). Introductory Econometrics: A Modern Approach, 4th edition. Mason, OH: South-Western Cengage Learning.

BIBLIOGRAPHY

1.

Agung,

Juda

et.al ,

“credit Crunch in Indonesia in the Aftermath if the Crisis facts, Causes and Policy Implications”, directorate of Economic Research and Monetary Policy, Bank of Indonesia, March 2001 2. Altman, Edward I. & Anthony Sauders , “An Analysis and Critique of the BIS Proposal on Capital Adequacy and Ratings”, Journal of Banking and finance, 2001, 2546 3. Agenor, P.R., J. Aizenman, A.Hoffmaister , “The Credit Crunch in East Asia: What Can Bank Excess Liquid Assets Tell Us?”, NBER Working Paper 7915, Ocotber 2000 4. Arrow, K, “The Theory of Risk Aversion”, In Essays in the Theory of Risk Bearing, New York: Markham, 1971, 90-120

5.

Bank

of

Finland

Institute for Economics in Transition , “BOFIT China Review Yearbook 2004”, 2005,

www.bof.fi/bofit 6.

Bank of Japan , “Japan’s

Nonperforming Loan Problem”, Quarterly Bulletin November 2002; 7.

Bank of Japan , “Bank of

Japan Annual Review 2004” 8.

Barr, R. L. Seiford & T. Siems, “Forcasing Banking Failure: A Non-Parametric Frontier stimation Approach”, Researches Economiques de Lovain (60), 1994, 417-429

9.

Bartel, John & Yiping Huang, “Dealing with the Bad Loans of the Chinese Banks”, Columbia University, APEC Study Centre Discussion Paper Series, July 2000

10.

Berger,

Allen

N.

&

Rpbert De Young , “Problem Loans and Cost of efficiency in Commercial Banks”, Journal of Banking and Finance, Vol. 21, 1997

11.

Berger, Allen, & Udell, Grogory F. , “Did Risk-Based Capital Allocate Bank Credit and cause A “Credit Crunch”

in the United-Sates?’, Journal of Money, Credit and Banking , Vol.26, No.3, Part 2: Federal Credit Allocation: Thoery, Evidence, and History, 1994, 585-628 12. Batrh, James R., Gerard Caprio Jr., & Ross Levine , “Banking Systems Around the Globe: Do Regulation and Ownership Affect Performance and Stability?”, 2001 http://econ.worldbank.org/docs/1079.pdf 13.

Bernanke, Bens, Cara S. Lown & Benjamin M.Friedman , “The Credit Crunch”, Brookings Papers on Econmic

Activity, Vol.1991, No.2 ,1991, 205-247;

14.

Berthelemy,

J.C.

&

Varoudakis, A. , “Economic Growth, Convergency Clubs, and the Role of Financial

Development”, Oxford Economic Papers, New Series, Vol.48 No.2, 1996, 300-328 15. Besanko, David & George Kanatas , “Regulation of Bank Capital: Do Capital Standards Promote Bank Safety?” Journal of Financial Intermediation 5, 1996, 160-183 16.

Borensztein, Eduardo & Jong-wha Lee , ‘Financial Crisis and the Credit Crunch in Korea: Evidence from Firm-

Level Data’, IMF working Paper WP/00/25, 2000 17. Calem, Paul & Rafeal Rob . “The Impact of Capital-Based Regulation on Bank RiskTaking”, Journal of Financial Intermediation 8,1999, 317-352 18. Central Bank of Malaysia , “Bank Negara Malaysia Annual Report 2004”, 23 March 2005 19.

Chan-Lau, Jorge A. & Zhao Hui chen , “Financial Crisis and Credit Crunch as a result of Inefficient Financial

Intermediation – with Reference to the Asian Financial Crisis”, 1998; 20. Chiang, Alpha.C , “Funamental Methods of Mathematical Economics” Third Edition, McGraw-Hill, 1984

21.

Chen, Johnny P. , “Non-

performing Loan Securitization in the people’s republic of China’, Stanford University, May 2004 22. Demirguc-Kunt, Asli & Enrica Detragiache , “The Determinants of Banking Crises in Developing and Developed Countries”, IMF Staff Paper 45 (1), 1998, 81-109 23.

Demirguc-Kunt,

Asli,

Enrica Detragiache, & Poonam Gupta , “Inside the Crisis: An Empirical Analysis of

Banking System in distress”, IMF Working Paper WP/00/56, October 2000 24.

Domic,

Ilker

&

Giovanni Ferri , “The Real Impact of Financial Shocks: Evidence from Korea”, The World Bank, Policy Research Working Paper Series No.2010, October 1998

25.

Dwight, Lawrence ,”The

Role of Non-Performing Loans in china: A Public Finance Approach”, 2004 26.

Ernst

&

Young,

“Nonperforming Loan Report: Asia 2002” 27.

Ernst & Young, “2003

review Non-Performing Loans in China” 28.

Ernst & Young , “Global

Nonperforming Loan Report 2004” 29. Furlong, Frederick T. & Keeley, Michael C. , “Bank Capital regulation and Asset Risk”, Economics Review Number 2, Federal Reserve Bank of San Francisco, spring 1987, 20-40 30.

Ghosh, Swati R & Atish R. Ghosh , “East Asia in the Aftermath: Was There a Crunch”’, IMF working Paper

WP/99/38, March 1999

Appendix-1

Bank AL Habib Ltd Data from the year 2000 to 2011 Rs in Millions Year

Gross Advances

Deposits

Profit Before Tax

Profit After Tax

NPLs

% of NPLs to Advances

2000

14,772

17,822

403

373

117

0.79%

2001

16,004

24,697

551

246

185

1.16%

2002

23,994

34,240

619

290

482

2.01%

2003

35,544

46,178

1,513

1,012

646

1.82%

2004

47,537

62,171

1,039

541

206

0.43%

2005

55,526

75,796

2,022

1,464

383

0.69%

2006

71,036

91,420

2,689

1,761

388

0.55%

2007

79,447

114,819

3,052

2,211

217

0.27%

2008

101,402

144,390

3,579

2,425

863

0.85%

2009

108,373

189,280

4,512

2,856

2,068

1.91%

2010

129,083

249,774

5,656

3,602

2,944

2.28%

2011

120,003

302,098

7,155

4,533

3,204

2.67%

(Source: Bank AL Habib Ltd Annual Reports - Details mentioned below)

Appendix-2

(Source : Bank AL Habib Annual Report)

Appendix-3


Appendix-4


Appendix-5


Appendix-6


Appendix-7


Appendix-8


Appendix-9


Appendix-10


Appendix-11


Appendix-12


Appendix-13


Rising Non-Performing Loans and Banks Profitability in Pakistan Banking Industry

Recommend Documents