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 Credit Risk Assessment policies under SBP

regulations.

Bank AL Habib Ltd as a case study from 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 10 (Corporate, SME & Consumer Loans) 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

3.2.1 Dependant Variable 47

3.2.2 Independent Variable 47

3.2.3 Moderate Variable 47

3.3 Theoretical Frame Work 48

3.3.1 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

3.4 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

APPENDIX 87

Appendix-1 Bank AL Habib Ltd (BAHL) Data Summary 87

Appendix-2 Profit & Loss Account of BAHL for the year 2011 88






Appendix-8 Balance Sheet of BAHL for the year 2011 94






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 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

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:

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.

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

on the debtor’s credit historical evaluation.

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 deposit growth rate.

H1: There is significant relationship with loan growth rate and deposit growth rate.

Hypothesis No. 2

Ho: There is no significant relationship with loan growth rate and capital growth rate.

H1: There is significant relationship between loan growth rate and 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 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. 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 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. 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 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 the 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 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. 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 securities. It is a 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 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 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 will be predicated upon mobilization of

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 at any point in time exceed 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 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:

(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, 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 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:

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 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 credit worthiness of 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 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 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 liquid 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, University 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. 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: 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 decades. 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 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 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 NPLs on GDP growth. They reduce growth estimates 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 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 literature in 2008 when the exchange rate is Rs 59, the 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 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 can now take into account 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 of the banks and DFIs was 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 severed the probability of huge accretion of bad 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 of

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 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 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

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 & 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 2nd 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, load-

shedding 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 banks 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.

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:

1,4,3,2,10, −++++= tititititi NPLGRaOEAGRaCGRaDGRaaLGR (1)

Where the index i is the index for individual banks and t is the index for time period. tiLGR , is

the loan growth rate, tiDGR , is the deposit growth rate in each time period t , tiCGR , is the

capital growth rate, tiOEAGR , is the other assets growth rate, and 1, −tiNPLGR is non-performing

loan growth rate of the previous year.

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 tiDGR , , 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

1,4,3,2,10, −++++= tititititi NPLGRaOEAGRaCGRaDGRaaLGR

Where the index i is the index for individual banks and t is the index for time period. tiLGR , is

the loan growth rate, tiDGR , is the deposit growth rate in each time period t , tiCGR , is the

capital growth rate, tiOEAGR , is the other assets growth rate, and 1, −tiNPLGR is non-performing

loan growth rate of the previous year.

REGRESSION ANALYSIS:

TABLE 4.2:

Model Summary ANOVA

Model R Square Adjusted R Square Model F Sig.

1 .851 .796 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), tiDGR , is the deposit growth rate in each time period, tiCGR , is the capital

growth rate, tiOEAGR , 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:


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

Coefficients

Unstandardized Coefficients

Standardized Coefficients

Collinearity Statistics

Model

B Std. Error Beta t Sig. Tolerance VIF

(Constant) LGR .017 .010 1.689 .113

DGR .000 .000 .0228 .523 .609 .045 9.306

CGR -.001 .000 -.0627 -1.284 .001 .569 1.498

OEAGR -.011 .013 -.0107 -.836 .417 .231 1.146

NPLGR .004 .002 .0340 1.867 .083 .431 2.318

1

Loan Loss Provision/Total Loans

-.300 .128 -.391 -2.346 .034 .379 15.947

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 R2 value (distinct from the overall

R2 of the model) and a Variance Inflation Factor (VIF). When those R2 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 Hypothesis t Sig Result 1 Hypothesis 1:

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

2.271

.029

Rejected

2 Hypothesis 2: There is no significant relationship between loan growth rate and capital growth rate

-.915

.366

Accepted

3 Hypothesis 3: There is no positive relationship other asset growth rate and loan growth rate

6.059

.000

Rejected

4 Hypothesis 4: There is no positive relationship between bank profitability and inflation rate

7.635

.000

Accepted

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

.547

.588

Rejected


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

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

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.

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

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

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 Sub-

Saharan 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 by 2,470 insured commercial

banks in the United States (US) over the 1979-85. Using NPLs net of charge-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 = β0i + β1lnNPL_Ai,t-1 + β2lnL_Ai,t + β3SIZEi,t + β4∆LOANSi,t +β5∆LOANSi,,t-1 + β6∆LOANSi,,t-2

+ β7lnRIRt + β8lnRIRt-1 + β9lnINFt + β10lnINFt-1 + β11∆GDPt + β12∆GDPt-1+ β13lnREERt + β14lnREERt-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; ∆GDPt and ∆GDPt-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; lnINFt and lnINFt-1 indicate the natural log of the annual inflation rate at time t and t-1;

SIZEi,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;

∆LOANSi,t, ∆LOANSi,,t-1 and ∆LOANSi,,t-2 represent the growth in loans for bank i in year t, t-

1,and t-2 respectively; and εi,t is the white noise error term. In the model, the coefficient β0i

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 effect 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), annual unemployment rate, broad money

supply (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 (RIRi,t) and with a lag of one year (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 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 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 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 domestic economy or credit policy of GNCB. Since the aim 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 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 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 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 are reflected in Figure 2, which shows a contraction 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. The strong co-movements between

NPLs and the various bank specific and macroeconomic factors are not only clearly 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 SIZEi,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 (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 (∆LOANSi,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 (REERt) 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 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 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.24-38.

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 Risk-Taking 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 stress-testing 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, 25-

46

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 Risk-

Taking”, 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)

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Appendix-13

rising non-performing loans and banks profitability in pakistan banking industry

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