impact of non-interest income in the profitability of commercial banks of nepal

7/25/2019 impact of non-interest income in the profitability of commercial banks of Nepal

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Impact of Non-Interest Income on Profitability of Commercial Banks in Nepal

Submitted By: Santosh Nepal

Roll No: 12220110

A Graduate Research Report submitted to

Ace Institute of Management

Pokhara University

Submitted for the degree of Master of Business Administration

Kathmandu

September 2015


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ACKNOWLEDGEMENT

There are several people and institutions that I would like to thank, without their support I would

never have finished this dissertation.

I would like to express my gratefulness to my supervisor, Mr. K.B. Manandhar for his great

perspectives, guidance, support and suggestion.

I would also like to thank Dr. Ram Kumar Phuyal for his suggestions and guidance in conducting

an efficient research and his tips on tackling the problem faced during conduction of the

research.

I would also like to thank Mr. Ujjwal Paudel for providing guidance and motivation during

conduction of the research.

Finally, I would also like to thank Mr. Prabhat Uprety (Faculty, Business Research

Methodology) for providing guidance and motivation for the study and also by sharing his

knowledge with me and Mr. Sohan Babu Khatri (Faculty, Financial Management), for his

lectures on various subjects and their continuous guidance in respective topics, without the

knowledge I gained in those lectures, this research would not have been completed.

Santosh Nepal

September, 2015


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CERTIFICATE OF AUTHORSHIP

I hereby declare that this report is my own work and to the best of my knowledge and belief, it

contains no material previously published or written by another person nor materials which to a

substantial extent has been accepted for the award of any other degree of a university or other

institution of higher learning, expect where due acknowledgements.

I also certify that the thesis has been written by me. Any help that I received in my research work

and the preparation of the thesis itself has been acknowledged. In addition, I certify that all

information sources and literature used are indicated in the thesis.

...

Santosh Nepal

Exam Roll No: 12220110

P.U. Registration No: 2012-2-57-0051


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APPROVAL SHEET

Recommendation for Approval

This GRP report prepared and submitted by Mr. Santosh Nepal in partial fulfillment of the

requirements for the degree of Master of Business Administration has been supervised by me and

recommend it for acceptance.

..

Name and Signatures of GRP Advisor

Date...

Acceptance of the External Examiner

I approve the GRP submitted by Mr. Santosh Nepal. The grade sheet has been submitted to the

Dean, School of Business and Pokhara University through the college on a separate evaluation

sheet.

Name and Signature of the External Examiner

Date...

Viva Examination

The candidate has successfully defined the GRP. We recommend it for acceptance. The grade

sheet has been submitted to the Dean, Pokhara University through the college on a separate

evaluation sheet.

External Examiner....

GRP Adviser

Other Members.

Date..


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TABLE OF CONTENTS

ACKNOWLEDGEMENTi

CERTIFICATE OF AUTHORSHIP...ii

APPROVAL SHEET..iii

TABLE OF CONTENTS....iv

LIST OF TABLES.vii

LIST OF FIGURES......viii

ABBREVIATIONS....ix

EXECUTIVE SUMMARY.......xii

CHAPTER ONE ................................................................................................................................. 1

INTRODUCTION............................................................................................................................... 1

1.1 Background of the Study....................................................................................................... 1

1.2. Statement of the Problem.................................................................................................... 6

1.3. Research Objective............................................................................................................... 6

1.4. Significance of the Study ...................................................................................................... 7

1.5. Research Hypothesis ............................................................................................................ 8

1.6. Limitation of the Study......................................................................................................... 8

1.7 Operational Definition /Assumptions ................................................................................. 10

1.8. Organizational Structure of the Study ............................................................................... 12

CHAPTER TWO .............................................................................................................................. 13

REVIEW OF LITERATURE AND THEORETICAL FRAMEWORK ......................................................... 13

2.1. Review of Literature........................................................................................................... 13

2.2. Research Gap...................................................................................................................... 25


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2.3. Theoretical Framework ...................................................................................................... 26

2.3.1 Specification of the Model ........................................................................................... 31

CHAPTER THREE ............................................................................................................................ 32

RESEARCH METHODOLOGY .......................................................................................................... 32

3.1. Research Design and Plan .................................................................................................. 32

3.2 Population and Sample Size ................................................................................................ 33

3.3. Instrumentation of Data..................................................................................................... 35

3.4. Data Collection Procedure ................................................................................................. 35

3.4.1. Primary Data................................................................................................................ 35

3.4.2. Secondary Data............................................................................................................ 36

3.5. Reliability and Validity of Data ........................................................................................... 36

3.6. Analysis and Plan: Method of Data Analysis ...................................................................... 37

3.6.1. Primary Data Analysis .................................................................................................. 37

3.6.2. Secondary Data Analysis.............................................................................................. 37

CHAPTER FOUR ............................................................................................................................. 38

RESULTS AND DISCUSSION ........................................................................................................... 38

4.1. Secondary Data Analysis .................................................................................................... 38

4.1.1. Descriptive Statistics.................................................................................................... 38

4.1.2. Variance Inflation Factor ............................................................................................. 42

4.1.3. Correlation Analysis..................................................................................................... 43

4.1.4. Multivariate Regression Analysis................................................................................. 50

4.1.6. Discussion of the Results ............................................................................................. 52

4.1.7 Variance Analysis .......................................................................................................... 61

4.2. Primary Data Analysis......................................................................................................... 62


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4.2.1 Analysis of Likert Scale.................................................................................................. 64

4.3. Summary of test in Hypothesis .......................................................................................... 67

4.3.1. Based on Secondary Data............................................................................................ 67

4.3.2. Based on Primary Data ................................................................................................ 67

CHAPTER FIVE ............................................................................................................................... 68

SUMMARY AND CONCLUSION...................................................................................................... 68

5.1 Summary of Findings........................................................................................................... 68

5.2 Conclusions.......................................................................................................................... 71

5.3 Recommendation................................................................................................................ 74

BIBLIOGRAPHY .............................................................................................................................. 76

ANNEXURE .................................................................................................................................... 82


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LIST OF TABLES

Table 3.1: List of Selected Commercial Banks 33

Table 4.1: Descriptive Analysis 39

Table 4.2: Summary of Trend Analysis of Selected Bank Variables 40

Table 4.3: Summary of Percentage Analysis of Commercial Banks 41

Table 4.4: Variance Inflation Factor 42

Table 4.5: Correlation Matrix of Selected Bank Variable with ROA and ROE 43

Table 4.6: Multivariate Regression Analysis with Dependent Variable ROA 50

Table 4.7: Multivariate Regression Analysis with Dependent Variable ROE 51

Table 4.8: Variance Analysis of ROA 61

Table 4.9: Variance Analysis of ROE 61

Table 4.10: Analysis of Factors Having High Concentration on NII 63

Table 4.11: Analysis of Variables Affecting the ROA of Commercial Banks 64

Table 4.12: Analysis of Variables Affecting the ROE of Commercial Banks 65


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LIST OF FIGURES

Figure 1.1: Yearly Weighted Average Interest Spread of Commercial Banks ` 4

Figure 2.1: Conceptual Framework 27

Figure 4.1: Analysis of Relevance of Non-Interest Income 62

Figure 4.2: Analysis of Risk in Non-Interest Income 63


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LIST OF ABBREVIATIONS

ADBL : Agricultural Development Bank

BOK : Bank of Kathmandu

CIT : Citizen Bank

DI : Dividend Income

EBIT : Earnings before Interest and Tax

EBL : Everest Bank Limited

EI : Exchange Income

FP : Financial Performance

GC : Guarantee Commission

GDP : Gross Domestic Product

GRD : Grand Bank

HB : Himalayan Bank

HHI : Herfindahl-Hirschman Index

JAN : Janata Bank

KUM : Kumari Bank

LAX : Laxmi Bank

LC : Letter of Credit

LUM : Lumbini Bank

MBL : Machhapuchre Bank


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MFI : Monetory Financial Institution

MTA : Money Transfer Activity

NAB : Nabil Bank

NB : Nepal Bangladesh Bank

NBE : National Bank of Ethiopia

NCC : Nepal Credit and Commerce Bank

NEB : Nepal Bank

NIBL : Nepal Investment Bank Limited

NII : Non-Interest Income

NIM : Net Interest Margin

NMB : NMB Bank

NOI : Non-Operating Income

NRB : Nepal Rastra Bank

OLS : Ordinary least-squares

PB : Prime Bank

PSA : Profit/(Loss) on Sale of Assets

RBB : Rastriya Banijya Bank

REF : Renewal Fee

RF: : Remittance Fee

ROA : Return on Assets

ROE : Return On Equity


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SAN : Sanima Bank

SBI : SBI Bank

SC : Service Charge

SCBN : Standard Chartered Bank Nepal

SID : Siddhartha Bank

SUN : Sunrise Bank


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EXECUTIVE SUMMARY

The main objective of this study is to investigate the relationship between selected internal bank

variables and bank profitability in terms of return on assets and return on equity and to find out

the dominant variables of commercial banks by considering yearly data from 2010-2014 of

profit/(loss) on sale of assets, dividend income, letter of credit, guarantee commission, remittance

fee, exchange income, service charge and renewal fee. Understanding this relationship is

important and worthwhile for all commercial banks managers regarding performance decision of

banks. As the development of banking sectors depends profoundly on strong decision making

that leads to the efficiency and performance.

The study is based on both primary and secondary data. The annual publication of banks were

used as secondary sources and analyzed through excel and statistical tools such as descriptive

statistics, percentage analysis, correlation, multiple regression and trend analysis. The study has

sampled 24 commercial banks out of 30 of the year 2010 to 2014 to examine the relationship

between selected bank internal variables and profitability measured in terms of return on assets

and return on equity. For the collection of primary data, survey technique was done based on the

questionnaire which was distributed to the managers and officers of all the sampled commercial

banks located inside the Kathmandu valley. Convenience and judgmental sampling technique

was used. Two methodologies were used in order to determine the relationships; first a

correlation test on the studied variable. Second, a multivariate regression analysis for the studied

variable where eight independent variables: profit/(loss) on sale of assets, dividend income, letter

of credit, guarantee commission, remittance fee, exchange income, service charge and renewal

fee while return on assets and return on equity treated as dependent variable.

Pearson correlation coefficient show that there is positive correlation between return on assets

with dividend income, letter of credit, guarantee commission, remittance fee, exchange income

and renewal fee whereas there is negative correlation with profit/(loss) on sale of assets. Besides,there is positive correlation between return on equity with dividend income, letter of credit,

guarantee commission, remittance fee, exchange income and renewal fee. In contrast there is

negative correlation with negative correlation with profit/(loss) on sale of assets. In addition, the

multiple regression analysis indicates that guarantee commission, remittance fee, exchange

income, service charge and renewal fee are significant in influencing return on assets and return


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on equity. The result shows the non-significant relationship between profit/(loss) on sale of

assets, dividend income, letter of credit with return on asset and return on equity.

From the primary data analysis, it is found that there is significant relationship between selected

internal bank variables and return on assets and return on equity. And study also found that

service charge has high concentration and dividend income has low concentration on Non-

Interest Income from the qualitative research. This is also important for regulators in order to

assist in the formulation and implementation of policies for future stability in the sustainable

development. This is also of interest to investors in terms of understanding stock price of

commercial bank to make necessary adjustments on their open positions. Knowing which force

leads to high performance can hold in determining the right operational procedures and aware

about what might happen in the financial market.

An implication of this analysis is that bank diversification into non-traditional activities should

be not hazardous. Banking institutions can reap diversification benefits as long as they well-

studied it and know just how much diversification would be necessary to achieve positive result

by considering its specific characteristics, capabilities and the risk level, and as they choose the

right niche.

Keywords: Commercial banks, non-interest income, fee based income, primary and secondary

data, and inferential analysis


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CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

Commercial banks play a vital role in the economic resource allocation of countries. They

channel funds from depositors to investors continuously. They can do so, if they generate

necessary income to cover their operational cost they incur in the due course. In other words for

sustainable intermediation function, banks need to be profitable. Beyond the intermediation

function, the financial performance of banks has critical implications for economic growth of

countries.

The traditional role of commercial banks has centered on intermediation and the generation of

net interest income through two core activities; namely, the collection of deposits on which

banks pay interest and the issuing of loans for which they receive interest income. Good financial

performance rewards the shareholders for their investment. This, in turn, encourages additional

investment and brings about economic growth. On the other hand, poor banking performance can

lead to banking failure and crisis which have negative repercussions on the economic growth.

On the research process, the circular of Nepal Rastra Bank draws attention about the fee based

income. This help to study about the financial literacy and performance of the commercial banks

by non-traditional activities. The researcher was curious to know about whether non-traditional

activities of commercial banks in Nepal will be sustainable for the high performance.

Banks exist to intermediate the transactions between demanders and suppliers of money at a

given consideration. Earnings from these transactions i.e from loans and deposits is traditional

income generating activities. However, critical analysis of financial statements for commercial

banks reveal a different trend, where over 40% of their net operating income comes from non-

intermediation income generating activities. (DeYoung & Rice, 2004)

The researcher intended to do the study on commercial banks because non-traditional incomes

are observed higher than traditional income in Nepalese Market. Therefore to know whether

commercial banks can survive if there is deregulation or any changes in regulation regarding


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non-traditional activities in future is the motivation of the researcher to do study on banks of

Nepal. The researcher intended to study the performance of commercial banks in terms of ROA

and ROE

The literature not only showed the greater function of banks in the economy but also stressed that

without the existence of a sound and efficient banking system, the economy can't function well.

When a bank fails, the whole of a nation's payment system is thrown in to jeopardy (Ikhide,

2000).

On the other front, studies also shown that bank performance also are influenced by the business

cycle or economic performance (Dermerguc and Huizinga, 2001). Both ways of arguments

justify the close link of banks and the economy which instigates the need to understand the

determinants of bank performance from both the bank and the aggregate economy wide

perspectives. Thus, financial performance analysis of commercial banks has been of great

interest to academic research.

The performance of commercial banks can be affected by internal and external factors (Flamini

et al., 2009). These factors can be classified into bank specific (internal) and macroeconomic

variables. The internal factors are individual bank characteristics which basically are influenced

by the internal decisions of management and board. The external factors are sector wide or

country wide factors which are beyond the control of the company and affect the profitability of

banks.

The shift of commercial bank from the traditional activities to the nontraditional activities is due

to regulated market and monetary policy tightened by the Nepal Rastra Bank. The fixed interest

spread leads to bank to diversify its product and services and researchers is motivated to find out

either the non-traditional activities are the major indicators of the profitability of the commercial

banks. Except Standard Chartered Bank Nepal, all commercial banks have brought their interest

rate spread below 5 percent. According to statistics, unveiled by NRB, the weighted interest rate

spread of commercial banks stood at 4.5044 percent which is illustrated by below figure.


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Fig 1.1: Yearly Weighted Average Interest Spread of Commercial Banks

This shift toward noninterest income has contributed to higher levels of bank revenue in recent

years, but there is also a sense that it can lower the volatility of bank profit and revenue, and

reduce risk. One potential channel is that noninterest income may be less dependent on overall

business conditions than traditional interest income, so that an increased reliance on noninterest

income reduces the cyclical variation in bank profits and revenue. Alternatively, expanded

product lines and cross selling opportunities associated with growing noninterest income may

offer traditional diversification benefits for a banks revenue portfolio.

Fundamentally, financial flows of commercial banks are from the intermediation process. It

comprises of interest paid on deposits, interest received from loans and securities and the

resulting net interest margin. However, over the decade commercial banks especial in developing

countries gradually expanded beyond these tradition sources of revenue toward fee earning,

trading profit and loss, commissions and other non-interest income sources. Across all types of

banks, study found increased share of non-interest income may adversely affect the return on

assets and equity. Therefore, researcher intends to find out whether diversification is the best

alternative to ensure good return on assets and equity.

Banks in recent times have changed their focus from depending heavily on interest income to

generating revenue from fee generating activities. It was found that smaller banks are more


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involved in non-interest earning activities, relative to their larger counterparts. The increasing

importance of non-interest income (NII), particularly in recent years, has stimulated research on

the factors which have underpinned its performance. International evidence has shown that bank

characteristics as well as environmental factors such as deregulation, globalization, and

investment in technology and developments in the financial architecture have played a

significant part in explaining trends in NII. More specifically, these bank-specific features

included the composition of the loan portfolio as well as the degree of personal service offered

by the banking institution. The findings for Craigwell and Maxwell (2005) showed that non-

interest income was positively related to both bank profitability and earnings volatility.

Examples, non-interest income include deposit and transaction fees, insufficient funds (NSF)

fees, annual fees, monthly account service charges; inactivity fees, check and deposit slip fees,

etc. Institutions charge fees that provide non-interest income as a way of generating revenue and

ensuring liquidity in the event of increased default rates. Fee income includes a wide range of

sources of income including fund management fees, loan arrangement fees, fees for advice, trust

and custody fees, and commission on sales of third party financial products such as insurance.

This study had used internal factors affecting non-interest income to find out the relationship

between profitability and the selected bank variables. Profit/(loss) on sale of assets, dividend

income, letter of credit, guarantee commission, remittance fee, exchange income, service chargeand renewal fee are those internal variables taken in consideration for the research.

In recent times, Ritter and Udell (1996) found that sources of revenue has became important in

banks as a result it have shifted from traditional interest income to nontraditional sources of

revenue, known as non- interest or fee income. These sources of income have a great growth

significant in non-interest income.

In recent years, though, the distinction between types of banks has become blurred, partly by

takeovers and partly by traditional retail banks going into fee-earning activities. In recent year,

the profitability of traditional banking activities such as business lending and deposit has been

diminished. Thus, the central bank formulated policy to reduce interest rate of treasury bills.


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As a result, there has been a huge shift to non-traditional financial activities as a way of

maintaining their position as financial intermediaries. The changes are relevant for financial

stability. The reason is straightforward. The more unstable is a banks or any other firms

earnings stream, the more risky the firm is.

Analysis of income and expense data of commercial banks shows that the dominant sources of

revenue is loan interest and discount. Fieldman and Schmidt (1999) found that over 20 years

non-interest income has transformed for supportive role into a major contributor of banks

revenue. In Kenya, the figure reflects downsized interest income despite growth in non-interest

income.

Omuodo (2003) found that as pressure mounts on the banking industrys profitability resulting

from over reliance on interest income by banks, it is strategically imperative that banks focus on

other revenue streams. National Industrial Credit Bank (NIC) has introduced new products to

diversify revenue and to keep its head above the water. Further, researcher stated that part of NIC

banks strategy has been to diversify revenues, by expanding the scope of its activities in

addition to its predominant asset finance focus and offering more general commercial banking

facilities and other products. Premium financing and provision of custodial services have

reduced over reliance on interest income.

The consequences of noninterest income for the financial performance of commercial banks are

not well understood. All else equal, an increase in noninterest income will improve earnings but

an increase in noninterest income seldom occurs without concomitant changes in interest

income, variable inputs, fixed inputs, and/or financing structure.

During 1990s noninterest income trended up, was generally believed that shifting banks

income away from intermediation-based activities in which bank income was subject to credit

risk and interest rate risk, and toward fee-based financial products and services, would reduce

banks income volatility. Moreover, it was conventionally believed that expansion into new fee -

based products and services reduced earnings volatility via diversification effects. But recent

empirical studies indicated that neither of these beliefs holds on average. (Jin & Young-Jae,

2009).


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1.2. Statement of the Problem

The globalization and financial de-regulation in banking sector prolonged the banking activities

produced different diversified product and services. The concentration on banking services in

recent year had been shifted from traditional activities to non-traditional activities. Most of the

Nepalese commercial bank is enjoying around half of its net income from non-traditional

activities based on sale of assets, dividend income, letter of credit, guarantee commission,

remittance fee, exchange income, service charge and renewal fee. In this regards, the research on

the Nepalese commercial banks would lead us to know the impact of non-interest income on the

performance. The study intends to explore the relationship between selected bank internal

variables and profitability. This research is focused on commercial banks of Nepal. In order to

know how banks are enjoying surplus of non-interest income and concentration of income in

non-traditional activities, this work is carried out.

Therefore the study focuses on following issues:

Is there any relationship between the profitability of bank and the selected variables

(bank internal variables)?

Which variable plays most important role (concentration) in determination of profitability

of bank?

1.3. Research Objective

The study has specified following objective:

To study the different dominant factors influence to Non-Interest Income.

To test the relationship between selected variables and financial performance (ROA &

ROE).


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1.4. Significance of the Study

This study includes the variable relating to performance of bank that is ROA and ROE. Thus the

purpose of the present study is to investigate whether there is relationship of selected bank

internal variables with performance of commercial Bank by using yearly data spanning form

year 2010-2014. Therefore this research will contribute to the researchers individual career

development as well as it is mainly concern for the academic purpose. Further the finding of the

study may have important implication for investors, decision makers, and other stake holders as

well. In addition, the present work improves the earlier studies performed in the Nepalese

context and offers the value addition to the existing literature.

The study will enable individual bank to evaluate interest and noninterest income and the

significant to its operation. To identify other forms of non-interest income organization may

venture into to enable the organization increase profitability and income stability.

The research will contribute to body of knowledge by documenting the contribution and

relationship of non-interest income to the whole organization and the profitability in financial

institution and enhance further research on the same.

The information will enable shareholder to know that their investment are yielding return and

also encourage investor to invest in the commercial banks that are diversifying portfolio. Howthe diversification will provide banks future profitability.

Bank managers income and professional reputations are clearly linked to bank earnings and

hence high instability or volatility of earning will fare poorly on their performance on the

extreme it will lead to insolvency. The benefit of this study is to provide the management to

rethink about the income diversification and investment management portfolio. It will also help

management to research the alternative way of non-traditional activities. The further practice of

traditional activities rather than fee based income will also help management to provide easyaccess of services to customers, suppliers, community and investors. The study will also help

management to create spirituality of work over employees to practice entrepreneurial in banking

activities.


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Bank regulators are vested with the responsibility of protecting the payment systems and also

protection of the customer from bank failure this necessitate bank to lay down mechanism of

measuring banks stability through its earning. This occurs when there is unstable earning. The

results of this paper may be useful to the financial institutions, Non-Governmental organization

and the Government of Nepal. It may provide a guide to remedial regulatory schemes and

supervisory programme to support the operation of financial institutions

The study will also help government to know either fee based income is an instrument of

sustainable economic development. As we are aware banking sectors have a key role in national

economic transformation process. The further study will help government to explore other

investable areas rather than practicing fee and commission based activities in the banks. Further

may give direction to the donor agencies, entrepreneurs and business people and importantly,

fills the research gap and provide further data for scholars.

1.5. Research Hypothesis

This research observes the relationship of selected variables with ROA and ROE. In order to

achieve the objective of the study the following hypothesis are developed:

HoROAi : There is no significant relationship of ith variable with ROA.

HoROEi : There is no significant relationship of ith variable with ROE.

where,

i= profit/(loss) on sale of assets, dividend income, letter of credit, guarantee commission,

remittance fee, exchange income, service charge and renewal fee.

1.6. Limitation of the Study

There are number of macroeconomic variables which have direct and indirect influence on

performance on commercial banks. However, this study only considers profit/(loss) on sale of

assets, dividend income, letter of credit, guarantee commission, remittance fee, exchange

income, service charge and renewal fee.


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Internal factors such as capital size, size of deposit liabilities, size and composition of credit

portfolio, interest rate policy, labor productivity, and state of information technology, risk level,

management quality, bank size and moderating variables such as liquidity ratio, leverage ratio,

stock market index, exchange rate, off-balance sheet items, investment portfolio and monetary

policy also plays important role in determining the performance of bank. Due to the time

constraint these variables has not been taken in consideration. It may be useful for future studies

to include either economic variable that might influence the performance in short run so that

dynamics between the variables could be addressed properly. However given that this research

was a preliminary investigation without much literature precedent.

The study has following limitations:

The study covers only 24 out of 30 commercial banks. Banks with recent mergers and

banks whose 5 years data are unavailable are excluded from the study.

Macroeconomic variables like GDP, inflation has not been taken in consideration.

Internal factors such as capital size, size of deposit liabilities, size and composition of

credit portfolio, interest rate policy, labor productivity, and state of information

technology, risk level, management quality, bank size and moderating variables such as

liquidity ratio, leverage ratio, stock market index, exchange rate, off-balance sheet items,

investment portfolio and monetary policy has not been taken into consideration. Conducting the research for academic purpose has always the limited time so due to

limited time frame, the in depth analysis in the subject matter may not be possible to

carry out.

In some cases, the data for individual variables could not be found among the sampled

bank and in this case the analysis is done neglecting the unavailability of data.

The primary information is based on the responses from 90 respondents.

Reliability of the study is based upon the accuracy of published data and the data given

by general respondent.


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1.7 Operational Definition /Assumptions

Performance

Performance of the financial institution is measured by the return on assets, return on

equity. The indicators of the performance for the research studies are return on assets and

return on equity.

Time Series

A Time series is a collection of observations of well defined data items obtained through

repeated measurements over time.

Correlation

Correlation is the statistical analysis that defines the variation in one variable by the

variation in another, without establishing cause and effect relationship. The coefficient of

correlation is a measure of the strength of the relationship between the variables: that is

how well changes in one variable can be predicted by changes in another variable.

Auto-Correlation

Autocorrelation is the correlation (relationship) between members of a time series of

observations such as yearly selected bank variables.

Regression

Regression Analysis is a statistical tool for the investigation of relationship between

variables.

Trend Analysis

Trend analysis is an aspect of technical analysis of the data that tries to predict the future

movement of historical data. Trend analysis is based on idea that what has happened in

the past gives us an idea of what will happen in the future.

Profit/ (Loss) on Sale of Assets

Profit/ (loss) on sale of assets of commercial bank is defined as the profit or loss of land

and building and assets owned by commercial bank. The assets are classified as land,

vehicles, buildings, intellectual property and default loan property. The profit is earned


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when the market price is greater than the cost price owned and loss vice versa. A gain

resulting from selling an asset at a price higher than the original purchase price.

Dividend Income

Dividend income is earned from the dividend paid by company listed in Nepalese

Market. As per NRB directives, commercial bank can invest in insurance companies,

hydropower, microfinance, hotels and other manufacturing companies listed.

Letter of Credit

Letter of credit means an instrument issued by a bank to another bank instructing to

accept cheque, draft, hundi or bill of exchange drawn by specified person up to the limit

of specified amount. It includes Telex or communication charges to buyers bank

couriers, postage reimbursement bank charge, controllable fees and buyers letter of

credit.

Guarantee Commission

A contract of guarantee is a contract to perform the promise or discharge of liability of

third person in case of his default. In other words, if the debtor fails to settle a debt, the

bank will cover it. This is the commission for the bank on behalf of being guarantee.

Bank Guarantee is generally of two types: financial and performance guarantee.

Remittance Fee

Remittance transaction includes a fee charged by the sending agent, who is paid by the

sender and a currency conversion fee for delivery of local currency to the beneficiary in

recipient country. In such a transaction, money transfer operators require the beneficiary

to pay a fee to collect remittances. This fee may be charged to account for frequent

exchange rate movements.

Exchange Income

Exchange income is the income earned from purchase and sale of foreign exchange or the

acts of borrowing, giving credits, and of accepting or providing foreign exchange in any

manner. In finance, an exchange rate between two currencies is the rate at which one

currency will be exchanged for another. The commercial bank earned income through the

fluctuation in exchange rate and book up foreign currency.


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Service Charge

Service charge is a fee incurred by a company for the expenses associated with its

account transactions. The term service charge covers all charges and fees made by a bank

to their customers. In common parlance, the term often relates to charges in respect of

loan and services its rendered

Renewal Fee

Renewal fee comprises of renewal of overdraft account, loan, account renewal, insurance

renewal, card renewal and other services rendered through commercial bank.

1.8. Organizational Structure of the Study

The report on this study consists of five chapters. Second chapter deals with review of literature

where in depth analysis of existing selected literature about the subject matter is reviewed. This

includes Review of Literature, Research Gap and Theoretical Framework. Third chapter deals

with the Research Methodology which includes six sub heads namely, Research Design and

Plan, Population and Sample Size, Instrumentation of Data, Data Collection Procedure,

Reliability and Validity of Data, and Method of Data Analysis. Fourth chapter includes about

Data Presentation and Analysis. Fifth chapter is dealt with Summary of Findings, Conclusion

and Recommendations.


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CHAPTER TWO

REVIEW OF LITERATURE AND THEORETICAL FRAMEWORK

2.1. Review of Literature

Stiroh (2002) made an effort to study the link between the growing reliance on noninterest

income and the volatility of bank revenue and profits in the U.S. banking industry using panel

data from the year 1970 to 2001 adopting regression model for quantitative analysis.. The major

finding of the study was that both aggregate and bank data provide little evidence that this shift

offers large diversification benefits in the form of more stable profits or revenue. At the

aggregate level, declining volatility of net operating revenue reflects reduced volatility of net

interest income, rather than diversification benefits from noninterest income, which is quite

volatile and has become more correlated with net interest income. At the bank level, growth rates

of net interest income and noninterest income have also become more correlated in recent years.

The study showed that there is a clear negative association between noninterest income shares

and profits per unit of risk in terms of bank risk and return.

Smith, Staikouras and Wood (2003) made an effort to examine the variability of interest and

non-interest income, and their correlation, for the banking systems of EU countries for the years

1994-98 using time series data. The study used 2,655 financial institutions of 15 EU countries by

using the balanced sample adopting time series and cross sectional analysis. The objective of the

study was to find the profitability and risk of noninterest activities relative to interest-bearing

banking activities, and the potential diversification benefits of non-interest activities to a banking

firm. The study found that non-interest income is much more volatile than interest income,

variability of non-interest income increases, and there exists a negative correlation between

interest and non-interest income in several countries. Another major finding of the study was that

the average non-interest income increases from 0.88% of total assets in1994 to 1.09% in 1998.

Only in Spain, Greece, Finland, Portugal, and Sweden non-interest income as a proportion of

total assets decreased in the last two years. The volatility of non-interest income was higher than

the volatility of interest income for all EU countries. So, although net interest income levels are

much higher than the respective for non-interest income, the volatility of the non-interest sources

of income is much larger.


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DeYoung and Rice (2004) made a study on the effect of non-interest income on operating

income in the US commercial banks from the year 1989 to 2009. The main objective of the study

was to find the empirical link between bank non-interest income and financial performance. The

study found that noninterest income now accounts for over 40% of operating income in the U.S.

commercial banking industry. The result indicates that well-managed banks expand more slowly

into noninterest activities, and that marginal increases in noninterest income are associated with

poorer risk-return tradeoffs on average. The study suggest that noninterest income is coexisting

with, rather than replacing, interest income from the intermediation activities that remain banks'

core financial services function.

Craigwell and Maxwell (2005) made an effort to study the trends in non-interest income at

commercial banks in Barbados between 1985 and 2001 using panel data regression model. The

main objective of the study was to investigate the determinants of non-interest income and its

impact on commercial bank financial performance. The study found that the incidence of

noninterest income in Barbados declined over the period 1985 to 2001, contrary to other

countries in the Caribbean and the wider developed world. Apparently, most of the major factors

that cause banks in the developed world to generate more non-interest income, like deregulation

and technological change for the development of loan securitization. Study concluded that the

result for Barbados may be attributed to the absence of some of the factors that were pinnacle to

the generation of noninterest income in developed countries, such as deregulation and

technological change, especially for the development of loan securitization and credit scoring.

Furthermore, the study found that increases in non-interest income are linked to greater bank

profitability but also to higher earnings volatility.

Lozano and Pasiouras (2008) made an effort to study the impact of non-traditional activities on

the estimation of bank efficiency by using unbalanced dataset of sample of 4,960 observations

from 752 publicly quoted commercial banks operating in 87 countries between 1999 and 2006.

All bank-specific data were obtained from Bank scope database of Bureau van Dijk and were

converted to US dollars where unconsolidated data were selected wherever available. The

Battese and Coelli model was used to investigate the inclusion of proxies for non-traditional

activities as an output in studies of bank efficiency. The major contribution of the study is the

average efficiency increases with the inclusion of either the off balance sheet or the non-interest


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income in the output vector. The inclusion of non-interest income in the profit function results in

mean efficiency scores that are significantly higher than the ones obtained from the traditional

model B1 in all cases. Researcher have found that on average cost efficiency increases whether

researcher use OBS or non-interest income as an indicator of non-traditional activities. The

results imply that the estimation of traditional models that do not account for non-traditional

activities through non-interest income will underestimate both cost and profit efficiency.

However, the impact of OBS on efficiency is in most cases insignificant and its influence can be

either positive or negative and varies across geographical regions and level of economic

development, as well as the specification of the model.

Kick and Busch (2009) made an attempt to study the determinants of non-interest income and its

impact on financial performance and the risk profile of German banks between 1995 and 2007.

The study has used data from the Bundesbanks prudential database which incorporates

information derived from bank balance sheets and the supervisory reports of individual German

banks, Bundesbanks borrowers statistics, and credit register for loans of 1.5 million euro or

more. The study has focused on the primary income sources of banks: interest and fee business.

An essential aspect of the analysis is the treatment of endogeneity between banks risk-return-

characteristics and their activity in fee income generating operations. For the treatment of

endogeneity the study has specified two models: one is a fixed-effects panel model with

regressors lagged by one year and, the second is a two-stage least squares estimator. The latter

approach allowed the researcher to analyze the factors determining a banks participation in the

fee income business. Thus the study applies both panel fixed-effects and cross-sectional two-

stage least squares models. The major finding of the study is that banks with a large share of fee

income exhibit a more favorable risk-return profile, i.e. they enjoy a higher risk-adjusted return

on equity (ROE) and total assets (ROA). Additionally, the study also found that for commercial

banks, strong involvement in the fee business is accompanied by higher ROE and ROA-volatility

and, therefore, with increased risk. In particular for commercial banks some fee income activitiesare associated with much higher risks than other income sources and, therefore, they could

contribute to destabilize both individual banks as well as the whole banking system.

William and Prather (2010) made an effort to study the impact on bank risk of portfolio

diversification between traditional margin income and feebased income for banks operating in


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Australia. The study considered several performance variables, which analysis compares the

benefits of diversification across different bank types relative to margin income and fee income.

Further, regression analysis considered bank risk and revenue concentration. The major finding

of the study was that feebased income is riskier than margin income but offers diversification

benefits to bank shareholders. While improving bank riskreturn tradeoff, these benefits are of

second order importance compared to the large negative impact of poor asset quality on

shareholder returns. The major contribution of the study was that its results suggest that

shareholders of banks will benefit from increased bank exposure to non interest income via

diversification. From a regulatory perspective, diversification reduces the possibility of systemic

risk, but caution must be offered with respect to banks pursuing absolute returns rather than

monitoring riskreturn tradeoffs, and so exploiting the benefits of the implied guarantee offered

by too big to fail.

Hong (2010) made an effort to analyze the ratio of non-interest income in the business income

and return on equity (ROE) using time series data based on the quarter data from 2002 to 2009 of

China Merchants Bank adopting augmented dickey-fuller test and co integration rank test. The

major contribution of the study was that there exists a co integration relationship between the

ratio of non-interest income in the business income and return on equity (ROE). To optimize the

income structure through developing the non-interest income business is the inevitable choice for

commercial banks under the increasingly fierce competition, which has become a worldwide

tendency for commercial banks. The study shows that Chinese commercial banks should take

measures to continuously improve the non-interest income business so as to enhance their

operation performance, and to narrow the gap with the commercial banks in developed countries.

The short-term fluctuation of ROE results from two factors, one is the short-term fluctuation of

NONINT, other one is the equilibrium error which indicates the degree the variable ROE

deviates from its long-run equilibrium. The short-term fluctuation of non-interest income

business has a significant impact on the short-term fluctuation of the operation performance of

commercial banks. Therefore, the study concludes that the commercial banks should maintain

the stable development of the non-interest income business so as to avoid excessive fluctuations

of the operation performance of the commercial banks due to the excessive fluctuation of non-

interest income.


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Mata (2010) made an effort to investigate the institutional factors that influence the MFI

decision-making process of entering the remittances market using observations of 225 MFIs

from 18 Latin American and Caribbean (LAC) countries considering the operational, managerial,

and financial performances as a potential explanatory factor. Results exhibit that financial

performance has the highest impact on the MFIs decision to diversify by offering a remittances

service. The major contribution of the study was that, MFIs with a higher probability to enter the

money transfer market are those that have better access to liabilities, including deposits (so they

have access to funds to invest in the new activity), while their operational capacities and their

profitability are not determinants in the decision-making process and should probably be

analyzed as a result of the decision. The result suggest that all other things being equal, the

probability of having an MFI operating on the remittances market will positively depend on its

capacity to finance investment (and probably operational) costs related to the new activity. The

study also suggests that MFIs enter the market because this could have an effect on their

profitability (considered as a result of the decision, not as a determinant). This implies that, to

some extent, as long as financial resources are available, MFIs will tend to launch a MTA

(Money Transfer Activity), regardless of the operational capacities and the managerial

performances available in the institutions.

Tapper (2010) made an effort to study the inter-linkages between non-interest income, financial

performance and the macro-economy to Jamaican panel data for the period March 1999 to

September 2010.The study also investigates the determinants of non-interest income in a context

of the increasing reliance by banking institutions on revenue generation from non-interest

income activities. The model used was seemingly unrelated regression (SUR) estimation method.

Also Herfindahl-Hirschman index (HHI) has been included in the analysis. The study shows that

ATM technology, personal lending and loan quality are among the main microeconomic factors

driving the performance in non-interest income in the commercial banking sector. Regarding the

macroeconomic environment, interest rate and foreign exchange rate volatility were the keyfactors which explained the performance in non-interest income. The study also found that

stronger performance in non-interest income not only leads to increased profitability but also

increased variability in performance. Also the finding for large banks shows that lower earnings

on investments lead to increases in service charges from loans and may reflect more aggressive

loans expansion by these increase institutions to increase fee income.


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Teimet et al., (2011) made an attempt to study income source diversification and financial

performance of commercial banks in Kenya. The main objective of the study was to find the

relationship between income source diversification and the performance of bank. The study used

secondary data and longitudinal approach to study the 5 years trends of income source

diversification. Diversification and focus were analyzed using HHI. The findings of the study

were ROA and ROE shows weak positive linear relationship with the ratios of earnings before

interest and tax to Asset and Equity respectively. Findings from the results of regression indicate

that all financial performance measures (NOI, EBIT, ROA, and ROE) reveal positively linear

relationship with diversification level, implying they increase with increase in diversification

level. ROA and ROE shows weak positive linear relationship and the model reveal HHI accounts

for little variance in ROA and ROE since ROA and ROE are ratios of EBIT to Asset and Equity

respectively.

Yang and Wu (2011) made an effort to test how non-interest income affects U.S. commercial

banks profitability for recent decade. The main objective of this research is to find out the

relationship between the banks Non-Interest Income and their profitability. The study uses

regression method to conduct an empirical estimation for the empirical data from Wharton

Research Data Services (WRDS). The data covers all the US Bank holding companies that had

reported to the Bank Regulatory from the year 2000 to 2010. The study uses two time segments

to conduct regression and analysis: one is year 2000 to 2006 (pre-crisis period), another period is

from year 2007 to 2010, the recent data. The study shows that, for asset value larger than one

billion, the traditional banking business (borrowing and lending) is not supporting the banks

profitability if the bank use ROA and ROE as the measurement. On the other hand no matter

what size the bank is, Non-Interest Income would always support the ROA and ROE of a bank

before and after financial crisis. The major finding of the study was that in the U.S. commercial

banking system, non-interest income contributes to as much as over 40% of net operating

income, compared to only 20% in 1980, which demonstrates non-interest income is playing avery important role. The results show that banks with higher non-interest income normally have

stronger power of profitability. It also indicates that the impact of non-interest income on bank

performance can be different, depending on how performance is measured.


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Karakaya and Er (2012) made an effort to study determinants of bank profitability and the

relation between noninterest (nonprofit) income and bank performance for an emerging market,

Turkey using 6 years data between 2005-2010 of the banks using cross sectional, time series and

panel data adopting random effects model and fixed effects models. The study comprises of 30

banks, 26 of which are commercial banks and 4 participation banks whose uninterrupted data

were available for the analysis period. The results indicate that banks and participation banks

which have adequate capital and which are large in size gain higher profits from their assets and

Noninterest (nonprofit) income margin increases equity capital profitability of banks. The study

shows that noninterest income which is the main revenue factor of commercial banks and

nonprofit income which is the main revenue factor of participation banks have effect on equity

capital profitability.

Erji et al., (2012) made an effort to investigate the effect of non-interest income, which includes

commission earned, trustee fee, and exchange gains on mean and variation of bank profits in

Taiwans banking industry. The study selected all available Taiwan commercial banks on an

annual basis from 1992 to 2009. Stirohs method was employed to estimate the diversification

effect of non-interest income instead of DEA method. The study analyzed the aggregate

volatility and cyclicality and then the cross-sectional correlation and bank-specific correlation

and set up an econometric model to analyze the impact on bank risk and return from increasing

non-interest income. At the aggregate level, Taiwan banking industry shows that non-interest

income appears much more volatile than net interest income, especially after 2002. At the bank

level, this study explores how there are diversification benefits from increasing non-interest

income, but not in large banks. There is positive significance to explain how diversification

benefits will decline as non-interest income enlarges. By analyzing the econometric model, the

continued expansion in the component of non-interest income will lower risk-adjusted returns.

From these overall results, the study concludes that inflation, along with nontraditional activities,

may not have diversification benefits in Taiwans banking industry. The study shows thatincreasing non-interest income shares will not improve profitability and diminish risk.

Nguyen (2012) made an effort to examine the determinant of bank net interest margin (NIM) and

non-traditional banking activities (NII). A system estimation approach was employed to control

for the simultaneity between NIM and NII for commercial banks in a group of 28 financially


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liberalized countries during the period between 1997 and 2004. Researcher found a statistically

significant negative relationship between NIM and NII for the period between 1997 and 2002. A

generally positive but statistically insignificant association between NIM and NII was found for

the subsequent period (20032004). Banks increasing involvement in non-traditional activities

was negatively correlated with risk-adjusted profitability measures in the former sub period,

suggesting no obvious diversification benefits. However, the share of noninterest income was

positively related to the return on assets (ROA) and the return on equity (ROE) for the latter

subsample.

Karanja (2012) made an effort to examine the relationship between non interest income and

financial performance of commercial Banks in Kenya. The study used a descriptive research

design of 43 commercial bank and one mortgage finance bank for the period of five (5) years

from 2007 to 2011 both years inclusive. The study used secondary data to investigate the

relationship between independent and dependent variables. The data was analyzed using

descriptive analysis, correlation analysis and regression analysis. This study used multiple linear

regression technique in data analysis. The major finding was that the banks with higher non-

interest income shares tend to exhibit contemporaneously higher ROA and equity-asset ratios.

Non-interest income expansion of commercial banks raises profit variability. While more

profitable banks tend to exhibit higher non-interest income ratios, banks with higher non-interest

income ratios do not necessarily show subsequently higher profitability. The study found a

positive significant relationship between noninterest income and financial performance of

commercial banks in Kenya. The data analysis confirms a non-linear relation between the non

interest income and financial performance.

Kiweu (2012) made an effort to investigate whether diversification of income sources for

Kenyan banks leads to better earnings and reduced individual bank and systemic risks. The main

objective of the study was to analyze the extent to which observed shift toward fees income

generating activities has improved bank performance and reduced volatility of revenue. The

study employs panel data on Kenyan commercial banks covering the years 2000 2012. To

measure income diversification, the Herfindahl Hirschmann Index (HHI) was computed for all

banks to account for diversification between the two major types of income generating activities

i.e. interest and non-interest income. The major contribution of the study was that established


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bigger banks are more diversified than small banks and tend to have higher returns. Findings

were clear that non-interest income is much more volatile than interest income as observed over

the sample period. Given increase in fee based income, Kenyan banks can expect increased

volatility in bank earnings and less benefits from income diversification. The findings also

revealed that lending rates were significantly correlated with net interest income.

Crouzille, Tacneng and Tarazi (2013) made an attempt to examine the impact of bank revenue

diversification on the performance of banks in an emerging economy. The study used two-way

fixed-effects panel regressions for the sample of 39 commercial banks (23 domestic and 16

foreign banks), in the Philippines from 1999 to 2005. The study also used the appropriateness of

estimation method using the Hausman test to check whether a fixed effects model is more

appropriate than a random effects model. The study found that most of the non-interest income

was drawn from trading activities (45.30%) compared to fee-based activities (38%).Trading in

government securities and foreign exchange profit were the largest source of trading income

(30.60% and 51.50%), while service charges dominated the fee-based income sources (61.40%).

Larger banks presented a higher level of non-interest income in total operating income (38.16 %

for large banks and 39% for medium-sized banks) than small banks (32.22%). Foreign exchange

profit and trading of government securities are the two main sources of trading income for all

types of banks. The major contribution of the study was that Philippine banks have a different

non-interest income structure. For an average Philippine bank, the share of trading activities in

non-interest income is relatively higher compared with an average U.S. bank. Whereas most of

the fee-based income is obtained from traditional bank intermediation activities, trading income

is nontraditional as its growth is less correlated with net interest income growth. From a standard

portfolio approach, the study indicates that there might be higher diversification benefits from

shifts towards trading income activities rather than shifts towards fee-based income activities.

Moussu and Arthur (2013) made a study on return on equity (ROE) as a central measure of

performance in the banking industry with a sample of 273 banks from 28 countries. The main

objective was to determine the relationship between ROE, risk and non-interest income. The

study used simple OLS model, where the dependent variable is the buy-and-hold return in the

crisis and the main independent variable is the ROE of the banks before the crisis and control

variables as bank capital measures, additional funding structure variables and the share of non-


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interest income activities in the banks operating income. The study has focused on the

importance of non-traditional activities using the ratio of non-interest income to total operating

income. Non-interest income includes net fee income, net commission income and net trading

income. The nature of the activities has been proven to have a significant impact on bank risk.

The statistics for the non-interest income are comparable to those for deposits. The average share

of non-interest income was around 38.7% but the standard deviation is rather high (22.7%),

revealing again substantial heterogeneity in the nature of activities across banks. It was

concluded that ROE was not only the main measure of bank performance from a risk

management perspective, ROE could be a good performance measure if the measurement and

disclosure of risks led to a perfect adjustment of the level of bank equity.

Kohler (2013) made an effort to analyze the impact of banks non-interest income share on risk

in the German banking sector for the period between 2002 and 2010 using panel data adopting

regression model for descriptive analysis. The study used 461 savings banks (4,026

observations), 1,291 cooperative banks (11,132) and 192 other banks (1,602) for sample. The

study used linear and quantile regression estimators for analyzing data. The study found that

banks with a more traditional, retail-oriented business model such as savings banks, cooperative

banks and other retail-oriented banks become significantly more stable if they increase their

share of non-interest income, while investment-oriented banks become significantly less stable.

The study also found that the impact of non-interest income on risk depends on the business

model of a bank. Overall, the study indicates that retail-oriented banks should increase their

share of non-interest income to become more stable. Investment-oriented banks, in contrast,

should decrease it. The result of the study imply that banks are significantly less risky if they

have a more balanced income structure and neither depend heavily on interest nor on non-interest

income. Also the study shows that decomposition of non-interest income into fee and

commission and trading income shows that impact on bank stability comes from fee and

commission income.

Ongore and Kusa (2013) made an effort to study on the determinants of financial performance of

commercial banks in Kenya. They have conducted the study of 37 commercial banks. The main

objective of this study was to find the relationship between the dependent (ROA, ROE, NIM)

and explanatory (bank specific and macroeconomic) variables. The main finding of the study


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was that there is no significant effect of the of bank specific factors on the financial performance

(dependent variables) of commercial banks. The liquidity variable as one of the bank specific

factor, which is positively related with bank performance, has no significant effect on the

financial performance of commercial banks in Kenya. A multiple linear regression model and t-

statistic were used to determine the relative importance (sensitivity) of each explanatory variable

in affecting the performance of banks. The moderating effect of ownership identity was also

evaluated by using ownership as a dummy variable.

Damankah, Tsede and Amankwaa (2014) made an effort to analyze the factors that are common

with banks that engage in non-interest earning activities in Ghana. The study used a panel dataset

constricted from the income statement and balance sheet of 20 universal commercial banks

operating in Ghana from 2002 through 2011. The study used the income statement components

of fees and commission as well as other income as the measure of bank non-interest income

(NII) and estimated two multiple regression equations to test for and established the significance

of some factors that influence banks engagement in non-interest earning activities. The major

contribution of the study was that interest income (INI), exposure to risk (ExpR), and liquidity

(LIQ) are the main driving factors of banks engagement in non-interest earning activities in

Ghana. Findings also established that smaller banks with lower levels of deposits, banks with

higher anticipated loan losses and high liquidity are mostly engage in non-interest earning

activities. The study also found that the Central Banks Prime rates also affect banking

operations and is positively related to banks engagement in nontraditional activities.

Saunders, Schmid and Walter (2014) made an effort to examine the relationship between the

ratio of non-interest to interest income, bank size, bank performance, and bank risk based on a

sample of 368,006 quarterly observations on 10,341 US banks during the period 2002-2013

using OLS regressions method and descriptive analysis. The study shows that trading income

accounts for approximately 18% of non-interest income across the entire sample of banks, on

average, and is often considered to the most controversial type of non-traditional bank income.

So, the study separately analyzed the ratio of trading income to interest income and found a

positive and significant relationship between trading income to interest income and bank

profitability which holds across all bank size groups and market regimes. The study also found

that banks that generate a higher fraction of their income in non-traditional business have a lower


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probability of insolvency than banks that focus more heavily on traditional banking activities.

The major finding of the study is that a greater reliance on non-interest income is associated with

higher bank profitability and lower risk at the individual bank level and non-interest income

seems to have bolstered bank-level profitability without adverse effects on individual bank risk.

Lelissa (2014) made an effort to study the determinants of Ethiopian commercial bank

performance considering bank specific and external variables on selected banks profitability for

the 1990-2012 periods. The empirical investigation uses the accounting measure return on assets

(ROA) to represent Banks performance. The study used data from secondary sources for

selected banks in the industry. The study involved both descriptive and econometrics techniques.

The econometrics method used in the study basically involved assessing the impact of selected

internal and external variables on the performance of the banking sector. Basic descriptive

statistics is applied for trend analysis and to identify outliers. The major contribution of the study

was that income diversification should also be focused. The share of income from foreign

operation in the form of service charge was found to be one of key drivers of the performance of

Ethiopian Banks. Hence, the study shows that banks should divert their attention towards

maintaining the proper mix of non-interest bearing assets which can generate fee incomes and

their loan exposures. The focus to introduce fee based services which are less exposed to credit

risk should be one of the areas that Ethiopian banks need to work for in the future if they need to

sustain their profitability performance.

Louzis and Vouldis (2015) made an effort to examine the determinants of interest and non-

interest income in the Greek banking system aiming to understand the primary drivers of overall

profitability for Greek banks. The study uses dynamic panel data techniques and a unique data

set, including supervisory data, covering the whole Greek commercial banking system from 2004

to 2011 on an annual basis adopting dealer model and correlation techniques. The major

contribution of the study was that net interest income is primarily affected by the banks market

power, their operating costs and their strategic choice to diversify their income sources by

enhancing non-interest income.. Moreover, interest and non-interest income are found to be

substitutes rather than complements, with non-interest income used as an indirect competition

instrument by efficient banks, instead of competing directly with their peers through prices in

loans and deposits. The major finding of the study was that non-interest income exhibited a


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higher degree of persistence, thus providing a buffer for adverse external shocks. Further

decomposing non-interest income, e.g. into fees and trading income would be especially relevant

since these sub-components were expected to exhibit different type of behavior and their

underlying determinants may differ. And finally it shows that in the Greek banking system

interest and non-interest income are substitutes rather than complements.

2.2. Research Gap

Much relationship between bank performance and CAMEL has been done before by many

scholars and academicians. There are many researches on the performance of banks by

macroeconomic variables and other internal variables like capital size, size of deposit liabilities,

size and composition of credit portfolio, interest rate policy, labor productivity, and state of

information technology, risk level, management quality, bank size, liquidity ratio, leverage ratio,

stock market index, exchange rate, off-balance sheet items, investment portfolio and monetary

policy and bank policy and management.

To the best in the knowledge of researcher it has been found that the research has been done only

in the foreign commercial banks. The research works done in other countries have not

incorporated the impact of the letter of credit or guarantee commission on performance of

commercial. The previous study covered only two to three variables at a time while this study

covers maximum variables under non-interest income which affect performance of commercial

banks in Nepal. As far as the knowledge of researcher, the study on non-interest income to the

performance of commercial bank was mostly done in foreign banks so covering the gap of

previous studies this research attempts to examine the relationship between selected bank

variables (non-interest income factors) which are profit/(loss) on sale of assets, dividend income,

letter of credit, remittance fee, guarantee commission, exchange income, service charge and

renewal fee and profitability of Nepalese commercial banks (i.e. ROA and ROE). The

performance based on non-interest income (selected variables) in context of Nepalesecommercial bank will be helpful for further researchers.


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2.3. Theoretical Framework

The theoretical framework is developed so that it serves as a foundation on which the entire

research is based. The possible systematic diagram based on conceptual framework is as follows.

This is a self-made model based on the assumptions that all selected variables have impact on the

performance of Banks. The research is based on the study conducted by Ngendo Karanja.

Karanja (2012) used regression model to analyze the relationship between non-interest income

and financial performance of commercial banks in Kenya. The study used independent variables

Fees and Commissions Income on Loans & Advances, Foreign Exchange Trading Income, Dividend

Income and Deposit and Transaction Fees and Other Account Fees and dependent variable as ROA.

The F statistic was significant suggesting that the model was fit to explain the relationship. The study

concluded that noninterest income had partial significant positive impact on financial performance.

The study recommends that in order for the financial performance of commercial bank to improve,

the management should not highly depend on non-interest income but diversify to other income

generating activities.


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Renewal Fee

Exchange Income

Service Charge

Profit on sale of Assets

Letter of Credit

Dividend Income

Remittance Fee


Return on Assets

(ROA)

Return on Equity

(ROE)

Figure 2.1: Conceptual Framework

Financial

Performance

(Profitability)

Independent Variables Dependent Variables


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Defining Variables

Return on Asset (ROA)

Return is expressed as net profit after tax and bonus and it is retrieved from profit and lossaccount of annual report of Nepalese commercial banks. While total assets is collected from

balance sheet of annual banks of Nepalese commercial banks.

Mathematically, the study used ROA is expressed as:

ROE= Net Profit / Total Assets

Return on Equity (ROE)

Return is expressed as net profit after tax and bonus and it is retrieved from profit and loss

account of annual report of Nepalese commercial banks. While equity is collected from

statement of changes in equity form in annual report.

Mathematically, the study used ROE is expressed as:

ROE= Net Profit / Total Equity

Profit/ (Loss) on Sale of Assets

Profit/ Loss Sale of Assets of commercial bank is defined as the profit or loss of land and

building and assets owned by commercial bank. The profit is earned when the market price is

greater than the cost price owned and loss vice versa. A gain resulting from selling an asset at a

price higher than the original purchase price.

Dividend Income

Dividend Income is earned from the dividend paid by company listed in Nepalese Market.

According to Nepal Rastra Bank Directives, commercial bank can invest in insurance companies,

micro finance companies, hydropower companies, private limited up to 25% of the core capital.

Distribution of earnings to shareholders that may be in the form of cash, stock, or property.


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Letter of Credit

A letter from a bank guaranteeing that a buyer's payment to a seller will be received on time and

for the correct amount. In the event that the buyer is unable to make payment on the purchase,

the bank will be required to cover the full or remaining amount of the purchase.

It comprises of compensating the advising bank for authenticating the L/C. sending the L/C to

the beneficiary logging the L/C into the banks liability system. It includes Telex or

communication charges to buyers bank couriers, postage reimbursement bank charge,

controllable fees and buyers letter of credit.


Banks guarantees are written obligations of the issuing bank to pay a sum on to a beneficiary on

behalf of its customers in the event that the customers themselves do not pay the beneficiary.

Through such guarantee letters, issuing bank undertakes responsibilities to provide fund

(guarantee amount), following a default by you of your contractual or other obligations.

Letters of Guarantee can be in the form of Bank Guarantees, Performance Bonds, Bid Bonds,

Shipping Guarantees, Advance Payment Guarantees, Counter Guarantees, Supplier Credit

Guarantees etc.

A guarantee from a lending institution ensuring that the liabilities of a debtor will be met. In

other words, if the debtor fails to settle a debt, the bank will cover it. This is the commission for

the bank on behalf of being guarantee.

Remittance Fee

The cost of remittance transaction includes a fee charged by the sending agent, who is paid by

the sender and a curren

impact of non-interest income in the profitability of commercial banks of nepal

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