Impact of Non-Interest Non-Interest Income on Profitability Profitability of Commercial Banks Banks in Nepal
Submitted By: Santosh Nepal Roll No: 12220110
A Graduate Research Report submitted to Ace Institute of Management Pokhara University
Submitted Submitted for the degree of Master of Business Business Administration Administration
Kathmandu September 2015
i
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 to express express my grateful gratefulness ness to my supervisor, supervisor, Mr. K.B. K.B. Manandhar Manandhar for his great 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 also like like to thank thank Mr. Ujjwal Ujjwal Paudel for providing providing guidance guidance and and motivation motivation during conduction of the research. Finall Finally, y, I would would also also like like to thank thank Mr. Prabha Prabhatt Uprety Uprety (Faculty (Faculty,, Busine Business ss Resear Research ch Methodology) for providing guidance and motivation for the study and also by sharing his knowle knowledge dge with with me and and Mr. Soha Sohan n Babu Babu Khatr Khatrii (Faculty (Faculty,, Financ Financial ial Manage Managemen ment), t), for for his his lectures on various subjects and their continuous guidance in respective topics, without the knowledge knowledge I gained in those those lectures, lectures, this research research would not have have been completed. completed.
Santosh Nepal September, 2015
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CERTIFICATE OF AUTHORSHIP
I hereby declare declare that this this report report is my own work and to the the best of my knowledge knowledge and belief, 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 Registration No: 2012-2-57-0051 2012-2-57-0051
iii 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 ACKNOWLEDGEMENT…………………………………………………………………………i 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
v 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
vi 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
vii 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
viii 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
ix 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
x 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
xi SAN
:
Sanima Bank
SBI
:
SBI Bank
SC
:
Service Charge
SCBN
:
Standard Chartered Bank Nepal
SID
:
Siddhartha Bank
SUN
:
Sunrise Bank
xii 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 c ommission, 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
xiii 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 NonInterest 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 wellstudied 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 nonintermediation 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
2 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.
3
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 bank’s 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
4 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 noninterest 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 charge and 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.
5 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 bank’s or any other firm’s 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 industry’s 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 bank’s 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 1990’s noninterest income trended up, was generally believed that shifting bank’s 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).
6 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).
7 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 researcher’s 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. How the diversification will provide banks future profitability. Bank manager’s 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 easy access 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.
8 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 i th 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.
9 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.
10 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
11 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 buyer’s bank couriers, postage reimbursement bank charge, controllable fees and buyer’s 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.
12
Service Charge
Service Service charge is a fee incurred incurred by a company for for the expenses associate associated d 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 it’s rendered
Renewal Fee
Renewal fee comprises of renewal of overdraft account, loan, account account renewal, insurance renewal, card renewal and other services rendered through commercial bank. 1.8. Organizational Structure of the Study
The report on this this study consists consists of five chapters. chapters. Second chapter chapter deals deals with review review of literature 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 Research Methodology Methodology which which includes includes six sub heads namely, Research Research Design and Plan, Plan, Popula Populatio tion n and Sample Sample Size, Size, Instru Instrument mentati ation on of Data, Data, Data Data Collec Collectio tion n Procedu Procedure, re, Reliability and Validity of Data, and Method of Data Analysis. Fourth chapter includes about Data Presentati Presentation on and Analysis. Analysis. Fifth Fifth chapter is is dealt with Summary Summary of Findings Findings,, Conclusion Conclusion and Recommendations.
13
CHAPTER TWO REVIEW OF LITERATURE AND THEORETICAL THEORETICAL FRAMEWORK 2.1. Review of Literature
Stiroh (2002) made an effort effort to to study the link between between the growing growing reliance reliance on noninterest noninterest income and and the volatility volatility of bank revenue and profits in the U.S. banking banking industry using panel panel data from the year 1970 to 2001 adopting regression model for quantitative analysis.. analysis. . The major finding finding of the study was that both aggregate aggregate and bank data provide provide little little evidence evidence that this 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 is a clear negative association between noninterest income shares and profits per unit of risk in terms of bank risk and return. Smith, Staikour Staikouras as and Wood (2003) made an effort effort to examine the variability variability of interest interest and non-interest income, and their correlation, for the banking systems of EU countries for the years 1994-98 using using time series series data. The study used used 2,655 financial financial institut institutions ions of 15 EU countries 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 non -interest activities to a banking firm. The study found that non-interest non-interest income income is much more volatile volatile than interest interest income, variability of non-interest income increases, and there exists a negative correlation between interest interest and non-interest non-interest income income in several several countries. countries. Another Another major finding finding of the study was 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.
14 DeYoung and and Rice (2004) made made a study on the effect of non-interest non-interest income income on operating operating income in the US commercial 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 Craigwell and Maxwell Maxwell (2005) made an effort effort to study the trends trends in non-interest non-interest income income at commercial commercial banks in Barbados Barbados between 1985 and 2001 using panel data 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 Pasiouras (2008) made made an effort to study the impact impact of non-traditiona non-traditionall activities 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-specifi bank-specificc data were obtained obtained from Bank scope database database of Bureau van Dijk and were 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 major contribution of the the study is the average efficiency increases with the inclusion of either the off balance sheet or the non-interest
15 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 Bundesbank’s prudential database which incorporates information derived from bank balance sheets and the supervisory reports of individual German banks, Bundesbank’s 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-returncharacteristics 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 twostage 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 activities are 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 fee ‐ based income for banks operating in
16 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 fee ‐ based income is riskier than margin income but offers diversification benefits to bank shareholders. While improving bank risk ‐return 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 risk ‐return trade‐offs, 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 noninterest income.
17 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 key factors 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.
18 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 bank’s 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 a very 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.
19 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 Taiwan’s banking industry. The study selected all available Taiwan commercial banks on an annual basis from 1992 to 2009. Stiroh’s 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 Taiwan’s banking industry. The study shows that increasing 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
20 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 (2003–2004). 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 noninterest 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
21 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-
22 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
23 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 tstatistic 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 bank’s 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 bank’s 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 Bank’s Prime rates also affect banking operations and is positively related to bank’s 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
24 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 noninterest 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
25 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 Nepalese commercial bank will be helpful for further researchers.
26 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.
27 Independent Variables
Dependent Variables
Profit on sale of Assets
Dividend Income
Letter of Credit
Guarantee Commission
Return on Assets (ROA) Return on Equity (ROE)
Remittance Fee
Exchange Income
Service Charge
Renewal Fee
Figure 2.1: Conceptual Framework
Financial Performance (Profitability)
28
Defining Variables Return on Asset (ROA)
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 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.
29 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 bank’s liability system. It includes Telex or communication charges to buyer’s bank couriers, postage reimbursement bank charge, controllable fees and buyer’s letter of credit. Guarantee Commission
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 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.
30 The commission bank gets on the process of sending money to remove an obligation. This is most often done through an electronic network, wire transfer or mail. The term also refers to the amount of money being sent to remove the obligation. Exchange Income
In finance, an exchange rate (also known as a foreign-exchange rate, forex 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. Service Charge
It 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 it’s rendered. These charges may take many forms, including: •
monthly charges for the provision of an account
•
charges for specific transactions (other than overdraft limit excesses)/ or service processing fee.
•
interest in respect of overdrafts (whether authorized or unauthorized by the bank)
•
charges for exceeding authorized overdraft limits, or making payments (or attempting to make payments) where no authorized overdraft exists
Renewal Fee
It comprises of renewal of overdraft account, loan , account renewal , insurance renewal, card renewal and other services rendered through commercial bank.
31 2.3.1 Specification of the Model
Multiple linear regression attempts to model the relationship of selected bank internal variables with the profitability. ROA= β0 + β1PSA + β2DI+ β3LC+ β4GC+ β5RF+ β6EG+ β7SC + β8REF+ e……(i) ROE= α0+α1PSA +α2DI+α3LC+α4GC+α5RF+α6EG+α7SC+α8REF+ e…….(ii) Where,
ROA: Return on Assets
ROE: Return on Equity
PSI: Profit/Loss on Sale of Assets
DI: Dividend Income
LC: Letter of Credit
GC: Guarantee Commission
RF: Remittance fee
EI: Exchange Income
SC: Service Charge
REF: Renewal Fee Where,
β1……………………….. β 8: Coefficient of selected bank variables with ROA β0: Regression Constant with ROA α1……………………….. α 1: Coefficient of selected bank variables with ROE α0: Regression Constant with ROE e: Stochastic Term
32
CHAPTER THREE RESEARCH METHODOLOGY Research Methodology is a way to systematically solve the researcher’s problem. It facilitates the researchers work and provides reliability and validity. Kothari (1988) defined research as a scientific systematic research for pertinent information on a specific topic. In this chapter, the research design, data collection procedure and procedure concerning analysis of data are described thoroughly. Analysis is conducted by using appropriate financial and statistical tools and findings are presented in a systematic way. 3.1. Research Design and Plan
Research Design is a plan, structure and strategy of investigation. It is a blue print for the collection, measurement and analysis of data. A research design is the arrangement of conditions and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure. It is the overall operational pattern of framework for the project that stipulates what information is to be collected from which source and by what procedures. The study is based on both primary and secondary data. This study focuses on understanding the non-interest income factors that affects bank’s performance of Nepalese banks. This study mainly concerned with historical research. Descriptive and analytical approaches are used to show the outcome of the study. But generally, to show the variables affecting banks performance, past historical data are used. The relevant and needed data has been collected from various publications of different commercial banks and Nepal Rasta Bank. The study investigates the relationship of selected bank variables on performance of commercial banks. Many of the researchers in various countries have found the significant relationship between non-interest income and profitability of commercial banks. These studies were concern multifactor model to explain the factor of variation on performance. This study will follow a multifactor model in finding the relationship of selected variables with ROA and ROE.
33 3.2 Population and Sample Size
The term ‘population’ or ‘universe’ for research means the universe of research study in which the research is based. There are 30 commercial banks operating in the country. The population of the primary data for this research is each and every class “A” commercial banks operating in the country. For the secondary data analysis whole population is not considered due to data unavailability and merger and acquisition in banks. As per the requirement of the study, only those banks are taken whose data are available for 5 years. The total population of the banks has been categorized in terms of availability of data. So out of 30 commercial banks, 24 have been taken as the sample size for this study. Table 3.1 List of Selected Commercial Banks
S.N.
Name of Banks
Years
No of Observations
Covered 1.
Standard Chartered Bank Nepal
2010-2014
5
2.
Sanima Bank
2010-2014
5
3.
Laxmi Bank
2010-2014
5
4.
Nabil Bank
2010-2014
5
5.
SBI Bank
2010-2014
5
6.
Bank of Kathmandu
2010-2014
5
7.
Nepal Bank
2010-2014
5
8.
Everest Bank
2010-2014
5
9.
Nepal Investment Bank
2010-2014
5
10.
Himalayan Bank
2010-2014
5
34 11.
Kumari Bank
2010-2014
5
12.
NMB Bank
2010-2014
5
13.
Sunrise Bank
2010-2014
5
14.
Siddhartha Bank
2010-2014
5
15.
Citizen Bank
2010-2014
5
16.
Lumbini Bank
2010-2014
5
17.
Janata Bank
2010-2014
5
18.
Agricultural Development Bank
2010-2014
5
19.
Nepal Bangladesh Bank
2010-2014
5
20.
Prime Bank
2010-2014
5
21.
Nepal Credit and Commerce Bank
2010-2014
5
22.
Machhapuchhre Bank
2010-2014
5
23.
Grand Bank
2010-2014
5
24.
Rastriya Banijya Bank
2010-2014
5
For collection of primary data, questionnaire has been distributed to the managers and officers of all the sampled commercial banks located inside the Kathmandu valley. 90 samples were collected. Sampling was done on the basis of convenience and judgmental sampling. Convenience sampling was used, as it would be difficult and inconvenience to visit all bank located outside valley in a given specified time frame and judgmental sampling involved collecting information from those members of the population who were in the best position to provide the information.
35 3.3. Instrumentation of Data
The study focuses on both qualitative and quantitative aspect. In order to collect the primary data, structured questionnaire is used whereas for secondary data, an audited financial statement of the sampled banks is used. The software that has been used to input the primary and secondary data is SPSS version 16.0. For the analysis of the qualitative aspect, primary data were collected from the respondent of different banks. This has been broken down into two phase. The questionnaire is composed of instruction on how to answer the question and the statements in the questionnaire paper. Coding system has been used for likert scale questions. To gather data, the questionnaire was designed in a structure format that included:
Single-choice response question
Multiple-choice response question
Ranking scale
Likert scale
For the analysis of the quantitative data, financial data of the sampled banks from annual reports were taken from websites of sampled banks and visit to bank whose data are unavailable in websites. The tools that are used for interpretation of quantitative data are pearson’s correlation coefficient, multiple regression analysis, significance 2 tailed, variable inflation factor, percentage analysis and trend analysis. 3.4. Data Collection Procedure 3.4.1. Primary Data
A questionnaire consisting of 9 set of question relating to non-interest income variables affecting banks profitability has been used. The structured questionnaire is presented in Annexure 1. Out of 125 distributed questionnaires, 90 questionnaires were collected on time. The questionnaires were given to the banks managers/ officers on hard copy format.
36 3.4.2. Secondary Data
For secondary sources, the required information such as Profit and Loss Account, Balance Sheet, Dividend Income, Profit/ Loss on Sale of Assets, Letter of Credit, Guarantee Commission, Remittance Commission, Exchange Income, Service Charge, Renewal Fee were collected from various sources which include:
Annual Reports NRB- Banking and Financial Statistics
3.5. Reliability and Validity of Data
Validity and Reliability are the two crucial factors that play a vital role throughout the study i.e. since designing of the study till examining the study. Validity is checked for both the primary and secondary. For valid and reliable primary data, the questionnaire was designed by taking expert opinions. Further, to minimize the errors generated from the non-response of the questionnaire, cross check was done and respondents were given clear instruction to fill the questionnaire in correct manner. Further the reliability of primary data is checked by Cronbach’s Alpha. In this research the Cronbach’s Alpha records 0.932 which is greater than 0.7, thus the data taken into considerations for the study are reliable. For the secondary data analysis, multi co-linearity and correlation had been carried out along with cross checking of data in order to check the reliability and validity of data. Variance Inflation Factor (VIF) were checked in the selected variables and found to be in the model range 1.05 to 2.78 which suggests that there is no multi-collinearity in the data. Since all data used for the study are based on audited annual reports and NRB statistics, the analysis is considered as a valid for the study purpose. The reliability of a measure indicated the stability and consistency with which the instrument is measuring the concept and helps to access the goodness of a measure.
37 3.6. Analysis and Plan: Method of Data Analysis
The main purpose of the study is to know the impact of non-interest income on the profitability of commercial banks. Various statistical and econometric models are used in order to analyse primary and secondary data. Under this study, data analysis is per formed as per the nature of data and its availability. 3.6.1. Primary Data Analysis
The primary data questionnaire comprises of single response questions, multiple response questions, ranking question and likert scale questions. The questionnaire also included some variables/factors that may have direct impact on banks performance, which are not included in secondary analysis. Once the data were gathered, they were entered in SPSS software. The analysis such as descriptive analysis, one sample statistics, and frequency analysis were obtained from these data. The output drawn from the SPSS were firstly copied to MS-Excel for the effective presentation and later they were inserted into the report. 3.6.2. Secondary Data Analysis
Data are analyzed through descriptive statistics, correlation matrix and econometric model. Mean, Minimum, Maximum, Standard Error, Standard Deviation, Skewness and Kurtosis are used as descriptive statistics to describe and summarize the data. Percentage analysis is used to analyze the changes in commercial bank performance on the basis of selected internal variables. Variance analysis is used in factorial design to minimize replication and combined groups when effects are found to be statistically or practically significant. Similarly, Correlation matrix is used to find the correlation between the independent and dependent variables. Lastly, multivariate regression analyses are used on the two econometric models.
38
CHAPTER FOUR RESULTS AND DISCUSSION This chapter includes the presentation and analysis of the data for the achievement of the objectives of the research. The purpose of this section is to provide the empirical results of relationship between selected bank internal variables and profitability measured by return on assets and return on equity. Chapter has been divided into two sections. The first section covers descriptive and inferential analysis of the secondary data and the second section covers the descriptive and inferential analysis of the primary data. The secondary data analysis consists of: (i) Descriptive statistics, (ii) Trend Analysis (iii) Percentage Analysis of Dependent Variables (iv) Percentage Analysis of Commercial bank’s selected bank internal variables (iv) Correlation analysis (v) Regression Analysis and (vi) Variance Analysis The Primary data analysis consists of (i) Descriptive statistics and (ii) One sample t-test statistics. 4.1. Secondary Data Analysis 4.1.1. Descriptive Statistics
The study used mean, minimum, maximum, standard deviation, standard error, skewness and kurtosis to analyze the data.
39 Table 4.1: Descriptive Analysis N
Minimum
Maximum
Mean
Std. Deviation
Skewness
Varibales
Statistic
Statistic
Statistic
Statistic
Statistic
Statistic
Std. Error
Statistic
Profit/(Loss) on Sale of Assets
119
-13431.7
546667.2
11465.25
54770.04
8.473677
0.221782
79.69042
Dividend Income
119
-2074.29
154051.1
5838.771
18873.46
5.767672
0.221782
37.41964
Letter of Credit
119
0
107843
19886.47
19913.57
2.091405
0.221782
5.148326
Guarantee Commission
119
149.63
233974.4
35793.07
37806.54
2.289559
0.221782
7.216432
Remittance Fee
119
10.61
170827.4
27117.8
32521.94
1.674475
0.221782
3.017153
Exchange Income
119
-73908
529995.6
99784.78
133952.8
1.834575
0.221782
2.589829
Service Charge
119
0
412068.2
61946.08
65196.49
3.312893
0.221782
14.2303
Renewal Fee
119
0
38995.7
3719.066
6205.581
2.487975
0.221782
9.021179
Return on Assets
119
0
8.15
1.670084
1.078576
2.23621
0.221782
10.26659
Return on Equity
113
-361.36
102.96
13.07168
38.72202
-8.07944
0.227447
79.67355
Kurtosis
Table 4.1 represents the summary statistics of the variables under the study. The average yearly index of ROA and ROE 1.67 and 13.07 during the study period (2010-2014) records a standard deviation 1.078 and 38.722 respectively implying a medium volatility in commercial banks. The mean of profit/loss on sale of assets records 11465.25(in 000’s), dividend income 5838.77(in 000’s) and renewal fee 3719.06(in 000’s). The result reveals that most of the high concentration on mean of commercial banks is on letter of credit, guarantee commission, remittance fee, exchange income and service charge. Skewness and Kurtosis are both numeric measures for normality test that are used to determine whether a data set is modeled for normal distribution or not. If either of these values is not close to zero, then our data set is not normally distributed. Positive value on skewness tests for ROA, profit/(loss) on sale of assets, dividend income, letter of credit, guarantee commission, remittance fee, exchange income, service charge and renewal fee suggest that these variables have long right tails, while negative value of the skewness tests for ROE had extreme values during the study period which suggest that these variables have long
40 left tails. It indicates a deviation from normal distribution of the data and volatility in that parameter. Given that Kurtosis value for exchange income 2.58 are less than three, the distribution of these variables exhibit non-normality. The kurtosis value of roa, roe, profit/ loss on assets, dividend income, letter of credit, guarantee commission, remittance fee, service charge and renewal fee are more than three, the distribution of these variables exhibit normality. The kurtosis values of all variable indicated platykurtic distribution flatter than a normal distribution with a wider peak. The probability for extreme value is less than for normal distributions and values are wider spread around the mean. Table 4.2: Summary of Trend Analysis of Selected Bank Internal Variables Variables
Return of Assets Return of Equity Profit on Sale of Asset Dividend Income Letter of Credit Guarantee Commission Remittance Commission Exchange Income Service Charge Renewal Fee
Year (Highest)
2010 2010 2014 2014 2014 2014 2014 2014 2014 2014
Value
Year (Lowest)
2.0454 17.23 23327 11182 27374 52122 31094 133353
2012 2013 2013 2013 2010 2010 2011 2011 2011 2010
91693 5476.9
Value
1.4608 4.93 4597.5 2141 15024 23158 22950 70451 45003 2214.1
Table 4.2 shows the year selected bank internal variables perform high and low. Among the taken 5 years of period from 2010-2014, it is seen that there is remarkable improvement and high earning of selected variables in the year 2014. The highest earning from PSA, DI, LC, GC, RF, EI, SC and REF is in year 2014. The ROE and ROA was highest in the year 2010.
41 The following table 4.3 deals with the percentage analysis on the attribution as follows. Table 4.3: Summary of Percentage Analysis of Commercial Banks Variables
Highest
Lowest
Return on Assets
Nabil, ADBL and NB
Nepal Bank, MBL and Grand
Return on Equity
SCBN, EBL, and Nabil
Janata, Grand and Nepal Bank
Profit(Loss) on Sale of Assets
NCC, Nepal Bank and MBL
Citizen, Nabil and BOK NB, Citizen and Sunrise
Letter of Credit
Himalayan, Nepal Bank and RBB BOK, Sunrise and MBL
Guarantee Commission
SBI, NCC and MBL
NMB, RBB and Sanima NMB, NCC and Citizen
Exchange Income
Siddhartha, MBL and Nepal Bank Sunrise, SCBN and MBL
Service Charge
Citizen, Sunrise and MBL
NB, NMB and NIBL
Renewal Fee
Nepal Bank, Citizen and MBL
NB, NMB and NCC
Dividend Income
Remittance Fee
NB, ADBL and RBB
RBB, ADBL and NMB
Table 4.3 shows Machhapuchhre bank has earned high from non-interest income during the research period. Looking at the result of Machhapuchhre bank, PSA, LC, GC, EI, SC and REF seems to be higher than other banks. Nepal Bank earned highest from remittance fee among all other banks, and Rastriya Banijya bank earned highest from dividend income in the study period. It is seen that NB bank earns lowest from REF, SC, DI, LC whereas NMB earns lowest from RF and GC compared to other banks in the study period. From the study it is seen that Citizen, Nabil and BOK incurred loss from sale of assets in the study period. Also from the table it can be seen that NB bank has highest ROA and Grand Bank has lowest ROA. Likewise, Nabil bank has highest ROE and Nepal bank has lowest ROE.
42 4.1.2. Variance Inflation Factor
Before we analyze the data we should detect if there is any multi-collinearity among the variable chosen. The variance inflation is performed to support the validity of the regression results In case of VIF, if the results are below the 10, suggest no multi-collinearity (Gujarati, 2004). Table 4.4: Variance Inflation Factor Variables
VIF
Profit/(Loss) on Sale of Assets
1.05
Dividend Income
1.81
Letter of Credit
2.78
Guarantee Commission
1.65
Remittance Fee
2.01
Exchange Income
2.67
Service Charge
1.25
Renewal Fee
1.70
The result reveals that VIF is reasonably good. The values of variance inflation factor for the variables are in the model range 1.05 to 2.78 for variables profit/(loss) on sale of assets, dividend income, letter of credit, guarantee commission, remittance fee, exchange income, service charge and renewal fee.
43 4.1.3. Correlation Analysis
Table 4.5: Correlation Matrix of selected bank variable with ROA and ROE Correlations ROA Pearson ROA
Correlation
1.00
Pearson ROE
Correlation Sig. (2-tailed) N Pearson
PSA
Correlation Sig. (2-tailed) N Pearson
DI
Correlation Sig. (2-tailed) N Pearson
LC
Correlation Sig. (2-tailed) N Pearson
GC
Correlation Sig. (2-tailed) N Pearson
RF
Correlation Sig. (2-tailed) N Pearson
EI
Correlation Sig. (2-tailed) N Pearson
SC
Correlation Sig. (2-tailed) N Pearson
REF
Correlation Sig. (2-tailed) N
PSA
DI
LC
GC
RF
EG
SC
REF
0.21
-0.13
0.07
0.22
0.46
0.18
0.28
0.23
0.08
0.02
0.17
0.45
0.02
0.00
0.05
0.00
0.01
0.37
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
0.21
1.00
-0.11
0.03
0.17
0.16
0.08
0.15
0.03
0.06
0.22
0.75
0.06
0.08
0.37
0.11
0.78
0.52
Sig. (2-tailed) N
ROE
0.02 119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
-0.13
-0.11
1.00
0.00
-0.07
-0.05
0.03
-0.06
-0.01
0.13
0.17
0.22
1.00
0.48
0.59
0.77
0.49
0.92
0.17
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
0.07
0.03
0.00
1.00
0.02
0.02
0.60
0.08
0.03
0.23
0.45
0.75
1.00
0.81
0.86
0.00
0.38
0.72
0.01
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
0.22
0.17
-0.07
0.02
1.00
0.40
0.32
0.70
-0.03
0.43
0.02
0.06
0.48
0.81
0.00
0.00
0.00
0.73
0.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
0.46
0.16
-0.05
0.02
0.40
1.00
0.19
0.43
0.37
0.31
0.00
0.08
0.59
0.86
0.00
0.03
0.00
0.00
0.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
0.18
0.08
0.03
0.60
0.32
0.19
1.00
0.41
0.10
0.19
0.05
0.37
0.77
0.00
0.00
0.03
0.00
0.26
0.04
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
0.28
0.15
-0.06
0.08
0.70
0.43
0.41
1.00
0.02
0.08
0.00
0.11
0.49
0.38
0.00
0.00
0.00
0.87
0.36
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
0.23
0.03
-0.01
0.03
-0.03
0.37
0.10
0.02
1.00
-0.04
0.01
0.78
0.92
0.72
0.73
0.00
0.26
0.87
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
0.08
0.06
0.13
0.23
0.43
0.31
0.19
0.08
-0.04
1.00
0.37
0.52
0.17
0.01
0.00
0.00
0.04
0.36
0.68
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
119.00
0.68
119.00
44 Table 4.5 represents the result of correlation test. Estimated correlation coefficient should be tested to determine whether there’s a significant relationship between the dependent variables and independent variables or not. Significant tests allow us to assess whether apparent relationship between random variables are the result of chance. If we decide that the relationship do not result from chance, we will be inclined to use this information in prediction because a good prediction of one variable will help us to predict other variable. If the corresponding value is higher than the level of significant we do not reject the null hypothesis and infer that there’s no significant relationship between the dependent variable and the independent variable. Data Analysis and Discussions:
The variables that were taken to measure the profitability of commercial banks were Return on Assets (ROA) and Return on Equity (ROE). Thus, in this section the study will discuss the inferential analysis about the relationship of profitability and selected bank internal variables. Relationship between Return on Assets and Profit/ (Loss) on Sale on Assets
The result reveals that the corresponding p-value of profit/(loss) on sale on assets -0.13 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of negative sign, it concludes that as one variable increase in value, the second variable decrease in value. The sig.(2-tailed) value record 0.17 is more than the significance level 0.05 concluding that there is no statistically significant correlation between return on assets and profit/loss on sale of assets. This means, increase or decrease in one variable do not significantly relate to increase or decrease in second variable. Relationship between Return on Assets and Dividend Income
The result reveals that the corresponding p-value of dividend income 0.069 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value.
45 The sig.(2-tailed) value record 0.454 is more than the significance level 0.05 concluding that there is no statistically significant correlation between return on assets and Dividend income. This means, increase or decrease in one variable do not significantly relate to increase or decrease in second variable. Relationship between Return on Assets and Letter of Credit
The result reveals that the corresponding p-value of letter of credit 0.21 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value. The sig.(2-tailed) value record 0.01 is less than the significance level 0.05 concluding that there is a statistically significant correlation between return on assets and letter of credit. This means, increase or decrease in one variable do significantly relate to increase or decrease in second variable. Relationship between Return on Assets and Guarantee Commission
The result reveals that the corresponding p-value of guarantee commission 0.46 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value. The sig.(2-tailed) value record 0.00008 is less that the significance level 0.05 concluding that there is a statistically significant correlation between return on assets and guarantee commission. This means, increase or decrease in one variable do significantly relate to increase or decrease in second variable. Relationship between Return on Assets and Exchange Income
The result reveals that the corresponding p-value of exchange income 0.276 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value.
46 The sig.(2-tailed) value record 0.002 is less that the significance level 0.05 concluding that there is a statistically significant correlation between return on assets and Exchange income. This means, increase or decrease in one variable do significantly relate to increase or decrease in second variable. Relationship between Return on Assets and Remittance Fee
The result reveals that the corresponding p-value of remittance fee 0.178 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value. The sig.(2-tailed) value record 0.05 is equal to that of the significance level 0.05 concluding that there is a statistically significant correlation between return on assets and remittance fee. This means, increase or decrease in one variable do significantly relate to increase or decrease in second variable. Relationship between Return on Assets and Service Charge
The result reveals that the corresponding p-value of service charge 0.234 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value. The sig. (2-tailed) value record 0.01 is less that the significance level 0.05 concluding that there is a statistically significant correlation between return on assets and service charge. This means, increase or decrease in one variable do significantly relate to increase or decrease in second variable. Relationship between Return on Assets and Renewal Fee
The result reveals that the corresponding p-value of renewal fee 0.08 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value.
47 The sig. (2-tailed) value record 0.37 is more that the significance level 0.05 concluding that there is no statistically significant correlation between return on assets and service charge. This means, increase or decrease in one variable do not significantly relate to increase or decrease in second variable. Relationship between Return on Equity and Profit/Loss on Sale on Assets
The result reveals that the corresponding p-value of profit/(loss) on sale on assets -0.127 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of negative sign, it concludes that as one variable increase in value, the second variable decrease in value. The sig.(2-tailed) value record 0.178 is more than the significance level 0.05 concluding that there is no statistically significant correlation between return on equity and profit/loss on sale of assets. This means, increase or decrease in one variable do not significantly relate to increase or decrease in second variable. Relationship between Return on Equity and Dividend Income
The result reveals that the corresponding p-value of dividend income 0.018 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value. The sig. (2-tailed) value record 0.844 is more than the significance level 0.05 concluding that there is no statistically significant correlation between return on equity and Dividend income. This means, increase or decrease in one variable do not significantly relate to increase or decrease in second variable. Relationship between Return on Equity and Letter of Credit
The result reveals that the corresponding p-value of letter of credit 0.15 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value.
48 The sig.(2-tailed) value record 0.1066 is more than the significance level 0.05 concluding that there is no statistically significant correlation between return on equity and letter of credit. This means, increase or decrease in one variable do not significantly relate to increase or decrease in second variable. Relationship between Return on Equity and Guarantee Commission
The result reveals that the corresponding p-value of guarantee commission 0.14 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value. The sig. (2-tailed) value record 0.12 is more than the significance level 0.05 concluding that there is no statistically significant correlation between return on equity and guarantee commission. This means, increase or decrease in one variable do not significantly relate to increase or decrease in second variable. Relationship between Return on Equity and Exchange Income
The result reveals that the corresponding p-value of exchange income 0.133 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value. The sig.(2-tailed) value record 0.15 is more than the significance level 0.05 concluding that there is no statistically significant correlation between return on equity and exchange income. This means, increase or decrease in one variable do not significantly relate to increase or decrease in second variable. Relationship between Return on Equity and Remittance Fee
The result reveals that the corresponding p-value of remittance fee 0.05 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value.
49 The sig. (2-tailed) value record 0.54 is more than the significance level 0.05 concluding that there is no statistically significant correlation between return on equity and remittance fee. This means, increase or decrease in one variable do not significantly relate to increase or decrease in second variable. Relationship between Return on Equity and Service Charge
The result reveals that the corresponding p-value of service charge 0.006 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value. The sig.(2-tailed) value record 0.94 is more than the significance level 0.05 concluding that there is no statistically significant correlation between return on equity and service charge. This means, increase or decrease in one variable do not significantly relate to increase or decrease in second variable. Relationship between Return on Equity and Renewal Fee
The result reveals that the corresponding p-value of renewal fee 0.06 means there is a weak relationship among the variables implying that changes in one variable are not correlated with changes in the second variable. As of positive sign, it concludes that as one variable increase in value, the second variable also increase in value. The sig.(2-tailed) value record 0.52 is more than the significance level 0.05 concluding that there is no statistically significant correlation between return on equity and service charge. This means, increase or decrease in one variable do not significantly relate to increase or decrease in second variable.
50 4.1.4. Multivariate Regression Analysis A. Determinants of ROA
Table 4.6: Multivariate Regression Analysis with Dependent Variable ROA B (Constant) Profit/(Loss) on Sale of Assets Dividend Income Letter of Credit Guarantee Commission Remittance Fee Exchange Income Service Charge Renewal Fee
R Square
0.2456
Beta
S.E
T
Sig
1.1817
0.1690
6.9921
0.0000
-0.09
0.000004
-0.9811
0.3288
0.01
0.000006
0.0585
0.9535
-0.03
0.000008
-0.2360
0.8139
0.39
0.000003
3.5192
0.0006
0.09
0.000004
1.6662
0.0034
0.08
0.000001
1.0155
0.0023
0.07
0.000002
2.5916
0.0013
0.02
0.000019
1.7660
0.0480
Adjusted R Square
0.1907
Durbin-Watson
1.8907
Table 4.6 shows that there is significant relationship of ROA with guarantee commission, remittance fee, exchange income, service charge and renewal fee and there are no significant relationship with profit/(loss) on sale of assets, dividend income and letter of credit. The data also shows the summary of regression analysis with moderate explanatory power of regression model with return on assets. Adjusted R 2 has a value of about 0.19 meaning that the selected bank internal variable explain only 19.07% if changes in return of assets for sampled commercial banks. The study used Durbin-Watson test to determine the auto correlation of all variables. The study confines there is no auto correlation in Nepalese commercial bank and selected bank internal variables since the value is near to 2.
51 B. Determinants of ROE
Table 4.7: Multivariate Regression Analysis with Dependent variable ROE B (Constant) Profit/(Loss) on Sale of Assets Dividend Income Letter of Credit Guarantee Commission Remittance Fee Exchange Income Service Charge Renewal Fee R Square
0.156926
Beta
S.E
T
Sig
3.4589
6.7100
0.5155
0.6072
-0.0963
0.0001
-1.0111
0.3142
0.0203
0.0003
0.1621
0.8715
0.1169
0.0003
0.7464
0.4570
0.1199
0.0001
1.9913
0.0034
0.0172
0.0002
2.1296
0.0410
0.0031
0.0000
1.0200
0.0021
0.0183
0.0001
2.1765
0.0013
0.0236
0.0008
1.1935
0.0140
Adjusted R Square
0.0902
Durbin-Watson
1.82126
Table 4.7 shows that there is significant relationship of ROE with guarantee commission, remittance fee, exchange income, service charge and renewal fee and there is no significant relationship with profit/(loss) on sale of assets, dividend income and letter of credit. In this regression model summary analysis, the study has excluded two banks from sample namely Nepal Bank and Grand Bank as they have extreme negative ROE and their adjusted R square is near to zero. So after excluding the two banks from sample, adjusted R 2 value of ROE excluding two banks is 0.0902 meaning that the selected bank internal variables explain only 9.03 % if changes in return on equity for sampled commercial banks. DW test results that there is no autocorrelation as its value is near to 2. The equation goes as follows: ROA= 1.817 -0.09 PSA + 0.01 DI- 0.03LC+ 0.39 GC+ 0.09RF+ 0.08EI+ 0.07SC+ 0.02REF ROE= 3.45- 0.0963 PSA +0.020DI+0.11LC+ 0.1199GC+ 0.0172RF+ 0.0031EI+ 0.0183SC+ 0.0236REF
52 4.1.6. Discussion of the Results Profit/ Loss on Sale of Assets
Finding of profit/loss on sale of assets with regard to ROA and ROE seems to have negative coefficient inferring there is negative relationship between them. In contrast p- value approach the result seems different in nature. The finding shows that profit/(loss) on assets have insignificant relation with ROE and ROA. It seems to be logical since the core activities of commercial bank is not leasing activities of land, building and other intellectual property. In this regard, researcher enticed that commercial bank NPA is minimal i.e. below 5% on average in the study period (NRB Ba nking and Financial Statistics, Vol.60, 2014). Higher NPA results commercial bank to transfer power of attorney to institution and sell those assets accommodating the cost price of the investment. Secondly, selected commercial banks are not engaged in leasing activities of land, building and other assets. In context of Nepalese economy, investment included in assets are associated with cost of holding them for tenure; short and long term risk trade off among the assets, market price of assets, management decision, land and building tax reform, legal rigid process which affect on purchase, sales and hold of assets in long term yielding minimal profit in commercial banks. Management plays imperative role in investing in such assets. This decision is influenced by personality and style of management (Myers & Majluf, 1984), which researcher had not taken in consideration in study. In the study period, the market had suffered the real estate bubble burst (spotlightnepal.com, Sept 30. 2015). It is the condition where most of the commercial bank invested in real estate but later felt like the price on investment is not as worthy as it seems on the market. Due to upsurge in loan in real estate, the NRB has issued some regulatory directives to banks and financial institutions to limit the loan flow in real estate (NRB Unified Directives, 2067) which resulted the bank limit their investment in single sector. Since the assets in question are the sort that are valued on the balance sheet - namely, fixed assets and not intangible assets like people or ideas- ROA is not always useful for comparing one company against another. Some companies are 'lighter', having their value based on things such as trademarks, brand names and patents, which accounting rules don't recognize as assets. A
53 software maker, for instance, will have far fewer assets on the balance sheet than a car maker. As a result, the software company's assets will be understated, and its ROA may get a questionable boost. Demand and supply theory also works in allocation of real estate price. There are numerous reasons that affect the price as customers are aware of financial crisis and real estate bubble price due to technological advancement (media and internet) in Nepal. Thus, the income on sale of asset is negligible and doesn’t have much influence on the overall bank profit volume thus resulting insignificant relationship on ROE and ROA. Also the dataset reveals that some banks did not trade in assets within the study period, thus their impact made the results insignificant. Dividend Income
The result reveals that there is positive coefficient inferring positive relationship between them. In contrast p-value approach, the result seems different. The study found that there is insignificant relationship with ROA and ROE. It seems to be relevant in commercial banks as bank core investment is on loan and other competitive instruments in the market. There are many variables that play important factor in the investment in stock market by management of commercial banks. Mainly, it depends on the psychology of the management which is driven by the market factors, company risk, investment policy
constraint,
internal
decision
and
macro-economic
variables
(Kengatharan
&
Kengatharan 2014). Also, the bank must utilize its investment in loan and treasury market and ,
other competitive market products to reap the benefit of those investments. Bank is not necessarily involved in investing in stocks and seeking dividend incomes from other companies. Banks main business is lending money as loans. Besides that banks also involve in non-interest income generating transactions. Dividend income is also one of the non interest incomes generating transaction. But in case of Nepalese banks, dividend income doesn’t have significant relationship with bank performance which shows that Nepalese banks do not consider investing in other companies for dividend income. The size of dividend income in commercial banks is very low since commercial banks cannot invest on stock of other commercial banks as per the prevailing laws of Nepal, hence losing the
54 opportunity to reap dividend income from these these banks. As per all FY FY data, there is more than 70% market share capitalization capitalization of financial institution. Whereas, if compared with banks total assets or equity, the the dividend income size is insignificant with no any effect on ROA and ROE. The dataset also reveals that all banks did not invest in other company’s stock within the 5-year period thus their impact made the results be insignificant. Letter of Credit
The result result found found that the the coefficient coefficient of the letter letter of credit credit with ROA ROA is negative negative and ROE is positive inferring there is negative and positive relationship among the variables respectively. In addition, p-value approach shows that there is no significant relationship of letter of credit with return on asset and return on equity. The results results seem logical logical as letter of credit credit is risky in terms of internati international onal market market and currency risk. Commerc Commercial ial banks assess the risk risk in customer customer profile, profile, internat international ional market, market, exchange exchange rate rate risk and environment risk. Since customer have to buy the good and pay the party within the stipulated times which result bank to assess and evaluate the customer profile and delivery risk from the third third party. party. Banks lend lend their creditwor creditworthiness thiness to to the beneficiaries beneficiaries by issuing issuing LCs to get commission from the customers for these facilities. These facilities facilities add to risk exposure, so it is is extended to effective/potential parties only. Also, there there is strife in market market place among among the commercial commercial bank. bank. The rigid process, beneficiary beneficiary credib credibili ility, ty, impo importer rter risk, risk, inte interna rnatio tional nal payme payment nt sys system tem,, charge charge fee base based d on differe different nt commercial bank and service rendered by them would also play in determining the performance in commercial commercial bank bank which affects the income income from the letter letter of credit. Also there are other alternatives to LC in Nepalese commercial banks provides which are collections, telegraphic transfer, other different products like advance payments/collection, document against payments (collection) so that there is minimal transaction with LC which in turn have insignificant impact on ROA and ROE of Nepalese commercial banks. Since, Nepalese trading house has low trust among the foreign manufacturers resulting them to make transaction in telegraphic transfer rather than letter of credit. Whereas, we can see some business house specially the trading involving in LC process and the contribution of these
55 business house on bank b ank profit is again negligible n egligible and do not have much impact on overall bank huge assets and equity resulting the low effect on overall ROA and ROE. Letter of credit is the matter of trust. And it is done for international business dealings. Most of the Nepalese banks are small in size and lack reputation and trust in international market. So issuance issuance of LC is not the major source of income for for them. As from the data extracted extracted from annual reports of commercial commercial banks, banks, it is seen that that only few banks banks in Nepal Nepal are involved involved in major LC transac transactio tions. ns. Due to these these fact factors ors LC does not impact impact the perform performanc ancee of of Nepale Nepalese se commer commercia ciall banks. banks. Guarantee Commission
The study study found found that that coeffi coefficie cient nt of guarant guarantee ee commis commissio sion n with with ROA and ROE is positiv positivee inferring that there is a positive relationship among the variables. More ever, p- value approach shows that there is significant relationship of guarantee commission with return on asset and return on equity. As from the trend analysis we can see that the guarantee commission is increasing every year in the study period. period. Also it is seen that annual foreign foreign trade is increased increased in the study period. period. Though export is not so significantly increased but import data shows that there is significant increment every year year (NRB Banking and Financia Financiall Statisti Statistics, cs, Vol Vol 60, 60, 2014), which ultimately ultimately needs guarantee by the outside party and thus the business house of Nepal needs to have bank guarantee. guarantee. Guarantee Guarantee is done done in high values values which which in turn generate generatess higher higher commission. commission. Bigger Bigger companies needs to have bank guarantee to do business for their security purposes so increasing the bank guarantee increases the profit and positively impacts on ROA and ROE. Also there is increment increment of remittance remittance every every year (NRB Economic Economic Bulletin, Bulletin, Vol 49, 2014) 2014) which creates creates more consumption and thus to fulfill the higher consumption there are more business transaction which needs more bank guarantee and thus the bank earns good profit from the guarantee commission and have significant impact on ROA and ROE of commercial banks. The finding seems seems to contextual in the Nepalese Nepalese market as in the study period, period, Nepal Tourism Tourism Year Year 2011, 2011, Nepal Nepal Invest Investmen mentt Year Year 2012-13 2012-13 and openi opening ng of speci special al indus industry try zone zone as per the the budget plan was implemented. In the study period major infrastructural works were carried out whic which h ulti ultima mate tely ly neede needed d to have have bank bank guara guarant ntee. ee. Many Many hydro hydropo powe werr comp compan anie iess like like Uppe Upper r
56 Tamakoshi, Sanjhen, Rahughat were were in work (Thanju, 2013) and other major telecommunication telecommunication projects like Huawei, ZTE were started in Nepal in the study period. Also due to high imports, there was more business business house making making banking guarantee guarantee to import goods. Bank Guarant Guarantee ee is a common way of bank lending. From this banks are definitely having impacts on the performance. Either the guarantee defaults or they meet the obligation ob ligation firms fi rms performance will be directly impacted. Remittance Fee
The study found that coefficient of remittance fees with return on assets and return on equity is positive inferring that there is positive relationship among the variables. In contrast, p-value approach shows that there is significant relationship of remittance fee with return on asset and return on equity. There is a trend trend of increment increment of of remittanc remittances es income income every year year in the study study period period (NRB Economic Bulletin, Vol 49, 2014). First, the fixed costs costs of sending remittances make make the flows lumpy, providing households with excess cash for some period of time. This might potentially increase increase their demands demands for banking services services and hence foster banking banking outreach outreach and depth. The Nepalese economy over the period has been driven by the remittance money since millions of Nepalese manpower is heading foreign countries as workers, students etc. and the banking sector are actively involved in between. This provides bank with remittance fee for the service and the fee which seems noticeable noticeable because because the frequency of transact transaction ion is is huge. huge. The The bank bank profit profit seems to be directly directly affected affected by these remittan remittance ce fees hence with with noticeable noticeable impact on on overall ROE ROE and ROA. Since bank offers households a safe place to store this temporary excess cash. Second, interbank and transfer transfer might be collected collected in bank branches branches are important important means means of receiving remitta remittances. nces. Bank Bank charge charge and and proce processi ssing ng fees fees for remi remitt ttan ance ce can can be a sig signi nifi fica cant nt sou sourc rcee of of inc incom omee for for commercial banks in receiving countries. The potential to collect collect these fees might induce bank to expand their outreach and locate close to remittance recipient. Third, a substantial portion of remittance flow to households that is likely to be unbanked households households in the middle middle and lower parts of the income distribut distribution. ion. Thus, banks banks acting as remittance paying agents are well-positioned to offer other services to unbanked households
57 receiving remittances. Fourth, processing remittance flows provides banks with information on the income of recipient households. This information may make banks better able to extend loans to otherwise opaque borrowers. Remittance is one of the major sources of income in Nepal. Being so, the fee, bank charges for remittances creates significant amount of profit for banks which cannot be ignored. In fact these days remittance is one of the major non-interest incomes of banks. This research also shows the significant relationship of remittance fee with banks performances. Exchange Income
The study found that coefficient of exchange incomes with return on assets and return on equity is positive inferring that there is positive relationship among the variables. In contrast, p value approach shows that there is significant relationship of exchange income with return on asset and return on equity. We can see the figures of import (NRB Banking and Financial Statistics, Vol 60, 2014) are increasing every year in the study period. So higher import results in higher foreign currency to be exchanged inside county and in turn generates higher income by commercial banks. In the study period it has been seen that there is trend of constant growth of foreign exchange rate except in 2011. Due to the constant growth term, the treasury department in commercial bank had earned good amount of profit from the exchange fluctuation/ or forward buying. Also the increasing remittances in the study period caused the banks to earn high foreign currency exchange income. Also due to high inflow of tourists due to SAARC Summit, Tourism Year 2011, Nepal Investment Year 2012-13 in the study period, the banks got opportunity to earn from foreign exchange. Data also shows foreign exchange reserve has increased every year significantly (NRB Economic Bulletin, Vol 49, 2014) which indicates that banks have earned high foreign currency and while earning the income was also significant. High FDI flow has also helped banks earn foreign currencies. Paudyal (2012) study shows the changing pattern of major contributors of foreign exchange earnings. Workers’ remittances (58%), merchandise exports (16%) and tourism receipts(7%) are the major sources of the foreign exchange earnings in the country which together made about 81% of total foreign exchange earnings in 2009/10.
58 Except in the year 2011, there is a growing trend of exchange income in the commercial banks between the study periods 2010-2014. Since, the exchange market during the study period observes greater flexibility but the point to be notice is the flexibility was much on Dollars and Euros whereas Nepal has more of its tourist and visitors from Asian countries (mainly Indian and Chinese) and Nepalese currencies being pegged with Indian currency, the flexibility has less effect on exchange. Likewise the lower fluctuation on other incoming currencies, Nepalese banks are being able to reap maximum benefit out of it. Nepalese currency had been weak compared to the US Dollar on average and other convertible currencies in the study period from 2010-2014 due to its peg with Indian rupees, remained fairly stable during the few months of the study period. (NRB Economic Bulletin, Vol 10, 2014). On the other hand the value of USD has been fluctuating since 2007-8 financial crises; key reason initially is the fall in the interest rates was hurting the treasury and debt investors and outflow from US created fluctuation in the value of USD due to which the commercial banks could make good income from these fluctuations. Exchange income is one of the major source of noninterest income of commercial banks of Nepal. Being so the profit bank makes from currency fluctuations and charges for currency exchange creates a significant amount of profit for banks which cannot be ignored. This research also shows the significant relationship of exchange income with banks performances. This period was associated with the acceleration of remittance inflows, the continuation of the peace process, and the financial crisis in the developed world. Service Charge
The study found that coefficient of service charge with return on assets and return on equity is positive inferring that there is positive relationship among the variables. In contrast, p value approach shows that there is significant relationship of service charge with return on asset and return on equity. Bank in terms of generating income rely mostly on service charge after interest income. Banks involve in many transactions of its customers/depositors directly and indirectly and charge the certain service charge out of it. Since, Nepalese investors aren’t secured in terms of their
59 investment therefore seeks the bank service like loan processing, guarantee which in turn facilitate banks with high service charge thus enhancing bank profit. First, technological and regulatory changes opened up new sources of noninterest income. Second, noninterest income was believed to provide favorable attributes to a bank's revenue stream. Most generally, the advances made in computing and telecommunications make it possible for banks to directly market service charges in a manner not previously possible. (Hawtrey, 2003). Furthermore, there has been a change in technology like automated teller machines and internet banking which also aff ects banks’ cost structure. Payments systems are heavily reliant upon deposit-based instruments, banks are in a unique position to profit by cross-selling payment-based, payment-related and non-payment-related products and services to their deposit customers. For example, banks offer customers a broad menu of payment methods with which to access the funds in their deposit accounts-such as checks, debit cards, direct debit for paying bills, direct deposit for receiving paychecks, and online bill paying and offer payment products peripheral to customers account, such as credit cards. Depending on their business model and competitive strategy, banks can and do charge fees for these payment-related services. Banks have become increasingly aware that their profits can be enhances by offering costly new relationship -based services (ATMs) at low prices or for a fee. Service income from credit cards includes late payments, interest on credit card balances above the cost of a traditional loan, finance chargers for cash advances, fees for handling transactions on behalf of merchants and card holders and interchange fees for credit card purchase (DeYoung & Rice, 2003). Renewal Fee
The study found that coefficient of renewal fee with return on assets and return on equity is positive inferring that there is positive relationship among the variables. In contrast, p value approach shows that there is significant relationship of renewal fee with return on asset and return on equity. It seems relevant in the context of Nepal as renewal fee comprises of different accounting fees segregated in different heading. The renewal fee of commercial banks includes renewal of
60 overdraft, renewal of credit, card renewal, service and product rendered by banks. The customers are liable to deposit fees for the service and product they use. The high dependency of Nepalese investors on bank and renew of their services at every fiscal year or periodic renewal tend to make high inflow of profits out of it. Since the trend of renewal fee in the study period is increasing every year, it can be inferred that most banks are making good profits from these. Since the technological advancement like e payments, mobile payments, credit/debit cards, the banks earn income from their annual or periodic renewal of given services so that there are plenty of titles under which Nepalese commercial banks can earn good profits. Also, there is increasing loan in commercial banks (NRB Banking and Financial Statistics, Vol 60, 2014), which ultimately leads to renew of the loans annually which create income under the heading renewal fee thus increasing non-interest income. Due to these factors, there is significant relationship with ROA and ROE of Nepalese commercial banks. In the context of the globalization and technological innovation, many e-products, Damankah et al., (2014) found 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. There are different other moderating and driving variables which the study has not taken into consideration. DeYoung and Roland (2001) suggests why non-interest income may increase bank earning. Most bank loans are relationship based and as a result have high switching cost, while most renewal fee based activities are service and loan based. Thus, despite credit risk and fluctuations in interest rates interest income from loans may be more volatile than non-interest income from fee based activities. In addition, within the context of an ongoing lending relationship, the main input needed to produce more renewal fee is typically for the services rendered by banks and more loans provided to the customer. Thus, fee based activities may require greater operating leverage than lending activities, which makes bank earning achieve growth in the long term.
61 4.1.7 Variance Analysis
It is a collection of statistical models, the difference among group means and their associated procedures used as variation among and between groups. A variety of techniques are used with multiple factor ANNOVA to reduce cost. One techniques used in factorial designs is to minimize replication and to combine groups when effects are found to be statistically (or practically) insignificant. An experiment with many insignificant factors may collapse into one with a few factors supported by many replications. Table 4.8: Variance Analysis with ROA
Model 1
Sum Squares Regression Residual Total
of Df
33.716 103.556 137.272
8 110 118
Mean Square 4.215 0.941
F
Sig.
4.477
.000
Table no. 4.8 shows the output of the ANNOVA analysis and whether we have a statistically significant difference between group means. The data reveals that the significance level is 0.000 which is less than 0.05 concluding there is a significant difference in the mean of selected bank internal variables with return on assets. Table 4.9: Variance Analysis with ROE
Model 1
Sum Squares Regression Residual Total
9562.202 178628.1 188190.3
of df 8 110 118
Mean Square 1195.28 1623.89
F
Sig.
0.736
0.659
Table no. 4.9 shows the output of the ANNOVA analysis and whether we have a statistically significant difference between group means. The data reveals that the significance level is 0.659 which is greater than 0.05 concluding there is no significant difference in the mean of selected bank internal variables with return on equity.
62 4.2. Primary Data Analysis
The output derived from the study of primary data provides the insight on the real world experience of financial managers in determining the factors affecting the bank profitability. α = 0.932 The study found that Cronbach's Alpha records 0.932 which is greater than 0.7, thus the data taken into consideration for the study are reliable.
Fig. 4.1: Analysis of Relevance of Non-Interest Income.
Figure no.4.1 shows that, out of 90 respondents, 8.77% i.e. 8 respondent feel that noninterest income is not significant relevant to the net profit of the commercial banks. Similarly, 31.57% i.e. 28 respondent feels that it is highly significant relevant to the net profit of the commercial banks. However, larger chunk 59.64% i.e. 54 respondent feels that the non-interest income is significantly relevant to the net profit of the commercial bank.
63
Fig. 4.2: Analysis of Risk in Noninterest Income
Figure no. 4.2 shows that, out of 90 respondents, 26.31% i.e. 23 respondent perceive that there is low risk in noninterest income. Similarly, 14.03% i.e. 13 respondent perceive that there is high risk in noninterest income. However, larger group 59.64% i.e. 54 respondent feels that there is moderate risk in non-interest income. Table: 4.10 Analysis of Factors Having High Concentration on NII Factors having high concentration on non-interest income Minimum
Maximum
Mean
Rank
Profit/Loss on Sale of Asset
1
8
4.63
4
Dividend Income
1
8
5.18
1
Letter of Credit Charge/Fee
1
8
4.67
3
Guarantee Commission
1
8
4.37
5
Remittance Fee
1
8
4.00
7
Exchange Income
1
8
4.16
6
Service Charge
1
8
3.95
8
Renewal Fee
1
8
4.89
2
Table no. 4.10 shows the factors that have high concentration on non-interest income. In the above table Rank 8 indicates the most concentrated factors and Rank 1 indicates the least concentrated factors. As per the response, service charge and remittance fee is considered to be the two highly concentrated factors among eight factors and dividend income and renewal fee as the two least concentrated factors among eight factors.
64 4.2.1 Analysis of Likert Scale Table 4.11: Analysis of Variables Affecting the ROA of Commercial Banks One-Sample Test Min.
Max.
Mean
Statement
Std.
t
Sig. (2-tailed)
Deviation
Profit on Sale of Asset affect ROA
1
5
3.58
0.96
4.54
0.00
Dividend Income affect ROA
2
5
3.67
0.81
6.22
0.00
Letter of Credit affect ROA
2
5
3.82
0.85
7.34
0.00
Guarantee Commission affect ROA
1
5
4.04
1.02
7.68
0.00
Remittance Fee affect ROA
1
5
4.07
0.98
8.25
0.00
Exchange Income affect ROA
1
5
4.07
0.92
8.75
0.00
Service Charge affect ROA
2
5
4.16
0.77
11.29
0.00
Renewal Fee affect ROA
1
5
4.04
0.94
8.28
0.00
Table no. 4.11 shows the analysis of likert scale data to test the significance of various factors. Eight statements of different variables are provided to analyze the significant test. To signify the importance between profitability and other variables test value of 3 has been taken. The interpretation of each statement is as follows:
Profit/(loss) on sale of asset is one of the determinants that affect ROA. This statement has been agreed by all the respondents. The agreement level of this statement is strong enough that the mean value of the satisfaction is 3.58. One sample t-test has been used to test the significance of profit/loss on sale of assets of commercial banks. The p-value is 0.00 which is less than 0.05, indicating it is a significant factor that affects profitability.
For the statement dividend income affecting the ROA, mean value of satisfaction is 3.67. This proves respondents agree to the above statement. The p-value is 0.00 which indicates a significant relationship.
For the statement letter of credit affecting ROA, the mean value is 3.82 which is agreement level to this statement. The p value is 0.00 i.e. <0.05, which indicates that the statement is significant.
65
For the statement guarantee commission affecting the ROA, mean value is 4.04 which is high level of agreement to the above statement. The p-value is 0.00 which indicates a significant relationship.
For the statement remittance fee affecting the ROA, mean value is 4.07 which is high level of agreement to the above statement. The p-value is 0.00 which indicates a significant relationship.
For the statement exchange income affecting the ROA, mean value is 4.07 which is high level of agreement to the above statement. The p-value is 0.00 which indicates a significant relationship.
For the statement service charge affecting the ROA, mean value is 4.16 which is high level of agreement to the above statement. The p-value is 0.00 which indicates a significant relationship.
For the statement renewal fee affecting the ROA, mean value is 4.04 which is high level of agreement to the above statement. The p-value is 0.00 which indicates a significant relationship.
Table 4.12: Analysis of Variables Affecting the ROE of Commercial Banks One-Sample Statistics Sig. (2-tailed) Std.
t
Statement
Min.
Max.
Mean
Deviation
Profit on Sale of Asset affect ROE
1
5
3.82
0.78
7.96
0.00
Dividend Income affect ROE
1
5
3.79
0.80
7.49
0.00
Letter of Credit affect ROE
2
5
3.82
0.95
6.57
0.00
Guarantee Commission affect ROE
2
5
4.07
0.90
8.94
0.00
Remittance Fee affect ROE
2
5
4.16
0.77
11.29
0.00
Exchange Income affect ROE
2
5
4.16
0.82
10.67
0.00
Service Charge affect ROE
1
5
4.16
0.96
9.11
0.00
Renewal Fee affect ROE
2
5
4.14
0.85
10.08
0.00
Table no. 4.12 shows the analysis of likert scale data to test the significance of various factors. Eight statements of different variables are provided to analyze the significance test. To signify
66 the importance between profitability and other variables test value of 3 has been taken. The interpretation of each statement is as follows:
Profit/(loss) on sale of asset is one of the determinants that affect ROE. This statement has been agreed by all the respondents. The agreement level of this statement is strong enough that the mean value of the satisfaction is 3.82. One sample t-test has been used to test the significance of profit/loss on sale of assets of commercial banks. The p-value is 0.00 which is less than 0.05, indicating it is a significant factor that affects profitability.
For the statement dividend income affecting the ROE, mean value of satisfaction is 3.79. This proves respondents agree to the above statement. The p-value is 0.00 which indicates a significant relationship.
For the statement letter of credit affecting ROE, the mean value is 3.82 which is agreement level to this statement. The p value is 0.00 i.e. <0.05, which indicates that the statement is significant.
For the statement guarantee commission affecting the ROE, mean value is 4.07 which is high level of agreement to the above statement. The p-value is 0.00 which indicates a significant relationship.
For the statement remittance fee affecting the ROE, mean value is 4.16 which is high level of agreement to the above statement. The p-value is 0.00 which indicates a significant relationship.
For the statement exchange income affecting the ROE, mean value is 4.16 which is high level of agreement to the above statement. The p-value is 0.00 which indicates a significant relationship.
For the statement service charge affecting the ROE, mean value is 4.16 which is high level of agreement to the above statement. The p-value is 0.00 which indicates a significant relationship.
For the statement renewal fee affecting the ROE, mean value is 4.14 which is high level of agreement to the above statement. The p-value is 0.00 which indicates a significant relationship.
67 4.3. Summary of test in Hypothesis 4.3.1. Based on Secondary Data
Multiple Regression Findings Independent Variables
Relationship with ROA
Relationship with ROE No Significant
Profit/ (Loss) on Sale of Assets
No Significant
Dividend Income
No Significant
No Significant
Letter of Credit
No Significant
No Significant
Guarantee Commission
Significant
Significant
Exchange Fee
Significant
Significant
Remittance Fee
Significant
Significant
Service Charge
Significant
Significant
Renewal Fee
Significant
Significant
4.3.2. Based on Primary Data
One Sample t-test Independent Variables
Relationship with ROA
Relationship with ROE
Significant
Significant
Significant
Significant
Letter of Credit
Significant
Significant
Guarantee Commission
Significant
Significant
Exchange Fee
Significant
Significant
Remittance Fee
Significant
Significant
Service Charge
Significant
Significant
Renewal Fee
Significant
Significant
Profit/ (Loss) on Sale of Assets Dividend Income
68
CHAPTER FIVE SUMMARY AND CONCLUSION 5.1 Summary of Findings
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 commission, exchange income, service charge and renewal fee. Many of the variables like dividend income, profit/ loss on sale of assets, letter of credit, guarantee commission, remittance commission, exchange income, service charge, renewal fee also impact on the profitability of the bank. The study has investigated the relationship of this variable with profitability of commercial bank. The indicator of the bank’s performance is determined by return on assets and return on equity. The specified objective of this study is to investigate the relationship between selected internal bank variables and banks profitability in terms of ROA and ROE and to find out the different dominant factors influencing non-interest income of commercial banks of Nepal. After conducting the research on non-interest income factors affecting performance on commercial banks, a number of positive, negative, significant and no significant results were observed. The major findings from secondary data analysis are:
The average yearly index for ROA and ROE are 1.67 and 13.07 during the study period (2010-2014) with a standard deviation i.e. 1.078 and 38.72 respectively implying a medium volatility in commercial bank.
ROA and ROE records minimum of 0.00, -361.36 and maximum of 8.15 and 102.96 respectively. The mean of profit/(loss) on sale of assets records 11465.25 (in 000’s), dividend income 5838.77 (in 000’s) and renewal fee 3719.06 (in 000’s).
Positive value on skewness tests for ROA, PSA, DI, LC, GC, RF, EI, SC and REF suggest that these variables have long right tails, while negative value of the skewness
69 tests for ROE had extreme values during the study period which suggest that these variables have long left tails. It indicates a deviation from normal distribution of the data and volatility in that parameter.
Kurtosis value of EI is less than three, the distribution of these variables exhibit nonnormality. The kurtosis value of ROA, ROE, PSA, DI, LC, GC, RF, SC and REF are more than three, the distribution of these variables exhibit normality.
The value of VIF factor suggests that there is no multi-collinearity among chosen variables.
According to the trend analysis, selected banks has earned highest in the year 2014 from all non-interest income. The income from NII is increasing every year except for income from PSA and DI which have fluctuation in their earnings.
According to the percentage analysis of ROA of commercial banks, the top 5 which records highest are Lumbini, Standard Chartered, Nabil, Agriculture Development and Nepal Bangladesh. In contrast, the lowest 5 commercial banks that record lowest return on assets are Grand Bank, Machhapuchre bank, Nepal bank, Janata bank, and Sunrise bank.
According to the percentage analysis of ROE of commercial banks, it shows that Grand Bank Limited and Nepal Bank Limited record the negative return on equity. Similarly, Janata bank, Machhapuchre bank and Sunrise bank records lowest return on equity whereas five commercial banks; Nepal Bangladesh bank, Nepal Investment bank, Standard Chartered bank, Everest bank and Nabil bank records highest ROE.
According to Pearson Correlation Coefficient, there is positive correlation between ROA with DI, LC, GC, RF, EI, SC and REF whereas there is negative correlation with PSA.
Similarly, there is positive correlation between ROE with DI, LC, GC, RF, EI, SC and REF whereas there is negative correlation with PSA
Likewise, the corresponding p-value shows that there is no statistical significant correlation of PSA, DI, REF with ROA whereas there is statistical significant relationship between ROA and LC, GC, EI, RF, SC. Also p-value shows that there is no statistical significant relationship between ROE and selected bank internal variables.
70
The multiple regression analysis indicates that PSA, DI, and LC are insignificant in influencing return on assets. Also there exists significant relationship of ROA with GC, RF, EI, SC and REF.
The regression analysis with ROE model indicates there is significant relationship of ROE with GC, RF, EI, SC and REF and insignificant relation with PSA, DI and LC.
The study confine there is no auto correlation in Nepalese commercial bank and selected bank internal variables since the value is near to 2.
According to variance analysis, there is significant difference in the mean of selected bank internal variables with ROA and no significant differences in the mean of selected bank internal variables with ROE.
The major findings from primary data analysis are:
According to the survey, larger group i.e 59.64% of respondent felt that the non-interest income is significant relevant to the net profit of the commercial bank.
In the analysis of risk, larger group i.e. 59.64% respondent feels that there is moderate risk in non-interest income.
In the analysis of lesson learned for banker from loss incurred due to reliance in NII, 57.89% of respondent thought that tightening of internal process and policy could be the lesson, 59.65% of respondent thought good corporate governance could be the lesson and 42.11% of respondent thought in-depth study of customer profile could be the lesson for banker.
The survey shows that service charge and remittance commission are the two highly concentrated factors among eight factors and dividend income and renewal fee are the two least concentrated factors among eight factors that have high concentration on NII.
The result shows that 49.12% of respondent gave their opinion that NRB regulation could be the reason for difficulty in increasing non-interest income. Similarly, 57.89% of respondent gave their opinion that market competitiveness could be the reason, 43.86% gave their opinion that depth study research and development could be the reason.
Likert scale analysis shows that all selected bank internal variables (PSA, DI, LC, GC, RF, EI, SC and REF) have significant relationship with both ROA and ROE.
71 5.2 Conclusions
The research was conducted to study the impact of bank specific variables on banks profitability. The analysis of the secondary data and primary data showed that the performance of the commercial banks in Nepal is influenced by non-interest income which is consistent with the finding of Saunders et al., (2014) who found that higher ratio of non-interest income to interest income is associated with a higher profitability across the banking sector and under different market regimes. The outcome of the study revealed that there is no statistical significant correlation between ROA with profit/ (loss) on sale of assets, dividend income, renewal fee. In contrast there is a statistical significant correlation between ROA with letter of credit, guarantee commission, remittance fee, exchange income and service charge. Sherene (2010) found that service charges and fees in particular are highly correlated with net income which is consistent to our finding. Similarly, there is no statistical significant correlation between ROE with all s elected bank internal variables which is in contrast with the finding of Gamra (2011) who found that foreign exchange income and other income have a statistical significant relationship with risk adjusted ROE and fee and commission income has shown insignificant relationship. As per regression coefficient, the study found the significant relationship between majority of bank variables and performance of selected commercial banks. Study found out that there is negative and no significant relationship of ROA with profit/ (loss) on sale of assets, and letter of credit. However there is positive and no significant relationship with dividend income. Also the result founds positive and significant relation of ROA with guarantee commission, remittance fee, exchange income and service charge and negative and significant relation with renewal fee. In regard with ROE, there is negative and no significant with profit/ (loss) on sale of assets, whereas positive and no significant relation with dividend income, and letter of credit. Furthermore, there is positive and significant relation with guarantee commission, remittance fee, exchange income, service charge and renewal fee. The finding of our study is consistent with theories and findings of other scholars. Craigwell and Maxwell (2015) found that bank characteristics and the ATM technology (service charge and
72 renewal fee) as the most influential factors shaping the trend of non-interest income in the banking industry in Barbados and suggests that non -interest income is positively related to both bank profitability and earnings volatility. Damankah (2014) found that ATM technology (fee income) is one of the main microeconomic factors driving the performance in non-interest income in the commercial banking sector and interest rate and foreign exchange rate volatility are the key macroeconomic factors which explain the performance in non-interest income. Same study 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. The researcher used variance (ANNOVA) analysis assumptions, including and excluding Grand bank and Nepal Bank from the sample. Since Grand Bank and Nepal Bank have extreme negative ROE during the study period. Researcher excluded these banks from sample and found out the significant difference in the mean of selected bank internal variables with ROE. However including those banks in the sample, the study found the no significant difference in the mean of selected bank internal variables with ROE. Consequently, the study found there is no difference in significance with ROA while including and excluding the Grand and Nepal Bank. The study found there is significant difference in mean of selected bank internal variables with ROE in both the cases. Also from trend analysis, it can be concluded that all the selected variables was highest in the year 2014 among the study period. As 2014, enjoyed good indicators in market due to high remittance inflow, tourism flow due to Nepal Tourism Year 2014 and other macro-economic variables trend analysis also seems to record remarkable improvement in data of selected commercial bank in study period. Also the year 2014 was sparked with a new flicker of hope of making new constitution by CA due to which the investment was also high. Also there was a hope of political stability and creation of investment friendly environment. The year 2014 was also the hosting of 18th summit of South Asian Association for Regional Cooperation. The year was also indicated by the increment of imports mainly industrial products by 25.7%. The balance of payment surplus ended at the end of first quarter of 2013/14 stood at $513.505m and foreign exchange reserves $6.615b which indicated the increment in exchange income. (FES, Nepal)
73 Apropos of the study objective, the researcher used qualitative and survey techniques to find out the significant relationship of performance and selected bank internal variables. The primary respondent of the study were the employees of the selected commercial bank of different post. These were selected for the real implication of research to understand the viewpoint of financial future managers in determining the factor affecting the bank performance. Among the variables taken into consideration, the researcher used mean analysis to find out the high concentration of selected variables on non-interest income. Hence, the study found that service charge has high concentration and dividend income has low concentration on NII which is consistent with the finding of Lelissa (2014) who found the ratio of service charge to gross income which mainly is a measure of income from foreign operation and hence the level of income diversification or business mix has a positive impact on profitability. This paper discusses the impacts of non-interest income in commercial banks of Nepal, as well as examines the important variables that have significant impact on non-interest income in the Nepalese banking system. It finds that the incidence of noninterest income in Nepalese commercial bank is significant consistent to other countries in the 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 and credit scoring, have not yet taken root in Nepal. Guarantee commission, remittance fee and service charge seems to be the most influential factors shaping the pattern of non-interest income in the banking industry in Nepal, results confirmed by an empirical model using panel data. Furthermore, increases in non-interest income are linked to greater bank profitability and increased performance measured by ROA and ROE. 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 wellstudied 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.
74 5.3 Recommendation
The finding provides an insight into the characteristics and practices of successful commercial banks in terms of profitability. In the view of these, the following recommendations can be made which may be useful for bank management, policy makers, shareholders and further researchers.
The format of financial reports and extent of disclosure seemed to vary among the banks. As a result, continuous data for the full sample period were not available for some explanatory variables, which had to be neglected for the purpose of the study. Also the unavailability of financial data in the website becomes a difficulty for researcher to extract the data.
Banks which have been ignored from this study due to unavailability of data can be considered for the purpose of further study.
For this study, only class “A” commercial banks has been considered. Further research can be done by taking into consideration of “B” and “C” class financial institution
As the data reveals that remittance has significant relationship with ROA and ROE, the recommendation drawn from these result for commercial banks would be new reforms and innovation in remittance funding that would increase credit easing of commercial banks. Since remittances might help relax households’ financing constraints, the demand for and the overall level of credit might fall as remittances increase. Regardless of remittance recipients’ demand for credit, overall credit levels might still increase in remittance receiving areas if banks channel the increased liquidity from remittance deposits to previously unfunded or underfunded projects.
Exploring the primary data, the researcher found 59.65% respondent think that good corporate governance should be lesson for the bank. Hence, the researcher recommends commercial bank should practice good corporate governance to increase the performance from non-interest income.
The researcher further suggests that commercial bank should practice rigid customer profile process to prevent any further losses arising from non-interest income.
75
The study found out 57.89% of respondent gave their opinion that NRB regulation could be the reason for difficulty in increasing noninterest income. Hence, to increase the performance of commercial bank and market efficiency, NRB should deregulate and introduce financial reform in the country to increase the sustainability of Nepalese commercial banks.
The study recommends that there is also need for the finance manager of commercial banks to note that there is some evidence to suggest that higher noninterest income may lead to improve financial performance. This may be attributed to stability of income flow in the financial institution.
76
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80 Rogers, K., and Sinkey, F.J. (1999). An analysis of nontraditional activities at US commercial banks. Review of Financial Economics, Vol ( 8), pp. 25-29. Saunders, A., Schmid, M. and Walter, I. (2014). Non-interest income and bank performance: Is banks increased reliance on non-interest income bad? Working paper on finance no. 2014/17 of Swiss Institute of Banking and Finance, University of St. Gallen, Switzerland. Silber, R. and Udell (1996). Money Banking and Financial Markets, 11th edition. New York: Pearson Education. Smith, R., Staikouras, C., and Wood, G. (2003). Non-interest income and total income stability An unpublished working paper no.198, 2003 of Bank of England. Stiroh, K.J. (2002). Diversification in Banking. Is non-interest income the answer? An unpublished staff research report of Federal Reserve Bank of New York, New York. Tapper, S.A. (2010).Non-interest income, financial performance and the macro economy: Evidence on Jamaican panel data. A CBB working paper 2012 of Central Bank of Barbados,Research and Economic Analysis Depar tment.
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81 Website Visited http://bfr.nrb.org.np/circular/2071-72/2071_72_For_A_%2c_B_&_C_Class--Circular_13Provision%20regarding%20Consumer%20Protection%20&%20Financial%20Literacy.pdf http://bfr.nrb.org.np/directives/Guidelines--Banking_Service_Fee_Guidelines_2067.pdf http://www.fesnepal.org/reports/2014/annual_report/annual_report_2014.htm (Nepal in the Year 2014: A Glance) http://www.nrb.org.np/red/publications/study_reports/Study_Reports-A_Report_on_Real_Estate_Financing_in_Nepal%20-%20A_Case_Study.pdf http://www.nrb.org.np/bfr/statistics/bank_fina_statistics/Banking_and_Financial_Statistics- No_60%20July%202014.pdf http://www.nrb.org.np/red/publications/economic_bulletin/Quarterly_Economic_Bulletin--201410_(Mid_October).pdf http://www.spotlightnepal.com/News/Article/CRISIS-IN-BANKS-Banking-on-Un-Real-Estatehttp://www.nepalenergyforum.com/hydropower-promise-in-nepal/
82
ANNEXURE
A1: Questionnaire
Dear Sir/Madam, My name is Santosh Nepal, MBA student at Ace Institute of Management and I am conducting this survey as partial fulfillment for the completion of Masters in Business Administration at Ace Institute of Management affiliated to Pokhara University. The general purpose of this study is to evaluate the “Impact of non-interest income on performance of commercial banks of Nepal”. I would like to invite your participation in this survey by filling up this questionnaire. All information will be treated with strict confidentiality. All the questions are for academic purpose only. Part I- GENERAL INFORMATION
1. How long you have been associated with banking sector? 1. 1-3 yrs
2. 3-6 yrs
3. 6-9 yrs
4. 9 yrs and above
Part II- BANK PERFORMANCE QUESTIONAIRE
2. Tick the relevance of non- interest income to the net profit of commercial banks. 1.
Non-Significant
2. Significant
3. Highly Significant
3. How do you perceive risk in non-interest income? 1. Low
2. Moderate
3. High
4. What could be the lesson for banker from loss incurred due to reliance in Non-Interest Income? (e.g. Melamchi, Rahughat Project Related) (You can tick in more than one )
1.
Tightening of internal process and policy
2.
Good corporate governance
3.
In-depth study of customer profile
4.
Any other Factor:………………………..
83 5. Give your opinion about the difficulty in increasing non-interest income? (You can choose more than one )
1.
NRB Regulation
2.
Market Competitiveness
3.
Lack of Good Customer
4.
Depth study Research and Development
5.
Slackness in Economy
6.
Any other Factor: ……………………………….
6. Rank the factors that have high concentration on non-interest income.(Rank 1 being the lowest and 8 being the highest)
1.
Profit on sale of asset
2.
Dividend income
3.
Letter of Credit Charge/Fee
4.
Guarantee Commission
5.
Remittance Commission
6.
Exchange Income
7.
Service Charge and Processing fee
8.
Renewal Fee
7. Reason bank divert from interest income activities to non-interest income activities (fee and charge based income) 1.
Market condition
2.
Competition
3.
Investment opportunity
4.
Risk reduction
5.
Higher profit
6.
Diversion in Income
84 8. Please give your opinion on variables affecting the ROA of commercial banks. Strongly
Disagree
Neutral
Agree
Disagree
Strongly Agree
Profit on Sale of Asset affect ROA Dividend Income affect ROA Letter of Credit affect ROA Guarantee Commission affect ROA Remittance Commission affect ROA Exchange Income affect ROA Service Charge and Processing Fee affect ROA Renewal Fee affect ROA
9.
Please give your opinion on variables affecting the ROE of commercial banks. Strongly Disagree
Profit on Sale of Asset affect ROE Dividend Income affect ROE Letter of Credit affect ROE Guarantee Commission affect ROE Remittance Commission affect ROE Exchange Income affect ROE Service Charge and Processing Fee affect ROE Renewal Fee affect ROE
Disagree
Neutral
Agree
Strongly Agree
85 A2: Trend Analysis of Selected Variables of Commercial Banks
86
87
88
A3: Percentage Analysis
89
90
