1.1 Background of the Study
Banking is an integral part of our everyday life. Banks have a vital function in the economy. They have easy access to funds through collecting savers’ money, issuing debt securities, or borrowing on the inter-bank markets. The funds collected are invested in short-term and long-term risky assets, which consist mainly of credits to various economic actors (individuals, companies and governments) (Striroh, 2006).
Traditional functions of banks are to take deposits and make loans, and they profit by the difference between the costs of the former and the earnings from the latter activities. However, the profitability of traditional banking activities such as business lending and raising deposits has diminished in recent years as described by Abreu ; Mendes, (2000). As a result, banks have increasingly turned to new, nontraditional financial activities as a way of maintaining their position as financial intermediaries. The changes are of importance for financial stability. Hoggarth et al., (1998) concluded that the more unstable is a bank’s earnings stream, the more risky is the firm.
Financial Performance refers to subjective measure of how well a firm can use assets from its primary mode of business and generate revenues. This term is also used as a general measure of a firm’s overall financial health over a given period, and can be used to compare similar firms across the same industry or to compare industries or sectors in aggregation. The financial position and performance is affected by the operation decision when assets are used effectively to increase profit (Abreu and Mendes, 2000). Operation decision indicates the effectiveness of the company management in making profit from asset. Therefore, operational efficiency can be achieved by dividing sale or revenue with total assets (Saira et al., 2011). Thus, financial performance analysis of banks has been of great interest to academic research since the Great Depression Intern the 1940’s. In the last two decades studies have shown that banks in Sub-Saharan Africa (SSA) are more profitable than the rest of the world with an average Return on Assets (ROA) of 2 percent ( Flamini et al., 2009).
Banks derive their income from interest, non interest and other sources of incomes, but heavily relying on interest income. However, banks are nowadays experiencing significant new competition and have somehow lost valuable regulatory protection. The implication of this is reduction in profit margins and deposit intermediation. Thygerson (1995), argues that regulations that for instance facilitated banks to earn interest rate on loan at market rate, while on the other hand paying depository at rate below the market rate to some extent guarantee positive net interest margins. With introduction of financial sector liberalization coupled with heavy capital requirements by the regulator, banks have been exposed to intense competition, even from non banking institutions leading to downward pressure on intermediation profit margin. A bank is a financial institution licensed to receive deposits and make loans as stated in BAFIA Act of 1991. Banks channel funds from the ones with excess (i.e. Surplus Spending Units) to the ones with deficits (i.e. Deficits Spending Units), as a main role banks play in the economy of every country in the world including Tanzania. Banks do influence the growth of economy through channeling financial resources to the most productive sectors. For example, in Tanzania, liberalization of the financial system in 1991 has led to introduction of many banks and this to some extent has led to an increase of GDP in Tanzania. On the other hand, banks should also generate profits for the owners.
Non-Interest Income has been defined by Khrawish, (2011) as revenue that banks earn from areas outside their lending operation or any income that bank earns from activities other than their core intermediation business (taking deposit and making loans) or from investment. Examples of 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 (Kaufman & Mote, 1994).
Fee income covers most income that is neither interest income nor bank charges. This 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 (Brealey & Myers, 2011). Non-interest income consists of statement of comprehensive income line items which are;- net fee and commission income, net insurance premium income, net insurance claims and benefits paid, changes in investment contracts and insurance contract liabilities, gains and losses from banking and trading activities, gains and losses from investment activities as well as other operating income, (Kaufman & Mote, 1994).
Advances in information and communications technology (for example, the Internet and Automatic Telling Machines (ATMs), new intermediation technologies for processes like loan securitization and credit scoring, and the introduction and expansion of financial instruments and markets (high yield bonds, commercial paper, financial derivatives) all impacted on the levels and types of non-interest income at commercial banks ( Lown et al., 2000).
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 found out (Short ,1979). As noninterest income trended up during the 1990s, it was generally believed that shifting banks’ income away from intermediation-based activities (in which bank income was subject to credit risk and interest rate risk), and toward fee-based financial products and services, would reduce banks’ income volatility (Hoggarth et al., 1998). Moreover, it was conventionally believed that expansion into new fee-based products and services reduced earnings volatility due to diversification effects.
According to Ritter & Udell (1996), this source of revenue has become more important in recent times as banks have shifted from traditional interest income to more non-traditional sources of revenue, known as non-interest or fee income. These sources of income have a great growth significant in non-interest income. There are various sources of non-interest income that have been discussed according to Ngugi (2003), who suggest that noninterest income is generated as a result of three information function of intermediation namely origination services and portfolio management.
Bank loans are relationship based and as a result have high switching costs, while most fee-based activities are not relationship based (Angbazo,1997).Thus, despite credit risk and fluctuations in interest rates, interest income from loans may be less volatile than non-interest income from fee-based activities. Within the context of an ongoing lending relationship, the main input needed to produce more loans is variable (interest expense); in contrast, the main input needed to produce more fee-based products is typically fixed or quasi- fixed (labor expense). Thus, fee-based activities may require greater operating leverage than lending activities, which makes bank earnings more vulnerable to declines in bank revenues (Williams and Prather, 2010).
Fee-based activities require banks to hold little or no fixed assets, so unlike interest based activities like portfolio lending, fee-based activities like trust services, mutual fund sales, and cash management require little or no regulatory capital. Thus, fee based activities likely employ greater financial leverage than lending activities reports CBK (2007). Using data from U.S. banks during the 1990s, the authors demonstrate that three traditional streams of income from intermediation activities – interest from loans, interest from securities, and service charges from deposits – were all less volatile than income from fee-based activities (Stiroh and Rumble, 2006).
According to Ritter and Udell (1996), this source of revenue has become more important in recent times as banks have shifted from traditional interest income to more non-traditional sources of revenue, known as non- interest or fee income. These sources of income have a great growth significant in non –interest income. They are various sources of non- interest income that have been discussed according to Thygerson (1993), that suggest noninterest income is generated because of three information function of intermediation namely origination, services and portfolio management.
According to Nairobi stock market report (2007), commercial banks in Kenya recorded a decrease in interest income by about 49% in the same period previous year. This long-term downward pressure on net interest margins have forced commercial bank to think of alternative sources of revenue that will ensure earning stability and also mitigate risk exposure (Thygerson, 1995). It is generally believed that diversification by a firm reduces risk, just as diversification of investments by an individual does. In both cases, however, whether the desired risk reduction effect is achieved does of course depend on the correlation between the different activities or lines of business and on the correlation between the prices of the different investments. Hence, there is need for bank to focus on other sources of revenue through value adding activities such as service charges, fees, commissions and foreign exchange dealing. According to Ritter & Udell (1996), this source of revenue became more important in recent times as banks have shifted from traditional interest income to more non traditional sources of revenue, known as non interest or fee income. These sources of income have a great growth significant in non interest income.
Analyzing of income and expense data of commercial banks shows that the dominant sources of revenue is loan interest and discount, Fieldman & Schmidt (1999) found that over 20 years non- interest income has transformed for supportive role into a major contributor of banks revenue.
The Tanzania banking industry is one of the broadest and most developed in Sub-Saharan Africa (SSA) with 59 banking financial institutions. The profitability of Tanzania’s banking industry in the recent past has been a subject of public interest and debate. Advances in information and communications technology (for example, the Internet and Automatic Telling Machines (ATMs), new intermediation technologies for processes like loan securitization and credit scoring, and the introduction and expansion of financial instruments and markets (high yield bonds, commercial paper, financial derivatives). All impacted on the levels and types of non-interest income at banks, helped by the process of deregulation.
The conventional view of bank shareholders is that they can diversify away any increases in idiosyncratic risk associated with increased non-interest income. Shareholders may be concerned about bank total risk due to the impact upon foregone investment and the need for active risk management by bank managers addressing information asymmetry and agency problems (Laeven ; Levine, 2007). Likewise, bank, total risk is important to bank regulators due to their concern about systemic risk and the potential for contagion to other banks within their regulatory ambit. Borrowers are also concerned about bank survival as they face information and agency costs in the event of bank failure requiring a change of borrowing relationship, the resulting switching costs reduce the intrinsic value of the bank-client relationship (Stiroh, 2004b).
1.2 Statement of the Problem
In Tanzania for the last two decade, the banking sector has been improving. The study indicated that deregulation and new technology have eroded bank comparative advantage and made it easier for non bank competitor to enter these market, hence their need to evaluate other form of portfolio other than depended on the deposit portfolio and loan interest. In case of Tanzanian market, the introduction of M-Pesa Services, M-PAWA Services and other mobile financial services has seen many bank transfer services as well as deposit services affected. The sharply drop in interest income have necessitated that bank should increase non-interest and other income to compliment the interest income, these will enable banks to maintain earning stability and as well as increase profit flow. Young ; Roland (1999) suggested that bank’s have responded to this phenomenon by shifting their product mix toward noninterest income by selling mutual funds and investment in money market / financial market or government securities.
Banks rely mainly on non-interest income sources so that they can achieve risk diversification. Thygerson (1995) argued that noninterest income is less susceptible to economic recession that may lead to loan delinquencies and losses, its then to offset loss brought by interest income. Roland (1997) observes that there are abnormal returns in the short run for fee-based activities.
Gardner & Cooperman (2002) stated that one measure of depository and institution risk exposure is their earnings volatility as depicted by volatility of their net interest Margin, return on assets and return on equity as measured by their standard deviation over time. In general, studies conducted find that combining banking and non-banking activities has the potential to reduce earnings instability of banks.
This study seeks to investigate the extent to which banks in Tanzania have adopted revenue diversification into non-interest sources and whether the effect of diversification has lead to earning stability. Does the diversification to non-interest income increase the performance of banks in Tanzania? The relationship between non-interest income and the financial performance of banks in Tanzania has not been studied and in particular, the use ROE and ROA as independent variables. In addition, similar studies in different countries show inconsistent results for example, the study by Köhler et.al (2013), Chiang et.al (2014), and Saunders et.al (2014). Thus, this study will be conducted with the intention of filling this gap.
1.3 Objective of the Study
1.3.1 Main Objective
The main objective of the study is to analyze the effect of non-interest income and financial performance of commercial banks in Tanzania
1.3.2 Specific Objectives
i) To find out the effect of non-interest income on Return on Asset
ii) To find out the effect of non-interest income on Liquidity Ratio
iii) To establish the effect of non-interest income on Asset Quality
iv) To find out the effect of inflation on Return on Asset
1.4 Hypothesis Development
Köhler et.al (2013), analyzed the impact of banks’ non-interest income share on risk in the German banking sector for the period between 2002 and 2010. Using linear and quantile regression estimators, they found out that the impact of non-interest income on risk depends on the business model of a bank. Their study has two important varying implications. First, they indicate that it might be beneficial for retail-oriented banks to increase their share of non-interest income to become more stable. However, investment-oriented banks, in contrast, become significantly less stable if they increase their non-interest income share. Their results generally imply that banks are more stable if they have a more diversified income structure and depend neither heavily on interest nor on non-interest income. Second, the decomposition of non-interest income into fee and commission and trading income shows that impact on bank stability comes from fee and commission income.
Saunders et.al (2014), studied the relationship between non-interest income and bank performance, to answer the question: Is Banks’ Increased Reliance on Non-Interest Income Bad? The study was based on a sample of 368,006 involving quarterly observations on 10,341 US banks. The study period was the years from 2002-2013. They found that a higher ratio of non-interest income to interest income is associated with a higher profitability across the banking sector and under different market regimes. Banks with a higher fraction of non-traditional income are also shown to have a lower insolvency risk as measured by the Z-score, and recovered faster after the 2007-09 crisis. Their results hold across bank size groups and are robust to the inclusion of bank fixed effects, bank size, and various measures of leverage and asset quality in the regressions.
Williams (2014), studied on the Impact of non-interest Income on Bank Risk in Australia. They concentrated on the relationship between bank revenue composition and bank risk in Australia, using data drawn from Australian bank confidential regulatory returns. This study found that those banks with lower levels of non-interest income and higher revenue concentration are less risky. Non-interest income is found to be risk increasing, but it is proposed that some types of non-interest income may be risk reducing when bank specialization effects are considered. It is concluded that care must be taken when selecting the appropriate peers for benchmarking, to reflect difference in income composition.
Chiang et.al (2014), did cross-country analysis to examine the non-interest income, profitability, and risk in banking industry: A cross-country analysis. The study used a bank accounting data for 22 countries in Asia over the period 1995–2009 and applied the dynamic panel generalized method of moments technique to investigate the impacts of non-interest income on profitability and risk for 967 individual banks. The study found that non-interest activities of Asian banks reduce risk and decrease profitability as well as increases risk for savings banks. On the other hand, non-interest activities raise risk for banks in high income countries, while increasing profitability or reducing risk for banks in middle and low income countries. Finally, authors revealed that the persistence of risk is greatly affected by bank specialization and a country’s income level.
As from the above results, the hypothesis has been developed to measure the impact of non-interest income on banks performance in Tanzania.
H01: There is no effect of non interest income on Return on Asset.
H02: There is no effect of non-interest income on Liquidity Ratio
H03 : There is no effect of non-interest income on Asset Quality
H04 : There is no effect of inflation on return on Asset
1.5 Significance of the Study
This study seeks to investigate the relationship between non-interest sources and financial performance of banks in Tanzania and the nature extent of the relationship. 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 the banks may venture into to mitigate the changing business environment and increase profitability.
The research will contribute to body of knowledge by documenting the contribution and relationship of interest and non-interest income to the banks and the profitability in financial institution. 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. The information will enable shareholder to know which banks are able to invest and mitigate the uncertainty of future income through diversification and hence maximize the returns. The conventional view of bank shareholders is that they can diversify away any increases in idiosyncratic risk associated with increased non-interest income.
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. Bank total risk is important to bank regulators due their concern about systemic risk and the potential for contagion to other banks within their regulatory ambit. Borrowers are also concerned about bank survival as they face information and agency costs in the event of bank failure requiring a change of borrowing relationship, the resulting switching costs reduce the intrinsic value of the bank-client relationship.
1.6 Organization of the Study
The study consists of five Chapters. Chapter 1 describes background of research problem, statement of the problem, research objectives, hypothesis development, significance of the study and lastly the organization of the study. Chapter 2 is on the review of the literature related to the study while the Chapter 3 describes on the methods used in data collection and how such data were analyzed. Chapters 4 presents the data and findings from the research and provide a discussion of the results, it exhausts the findings in relation to the objectives put forward in the study. Chapters 5 conclude and provide recommendations of the study. The researcher concludes the study and put forward some recommendations in relation to the study.
This chapter present a review of literature related to purpose of the study. The chapter is organized according to specific objectives in order to ensure relevance of research problem.
The review has been undertaken in order to eliminate duplication of what has been done and provide a clear understanding of existing knowledge based on the problem area. The review is based on authoritative, recent and original sources such as journals, books theses and dissertations. The chapter covers the theoretical literature, empirical literature, the research gap and the conceptual framework.
2.2 Historical Background of Financial System in Tanzania
Monetary arrangements in Tanzania prior to l919 were different on the Mainland from those for Zanzibar, since the former was under German rule, while the latter had its own government. The currency on the Mainland was the German Rupee, made of silver, while the subsidiary coin was the Heller, which was 1/100 of the Rupee. In Zanzibar, the Indian Silver Rupee and its subsidiary coins were in circulation. In addition, shells and cattle were used to serve as a store of value, and, to a certain extent, even as a medium of exchange. Commercial banking was introduced in the country in 1905, when the Deutsch-Ostafrikanische Bank opened its office in Dar es Salaam. This bank had a concession from the German Government to issue its own notes and coins, which helped the bank to meet the demand for coins in exchange for its notes. A temporary mint was set up in Tabora. In 1911, another German bank, namely the Handelsbank fuer Ostafrika, opened a branch in Tanga. There also was an official savings bank.
After World War I (1914-1918), the Mainland became a mandate territory of the United Kingdom (UK) and its monetary system was aligned to that of Kenya and Uganda, mainly in two aspects, through the establishment of the EACB in December 1919 and by auctioning off the assets of the German banks and permitting British banks to open their offices.
The regulations defining the Constitution, Duties, and Powers of the EACB stated that it had been constituted to provide for, and to control the supply of, currency in the East African Protectorate, the Uganda Protectorate, and any other dependencies in East Africa, which might be added by the Secretary of State. This was to ensure that the currency was maintained in satisfactory condition, and generally, to watch over the interest of the dependencies as far as currency was concerned. Originally, the EACB operated in Tanzania Mainland, Kenya, and Uganda. Zanzibar adopted its currency in 1936. Other occupied countries joined the Board later, but withdrew from it again after some time. The Board itself stopped functioning in 1966, when Central Banks came into existence in Tanzania, Kenya, and Uganda.
The Board was authorized to issue its own currency notes and mint coins according to the designs approved by the Secretary of State for circulation in its area of operations. The Secretary of State fixed the rate of exchange between the Board’s currency and the pound sterling. Board currency was essentially issued in exchange for pound sterling, indicating that the EACB’s currency was backed predominantly by pound sterling.
The first President of Tanzania Mwalimu Julius K. Nyerere opened following the decision to dissolve the EACB and to establish separate Central Banks in Tanzania, Kenya, and Uganda, the National Assembly passed the Bank of Tanzania Act, 1965, in December 1965, and the Bank on June 14, 1966.
After independence the Government established new financial institutions, which are Tanzania Bank of Commerce in 1965 and People’s Bank of Zanzibar were formed in 1966 in Zanzibar, act as a Government Banker as well to provide financing to state owned companies in Zanzibar. Specialized financial institutions were formed such as Agriculture Credit Agency was established in 1962, later converted to National Development and Cooperative Bank in 1964.
The Act empowered the Bank of Tanzania to perform all the traditional central banking functions. However, within eight months of the inauguration of the Bank, in February 1967, the Arusha Declaration was proclaimed, and, with it, the Bank had to advance its roles and policies. Most of the traditional instruments of indirect monetary policy stipulated in the Act became inoperative, as there was no longer an environment of the type that existed in a competitive system, where indirect instruments were effective.
The Annual Finance and Credit Plan (AFCP), supported by a system of administered interest rates, were devised as the main instrument of monetary policy from 1971/72. Similarly, the Foreign Exchange Plan (FEP) was devised to control the use of foreign exchange in accordance with national priorities. The plans were formulated in the Ministry of Development Planning, in consultation with the Bank. However, the Bank and the banking system were responsible for their implementation. A system of direct controls was used for this purpose, as stipulated in the Exchange Control Ordinance and the Import Control Ordinance.
The 1960s and 1970s were broadly characterized by direct monetary control, marked by stringent exchange controls, direct credit to priority sectors preferential interest rates, excessive government borrowing, accumulation of bad debts by Commercial Banks, partly due to excessive interference by Government, lack of adequate supervision of financial institutions and pursuit of multiple policy objectives. The above situation resulted into a weak financial system characterized by among others, double digit inflation, negative real interest rates, decline in returns on formal financial assets, parallel markets, financial disintermediation and increase in cash transactions due to weaknesses necessitated the need to carry out a wide range of economic and financial sector reforms from the 1980s.
In 1967, after Arusha Declaration was proclaimed, and, with it, the Bank had to advance its roles, policies, led to all private banks were nationalized, and their assets and liabilities were merged resulting into establishment of one big bank named National Bank of Commerce that was wholly owned by Government. The period after the Arusha Declaration was marked by rapid development of non bank and development financial institutions. This was due to the increased role of public sector in development and the need to mobilize long-term funds to finance various productive sectors in the country. Tanzania Investment Bank (TIB) was established in 1970 to provide development finance to country productive sectors especially large-scale industry.
Another non-bank financial institution was Tanzania Rural Development Bank (TRDB), which was established in 1972 to provide financing to the rural sector. The Bank was later restructured and changed its name to Cooperative and Rural Development Bank (CRDB). In the same year, Tanzania Housing Bank (THB) was established to specialize in financing of rural and urban residential, office and commercial buildings. Other non- bank financial institutions, which were formed, are the National Insurance Corporation, Pension funds, and the Postal Office Savings Bank. Except for Tanzania Development Finance Limited (TDFL) and Diamond Jubilee Trust Fund, the Government wholly or partially owned all banks and financial institutions in the country and their managements were under Government direct control. The bulk of the lending from banks and financial institution was directed to government owned parastatal organizations in accordance with government approved national credit plans. In 1988, the government formed a presidential commission this was due to the banking industry was performed poorly, increased losses and non performing assets, increase of subsidies to the banks which was a burden to the Government. The presidential Commission of enquiry was formed 1988 to set the milestone for liberalization of the financial sector in Tanzania. The 1990s was the turning point in financial sector reforms spearheaded by the Bank of Tanzania.
Based on Commission, the bank embarked on a series of reforms in an effort to promote the development of a market based financial sector as a strategy to turn around the deteriorating economy, and accelerate economic growth. The Banking kicked off the strategy and Financial Institutions Act was adopted in 1991, which paved way for entrance of private, foreign and domestic investors in the financial sector.
Following recommendations come out from the commission Banking and Financial Act was established in 1991 to govern the conduct of banking business in Tanzania. The Act gave power to Bank of Tanzania to license, regulate, and supervise banks and financial institutions. It allowed entry of domestic banks and foreign banks in the market, thus introduce competition in financial sector.
In general, emphasis of the first generation of the financial sector reforms was to put in place a nice environment for a free market to operate and to provide quality and reliable financial services. The reform based to bring about a new financial landscape in Tanzania and also a new culture of doing business. The banking industry which comprises of three banks before the reforms saw a rapid increase in banking industry up to 59 financial institutions were registered at the end of 2016 with new products and services.
In Tanzania, we have Commercial Banks, Non commercial Banks as formal financial institutions and informal financial institutions such as Savings and Credit Cooperatives, Microfinance and others. Main players in the banking system are commercial banks since they are the largest and most significant funds providers in the banking system. Banking industry are the largest in the economy in deposit taking institutions.
2.3 Theoretical Review
2.3.1 Arbitrage Pricing Theory
Arbitrage Pricing Theory, first developed by Ross (1976) is an asset pricing theory that states that the anticipated investment return or financial assets can be modeled to form a linear correlation of different macroeconomic variables. The change in correlation extent is represented by a beta coefficient. Ross (1976) initiated the Arbitrage Pricing Theory to form an alternative to Capital Asset Pricing Model (CAPM) as a result of decreased satisfaction of applicability of CAPM on a theoretical and empirical basis. CAPM is measuring a single correlation between anticipated return and risk, using the beta, is based on the effectiveness of mean standard deviation of the market portfolio. CAPM, derived from initial principles of expected utility theory is consistent with recognized empirical view, and there is a normal variability in asset prices. However, Ross (1976) asserted that the suppositions of fundamentally expected utility theory did not employ standard variability, but CAPM made distinct between non-diversifiable and diversifiable risks. CAPM’s model is a linear model in which the typical variation in returns is due to one variable, and the real returns deviate from the standard variable by an extra random disturbance. This results in the assumption that the model is composed of two parts, one being random and other systematic. However, there exists a possibility of diversifying the random component, leaving investors with systematic risk. In APT, there are at least two variables and one not being an actual market value. APT model maintains majority intuitive CAPM outcomes and is developed on linear return generating process as one principle, but does not use any utility proposition apart from monotonicity and concavity for greed and risk aversion.
Arbitrage Pricing Theory is essential to this study to determine the correlation between non-interest income and profitability of banks in Tanzania, as it will give the opportunity to analyze variant variables. The strict testing of this model by Roll & Ross (1980), Chen, Roll & Ross (1986) as well as Lehmann and Modest (1988) makes it viable for this study.
2.3.2 Modern Portfolio Theory
Modern Portfolio Theory is also popularly known as MPT model. The MPT of economic theory considers the return of an asset as a random variable and considers the portfolio as the weighted combination of assets. Hence, the return of a portfolio, according to Modern Portfolio Theory, is defined as the weighted combination of the returns of the assets. The random variable taking the portfolio’s return has an expected value and a variance also. According to this model, risk is defined as the standard deviation of the return of portfolio (Brealey et al., 2011).
According to Modern Portfolio Theory, a quadratic utility function describes the investor’s risk and reward preference. This theory assumes that only the volatility and expected return of the portfolios matter to the investors. It has been seen that the investors are indifferent about the skew and kurtosis of the returns. In this theory, the volatility is considered as the proxy for risk and the return is the expectation on the future (Hickman et al., 2002).
2.4 Interest and Non-Interest Income and Profitability
According to the ECB survey (2000), drawing on a survey among EU supervisory authorities, net interest income as a percentage of total assets (the interest margin) continuously declined, as an EU average, over the period 1989-98. By contrast, during the same period, an increasing trend can be observed for the non-interest income to assets ratio (from 0.94% to 1.15% in the period 1995-98). Within Europe, a wide range of non-interest variation was observed. They also noted that non-interest income is less volatile in Europe than in the
United States. With regard to the most recent years, there has been a noteworthy increase from 32% in 1995 to 41% in 1998 in the relative importance of non-interest income (as a percentage of total operating income) in the EU.
The growth of non-interest income seems to have a positive effect on bank profitability. The positive impact on profitability has, however, been limited by the increased operating costs associated with the development of activities generating non-interest income.
Commercial bank main earnings can be classified as interest and non interest income. Couto (2002) provide a framework for the analysis of bank earnings. He classifies determinants of earnings in structural and secondary categories. Structural categories include net interest income, fee income operating expenses. Secondary determinants include provisions of loan losses, incomes after secondary charges, profit/ loss from banking activities and non-banking subsidiaries. Moreover, they identify that the sensitivity of earnings to changes in interest rates, spreads, loan volumes, delinquency and other factors is an important questions in the analysis of earnings.
2.5 Factors Affecting Bank Profitability
Bank profitability is influenced by both internal factors and external factors which management or shareholders of firms can’t control. This section will debate a number of factors that affect bank profitability with empirical evidence.
2.5.1 Non-interest Income
Bank’s non-interest income is the proceeds mainly from service and penalty charges, asset sales and property leasing. Commercial banks sources of income include interest income, non-interest income and other incomes. Interest income is also known as traditional source of income. Most commercial banks in Kenya rely significantly on traditional source of income. However, this source of income has lost important regulatory protection as new competition has emerged from non-bank financial institutions that have significantly reduced interest income earned by commercial banks (Atellu, 2014).
In his study, Köhler et al., (2013), established that banks with a 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. On contrary, investment-oriented banks become significantly less stable so he recommends that larger and more investment-oriented banks should increase their share of interest income to become more stable. This shows that non-interest income affects bank profitability.
2.5.2 Capital Adequacy
Capital adequacy the ratio of total capital to total risk weighted assets. The Signaling Theory argues that there is a positive relationship between a bank`s profits and its level of capital. One of the indicators of bank`s profitability is capital adequacy. From the literature, this variable is measured by the ratio of capital and reserves of each commercial bank to total assets or as the ratio of equity to total assets of a bank. Generally banks with high capital ratio, other factors held constant will face relatively lower financial difficulties during general financial crisis within the economy and this will translate to high profits. Well capitalized banks are able to meet the capital requirements set by central bank while the excess can be used to provide loans (Onounga, 2014).
Guyo, (2013) in his study showed that bank characteristic variables such as interest spread, capital adequacy, size, and liquidity have positive and strong influence in the performance of commercial banks, while management efficiency and asset quality recorded strong and negative association to profitability. In the study of banks profitability for twelve countries selected from Europe, North America and Australia, Bourke (1989), observed a significant positive association between capital adequacy and bank profitability. This means that the higher the capital ratio the more profitable the bank will be.
Liquidity of a business is its ability to pay off its short-term debt obligations. It is measured by the ratio of net liquid assets to net liquid liabilities.. Liquidity has an impact on the profitability of banks. DeYoung and Roland (2001), explained that the reason why banks, and more generally financial intermediaries exist is so that can they mitigate a host of problems that otherwise prevent liquidity from flowing directly from agents with excess liquidity (depositors) to agents in need of liquidity (borrowers). These problems arise because of informational asymmetries, contracting costs, and scale mismatches between liquidity suppliers and liquidity demanders.
The importance of liquidity goes beyond the individual bank as a liquidity shortfall at an individual bank can have systemic repercussions. When banks hold high liquidity, they do so at the opportunity cost of some investment, which could generate high returns. Literature has proven that a nexus exists between liquidity and profitability. Amankwaa et al.. (2014) concluded that customer deposits, exposure to risk and liquidity are common factors among banks in Ghana that affect profitability.
2.5.4 Bank Size
Bank size is measured by its assets. Commercial banks should make every effort to increase their size by diversifying their products through investing in for instance, in financial market and selling mutual funds in the market. Size of a firm in general is the speed and extent of growth that is ideal and this growth can be in terms of revenue, profits, assets or number of employees which are all essential for increased financial performance and profitability. Large firms are more likely to manage their working capitals more efficiently than small firms. Most large firms enjoy economies of scale and thus are able to minimize their costs and improve on their financial performance (Yuqi, 2007).
Almajali et al., (2012), argued that the size of the firm can affect its financial performance. However, for firms that become exceptionally large, the effect of size could be negative due to bureaucratic and other reasons. Onounga, (2014) in his analysis of internal determinants of Profitability of Kenya’s Top Six Commercial Banks revealed that bank size, capital strength, ownership, operations expenses, diversification do significantly influence profitability of the top six commercial banks.
2.5.5 Operational Efficiency
The operating efficiency of a business in relation to the efficient utilization of the assets is reflected in net profit margin.
In the banking industry, It will be measured as a ratio of total costs to total income. Although a high return margin reflects better performance, a lower margin does not automatically indicate a lower rate of return on assets turnover. Operational efficiency is the capability of a business to deliver quality commodities to customers in the most cost-effective manner possible. According to Kalluru ; Bhat (2009), Operational efficiency is the proficiency of a corporation to curtail the unwelcome and maximize resource capabilities so as to deliver quality products and services to customers.
In his study, Njenga (2014), main findings are that income of commercial banks in Kenya is affected by micro and macroeconomic factors. His results show that among the microeconomic variables, managerial efficiency and operational efficiency are negatively and significantly related to income.
2.6 Why Bank Invest in Non -Interest Income
Several studies have been advanced as to why the bank invest in noninterest income these include;-
2.6.1 New Technology
New technological developments have resulted to very high competition.
According study carried out by Fieldsman and Schmidt (1996), indicated that deregulation and new technology have eroded bank comparative advantage and made it easier for non bank competitor to enter these market, hence their need to evaluate other form of portfolio other than depended on the deposit portfolio and loan interest.
2.6.2 Risk Reduction
Bank that increase non- interest could reduce risk, and it’s increase could lead to more diversification. DeYoung and Roland (1999) criticize the conventional wisdom in the banking industry that earnings from fee-based products are more stable than loan-based earnings and that fee-based activities reduce bank risk via diversification. They show that as the average bank tilts its product mix toward fee-based activities and away from traditional lending activities its earnings volatility increases. Saunders & Walters (1994) found that the expansion of banks’ activities reduces risk, with the main risk-reduction gains arising from insurance rather than securities activities.
2.6.3 J Pressure on Net Interest Margin
Gardner ; Cooperman (2002) stated that one measure of depository and institution risk exposure is their earnings volatility as depicted by volatility of their net interest Margin, return on assets and return on equity as measured by their standard deviation over time.
The interest income has been experiencing reduction due to downward trend interest rate on loan and deposit. Interest rate on other interest income have been greatly affected the Libor rate and interest on risk free rate hence reducing drastically the interest income.
Pressure by central bank to reduce interest rate on loans to customers has seen the decline of interest income. Hence, there is need for banks to increase diversification to non interest to counteract the pressure on interest income. This long-term downward pressure on net interest margins have forced commercial bank to think of alternative sources of revenue that will ensure earning stability and also mitigate risk exposure (Thygerson, 1995)
2.6.4 Less Subject to Business Cycle
Interest income is known to be affected by economic condition prevailing in a country example the financial crisis lead to downward trend in interest rate hence leading to decreased interest income. Whereas non-interest income is not highly affected by economic recession according to Thygerson (1995), he argued that noninterest income is less susceptible to economic recession that may lead to loan delinquencies and losses, its then to offset loss brought by interest income.
2.7 Empirical Literature
Financial performance analysis of commercial banks has been of great interest to academic research since the Great Depression Intern the 1940’s. In the last two decades studies have shown that commercial banks in Sub-Saharan Africa (SSA) are more profitable than the rest of the world with an average Return on Assets (ROA) of 2 percent (Flamini et al., 2009). One of the major reasons behind high return in the region was investment in risky ventures. The other possible reason for the high profitability in commercial banking business in SSA is the existence of huge gap between the demand for bank service and the supply thereof. That means, in SSA the number of banks are few compared to the demand for the services; as a result there is less competition and banks charge high interest rates (Flamini et al., 2009).
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 Saunders & Water (1994). 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 (Staikouras & Wood, 2004). Alternatively, expanded product lines and cross selling opportunities associated with growing noninterest income may offer traditional diversification benefits for a Bank’s revenue portfolio. If noninterest income and net interest income are negatively or only weakly correlated, for example, noninterest income may diversify bank revenue and improve the risk/return trade-off (Stiroh, 2004b).
Bush ; Kick (2009) analyzed the impact of non-interest income on financial performance and the risk profile of banks, and the results showed that a strong engagement in fee-generating activities correlates with higher risk for commercial banks and higher risk-adjusted returns on equity and total assets. DeYoung and Roland (2001) indicated that non-interest income was generated by traditional and nontraditional activities, leading to higher bank profitability and risk associated with the increase in earnings volatility. Baele et al., (2007) examined European banks over the period 1989-2004 and used market-based measures of return potential and bank risk to find that diversification will increase banks? expected returns and systematic risk. Stiroh (2006) further showed that an increase of non-interest revenue did not lead to higher share of market returns but did cause increased market risk.
Mercieca et al., (2007) concluded that banks financial stability is negatively affected by a reliance on non-interest income, but shifting into non-interest income creates an inefficient trade-off between risk and return. Ramona ; Thomas (2009) analyzed the impact of non-interest income on financial performance and the risk profile of German banks between 1995 and 2007, and the results showed that a strong engagement in fee-generating activities correlates with higher risk for commercial banks and higher risk-adjusted returns on equity and total assets.
The ability to reduce risk is obviously a topic of considerable importance for individual banks, as well as their regulators and supervisors. If noninterest income lowers the volatility of bank profits and reduces risk, for example, it might be reasonable to reduce capital requirements for banks with a diversified revenue portfolio and for supervisors to reallocate their scarce resources. Similarly, the costs of bank supervision are tied to the perceived riskiness of the institution, so banks have additional incentives to reduce risk. Managers with large equity interests in banks with franchise values have further incentives to reduce risk and maintain that value (Stiroh, 2004b).
Thygerson (1993) also argued that noninterest income is less susceptible to economic recession, which may lead to loan delinquencies and losses, its then to offset loss brought by interest income. Profit is the ultimate goal of commercial banks. All the strategies designed and activities performed thereof are meant to realize this grand objective. However, this does not mean that commercial banks have no other goals. Commercial banks could also have additional social and economic goals (Michael ; Yan, 2010). Roland (1997) found that high returns from fee-based activities were less persistent than those from lending and deposit taking. Most recently, De Young and Roland (1999) found that as banks move towards fee-earning activities, revenue volatility increases, as do both total leverage and earnings.
Thygerson (1995, also argued that noninterest income is less susceptible to economic recession which may lead to loan delinquencies and losses, its then to offset loss brought by interest income.
Köhler et.al (2013), analyzed the impact of banks’ non-interest income share on risk in the German banking sector for the period between 2002 and 2010. Using linear and quantile regression estimators, they found out that the impact of non-interest income on risk depends on the business model of a bank.
Saunders et.al (2014), studied the relationship between non-interest income and bank performance, to answer the question: Is Banks’ Increased Reliance on Non-Interest Income Bad? The study based on a sample of 368,006 involving quarterly observations on 10,341 US banks. The study period was the years from 2002-2013. They found that a higher ratio of non-interest income to interest income is associated with a higher profitability across the banking sector and under different market regimes. Banks with a higher fraction of non-traditional income are also shown to have a lower insolvency risk as measured by the Z-score, and recovered faster after the 2007-09 crisis.
Williams (2014), studied on the Impact of non-interest Income on Bank Risk in Australia. They concentrated on the relationship between bank revenue composition and bank risk in Australia, using data drawn from Australian bank confidential regulatory returns. This study found that those banks with lower levels of non-interest income and higher revenue concentration are less risky. Non-interest income is found to be risk increasing, but it is proposed that some types of non-interest income may be risk reducing when bank specialization effects are considered. It concluded that care must be taken when selecting the appropriate peers for benchmarking, to reflect difference in income composition.
Chiang et.al (2014), examined the non-interest income, profitability, and risk in banking industry: A cross-country analysis. The study used a bank accounting data for 22 countries in Asia over the period 1995–2009. His article applied the dynamic panel generalized method of moments technique to investigate the impacts of non-interest income on profitability and risk for 967 individual banks. The findings were that non-interest activities of Asian banks reduce risk, but do not increase profitability on a broad sample basis. Non-interest activities decrease profitability as well as increases risk for savings banks. On the other hand, non-interest activities raise risk for banks in high income countries, while increasing profitability or reducing risk for banks in middle and low income countries. Finally, our results reveal that the persistence of risk is greatly affected by bank specialization and a country’s income level, as all risk variables present persistence from one year to the next
Amankwaa et al., (2014) in their study on the analysis of Non-Interest Income of Commercial Banks in Ghana, identifies and discusses some factors common with banks that engage in non-interest earning activities in Ghana. It was found that smaller banks are more involved in non-interest earning activities, relative to their larger counterparts. Higher interest income, customer deposits, exposure to risk and liquidity are also found to be common factors among banks in Ghana that concentrate more non -interest income generation. The Central Bank’s Prime rates also affect banking operations and is positively related to bank’s engagement in nontraditional activities. These results have implications for bank regulators, who must institute regulations toward harmonizing the various sources of bank income as against likely exposures to risk.
Murithi (2013) studied the effect of Revenue Diversification into Non-Interest Income on Financial Performance of Commercial Banks in Kenya. The research adopted an exploratory design where the population of interest was drawn from the five most profitable commercial banks in Kenya; KCB, Equity Bank, Barclays, Standard Chartered and Cooperative Bank. Stratified random sampling was used to select the sample, taking a sample of 30% from each stratum. The study used both primary data and secondary data. The questionnaires included structured and unstructured questions and was administered through drop and pick method to respondents who were the top, middle and low level managers in the organizations. Data was analyzed using descriptive statistics. The study established that all the banks in the study had diversified into non-interest income.
Guyo (2013) examines empirically the factors influencing the financial performance of Islamic versus conventional banks in Kenya. The period of his study was (2009 –2012). The study employed causal comparative research design as the main approach to guide the study. A simple random sampling technique was used to select sample of two Islamic and eight conventional banks from a stratified groups, based on CBK weighted composite index of small and large banks. Data was analyzed using correlation and regression analysis and the results presented in graphs and tables. The study findings showed that bank characteristic variables such as interest spread, capital adequacy, size, and liquidity have positive and strong influence in the performance of commercial banks. On the impacts of the industry specific factors, the results was mixed; whereas the banking sector development variable proxy as credit to private sector have a positive and insignificant influence on bank performance, the stock market capitalization indicator recorded negative and insignificant influence on banks profitability. Lastly, the study found that the macroeconomic determinants such as real GDP growth rate showed positive and strong association to banks profitability, while Inflation have negative and insignificant impacts on profitability.
Atellu (2014) investigated the determinants of non-interest income in Kenya’s commercial banks. A panel data of 2003-2012 was used in this research paper. The main findings are that non-interest income of commercial banks in Kenya is affected by management efficiency, bank’s size, technological development and macroeconomic factors. Bank size and management efficiency is positively and significantly related to non-interest income while ATM development, inflation and growth of gross domestic product are negatively and significantly related to non-interest income. He recommends that commercial banks should make every effort to increase their size by diversifying their products through investing in financial market and selling mutual funds in the market. To increase their equity to asset ratio banks should issue more shares through rights issue or post incorporation issue so as to diversify their investments towards non- interest income.
A publication by Aggeler and Feldman (1998) show that while net interest income of the US banks rose by 12% over the period 1992-97, the biggest gain in bank earnings came from non-interest income. Non-interest income grew by 34% in that period – nearly three times as fast as interest income. In addition, the most important difference in profitability between large banks (banks with $1 billion or more in total assets) and small banks concerns the source of income. Non-interest income made up an average of 27% of total income in the large banks between 1992 and 1997, compared with 12% for smaller banks. Since 1992, non-interest income as a percent of assets increased by 83% in the largest banks but was essentially flat in smaller banks.
Onounga (2014) did an analysis of internal determinants of Profitability of Kenya’s Top Six Commercial Banks. The period of study ran from 2008 to 2013. Generalized least squares method was used to estimate the impact of bank assets, capital, and loans, deposits and assets quality on banks profitability. He used return on assets (ROA) as a measure of profitability. The findings revealed that bank size, capital strength, ownership, operations expenses, diversification do significantly influence profitability of the top six commercial banks. The result suggests that the Kenyan Government should set policies that encourage commercial banks to raise their assets and capital base, as this will enhance the performance of the sector. Another implication of the study is that commercial banks need to invest in technologies and management skills that minimize costs of operations, as this will affect positively on their growth and survival.
Njenga (2014), set out to investigate the determinants of non-interest income in Kenya’s commercial banks. He carries out an empirical analysis to determine the impact of bank specific characteristics, technological development and macroeconomic factors on commercial banks non- interest income. A panel data of2003-2012 is used in this research paper. The main findings are that non-interest income of commercial banks in Kenya is affected by management efficiency, bank’s size, technological development and macroeconomic factors. Bank size and management efficiency is positively and significantly related to non-interest income while ATM development, inflation and growth of gross domestic product are negatively and significantly related to non-interest income.
2.8 The Research Gap
Despite the various literature review carried they is no clear cut relationship between noninterest income and the financial performance of banks. The empirical review carried out show conflicting results in different countries where similar study has been conducted. For example, Saunders et.al (2014), found that a higher ratio of non-interest income to interest income is associated with a higher profitability across the banking sector and under different market regimes. Similarly, Köhler et.al (2013), found out that the impact of non-interest income on risk depends on the business model of a bank and they indicate that it might be beneficial for retail-oriented banks to increase their share of non-interest income to become more stable. In contrast, Chiang et.al (2014), findings were that non-interest activities of Asian banks reduce risk, but do not increase profitability on a broad sample basis. Non-interest activities decrease profitability as well as increases risk for savings banks. On the other hand, non-interest activities raise risk for banks in high income countries, while increasing profitability or reducing risk for banks in middle and low income countries Murithi, (2013). The growth of non-intermediation income activities suggests intermediation activities are becoming less important part of banking business strategies and strategically, banks have shifted their sales mix by diversifying in income sources. These contrasting findings motivate further investigations on the effect of non-interest income on profitability, specifically on banks in Tanzania to indorse the more comprehensive results.
2.9 Conceptual Framework
This study seeks to find the relationship between non-interest income and bank financial performance in Tanzania. The dependent variable is the financial performance which is measured by the Return On Asset (ROA). ROA is the ratio of Net income to total assets of a bank. The independent variable in the model is the non-interest income (NII) which is measured by non-interest income ratio, calculated as total noninterest income divided by total operating income (TOI). Since there are several other factors affecting banking performance. These factors may be grouped into two main groups namely; bank specific factors and macroeconomic factors. Bank-specific factors include log asset size, equity capital to asset ratio, impaired loan ratio, ROA, loan ratio, net interest margin, and cost-income ratio. Rogers and Sinkey (1999) include bank asset size, equity capital to asset ratio, and net interest margin in their regression models to explain the share of non-interest income for US commercial banks. They also include other variables such as the ratio of the provision for loan losses to total assets as a measure of credit risk. The macroeconomic factors include GNI per capita in US dollars, real GDP growth rate, real interest rate, inflation rate, and stock market capitalization relative to nominal GDP. However, this study will include Liquidity Ratio, Asset Quality, Bank Size and Inflation as the control variables in the model so as to make the model reliable and have reliable results.
Figure: 2.1 The Conceptual Framework
This chapter shows how the data will be collected, analyzed and presented so as to meet the research objectives in chapter one and solve the problem of the research gap in chapter two . It discusses the design and methodology to be used during the research. It presents the research design, target population, data collection and analysis techniques, modeling and the test of significance on order to draw a correct conclusion.
3.2 Research Design
Research design is the overall plan used in collecting information useful in fulfilling research objectives (Hakim, 2000). The research design acts as a blueprint since it illustrates all the major parts of the research project and how the goals are achieved. The descriptive research design will be employed in this study. Kothari (2004) defined descriptive research as the statistical study that identifies and explains trends in the study population about the study topic. Descriptive research is a process of collecting data in order to test hypotheses or to answer questions concerning the status of the subject in the study (Mugenda ; Mugenda, 2003). This scientific approach aims primarily at gathering knowledge (descriptions and explanations) about the object of study but does not wish to modify the object. The target is to find out how things are, or how they have been. This is a design that illustrates the characteristics of a variable in the study population.
Descriptive research is used in this study since it tends to answer “what is the relationship” question. It also intensely uses descriptions as a method of data analysis that is typically grouped into patterns for analysis. Descriptive research method may be successfully used to identify the relationship between two variables, revealing summary statistics and always help in recognizing alteration needed (Richey ; Klein, 2002). Appropriately, Ndichu (2014) and Ngigi (2009) successfully used descriptive research in their projects and so its applicability in this study.
3.3 Target Population
The target population is the particular group of objects, people or institution from which information is collected. Ngechu (2008), further defines populations group of people, services elements, or things that are under scrutiny. Mugenda ; Mugenda (2003), illustrates a population as a set of objects, persons or institutions that have some common characteristics that can be generalized. The target population comprises of 51 banks operating in Tanzania and regulated by the Bank of Tanzania. The period of study is eight years from 2009 to 2016. The choice of eight years was taken into consideration because of adequacy of data and it will be reasonable because of average ratios shift over time and the availability of necessary data.
3.4 Sources of Data and Collection
Data collection is the gathering of information related to the research topic from the target population on selected variables, in an organized and objective-oriented manner. The collected data should enable the researcher answer the research questions and make inferences. This study will use secondary data derived from reputable sources for example the Bank of Tanzania (BOT), and annual reports of banks. Mugenda ; Mugenda, (2003), define the secondary data as the information already collected by other researchers on the same topic of study. This research covered years between 2009 and 2016 on the sampled banks. The eight years span is suitable to give enough information on the trend in the variables in determining the effects on non-interest on profitability of banks. The study collected data from 40 banks in a period of 8 years that constitutes of 320 data points (40 multiply by 8 years).
3.5 Data Analysis and Presentation
Data analysis is the method through which gathered information is evaluated using a defined strategy to make conclusions from the information (Mugenda ; Mugenda, 2003). Data analysis consists of examining, categorizing, tabulating, testing or recombining both quantitative and qualitative evidence to address the initial proposition of the study. It also involves cleaning, organizing and determining the relationships between variables using descriptive analysis. Multiple linear regression analysis will used to establish the relationship. Regression analysis is a technique for using data to identify relationships among variables and use these relationships to make predictions, Ime at all, (2014). The study will incorporate the descriptive statistics techniques. It involves calculating the mean, median, standard deviation, J.B Test and range of the All Share Index based on three years daily analysis. The strength of the relationship between non-interest income and financial performance will be tested using correlation coefficient and F-statistic. The analysis of quantitative data will be carried out using STATA Version 11 and will be presented in tables. T-tests will be used to determine whether there was significant difference in financial performance when the non-interest income is high and vice versa.
The J.B Test is a large sample test that computes the skewness and kurtosis measures and is calculated as follows:
JB = nS2/6 + (K-3)2/24
Where n represents the sample size, S represents the skewness coefficient and K represents the Kurtosis coefficient. It is required that a normally distributed variable will have S = 0 and K = 3.
The performance indicator will be Return on Asset (ROA).
The regression model to be used in this study is adopted from Kabiru (2014) and modified to fit the objectives of the study. The Multivariate regression model is as shown in equation (1), (2) and (3) below:-
ln??ROA=?_0+?_1 ln??NIIR+?_2 ln?LR ? ?+?_3 lnAQR+?_4 lnBS+?_5 INF+? ………..………(1)
ln??LR=?_0+?_1 ln??NIIR+?_2 ln?ROA ? ?+?_3 lnAQR+?_4 lnBS+?_5 INF+? ……………..…..(2)
ln??AQR=?_0+?_1 ln??NIIR+?_2 ln?LR ? ?+?_3 lnROA+?_4 lnBS+?_5 INF+? ………….…….. (3)
ROA= Return on Asset (ROA is defined as net income divided by total Assets)
NIIR= Total noninterest income, calculated as (TNII)/Total operating income (TOI)
LR= Liquidity Ratio, calculated as (Quick assets/Total liabilities)
AQR= Assets quality, calculated as (Total Nonperforming loans /Total loans advances)
BS= Size of the bank, expressed as the Natural Log of total assets.
INF= Inflation (Yearly average rate)
3.6 Test of Significance
According to Robinson (2002), research validity is the degree to which study results is a real presentation of the trend in the study population. It entails how accurate the results map the existing patterns. The credible sources of information, BOT and Financial Statements Reports of banks will also boost reliability and credibility of the research.
The study will test statistical significance at 95% confidential level. The high significance level will check if the information collected honestly maps the trends in the study populations. The researcher used analysis of variance (ANOVA) to determine this significance level using the received data. If the researcher obtains a result with significance level falling within 95%, that will mean the data collected is a true representative of the study population.