effects of banking sectoral factors on the profitability ...€¦ · specific factors had a...

Economics and Finance Review Vol. 1(5) pp. 01 – 30, July, 2011 ISSN: 2047 - 0401

Available online at http://wwww.businessjournalz.org/efr

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EFFECTS OF BANKING SECTORAL FACTORS ON THE PROFITABILITY OF

COMMERCIAL BANKS IN KENYA

Tobias Olweny

Lecturer Department of Commerce and Economic Studies

JKUAT Kenya

Themba Mamba Shipho

Masters in Banking (Student)

Kenya School of Monetary Studies

Kenya

ABSTRACT

The first objective of this study was to determine and evaluate the effects of bank-specific factors; Capital adequacy,

Asset quality, liquidity, operational cost efficiency and income diversification on the profitability of commercial

banks in Kenya. The second objective was to determine and evaluate the effects of market structure factors; foreign

ownership and market concentration, on the profitability of commercial banks in Kenya. This study adopted an

explanatory approach by using panel data research design to fulfill the above objectives. Annual financial

statements of 38 Kenyan commercial banks from 2002 to 2008 were obtained from the CBK and Banking Survey

2009. The data was analyzed using multiple linear regressions method. The analysis showed that all the bank

specific factors had a statistically significant impact on profitability, while none of the market factors had a

significant impact. Based on the findings the study recommends policies that would encourage revenue

diversification, reduce operational costs, minimize credit risk and encourage banks to minimize their liquidity

holdings. Further research on factors influencing the liquidity of commercials banks in the country could add value

to the profitability of banks and academic literature.

Keywords: Assets Quality, Banking Sectoral Factors, Bank-specific factors

1.1 BACKGROUND TO THE STUDY

The stream of bank failures experienced in the USA during the great depression of the 1940s prompted considerable

attention to bank performance. The attention has grown ever since then (Heffernan, 2005). The recent global

financial crisis of 2007/2009 also demonstrated the importance of bank performance both in national and

international economies and the need to keep it under surveillance at all times. Arun and Turner (2004) argued that

the importance of banks is more pronounced in developing countries because financial markets are usually

underdeveloped, and banks are typically the only major source of finance for the majority of firms and are usually

the main depository of economic savings (Athanasoglou et al, 2006).

There are many aspects of the performance of commercial banks that can be analyzed. This study focuses on the

profitability performance of commercial banks in Kenya. Aburime (2009) observed that the importance of bank

profitability can be appraised at the micro and macro levels of the economy. At the micro level, profit is the essential

prerequisite of a competitive banking institution and the cheapest source of funds. It is not merely a result, but also a

necessity for successful banking in a period of growing competition on financial markets. Hence the basic aim of

every bank management is to maximize profit, as an essential requirement for conducting business.

At the macro level, a sound and profitable banking sector is better able to withstand negative shocks and contribute

to the stability of the financial system. Bank profits provide an important source of equity especially if re-invested

into the business. This should lead to safe banks, and as such high profits could promote financial stability (Flamini

et al, 2009). However, too high profitability is not necessarily good. Garcia-Herrero et al (2007) observed that too



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high profitability could be indicative of market power, especially by large banks. This may hamper financial

intermediation because banks exercising strong market power may offer lower returns on deposit but charge high

interest rates on loans. Too low profitability, in turn, might discourage private agents (depositors and shareholders)

from conducting banking activities thus resulting in banks failing to attract enough capital to operate. Furthermore,

this could imply that only poorly capitalized banks intermediate savings with the corresponding costs for sustainable

economic growth.

The banking environment in Kenya has, for the past decade, undergone many regulatory and financial reforms.

These reforms have brought about many structural changes in the sector and have also encouraged foreign banks to

enter and expand their operations in the country (Kamau, 2009). Kenya‟s financial sector is largely bank-based as

the capital market is still considered narrow and shallow (Ngugi et al, 2006). Banks dominate the financial sector in

Kenya and as such the process of financial intermediation in the country depends heavily on commercial banks

(Kamau, 2009). In fact Oloo (2009) describes the banking sector in Kenya as the bond that holds the country‟s

economy together. Sectors such as the agricultural and manufacturing virtually depend on the banking sector for

their very survival and growth. The performance of the banking industry in the Kenya has improved tremendously

over the last ten years, as only two banks have been put under CBK statutory management during this period

compared to 37 bank-failures between 1986 and 1998 (Mwega, 2009).

The overall profitability of the banking sector in Kenya has improved tremendously over the last 10 years. However

despite the overall good picture a critical analysis indicates that, not all banks are profitable. For example the small

and medium financial institutions which constitute about 57 % of the banking sector posted a combined loss before

tax, of Ksh 0.09 billion in 2009 compared to a profit before tax of Ksh 49.01 billion posted by the big financial

institutions (CBK, 2009). The huge profitability enjoyed by the large banks vis-a-avis the small and a medium bank

indicates that there are some significant factors that influence the profitability of commercial banks. Flamini et al

(2009) and other several studies have shown that bank profitability is influenced by bank-specific factors and

industry specific factors. However, these studies were based on data from other countries and their findings may not

be applied to the local banking sector. Locally, to the researcher‟s knowledge, no studies have been done to

determine the key factors that influence the profitability of commercial banks. The aim of this study then was to

close this gap in knowledge by investigating the factors, within the banking sector that influence the profitability of

commercial banks in Kenya.

1.2 Research Objectives

The general objective of this study was to determine and evaluate the effects of banking sectoral factors on the

profitability of commercial banks in Kenya. Specific objectives derived from the general objective of the study

were as follows;

(i) To determine and evaluate the effect of bank-specific factors on the profitability of commercial banks in

Kenya

(ii) To determine and evaluate the effect of market factors on the profitability of commercial banks in Kenya

2.1 THEORIES AND MODELS OF BANK PROFITABILITY

Studies on the performance of banks started in the late 1980s/early 1990s with the application of two industrial

organizations models: the Market Power (MP) and Efficiency Structure (ES) theories (Athanasoglou et al, 2006).

The balanced portfolio theory has also added greater insight in to the study of bank profitability (Nzongang and

Atemnkeng, 2006).Applied in banking the MP hypothesis posits that the performance of bank is influenced by the

market structure of the industry. There are two distinct approaches within the MP theory; the Structure-Conduct-

Performance (SCP) and the Relative Market Power hypothesis (RMP). According to the SCP approach, the level of

concentration in the banking market gives rise to potential market power by banks, which may raise their

profitability. Banks in more concentrated markets are most likely to make „abnormal profits‟ by their ability to



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lower deposits rates and to charge higher loan rates as a results of collusive (explicit or tacit) or monopolistic

reasons, than firms operating in less concentrated markets, irrespective of their efficiency (Tregenna, 2009). Unlike

the SCP, the RMP hypothesis posits that bank profitability is influenced by market share. It assumes that only large

banks with differentiated products can influence prices and increase profits. They are able to exercise market power

and earn non-competitive profits.

The ES hypothesis, on the other hand posits that banks earn high profits because they are more efficient than others.

There are also two distinct approaches within the ES; the X-efficiency and Scale–efficiency hypothesis. According

to the X-efficiency approach, more efficient firms are more profitable because of their lower costs. Such firms tend

to gain larger market shares, which may manifest in higher levels on market concentration, but without any causal

relationship from concentration to profitability (Athanasoglou et al, 2006). The scale approach emphasizes

economies of scale rather than differences in management or production technology. Larger firms can obtain lower

unit cost and higher profits through economies of scale. This enables large firms to acquire market shares, which

may manifest in higher concentration and then profitability.

The portfolio theory approach is the most relevant and plays an important role in bank performance studies

(Nzongang and Atemnkeng, 2006). According to the Portfolio balance model of asset diversification, the optimum

holding of each asset in a wealth holder‟s portfolio is a function of policy decisions determined by a number of

factors such as the vector of rates of return on all assets held in the portfolio, a vector of risks associated with the

ownership of each financial assets and the size of the portfolio. It implies portfolio diversification and the desired

portfolio composition of commercial banks are results of decisions taken by the bank management. Further, the

ability to obtain maximum profits depends on the feasible set of assets and liabilities determined by the management

and the unit costs incurred by the bank for producing each component of assets (Nzongang and Atemnkeng, 2006).

The above theoretical analysis shows that MP theory assumes bank profitability is a function of external market

factors, while the ES and Portfolio theory largely assume that bank performance is influence by internal efficiencies

and managerial decisions. Several models of the banking firm have been developed to deal with specific aspects of

bank behavior but none is acceptable as descriptive of all bank behavior. Some of these approaches are: univariant

analysis, multiple discriminant analysis, multiple regression analysis, canonical correlations analysis and neural

network method. Olugbenga and Olankunle (1998) noted that a major limitation of the univariant analysis approach

is that it does not recognize the possibility of joint significance of financial ratios, while the canonical correlations

method precludes the explicit calculation of marginal value of independent variables on the dependent variable. Nor

can the significance of individual explanatory factors be ascertained. They noted that multiple regression approaches

correct for these limitations and they produce comparable results to the discriminant analysis method.

Bakar and Tahir (2009) evaluated the performance of the multiple linear regression technique and artificial neural

network techniques with a goal to find a powerful tool in predicting bank performance. Data of thirteen banks in

Malaysia for the period 2001-2006 was used in the study. ROA was used as a measure of bank performance and

seven variables including liquidity, credit risk, cost to income ratio, size, concentration ratio, were used as

independent variables. They note that neural network method outperforms the multiple linear regression method but

it lacks explanation on the parameters used and they concluded that multiple linear regressions, not withstanding its

limitations (i.e. violations of its assumptions), can be used as a simple tool to study the linear relationship between

the dependent variable and independent variables. The method provides significant explanatory variables to bank

performance and explains the effect of the contributing factors in a simple, understood manner. This study adopted

this approach together with the correction analysis to determine the effects of banking sectoral factors on bank

profitability in Kenya.



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2.2 Factors Influencing Bank Profitability

In accordance with the above theories and models, many studies have introduced some useful variables in the profit

function of commercial banks to shed light on key factors that make a difference in bank profits. Such studies are

not without ambiguity especially with regard to the measurement of the variables and the results reported thereafter.

However there is general agreement that bank profitability is a function of internal and external factors. Koch (1995)

observed that the performance differences between banks indicate differences in management philosophy as well as

differences in the market served. Athanasoglou et al, (2006) concurred and argued that profitability is a function of

internal factors that are mainly influenced by a bank's management decisions and policy objectives such as the level

of liquidity, provisioning policy, capital adequacy, expense management and bank size, and the external factors

related to industrial structural factors such as ownership, market concentration and stock market development and

other macroeconomic factors.Though most of the studies on bank profitability are based on developed countries

especially the USA and Europe, a couple of studies focusing on developing countries (Naceur (2003), Flamini et al

(2009), Sufian and Chong (2009)) have also used more or less the same variables to study the determinants of bank

profitably.

To identify the relevant factors influencing commercial bank profitability in Kenya, this study concentrated on bank-

specific factors based on the CAMEL framework and market structural factors; ownership and market concentration.

CAMEL is a widely used framework for evaluating bank performance. The Central Bank of Kenya also uses the

same to evaluate the performance of commercial banks in Kenya. Ownership and Market concentration are chosen

because the ownership structure of banks in Kenya has somewhat changed over last decade. More foreign banks

have expanded their operations in the country thus changing the structure of the banking industry.

2.3 The effect of Bank-specific factors on Bank Profitability

Several studies (Elyor (2009), Uzhegova (2010)) have used CAMEL to examine factors affecting bank profitability

with success. CAMEL stands for Capital adequacy, Asset quality, Management efficiency, Earnings performance

and Liquidity. The system was developed by the US Federal Deposit Insurance Corporation (FDIC) for “early

identification of problems in banks‟ operations” (Uzhegova, 2010). Though some alternative bank performance

evaluation models have been proposed, the CAMEL framework is the most widely used model and it is

recommended by Basle Committee on Bank Supervision and IMF (Baral, 2005).

2.3.1 Capital Adequacy and its effect on Profitability

Capital adequacy refers to the sufficiency of the amount of equity to absorb any shocks that the bank may

experience (Kosmidou, 2009). The capital structure of banks is highly regulated. This is because capital plays a

crucial role in reducing the number of bank failures and losses to depositors when a bank fails as highly leveraged

firms are likely to take excessive risk in order to maximize shareholder value at the expense of finance providers

(Kamau, 2009).

Although there is general agreement that statutory capital requirements are necessary to reduce moral hazard, the

debate is on how much capital is enough. Regulators would like to have higher minimum requirements to reduce

cases of bank failures, whilst bankers in contrast argue that it is expensive and difficult to obtain additional equity

and higher requirements restrict their competitiveness (Koch, 1995). Beckmann (2007) argue that high capital lead

leads to low profits since banks with a high capital ratio are risk-averse, they ignore potential [risky] investment

opportunities and, as a result, investors demand a lower return on their capital in exchange for lower risk.

However Gavila et al (2009) argues that, although capital is expensive in terms of expected return, highly

capitalized banks face lower cost of bankruptcy, lower need for external funding especially in emerging economies

where external borrowing is difficult. Thus well capitalized banks should be profitable than lowly capitalized banks.



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Neceur (2003) using a sample of 10 Tunisian banks from 1980 to 2000 and a panel linear regression model, reported

a strong positive impact of capitalization to ROA. Sufian and Chong (2008) also reported the same results after

examining the impact of capital to the performance of banks in Philippines from 1990 to 2005. The banking sector

in Kenya provides an interesting case to examine the impact of capital because the minimum statutory requirement

has been upgraded to Ksh, 1billion in 2012. Capital adequacy is divided into Tier I and Tier II. Tier I capital is

primary capital and Tier II capital is supplementary capital, but this study will focus on total equity of the banks as

opposed to the minimum requirements.

2.3.2 Assets Quality and its effect on Profitability

Credit risk is one of the factors that affect the health of an individual bank. The extent of the credit risk depends on

the quality of assets held by an individual bank. The quality of assets held by a bank depends on exposure to specific

risks, trends in non-performing loans, and the health and profitability of bank borrowers (Baral, 2005). Aburime

(2008) asserts that the profitability of a bank depends on its ability to foresee, avoid and monitor risks, possibly to

cover losses brought about by risks arisen. Hence, in making decisions on the allocation of resources to asset deals, a

bank must take into account the level of risk to the assets.

Poor asset quality and low levels of liquidity are the two major causes of bank failures. Poor asset quality led to

many bank failures in Kenya in the early 1980s. During that period 37 banks collapsed following the banking crises

of 1986-1989, 1993-1994 and 1998 (Mwega, 2009). According to Waweru and Kalani (2009) many of the financial

institutions that collapse in 1986 failed due to non-performing loans (NPLs) and that most of the larger bank-

failures, involved extensive insider lending, often to politicians.The CBK measures asset quality by the ratio of net

non-performing loans to gross loans. However Koch (1995) argues that a good measure of credit risk or asset quality

is the ratio of loan loss reserve to gross loans because it captures the expectation of management with regard to the

performance of loans. Hempel et al (1994) observed that banks with high loan growth often assume more risk as

credit analysis and review procedures are less rigorous, however returns are high in such loans indicating a risk and

return trade-off.

Kosmidou (2008) applied a linear regression model on Greece 23 commercial banks data for 1990 to 2002, using

ROA and the ratio of loan loss reserve to gross loans to proxy profitability and asset quality respectively. The results

showed a negative significant impact of asset quality to bank profitability. This was in line with the theory that

increased exposure to credit risk is normally associated with decreased firm profitability. Indicating that banks

would improve profitability by improving screening and monitoring of credit risk.

2.3.3 Liquidity Management and its effect on Profitability

Another important decision that the managers of commercial banks take refers to the liquidity management and

specifically to the measurement of their needs related to the process of deposits and loans. The importance of

liquidity goes beyond the individual bank as a liquidity shortfall at an individual bank can have systemic

repercussions (CBK, 2009). It is argued that when banks hold high liquidity, they do so at the opportunity cost of

some investment, which could generate high returns (Kamau, 2009). The trade-offs that generally exist between

return and liquidity risk are demonstrated by observing that a shift from short term securities to long term securities

or loans raises a bank‟s return but also increases its liquidity risks and the inverse in is true. Thus a high liquidity

ratio indicates a less risky and less profitable bank (Hempel et al, 1994). Thus management is faced with the

dilemma of liquidity and profitability. Myers and Rajan (1998) emphasized the adverse effect of increased liquidity

for financial Institutions stating that, “although more liquid assets increase the ability to raise cash on short-notice,

they also reduce management‟s ability to commit credibly to an investment strategy that protects investors” which,

finally, can result in reduction of the “firm‟s capacity to raise external finance” in some cases (Uzhegova, 2010).



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In Kenya the statutory minimum liquidity requirement is 20%. However, according to CBK Bank Supervision

Annual Report (2009), the average liquidity ratio for the sector was 39.8% in 2009, 37.0 % in 2008, and way above

the minimum requirements. This has baffled many financial analysts as to how could banks withhold such amount

of cash in a credit needy economy such as Kenya (Kamau, 2009). The CBK attributes this to the banking industry‟s

preference to invest in the less risky government securities, while Ndung‟u and Ngugi (2000) as cited by Kamau

(2009) attributes this liquidity problem to the restrictions placed on commercial banks at the discount window,

coupled with thin interbank market, a high reserve requirement and preference of government securities. Thus given

the above foregoing analysis, the given Kenyan banking sector provides an interesting case to assess the effects of

liquidity on profitability.

2.3.4 Operational Costs Efficiency and its effect on Profitability

Poor expenses management is the main contributors to poor profitability (Sufian and Chong 2008). In the literature

on bank performance, operational expense efficiency is usually used to assess managerial efficiency in banks.

Mathuva (2009) observed that the CIR of local banks is high when compared to other countries and thus there is

need for local banks to reduce their operational costs to be competitive globally. Beck and Fuchs (2004) examined

the various factors that contribute to high interests spread in Kenyan banks. Overheads were found to be one of the

most important components of the high interests rate spreads. An analysis of the overheads showed that they were

driven by staff wage costs which were comparatively higher than other banks in the SSA countries.

Although the relationship between expenditure and profits appears straightforward implying that higher expenses

mean lower profits and the opposite, this may not always be the case. The reason is that higher amounts of expenses

may be associated with higher volume of banking activities and therefore higher revenues. In relatively

uncompetitive markets where banks enjoy market power, costs are passed on to customers; hence there would be a

positive correlation between overheads costs and profitability (Flamini et al, 2009). Neceur (2003) found a positive

and significant impact of overheads costs to profitability indicating that such cost are passed on to depositors and

lenders in terms of lower deposits rates/ or higher lending rates.

2.3.5 Diversification of Income and its effect on Profitability

Financial institutions in recent years have increasingly been generating income from “off-balance sheet” business

and fee income. Albertazzi and Gambacorta (2006) as cited by Uzhegova (2010) noted that the decline in interest

margins, has forced banks to explore alternative sources of revenues, leading to diversification into trading

activities, other services and non-traditional financial operations. The concept of revenue diversifications follows

the concept of portfolio theory which states that individuals can reduce firm-specific risk by diversifying their

portfolios. However there is a long history of debates about the benefits and costs of diversification in banking

literature. The proponents of activity diversification or product mix argue that diversification provides a stable and

less volatile income, economies of scope and scale, and the ability to leverage managerial efficiency across products

(Choi and Kotrozo, 2006). Chiorazzo et al (2008) noted that as a result of activity diversification, the economies of

scale and scope caused through the joint production of financial activities leads to increase in the efficiency of

banking organizations. They further argued that product mix reduces total risks because income from non-interest

activities is not correlated or at least perfectly correlated with income from fee based activities and as such

diversification should stabilize operating income and give rise to a more stable stream of profits (Uzhegova, 2010).

The opposite argument to activity diversification is that it leads to increased agency costs, increased organizational

complexity, and the potential for riskier behavior by bank managers. Kotrozo and Choi (2006) mentioned that

activity diversification results in more complex organizations which “makes it more difficult for top management to

monitor the behavior of the other divisions/branches. They further argued that the benefits of economies of

scale/scope exist only to a point. The costs associated with a firm‟s increased complexity may overshadow the

benefits of diversification. As such, the benefits of diversification and performance would resemble an inverted-U in



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which there would be an optimal level of diversification beyond which benefits would begin to decline and may

ultimately become negative

Using annual bank level data of all Philippines commercial banks Sufian and Chong (2008) found a positive

relationship between total non-interest income divided by total assets, a proxy for income diversification and bank

profitability. Uzhegova (2010) using a HH index of interest income, commissions, fee income, trading income, non-

interest income and other operating income found empirical support of the idea that banks involved in

diversification activities expect some benefits. While Kotrozo and Choi 2006, using a similar index found that

activity diversification tends to reduce performance compared to banks more focused in their activities.

2.4 The Effects of Market Structural Factors on bank profitability

2.4.1 Ownership and its Effects on Profitability

Claessens and Jansen (2000) as cited by Kamau (2009) argued that foreign banks usually bring with them better

know-how and technical capacity, which then spills over to the rest of the banking system. They impose competitive

pressure on domestic banks, thus increasing efficiency of financial intermediation and they provide more stability to

the financial system because they are able to draw on liquidity resources from their parents banks and provide access

to international markets. Beck and Fuchs (2004) argued that foreign-owned banks are more profitable than their

domestic counterparts in developing countries and less profitable than domestic banks in industrial countries,

perhaps due to benefits derived from tax breaks, technological efficiencies and other preferential treatments.

However domestic banks are likely to gain from information advantage they have about the local market compared

to foreign banks.

However the counter argument is that unrestricted entry of foreign banks may result in their assuming a dominant

position by driving out less efficient or less resourceful domestic banks because more depositors may have faith in

big international banks than in small domestic banks. They cream-skim the local market by serving only the higher

end of the market, they lack commitment and bring unhealthy competition, and they are responsible for capital flight

from less developed countries in times of external crisis.(Bhattachrya,1994)

The ownership structure of banks in Kenya has changed over the last few years. For example according CBK Bank

Supervision Annual report of 2000 and 2008, in the year 2000, there were five banks in which government had a

significant ownership, but in 2008, the number had reduced to three banks. During the same period the number of

locally incorporated foreign banks increased from four to eight, while the number of branches of foreign-owned

banks decreased from seven to five. This shows that there is now less state involvement in the industry and more

foreign banks have been allowed to expand their operations in the country. Kamau (2009) used a sample of 40 banks

in Kenya from1997-2006 and linear regression method to analyze factors of X-inefficiencies. The results showed

that an increase in the degree of foreign ownership in Kenya is associated with a reduction of cost X-inefficiencies,

suggesting that the degree of foreign-owned banks influences the performance of the local banking sector.

2.4.2 Market Concentration and its Effect on Profitability

The market power theory, as it was discussed under bank performance theories, posits that the more concentrated the

market, the less the degree of competition (Tregenna, 2009). According to Nzongang and Atemnkeng (2006) high

degrees of market share concentration are inextricably associated with high levels of profits at the detriment of

efficiency and effectiveness of the financial system to due decreased competition. Secondly, since commercial banks

are the primary suppliers of funds to business firm, the availability of bank credit at affordable rates is of crucial

importance for the level of investments of the firms, and consequently, for the health of the economy. In situation of

increased concentration, the possibility of rising costs of credits is reflected by a reduction of the demand for bank

loans and the level of business investments. The effect multiplies many folds in as much as bank management

capitalizes on the market share concentration factor.



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However there is a long held view that market power is necessary to ensure stability in banking. Banks that are

profitable and well-capitalized are best positioned to withstand shocks to their balance sheet. Hence banks with

market power, and the resulting profits, are considered to be more stable Northoctt (2004). Large banks with market

power have typically been viewed as having incentives that minimize their risk-taking behavior and improve the

quality of their assets (the screening theories). Keeley (1990) as cited by Northoctt (2004) argues that the rise in

bank failures in the United States during the 1980s was due in part to an increase in competition in the banking

industry. Flamini et al (2009) noted that if high returns are the consequence of market power, this implies some

degree of inefficiency in the provision of financial services. In this case it should prompt policymakers to introduce

measures to lower risk, remove bank entry barriers if they exist, as well as other obstacles to competition, and

reexamine regulatory costs. But bank profits are also an important source for equity. If bank profits are reinvested,

this should lead to safer banks, and, consequently high profits could promote financial stability.

Tregenna (2009) using a sample of USA commercial banks and savings institutions from 1995 to 2005 and a linear

regression panel model, found robust evidence that concentration increases profitability in USA banks and then

concluded than the high profitability of banks in the USA before the 2007/2008 financial crisis was not earned

through efficient processes, but through market power and the profits were not reinvested to strengthen the capital

base of the financial institutions. Nzongang and Atemnkeng (2000) examined the effects of concentration to the

profitability of Cameroonian commercial banks from 1987 to 1999. Unlike Tregenna (2009), who used the

concentration ratio of the 3 largest banks in the USA to model market concentration, Nzongang and Atemnkeng

(2000) used the Herfindahl-Hirschman index to measure market concentration in Cameroon. The results indicate

that market concentration power is of paramount importance in the determination of bank profitability.

The banking sector in the Kenya looks very competitive judging by number of local and foreign banks in the

industry. CBK Bank Supervision Report (2009) as 31 December 2009 there were 44 commercial banks, 13 of which

are foreign-owned. However Beck and Fuchs (2004) noted that most customers in Kenya below the top tier of

corporate and wealthy borrowers face a non-competitive banking market and are often effectively tied to one bank,

with very high switching costs hence the interest rate spread and margins in the country.

The review of literature has revealed that bank profitability can be influenced by bank-specific factors and external

factors. Bank-specific factors are those factors within the direct control of managers and can be best explained by

the CAMEL framework, while external factors include industry-specific and macroeconomic factors. This study

focuses only on industry-specific factors as external factors. The review of literature also revealed that the multiple

linear regressions method is the most used in modeling the relationship between bank profitability and its factors.

The relevant interrelationships among bank-specific factors and market specific factors and their impact on bank

profitability, as revealed by the reviewed of literature, are depicted in the conceptual framework (Figure 2.1).

Finally, it is clear from the reviewed literature that few local studies have been dedicated on this particular area of

bank performance and that studies that have attempted to do so have tended to study each factor of performance to

the exclusion of other factors.

2.5 Conceptual Framework

The conceptual schema of the relation between the independent variables and dependent variable distilled from the

literature review by the researcher is shown on Figure 2.1 below. It assumes that the relationship between the

independent variable and dependent variables is linear.



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Figure 2.1: Schematic Diagram showing relationships between variables

Independent Variables

Dependent Variable

Affects

3.1 RESEARCH DESIGN

The main objective of this study was to determine and evaluate the effects of banking- sectoral factors on the

profitability of commercial banks in Kenya. This study adopted an explanatory approach by using panel research

design to fulfill the above objective. The advantage of using panel data is that it controls for individual

heterogeneity, less collinearity variables and tracks trends in the data something which simple time-series and cross-

sectional data cannot provide (Baltagi, 2005).

3.2 Target Population

The population of this study comprised of all licensed commercial banks in Kenya between the period of 2002 and

2008. As at 31 December 2008, there were 43 registered commercial banks comprising of 14 large banks and, 29

small and medium banks (Appendix 1).

3.3 Sample Design

All the banks were considered for this study. However, commercial banks which discontinued or started their

operation in the middle of the period under review were not considered. As results, out of the 43 commercial banks,

38 (88%) banks formed the sample of this study. The 38 banks compromised of 13 large banks and 25 small and

medium banks.

3.4 Data Collection

The study employed secondary data. The data was collected from the Central Bank of Kenya and Banking Survey

2009. The banking Survey is an annual publication that publishes annual financial statement of all banks in Kenya

covering a period 10 years, while the Central Bank of Kenya publishes annually, major financial indicators of the

sector.

Bank-specific Factors

Capital adequacy

Assets Quality

Liquidity management

Operational cost

efficiency

Income diversification

Market Structure Factors

Foreign Ownership Structure

Market Concentration

Bank Profits

ROA



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3.5 Data Analysis

The collected data was analyzed using descriptive statistics, graphs, correlations, multiple linear regression analysis

and inferential statistics.

3.5.1 Descriptive Statistics and Relationship Analysis

Mean values and graphs were used to analyze the general trends of the data from 2002 to 2008 based on the sector

sample (38 banks), large banks sample (13 banks), and small and medium banks sample (25 banks). Scatter plots

and a correlation matrix were used to examine the relationship between the dependent variable and explanatory

variables.

3.5.2 Operationalization of the Study Variables

This section presents the measurements that were used to operationalise the study variables before the application of

the linear multiple regression analysis

Table 3.1: Operationalization of the study variables

Variable Measurement

Profitability Ratio of profit before tax to total assets.

Bank-Specific variables

Capital

Adequacy Ratio of total equity to total assets

Asset

Quality

Ratio of non-performing loans to gross loans. Higher ratio indicates poor asset quality

Liquidity Ratio of liquid assets to total liability deposits.

Operational

Cost

efficiency

Ratio of operating costs (staff wages and administrative expenses) to net operating income (net

interest income, net foreign exchange income, net fees and commission, and other income).

Higher ratio indicates inefficiency

Income

Diversificati

on

1-(HHI of net interest income, foreign exchange income, commissions and fees, and other

income). Index ranges from 1 to 0. Where 1 indicates complete diversification, 0 indicates

complete focus

Market Factors

Foreign

Ownership

Ratio of foreign annual assets held by foreign banks to total annual banking sector assets

Market

Concentrati

on

HH index of the annual deposits of all commercial banks in the market. Index ranges from 10,000

to 0. Indicating an uncompetitive market to a competitive market

3.6.3 Multiple Linear Regression Analysis

A multiple linear regression model and t-statistic were used to determine the relative importance of each

independent variable in influencing profitability. The t-statistic was used to test the two hypotheses at a maximum of

10% significance level. The multiple linear regressions model is shown on equation 1 below. This model was run

using Eviews 5. The analysis was based on the sector sample (38 banks), large banks sample (13), and small and

medium banks (25 banks).

ROAit = Ci+α1CAPit +α2ASQit+α3LIQit+α4CIRit+α5RDIit + β1FGNt +β2MKTt + ei…… (1)



11

Where;

ROAit = Profitability of bank i at time t

CAPit = Capital adequacy of bank i at time t

ASQit = Asset quality of bank i at time t

LIQit = Liquidity of bank i at time t

CIRit = Operational cost efficiency of bank i at time t

RDIit = Income diversification of bank i at time t

FGNt = Market foreign ownership structure at time t

MKTt = Market Concentration at time t

Where t = 2002….2008, Ci = constant for each bank (fixed effects), α= bank specific factors coefficients, β= Market

factors coefficients.

The above model can three forms: pooled model, fixed effects model and random effects model. Pooled model

assumes homogeneity in the study units, while the other two assume heterogeneity in the study units. Kennedy

(1998) and (Baltagi (2005) argued that the fixed effects model is suitable if the data exhaust the population, the

study is focusing on a specific set of N firms and the inference is restricted to the behavior of these sets of firms. The

foregoing argument suggested that the fixed effects model would be suitable for this particular study. To test for

suitability of the fixed effects model, the F-statistic was used (Gajarati, 2007). The null hypothesis of the F-statistic

is that the study units are homogeneous and as such the pooled model is better, while the alternative is that the study

units are heterogeneous and therefore they cannot be pooled. The F-statistic is given as follows;

Where RRSS is the restricted sum of residual squares (pooled model) and URSS is the unrestricted sum of the

squares (Fixed effects model). N is the number of cross-section, T is the number of time periods and K is the number

of parameters to be estimated. The null hypothesis is accepted when the test statistic is less than the appropriate

critical value. Rejection of the null hypothesis leads to the acceptance of the fixed effects model.

The pooled model was estimated and a RRSS of 1146.206 was obtained. The fixed effects model was estimated to

get URSS of 842.31. Applying the above formula, an F statistic of 2.145 with 37 and 220 degrees of freedom was

obtained. The F-statistic critical value of 37 and 220 degrees at 1% is 1.710 and as such the null hypothesis of

homogeneity was rejected at 1% significant level hence the fixed effects model was used. However in the sample of

large banks, and small and medium banks sample the pooled model was used.

3.6.3 Model Assumptions and Data properties

The following diagnostic tests were carried out to ensure that the data fits the basic assumptions of linear regression

models;

Normality: Descriptive statistics were taken to examine the distribution of data. Upon examination the Skewness

and Kurtosis of the data it was clear that most of the variables were close to normal distribution.

Multicollinearity: Schindler and Cooper (2009) suggested that a correlation above 0.8 between explanatory

variables should be corrected for. To ensure that none of the explanatory variables were highly correlated to each

other, a correlation matrix was used and none of the variables were highly correlated to each other. After all, one

advantage of panel data models is the ability to control for multicollinearity.



12

Heteroscedasticity: Finally, The model was estimated in Eviews assuming cross-section heteroscedasticity (Whites‟

cross-section weights) to control for the possible effects heteroscedasticity in the error variance (Gujarati, 2007)

4.1 FINDINGS AND DISCUSSIONS

4.2 Trend Analysis of Profitability and Banking-Sectoral Factors

This section of the study aimed at establishing the general trend of profitability and the seven banking-sectoral

factors in the Kenyan banking sector from 2002 to 2008.

4.2.1 Trend Analysis of Profitability

Table 4.1 reports the mean scores of ROA from 2002 to 2008. The mean of score of ROA for the whole sector was

1.4% and rose to 2.4% in 2008 showing an increase of 71.4%. For large banks ROA was 1.5% in 2002 and rose to

4% showing an increase of 166.7%. ROA for small and medium was 1.4% in 2002 and rose to 1.8% by only 28%.

Table 4.1: The Annual Mean Scores of Profitability from 2002 to 2008

Variable Category 2002 2003 2004 2005

2006 2007 2008

% ∆ since

2002

Sector (%) 1.4 1.5 1.3 1.7

2.2 2.8 2.4 71.4

ROA Large banks (%) 1.5 2.5 2.3 2.9

3.6 3.8 4 166.7

Small & medium (%) 1.4 1.1 0.8 1.2

1.6 2.4 1.8 28.6

Source: Research Data, 2010

The reported results in table 4.1 mean that the profitability of the sector increased from 2002 to 2008. In the banking

industry, ROA of more than 1.5% indicates good performance (Flamini et al, 2009). Therefore this means the

performance of the sector was comparable to international standards. This is very important for the development of

this country as banks play a very important role of financial intermediation. However analysis by bank size indicates

that, large banks enjoyed more profit increase than small and medium banks during this period. From 2002 to 2008

the average profitability of the large banks increased by 166.8%, while for small and medium banks increased by

only 28.6%. This lends support to the argument that the local banking market is largely dominated by larger banks.

4.2.2 Trend Analysis of Bank-specific Factors

This section analyses the average performance of the banking sector in terms of the five bank-specific factors

between 2002 and 2008 and the mean scores are reported in 4.2. The mean value of CAP for the whole sector from

2002 to 2008 was 18%, for large banks was 12.23% and for small and medium banks was 20.66%. The mean score

of ASQ for the sector was 16.43%, for the large banks 12.12% and for the small and medium banks was 18.19%.

The average mean value of LIQ, a proxy for liquidity, was 43.08% for the sector, for large banks it as 41.07% and

for small and medium banks it was 44.97%. CIR which represents operational costs was 65.84% for the sector,

57.66% for large banks and 69.17% for small and medium banks. Lastly the mean score of RDI, which measures the

ability of banks to generate revenue from different sources, was 0.48 for the whole sector, 0.53 for large banks and

0.46 for small and medium banks.



13

Table 4.2 Aggregate Mean Scores of Bank-specific Factors

Variables

Banking Sector

(%)

Large

banks

(%)

Small & Medium

banks

(%)

CAP 18.22 12.23 20.66

ASQ 16.43 12.12 18.19

LIQ 43.08 41.07 44.97

CIR 65.84 57.66 69.17

RDI 0.48 0.53 0.46


The capital adequacy results suggest that about 18% of the total assets of the sector were financed by shareholders‟

funds while the remaining 82% was financed by deposit liabilities. The high leverage is not surprising because the

business of banking is to mobilize more deposits from customers. The CBK stipulates that banks must keep core

capital of not less than 8% of total deposit. This implies that Kenyan banks on average operated above minimum

statutory levels. However an interesting observation is that small and medium banks seem to use more shareholders

funds to finance their assets that large banks as the mean of CAP for small banks is (20.66%) higher that the mean

score of CAP for large banks (12.23%). One possible reason is that the fixed minimum capital requirement of Ksh,

350 million is very high for small and medium banks relative to their growth, while it is low for large banks.

The mean ratio of assets quality (ASQ) indicates that small and medium banks had a poor loan book than large

banks as the mean value of ASQ for large banks (12.2%) was less than the mean score of small banks (18.19%).This

is not surprising because most small and medium banks do not have the capacity to invest in stringent credit risk

management practices compared to big banks. The mean score of liquidity (LIQ) shows that the sector was very

liquid, two times more than the minimum statutory liquidity ratio of 20% set by CBK. The higher liquidity ratio

indicates that banks in the country prefer to invest in safe, short-term investments than credit loans. The average

ratio of cost to income (CIR) was 65.84% an indicator that overheads are high in the local banking sector. It is even

worse for small and medium banks as the mean was 69.17% against 57.66% for large banks. Lastly the income

diversification index indicates that the revenue income of local banks was poorly diversified as the average was

0.48, with the income of large banks more and better diversified than for small and medium banks.

The foregoing analysis shows that the profitability of the sector improved during the period under review, but large

banks were dominant. Furthermore, the performance of small and medium banks in terms of asset quality,

operational cost efficiency and income diversification was poor compared to the large banks, while in terms capital

adequacy and liquidity they were comparable to the larger banks.

4.1.3 Trend Analysis of Market Factors

In this section, the study sought to analyze the trend of foreign ownership and market concentration in the banking

sector from 2002 to 2008. The degree of foreign ownership is given by the percentage ratio of total assets held by

foreign-owned banks to the total assets of the banking sector in each year, while market concentration is given by

the HHI index using total deposit for each bank in each year in figure 4.1 below.



14

Figure 4.1: Analysis of Market Concentration and Foreign Ownership


The Figure above shows that foreign-owned banks, though small in number (about 27%) controlled about 50% to

40% of total assets in the sector between 2002 and 2008. This demonstrates the influence of foreign banks in the

sector and seems supports the counter argument that unrestricted entry of foreign banks may result in their assuming

a dominant position by driving out less efficient or less resourceful domestic banks because more depositors may

have faith in big international banks than in small domestic banks.

The HHI index, on the other hand indicates that the market structure is moving from high concentration to low

concentration. An index above 1800 represents a highly concentrated industry, which indicates the presence of

oligopoly (Kamau, 2009). Therefore the concentration of the local banking market is exhibiting a loose oligopoly. A

highly concentrated market results in market power and less competitive strategies which lead to high interest

margins.

4.3 The Relationship between Profitability and Banking-Sectoral factors

It is always important for a researcher to assess the general relationship between two variables before subjecting

them to a linear regression analysis to ascertain whether they are linearly related or not. This section therefore aimed

at establishing the relationship between the profitability of commercial banks and the seven explanatory variables.

4.3.1 The Relationship between Profitability and Capital adequacy

The results presented in figure 4.2 indicate that the capital ratio (CAP) is positively related to return on assets

(ROA), the profitability measure. The coefficient of correlations is 0.176 which indicates that the relationship may

not be very strong. However it is clear that the weak positive relationship is due to the two extreme banks, Eco Bank

and Oriental Bank which had relatively sufficient capital levels but posted poor profitability results. These results

provide reasonable evidence to the consistent view that, the higher the capital levels, the higher the profitability.

Generally a bank that depends more on leverage will experience more volatile earnings and this also affects the

credit creation and liquidity function of the bank.



15

Figure 4.2: The Relationship Analysis, Profitability and Capital Adequacy


4.3.2 The Relationship between Profitability and Assets Quality

Figure 4.3 presents the relationship between assets quality or credit risk and profitability. It is clear from the this

figure that there is a negative and strong relationship between poor assest quality and profitability as the plots are

clustered strongly aroung the trend and the coefficient of correlation is -0.71. This means banks which fail to

monitor their credit loans tend to be less profitable than those which pay particular attention to assets quality.

Again, as it was observed under desctriptive statistics, the small and medium banks (Oriental, Eco bank, City Finace

bank) that had the highest ratio of non-performing loans to gross loans are associated with low profitability. This is

inline with the theory that increased exposure to credit risk is normally associated with decreased bank profitability

(Kosmidou, 2008).



16

Figure 4.3: Relationship Analysis, Profitability and Asset Quality


4.3.3 The Relationship between Profitability and Liquidity

In the literature review, the divergent views regarding the relationship and the effect of liquidity on profitability was

explored. Furthermore, the descriptive analysis in section 4.1 above showed that local banks prefer to invest in short

term liquid assets as demonstrated by the high liquidity ratios. Figure 4.4 shows a correlation coefficient of 0.176

between profitability and liquidity, indicating a positive correlation between the two variables.

With the exception of Eco bank, Oriental and City Finance, there is evidence that liquid banks are associated with

better profitability. Notably, National bank of Kenya was the least liquid bank, Habib Zurich and Habib bank Ltd

were highly liquid. These findings seem to be against the argument that liquidity has a negative effect on

performance (Kamau,2009), but they seem to support the counter-argument that illiquidity force banks to borrow

from the money market expensive funds, or to prematurely liquidate their long-term investments at „fire prices‟ to

cover their immediate cash needs, thus reducing their profitability (Elyor,2009). However the, such results need to

be read with caution given the relatively weak coefficient of correlation.



17

Figure 4.4: Relation Analysis, Profitability and Liquidity


4.3.4 The Relationship between Profitability and Operational Costs Efficiency

The nature of the relationship that exists between operating costs and profitability is presented in Figure 4.5. The

coefficient of correlation(r) of -0.76, suggests a strong negative correlation between profitability and Operational

costs. These findings are not surprising, as the issue of high operative costs was covered extensively in the literature

review and the descritive analysis showed that operating costs are higher in the sector. For example Oriental Bank

had a high ratio of operating cost to income and as a results made an aggregate loss of about -6%, whilst Standard

Charterd was amongst the lowest and made a profit of about 4%.



18

Figure 4.5: Relationship Analysis, Profitability and Operational Costs efficiency

Souce: Research Data, 2010

However an important findings is that the local market seems to be competitive. In relatively uncompetitive markets

where banks enjoy market power, costs are passed on to customers; hence there would be a positive correlation

between operating costs and profitability (Flamini et al, 2009).

4.3.5 The Relationship between Profitability and Income diversification

The descriptive analysis in section 4.2 showed that revenue diversification in the sector is average, with large banks

showing a higher diversification index that small and medium banks. Figure 4.5 displays the relationship between

proftability and diversification of income and the coefficient of correlation is 0.26. indicating that the more banks

generate their revenue from different activities, the more profitable they become.



19

Figure 4.6: Relationship Analysis, Profitability and Income Diversification


As discussed above, large banks seem to be well diversifed in terms of their income than small banks. The same

pattern is being observed in Figure 4.5 above. The Bank of Baroda, and Oriental is the least diversifed, while most

of the big banks ( KCB, Barclays, Citibank, and Standard chartered bank) appear to be the most divesfied in terms of

revenue. This relationhip supports the argument that product mix reduces total risks because income from non-

interest activities is not correlated or at least perfectly correlated with income from fee based activities and as such

diversification stabilizes operating income and gives rise to a more stable stream of profits

4.3.6 The Relationship between of Profitability and Market factors

The aim of this section was to establish the relationship between the market factors; degree of foreign onwership,

market concetration and profitability and the results are reported in table 4.3. The coefficient of correlation between

proftability (ROA) and the degree of foreign ownership (FGN) is -0.116, while the coefficinet of correlation

between markert concetration (MKT) and profitability is -0.128.

Table 4.3: Correlation matrix of Profitability and Market Factors

ROA FGN MKT

ROA 1.0000 -0.116 -0.128

FGN -0.116 1.0000 0.800

MKT -0.128 0.800 1.0000




20

This means that bank foreign ownership is negatively associated with profitabilty which does not supports the

argument that foreign banks usually bring with them better know-how and technical capacity, which then spills over

to the rest of the banking system. The negative correlations of market concetration and profitability on the otherhand

is also not in support of the market power hypothesis. In both cases the coefficients of correlation is weak

suggesting a weak relationship.

4.3 Regression Results for the Effects of Banking-Sectoral Factors on Profitability

The above relationship analysis has shown that all the variables are somehow related to profitability. The aim of this

section is to explore in detail the above relationships by using regression analysis which is more robust that the

scatter plot analysis. The regression results are reported as follows; Section 4.3.1 reports the summary statistics of

the regression model, Section 4.3.2 and 4.3.3 reports the regression results in terms of the specific objectives of the

study. The detailed Eviews results are found in appendix 2.

4.3.1 Summary Statistics of the Regression Model

Table 4.4 reports the summary statistics of the regression model. The second column gives summary statistics of the

regression model based on the full sample. The R2 of the sector sample regression was 0.948, F-statistic was 90.79

with p-value of 0 and the DW statistic was 1.877.

Table 4.4: Summary Statisitics of Regression Model

Statistic Sector Large banks Small & Medium banks

R-squared 0.948 0.79 0.71

Adjusted R-squared 0.937 0.77 0.70

F-statistic 90.79 45.34 59.59

Probability(F-stat) 0.000 0.000 0.000

DW statistic 1.877 1.12 1.39


The R2 is a measure of the goodness of fit of the banking-sectoral factors variables in explaining the variations in

bank profitability. This means the variables jointly explain about 95% of the variation in the profitability of banks.

Thus these variables collectively, are good explanatory variables of the profitability of commercial banks in Kenya.

The null hypothesis of F-statistic (the overall test of significance) that the R2 is equal to zero was rejected at 1% as

the p-value was sufficiently low. Secondly the D.W. statistic was about 1.88 implying that there was no serious

evidence of serial correlation in the data

4.3.2 Regression Results for the Effects of Bank-Specific factors on Profitability

The first objective of the study was to determine and evaluate the effects of bank specific factors on profitability.

These effects were investigated by testing the hypothesis that;

‘Bank-specific factors affect the profitability of commercial banks significantly in Kenya’

The multiple linear regression and t-statistic results used to test this hypothesis are reported in Table 4.5. The

coefficient of CAP is 0.076 with a t-statistic of 5.464 in the main sample, 0.034 and t-statistic of 1.840 in the large

banks sample and 0.054 and t-statistic of 4.672 in the sample of small and medium banks. The positive coefficients

mean an increase in capital leads to an increase in profitability and the high t-statistic value indicates that the impact

is statistically significant at 1 % test level.

ASQ has a negative beta of -0.048 with a t-statistic of -5.087 in the main sample, coefficient of -0.028 and t-statistic

of -2.877 in the sample of large banks, coefficient of -0.056 and t-statistic of -6.24 in the sample of small and

medium banks. This means poor asset quality leads to lower profitability to all banks. This negative impact is



21

significant at 1% test level. The effect of Liquidity (LIQ) to ROA is 0.009 with t-value of 2.095 in the main sample,

0.003 (0.573) in the sample of large banks and 0.010 (1.734) in the sample of small and medium banks. This means

an increase in liquidity leads to an increase in profitability. This impact is significant at least, at 10% test level in all

samples. However the coefficient is weak, implying a weak positive impact.

The results for CIR are as follows; in the main sample the impact is -0.068(-16.972), in the sample of large banks is

-0.075 (-10.14) and in the sample of small and medium banks the impact is -0.063 (-10.348). This means operational

costs inefficiency leads to poor profitability. The effect is more on large banks than in small and medium banks and

it is significant at 1% test level in all samples

Table 4.5: Regression Results for the effects of Bank-Specific factors on Profitability

Sector Large banks

Small

& Medium banks

Variables Coefficient Coefficient Coefficient

C

4.935***

(7.017)

6.187***

(4.547)

3.619***

(2.688

CAP

0.076***

(5.464)

0.034*

(1.840)

0.054***

(4.672)

ASQ

-0.048***

(-5.087)

-0.028***

(-2.877)

-0.056***

(-6.269)

LIQ

0.009**

(2.095)

0.003

(0.573)

0.010*

(1.734)

CIR

-0.068***

(-16.972)

-0.075***

(-10.14)

-0.063***

(-10.348)

RDI

0.017**

(2.456)

0.026***

(3.249)

0.028***

(2.506) ***

Significant at 1%; **

Significant at 5%; *Significant at10%; t-statistic in brackets


Finally, the impact of RDI is 0.017 (2.456) in the main sample, 0.026 (3.249) in the sample of small and medium

banks and 0.026 (2.506) in the sample of large banks. This means income diversification or product mix leads to

increased profitability. This impact is statistically significant at least, at 5% test level. Clearly the above results

indicated that all the bank-specific factors had a significant impact on the profitability of banks during the period

understudy at least, at 10% test level. This means that Bank-specific factors affect the profitability of commercial

banks significantly.

4.3.3 Regression Results for the Effects of Market Factors on Profitability

The second objective of the study was to determine and evaluate the effects of market factors on profitability.

Market factors do not significantly affect the profitability of commercial banks significantly in Kenya

Table 4.6 reports the results for the effects of market factors on profitability. The impact of foreign ownership

(FGN) is 0.004, with t-statistic of 0.215 in the main sample, -0.035 and t-statistic of -0.842 in the sample of large

banks, and 0.046 with a t-statistic of 1.186 in the sample of small and medium banks. The effect is positive in the

sample of small and medium banks, but negative in the sample of large banks and it is statistically insignificant in all

samples.

With regard to market concentration, the effect is -0.001, t-statistic of -0.708 in the main sample, clearly

insignificant, weak coefficient and not in support of the SCP hypothesis. In the sample of large banks, it has a weak



22

positive coefficient of 0.001, and statistically insignificant. And in the sample of small and medium banks it is

negative and insignificant as well.

Table 4.6: Regression Results for the Effect of Market factors on Profitability

Sector Large banks

Small

& Medium banks

Variables Coefficient Coefficient Coefficient

FGN

0.004

(0.215)

-0.035

(-0.842)

0.046

(1.186)

MKT

-0.001

(-0.708)

0.001

(0.535)

-0.002

(-0.962) ***

Significant at 1%; **

Significant at 5%; *Significant at 10%; t-statistic in brackets

From the above results it is evident that market factors had little effect on the profitability of banks during this

period. The low t-statistics and high p-values (i.e. not significant at least at 10% test level) of both variables indicate

that the null hypothesis of the t-value; that the true population coefficients are equal to zero is not rejected

4.4 Discussion of Results

The scatter plot analysis and multiple regression analysis have shown that bank-specific factors are not only related

to the profitability of banks, but they also influence the profitability of commercial banks in Kenya significantly.

Elyor (2009 argued that well capitalized banks have a stronger revenue generating capacity and can collect more

deposits. The analysis revealed that capital adequacy is the most robust and important factor influencing profitability

in the sector. The results showed that a 1% increase in capital adequacy could result in 0.076% increase in

profitability. This was statistically significant at 1% (5.464) confidence level. The same statistically significant and

positive impact was found in the sample of small and medium banks, and large banks. Similar results were also

found by Neceur (2003) when evaluating the determinants of bank profitability in Tunisia. Suffian and Chong

(2008) also reported the same results after examining the impact of capital on the performance of banks in

Philippines.

This result means banks should focus on improving their capital levels in order to improve their profitability. This

will enable the banks, not only to be cushioned against exogenous shocks, but also to take full advantage of business

opportunities as they come and increase their profitability in process. Thus this finding provides support to the

argument that well capitalized banks face lower cost of bankruptcy and lower need for external funding especially in

emerging economies where external borrowing is difficult and costly. It also provides evidence that supports the

CBK‟s move to gradually increase capital levels by 2012.

Operational costs efficiency was also found to be the next critical factor influencing profitability. The study found

that a 1% increase in operational costs could results in 0.068% decrease in profitability and this finding was

statistically significant at 1% (-16.972) level. Flamini et al (2009) and Neceur (2003) also found the same results for

SSA and Tunisian banks respectively. The importance of efficient overhead management cannot be over emphasized

in this study. The descriptive analysis of this factor showed that operating expenses are as high as 65.84% of

operating income on average in the sector, 69.17% small and medium banks and 57.66% large banks. It is therefore

obvious that a lot needs to be done to reduce staff wage costs and administrative costs in the sector to improve

profitability. The strong negative impact of CIR indicates that banks are not able to pass all their operating cost to

customers which may be an indicator of the competiveness and lack of market power in the sector.

Asset quality showed a negative effect of -0.048, statistically significant 1% level, meaning a 1% increase in the

asset quality ratio (indicating deteriorating asset quality), could lead to 0.048 % reduction in profitability. The effect



23

was the same in the sample of small and medium banks, and large banks. These results are consistent with previous

findings by Kosmidou (2008) and Flemini et al (2009). This means local banks need to improve their processes of

screening credit customers and monitoring of credit risk especially the small and medium sector as the descriptive

analysis showed that small and medium banks have a poor loan book (18.19%) compared to large banks

(12.12%).This is an important indicator because local banks have had serious problem with non-performing in the

past which led to collapse of many banks. Again these results provide support for the CBKs move to establish the

credit bureau reference as this is expected to go a long way in helping banks reduce the rate of bad loans in the

industry and thus improving profitability.

Another important finding after assets quality is income diversification. This factor had a positive effect of 0.017

(2.456), statistically significant at 5% confidence level. It was statistically significant in large banks and in the small

and medium banks. Investigating the relative importance of bank specific factors on the profitability of banks

operating in developed, advanced emerging, secondary emerging and frontier markets, Uzhegova (2010) also found

that income diversification leads to increased profitability. This means banks that diversify their source of revenue

between, interest income, fees and commissions, foreign exchange activities and other, are profitable than those that

largely depend on a single source of income. This is line with argument that diversification provides a stable and

less volatile income, economies of scope and scale, and the ability to leverage managerial efficiency across products

(Chiorazzo et al, 2008)

Finally the effect of liquidity was 0.009 (2.095) statistically significant at 5% significance level, indicating that

liquidity positively influences profitability. The implication of this finding is that investing in short-term, less risky

securities like government bonds leads to increased profitability. However, all the coefficients were very weak

implying a weak impact. Thus these results support the risk and return theory. The descriptive statistics analysis

showed that liquidity in the sector is well above statutory limits and that small and medium banks are more liquid

that large banks. This finding suggests that these funds are underutilized. Past studies regarding the effect of

liquidity on profitability are mixed but these findings are consisted with Kosmidou et al (2008) and Ghazali (1999

However to the contrary, market factors do not have any significant influence to the profitability of banks in Kenya.

The impact of foreign ownership in the sector was positive (0.004) but not statistically significant (0.215). The

results were almost the same in all samples indicating that foreign ownership is not a critical factor of profitability in

the sector and as such a public policy to encourage the presence of foreign banks may, therefore, not yield any

advantage in terms of bank profitability. This finding is diametrically against the argument that foreign banks bring

with them better know-how and technical capacity, which then spills over to the rest of the banking system and thus

improve profitability (Jansen, 2000; Kamau, 2009). Flamini et al (2009) obtained similar results and they concluded

that foreign-owned banks face the same local conditions as local banks, with regard to risk and the performance of

the domestic economy.

With regard to market concentration, the main hypothesis underlying this factor is the SCP hypothesis which

postulates that market concentration has a positive impact on the performance of banks, indicating that large banks

are able to exercise market power. The market concentration index showed that the local banking industry is moving

from high concentration to low concentration. The overall regression results showed that market concentration had a

negative effect on profitability. However in the sample of large banks the effect was positive indicating that large

banks may have been able to exercise market power in line with SCP hypothesis. In the sample of small and medium

banks the effect was negative indicating that market concentration was not beneficial to these banks. However in all

samples the coefficients were weak and were statistically not different from zero. Clearly these results failed to

support the Structure-conduct-performance or market power hypothesis. This might mean concentration is less

beneficial in terms of profitability to the Kenyan commercial banks than competition (Kosmidou, 2008)



24

4.6 Conclusion

The main objective this study was to determinate and evaluate the effects of banking sectoral-factors on the

profitability of commercial banks in Kenya. Two specific objectives were derived from the main objective. The first

specific objective was to determine and evaluate the effects of bank-specific factors expressed within the CAMEL

framework. The second objective was to determine the effects of market structure factors; foreign ownership and

market concentration. Panel data from 2002 to 2008 of 38 commercial banks was analyzed using multiple linear

regressions method. From the discussion of the findings above, it can be concluded that the bank-specific factors are

the most significant factors influencing the profitability of commercial banks in Kenya than market factors. The

study revealed that profitable commercial banks are those that strive to; improve their capital bases, reduced

operational costs, improve assets quality by reducing the rate of non-performing loans, employ revenue

diversification strategies as opposed to focused strategies and keep the right amount of liquid assets. Indeed the

descriptive analysis of these factors by bank size showed that large banks perform better than the small and medium

banks hence the superior profitability performance. Thus it can be concluded that profitability in the Kenyan

banking sector is largely driven by managerial decision than market factors.

4.7 Future Research

The study sought to investigate factors that influence profitability of commercial banks in Kenya. However the

variables used in the study were not exhaustive. Future research could incorporate macroeconomic variables such as

GDP, inflation and exchange rates. Also a study on the factors influencing the liquidity position of commercial bank

in the country could add great value to the performance of local banks and academic literature.

REFERENCES

Athansasoglou, P., Brissimis, S. & Delis, M. (2006). Bank-Specific, Industry-Specific and Macroeconomic

Determinants of Bank Profitability. Journal of International Financial Markets, Institutions and Money.

[Online] 121-136. Available from: http://ssrn.com/abstract=1106825. [Accessed: 03/06/2010]

Aburime, U. (2008). Determinants of Bank Profitability: Company-Level Evidence from Nigeria. [Online].October

2008. Available from: http://ssrn.com/abstract = 1106825. [Accessed: 10 June 2010]

Abu Bakar, N. & Tahir, I. M. (2009). Applying Multiple Linear Regression and Neural Network to Predict Bank

Performance. International Business Research. [Online]. Vol.2 No.4 . Available from:

www.ccsenet.org/journal.html. [Accessed: 20/06/2010].

Bobáková, V. (2003). Raising the Profitability of Commercial Banks. XI 4/2003. [Online]. Available from:

www.nbs.sk/img/documents/biatec/bia0403/2125.pdf. [Accessed: 9 June 2010]

Baral, K. J. (2005). Health Check-up of Commercial Banks in the Framework of CAMEL: A Case Study of Joint

Venture Banks in Nepal. The Journal of Nepalese Business Studies.[Online] Vol II No.1. Available

from:http://www.nepjol.info/ index.php/JNBS/ article/viewFile/55/483. [Accessed: 20/06/2010]

Berger, A. (1995). The Profit-Structure Relationship in Banking-Test of Market Power and Efficient-Structure

Hypothesis. The Journal of Money, Credit and Banking. [Online] 27 (2). Available from:

http://www.jstor.org/stable/2077876. [Acessed: 26/06/2010]

Beck, T. & Fuchs, M. (2004). Structural Issues in the Kenyan Financial System: Improving Competition and Access.

W/P 3363. [Online]. July 2004. Available from: http://siteresources.worldbank.org. [Accessed: 20 June 2010]

Baltagi, B.H. (2005). Econometric Analysis of Panel Data. John Wiley & Sons Publish. Chichester.

Central Bank of Kenya (2009). Bank Supervision Annual Report 2009. Central Bank of Kenya, Nairobi.

Cooper, D. C., & Schindler, P. S. (2009). Business Research Methods. 9th

edn.Tata McGraw-Hill. New Delhi

Choi, S. & Kotrozo, J. (2006). Diversification, Bank Risk and Performance: A Cross-country Comparison.

[Online]. October 2006. Available from: http://ssrn.com/abstract=1013430. [Accessed: 15 August 2010]

Dougherty, C. (2007). Introduction to Economics. 3rd

edn. Oxford University Press Inc. New York

Elyor, S. (2009). Factors Affecting the Performance of Foreign Banks in Malaysia. A Thesis Submitted in Partial

Fulfillment of the Requirements of Universiti Utara Malaysia for the Degree of Master of Science (Banking).

[Online]. October 2009. Available from:www.ep3.uum.edu.my/1760/1/ Saidov_Elyor_Ilhomovich.pdf

http://ssrn.com/abstract=1106825

http://ssrn.com/abstract%20=%201106825

http://www.ccsenet.org/journal.html

http://www.nbs.sk/img/documents/biatec/bia0403/2125.pdf.%20%20%20%5bAccessed

http://www.nepjol.info/%20index.php/JNBS/%20article/viewFile/55/483

http://www.jstor.org/stable/2077876

http://siteresources.worldbank.org/


http://www.ep3.uum.edu.my/1760/1/



25

Flamini, V., McDonald, C. & Schumacher, L. (2009). The Determinants of Commercial Bank Profitability in Sub-

Saharan Africa. WP/09/15. [Online]. January 2009. Available from:

www.imf.org/external/pubs/ft/wp/2009/wp0915.pdf. [Accessed: 26 May 2010]

Gavila, S. & Santabarbara, D. (2009). What Explains the low Profitability in Chinese Banks.W/P30. [Online]

March2006. Available from: http://ssrn.com/abstract= 1413123 [Accessed: 24 May 2010]

Ghazali, M. (2008). The Bank-Specific and Macroeconomic Determinants of Islamic Bank Profitability: Some

International Evidence. [Online] November 2008. Available from:

http://dspace.fsktm.um.edu.my/bitstream/1812/752/1/abstract.pdf [Accessed: 23 May 2010]

Guranti, D. N. (2007). Basic Econometrics. McGraw-hill. New York

Heffernan, S. (2005). Modern Banking. John Wiley & Sons. London

Hempel, G. H., Simonson, D. G. & Coleman, A.B. (1994). Bank Management. 4th

edn. John Wiley & Sons. New

York

Kosmidou, K. (2008). The determinants of banks „profits in Greece during the period of

EU Financial integration. Journal of Managerial Finance. [Online] 34 (3). Available from:

http://www.emeraldinsight.com. [Accessed: 05/06/2010]

Kamau, A.W. (2009). Efficiency in the Banking Sector: An Empirical Investigation of Commercial Banks in Kenya.

A thesis submitted in partial fulfillment of the Requirements of Nairobi University for the Degree of Doctor of

Philosophy. Nairobi: University of Nairobi

King, G.R. & Levine, R. (1993). Finance and Growth: Schupter Might be Right. The Quarterly Journal of

Economics. [Online]. 108 (3). pp. 717-737. Available from: www.jstor.org [Accessed: 01/06/2010]

Kennedy, P. (1998). A guide to Econometrics. 4th

edn. Blackwell. Malden

Koch, T.W. (1995). Bank Management. 3rd

edn. The Dryden Press. London

Mathuva, D. (2009). Capital Adequacy, Cost Income Ratio and the Performance of Commercial Banks: The Kenyan

Scenario. International journal of Applied Economics and Finance. [Online]. ISSN 199-0886. Available from:

http://www. ScienceAlert.com. [Acessed: 29 May 2010]

Mwega, F.M. (2009). Global Financial Crisis: Kenya: Discussion series. Paper 7. [Online]. May 2009. Available at

www.odi.org.uk/resources/download/3312.pdf. [Accessed: 25 May 2010]

Naceur, S. (2003). The Determinants of the Tunisian Banking Industry Profitability: Panel Evidence.

[Online].October 2003. Available from: www.mafhoum.com/press6/ 174E11. pdf [Accessed: 29 May 2010]

Ngugi, R. (2001). An empirical analysis of interest rate spread in Kenya. [Online]. May 2001. Available at

http://www.aercafrica.org/documents/rp106.pdf. [Accessed: 25 May 2010]

Ngugi, N., Amanja D. & Maana L. (2006). Capital Market, Financial Deepening and Economic growth in Kenya.

[Online]. Available from: http://www.csae.ox.ac.uk/ conferences/2009-EDiA papers/513-Isaya.pdf [Accessed:

27 May 2010]

Northcott, C. (2004). Competition in Banking: Review of Literature. WP 2004-24. [Online]. June 2004. Available

from: http://www.bankofcanada.ca/en/res/wp/2004/wp04-24.pdf [Accessed: 7 June 2010]

Nzongang, T. & Atemnkeng, J. (2006). Market Structure and Profitability Performance in the Banking Industry of

CFA countries: the Case of Commercial Banks in Cameroon. [Online] May 2006. Available from:

http://www.jsd-africa.com/Jsda/Summer_2006/PDF [Accessed: 28 June 2010]

Okpara, G. C. (2009). A Synthesis of the Critical Factors Affecting performance of the Nigerian Banking System.

European Journal of Economics and Administrative sciences. [Online] Issue 17 ISSN 1450-2887. Available

from: http://www.eurojournals.com [Accessed: 05/06/2010]

Olugbenga, S. & Olankunle, A. (1998). Bank Performance and Supervision in Nigeria: Analysing the Transition to

Deregulated Economy. RP 71. [Online]. March 1998. Available from: http://idl-

bnc.idrc.ca/dspace/bitstream/10625/22919/1/113309.pdf [Accessed: 8 June 2010]

Oloo, O. (2009). The Banking Survey 2009. Think Business. Nairobi

Sufian, F. & Chong, R. R. (2008). Determinants of Bank Profitability in a Developing Economy: Empirical

Evidence from Philippines. Asian Academy of Management Journal of Accounting and Finance. [Online] 4(2)

p.91-112. Available from:http:// www.usm.my/journal/aamjaf/vol%204-2-2008/4-2-5.pdf. [Accessed:

06/06/2010

Tregenna, F. (2009), The fat years: the structure and profitability of the US banking Sector in the pre-crisis

period.Camridge Journal of Economics. [online] 33(10) p. 609-63. Available from:

www.oxfordjournals.org/cgi/content/abstract/33/4/609. [Accessed: 06/06/2010]

http://www.imf.org/external/pubs/ft/wp/2009/wp0915.pdf.%20%5bAccessed

http://ssrn.com/abstract=%201413123

http://dspace.fsktm.um.edu.my/bitstream/1812/752/1/abstract.pdf

http://www.emeraldinsight.com/

http://www.jstor.org/

http://www.odi.org.uk/resources/download/3312.pdf

http://www.mafhoum.com/press6/%20174E11.%20%20pdf

http://www.csae.ox.ac.uk/%20conferences/2009-EDiA%20papers/513-Isaya.pdf

http://www.bankofcanada.ca/en/res/wp/2004/wp04-24.pdf

http://www.jsd-africa.com/Jsda/Summer_2006/PDF

http://www.eurojournals.com/

http://idl-bnc.idrc.ca/dspace/bitstream/10625/22919/1/113309.pdf

http://idl-bnc.idrc.ca/dspace/bitstream/10625/22919/1/113309.pdf

http://www.oxfordjournals.org/cgi/content/abstract/33/4/609



26

Uzhegova, O. (2010). The Relative Importance of Bank-specific Factors for Bank Profitability in Developing

Economies/P 2010/02. [Online]. April 2010. Available from: http://ssrn.com/abstract=1595751. [Accessed: 10

June 2010]

Waweru, N. and Kalani, V. (2009).Commercial Banking Crises in Kenya: Causes and Remedies. African Journal of

accounting Economics, Finance and Banking Research. [Online] 4 (4). Available from,

http://globip.com/pdf_pages/african-vol4-article2.pdf. [Accessed: 28/05/2010]

Appendices

Appendix 1: List of Commercial banks as at 31 December 2008

Table 1: List of Commercial Banks

Registered banks as at 31 December 2008

N Large (Assets >Ksh.15 billion) Abbreviation

1 Kenya Commercial Bank Ltd KCB

2 Barclays Bank of Kenya Ltd BARCL

3 Standard Chartered Bank Ltd STD

4 Co-operative Bank of Kenya Ltd COP

5 CFC Stanbic Bank Ltd CFC

6 Equity Bank Ltd**

7 Commercial Bank of Africa Ltd CAFR

8 Citibank, N.A. CITB

9 NIC Bank Ltd NIC

10 National Bank of Kenya Ltd NBK

11 Diamond Trust Bank Ltd DIAM

12 I & M Bank Ltd I&M

13 Prime Bank Ltd PRM

14 Bank of Baroda Ltd BAR

Medium(Ksh 5 billion <Assets < Ksh 15 billion)

15 Imperial Bank Ltd IMP

16 Bank of Africa Ltd BAFR

17 Bank of India INDIA

18 Ecobank Ltd 10 ECO

19 Family Bank Ltd**

20 Chase Bank Ltd CHS

21 Fina Bank Ltd FIN

22 K-Rep Bank Ltd KRP

23 African Banking Corporation Ltd ABC

24 Habib AG Zurich HABZ

25 Development Bank of Kenya Ltd DEV

26 Giro Commercial Bank Ltd GIR

27 Guardian Bank Ltd GDN

28 Southern Credit Banking Corp. Ltd SCRDT

29 Gulf African Bank Ltd**


http://globip.com/pdf_pages/african-vol4-article2.pdf



27

Small(Assets< Ksh. 5 billion)

30 Consolidated Bank of Kenya Ltd CONS

31 Habib Bank Ltd HAB

32 Victoria Commercial Bank Ltd VTR

33 Equatorial Commercial Bank Ltd EQTRL

34 Fidelity Commercial Bank Ltd FED

35 Credit Bank Ltd CREDT

36 Transnational Bank Ltd TRANS

37 Middle East Bank Ltd MDL

38 First Community Bank Ltd**

39 Paramount-Universal Bank Ltd PMNT

40 Oriental Commercial Bank Ltd ORTL

41 Dubai Bank Ltd DUB

42 City Finance Bank Ltd CITF

43 Charterhouse Bank Ltd **

** Not included in the sample

Appendix 2: Detailed Eviews5 Regression Results

Table 1: Regression Results, Main Sample

Dependent Variable: ROA?

Method: Pooled EGLS (Cross-section weights)

Date: 08/17/10 Time: 10:45

Sample: 2002 2008

Included observations: 7

Cross-sections included: 38

Total pool (balanced) observations: 266

Linear estimation after one-step weighting matrix

Variable Coefficient Std. Error t-Statistic Prob.

C 4.935231 0.703327 7.016975 0.0000

CAP? 0.076043 0.013918 5.463834 0.0000

ASQ? -0.048364 0.009507 -5.087166 0.0000

LIQ? 0.009427 0.004499 2.095256 0.0373

CIR? -0.068347 0.004027 -16.97155 0.0000

RDI? 0.017143 0.006981 2.455622 0.0148

FGN? 0.003864 0.017943 0.215360 0.8297

MKT? -0.000573 0.000809 -0.707602 0.4799

Fixed Effects (Cross)

_ABC--C 0.484091

_BOA--C 0.815694

_BOB--C -0.110478

_BOI--C -0.489618



28

_BBK--C 2.360983

_CFC--C 0.390212

_CHS--C -0.806784

_CTB--C 0.002003

_CTF--C -4.544482

_CBA--C 0.171732

_CNS--C 0.544505

_COP--C 1.045727

_CRB--C 0.625829

_DEV--C -0.163489

_DIA--C -0.139525

_DUB--C -1.821864

_ECO--C -2.855662

_EQT--C -0.310616

_FED--C -0.153557

_FED--C -0.153557

_GIR--C 0.025673

_GDN--C -0.074752

_HBZ--C 0.042821

_HBB--C 0.042530

_IMB--C 0.054841

_IMP--C 1.913436

_KCB--C 0.508912

_KRP--C 0.614073

_MDL--C -0.187432

_NBK--C 1.279710

_NIC--C 0.185694

_RTL--C -2.368763

_PMT--C 0.256951

_PRM--C -0.433202

_SCR--C 0.390778

_STD--C 1.611831

_TRS--C 2.068476

_VTR--C -0.822720

Effects Specification

Cross-section fixed (dummy variables)

Weighted Statistics

R-squared 0.947581 Mean dependent var 6.667342

Adjusted R-squared 0.937144 S.D. dependent var 7.253862

S.E. of regression 1.818616 Sum squared resid 730.9276

F-statistic 90.79585 Durbin-Watson stat 1.877006

Prob(F-statistic) 0.000000



29

Unweighted Statistics


Sum squared resid 872.0177 Durbin-Watson stat 2.038110

Table 2: Regression Results, Large banks Sample



Date: 08/21/10 Time: 22:28

Sample: 2002 2008






C 6.187146 1.360453 4.547858 0.0000

CAP? 0.034731 0.018876 1.839932 0.0694

ASQ? -0.028027 0.009740 -2.877421 0.0051

LIQ? 0.002842 0.004959 0.573195 0.5681

CIR? -0.075150 0.007411 -10.14009 0.0000

RDI? 0.025815 0.007970 3.238767 0.0017

FGN? -0.034958 0.041515 -0.842064 0.4022

MKT? 0.000983 0.001836 0.535471 0.5938

Weighted Statistics









Table 2: Regression Results, Small and medium banks Sample



Date: 08/21/10 Time: 22:24

Sample: 2002 2008



30






C 3.619236 1.346383 2.688119 0.0079

CAP? 0.054203 0.011600 4.672862 0.0000

ASQ? -0.056343 0.008987 -6.269490 0.0000

LIQ? 0.007369 0.004251 1.733594 0.0848

CIR? -0.062805 0.005785 -10.85671 0.0000

RDI? 0.028475 0.011364 2.505842 0.0132

FGN? 0.045757 0.038577 1.186146 0.2372

MKT? -0.001630 0.001694 -0.962402 0.3372

Weighted Statistics









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