Mercy Nderitu MBA Research Project 2019.docx

EFFECTS OF SELECTED MACRO-ECONOMIC VARIABLES
ON THE FINANCIAL PERFORMANCE OF THE BANKING
INDUSTRY IN KENYA
MERCY NEHEMA NDERITU
A RESEARCH PROJECT SUBMITTED IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD
OF THE DEGREE OF MASTER OF BUSINESS
ADMINISTRATION, SCHOOL OF BUSINESS, UNIVERSITY OF
NAIROBI
NOVEMBER, 2019

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DECLARATION
I, the undersigned, declare that this is my original work and has not been presented to
any institution or university other than the University of Nairobi for examination.
Signed: _____________________Date: __________________________
MERCY NEHEMA NDERITU
D61/80247/2012
This research project has been submitted for examination with my approval as the
University Supervisor.
Signed: _____________________Date: __________________________
DR. CYRUS IRAYA
Department of Finance and Accounting
School of Business, University of Nairobi

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ACKNOWLEDGEMENT
I sincerely acknowledge my supervisor, Dr. Cyrus Iraya, for his immense support,
guidance, and patience; without his constructive criticism and advice, this work would
not have been complete.
I also thank the university administration for providing a conducive environment
regarding the infrastructure and other support directly and indirectly linked to this
study. Thanks to the staff of the suppliers sampled who provided data and allowed me
to use the information they provided as results of the investigation.
Thank you to all my friends who contributed to the completion of this academic
document both directly and indirectly. They provided me with logistical and moral
support that gave me every reason to work harder and ensure that this study became a
success.

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DEDICATION
This project paper is dedicated to my two sons, Giovanni and Gandhi. They have been,
and still are, the pillar of strength in my life. I thank you.
To my friends, finishing this project would have been impossible if it were not for your
constant impetus in concluding this project. Also, for your excellent support and
significant input. You are much appreciated.

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TABLE OF CONTENTS
DECLARATION..........................................................................................................ii
ACKNOWLEDGEMENT......................................................................................... iii
DEDICATION.............................................................................................................iv
LIST OF TABLES................................................................................................... viii
LIST OF ABBREVIATIONS ....................................................................................ix
ABSTRACT..................................................................................................................x
CHAPTER ONE: INTRODUCTION........................................................................1
1.1 Background of Study ..........................................................................................1
1.1.1 Selected Macroeconomic Variables...............................................................2
1.1.2 Financial Performance...................................................................................4
1.1.3 Selected Macro-Economic Variables and Financial Performance.................5
1.1.4 Banking Industry in Kenya............................................................................6
1.2 Research Problem ................................................................................................7
1.3 Research Objective ..............................................................................................9
1.4 Value of study......................................................................................................9
CHAPTER TWO: LITERATURE REVIEW.........................................................10
2.1 Introduction........................................................................................................10
2.2 Theoretical Framework......................................................................................11
2.2.1 Modern Portfolio Theory.............................................................................11
2.2.2 Arbitrage Pricing Theory.............................................................................12
2.2.3 Purchasing Power Parity Theory .................................................................13
2.3 Determinants of Financial Performance ............................................................14
2.3.1 Interest Rates ...............................................................................................14
2.3.2 Inflation .......................................................................................................15
2.3.3 Economic Growth........................................................................................16
2.3.4 Exchange Rates............................................................................................16

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2.3.5 Firm-Specific Factors ..................................................................................17
2.4 Empirical Review...............................................................................................18
2.4.1 Global Studies..............................................................................................18
2.4.2 Local Studies ...............................................................................................20
2.5 Conceptual Framework......................................................................................22
2.6 Summary of the Literature Review....................................................................23
CHAPTER THREE: RESEARCH METHODOLOGY ........................................25
3.1 Introduction........................................................................................................25
3.2 Research Design.................................................................................................25
3.3 Data Collection ..................................................................................................25
3.4 Diagnostic Tests.................................................................................................26
3.5 Data Analysis .....................................................................................................26
3.5.1 Analytical Model .........................................................................................27
3.5.2 Tests of Significance....................................................................................27
CHAPTER FOUR: DATA ANALYSIS, RESULTS,AND DISCUSSION ...........28
4.1 Introduction........................................................................................................28
4.2 Descriptive Analysis ..........................................................................................28
4.3 Diagnostic Tests.................................................................................................29
4.3.1 Multicollinearity Test...................................................................................29
4.3.2 Normality Test .............................................................................................30
4.3.3 Autocorrelation Test.....................................................................................30
4.3.4 Heteroskedasticity Test................................................................................31
4.4 Correlation Analysis ..........................................................................................32
4.5 Regression Analysis...........................................................................................33
4.6 Discussion of Research Findings .......................................................................36
CHAPTER FIVE: SUMMARY, CONCLUSION, AND RECOMMENDATIONS
......................................................................................................................................38

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5.1 Introduction........................................................................................................38
5.2 Summary of Findings.........................................................................................39
5.3 Conclusion .........................................................................................................40
5.4 Recommendations..............................................................................................42
5.5 Limitations of study ...........................................................................................43
5.6 Suggestions for Future Studies ..........................................................................43
REFERENCES...........................................................................................................45
APPENDICES............................................................................................................54
Appendix I: Research Data ......................................................................................54

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LIST OF TABLES
Table 4.1: Descriptive Statistics ..................................................................................28
Table 4.2: Multicollinearity Test .................................................................................29
Table 4.3: Normality Test............................................................................................30
Table 4.4: Autocorrelation Test...................................................................................30
Table 4.5: Heteroskedasticity Test...............................................................................31
Table 4.6: Correlation Analysis ...................................................................................33
Table 4.7: Model Summary .........................................................................................34
Table 4.8: Analysis of Variance...................................................................................34
Table 4.9: Coefficients of Determination ....................................................................35

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LIST OF ABBREVIATIONS
ANOVA Analysis of Variance
APT Arbitrage Pricing Theory
CAPM Capital Asset Pricing Model
CBK Central Bank of Kenya
CPI Consumer Price Index
FP Financial Performance
GDP Gross Domestic Product
GNP Gross National Product
KNBS Kenya National Bureau of Statistics
NIM Net Interest Margin
NPL Non-Performing Loans
NSE Nairobi Securities Exchange
PPP Purchasing Power Parity
ROA Return on Assets
VIF Variance Inflation Factors

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ABSTRACT
Both empirically and theoretical literature embrace that the thriving of a nation is
directly associated with the economy; this includes variables like unemployment, GDP,
Inflation, remittances, interest rate, exchange rate, and money supply. The study aimed
to ascertain the extent to which macroeconomic variables influence the financial
performance of the commercial banking sector in Kenya. The researcher ran a
descriptive and inferential analysis of all commercial banks in Kenya between January
2009 and December 2018. Data were analyzed using SPSS software version 22 and
presented using graphs and frequency tables. The Central bank's quarterly financial
reports acquired secondary data on quarterly bank performance. In contrast, data on
macro-economic variables was obtained from both CBK and KNBS and analyzed
through descriptive and inferential statistics.
Return on assets was used to measure financial performance. In contrast, quarterly
interest rates, quarterly exchange rates (USD/KSH), quarterly GDP growth rates, and
quarterly inflation rates were used to gauge interest rates, exchange rates, economic
growth, and inflation rates, respectively. The study's results indicated a strong
relationship (R=0.728) between macroeconomic variables and the financial
performance of commercial banks. The study additionally generated an R-square value
of 0.531. This implies that 53.1% of the total variation in the financial performance of
the commercial banking sector in Kenya can be related to macroeconomic variables.
ANOVA statistics show that the whole model was significant, with a P value of 0.000.
The study further established that interest rates and economic growth affect the
financial performance of the commercial banking sector positively and significantly,
while exchange rates and inflation rates negatively affect the banking sector's financial
performance. The study recommends that the commercial banking sector in Kenya
policymakers consider macroeconomic variables in their policy formulation to manage
their effect on the financial performance of the banking sector. The government of
Kenya, through the CBK, should formulate policies that create an enabling and
conducive environment for the operation of commercial banks, as it will lead to
economic growth.

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CHAPTER ONE: INTRODUCTION
1.1 Background of the Study
Financial performance (FP) is a domain of management that has remained and will
continue to be the focus of management executives and scholars for a long time to come
because of its centrality in the life of an organization. Moreover, because of the
importance of financial performance, great attempts have been made to understand it
over time in terms of factors contributing to its realization or none utilization (Abata,
2014). Therefore, the relationship existing between macroeconomic factors and firms'
performance is a subject that has interested many scholars and practitioners. Often, it is
proved that a firm's performance is dictated by essential macroeconomic variables like
rate of interest, gross domestic product, Inflation, and exchange rate (Gan, Lee &
Zhang, 2006).
This research was anchored on various theories, including the modern portfolio theory,
the purchasing power parity theory, and the arbitrage pricing theory that shed light on
associations between macroeconomic factors and financial performance. The current
portfolio theory supports this study in that the prices in the financial markets mirror
occurrences in the macroeconomic variables disparity. The influence of
macroeconomic factors on financial market returns is then reflected in financial
development. Additionally, Ross's (1976) classical model of Arbitrage Pricing Theory
(APT) linked the macroeconomic variables to returns of financial assets. Finally,
purchasing power parity relates prices to exchange rates, implying that prices of goods
and services will tend to change with unit changes in the exchange rates. Financial
performance, being not an exception from these prices described in theory, will

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therefore change in relation to exchange rate changes if the assumptions of the PPP
theory are to hold (Kanamori & Zhao, 2006).
The study focused on Kenyan commercial banks, the choice arising from the fact that
the commercial banking industry has been one of the most demanding on managers in
terms of performance improvement. The sector has been concentrating on improving
its performance due to stiff competition within this industry. In addition, the country's
economy depends on the success of financial institutions (Waithanji, 2016). As a result,
the financial performance of most commercial banks has been on the rise in the last ten
years. However, there have been periods where performance either experienced
significant fluctuations or deepened. Therefore, it is imperative to study the role of
macroeconomic variables on the FP of the Kenyan banking sector.
1.1.1 Selected Macroeconomic Variables
The macroeconomic variables refer particularly to the factors of overall importance to
the position of the country's economy at the regional and national face. These factors
impact a considerable proportion of the population (Sharma & Singh, 2011). Due to
their influence on the economy's overall performance, macroeconomic variables are
closely scrutinized by businesses, governments, and consumers. In their study, Kwon
and Shin (1999) concluded that GDP, interest rates, currency exchange rate, Inflation,
market risk, and money supply are the most impactful macroeconomic variables.
Mishkin (2004) defines macroeconomic variables as the factors relevant to an economy
and shake a greater populace relatively than a select few. The GDP, unemployment,
exchange rate, and Inflation were identified as the variables that significantly
influenced the economy.

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The price at which a debtor pays interest for the utilization of the funds borrowed is
referred to as the interest rate. Interest rates are rarely static, often changing with
changes in the macroeconomic environment (Ali, 2014). Sill (1996) explains that
interest rates react to events in the international and domestic markets, Inflation, and
national economic prospects. The nominal interest rate was a combination of Inflation
and real interest rate (Fisher, 1930). As inflation increases, investors demand higher
returns to compensate them for the reduction in the value of their investment.
The inflation rate refers to the rate at which general product price levels increase with
the decrease in the currencies' purchasing power. Simply put, it is a scenario in which
few goods are chased by too much money while currency devalues (Sharma & Singh,
2011). Therefore, the CPI is often used as an inflation proxy, and it is used to measure
the current price level relative to the base year selected. In addition, the CPI measures
fluctuations in prices at the retail level and further indicates the purchase price of goods
and services used by private households (Subhani, Gul & Osman, 2010).
The expansion of the economy is termed economic growth. The economy refers to the
global physical subsystem composed of wealth, stock composition, and the flow
between consumption and production (Mishkin & Eakins, 2009). It can also be
described as the economic expansion to generate more goods and services. Abbas
(2005) defines it as a rise in the production and consumption of commodities. Economic
growth is mainly measured through the GDP and GNP.
The price of a currency reflected in another is referred to as the exchange rate (Mishkin
& Eakins, 2009). An exchange rate can either be a direct or an indirect quotation. A
direct quote refers to the number of units of foreign currency that a unit of home
currency could buy. In contrast, an indirect quotation refers to the amount of foreign

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currency obtainable from a unit of the home currency (Howells & Bain, 2007). The
exchange rate is said to be the nominal exchange rate when it includes inflation effects
and is referred to as the real exchange rate when inflationary effects are excluded
(Lothian & Taylor, 1997). Before 1972, nearly all countries in the world operated on a
fixed exchange rate system whereby their individual country's currencies had a fixed
rate relative to the US dollar.
1.1.2 Financial Performance
As per Almajali, Alamo, and Al-Soub (2012), financial performance denotes a firm’s
capacity to attain a range of set financial goals, such as profitability. Financial
performance is a degree of the extent to which a firm's financial benchmarks have been
achieved or surpassed. It shows the extent to which financial objectives are
accomplished. Baba and Nasieku (2016) state that financial performance shows how a
company utilizes assets to generate revenues. Thus, it gives direction to the stakeholder
in their decision-making. Nzuve (2016) asserts that industrial banking health largely
depends on financial performance, indicating individual banks' strengths and
weaknesses. Moreover, the government and regulatory agencies are interested in how
banks perform for regulatory purposes.
The focus of financial performance is majorly directed on items altering statements of
finance or a firm financial reports (Omondi & Muturi, 2013). The firm's performance
is the main external parties' tool of appraisal (Bonn, 2000). Hence this explains why a
firm's performance is used as the gauge. The attainment level of the objectives of the
firm describes its performance. The results obtained from achieving a firm's internal
and external objectives are the financial performance (Lin, 2008). Several names are

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given to performance, including growth, competitiveness, and survival (Nyamita,
2014).
Measurement of financial performance is done using several ratios, for instance, Return
on Assets (ROA) and Net Interest Margin (NIM). This measure indicates the bank's
capability to use the available assets to make profits (Milinović, 2014). ROA is derived
by dividing operating profit by the total asset ratio, which calculates earnings from all
company's financial resources. On the other hand, NIM measures the spread of the paid-
out interest to the banks' lenders, for instance, liability accounts and interest income
that banks compared to assets. Therefore, dividing net interest income by total earnings
assets expresses the NIM variable (Crook, 2008).
1.1.3 Selected Macro-Economic Variables and Financial Performance
Both empirical and theoretical works of literature embrace that the thriving of a nation
is directly associated with the economy; this includes variables such as unemployment,
GDP, Inflation, remittances, interest rate, exchange rate, and money supply. Variations
influence the share price movements in economic fundamentals, and these
fundamentals affect prospects. Therefore, the stock share price movement measures
market performance over a long period (Aduda, Masila & Onsongo, 2012). According
to Gazi, Uddin, and Mahmudul (2010), a rising index or persistent share price growth
indicates a developing economy. In contrast, fluctuations in share prices indicate
economic instability in a country.
McKinnon's (1973) theory argues that macroeconomic variables, for instance, real
interest rates, exchange rates, and Inflation, should be monitored as they influence the
diverse economic fundamentals and economic status. For example, they position that
holding the interest rates below market equilibrium increases the investments' demand

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and not the real investment. However, according to market efficiency theory, the prices
of all variables should not be influenced by other factors apart from demand and supply.
According to Fama (2000), if the stock prices indicate all the information regarding the
market, it is an efficient market.
The theory of efficient market hypothesis by Fama (1970) argues that security prices
will always reflect all the available information in an efficient market. Bank managers,
as such, therefore, ought to react fast and accurately to actual and anticipated
macroeconomic variable changes by adapting the said changes or planning for them
well in advance. Such prudence assists in assuring financial performance not only in
the present but also in the future. Macroeconomic variables affect firms' profitability
(Gerlach, Peng & Shu, 2005). Changes in macroeconomic variables present
opportunities and threats to the industry players. Those prepared for the changes shall
realize gains from opportunities that arise, thus fostering financial performance. At the
same time, unprepared players might suffer from threats that negatively impact their
financial performance.
1.1.4 Banking Industry in Kenya
CBK defines a bank as a business that carries out or intends to conduct banking
activities in Kenya. Commercial banking involves accepting deposits, giving credit,
money remittances, and other financial services. The industry performs a vital role in
the financial sector with a lot of emphasis on mobilizing savings and credit provision
in the economy. According to the Bank Supervision yearly Report (2018), the banking
industry comprises the CBK as the legislative authority. The sector has one mortgage
finance, 42 commercial banks, and 13 microfinance banks. Among the 42 commercial

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banks in the country, 30 have local ownership, while 12 have foreign ownership. 11 of
the 42 are listed at the NSE.
The Kenyan banking sector has faced a challenging macroeconomic environment,
including the capping of interest rates that was effected on August 2016. Other
macroeconomic challenges that have affected the industry include; increasing levels of
prices, the unpredictability of interest rates, and exchange rate variability. Furthermore,
the Kenyan currency has declined consistently over the last decade, which might impact
the banking sector. In addition, the country's inflation levels have also fluctuated
significantly. These unfavorable macroeconomic developments may result in
significant problems in the banking industry (CBK, 2018).
Commercial banks in Kenya have registered growth in financial performance over the
past decade (CBK, 2016). With respect to macroeconomic variables, the central bank's
monetary policy committee is charged with setting the lending base rate periodically.
The set base rate affects the lending interest rates in the economy and, indirectly, the
foreign exchange rate. The central bank also put two banks into receivership in 2015;
the banks experienced liquidity challenges, which triggered their closure (Adembesa,
2014). Furthermore, in September 2016, the banking amendment Act (2016) to cap
interest rates was passed, which affected the rate at which banks borrow and lend money
(CBK, 2018).
1.2 Research Problem
Central in the field of finance is financial performance. The need to explain how two
firms operating within the same environment perform differently is a concern, and
several research works in finance have been devoted to understanding this mystery.
This led to studies focusing on various external factors and internal issues that were

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thought to cause differing financial performance. Macroeconomic variables like money
supply, exchange rate, interest rate, GDP, and Inflation affect the banking sector's
financial performance in many ways (Levine, 1996). Economies with profitable
banking sectors exhibit high tolerance to adverse shocks, leading to stable financial
systems (Bashir, 2003). Therefore, the bank needs to identify factors influencing its
financial performance to develop initiatives that increase profitability through effective
management of dominant determinants (Athanasoglou et al., 2005).
After the review of CBK regulation on commercial banks in 2013, we witnessed three
large commercial banks, Chase bank, Imperial bank, and Dubai bank, being placed in
receivership by CBK.
The Kenyan banking sector has faced a challenging macroeconomic environment,
including the capping of interest rates that was effected on August 2016. Other
macroeconomic challenges that have affected the sector include; increasing levels of
prices, the unpredictability of interest rates, and exchange rate variability. These
unfavorable macroeconomic developments may result in significant problems in the
banking industry.
Several research studies have been done in this area in the international context.
Osamwonji and Chijuka (2014) investigated how macroeconomic factors influence the
profitability of commercial banks. The study finds a significant positive correlation
between ROE and GDP, a meaningful negative relationship between ROE and interest
rate, and an insignificant negative relation involving inflation rate. San and Heng (2013)
found macroeconomic variables like gross domestic growth and Inflation do not affect
profitability. Bank-specific determinants, however, affect bank performance. Kanwal

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and Nadeem (2013) find macroeconomic variables negatively affecting commercial
banks' earnings.
Locally, Tora (2018) examined macroeconomic factors' influence on Kenya's
commercial banking sector FP. The study established that interest rates affect the FP of
the commercial banking industry positively and significantly. At the same time, the rest
of the selected macroeconomic variables had no significant effect on the banking sector
FP. Simiyu and Nile (2015) conducted a study to analyze how the profitability of listed
commercial banks in Kenya is affected by macroeconomic variables. The census study
finds an insignificant positive effect of GDP on profitability; also, the study finds a
significant negative relationship between profitability and interest rate and a significant
positive impact between profitability and exchange rate. Finally, Ongeri (2014)
explored the influence of chosen macroeconomic variables on non-bank institutions'
financial performance and found that GDP, interest rate, and currency exchange had a
positive relationship with financial performance. Although there are previous studies
done before in this area, the findings have been inconsistent. This study sought to
contribute to this debate by answering the research question; what is the effect of
selected macroeconomic variables on the financial performance of the banking industry
in Kenya?
1.3 Research Objective
The objective of this study was to determine the effect of selected macroeconomic
variables on the financial performance of the banking industry in Kenya.
1.4 Value of the Study
The research results are important to future researchers since they can be a reference
point. The findings might also be significant to scholars and researchers in identifying

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the research gaps on the related topics of the study and reviewing the empirical literature
to institute further areas of research.
The banking industry stakeholders will find this research very useful as this study will
generate vital information in the management of the industry. These stakeholders
include researchers, managers, and the sector's legislative authorities. The management
of banks will derive the most out of this since it illuminates how they can utilize
macroeconomic factors to improve their banks' performance
.
The study will also be vital to the government and other policymakers as they may use
its findings to generate effective policies to mitigate the impacts of macroeconomic
factors on the FP of the banking sector.
CHAPTER TWO: LITERATURE REVIEW
2.1 Introduction
This section will present a review of theories forming this study's foundation. In
addition, previous research on the topic and related areas is discussed. The other
sections of this chapter include determinants of financial performance, a conceptual

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framework showing the relationship between variables of the study, and a literature
review summary.
2.2 Theoretical Framework
Relevant theories that explain the relationship between macroeconomic factors and FP
are explored in this section. Theoretical reviews covered are modern portfolio theory,
arbitrage pricing theory, and purchasing power parity theory.
2.2.1 Modern Portfolio Theory
Markowitz (1952) coined the theory in his write-up for portfolio mixture. This theory
emphasizes how expected returns can be maximized by establishing portfolios that are
weighed through risk levels. Markowitz concluded that institutions could construct a
portfolio that would give the highest expected returns at a manageable risk level. This
theory tries to maximize profits in a given portfolio risk or equally reduce the risk in a
given level of anticipated returns by carefully selecting the proportion of different
investments (Fabozzi, Gupta, & Markowitz, 2002).
This theory identified two types of risks that investors need to be conscious of,
systematic and unsystematic. Systematic risk is inherent in the volatility of the entire
market or some part of it. In contrast, unsystematic risk is associated with the extent to
which an individual investment is volatile. Investors are therefore instructed to combine
portfolios by guaranteeing that the specific risk carried by that particular investment in
the portfolio is offset by a lower specific risk in another investment.
According to Brueggeman and Fisher (2011), macroeconomic variables generally
influence the business environment within the economy. An environment of volatile
economic variables, including inflationary pressures and volatile exchange rates, infer
that returns to businesses, firms, and financial firms, in particular, shall fluctuate.

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Unstable returns, therefore, dominate the performance of financial firms in such
environments fluctuates, thus affecting their growth and financial performance.
Therefore, policymakers should be keen on macroeconomic variables as they can
impact financial performance. Therefore, this study is relevant to the current research
as it recognizes macroeconomic factors as variables that can influence the performance
of firms.
2.2.2 Arbitrage Pricing Theory
The model was advanced by Ross (1976). The theory presumes that the returns of a
given instrument are affected by different economic factors through their effect on
future dividends and discount rates (Subedi & Shrestha, 2015). APT correlates with the
market portfolio concept. According to arbitrage theory, persons exhibit a varying
portfolio of investments with their specific systematic risk. The APT is a multifactor
model, and most empirical literature argues that APT proposes better results than
CAPM since it uses multiple factors to demonstrate risks.(Waqar & Mustabsar, 2015).
The theory established a theoretical framework that links share returns with some
variables that have the potential to influence sources of income volatility (Shrestha &
Subedi, 2015). Arbitrage Pricing theory (APT) uses macroeconomic variables to
determine asset prices. The theory assumes that various macroeconomic variables affect
asset prices other than systematic risk beta (Waqar & Mustabsar, 2015).
Macroeconomic parameters that impact asset prices of financial instruments include the
Gross national product(GDP), internal government borrowing, the rate of Inflation, the
balance of payments (BOP), investor confidence levels, general levels of
unemployment, the changes in expected returns on securities, and changes in the
interest yield curve. (Amarasignhe, 2015). Based on this linear correlation between

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equity prices and macroeconomic variables, it purports that macroeconomic factors
affect the value of securities. Consequently, the asset's value or security can be
described as the total of the expected return and any unexpected returns on the asset
(Cuthbertson, 2004). This study relates macroeconomic factors to the returns of firms,
and therefore it is relevant to the current study.
2.2.3 Purchasing Power Parity Theory
Swedish economist Cassel (1918) was the originator of this theory defining the
theoretical nominal exchange rate as a report between national and foreign prices.
However,r the market value of the exchange rate could deviate from the former value
(over or under deviations) of the national currency. Therefore, Cassel (1918) selected
various hypotheses to be fulfilled before validating the theory. These hypotheses
included the working of the international arbitrage mechanism, the presence of perfect
competition in both home and foreign countries, and capital movements free from
barriers such as taxes or any other restrictions. Consequently, non-tradable goods will
trade at a lower price than those in more developed countries.
According to the PPP, selling identical goods at the same price by all countries will be
when the particular country's price level increases resulting in a decline in the exchange
rate compared to other nations. This theory suggests that when the Law of One Price
holds, an exchange rate change is usually offset by relative price indices/inflation. PPP
functions on par with the one-price law, which states will sell identical goods at similar
prices in competitive markets. The PPP version relates to a specific product and its
generalization. The relative PPP does not relate to absolute price levels but to variations
in exchange rates and prices (Hau, 2002).

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The assumptions for PPP to hold include; no information gaps, goods being identical
and tradable, no transportation costs, no tariffs, no taxes, no trade restrictions, and
relative inflation rates influence exchange rates. Because of the violation of one price
law and these restrictive assumptions, the monetary models for determining exchange
rates were adopted. This is because of the consideration that exchange rates refer to
asset prices that constantly adjust to balance between financial assets and international
trade. Therefore, future expectations determine exchange rates because they are asset
prices (Hosfstrand, 2006). when Relying on the theory, it is possible to draw a
correlation between exchange rate movements and firms' future performance, which a
fluctuating performance will most certainly follow in the industry.
2.3 Determinants of Financial Performance
Several factors can ascertain the determination of the FP of an organization; these
factors are either internal or external. Internal factors differ from one bank to the next
and are within a bank's scope of manipulation. These consist of labor productivity,
capital size, quality of management, efficiency of management, deposit liabilities,
credit portfolio, interest rate policy, ownership, and bank size. External factors affecting
a bank's performance are mainly gross domestic product, Inflation, stability of
macroeconomic policy, Political instability, and the rate of Interest (Athanasoglou,
Brissimis & Delis, 2005).
2.3.1 Interest Rates
The interest rate is considered an outlay of funds, and an upward or downward
movement in interest rate could influence the savings choice of the financiers (Olweny
& Omondi, 2010). According to Rehman, Sidek, and Fauziah (2009), using an interest
cap causes banks to decrease loans and provoke many of these foundations to abscond

15
rural areas due to the high cost of production and rate of perils. This, in turn, will lead
to slowed growth of the banks. The banks can mitigate this situation by skyrocketing
fees and other levies to arrest the situation. Barnor (2014) stated that unexpected interest
rate change impacts investment decisions; hence, investors tend to adjust their savings
arrangement, generally from the capital market to fixed-profits securities.
According to Khan and Sattar (2014), interest rate affects performance positively or
negatively depending on their movement. For example, a decrease in interest rate to the
depositors and an increase in spread discourage savings. On the other hand, an
increasing interest rate to the depositor adversely affects the investment. The banking
sector is the most sensitive to movements in interest rates compared to other sectors
because the most significant proportion of banks' revenue comes from the differences
in the interest rate that banks charge and pay to depositors.
2.3.2 Inflation
Higher rates of Inflation will lead to higher prices for consumers slowing down business
and thus reducing firms’ earnings. High prices also trigger a regime with a higher
interest rate (Hendry, 2006). According to Fama (1998), real economic activity would
be negatively affected by Inflation, which consequently would positively affect market
performance. Thus, financial performance should be negatively correlated with
anticipated price level, with interest rates in the short-term acting as a proxy similar to
International Fisher Effect (IFE).
Inflation affects a bank's financial performance positively or negatively depending on
the ability of a bank to anticipate it. When a country anticipates Inflation, banks adjust
the rate of interest to ensure that revenues generated are higher than the cost of
operation. Banks that do not anticipate Inflation fails to make the proper adjustment,

16
and as a result, the cost of operations increases at a higher rate than the revenue
generated. A rise in interest rates resulting from Inflation is expected to discourage
borrowers from borrowing funds and will likely reduce lending levels. Boyd, Levine,
and Smith (2001) reported a negative relationship between Inflation and loan volumes.
However, Ameer (2015) asserts that most studies have found a positive impact of
Inflation on loan volumes.
2.3.3 Economic Growth
A growing economy exhibits positive GDP, raising demand for loans (Osoro & Ogeto,
2014). Any rise in economic output may increase expected cash flows and, hence,
trigger a rise in banks' financial performance, with the reverse impact during a recession
being justified (Kirui et al., 2014). Existing empirical evidence indicates that the
financial systems of advanced nations are more efficient (Beck et al., 2003). Banking
sector development is also positively related to economic stability and monetary and
fiscal policies. Countries with higher incomes have more advanced banking sectors than
countries with low incomes (Cull, 1998).
Investors are mainly concerned with GDP reports since the overall economic health can
be established through measurement. The long-run implication of healthy economic
growth is higher corporate profits and improvement of bank lending levels leading to
long-term growth. In contrast, the short-term implication is unpredictable market trends
even during good economic growth seasons (Beck et al., 2003).
2.3.4 Exchange Rates
Exchange rates significantly influence FP when there is variation in the exchange rate
of currencies which affects the price of imports, production cost, and CPI. The imported
consumption goods networks transmit discrepancies to local prices, and exchange rate

17
movement influences domestic prices directly. Demand increases for domestic goods
when factors affecting prices cause a rise in the price level of imported goods and
services; hence reduction in completion is experienced (Magweva & Marime, 2016).
This shift in equilibrium results in pressure mounting on domestic prices and nominal
wages as demand increases. Rising pressure will be applied to domestic prices due to
rising wages. Depreciation in the rate of exchange can merely safeguard the local
industry as local production cost rises much less than the rate of depreciation compared
to the cost of imported equivalent rises by the total amount of the depreciation. This
scenario of currency depreciation leads to an improved and conducive environment for
indigenous industry production (Nwankwo, 2006).
2.3.5 Firm-Specific Factors
Firm-specific factors also affect their financial performance, as reviewed hereunder.
For example, the capital Adequacy Ratio (CAR) defines the ability of the firm to
overcome situations that may threaten profits. The higher the CAR, the lower the risk
and the higher the profitability due to the ability to absorb losses and minimize risk
exposure. However, over-reliance on the CAR might reduce bank profitability by
reducing the need for deposits and other cheaper sources of capital, leading to slowed
lending levels. Therefore, banks must ensure they maintain a quality portfolio of these
assets as it determines their lending levels (Dang, 2011).
Asset quality shows a bank's asset risk situation and financial strength. Asset quality
forecasts the degree of credit risk among the dynamics that affect banks' health status.
The value placed on assets controlled by a specific bank relies on the amount of credit
risk, and the quality of the assets controlled through the bank also relies on liability to
particular risks, tendencies on NPLs, and the cost-effectiveness of the debtors to the

18
bank (Athanasoglou et al., 2005). Preferably, this ratio ought to be at a minimum. If the
lending books are vulnerable to risk in a smoothly operated bank, this would be
reflected by advanced interest margins. On the other hand, if the ratio decreases, it
entails that margins are not appropriately recompensing the risk.
The feasibility of a firm's future depends on its ability to make superior returns by using
its assets. The power of a firm to earn enables it to raise more funds, increase capital
and stand out competitively. The earning capability can be represented by the net
interest rate margin, which shows the difference between the cost of interest bank's
borrowed capital and the bank income of interest received on loans and securities
(Owoputi, Kayode & Adeyefa, 2014).
Firm failures have been associated with insufficient liquidity. Holding liquid assets can
help a firm to generate higher returns. Murerwa (2015) asserts that a positive correlation
exists between the adequate level of bank liquidity and financial performance. Liquid
assets protect firms against deposits that might require on-demand payment, and thus a
firm's liquidity minimizes this risk. However, liquid assets reduce the amount of funds
for lending, reducing bank profitability and, in essence, growth indicating a negative
relationship between liquidity and financial performance.
2.4 Empirical Review
Local and international studies have been done supporting the relationship between
macroeconomic variables and financial performance though the studies have generated
different reactions.
2.4.1 Global Studies
Zhang and Daly (2013) investigated how macroeconomic and bank-specific variables
affect the performance of banks in China. The period covered for the study was from

19
2004 to 2010. The study population comprised all banks in China; the sample size
included 124 banks with complete secondary data ROA was used as the profitability
proxy, and Regression analysis was run on the data. The research study indicates that
banks with lower and well-capitalized credit risk are more profitable, while banks with
higher expense preferences have undesirable performance effects. Banks also grow
along with economic growth; greater economic amalgamation increases bank
profitability.
Owoputi, Kayode, and Adeyefa (2014) studied the influence of variables (industry
specific, macroeconomic, and bank-specific) on Nigerian bank performance. The
study’s data was from the central bank of Nigeria's Central bank publications of ten
banks' financial statements from 1998 to 2012. Three macroeconomic variables were
analyzed in this study: interest rate, inflation rate, and GDP. After applying a random-
effect model, the researchers found a notable and positive influence of bank size and
capital adequacy on profitability. Liquidity ratio and credit risk negatively affect the
bank's FP. It was found that industry-specific variables did not affect the bank FP. Out
of the three macroeconomic variables investigated in this study, the empirical results
showed a significant and negative impact of interest rate and inflation rate on bank
profitability. At the same time, GDP growth has an insignificant relationship.
Osamwonji and Chijuka (2014) studied the influence of macroeconomic variables on
commercial banks' profitability. Secondary data for the study was based on data from
1990 to 2013 obtained in Nigeria. The secondary data was obtained from the central
bank as well as firms annual reports and financials. Macroeconomic variables studied
are GDP, interest rate and inflation rate; the proxy for profitability is return on equity.
Data analysis was by way of ordinary regression. The study finds a significant and

20
positive relationship between GDP and ROE, significant and negative relationship
between ROE and interest rate, and finally insignificant and negative relation involving
inflation rate. This study however fails to indicate neither the population of the research
nor the sample used.
Mazlan, Ahmad and Jaafar (2016) examined factors affecting of quality of bank assets
and profitability for Indian banks. The study employed panel data analysis between
1997 and 2009 and the research findings revealed an inference contrary to the
established and expected outcome. The study found that non-performing assets had no
significant influence on profitability of commercial banks and that asset size of the bank
has insignificant effect on level of commercial banks profitability.
Akben-Selculk (2016) did a study spanning amid 2005 to 2014, the study sought to
explore factors that influenced the competitiveness of a firm in Borsa Istanbul, panel
data was utilized. A longitudinal design was employed, and panel data and the findings
disclosed that ROA was positively associated with the size, growth, gross sales, and
liquidity. Similarly, ROA was adversely associated with R&D outflows and leverage.
Additionally, there was a higher Tobin's Q ratio when debt and liquidity levels were
high. The study's limitation is that it was conducted in a developed economy, with
broadness and firm competitiveness as the dependent variable.
2.4.2 Local Studies
Kiganda (2014) also investigated macroeconomic factors' influence on commercial
bank profitability performance. This case study of Equity bank limited used a
correlation research design and obtained secondary data covering the five years, 2008
to 2012. Data analysis was undertaken via ordinary least squares regression. The study
finds macroeconomic variables (inflation rate, exchange rate and GDP) have no

21
significant effect on profitability and concludes that the factors do not affect banks FP
in Kenya. However, the case study might have resulted in skewed findings;
generalization of findings to the over forty banks in Kenya might not beholdbe held.
Simiyu and Nile (2015) undertook a research study to analyze how macroeconomic
factors affect listed Kenyan commercial banks profitability. The census study used a
population of ten commercial banks and obtained secondary data covering 2001 to
2012. Data acquired was analysed using fixed effects panel data analysis.
Macroeconomic variables studied included GDP, exchange rate, and interest rate; ROA
was the proxy for profitability. In this study, the researchers find an insignificant
positive effect of GDP on profitability; also, the study finds a significant and negative
relationship between interest rate and profitability and, finally positive and significant
effect between exchange rate and profitability.
Ng’ang’a (2016) undertook a study investigating the association between
macroeconomic determinants and the FP of the insurance industry in Kenya. The FP
was regressed against the macroeconomic indicators; average interest rates computed
by Central Bank rate, GDP growth rate, real exchange rate (Ksh/USD), inflation rate
calculated by CPI and unemployment rate. Secondary data collected quarter yearly was
used while descriptive research design was utilized. The study was carried out in a ten-
year period from 2006 to 2015. Correlation together with multiple regression analysis
were used for data analysis. Findings reveal that exchange, interest, and unemployment
rates are not significant predictors of the insurance industry's financial performance.
Nzuve (2016) investigated on influence of macroeconomic determinants on the FP of
deposit taking microfinance institutions. The study applied secondary data from 9
Kenyan microfinance for a timespan of ten years from 2005 and 2014 and which was

22
analyzed by use of multiple linear regression. The findings discovered a negative
association between inflation rate and financial performance, positive correlation
between GDP, national savings, exchange rates, employment rate and financial
performance. Therefore, the study recommended government policy intervention on
macroeconomic factors to spur greater financial performance.
Tora (2018) examined how macroeconomic variables affect FP of the banking industry
in Kenya. The research utilized a descriptive survey design. The study targeted all the
42 banks that were in operation for the study period and have financial data for the five
years from 2013-2017. The data was analyzed with the aid of SPSS where descriptive
and inferential statistics were generated. The study revealed that interest rates affect the
FP of the commercial banking sector positively and significantly. At the same time, the
rest of the selected macro-economic variables have no significant effect on the FP of
the banking sector.
2.5 Conceptual Framework
The conceptual model below demonstrates the expected association among the study
variables. The independent variables were Average quarterly lending rates represented
the interest rates. The quarterly rate of Inflation represented the inflation rate, the
quarterly GD growth rate represented the economic growth, and the quarterly exchange
rate of KSH/USD represented the exchange rate. The dependent variable which was the
FP represented by return on assets quarterly.

23
Figure 2.1: The Conceptual Model
Independent variables Dependent variable
Source: Researcher (2019)
2.6 Summary of the Literature Review
A number of theoretical frameworks have explained the theoretically expected
relationship between selected macroeconomic variables and the Kenyan banking
industry's financial performance. This review covers arbitrage pricing, modern
portfolio, and purchasing power parity theories. Some of the primary influencers of
financial performance have also been explored in this chapter. In addition, several local
Interest rates
 Quarterly average lending
rates
Inflation
 Quarterly inflation rate
Exchange rate
 Ln quarterly KSH/USD
Economic growth
 GDP growth rate
Financial performance
 Return on Assets
(ROA)

24
and international empirical studies exist on macroeconomic factors and FP. The
findings of these studies have also been analyzed in this section.
Tora (2018) conducted a study to examine the influence of macroeconomic factors on
the FP of the Kenyan commercial banking sector. The study established that interest
rates affect the financial performance of the commercial banking industry positively
and significantly. At the same time, the rest of the selected macroeconomic variables
had no significant effect on the FP of the banking sector. Simiyu and Nile (2015) had a
study to analyse how the profitability of listed commercial banks in Kenya is affected
by macroeconomic variables. The census study finds an insignificant positive effect of
GDP on profitability; also, the study finds a significant and negative relationship
between profitability and interest rate and a positive and significant effect between
profitability and exchange rate. Finally, Ongeri (2014) explored the influence of chosen
macroeconomic variables on non-bank institution's financial performance and
established that GDP, interest rate and currency exchange have positive relationships
with financial performance. However, lack of consensus among previous researchers
was reason enough to conduct further studies. This study sought to add to this debate.

25
CHAPTER THREE: RESEARCH METHODOLOGY
3.1 Introduction
To ascertain how selected macroeconomic variables influence the financial
performance of the banking industry in Kenya, a research methodology was necessary
to outline how the research was carried out. Therefore, this chapter has four sections,
namely; research design, data collection, diagnostic tests, and data analysis.
3.2 Research Design
The study used descriptive research design to determine the relationship between
selected macroeconomic determinants and financial performance. Descriptive method
was utilized as the researcher's interest was finding out the state of affairs as they exist
(Khan, 2008). This design is more appropriate since the researcher is familiar with the
phenomenon under study but is more interested in finding the nature of relationships
between the study variables. In addition, descriptive research aims to provide a valid
and accurate representation of the study variables, which helps respond to the research
question (Cooper & Schindler, 2008).
3.3 Data Collection
Secondary data was solely used for the study. There exists a regulatory demand for all
banks to forward their annual values to CBK. Quarterly data was acquired for ten years
(January 2009 to December 2018). Independent variables data; interest rates and
exchange rates (KSH/USD) were collected from the CBK while data on Inflation and

26
economic growth was acquired from the KNBS. Data for the independent variable;
financial performance referenced by return on assets was obtained from CBK.
3.4 Diagnostic Tests
The assumption of linearity states that an association between two variables X and Y
can be illustrated using an equation Y=bX with c as a constant factor. The linearity test
was obtained through the scatterplot testing or F-statistic in ANOVA.
The stationarity test is a process where the statistical properties such as mean, variance,
and autocorrelation structure remain unchanged with time. Stationarity was obtained
from the run sequence plot. Normality tests the presumption that the residual of the
response variable has a normal distribution around the mean. In testing for normality
Shapiro-will test or Kolmogorov-Smirnov test was utilized. Autocorrelation measures
how similar a particular time series is compared to a lagged value of the same time
series between successive time intervals. This was measured by the Durbin-Watson
statistic (Khan, 2008).
Multicollinearity occurs when an exact or near exact relation that is linear is observed
between two or several predictor variables. The determinant of correlation matrices was
used as a test for Multicollinearity, which ranges from zero to one. The orthogonal
predictor variable indicates that for a complete linear dependence to be ascertained
between the variables, the determinant should remain one while it is at zero, and
Multicollinearity increases as it moves closer to zero. Variance Inflation Factors (VIF)
and tolerance levels were determined to show how strong Multicollinearity is (Burns &
Burns, 2008).
3.5 Data Analysis
The SPSS software version 22 was used in the analysis of the data. The researcher

27
quantitatively presented the findings using graphs and tables. Descriptive statistics were
used to summarize and explain the study observed in banks. The results were presented
using percentages, frequencies, measures of central tendencies and dispersion displayed
in tables. Inferential statistics include Pearson correlation, multiple regressions,
ANOVA and coefficient of determination.
3.5.1 Analytical Model
The regression model below was used:
Y= β0 + β1X1+ β2X2+ β3X3 + β4X4+ε.
Where: Y = Financial performance given by return on assets on an annual basis
β0 =y intercept of the regression equation.
β1, β2, β3, β4 =are the slope of the regression
X1 = Interest rate as measured by average quarterly average bank lending rates
X2 = Inflation as measured by average quarterly inflation rate
X3 = Exchange rates as measured by average quarterly exchange rate of ln
KSH/USD
X4 = Economic growth as measured by quarterly GDP growth rate
ε =error term
3.5.2 Tests of Significance
The researcher carried out parametric tests to establish the statistical significance of the
overall model and individual parameters. The F-test was used to determine the
significance of the overall model, and it was obtained from Analysis of Variance
(ANOVA). At the same time, a t-test established the statistical significance of
individual variables.

28
CHAPTER FOUR: DATA ANALYSIS, RESULTS, AND
DISCUSSION
4.1 Introduction
In this chapter, the analysis, findings, and interpretation of the data are presented. The
study sought to ascertain the impact of the chosen macroeconomic variables on the FP
of the Kenyan banking sector. The selected macro-economic variables were GDP
growth rate, rate of Inflation, exchange rates and interest rates. Regression analysis was
applied to test the correlation between the variables under study and the study's
objectives. In testing the suitability of the analytical model, the ANOVA was utilized.
Finally, the outcomes were represented in figures and tables.
4.2 Descriptive Analysis
This section discusses the trend of the banking sector financial performance, GDP
growth rate, inflation rate, exchange rates and interest rates covering the period from
2009 to 2018 quarterly.
Table 4.1: Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Financial performance 40 2.5000 4.7000 3.322500 .6930007
Interest rate 40 5.8333 18.0000 9.585415 2.8841915
Inflation 40 4.0333 16.8333 8.074000 3.6064131
Exchange rate (KSH/USD) 40 75.1380 103.5177 90.836535 9.5118023
Economic growth 40 1.7000 11.6000 5.625000 1.6566494
Valid N (listwise) 40
Source: Research Findings (2019)
The study discovered that financial performance recorded an average of 3.3225 over
the study period. During the same period, interest rates produced an average of 9.5854
while the GDP growth rate recorded an average of 5.625. Further, Inflation and

29
exchange rates recorded an average of 8.074 and 90.8365, respectively. The standard
deviation indicated that financial performance, interest rates, economic growth,
exchange rates, and inflation rates varied over the study period. The exchange rate
recorded the most significant variation (9.5118), followed by inflation rates (3.6064).
4.3 Diagnostic Tests
Before running the regression model, Diagnostic tests were done. In this case, the tests
conducted were Multicollinearity, normality, autocorrelation and heteroskedasticity.
4.3.1 Multicollinearity Test
Multicollinearity can be defined as a statistical situation in which several predictor
variables in a multiple regression model have a high correlation. The situation is
unwanted where a strong correlation exists among the predictor variables. A
combination of variables is said to be perfectly multicollinear in case there is one or
more 100% linear relationship among a number of the variables.
Table 4.2: Multicollinearity Test
Collinearity Statistics
Variable Tolerance VIF
Interest rates 0.360 2.778
Inflation rate 0.392 2.551
Exchange rate 0.372 2.688
Economic growth 0.376 2.660
Source: Research Results (2019)
VIF value was utilized in the study where a value lower than 10 for VIF meant lack of
Multicollinearity. For multiple regressions to be useful, the variables should exhibit a
weak relationship. The variables in the study showed a VIF value of <10, as shown in
Table 4.2, which could be interpreted to mean that the variables had no statistical
significant Multicollinearity among them.

30
4.3.2 Normality Test
To test for normality, the researcher used the Shapiro-Wilk and Kolmogorov-Smirnov
tests. The null and alternative hypotheses are as shown below.
H0: the secondary data was not normal.
H1: the secondary data is normal
A p-value exceeding 0.05, would lead the researcher to reject the null hypothesis and
vice versa. The test results are illustrated in table 4.3.
Table 4.3: Normality Test
ROA
Kolmogorov-Smirnova
Shapiro-Wilk
Statistic Df Sig. Statistic Df Sig.
Interest rates .180 40 .264 .894 40 .790
Inflation .176 40 .264 .892 40 .784
Exchange rate .178 40 .264 .893 40 .787
Economic growth .181 40 .264 .896 40 .792
a. Lilliefors Significance Correction
The data revealed a p-value exceeding 0.05 hence the researcher used only the
alternative hypothesis and concluded that the utilized data was evenly distributed. This
data was used to conduct parametric tests and statistical analyses like ANOVA,
regression and Pearson’s correlation.
4.3.3 Autocorrelation Test
Correlation of error terms in varying periods were checked by conducting a serial
correlation test. The Durbin Watson test for serial correlation was used to assess
autocorrelation in the linear panel. It is a major challenge in panel data analysis and
must be considered to get the right model specifications. Below are the results.
Table 4.4: Autocorrelation Test

31
The null hypothesis suggests that autocorrelation does not exists. For example, the
Durbin Watson statistic of 2.015 is between 1.5 and 2.5, implying that serial correlation
doesn't exist.
4.3.4 Heteroskedasticity Test
It checked for heteroskedasticity using Likelihood Ratio (LR) as indicated in the Table.
This test used the alternative hypothesis that the error was homoscedastic. The
likelihood-ratio test produced a chi-square value of 36.84 with a 0.0000 p-value. The
chi-square esteem was significant at 1 percent level, in this manner the invalid
speculation of consistent fluctuation was rejected, meaning the nearness of
heteroskedasticity in the examination information as suggested by Poi and Wiggins
(2001). To deal with this issue the examination utilized the FGLS estimation method.
Table 4.5: Heteroskedasticity Test

32
4.4 Correlation Analysis
Pearson's correlation analysis was utilized to test for correlation amongst the variables.
A p-value of 0.05 or less was used to indicate significant correlations. Table 4.4 below
displays the outcomes.
It was revealed that there was a negative and statistically significant correlation (r = -
.330, p = .038) between exchange rate and the FP of the Kenya banking sector.
Additionally, it was discovered that there exists a positive but insignificant correlation
between interest rates, economic growth and FP of the banking sector as supported by
(r = .123, p = .450) and (r = .173, p = .285) respectively while inflation rate had an
insignificant negative correlation with ROA.

33
Table 4.6: Correlation Analysis
4.5 Regression Analysis
Regression analysis of the Kenyan banking sector’s financial performance against the
four chosen predictor variables; economic growth, interest rates, exchange rate and
inflation rate was undertaken at a 5% significance level. Table 4.7 below illustrates the
model summary.

34
Table 4.7: Model Summary
The study ought to ascertain the influence of selected macroeconomic variables on
financial performance. The results revealed an overall strong and positive relationship
(R= 0.728) between the selected macroeconomic variables and the financial
performance. Additionally, an R square of value 0.531 was generated. The implication
is that the selected macroeconomic variables can justify 53.1% of the disparities in the
financial performance. On the other hand, residual variables from the Durbin-Watson
statistic of 2.015 mean no serial correlation existed since it was above 1.5.
4.5.2 Analysis of Variance
The ANOVA was used to confirm the overall model's appropriateness and the outcomes
given in Tableau 4.8.
Table 4.8: Analysis of Variance
From the ANOVA results above, the regression model was declared significant as
supported by the significance level of 0.00 which suggest suitability of the model in

35
forecasting of influence of the chosen macroeconomic variables and financial
performance since the value of p was below 0.05, the value of significance (p-value)
was less than 5%.
4.5.2 Model Coefficients
In indicating the strength and direction of the relationships between the selected
macroeconomic variables and the financial performance, the Model coefficients were
utilized. The confidence level at 95% and the value of p below 0.05 were deduced as a
statistical significance measure. As such, a p-value of more than 0.05 indicates a
relationship between the dependent and the independent variables is statistically
insignificant. Table 4.9 presents the results.
Table 4.9: Coefficients of Determination
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 30.845 4.697 6.568 .000
Interest rate .187 .041 .776 4.498 .000
Exchange rate -.963 .246 -.219 -3.918 .000
Inflation -.143 .036 -.743 -3.946 .000
Economic
growth
.120 .059 .288 2.049 .048
a. Dependent Variable: Financial performance
As per the finding above, it is apparent that interest rate and economic growth produced
positive and statistically significant values for this study (high t-values (4.498 and
2.049), p < 0.05). Exchange rate and Inflation generated negative and statistically
significant values for this study as supported by (t= -3.918 and -3.946, p< 0.05).

36
The following regression equation was formulated:
Y = 30.845 + 0.187X1- 0.963X2- 0.143X3+ 0.120X4
Where,
Y = Financial performance of the banking sector
X1= Interest rate
X2= Exchange rate
X3= Inflation
X4= Economic growth
From the above formulated regression model, the constant = 30.845 indicates that if the
nominated macroeconomic variables (economic growth, rates of exchange, rates of
interest, and rate of Inflation) were considered zero, the financial performance of the
banking sector would be 30.845. An increment in interest rate with a unit would cause
an increment in financial performance with 0.187. In contrast, an increment in
economic growth with a unit would increase financial performance by 0.120. On the
other hand, an increment in exchange rates with a unit and an inflation rate with a unit
would translate to a reduction in the banking sector's financial performance by -0.963
and -0.143 respectively.
4.6 Discussion of Research Findings
The study aimed to ascertain the influence of the chosen macroeconomic variables on
the FP of the banking sector. The study used secondary data covering 2009 to 2018 for
analysis. Data was then edited and cleaned for completeness. Finally, regression
analysis was applied to test the connection between the variables under study
concerning the objective. ANOVA analysis was used to confirm the regression
findings.

37
The study discovered a connection between selected macroeconomic variables and the
banking sector's FP. Interest rates and economic growth generated positive and
statistically significant values. This implies that the greater the interest rate gets and
economic growth, the more the FP of the banking sector. It was further discovered that
exchange rates and rate of Inflation have a significant negative impact on FP on the
Kenyan banking sector, implying that an increase in either of these two will lead to a
reduction in banking sector FP.
The findings agree with Osamwonji and Chijuka (2014), who studied the influence of
macroeconomic variables on banks' profitability. Secondary data for the study was
based on data from 1990 to 2013 obtained in Nigeria. The secondary data was obtained
from the central bank and firms’ annual reports and financials. The macroeconomic
variables studied are GDP, interest rate and inflation rate; the proxy for profitability is
return on equity. Data analysis was by way of ordinary regression. The study finds a
significant and positive relationship between GDP and ROE, significant and negative
relationship between ROE and interest rate, and finally insignificant and negative
relation involving inflation rate.
The study also differs with Tora (2018) who examined how nominated macroeconomic
variables affect the FP of the banking industry in Kenya. The research utilized a
descriptive survey design. The 42 banks in operation for the study period had financial
data for the five years of the study from 2013-2017 were targeted. The data were
analyzed using SPSS, where descriptive and inferential statistics were generated. The
study discovered that interest rates affect the FP of the commercial banking sector
positively and significantly. At the same time, the rest of the chosen macro-economic
variables have no significant effect on the FP of the banking sector.

38
CHAPTER FIVE: SUMMARY, CONCLUSION, AND
RECOMMENDATIONS
5.1 Introduction
The chapter presents the study summary, discussions, and conclusions. Major
limitations of the study are also presented as well as the recommendations.

39
5.2 Summary of Findings
The study sought to ascertain the impact of chosen macro-economic variables on the
FP of the Kenyan banking sector. The chosen macro-economic variables were GDP
growth rate, exchange rate, inflation rate, and interest rates. Regression analysis was
applied in testing the correlation between the variables based on the study objectives.
In addition, the appropriateness of the analytical model was tested using ANOVA.
Tables and figures were used in presenting the outcomes.
Diagnostics tests were done on the data obtained with a null hypothesis that the
secondary data was not normal. P-values exceeding 0.05 was generated in Kolmogorov-
Smirnova, and Shapiro-Wilk tests, thus rejecting the null hypothesis. Hence it was
appropriate to utilize the data in conducting the parametric tests. The study revealed
that economic growth, rate of interest, rates of exchange, and rates of Inflation in Kenya
fluctuated during the study period (2009-2018).
In testing the correlation among the variables, Pearson correlation analysis was utilized.
As a result, a negative and statistically significant correlation between exchange rates
and the banking sector's financial performance was revealed. Further, it was revealed
that a negative and insignificant correlation between inflation rate and banking sector
FP existed and an insignificant positive correlation between GDP growth rate and
interest rate on the banking sector's financial performance. Nevertheless, the study
never recorded any significant correlation among the independent variables. The
implication was that Multicollinearity was not there; hence the independent variables
were appropriate to use as a determinant of financial performance in regression
analysis.

40
Regression analysis findings discovered a strong relationship (R= 0.728) between
selected macroeconomic variables and financial performance. The result additionally
indicated that the R2
value is 0.531. This implies that independent variables investigated
in the study could account for or explain only 53.1% of the dependent variable. The
remaining 46.9% were associated with other factors that were not the study's subject.
According to the literature review, there is a notable lack of consensus on the effect of
macro-economic variables on FP; Simiyu and Nile (2015) undertook a research study
to analyze how the profitability of listed commercial banks in Kenya is affected by
macroeconomic variables. The census study finds an insignificant positive effect of
GDP on profitability; also, the study finds a significant negative relationship between
profitability and interest rate and a significant positive effect between profitability and
exchange rate. Kiganda (2014), however, concluded that bank performance in Kenya is
not affected by macroeconomic factors. Ongeri (2014) finds that macroeconomic
variables positively affect nonbanking financial institutions' profitability. Kungu (2013)
concludes that the FP of private equity firms is influenced by macroeconomic factors
but finds the exchange rate to have a weak negative relationship with return on
investment. This study found that interest rate and economic growth have a notable
positive influence among the four selected macro-economic variables. In contrast,
Inflation and exchange rate have a notable negative influence.
5.3 Conclusion
The research aimed to determine the influence of chosen macroeconomic variables
(inflation rate, economic growth rate, interest rates, and exchange rates) on the FP of
the Kenyan banking sector. The study concludes that the existence of a strong
relationship between the selected macroeconomic variables and the financial

41
performance of the banking sector is evident. Further, it was revealed that interest rate
and economic growth positively affect the Kenyan banking sector's FP while exchange
rate and inflation rate negatively influence the banking sector’s FP.
In testing the correlation amongst the variables, Pearson correlation analysis was
utilized. As a result, exchange rates were discovered to be negatively as well as
significantly correlated with the FP of the banking industry. The study also discovered
that existence of a negative but insignificant correlation between inflation rates and
financial performance of the banking sector and an insignificant positive correlation
between interest rate, GDP growth rate and FP. However, the study never recorded any
significant correlation among the independent variables. Implication thereof was that
Multicollinearity was not there hence the independent variables were appropriate to use
as determinant of financial performance in regression analysis.
The study concurs with Kanwal and Nadeem (2013) who is a research study aimed to
ascertain the relationship that exists between macroeconomic variables (GDP, inflation
rate, interest rate) and profitability (measured by ROA, return on equity, and equity
multiplier) of public, commercial banks in Pakistan. This study covered a period from
2001-2011 (ten years). The population comprised thirty-eight banks; a sample of
twenty-three listed banks were studied. Data was sourced from secondary sources and
analyzed using correlation, descriptive statistics, and pooled ordinary least squares
regression analysis. As a result, the researchers find a strong positive association
between profitability and interest rate, an insignificant positive association between
GDP and profitability and a weak negative relationship between inflation rate and bank
profitability.

42
The study's findings disagree with Ng’ang’a (2016) undertook a study investigating the
association between macroeconomic determinants and FP of the insurance industry in
Kenya. The FP was regressed against the macroeconomic indicators; average interest
rates computed by Central Bank rate, GDP growth rate, real exchange rate (Ksh/USD),
inflation rate computed by CPI and unemployment rate. Secondary data collected
quarter yearly was used while descriptive research design was utilized. The study was
carried out in a ten-year period from 2006 to 2015. Correlation together with multiple
regression analysis were used for data analysis. Findings reveal that exchange, interest,
and unemployment rates are not significant predictors of the insurance industry's
financial performance.
5.4 Recommendations
The study unveiled that interest rate significantly and positively influence the FP of the
banking industry. The implication is that increment in interest rates causes an increment
in the banking sector in Kenya. The reality can justify this that interest income is banks'
major revenue stream. So an increase in interest rates translates to an increment in
financial performance. Therefore, policymakers such as the Central bank should sustain
interest rates at a level that will maximize the banking sector's financial performance
while considering the negative effect of higher interest rates on the economy.
It was discovered that there is a positive influence of economic growth on the banking
sector FP. The influence is also statistically significant. The study recommends the need
to develop measures that can boost economic growth as this will translate to better
financial performance of banks. Inflation rate and exchange rate were found to
negatively influence the FP of the banking sector; the current study recommends the
need to regulate the prevailing levels of these two variables.

43
The commercial banking sector in Kenya should consider macro-economic variables
like interest rates, economic growth, inflation rates and exchange rates in their policy
formulation to manage their effect on the financial performance. In addition, the
government using its agencies like the CBK should formulate policies that generate an
enabling and favorable environment for banks. An improved FP in the banking sector
will translate to the country's economic growth.
5.5 Limitations of the Study
This study mainly relied on the data provided by KNBS and CBK. This implies that the
accuracy of the data obtained was contingent on the information provided. The
researcher did not have any control over this accuracy. This is usually a general problem
when dealing with secondary data. To handle this challenge, the researcher had to
counter check the data from KNBS and CBK for any differences.
The research concentrated on 10 years (2009 to 2018). It is not certain whether the
findings would hold for a longer time frame. It is also unclear whether similar outcomes
would be obtained beyond 2018. The study should have been executed longer to
incorporate major forces such as booms and recession.
In the analysis of data, multiple linear regression model was applied. The study's
findings cannot be generalized authoritatively because of the weaknesses arising when
using regression models like errors and misleading a result of change in variables. If
data were to be added to the functional regression model over and over, the results may
not hold anymore regarding the relationship between two or more variables.
5.6 Suggestions for Future Studies
The study sought to ascertain the influence of selected macroeconomic variables on the
FP of the Kenyan banking sector. The selected macroeconomic variables were rate of

44
interest, growth in the economy, rates of exchange, and rates of Inflation, which could
only account for 53.1% of the total variance in the Kenyan banking sector's financial
performance. This implies that other key macro-economic variables impact the banking
sector's FP. Therefore, in the future, researchers should seek to know the other
determinants of FP in the banking sector. This will enable them to make more adequate
conclusions concerning the effect of macroeconomic variables on the banking sector’s
FP.
This study focused on selected macro-economic variables and the banking sector's FP
and utilized secondary data. A subsequent study utilizing primary data, which is much
superior to secondary data, should be done using interviews or questionnaires to
complement or criticize this study's findings.
This study
's attention was drawn to the latest ten years because of the readily available
information. However, subsequent studies may cover a big time frame like ten or twenty
years which can be very impactful on this study by either complementing or
disregarding the findings of this study. Lastly, adoption of other models for example
,Vector Error Correction Model (VECM) should be considered in explaining
relationships among variables because of the challenges encountered using regression
models.

45
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54
APPENDICES
Appendix I: Research Data
Year
Financial
performanc
e
Interest
rate
Exchange rate
(KSH/USD)
Inflatio
n
Economic
growth
2009 Q1 2.6000 8.4167 79.5813 16.8333 6.6000
2009 Q2 2.6000 8.0833 78.4460 15.9200 7.6000
2009 Q3 2.6000 7.7500 76.2427 13.3933 7.9000
2009 Q4 2.6000 7.2500 75.1380 10.3000 11.6000
2010 Q1 3.7000 6.9167 76.4877 7.8500 7.5000
2010 Q2 3.4000 6.7500 78.9377 5.8667 6.6000
2010 Q3 3.5000 6.0000 80.9260 4.7067 6.1000
2010 Q4 3.7000 6.0000 80.5807 4.0333 4.4000
2011 Q1 3.7000 5.8333 82.2360 4.1567 4.2000
2011 Q2 3.4000 6.0833 86.1240 6.0133 4.3000
2011 Q3 3.3000 6.5000 93.0137 9.0200 5.0000
2011 Q4 3.3000 15.1667 93.8697 12.7767 4.7000
2012 Q1 3.8000 18.0000 84.1387 15.8267 6.1000
2012 Q2 4.0000 18.0000 84.1203 16.2900 7.5000
2012 Q3 3.7000 15.3333 84.2760 14.2967 6.4000
2012 Q4 4.6000 11.6667 85.5783 10.6967 3.5000
2013 Q1 4.7000 9.5000 86.7213 7.2567 5.2000
2013 Q2 4.7000 8.8333 84.6077 5.0433 6.0000
2013 Q3 4.7000 8.5000 87.2550 4.5633 4.6000
2013 Q4 4.7000 8.5000 85.9073 5.3867 5.6000
2014 Q1 3.4000 8.5000 86.3270 6.2033 5.7000
2014 Q2 3.4000 8.5000 87.2467 6.8267 5.6000
2014 Q3 3.4000 8.5000 88.2383 7.2367 6.1000
2014 Q4 3.4000 8.5000 89.8780 6.9767 5.5000
2015 Q1 2.5000 8.5000 91.5247 6.6667 5.0000
2015 Q2 2.5000 9.0000 95.8440 6.6567 6.2000
2015 Q3 2.5000 11.5000 102.9673 6.3900 5.2000
2015 Q4 2.9000 11.5000 102.3807 6.4367 7.2000
2016 Q1 3.4000 11.5000 101.9100 6.8400 5.2000
2016 Q2 4.2000 10.8333 101.0350 6.5900 4.5000
2016 Q3 3.3000 10.5000 101.3377 6.4700 4.5000
2016 Q4 2.5000 10.5000 101.7343 6.4033 5.3000
2017 Q1 2.9000 10.0000 103.4147 6.4833 6.6000
2017 Q2 2.8000 10.0000 103.3593 7.7233 6.4000
2017 Q3 2.7000 10.0000 103.5177 8.3233 6.4000
2017 Q4 2.7000 10.0000 103.3513 8.1533 5.9000

55
Year
Financial
performanc
e
Interest
rate
Exchange rate
(KSH/USD)
Inflatio
n
Economic
growth
2018 Q1 2.7000 9.5000 101.8330 7.3600 4.7000
2018 Q2 2.8000 9.0000 100.7590 5.6833 3.5000
2018 Q3 2.8000 9.0000 100.7063 4.7033 1.7000
2018 Q4 2.8000 9.0000 101.9083 4.6033 2.4000

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