Download TO DETERMINE INTEREST AND LOAN DEFAULT RATES AMONG

TO DETERMINE INTEREST AND LOAN DEFAULT
RATES AMONG COMMERCIAL BANKS IN KENYA
BY
SHEM, ORONI MOTARI
A Management Research Project Submitted in Partial
Fulfillment of the Requirements for the Award Degree of
Master of Business Administration, School of Business,
University of Nairobi
NOVEMBER, 2013
DECLARATION
I declare and confirm that the contents of this document are my own derivatives and
compilation and that to the best my knowledge they are original; and where other
researchers and authors have been u s e d o r c i t e d , this has been acknowledged
through references.
__________________________
_______________________
SHEM, ORONI MOTARI
DATE
D61/75590/09
SUPERVISOR
This project has been presented with approval from me as
__________________________
_______________________
Mr. LUTHER OTIENO
DATE
University Supervisor
i
ACKNOWLEDGEMENT
I express my sincere gratitude to all those who directly or indirectly contributed to the
successful completion of this project. It is by God's love and grace that this project has
been successfully completed. I give thanks to the Lord God for His faithfulness, grace
and favour and for granting health that has helped me have this work done to completion.
I give gratitude to my supervisor Mr. Luther Otieno and indeed all my lecturers at the
University of Nairobi for all their professional advice and the drive towards achieving
this great goal. They imparted useful knowledge and made me more valuable to the
society. I must also remember my colleagues in class who kept encouraging me both in
class and during group discussions.
I also wish to sincerely acknowledge my wife Josephine, and children for their patience
and encouragement all the way during the course of my studies. The entire family too
gave me material, financial and moral support towards completion of this project. To
them I give great honour.
If this research contains anything valuable, it is the collective work I did with - the
individuals and institutions I share the credit with them all. I take full responsibility for
any errors, omissions and distortions that the reader may find in this research work.
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DEDICATION
I would wish to dedicate this Research Project to my dear wife Josephine Motari and my
dear children Reagan and Sylvia Motari. God bless you all.
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TABLE OF CONTENTS
Declaration……………………………………………………………………..
i
Acknowledgment……………………………………………………………….
ii
Dedication………………………………………………………………………
iii
Abstract…………………………………………………………………………
viii
CHAPTER ONE………………………………………………………………
1
1.0 INTRODUCTION…………………………………………………………
1
1.1
Background to the Study……………………………………………….
1
1.1.1 Interest Rate…………………………………………………………….
3
1.1.2 Concept of Loan Default……………………………………………….
4
1.1.3 Interest of Loan Default……………………………………………….
6
1.1.4 Commercial Banks in Kenya…………………………………………
8
1.2
Research Problem…………………………………………………….
10
1.3
Objectives of the study………………………………………………
12
1.4
Value of the study……………………………………………………
12
CHAPTER TWO ……………………………………………………..
2.0 LITERATURE REVIEW………………………………………………
14
14
2.1
Introduction………………………………………………………….
14
2.2
Term Structure Theory ……………………………………………….
14
2.3
Interest Rate …………………………………………………………
16
2.4 Loan default in Commercial Banks ………………………………………… 21
2.5 Relationship between interest rates and the default of loans………………… 25
iv
CHAPTER THREE……………………………………………………………. 28
3.0 RESEARCH METHODOLOGY…………………………………………. 28
3.1 Introduction…………………………………………………………………. 28
3.2 Research Design…………………………………………………………….
28
3.3 Population of study…………………………………………………………. 28
3.4 Data Collection……………………………………………………………..
29
3.5 Data Analysis………………..……………………………………………..
29
CHAPTER FOUR..……………………………………………………………. 30
4.0 FINDINGS AND DISCUSSIONS …………………………………………. 30
4.1 Introduction…………………………………………………………………. 30
4.2 Descriptive Statistics: Interest rates on non-performing loans………………. 30
4.3 Impact of all interest on gross non-performing loans, deposits, savings,
lending and overdrafts………………………………………………………. 33
4.4 The impact of all interest on total net non-performing loans, deposits, savings,
lending and overdrafts………………………………………………………
35
4.5 The impact of interest rates on total net non-performing loans, deposits
and lending ………………………………………………………………..
37
4.6 The impact of interest rates on gross non-performing loans, deposits
and lending ………………………………………………………………..
38
4.7 The impact of interest rates on gross non-performing loans, deposits,
lending and overdrafts ……………………………………………………..
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39
4.8 The impact of interest rates on total net non-performing loans, deposits,
lending and overdrafts ……………………………………………………..
41
CHAPTER FIVE..…………………………………………………………….
44
5.0 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS…………. 44
5.1 Summary ……………………………………………………………………. 44
5.2 Conclusions ….…………………………………………………………….
45
5.3 Recommendations…………………………………………………………
45
5.4 Limitations…………………………………………………………………
46
5.5 Suggestions for Further Studies…………………………………………..
46
REFERENCES…………………………………………………………………
47
Appendix 1: Letter of Identification…………………………………………..
50
Appendix 2: Loan Default of Commercial Banks in Kenya …………………..
51
Appendix 3: Annual Monthly Observation……………………………………
54
Appendix 4: Average percentage interest rates and loan default……………..
58
Appendix 5: Licensed Commercial Banks……………………………………..
59
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LIST OF TABLES
Table 1: Descriptive Statistics of non performing Loans and Interest Rates…… 30
Table 2: Gross Non-performing Loans and Advances (GNperL)..……………… 31
Table 3: Total Net Non performing Loans and Advances ……………………… 32
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ABSTRACT
Banks operate in an environment of considerable risk and uncertainty. This study
investigates the relationship between interest rates and non-performing loans for
commercial banks in Kenya. The period of analysis was five years from 2008 to 2012. The
findings indicate an increasing trend of average market interest rates ranging from 12.02 in
the year 2008 to 19.20 in the year 2012. This indicates improved performance in the macro
economic variables over the years under the study. The level of non-performing loans on
average declined in all commercial banks for the period under study. The decline was
however, more pronounced in privately owned banks than in the state owned.
The literature review focused on term structure theory as the guiding frame and other
researchers who explored the concepts of interest rates in banks, loan default in
commercial banks, and the relationship between interest rates and loan default rates. The
methodology is cross sectional descriptive design carried across all the 47 registered banks
in Kenya, library research and content analysis were used to collect and collate the data. The
findings also reveal that there is a positive relationship between interest rates and nonperforming loans, therefore it is evident that there are other factors an indication that when
interest rates increase, commercial banks should put in place mechanisms to deal with nonperforming loans to minimize their adverse effects on bank performance.
There are discussions and conclusions have been included in chapter five, which also gives
the recommendations for further studies. There are appendices to guide the user of this
document.
viii
CHAPTER ONE
1.0 INTRODUCTION
1.0 Background of the Study
Persistent loan defaults have become an order of the day in developing countries. There have
been hardly any bank or development financial institutions (DFI) in developing which has not
experienced persistent loan default. This is evidenced by the undercapitalization and illiquidity of
160 development financial institutions in 33 developing countries (Hoque 2004, World Bank
1993: and Calomiris and Himmelberg, 1993). This malaise in the development finance market has
not only impaired the existence of many development financial institutions, but also adversely
affected the economies of developing nations.
Despite the application of a number of remedial measures, such as supplying fresh loans, loan
rescheduling, imposition of penal interest rates, denial of additional credit to repeat defaulters,
management takeover of problem projects, and legal actions, loan actions, loan default problems
continued to reign the credit markets in developing countries. Available literature (Hoque 2004;
Gupta 1990; and Sinkey and Greenwalt 1991) suggests that loan default occurs when borrowers
are not able and/or willing to repay loans. There are borrowers who are willing but not able to
repay loans and there are borrowers who are able but not willing to repay loans.
Loan default occurs in either cases. This research paper advances this argument that
unless the banks and DFIs follow a rationalized interest rate policy, loan default and loan
loss will continue to haunt the financial institutions in developing countries.
Compensation is expected by resource owners for deferred spending generating savings,
1
which is the cost ultimately born by the borrowers willing to repay additional sums on the
borrowed moneys (Reilly and Brown, 2005). This cost of money is indeed the price,
demanded by surplus fund purveyors paid by consumers, conventionally known as
interest rate (Fabozzi and Peterson, 2003) and usually considered as a precipitating factor
responsible for economic shifts.
If oscillation in this cost result in increase of debt burden of consumers, according to
quantitative theory on consumer credit and default risk presented by Chatterjee, Corbae,
Nakajima and Rias-Rull (2002), borrowers begin to default according to Obamuyi
(2007), a loan is considered as performing if principal and interest is being paid in
accordance with the agreed contractual terms of repayment and otherwise in default i.e.
non-performing.
Generally around the globe and more specifically in the context developing countries,
non-performing loans are subdivided into three categories i.e. principal repayment or
interest payment is classified as "Substandard" if overdue by 90 days. After being
overdue for 180 days, loan is classified as "Doubtful" and after 01 year it is reported loss.
In Kenya, commercial banks are no exemption from these aspects of loan default. These
institutions are run to make a profit and owned by a group.
While commercial banks offer services to individuals, they are primarily concerned with
receiving deposits and lending to businesses. There are borrowers who are willing but not
able to repay loans and there are borrowers who are able but not willing to repay loans.
Loan default occurs in both cases. In Kenya interest rates were liberalized in July 1991.
2
However, nominal interest rates increased minimally immediately after liberalization, and
as inflation accelerated very high negative real rates were recorded. Interest rate spread
widened, indicating either inefficiency in the intermediation process with weak
institutional infrastructure, and/or macroeconomic instability, and/or a non-competitive
structure in the banking sector. Deposit rates remained at low and almost constant levels,
while lending rates began moving upwards.
1.1.1 Interest Rates
Interest rate is the price of money and cost of using lenders money for specified
period of time. There is interest rate at which banks are lending (the offer rate)
and interest rate they are paying for deposits (the bid rate). The difference
between them is called a spread. The spread between offer and bid rates provides
a cover for administrative costs of the financial intermediaries and includes their
profit. The spread is influenced by the degree of competition among financial
institutions.
With the cost for disposal of non-performing loans (direct write-off costs,
provisions for credit losses,) exceeding the profits from the core banking business
(net business profits), it can be said it erodes commercial banks' profitability.
An interest rate is the rate at which interest is paid by borrowers for the use of
money that they borrow from a lender. Specifically, the interest rate is a percent of
3
principal paid a certain amount of times (m) per period (usually quoted per
annum). For example, a small company borrows capital from a bank to buy new
assets for its business, and in return the lender receives interest at a predetermined
interest rate for deferring the use of funds and instead lending it to the borrower.
Interest rates are normally expressed as a percentage of the principal for a period
of one year.
Interest-rate targets are a vital tool of monetary policy and are taken into account
when dealing with variables like investment, inflation, and unemployment. The
central banks or reserve banks of countries generally tend to reduce interest rates
when they wish to increase investment and consumption in the country's
economy. However, a low interest rate as a macro-economic policy can be risky
and may lead to the creation of an economic bubble, in which large amounts of
investments are poured into the real-estate market and stock market.
1.1.2 Concept of Loan Default
Loan default can be defined as the failure to promptly pay interest or principal
when due. Default occurs when a debtor is unable to meet the legal obligation of
debt repayment. Borrowers may default when they are unable to make the
required payment or are unwilling to honor the debt. The failure to perform on a
futures contract as required by an exchange. Defaulting on a debt obligation can
place a company or individual in financial trouble.
4
The lender will see a default as a sign that the borrower is not likely to make
future payments. If for instance company XYZ is unable to make a coupon
payment on its bonds, the bondholders would place XYZ in bankruptcy. This
would give the company an opportunity to claim XYZ's assets as a form of
repayment for the debt. Defaulting on a futures contract occurs when one party
does not fulfill the obligations set forth by the agreement. The default usually
involves not settling the contract by the required date. A person in the short
position will default if he or she fails to deliver the goods at the end of the
contract. The long position defaults when payment is not provided by the
settlement date.
Other studies that analyzed bank loans recovery rates were by Asarnow and
Edwards (1995) and Eales and Bosworth (1998). The first study presents the
results of an analysis of losses on bank-loan defaults based on 24 years of data
compiled by Citibank; their database comprises 831 commercial and industrial
(C&I) loans, as well as 89 structured loans (highly collateralized loans that
contain many restrictive covenants). Their results (based on "ultimate" recoveries)
indicate a LGD of about 35% for C&I loans (with larger loans, above US$ 10
million, showing a somewhat lower loss rate of 29%); unsurprisingly, the LGD
for structured loans is considerably lower (13%), due to the role played by
collateral and covenants in supporting the early default-detection and recovery
processes .
5
In the second study, the authors report the empirical results on recovery rates from
a foreign bank operating in the United States - Westpac Banking Corporation. The
study focuses on small business loans and larger consumer loans, such as home
loans and investment property loans. Neto de Carvalho and Dermine (2003)
analyze the determinants of loss given default rates using a portfolio of credits
given by the largest private Portuguese bank, Banco Commercial Portugues.
Their study was based on a sample of 371 defaulted loans to small and medium
size companies, originally granted during the period June 1985-December 2000.
The estimates of recovery rates were based on the discounted cash flows
recovered after the default event. The authors report three main empirical results
which are consistent with previous empirical evidence that the frequency
distribution of loan losses given default is bi-modal, with many cases presenting a
0% recovery and other cases presenting a 100% recovery; the size of the loan has
a statistically significant negative impact on the recovery rate, and while the type
of collateral is statistically significant in determining the recovery, this is not the
case for the age of the bank-company relationship.
1.1.3 Interest Rates and loan Default
It's expected that as interest rates rise, the default rate also rise as loans become
increasing difficult to repay. Conversely as interest rates fall, the default rate also
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falls as loans become increasing easier to repay. Study conducted by Espinoza and
Prasad (2010) focused in macroeconomic and bank specific factors influencing
non-performing loans and their effects in GCC Banking System. After a
comprehensive analysis, they found that higher interest rates increase Nonperforming loans but the relationship was not statistically significant.
A similar study was recently conducted by Nkusu (2011) investigating the
macroeconomic determinants of loan defaults through panel regressions and panel
vector autoregressive models. Author in this article suggested that hike in interest
rates result in deterioration of borrower's repayment capacity and hence, cause of
increase in Non-performing loans. Interest rate policy plays a major role in NPL
growth rate of an economy.
Persistent loan defaults have become an order of the day in developing countries.
There have been hardly any bank or development financial institutions (DFI) in
developing which has not experienced persistent loan default. This is evidenced
by the undercapitalization and illiquidity of 160 development financial institutions
in 33 developing countries (Hoque 2004, World Bank 1993: and Calomiris and
Himmelberg, 1993). This malaise in the development finance market has not only
impaired the existence of many development financial institutions, but also
adversely affected the economies of developing nation.
7
1.1.4 Commercial Banks in Kenya
Banks are institutions which accept deposits, make business loans, and offers
related services. Commercial banks also allow for a variety of deposit accounts,
such as checking, savings, and time deposit. These institutions are run to make a
profit and owned by a group.
While commercial banks offer services to individuals, they are primarily
concerned with receiving deposits and lending to businesses. Overall Banking
Sector in Kenya is made up of 45 licensed institutions to carry out the business of
financial intermediation. They are guided by prudential guidelines issued by the
Central Bank of Kenya. Of the 45, 2 are mortgage finance companies and one is
non-bank financial institution. Out of the 45 institutions 35 are locally owned and
10 are foreign owned. 3 locally owned banks have significant government
shareholding.
The supervisory and regulatory role is played by the central bank which is
established by an Act of parliament, Central Bank of Kenya Act Cap 491. The
principal object of the Bank is to formulate and implement monetary policy
directed to achieving and maintaining stability in general level of prices in Kenya.
The second principal objective is to foster liquidity, solvency and proper
functioning of stable market-based financial system. Central Bank of Kenya
(CBK) Other secondary objectives of CBK are; to formulate and implement
8
foreign exchange policy; to hold and manage Kenya's foreign exchange reserves;
to licenses and supervise authorized dealers in money market; to promote the
smooth operation of payments, clearing and settlement systems; to act as a banker
and advisor to, as fiscal agent to the government of Kenya and issue currency
notes and coins.
In Kenya interest rates were liberalized in July 1991. Financial repression theory
predicts that after liberalization positive real interest rates should be realized as
nominal interest rates increase from the government set low levels when price
stability is achieved. The financial system also gains efficiency in the
intermediation process such that the interest rate spread between the lending and
deposit rate narrows. In Kenya, however, nominal interest rates increased
minimally immediately after liberalization, and as inflation accelerated very high
negative real rates were recorded. Interest rate spread widened, indicating either
inefficiency in the intermediation process with weak institutional infrastructure,
and/or macroeconomic instability, and/or a non-competitive structure in the
banking sector. Deposit rates remained at low and almost constant levels, while
lending rates began moving upwards.
Lending rates increased gradually after liberalization and we reaccelerating as the
sector faced a more risky environment. In addition, Treasury bill rates were kept
high so that the lending rates tended to follow the Treasury bill rate over time.
Even with a favorable environment for flexible interest rates, the lending rates
9
were sticky downwards, and even when they did decline they settled at relatively
high levels.
The persistently high lending rates were attributed to inflationary expectations,
expectations regarding exchange rate depreciation, high implicit taxes, poor loans
portfolios, a non-competitive financial system, and an inefficient intermediation
process.
1.2 Research Problem
Increasing interest rates with rising non-performing loans destruct private sector growth
and increase losses that result attrition of banks' equity similar to self-cannibalism
situation (Fofack, 2005). Keeping in view the above fact, researchers attempted to
empirically test the impacts of lending interest rates on the surging non-performing loan
portfolio of banks with specific reference to Developing countries' crippling economy.
Increase in loan defaults is critical source of economic distortion and stagnation which
must be controlled and monitored [Hou, (2007); Obamuyi (2007); Asariet al. (2011)],
they also stressed on policy makers of developing countries to take adequate measures on
high default rates which is a major apprehension. Therefore, area of loan defaults i.e.
non-performing loans (NPL) requires continues in-depth research to avoid distresses in
economic and financial system which is trusted by millions of individuals and business
concerns.
10
Research conducted by Asari, et al. (2011) concluded that Non-performing loans and
interest rates have significant relations. Fridson, German and Wu, (1997) as cited in
Kaplin, et al. (1999) focused on the correlation between default rates and real interest
rates. They used Moody's quarterly default rate on high-yield bonds from 1971-1995 and
found its weak but positive correlation with nominal interest rates. Further they
concluded moderate but positive correlation with lagged 2-year real interest rates and
default rates. They argued that the firm would run into financial insolvency or bankruptcy
in case the return on investments and assets are lower than cost of capital. Accordingly
they indicated positive relationship between default rate and real interest rates. Other
studies that analyzed bank loans recovery rates were by Asarnow and Edwards (1995)
and Eales and Bosworth (1998). The first study presents the results of an analysis of
losses on bank-loan defaults based on 24 years of data compiled by Citibank; their
database comprises 831 commercial and industrial (C&I) loans, as well as 89 structured
loans (highly collateralized loans that contain many restrictive covenants).
There have been many local studies focusing on interest rates and credit, Njuguna (2000)
and Phiri (2011) studied the factors affecting interest rate spread in Kenya. Njuguna
concluded that 75% of the decisions on interest rates are determined by forces of demand
and supply and government influence through the central bank policy frame has upto
20% effect on interest rate charged by commercial banks. They also tried to explain the
main factors that determine the levels of interest rates in Kenyan commercial banks.
Ngingi (1998) studied financial sector reforms and interest rate liberalization and dwelt
mainly on the historical aspects of financial sector reforms and how they impacted on the
11
interest rates. Kibet (2012) surveyed the application of term structure of interest rates by
commercial banks in Kenya. While these pieces of academic work are independent and
valid in their own right, none of these studies or any other known to the researcher
addresses the relationship between loan default and interest rates in commercial banks in
Kenya, hence the gap for study; and leads to the question that, what is the relationship
between loan default and interest rates among commercial banks in Kenya?
1.3 Objective of the study
The objective of the study is to establish the relationship between interest rates and the
rate of default on loans.
1.4 Value of the study
This research work will be source information for policy makers and banking
professionals to understand, control and to reduce the cancer of increasing nonperforming loans from the economy. Extent of this study is restricted to analyze the
impacts of the flux of borrowing cost, ex ante to any financial crisis which can be
expected on the basis of recent tremendous NPL growth in Developing countries.
Increase in loan defaults is a critical source of economic distortion and stagnation which
must be controlled and monitored; policy makers of developing countries should take
adequate measures on high default rates which is a major apprehension. Therefore, area
12
of loan defaults i.e. non-performing loans (NPL) requires continued in-depth research to
avoid distresses in economic and financial system which is trusted by millions of
individuals and business concerns.
This study is accordingly focused to contribute the knowledge world by analyzing the
relationship of volatile macroeconomic factor i.e. lending interest rate in context of skyhigh non-performing loans in the developing countries.
13
CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 Introduction
In this chapter, the theories and facts based on other scholars findings and researches are
presented. The chapter discusses, the theory of term structure as the factors determining
interest rates, loan default, relationship between interest rates and loan default.
2.2 Term Structure Theory
This study attempts to examine the term structure of interest rates, in order to explain the
relationship between interest rates and loan default rates among commercial banks in
Kenya. Interest rate structure is the relationship between the various rates of interest in an
economy on financial instruments of different lengths (terms) or of different degrees of
risk (Vinci, 2010). It is a yield curve displaying the relationship between spot rates of
zero-coupon securities and their term to maturity. The term structure of interest rate is the
relationship between the term to maturity of a bond and its yield to maturity (Haugen,
2005).
The interest term structure yield curve is explained by the following theories. Liquidity
preference theory predicts that investors are risk averse and prefer short-term securities.
14
Interest rates are determined based on the preferences of households to hold money
balances rather than spending or investing those funds (Sharpe, 2008). Money balances
can be held in the form of currency or checking accounts, however it does earn a very
low interest rate or no interest at all. A key element in the theory is the motivation for
individuals to hold money balance despite the loss of interest income. The quantity of
money held by individuals depends on their level of income and, consequently, for an
economy the demand for money is directly related to an economy’s income. (Gibson,
2001).
Pure expectation theory holds that forward rate represents the average opinion of what
the expected future spot rate for that period will be (Sharpe, 2008), According to this
theory, longer-term rates are determined by investor expectations of future short-term
rates. The pure expectations theory assumes that investors are indifferent between
investing for a long period on the one hand and investing for a shorter period with a view
to reinvesting the principal plus interest on the other hand (Vinci, 2010).
Preferred habitat theory purports that investors can move between segments if there
sufficient incentives exist. The preferred habitat theory allows for some substitutability
between maturities. However the preferred habitat theory views that interest premiums
are needed to entice investors from their preferred maturities to other maturities.
According to preferred habitat, government can have a direct impact on the yield curve.
Government borrow by selling bills and bonds, it will push up long-term interest rates (by
pushing down long-term bond prices) and cause the yield curve to be more upward
sloping or less downward sloping (Sharpe, 2008).
15
2.3 Interest Rates
Interest rate is the price of money and cost of using lenders money for specified period of
time. There is interest rate at which banks are lending (the offer rate) and interest rate
they are paying for deposits (the bid rate). Several factors determine the rate of interest
charged by commercial banks in Kenya include, legal and regulatory framework,
taxation, open market forces, money supply, capital requirements and risk factor. The
regulatory framework incorporates regulations by the monetary authority aimed at
achieving financial stability.
The major goal of financial liberalization is to achieve financial stability by creating a
strong regulatory framework. Financial instability with unsound and improper supervised
lending practices may results in high interest on loans and widening the spread because of
information asymmetry problem. With adequate supervision an increase in interest results
in banks rationing out credit instead of taking on new borrowers (Ndung'u and Ngugi,
2000). Imposing various regulations to banks results in financial sector instability by
diverting intermediation into informal, less regulated and less taxed part of the financial
system.
The legal framework incorporates the adequacy of commercial law and the efficiency
with which the judicial system makes and enforces legal decisions. Weaknesses in
enforcement of financial contracts will create credit management problems so that the
premium charged on credit increases. This is because banks face a credit risk associated
16
with their inability to make agreements that restrict the inability of the borrowers
to divert away funds from the intended purpose, disclose accurate information on
borrowers or make legal contracts. A weak legal system without clearly spelt out property
right hinder diversity of institutions thus denying them the opportunity to diversity risk.
Efficient legal system and better contract enforcement are associated with lower realized
interest rates (Demirguc-kunt and Huizinga, 1997).
The money supply is crucial determinant of the level of interest rates in the economy. The
equilibrium interest rate is determined when the quantity of money demanded is equal to
the quantity of money supplied (Haugen, 2005). The supply of money is determined by
the Central bank of Kenya through monetary policies. According to the loanable funds
theory, interest rate determination in financial market is as a result from factors that affect
the supply and demand for loanable funds (Saunders and Cornett, 2008) the aggregate
quantity of funds supplied is positively related to interest rates as long as competitive
forces are allowed to operate freely in a financial system (Saunders and Cornett, 2008).
Changes in underlying factors that determine the demand and supply of loanable funds
can cause continuous shifts in the supply and/or demand curve for loanable funds. Market
forces will react to the resulting disequilibrium with a change in the equilibrium interest
rate and quantity of funds traded in that market.
The Central bank of Kenya achieves market equilibrium on interest rate through open
market operation, when the demand for money is less than the supply, and the rate of
interest are high, there is surplus liquidity (Haugen, 2005), individuals will "attempt" to
17
get rid of it by buying government bills in securities market, thus lowering real rate of
interest. This will continue until interest rate has reached its equilibrium level. In the
same sense, if the rate of interest were below its equilibrium level, the supply of money
would be less than the demand. There will be shortage in liquidity; people would attempt
to increase liquidity by selling T- bills in security market. This will drive the real rate of
interest up to equilibrium level. I say "attempt" because only Central bank can really get
rid of money in the economy.
Commercial banks in Kenya are required to hold a certain percentage of their assets as
capital, a rate which may be established by the Central bank of Kenya or the banking
supervisor. The Basel capital accords sets the threshold at 8% of risk — adjusted assets
whereby certain assets such as government bonds are considered to have a lower risk and
are either partially or fully excluded from total assets for the purposes of calculating
capital adequacy, when at the threshold, a bank cannot extend another loan without
acquiring further capital on its balance sheet.
Reserve and liquidity requirements and mandatory investment and interest controls are
categorized as implicit taxes. A reserve requirement with no interest payment tends to
have a high opportunity cost as it squeezes the excess reserve available for banks to
advance credit, reducing the scope of the banks' income-earning assets. Similarly,
mandatory investment implies inefficient allocation of resources where banks continue
giving funds to prioritized sectors despite a non-optimal rate of return, while interest rate
controls limit the banks' effort to capture high-yielding investments.
18
Explicit taxes may provide a negative effective protection to the domestic financial
system and encourage financial intermediation abroad, especially if there is tax
discrimination. Discriminatory taxation reduces the flexibility of the system by
significantly reducing the funds available for discretionary lending. Tax discrimination
also leads to financial sector instability by driving intermediation into the informal, less
regulated and less taxed part of the market.
The presence of explicit and implicit taxes also discourages the development of an
interbank market level; all financial transactions make short-term overnight borrowing
uneconomical and increase the reliance on Central Bank discount facilities that provide
inexpensive and unlimited loans to banks in need of funds. Restrictive discount facility
results liquidity problems on banks thus resort to offering attractive deposit rates to
attract more deposits. There is a positive relationship between high interest rate spread
and high levels of taxation of the intermediation process (Demirguc-kunt, 1997).
A risk is a variable, price or quantity that affects cash flow or return and can change
unexpectedly for reasons beyond one's control (Stulz, 2003). The interest rates charged
by commercial banks in Kenya encompass a risk factor. Risk includes financial and
operational risks (Haugen, 2005). Financial risks are risks commercial bank interacts with
on its business. These risks include credit risks, which are risk of defaults by borrowers
on the loan amount on both interest and principle; liquidity risk is the inability to meet
current cash obligations and market risks that include interest rate risk, foreign exchange
and investment portfolio risk. Banks are exposed to risk because of uncertainty,
19
information asymmetry and the policy environment. When banks hold deposits and loans
with unmatched maturities they are exposed to interest rate risk as they adjust to the
available assets and liabilities at the end of the period by engaging in money and
secondary- market operations or roll over the deposits. A decline in market interest rate
lowers the present value of the outstanding amount of loan even if the credit risk is low,
especially when banks raise funds through short-term deposits to finance loans or
purchase security with a longer maturity period, and thus leads to a significant increase in
the volatility of market interest rate. This is because short- term interest rates are highly
volatile and affected by nominal shocks.
Banks are exposed to risk in the credit market as they do not know in advance the
proportion of loans that will perform. To cover this credit risk, banks charge a premium
whose magnitude depends on the credit policy, the interest rate on alternative assets,
amounts borrowed and types of clients. This increases the effective rate to borrowers and
t
may reduce the demand for loans. If lending interest is high, investors find it costly to
finance their loans, thus increases credit risk and the level of non-performing loans for
banks, thus widening the spread. According to Capital Asset Pricing model (CAPM), tries
to construct efficient portfolio. The expected return over the risk free rate is called the
risk premium. Risk premium is the reward commercial banks expect to receive for
bearing the risk associated with lending to the public (Stulz, 2003).
According to arbitrage pricing theory, assumes that returns depends partly on pervasive
macroeconomic influences or "factors" and partly on "noise" - events that are unique to
that market (Brealey and Meyers, 2003). Moreover, the returns are assumed to obey the
20
following simple relationship. Some commercial banks will be more sensitive to a
particular factor than other banks. An efficient financial market exists when interest rates
reflect all available public information about the economy, financial markets, and the
specific customer involved (Van home, 2002).The implication is that interest rates on
loans adjust very rapidly to new information.
2.4 Loan Default in Commercial Banks
Evidence from many countries in recent years suggests that collateral values and recovery
rates on corporate defaults can be volatile and, moreover, that they tend to go down just
when the number of defaults goes up in economic downturns. This link between recovery
rates and default rates has traditionally been neglected by credit risk models, as most of
them focused on default risk and adopted static loss assumptions, treating the recovery
rate either as a constant parameter or as a stochastic variable independent from the
probability of default.
This traditional focus on default analysis has been partly reversed by the recent
significant increase in the number of studies dedicated to the subject of recovery rate
estimation and the relationship between default and recovery rates. This link between
recovery rates and default rates has traditionally been neglected by credit risk models, as
most of them focused on default risk and adopted static loss assumptions, treating the
recovery rate either as a constant parameter or as a stochastic variable independent from
the probability of default. This traditional focus on default analysis has been partly
21
reversed by the recent significant increase in the number of studies dedicated to the
subject of recovery rate estimation and the relationship between
default and recovery rates. This paper presents a detailed review of the way credit risk
models, developed during the last thirty years, treat the recovery rate and, more
specifically, its relationship with the probability of default of an obligor.
Hoque and Hossain (2008) focused this issue and successfully tested association of loan
defaults and higher interest rates evidencing their view point using three regression
models. They suggested rationalizing of interest rate policy to enhance the repayment
capacity of borrowers for lowering the default rates. They found that loan "defaults were
highly correlated with higher interest rates which increase the debt burden on borrowers
and leads to defaults resulting in capital erosion of banks. Research conducted by Asari,
et al. (2011) was also on the same lines and concluded that Non-performing loans and
interest rates have significant relations. Their study suggested that increase in the Nonperforming loans result in deterioration of bank assets and subsequently capital erosion.
Interest rates and their volatility are among the most critical and closely watched
variables in the economy. Paper by Adela and Iulia (2010) demonstrated the connection
between average interest rate and non-performing loans. Dash and Kabra (2010) found
that commercial banks with aggressive lending strategies charging relatively higher rents
on lending incur greater Non-performing loans. Study conducted by Kaplin, et al. (2009),
empirically supported negative correlation between interest rate volatility and loan
defaults using data of large non-financial US firms for the period 1982-2008.
22
They found no correlation between interest rates and defaults after conditioning on
expected default frequency credit measure. Capozza, Kazarian and Thomson (1998),
while studying the mortgage defaults, found declining effects in defaults due to increase
in interest rate volatility. Based on their statistical and theoretical analysis, they suggested
that empirical studies may face difficulties in concluding significant effect of interest rate
volatility and loan defaults. Instability in interest rates poses a stronger risk for assets side
of the banks.
This ebb and flow increases the risk of returns for the banks and result in growth of nonperforming loans. Patnaik and Shah (2004) focused on interest rate volatility and relevant
risk to which Indian banks are exposed to. They suggested that measuring interest rate
risk exposure is an important issue for banks and regulators in policy evaluation with
specific reference to effects of interest rate shocks on banks' equity. It is concluded in
their research that Indian banks face relatively small problems of Developing countries as
the credit risk is controlled by management of interest rate risk exposures.
Volatile and increasing interest rates with rising non-performing loans destruct private
sector growth and increase losses that result attrition of banks' equity (Fofack, 2005)
similar to self-cannibalism situation. Keeping in view the above fact, researchers
attempted to empirically test the impacts of lending interest rates on the surging nonperforming loan portfolio of banks with specific reference to Developing countries'
crippling economy. Research conducted by Asari, et al. (2011) was also on the same lines
and concluded that Non-performing loans and interest rates have significant relations.
23
Fridson, German and Wu, (1997) as cited in Kaplin, et al. (1999) focused on the
correlation between default rates and real interest rates. They used Moody's quarterly
default rate on high-yield bonds from 1971-1995 and found its weak but positive
correlation with nominal interest rates.
Further they concluded moderate but positive correlation with lagged 2-year real interest
rates and default rates. They argued that the firm would run into financial insolvency or
bankruptcy in case the return on investments and assets are lower than cost of capital.
Accordingly they indicated positive relationship between default rate and real interest
rates.
Stiglitz and Weiss (1981) analyzed credit rationing and argued that higher interest rates
increase riskiness of bank's investments and accordingly the probability of default
contributing to NPL portfolio. They empirically justified their view point and explained
that increase in the interest rates foster borrowing for relatively riskier projects with
higher interest costs. Such increase in riskiness results in increased probability of
defaults. Increase in non-performing loan portfolios of banks is common in all those
economies where economic monitors including regulators and banking professionals lack
understanding of systemic risks and macroeconomic effects pertaining to Developing
countries. Reddy (2002) stressed the importance of issues related to systemic risk and
effects of macroeconomic variables faced by the economy specifically the banks by
comparing India with other countries including China, Japan, Korea and Thailand for
24
effectively solving critical problem of non-performing loans, as per their analysis, steep
rise in the interest rates was the root cause of majority Developing countries in Thailand.
2.5 Relationship between Interest Rates and the Loan Default
It's expected that as interest rates rise, the default rate also rise as loans become
increasing difficult to repay. Conversely as interest rates fall, the default rate also falls as
loans become increasing easier to repay. Study conducted by Espinoza and Prasad (2010)
focused in macroeconomic and bank specific factors influencing non-performing loans
and their effects in GCC Banking System. After a comprehensive analysis, they found
that higher interest rates increase Non-performing loans but the relationship was not
statistically significant.
A similar study was recently conducted by Nkusu (2011) investigating the
macroeconomic determinants of loan defaults through panel regressions and panel vector
autoregressive models. Author in this article suggested that hike in interest rates result in
deterioration of borrower's repayment capacity and hence, cause of increase in Nonperforming loans. Interest rate policy plays a major role in NPL growth rate of an
economy.
Hoque and Hossain (2008) focused this issue and successfully tested association of loan
defaults and higher interest rates evidencing their view point using three regression
models. They suggested rationalizing of interest rate policy to enhance the repayment
25
capacity of borrowers for lowering the default rates. They found that loan defaults were
highly correlated with higher interest rates which increase the debt burden on borrowers
and leads to defaults resulting in capital erosion of banks. Research conducted by Asari,
et al. (2011) was also on the same lines and concluded that Non-performing loans and
interest rates have significant relations. Their study suggested that increase in the Nonperforming loans result in deterioration of bank assets and subsequently capital erosion.
Interest rates and their volatility are among the most critical and closely watched
variables in the economy.
Paper by Adela and Iulia (2010) demonstrated the connection between average interest
rate and non-performing loans. Dash and Kabra (2010) found that commercial banks with
aggressive lending strategies charging relatively higher rents on lending incur greater
Non-performing loans. Study conducted by Kaplin, et al. (2009), empirically supported
negative correlation between interest rate volatility and loan defaults using data of large
non-financial US firms for the period 1982-2008. They found no correlation between
interest rates and defaults after conditioning on expected default frequency credit
measure.
Capozza, Kazarian and Thomson (1998) while studying the mortgage defaults, found
declining effects in defaults due to increase in interest rate volatility. Based on their
statistical and theoretical analysis, they suggested that empirical studies may face
difficulties in concluding significant effect of interest rate volatility and loan defaults.
Instability in interest rates poses a stronger risk for assets side of the banks. This ebb and flow
increases the risk of returns for the banks and result in growth of non-performing loans. Patnaik
26
and Shah (2004) focused on interest rate volatility and relevant risk to which Indian banks are
exposed to. They suggested that measuring interest rate risk exposure is an important issue for
banks and regulators in policy evaluation with specific reference to effects of interest rate shocks
on banks' equity. It is concluded in their research that Indian banks face relatively small problems
of Developing countries as the credit risk is controlled by management of interest rate risk
exposures.
27
CHAPTER THREE
3.0 RESEARCH METHODOLOGY
3.1 Introduction
This chapter discusses on how data will be collected, processed, analyzed and interpreted to
achieve the research objectives. Highlights research design, variables and population and how
validity and reliability will be determined.
3.2 Research Design
The study was carried out through cross-sectional and descriptive design. Cross-sectional
descriptive design is undertaken in order to ascertain and be able to describe
characteristics of variables of interest in a situation. Cross-sectional descriptive presents
data in a meaningful form thus it helps one to understand the characteristics of a group in
a given situation; think systematically about aspects in a given situation; offer ideas for
further probe and research (Chava and Nachmia, 1996).
3.3 Population of study
A population is a group of individuals, objects or items from which samples are taken for
measurement (Combo and Tromp, 2006). The populations will comprise of all the 43 commercial.
28
3.4 Data Collection
The study used secondary data. Secondary data will be gotten from records of loan performance
in the commercial banks and referred journals on the conceptual frame of the study.
3.5 Data Analysis
The data collected was edited to ensure that it is accurate and consistent with other facts
gathered. Content analysis was used to analyze the data. Qualitative content analysis has
been defined as a research method for the subjective interpretation of the content of text
data through the systematic classification process of coding and identifying themes,
patterns or categories important to a social reality (Hsieh & Shannon, 2005). It involves
doing a word frequency count to identify words of potential interest with an assumption
that the words that are mentioned most often are the ones that reflect the greatest concern.
This technique is suitable for analyzing recorded interviews and provides an empirical
basis for monitoring shifts in public opinion.
Quantitative data will be analyzed using simple linear function (Y=Mx+C) relating two
variables; where by loan default (Y) is a function of on Interest Rates (X) and other
factors which are independent of Interest Rates, these factors were farther analyzed by
use of simple descriptive statistics, mainly the mean and standard deviation to taste for
the significant of the relation between loan default and interest rates.
29
CHAPTER FOUR
4.0
4.1
FINDINGS AND DISCUSSION
Introduction
The objective of the study was to establish the changes in interest rates and loan default
rates. In this chapter, section 4.2 is the summary statistics of the two major variables
namely: - interest rates and loan defaults. Even the banks are not keen in disclosing
explicitly their default rates; the study uses the non performing loans as an indicator of
the default rate. In 4.3, is the impact of all interest rates on gross non performing loans,
deposits, savings, lending and overdrafts. The interest rates are on the deposits. In 4.4, is
again the impact of all the interest rates on total net non-performing loans, deposits,
savings, lending and overdrafts. In 4.5, is the impact of interest rates on deposit and
lending on total net non-performing loan, deposits and lending. In 4.6 again is the impact
of all interest rates on gross non performing loans, deposit and lending. In 4.7, is the
impact of all interest rates on gross non performing loans deposit, lending and overdraft.
In 4.8, is the impact of all interest rates on deposit, lending and overdraft on total net non
performing loans.
4.2
Descriptive statistics. Interest rates on non-performing loans.
The study used monthly data from January 2007 to august 2013. Over the period, January
2007 to august 2013. There were 12 months each but 2013 were only 8 months and the
summary statistics per month are presented on table 1 below.
30
TABLE 1: Descriptive Statistics of non-performing Loans and Interest Rates
Variable
N
Mean
StDev
Minimum
Maximum
GNPerL
80
50.05
6.535
41.900
65.000
TNPerL
80
23.1
4.247
16.700
37.600
Deposit
80
5.135
1.368
3.413
9.040
Savings
80
1.6121
0.1760
1.2518
2.1319
Lending
80
15.400
2.216
12.871
20.300
Overdraft
80
15.144
2.369
12.955
20.530
GNPerl=Gross non-performing loans and advances
TNPerl= Total Net non-performing loans and advances
The average gross non-performing loans, the lowest being 41.9 and the highest is 65 with
a high variability as indicated by the standard deviation of 6.535. However the difference
between gross non performing loans and net non performing loan and advances is the
general provision which is the difference between gross non performing loans and
advances and total net non performing loans and advances.
If we compare gross non performing loans to total net non performing loans, then we
find the standard deviation between gross non performing loans and total net bob
performing loans which is lower (4.247) indicating that the general –p[provisions of nonperforming loans varies from period to period. The bank overdraft attract high interest
rates, followed by lending rates and the savers are paid the least average of (1.6121),
31
while depositors are paid an average of 5.156, such that the spread between interest on
deposits and lending is (15.144 -5.135=10.011).
In table 2 and table 3 below are the main statistics for gross non performing loans and
advances, and total net non performing loans and advances. Highest in 2013=60.15 and –
owest on 2008=43.5. On the total net non performing loans, the highest is on 2013=30.66
and the lowers is on 2012=19.35.
TABLE 2: Gross Non-performing Loans and Advances (GNperl)
GNPerL
Year N
Mean
StDev
Minimum
Maximum
2007 12
53.54
9.84
42.10
65.00
2008 12
43.500
1.402
41.9
47.4
2009 12
51.608
3.164
45.1
56.4
2010 12
51.417
1.889
48.6
54.7
2011 12
47.333
2.185
41.9
49.2
2012 12
46.217
2.233
42.9
49.5
2013 8
60.15
5.81
51
65
32
TABLE 3: Total Net Non-Performing Loans and Advances
Variable
Year
N
Mean
StDev
Minimum Maximum
TNPerL
2007
12
22.442
1.734
19.8
25.3
2008 12
20.933
2.372
18.4
27.3
2009 12
26.183
2.514
21.7
29.6
2010 12
24.400
2.985
20.3
29
2011 12
20.583
1.431
17.5
22.3
2012 12
19.350
1.890
16.7
22.2
2013 8
30.66
5.38
23.2
37.6
4.3 Impact of all interest rates on gross non performing loans, deposits, savings,
lending and overdrafts.
The starting point of the analysis is to look at the impact or all independent variables
(various interest rates, and dependent variables) non-performing loans). The regression
below shows that when put jointly; various interest rates have different impact on the
level of non performing loans. The co efficiency of deposits is positive (3.68) indicating
that as interest rates and deposits increases also the level of non-performing loans
increases. Interest on savings indicates a negative co efficiency (-9.23) indicating that as
interest rates on savings increases the level on non-performing loans increases. Interest
rates on lending increases the level of non-performing loans which goes down. Interest
rates on overdrafts is negative (-0.27) and the level of non-performing loans goes down.
33
However from the level of statistical significance, it is only interest rates on deposits that
at 10% level of significance which shows a predictable power. Most o all P-values are
above the cut of level of 0.05. The F-value tells us of 1.09 and P of 0.367 of all the
predictable value. It tells us that looked together, interest rates do not tell changes on nonperforming loans.
The regression equation is
GNPerL = 81.7 + 3.68 Deposit - 9.23 Savings - 2.05 Lending - 0.27 Overdraft
Predictor
Coef
SE Coef
T
P
Constant
81.72
15.94
5.13
0.000
Deposit
3.681
2.028
1.82
0.074
Savings
-9.228
6.306
-1.46
0.148
Lending
-2.053
2.496
-0.82
0.413
Overdraft
-0.269
2.372
-0.11
0.910
S = 6.520
R-Sq = 5.5%
R-Sq(adj) = 0.5%
Analysis of Variance
Source
DF
Regression
4
Residual Error
Total
SS
MS
F
185.66
46.42
1.09
75
3187.83
42.50
79
3373.50
34
P
0.367
4.4
The impact of all interest rates on total net non-performing loans, deposits,
savings, lending and overdrafts.
In the analysis, we look at the impact of all independent variables (various interest rates)
on the dependent variables (non-performing loans). The regression below shows that
when they are put jointly, various interest rates have different impact on the level of nonperforming loans. The co efficiency of deposits is positive (2.56) indicating that as
interest rates on deposits increases, also the level of nonperforming loans increases.
Interest rates on saving indicates a negative co efficiency of (-0.907) which indicates that
as interests rates on savings increases, the level of non performing loans goes down.
Interest rates on lending as a positive co efficiency of (1.352) which indicates that as
interest rates increases on lending, the level of non-performing loans also increases.
However, from the level of statistical significance it is only interest rates on deposits that
at 10% level of significance which shows a predictable power. Most of all, P-values are
below the cut-off level of 0.05. The F-value tells us of 3.44 and P is 0.012. For
predictable values. It tells us that looked to together, interest rates do not tell any changes
on non-performing loans.
35
The regression equation is
TNPerL = 34.2 + 2.56 Deposit - 0.91 Savings + 1.35 Lending - 2.88 Overdraft
Predictor
Coef
SE Coef
T
P
Constant
34.208
9.798
3.49
0.001
Deposit
2.556
1.247
2.05
0.044
Savings
-0.907
3.876
-0.23
0.816
Lending
1.352
1.534
0.88
0.381
Overdraft
-2.875
1.458
-1.97
S = 4.007
R-Sq = 15.5%
0.052
R-Sq(adj) = 11.0%
Analysis of Variance
Source
DF
SS
MS
F
P
Regression
4
220.68
55.17
3.44
0.012
Residual Error
75
1204.40
16.06
Total
79
1425.08
36
4.5
The impact of interest rates on total net non-performing loans, deposits and
lending.
In the analysis, we look at the impact of all independent variables (interest rates) on the
dependent variables on (non-performing loans).The regression below shows that when
put together various interest rates have different impact on the level of non-performing
loans. The co efficiency of deposits is positive (2.7132) indicating that as interest rates on
deposits increases, also the level of non-performing loans increases. Interest rates on
lending, indicates a negative co efficiency of (- 1.7718), indicating that as interest rates
increase on lending; the level of nonperforming loans goes down. However, from the
level of statistical significance, it is only interest rates that at 10% level of significance
which shows a predictable power. Most of all P-values are below the cut-off level of
0.05l. All are most significant. The F-value tells us of 4.12 and P is 0.020 for predictable
values. It tells us that all looked together, interest rates show a positive change on nonperforming loans.
The regression equation is
TNPerL = 36.5 + 2.71 Deposit - 1.77 Lending
Predictor
Coef
SE Coef
T
P
Constant
36.503
4.998
7.30
0.000
Deposit
2.7132
0.9998
2.71
0.008
Lending
-1.7718
0.6171
-2.87
0.005
S = 4.089
R-Sq = 9.7%
R-Sq(adj) = 7.3%
37
Analysis of Variance
Source
DF
SS
MS
F
P
Regression
2
137.84
68.92
4.12
0.020
Residual Error
77
1287.24
16.72
Total
79
1425.08
4.6
The impact of interest rates on gross non-performing loans, deposits and
lending.
In the analysis, we look at the impact of or independent variables (interest rates) on the
dependent variables (non-performing loans). The regression bellow shows that when all
put together, various interest rates have different impact on the level of non performing
loans. The coefficiency of deposits is positive (1.662) which indicates that as interest
rates on deposits increases, also the level of non-performing loan increases. Interest rates
on lending indicates a negative (-1.15 co efficiency) which indicates that as interest rates
increases, on lending, the level of non-performing loans goes down.
However, from the level of statistical significance, it is only interest rates on deposits that
at 10% level of significance which shows a predictable power. Most of all P-values are
above the cut-off level of 0.05. The RF tells us of 0.69 and P is 0.504 for all the
predictable values. It tells us that all looked together interest rates shows a change on
non-performing loan.
38
The regression equation is
GNPerL = 59.2 + 1.66 Deposit - 1.15 Lending
Predictor
Coef
SE Coef
T
P
Constant
59.249
8.020
7.39
0.000
Deposit
1.662
1.604
1.04
0.303
Lending
-1.1510
0.9901
-1.16
0.249
S = 6.560
R-Sq = 1.8%
R-Sq(adj) = 0.0%
Analysis of Variance
Source
DF
Regression
2
Residual Error
Total
4.7
SS
MS
F
P
59.48
29.74
0.69
0.504
77
3314.01
43.04
79
3373.50
The impact of interest rates on gross non-performing loans, deposits, lending
and overdrafts.
In the analysis, we look at the impact of all independent variables (interest rates) on the
dependent variables (non-performing loans). The regression below shows that when all
put together, various interest rates have different impact on the level on non-performing
39
loans. The co efficiency of deposit is positive (1.881) indicating that as interest rates on
deposits increases, also the level of no-performing rates increases. The co efficiency of
lending is negative (-3.128) indicating that as interest rates on lending increases, the level
of non-performing loans goers down. The co efficiency of overdraft is positive (1.75)
indicating that as interest rates of overdrafts increases, the level of non-performing loans
goes up.
However, from the level of statistical significance, its only interest rates on deposits that
at 10% level of significance that shows a predictable power. Most of all P-values are
above the cut-off level of 0.05. The F tells us of 0.73 and P is 0.537 for all predictable
values. It tells us that all looked together, interest rates shows a change on nonperforming loans.
The regression equation is
GNPerL = 62.0 + 1.88 Deposit - 3.13 Lending + 1.75 Overdraft
Predictor
Coef
SE Coef
T
P
Constant
62.022
8.597
7.21
0.000
Deposit
1.881
1.624
1.16
0.251
Lending
-3.128
2.404
-1.30
0.197
Overdraft
1.753
1.942
0.90
0.369
S = 6.568
R-Sq = 2.8%
R-Sq(adj) = 0.0%
40
Analysis of Variance
Source
DF
Regression
3
Residual Error
Total
4.8
SS
MS
F
94.64
31.55
0.73
76
3278.85
43.14
79
3373.50
P
0.537
The impact of interest rates on total net non-performing loans, deposits,
lending and overdrafts.
Finally, in this analysis we look at the impact of all independent variables (interest rates)
on the dependent variables (non-performing loans). The regression below shows that
when all put together, various interest rates have various impacts on the level of nonperforming loans. The co efficiency of deposits is positive (2.3793) indicating that as
interest rates on deposits increases, also the level of nonperforming loans increases. The
co efficiency of lending is positive (1.25) indicating that as interest rates on lending
increases, the level of non-performing loans goes up. The co efficiency of overdraft is
negative (-2.68) indicating that as interest rates on overdrafts increases the level of non
performing loans goes up also.
However, from the level of statistical significance, its only interest rates on deposits that
at 10% level of significance that shows a predictable power. Most of all P-values are
below the cut-off level of 0.05. The F tells us of 4.62 and P is 0.05 for all predictable
41
values. It tells us that all looked together, interest rates shows a big change on nonperforming loans.
The regression equation is
TNPerL = 32.3 + 2.38 Deposit + 1.25 Lending - 2.68 Overdraft
Predictor
Coef
SE Coef
T
P
Constant
32.270
5.212
6.19
0.000
Deposit
2.3793
0.9848
2.42
0.018
Lending
1.246
1.457
0.86
0.395
Overdraf
-2.676
1.177
-2.27
0.026
S = 3.982
R-Sq = 15.4%
R-Sq(adj) = 12.1%
Analysis of Variance
Source
DF
Regression
3
Residual Error
Total
SS
MS
F
P
219.80
73.27
4.62
0.005
76
1205.28
15.86
79
1425.08
42
Summary of the Findings
In brief, the findings from the descriptive statistics of non-performing Loans and Interest
rates shows variations from period to period: whereby bank overdraft attracts highest
interest rates, followed by lending, while the savers are paid the least average: whereas
the impact of all interest rates on gross non-performing loans on deposit, savings, lending
and overdraft
shows negative co efficiencies other than only deposit showing no
significancy between the variables: when the co efficient are positive, it will show that
one variable (interest rates) has an impact on the other variable (non performing loans).
But when its negative, it will show that one variable (interest rates) has no impact on
other variable (non-performing loans).
43
CHAPTER FIVE
5.0 SUMMARY, CONCLUSION, AND RECOMMENDATION
5.1 Summary
In general, from the descriptive statistics on non performing Loans and Interest rates, it is
indicated that the general provisions of non performing varies from period to period.
Whereby Bank overdraft attracts high interest rates, followed by lending rates, then
savers are paid the least average and depositors are paid at an average showing a spread
between interest on deposit and lending: while the impact of all the interest rates and non
-performing loans on deposits, savings, lending and overdrafts have negative co efficient
and only deposit has a positive co efficiency showing no significance in the relationships.
But the impact of all interest rates and total net non-performing loans on deposits,
savings, lending and overdraft have both negative and positive co efficiencies. It is
positive for deposits and lending while negative for savings and overdrafts. Therefore,
various interest rates have various impacts on the level of non-performing loans if
deposits, savings, lending and overdrafts are given.
Finally, this will shows from the study that there is a relationship between interest rates
and the rate of default on loans.
44
5.2 Conclusions
Despite its importance, the exact nature of the relationship between lending and interest
rate risk is not quite clear: Given the mean, median, standard deviation, first quartile,
third quartile and the regression equation, there is a positive relationship between interest
and total non-performing loans to some extent from the studies given. Given that firms
must generate a higher rate of return on its assets to stay in business, than what the
government rates forms as the basis of the cost of capital.
But if the cost of capital is higher than the rate of return, for a particular firm, then that
firm will run into financial insolvency, it will be concluded that as interest rates increases
on lending and advances, the rate of non-performing loans will automatically increases
because the loans are now expensive and borrowers will be unable to pay it all default
will be higher. The relationship between default and risk and interest rates is positive and
is sensitive to some measure of where the economy is in the business cycle or other
Macro economic factors.
5.3. Recommendations
The regulators of banking in the country i.e. central bank can go further so that a part
from capping the rates to protect the customers and borrowers from exploitation by
banks; they should be able to step in to gibe each bank its standard rate to cab on cut
throat competition that knocks out small banks with little spread. Bank branches could
also be allowed leeway to set their won suitable rate to serve their own unique set of
borrowers.
45
5.4 Limitations
This study is particularly weak on its findings emanating from the fact that banks have
binding agreement with customer not to divulge information concerning customers and
their bank accounts. The information was gotten from bank branches whose headquarter
are in the capital city and this may have affected the credibility of the findings since they
are controlled by their headquarters in terms of policy and bank loan interest rates.
5.5. Suggestions for Further Studies
Other researchers can review the relationship between loan default and interest rates by
but use individual banks as case studies instead of all banks, further still the impact of
cooperative movement on the credit sale and interest rates need research.
46
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49
APPENDIX 1: LETTER OF IDENTIFICATION
50
APPENDIX 2: LOAN DEFAULT OF COMMERCIAL BANKS IN KENYA
LOAN DEFAULT
PER YEAR
BANK/YEAR
2012
2011
2010
2009
2008
2007
ABC
79
64
62
54
50
44
BANK OF AFRICA
85
64
63
54
45
44
BANK OF BARODA
70
56
57
54
44
43
BANK OF INDIA
84
63
66
55
46
45
BARCLAYS BANK
78
62
57
54
44
43
CFC STANBIC BANK
70
60
64
54
46
45
CHASE BANK
74
57
60
54
45
43
CITI BANK
76
59
57
51
43
42
COMMERCIAL B A
67
51
58
53
44
43
CONSOLIDATED
80
64
66
55
46
41
COOP BANK
82
68
64
55
44
42
CREDIT BANK
76
61
59
52
49
46
DEV. BANK
75
62
59
53
47
45
DIAMOND BANK
85
65
61
54
49
48
DUBAI BANK
77
65
60
54
48
46
ECO BANK
73
64
60
54
47
44
EQUITORIAL BANK
79
68
68
51
47
48
EQUITY BANK
72
62
56
54
50
49
51
FAMILY BANK
71
61
56
52
48
47
FAULU BANK
71
64
58
53
46
44
FIDELITY BANK
78
65
63
54
51
49
FINA BANK
83
66
56
56
51
47
FIRST BANK
71
65
58
53
50
49
GIRO BANK
72
62
55
54
50
51
GUARDIAN BANK
67
59
56
52
50
46
GULF BANK
74
58
54
53
48
42
HABIB BANK
67
59
55
54
48
46
HABIB ZURICH
82
71
62
55
47
44
I&M BANK
58
52
50
52
47
44
IMPERIAL BANK
77
58
61
54
48
47
JAMII BANK
89
68
68
54
49
45
KCB
75
58
57
52
40
43
K – REP
78
60
65
53
47
44
MIDDLE EAST BANK
73
58
56
53
47
45
NATIONAL BANK
80
58
57
54
50
48
NIC BANK
75
63
56
56
52
51
ORIENTAL BANK
86
69
62
55
52
49
PARAMOUNT BANK
81
72
62
53
48
46
PRIME BANK
89
69
64
55
51
47
STANDARD BANK
75
72
66
54
51
48
52
TRANS NATIONAL
84
81
71
56
52
48
UNITED BANK
87
65
59
54
51
48
VICTORIA BANK
83
70
65
52
49
47
TOTAL IN Ksh.
5320
4726
4599
2312
2058
1964
INTEREST RATE AV
16
8
6
8
8
8
Table showing secondary data derived modified from central bank of Kenya yearly credit survey reports
from 2007 - 2012
53
Appendix 3: Annual Monthly Observation
Observation
Year
Month
GNPerL TNPerL
Deposit
Savings
1
2007
Jan
65
2
2007
Feb
3
2007
4
25.3
4.35
1.42
13.78
14.11
65
24.6
4.21
1.41
13.64
14.05
Mar
63.5
23
4.19
1.43
13.56
13.95
2007
Apr
64.7
24.2
4.11
1.35
13.33
13.26
5
2007
May
63.3
22.9
4.14
1.57
13.38
13.35
6
2007
Jun
49.7
22.7
4.18
1.54
13.14
13.20
7
2007
Jul
49.5
22.5
4.33
1.65
13.29
13.34
8
2007
Aug
49.2
22.2
4.31
1.60
13.04
13.39
9
2007
Sep
43.1
20.3
4.34
1.67
12.87
13.26
10
2007
Oct
43.5
20.6
4.27
1.64
13.24
13.29
11
2007
Nov
43.9
21.2
4.33
1.65
13.39
13.43
12
2007
Dec
42.1
19.8
4.32
1.67
13.32
12.96
13
2008
Jan
42.2
19
4.37
1.72
13.78
13.41
14
2008
Feb
41.9
18.4
4.37
1.70
13.84
13.26
15
2008
Mar
43.6
20
4.43
1.72
14.06
13.48
16
2008
Apr
43.6
19.1
4.41
1.71
13.91
13.46
17
2008
May
43.4
21.1
4.45
1.71
14.01
13.53
18
2008
Jun
43.5
20.5
4.48
1.70
14.06
13.30
19
2008
Jul
43.7
21.4
4.54
1.67
13.90
13.46
20
2008
Aug
43.5
27.3
4.65
1.68
13.66
13.11
54
Lending Overdraft
21
2008
Sep
42.1
19.8
4.62
1.73
13.66
13.43
22
2008
Oct
43.8
20.7
4.65
1.74
14.12
13.91
23
2008
Nov
43.3
20.7
4.86
1.61
14.33
13.85
24
2008
Dec
47.4
23.2
4.89
1.65
14.87
14.39
25
2009
Jan
48.2
21.7
5.19
2.10
14.78
13.84
26
2009
Feb
47.9
22.7
5.23
2.13
14.67
13.46
27
2009
Mar
52.4
25.5
5.09
1.90
14.87
13.78
28
2009
Apr
52.8
27.6
5.12
1.91
14.71
13.66
29
2009
May
56.4
29.6
5.10
1.67
14.85
14.13
30
2009
Jun
53.4
28.2
5.28
2.08
15.09
14.41
31
2009
Jul
52.1
23.6
5.09
1.67
14.79
13.94
32
2009
Aug
53.5
27.3
5.00
1.65
14.76
13.90
33
2009
Sep
45.1
25.7
5.05
1.65
14.74
13.76
34
2009
Oct
51.4
27.2
5.03
1.85
14.78
14.03
35
2009
Nov
51.3
25.8
5.06
1.71
14.85
14.24
36
2009
Dec
54.8
29.3
4.84
1.73
14.76
14.13
37
2010
Jan
54.7
29
5.00
1.75
14.98
14.25
38
2010
Feb
53.2
28.4
4.89
1.81
14.98
14.25
39
2010
Mar
53.9
27.8
4.74
1.81
14.80
13.59
40
2010
Apr
52
26.1
4.49
1.85
14.58
14.50
41
2010
May
52
25.7
4.58
1.76
14.46
14.38
42
2010
Jun
51.4
24.9
4.45
1.75
14.39
14.23
43
2010
Jul
50.7
23.1
3.85
1.55
14.29
14.03
55
44
2010
Aug
50.4
22.3
3.74
1.50
14.18
13.97
45
2010
Sep
51
22.1
3.53
1.47
13.98
13.81
46
2010
Oct
50.5
21.6
3.58
1.46
13.85
13.64
47
2010
Nov
48.6
20.3
3.54
1.40
13.95
13.77
48
2010
Dec
48.6
21.5
3.59
1.45
13.87
13.69
49
2011
Jan
48.6
21.5
3.43
1.25
14.03
13.93
50
2011
Feb
48.3
20.3
3.41
1.41
13.92
13.65
51
2011
Mar
49
20.5
3.47
1.37
13.92
13.60
52
2011
Apr
48.9
20.5
3.47
1.38
13.92
13.68
53
2011
May
48.2
22.3
3.51
1.38
13.88
13.72
54
2011
Jun
47.9
20.9
3.68
1.37
13.91
13.59
55
2011
Jul
47
20.3
3.85
1.37
14.14
13.89
56
2011
Aug
46.8
20.5
4.07
1.37
14.32
14.28
57
2011
Sep
48
22
4.21
1.35
14.79
14.64
58
2011
Oct
49.2
22.2
4.83
1.33
15.21
14.87
59
2011
Nov
44.2
17.5
5.75
1.41
18.51
18.67
60
2011
Dec
41.9
18.5
6.99
1.59
20.04
20.20
61
2012
Jan
43.6
16.7
7.66
1.62
19.54
20.38
62
2012
Feb
43.5
17.5
8.01
1.69
20.28
20.53
63
2012
Mar
42.9
17.1
9.04
1.58
20.22
20.27
64
2012
Apr
45.8
17
8.42
1.59
20.12
20.41
65
2012
May
44.8
18.8
7.88
1.46
20.30
20.36
66
2012
Jun
45.7
20
8.25
1.66
20.15
19.96
56
67
2012
Jul
46.5
20.1
7.85
1.58
20.13
20.31
68
2012
Aug
47.8
21
7.40
1.55
19.73
19.81
69
2012
Sep
46.9
19.7
6.85
1.60
19.04
19.13
70
2012
Oct
49.5
22.2
6.69
1.57
18.70
18.80
71
2012
Nov
48.8
21.4
6.80
1.60
18.15
17.79
72
2012
Dec
48.8
20.7
6.51
1.65
18.13
17.79
73
2013
Jan
51
23.2
6.29
1.61
17.84
17.68
74
2013
Feb
52.7
23.4
6.54
1.42
17.78
17.48
75
2013
Mar
56.6
27.7
6.39
1.45
17.87
17.71
76
2013
Apr
64.8
31.1
6.53
1.53
17.45
17.60
77
2013
May
65
37.6
6.65
1.73
16.97
16.92
78
2013
Jun
62.7
33.1
6.59
1.64
17.02
17.00
79
2013
Jul
64
34.2
6.36
1.67
16.96
16.89
80
2013
Aug
64.4
35
6.55
1.64
16.86
16.42
GNPerL = Gross Non-Performing Loans and advances
TNPerL = Net Non-Performing Loans and advances
General Provisions is the difference between Gross Non-Performing Loans and advances
and Net Non-Performing Loans and advances
57
Appendix 4: Average percentage interest rates and loan default
YEAR
2008
2009
2010
2011
2012
INTEREST RATES %
8.4
7.9
6.2
8.2
16.2
LOANS DEFAULTED
27.9 %
24.2%
20.2%
12.16% 10.79%
Table adopted with modification from www.centralbankofkenya.co.ke
Graph developed from secondary data from central bank of Kenya
KEY
Series 1: Line showing trends in nonperforming loans
Series 2: Line showing trends in interest rates
58
Appendix 5: Licensed Commercial Banks
ABC Bank (Kenya)
Bank of Africa
Bank of Baroda
Bank of India
Barclays Bank
CFC Stanbic Bank
Chase Bank (Kenya)
Citibank
Commercial Bank of Africa
Consolidated Bank of Kenya
Cooperative Bank of Kenya
Credit Bank
Development Bank of Kenya
Diamond Trust Bank
Dubai Bank Kenya
Ecobank
Equatorial Commercial Bank
Equity Bank
Family Bank
Faulu deposit taking
Fidelity Commercial Bank Limited
Fina Bank
59
First Community Bank
Giro Commercial Bank
Guardian Bank
Gulf African Bank
Habib Bank
Habib Bank AG Zurich
I&M Bank
Imperial Bank Kenya
Jamii Bora Bank
Kenya Commercial Bank
K-Rep Bank
Middle East Bank Kenya
National Bank of Kenya
NIC Bank
Oriental Commercial Bank
Paramount Universal Bank
Prime Bank (Kenya)
Standard Chartered Kenya
Trans National Bank Kenya
United Bank for Africa
Victoria Commercial Bank
Source: Central bank of Kenya, 24/08/2012
60