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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. ii 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. iii 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 …………………………………………………….. v 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 vi 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 vii 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 6 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 REFERENCES Alvarez, F and Jermann, U.J (2000) „Efficiency, Equilibrium and Asset Pricing with Risk of Default‟, Econometrica, V.68, PG775-797 Andreeva, G. (2006) “European Generic Scoring Models Using Survival Analysis”, Journal of the Operational research Society, V.57, P. 1180-1187 Annual Conference on Economic development, supplemented to the World Bank Review and the Third World Research Observer: 1994, PP 113-123. Bangladeshi, Journal of Accounting, Business and Management; vol. 11, no.2, PP. 202213 Banasik, j., Crook, J., Thomas, L., (1999) “Not if but when will borrowers default”. Journal of the Operational Research Society, V. 50, P. 1185 - 1190 Bellotti, t., Crook, J. 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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