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CHAPTER THREE

RESEARCH METHODOLOGY

3.1       Introduction

In this chapter, a discussion on the research design, the population of the study, the description of the population, methods of data collection, as well as data analysis methods is done.

3.2       Research Design

This is a plan which offers guidance to a researcher on how to organize their research activities (Bryman & Bell 2003). This study will adopt quantitative methods of data collection. This is a research design that attempts to identify a causative relationship between an independent variable and a dependent variable (Kumar, 2009). The independent variable will be interest rate and loan demand as dependent variable.

3.3       Data type and source

The study used purely secondary data to analyse data findings. This was in form of reports on the bank’s loan disbursed and interest rates charged for the period under review.

3.3.1 Secondary data

Secondary sources describe, discuss, interpret, comment upon, analyze, evaluate, summarize, and process primary sources. Secondary source materials can be articles in newspapers or popular magazines, book or movie reviews, or articles found in scholarly journals that discuss or evaluate someone else’s original research.

3.4 Variables and their measurements

3.5       Data Analysis

Data analysis will be used through univariate and bivariate to determine the relationships and correlations using ANOVA whereby Y=Ժ+βχі

CHAPTER FOUR

4.0 Trend of loan demand

Source: Secondary Data

The graph above shows that the trend of loan demand was highest at 2016 January this indicates that loan demand keeps on changing basing on the level of interest rates in the country at the time . this figure further shows that loan demand is not constant showing that if interest rate is low loan demand is high and when the interest rates are high loan demand is high. This findings is also in line with watanabe (2012) who asserts that Interest rates  may be argued, may perhaps be the single most key motivation that influences credit markets and the access to issuance of credit facilities by lending institutions. The Monetary Policy Committee usually sets the benchmark lending rate on a monthly basis, and commercial banks reference this is setting out the interest rates to issue out credit at to the markets.

This findings is also further in line with Amonoo et al., (2003) who indicates that because of the unpredictability of interest rates there is always a constantly changing loan demand since when the interest rates are high the demand for loans decreases and vice versa.

Normality test

A regression was run and on clicking on the view-residual test-histogram-normality test, the histogram is bell-shaped, suggesting a normal curve shape, and the jarque-bera statistics has high p-value of greater than 0 indicating that the errors in the regression are normal that is to say; the statistics probability of 0.361311 is greater than zero and it has a percentage of 36% greater than 10%(36%>10%) thus the errors in the regression are normal.

Unit root test of the series 1^st difference

Ho:  Loan demand has a unit root

Ha:  Loan demand has no unit root

At first difference, absolute of the ADF t-statistic was found to be greater than that of the 5% critical value. Its P-value was also found to be less than 0.05, thereby rejecting the null hypothesis and concluding that Loan Demand has no unit root at first difference and is therefore stationary.

Descriptive statistics of loan demand on interest rates from 2015-2017

This involved establishing the basic descriptive statistics and the correlation matrix. The descriptive statistics of all the variables are displayed while the correlation matrix in table 4.3 demonstrates the relationship between loan demand and interest rates.

Source:

The Jarque-Bera tests the hypothesis that the series is normal. Since the probability value for loan and interest are less than five percent significant level, the null is accepted meaning the series is normal.

Co-integration tests

Among the variables that are integrated of order 1(1), an attempt was made to check whether Cointegration holds. The purpose of the Cointegration tests was to determine whether a linear combination of a group of non-stationary series is stationary. Engle and Granger (1987) pointed out that a linear combination of two or more non- stationary series may be stationary. The linear combination of loan demand and interest rates was checked to find out whether the residuals were stationary.

Table showing: Co-integration tests output

The next attempt involved testing the residuals for the order of integration. The application of the Augmented Dickey Fuller test statistic revealed that the residuals are stationary in levels. This confirmed that the linear combination of loan demand and interest rates is indeed stationary.

Correlations

Source: Primary Data

The results show a weak negative relationship between loan demand and interest rates this findings therefore indicates that increase in interest rates leads to a decrease in loan demand this is also in line with Were and Wambua (2013) who argued that a rise in the interest rates by the banking sector increases cost of credit this therefore leads to a decline in the loan demand because borrowers can’t afford to pay high interest rates. The higher the rates of interests charged, the more potential customers are driven further away, and this may severely limit the amount of credit issued out by commercial banks. When the interest rates are lower, demand for credit goes up, and commercial banks may be in a position to issue more credit.

Forecasting loan demand for 2018 and 2019

The forecast revealed a steady decrease in loan demand in December 2019 as compared to January 2018.

Graphical representation of the forecast between 2018 and 2019

Findings from the study indicates that loan demand will decline in 2018 and 2019 this results therefore shows that equity bank should lower the interest rates in order to enable it reverse this situation from happening.

REGRESSION ANALYSIS OF

The results above show that the R Square is 0.112 (11.2%) which implies that interest rates affects loan demand as interest rates increases, loan demand decreases and in this case therefore it is likely that the increase in interest rates at one time led to a decrease in loan demand. However, there are other factors that loan demand such availability of liquidity, the political environment, and the regulatory environment among others. For example availability of liquidity in bank affects loan demand if it is low, the bank reduces on the money it lends out in order to have more; the political climate/environment forces banks to lend to few customers because of the risks that they perceive may affect their business and the regulatory environment affects the amount of money lent.

CHI-SQUARE TEST

The chi-square test showed that it is 0.067 likely that interest rates affect the loan demand. This implies that as loan demand goes high the interest rates reduce and thus this confirms that there is a long run relationship between interest rates and loan demand.

CHAPTER FIVE

CONCLUSION AND POLICY RECOMMENDATION

INTRODUCTION

CONCLUSION

LONG RUN RELATIONSHIP BETWEEN LOAN DEMAND AND INTEREST RATE

The results further showed that the R Square is 0.112 (11.2%) which implies that interest rates affects loan demand as interest rates increases, loan demand decreases and in this case therefore the increase in interest rates at one time lead to a decrease in loan demand. However, there are other factors that loan demand such availability of liquidity, the political environment, and the regulatory environment among others.   For example availability of liquidity in bank affects loan demand if it is low, the bank reduces on the money it lends out in order to have more; the political climate/environment forces banks to lend to few customers because of the risks that they perceive may affect their business and the regulatory environment affects the amount of money lent.

The chi-square test showed further with P value of (0.067) the interest rates affects the loan demand. This implies that loan demand depend on interest rates as few people demand for loan,  the interest rates increases.

The forecast shows that loan demand will reduce from 53,616,666,667/= in 2017 to 49,980,555,556/= in 2018 and reduce further to 49,765,740,741/= in 2019 and the interest rates will further increase to 22.91% in 2018 from 22.08% in 2017 to 22.96% in 2019%. The reduction in loan demand may be due to reduced economic activity.

The findings furthermore agrees with Were and Wambua (2013) argued out that a rise in  the operational expenses and  costs of commercial banks will have an effect of driving up interest rates in an effort by the banks to cover up as much of the operational costs as possible. The higher the rates of interests charged, the more potential customers are driven further away, and this may severely limit the amount of credit issued out by commercial banks. When the interest rates lower, demand for credit goes up, and commercial banks may be in a position to issue more credit. Lower operating costs translating to lower interest rates on the other hand will have the positive impact of opening up an opportunity for more customers to access credit facilities, and this will drive up the levels of credit advanced by commercial banks.

Interest rates, it may be argued, may perhaps be the single most key motivation that influences credit markets and the access to issuance of credit facilities by lending institutions. The Monetary Policy Committee usually sets the benchmark lending rate on a monthly basis, and commercial banks reference this is setting out the interest rates to issue out credit at to the markets. Since the early 1990’s up to August 2016, there was a liberal interest rates regime in the country, whereby banks would determine their preferred basis point above the set out rate by the MPC. However, since September 2016, this has changed, as banks a required by law to set out their interest rates at a maximum of four basis points above the base rate set by the MPC.

The findings furthermore agree with according to Amonoo et al., (2003), credit helps in the bridging of the gap that may exist between enterprise owner’s financial assets and what may currently be the required financial assets an enterprise. As in most instances there exists an imbalance between the two, then forcing a demand of credit by enterprises.

The findings furthermore agree with According to Aryeetey et al., (1994), categorization of demand for credit can be put into three; demand that is perceived, potential demand and demand that is revealed. Demand that is perceived may arise in situations whereby enterprises that assume to be in need of finances mention cash as a constrain. On the other hand, Potential demand may arise in instances whereby an imperfection in the markets and institutions make it impossible to actualize the desire for credit. Demand that is revealed is the written application for financial support based on a given rate of interest prevailing at the time of application. Gale (1991) defines effective demand as what lending institutions are willing and able to disburse to borrowers.

There has been a continuous and endless debate on what really is the impact that interest rates have on the level of personal loans advanced by commercial banks and other financial institutions. Besley (1994) argued that loan seekers may face adverse selections occasioned by high interest rates. Financial institutions charge individuals perceived as being of higher risk and higher rates in order to cover for default risk. There are however, those who differ and argue that the rates of interest charged do not have an impact on levels of personal loans advanced or demanded in an economy. According to Aryeetey et al., (1994), the level of interest rates was not a major concern for SME’S seeking credit from financial institutions.

RECOMMENDATION OF RESULTS

In light of the above, the respondents recommended the following

That the central bank should intervene in the financial market and reduce on the interest rates as banks charge their own rates thus cheating unsuspecting customers.

REFERENCES

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Central Bank of Kenya, (2015). Bank Supervision Annual Report 2015. Nairobi, Kenya

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Crowley, J. (2007). Interest Rate Spreads in English-Speaking African Countries. IMF Working Paper, WP/07/101.

Drehman, M., Sorensen, S. & Stringa, M. (2008). “The Integrated Impact of Credit and Interest Rate Risk on Banks: An Economic Value and Capital Adequacy Perspective”, Bank of England Working Paper No.339

Emmanuelle, J. (2003).Monetary and fiscal policy. Kenya: University of Nairobi Financial Sector Deepening-Kenya (2009) Cost of collateral in Kenya; Opportunities for Reform. Nairobi. FSD Kenya.

Fofack, H. (2005). “Nonperforming loans in Sub-Saharan Africa: causal analysis and macroeconomic implications”. World Bank Policy Research Working Paper (3769).

The results above shows that

Source: primary data

Co-Integration

Interest Rates

Correlegram

Findings on