Journal of Economics, Finance and Business Analytics
2023; 1(2): 26 – 35
http://www.quantresearchpublishing.com
ISSN: 3006-0745 (Online)
Effect of Financial Distress Factors on Profitability of
Microfinance Banks Licensed by Central Bank of Kenya
Franciscah Kitheka
Finance and Accounting, KCA University, Nairobi, Kenya
Email address:
francykitheka84 (Franciscah Kitheka)
Suggested citation to this article:
Kitheka, F. (2023). Effect of Financial Distress Factors on Profitability of Microfinance Banks Licensed by Central Bank of Kenya. Journal
of Economics, Finance and Business Analytics, 1 (2), 26 - 35
Received: 11 15, 2023; Accepted: 12 12, 2023; Published: 12 21, 2023
Abstract: The study sought to examine the effect of financial distress factors on the profitability of microfinance banks
(MFBs) licensed by the Central Bank of Kenya (CBK). The research adopted a causal research design and targeted the 13
MFBs licensed by CBK and operated between 2016 to 2020. The study targeted a period of five (5) years beginning from 2016
to 2020 forming panel data. The study was a census of all the 13 licensed Microfinance banks by CBK hence no sampling was
carried out. The study collected annual secondary data from 2016 to 2020 from the 13 licensed Microfinance banks by CBK.
The data was panel in nature and Microsoft Excel was used as a secondary data collection template as it is capable of inputting
and managing the data. The collected panel data for the period between 2016 and 2020 was analyzed using Microsoft Excel.
Descriptive statistics included mean, standard deviation, minimum and maximum. Regarding inferential statistical analysis, the
OLS regression model was adopted to estimate the coefficients and associated p-values to enable the fitting of the model and
forecasting. The analysis of variances revealed that financial distress factors (financial leverage, liquidity and non-performing
loans) have a significant impact on profitability captured by ROA. Further, the effect of liquidity on the profitability of
licensed MFBs was inverse but not statistically significant. The study also revealed that financial leverage had a direct and
statistically significant effect on the profitability of licensed MFBs. Finally, the study revealed that the impact of non-
performing loans on profitability was inverse and statistically significant. The study thus concluded that distress factors
including financial leverage, liquidity and non-performing loans have a major effect on profitability among the licensed MFBs
in Kenya.
Keywords: Financial Distress, Profitability, Financial Leverage, Liquidity and Non-Performing Loans.
1. Introduction
Financial distress may lead a firm to default on a contract, and it may involve financial restructuring between the firm, its
creditors, and its equity investors (Bartram, Brown & Waller, 2015). Bartram, Brown & Waller (2015) further noted that cash
flow volatility could lead to situations where a firm's available liquidity is insufficient to fully meet fixed payment obligations,
such as wages and interest payments, on time. Likewise, lowering the chance of financial distress can increase the optimal
debt-equity ratio and therefore the associated tax shield of debt (Imdad, Akash, Hamid & Mahmood, 2020). Furthermore,
financial distress may stimulate profitability problems in firms through cash flow and revenue deterioration or operating
income. It is expected that financial distress in firms will have an effect on operating income causing short-term insolvency
which reduces the firm's ability by constraining working capital and increasing indebtedness. Financial distress is a broad
concept that comprises several situations in which firms face some form of financial difficulty and cannot meet or have
difficulty paying off their financial obligations, especially to their creditors. Financial distress means there is a tight cash
situation and if prolonged may lead to bankruptcy and even liquidation. Ombaba and Kosgei (2017) define financial distress as
severe liquidity problems that cannot be resolved without a sizable financial restructuring of the entity's assets and operations.
Financial distress may lead a firm to default on obligations and it usually needs financial restructuring between the firm, its
creditors, and its equity investors (Bartram, Brown & Waller, 2015). Further, cash flow volatility associated with financial
Journal of Economics, Finance and Business Analytics 2023; 1(2): 26 - 35 27
distress may lead to situations where a firm's available liquidity is insufficient to fully meet fixed payment obligations, such as
wages and interest payments when they fall due. Financial distress in banking institutions can be measured using three main
indicators including non-performing loans, liquidity and financial leverage. Nonperforming loans are a percentage of the gross
loans which have a higher likely hood of becoming default (Zelie, 2019). A higher non-performing loans to gross loans ratio
implies a greater financial distress situation. Financial leverage in the banking sector is often measured by the ratio of equity to
total assets. Banks that have higher levels of capital as a ratio to total assets post better financial results than their counterparts
who have less capital at their disposal (Staikouras & Wood, 2003). Finally, liquidity as used in banking refers to the degree to
which an asset or security can be easily sold in the market without the sale affecting its price. Insufficient liquidity of
commercial banks is considered to be one of the major reasons why they fail. Liquidity in the baking sector is often measured
by the ratio of cash and cash equivalents to total deposits (Mariano, Izadi & Pratt, 2021).
Profitability is the ability of a business to generate enough revenues to offset the expenses of running the organization and
compensate the owner (s). Profitability is an aspect of overall performance where a business organization generates adequate
revenues to cover the cost of operations and compensate for the risk-taking behaviours of the shareholders. According to
Tonchia and Quagini (2010), the stakeholders of a firm in general and shareholders in particular expect to earn a return on
investment after the firm offsets the cost of operations including payment to employees, suppliers, financiers, and government
taxation. All the stakeholders have a claim on the profits generated by a firm (Tanui, Magadi, Tanui & Rotich, 2018). The
profit figure can also be negative in cases where total revenues generated cannot settle the total cost incurred by a firm
completely, negative profits can be called losses (Quagini & Tonchia, 2010). The profitability in banking is often measured by
Return on assets (ROA) and return on equity (ROE) can be a positive figure when sales revenues are more than total costs
incurred. Return on assets is the ratio of profits after tax to the total assets of the bank (Estrin & Pelletier, 2018). The return on
assets is usually presented as a percentage. The higher the ROA, the higher the efficiency of the bank in utilising assets to
generate revenues (Waswa, Mukras & Oima, 2018). The second proxy of profitability is ROE. Return on equity is the ratio of
after-tax profit to the total equity of the bank. A higher ROE implies the bank is efficiently using equity to generate revenues
for the business (Dianova & Nahumury, 2019). The study will adopt ROA to measure the profitability of MFBs licensed by
central bank in Kenya. Micro-finance is the provision of a broad range of financial services such as deposits, loans, payment
services, money transfers, and insurance to poor and low-income households and their microenterprises. There are four types
of MFIs: formal institutions such as micro-finance banks, non-government organizations, cooperative organizations and
informal sources such as moneylenders. The microfinance industry in Kenya is under the umbrella of the Association of
Microfinance Institutions of Kenya (AMFI) Kenya. AMFI presently has 62 member institutions serving more than six million
five hundred thousand poor and middle-class families with financial services throughout the country.
Twelve of these member institutions are registered as Microfinance Banks [MFBs] (AMFI, 2018). The Microfinance Act
authorizes the Central Bank of Kenya to license, regulate, and supervise the activities of formally constituted deposit-taking
microfinance institutions in Kenya. The Act itself simply empowers the Central Bank as a regulator, but specific rules
subsequently released by the bank serve to govern micro-finance activity. In particular, the Bank has imposed core capital
requirements designed to ensure adequate liquidity of depository MFBs and established minimum corporate governance
standards and ownership limits (AMFI, 2018). The financial sector in Kenya has seen tremendous growth over the years. This
growth has been driven by the innovation and dynamism of the banking sector. Despite the impressive growth, the banking
sector in Kenya has faced numerous challenges. The most significant challenge is financial distress (Kithinji & Waweru, 2017).
Among the banking institutions, MFBs have faced profitability problems that may be blamed on financial distress. The
financial performance of MFBs has fallen in a major way in the last four years. The MFBs recorded a ROA of -2.0% in 2018
compared to -0.9 % in 2017. The decline in the overall financial performance of MFBs experienced a midst increase in deposit
funding by 5.3 per cent, growth in loans by 3.1 per cent and a fall in asset quality by 3% (CBK, 2018). The fall be profitability
among MFBs may be explained by financial distress factors including financial leverage, non-performing loans and liquidity
(CBK, 2019). The empirical review has examined various financial distress factors on their effect on profitability. However, a
few gaps have been identified; first, most of the studies have been based on commercial banks with slightly different operating
environments from MFBs. Secondly, most studies in Kenya were performed before the COVID-19 pandemic which has had a
major impact on banking sectors, especially regarding non-performing loan accumulation with various businesses that had
taken loans being affected. Thirdly, few studies have examined the combined effect of the financial distress factors that are of
focus in the current study. The current study thus sought to bridge the gap in the literature by examining the effect of financial
distress factors on the profitability of MFBs licensed by CBK.
2. Literature Review
2.1 Theoretical Review
28 Kitheka: Effect of Financial Distress Factors on Profitability of Microfinance Banks Licensed by Central Bank of Kenya
The theoretical review examines theories that underpin the relationship between financial distress factors and profitability. The
study focused on the Modigliani-Miller Theory, The Miller- Orr Model and Information Asymmetry Theory.
2.1.1 Modigliani-Miller Theory
The theory has a major proponent in Modigliani and Miller (1958). The theory was developed into two variant forms, the
capital relevant and capital irrelevant positions. The capital-relevant variant holds that the structure of capital for a firm is
critical in explaining the firm value while capital irrelevant theory holds that capital structure does not affect the value of the
firm. The irrelevant theory variant assumes the absence of corporate taxes hence a firm does not get any value from leverage.
The relevant variant explains that the structure of capital is very critical in explaining a firm's value (Hirshleifer, 1966). The
theory further argues with corporate tax, organizations practice leverage to benefit from tax exemptions. The theory further
holds that optimising the structure of capital of a business impacts WACC. Equity capital tends to be cheaper compared to debt
capital (Miller, 1977). Equity capital is less costly but does not allow the firm to get an exception from corporate tax as all the
income earned is taxed. Moreover, debt capital allows the business to enjoy tax exemptions as the income earned debt
repayment interest is first deducted before corporate tax is charged on the profits earned. Hence, a levered firm (which relies
much on debts) pays less tax compared to fully unlevered firms. However, debt finance exposes the firm to the risk of
liquidation from financiers (Auerbach & King, 1983). A firm must therefore find the right mixture of equity and debt financing
that ensures that WACC is optimised to ensure maximum profitability. The theory was relevant for the current research as it
explains the level of leverage that an MFB should accept. Based on the theory, the MFB firm ought to balance equity and debt
financing such that the WACC of capital is at its minimum point. The firm can maximise profits when their an optimal capital
structure.
2.1.2 The Miller- Orr Model
The model as developed by Merton, Miller and Daniel Orr (2013) was meant to deal with cash outflows and cash inflows,
which keep on changing randomly from one day to another. The model works based on the premise that cash needs daily are
normally distributed in the firm over a given period. On any specific day, the net cash flow may be an amount that was
expected or it may be different assuming a lower or a higher value than expected value. The model operates in terms of upper
(U) and lower (L) control limits with the targeted net cash balance being (Z). As postulated by this model, a business's net
cash balances oscillate between the upper and lower limits arbitrarily. As long as the cash balance is somewhere between the
upper and lower limit, the business does not make any transactions however, when the net cash balance goes beyond the upper
limit, the business is expected to sell units of marketable financial assets equivalent to (U-Z). This deliberate action is expected
to reduce the cash balance to the expected level (Z). On the other hand, when the net cash balance in the business goes below
the lower level (L), the firm should sell (Z-L) units of financial assets at its disposal to generate liquid cash to take the net cash
balance back to Z level. Therefore, transaction costs incurred during this process of buying or selling financial assets to and
from net cash balances depend on the number of expected transactions made in marketable securities during the period (Hillier
et al 2010). This model was relevant in in this study by informing the variable liquidity level. The liquidity (cash balances)
may depend upon the cost of making transfers between cash and securities holding, the opportunity cost of holding cash and
intense variability in the firm's cash flows. The model also enables MFBs to maintain cost-efficient transactional cash balances.
2.1.3 Information Asymmetry Theory
The theory builds on the economics of imperfect information that began to emerge during the 1970s with the seminal
contributions of Akerlof (1970). According to the theory, information asymmetry causes the market to become inefficient;
since all the market participants do not have access to the information, they need for their decision-making process. A situation
that arises when one party has insufficient knowledge about the party involved in a transaction makes it impossible to make
accurate decisions when conducting the transaction (Nyamweya and Obuya, 2020). Financial intermediaries make lending
decisions and the borrower is likely to have more information than the lender about the risks of the project for which they
receive funds (Obuya and Olweny, 2017). In the presence of asymmetric information, the market may break down completely
with the three distinct consequences emerging including adverse selection, moral hazard and monitoring cost. According to
Mavlanova, Benbunan-Fich and Koufaris, (2012), an asymmetric information problem arises before the transaction occurs. It
occurs when a potential borrower who is likely to produce an undesirable (adverse) outcome in the form of bad debt risk is the
one who actively seeks out and is the most likely to be selected. Moral hazard on the other hand arises after a transaction.
Moral hazard arises when a borrower engages in activities that reduce the likelihood of a loan being repaid. Kwambai and
Wandera (2013) posit that the theory of asymmetric information indicates that it may be complex to distinguish between good
and bad borrowers which may result in adverse selection and moral hazard problems. As a result, moral hazard and adverse
selection lead to a reduction in the efficiency of the transfer of funds from surplus to deficit units. This is because lenders may
decide in some circumstances that they would rather not make a loan and credit rationing may occur (Matthews & Thompson,
2008). Asset quality refers to the timely manner in which borrowers are meeting their contractual obligations (Alhassan et al.,
Journal of Economics, Finance and Business Analytics 2023; 1(2): 26 - 35 29
2014). The asset quality is therefore inversely related to the amount of non-performing Loans (NPLs). Adverse selection and
moral hazards have led to a significant accumulation of nonperforming loans in banks. The level of NPLs in the loan portfolio
of a commercial bank affects profitability. The theory explains how NPLs arise due to problems of moral hazards and adverse
selection. The commutation of Nonperforming loans means that loan losses rise and profitability falls.
2.2 Empirical Review
The sections examine empirical literature on financial distress factors and the profitability of financial institutions. The
financial distress factors to be considered include liquidity, leverage and non-performing loans.
2.2.1 Liquidity and Profitability
Banafa (2016) investigated the effect of Leverage, Liquidity and Firm Size of non-financial firms listed at the Nairobi Stock
Exchange. The study used panel data over five years (2009 to 2013) to examine the effect of Leverage, Liquidity, Firm size,
Day's accounts receivables and accounts payables on Returns on Equity and Assets on the financial performance of listed non-
financial firms. leverage, Liquidity and firm size Influence the financial performance of listed non-financial firms at the
Nairobi Securities Exchange positively. Kahuthu (2016) sought to examine if core capital requirement, liquidity levels,
allowance for loan loss and member retention had any significant impact on the deposit-taking Sacco's financial income. The
study used comparative design and a linear regression model to establish the impact of prudential requirements on Sacco’s
Financial Performance. Core capital, credit management, membership growth and liquidity were not strong predictors of
financial performance before regulations but after the prudential regulations, they all became strong predictors.
Caliskana and Lecuna (2020) investigate the determinants of the banking sector profitability in Turkey for the years between
1980 and 2017. In this context, we use return on assets (ROA) and return on equity (ROE) as profitability indicators and form
two models separately by taking them as dependent variables. Within this framework the study employed bank size, deposit
conversion ratio, and liquidity as banking sector variables; whereas inflation rate, interest rate and exchange rate as control
variables. To examine the study models, the study ran a regression analysis. According to findings showed that banking sector
variables such as assets, efficiency and liquidity are more crucial for profitability. Ramadhanti, Marlina and Hidayati (2019)
examined the effect of Capital Adequacy, Liquidity and Credit Risk toward Profitability. The population in this study are
banking companies listed on the Indonesia Stock Exchange 2015-2017. The technique of determination of the sample using the
method of purposive sampling obtained 27 banking companies with a research period of three years to obtain 81 units of
samples. Panel Regression Analysis showed that capital adequacy has a significant positive effect on profitability, liquidity has
a positive and significant effect on profitability credit risk has a negative effect and significant to profitability.
SM and Razimi (2018) examined the effect of capital, liquidity, and efficiency on profitability in Islamic Commercial Banks in
Indonesia. This type of research is quantitative descriptive research. This study uses secondary data, and the period used 3
years, namely the period 2013-2015. Data analysis used is Regression analysis to analyse the factors that influence the
profitability of Islamic Commercial Banks in Indonesia. Capital has a positive and significant effect on profitability, liquidity
has a positive and significant influence on profitability, and efficiency has a negative and significant effect on profitability.
Junaidi, Sulastri, Isnurhadi and Adam (2019) examined the effect of liquidity proxy Loan to Funding Ratio, asset quality proxy
by Non-Performing Loan and efficiency proxy by Operating Cost to Operating Income toward Sustainability Growth rate. The
sampling technique is purposive based on the criteria so that the selected 22 banks with the study period 2012-2107. Unit
analysis of as many as 132 observations. The analysis of data using panel data regression. The findings of the study showed
that liquidity, asset quality Non-Performing Loans and efficiency proxy had a significant negative effect on suitable loan
growth.
2.2.2 Financial Leverage and Profitability
Kirimi, Simiyu and Dennis (2017) examined the effects of debt finance on financial performance measured ROE. The study
investigated the effect of interest rate, loan tenure, debt/equity ratio, and interest coverage ratio on the financial performance of
savings and credit cooperative societies in Maara Sub-County, Tharaka Nithi County, Kenya. A causal research design of a
target population of 10 Sacco's and a census survey were used. A positive relationship was revealed between the debt-equity
ratio and ROE respectively. Chesang (2016) examine the effect of financial leverage on the profitability of agricultural firms
listed at the Nairobi Securities Exchange. The study used a descriptive research design. The study targeted 66 listed firms at
the Nairobi Securities Exchange and a target population of all the seven agricultural firms listed at the Nairobi Securities
Exchange. The study used a regression model to determine the effect of independent and dependent variables under study. The
study also collected secondary data. The study established that the debt-to-equity ratio and current ratio have a statistically
significant effect on the profitability of agricultural firms listed at the Nairobi Securities Exchange while long-term debt to
total capital employed and firm size did not have a statistically significant effect on the profitability of agricultural firms listed
at the Nairobi Securities Exchange.
30 Kitheka: Effect of Financial Distress Factors on Profitability of Microfinance Banks Licensed by Central Bank of Kenya
Mule and Mukras (2015) investigate the relationship between financial leverage and the financial performance of listed firms
in Kenya. The study used annual data for the period 2007 – 2011. Using various panel procedures, the study finds reasonably
strong evidence that financial leverage significantly and negatively affects the performance of listed firms in Kenya. However,
financial leverage negative but insignificant effect on ROE. Shibutse, Kalunda and Achoki (2019) evaluated the effect of
leverage and firm size on the financial performance of DT-SACCOs in Kenya. A positivist research philosophy was adopted
for this study utilizing a mixed research design. The target population for this study constituted the 174 DT SACCOs licensed
by SASRA in Kenya. The sample frame was obtained from the SASRAs 2017 list of licensed DT-SACCOs. Inferential
regression results show a significant negative relationship between leverage and financial performance and, a significant
positive relationship between firm size and financial performance. Budhathoki, Rai, Lamichhane, Bhattarai and Rai (2020)
examined the impact of liquidity, leverage, and total assets size of the bank on profitability. This study employed bank scope
data of all 28 commercial banks operating in Nepal from 2010 to 2016. Altogether, the 168 observations were used in the study.
Three ordinary least-squares models were applied to analyze the impact of liquidity, leverage, and the total size on the bank's
profitability. The first regression model reveals that liquidity was observed to hurt the bank's ROA. Higher equity to assets
ratio measuring leverage positively affected ROA and NIM.
2.2.3 Non-performing Loans and Profitability
Obuya and Olweny (2017) examined the effect of banks’ lending behaviour on loan losses of listed commercial banks in
Kenya. The study employed a descriptive research survey design. The target population encompassed 11 listed commercial
banks in Kenya. The study was a census of listed commercial banks in Kenya. The data was extracted from CBK Annual
reports and audited financial statements of individual commercial banks in Kenya. The inferential analysis; and correlation
analysis were used to test the relationship between banks' lending behaviour and loan losses of commercial banks in Kenya. A
simple OLS model was used to establish the causal effect relationship between lending behaviour and loan losses of listed
commercial banks in Kenya. The results of the study showed that total customer loans and Quality of loans had statistically
significant effects on loan losses of listed commercial banks in Kenya.
Ekinci and Poyraz (2019) examined the impact of credit risk on banks' profitability in the Turkish Banking Sector for the
period 2005-2017. To determine the relationship between credit risk management and profitability, ROA and ROE are used to
represent the bank's profitability and NPL/TL is used to represent the credit risk. The dataset is obtained from the Banks
Association of Turkey (TBB) and the Central Bank of Turkey (CBRT). Along with credit risk, control variables (bank-specific
and sector-specific) and macroeconomic variables are included in the model. The estimation results showed that there is a
negative relationship between credit risk and ROA as well as between credit risk and ROE. Chabachib, Yudha, and Udin
(2020) examined the effect of Non-Performing Loans, Net Interest Margin (NIM), Non-Interest Income, and Loan Deposit
Ratio on Return on Assets with size as a control variable for the period period 2012 – 2017. The sample of this study is 228
domestic and foreign banks listed on the Indonesia Stock Exchange for the period 2012 – 2017. The result of the analysis
shows that NPL hurts ROA; NIM has a positive effect on ROA. Further, size becomes a control variable and there is no
difference between domestic and foreign banks.
Munangi and Sibindi (2020) examined the impact of credit risk on the financial performance of 18 South African banks for the
period 2008 to 2018. Panel data techniques, namely the pooled ordinary least squares (pooled OLS), fixed effects and random
effects estimators were employed to test the relationship between credit risk and financial performance. The results of the study
documented that credit risk was negatively related to financial performance. Thus, the higher the incidence of non-performing
loans, the lower the profitability of the bank. Bank leverage and financial performance were negatively related. Afolabi,
Obamuyi and Egbetunde (2020) examined the effect of credit risk on the financial performance of microfinance banks in
Nigeria. Published financial reports of six purposively selected microfinance banks, covering the periods 2012 to 2018 were
used as panel data for the regression model. The panel Ordinary Least Squares (OLS) regression technique was used to
estimate the influence of the credit risk proxy by non-performing loans and loan-loss provisions on the financial performance
proxy by returns on assets of the banks. The results of the analysis revealed that non-performing loans have a significant and
negative effect on returns on assets.
2.3 Research Gap
The empirical review has examined various financial distress factors on their effect on profitability. However, a few gaps have
been identified; first, most of the studies have been based on commercial banks with slightly different operating environments
from MFBs. Secondly, most studies in Kenya were performed before the COVID-19 pandemic which has had a major impact
on banking sectors, especially regarding non-performing loan accumulation with various businesses that had taken loans being
affected. Thirdly, few studies have examined the combined effect of the financial distress factors that are of focus in the
current study. The current study thus seeks to bridge the gap in the literature by examining the effect of financial distress
factors on the profitability of MFBs licensed by CBK.
Journal of Economics, Finance and Business Analytics 2023; 1(2): 26 - 35 31
3. Methodology
The research adopted a causal research design. This method is preferred since establishes how a variable causes the changes
in other variables in the research. Causal studies enable the researcher to establish whether or not the explanatory variables
lead to variation in the outcome variable (Cooper & Schindler 2003). The focus was on how changes in financial distress
factors including liquidity, financial leverage and non-performing loans caused a change in profitability of MFBs in Kenya.
The research targeted the 13 MFBs licensed by CBK and operated between 2016 to 2020. The study targeted a period of five
(5) years beginning from 2016 to 2020 forming panel data. This period suited the purpose of the research as it incorporates
recent financial reforms in the microfinance sub-sector. Sample are the elements picked from the population that represent the
population. The study was a census of all the 13 licensed Microfinance banks by CBK hence no sampling was carried out.
When the population size is small, then the researcher can study all the elements in the population like in the current study. The
study collected annual secondary data from 2016 to 2020 from the 13 licensed Microfinance banks by CBK. The data was
panel in nature and Microsoft Excel was used as a secondary data collection template as it is capable of inputting and
managing the data. Annual panel data sources running from 2016 to December 2020 were adopted in the current study. Data
was sourced from the annual banking report by the Central Bank of Kenya. Data to compute Return on assets included Total
assets and net operating income before tax. Data to compute financial leverage included equity and total assets. Data to
compute liquidity included cash & cash equivalents and deposits. Data to compute the non-performing loans ratio included net
non-performing loans and gross loans. The collected data was recorded on a Microsoft Excel sheet capable of inputting and
managing the data. The collected panel data for the period between 2016 and 2020 was analyzed using Microsoft Excel.
Descriptive statistics included mean, standard deviation, minimum and maximum. Regarding inferential statistical analysis, the
OLS regression model was adopted to estimate the coefficients and associated p-values to enable the fitting of the model and
forecasting. The study was based on the regression model presented in equation [1].
ROAit = αo+ α1LQit+α2LEVit+α3NPLit+ µit..............................................................................................................................[1]
Where:
ROA- Returns on Assets
LQ – Liquidity
LEV – Financial Leverage
NPL- Non-performing Loans
the µ-error term, αi -coefficients measuring the magnitude of changes in the dependent variable.
α0 – intercept term, t – Current time
i- Cross-sectional Units (licensed MFBs)
The independent variable is financial distress factors (liquidity, leverage and non-performing loans). The dependent variable is
profitability. The variables were operationalised in Table 1.
Table 1: Operationalization of Variables
Variable
Notation
Measurement
Expected Sign
Dependent variable
ROA
Profitability
ROA
Return on Assets = net income divided by total
assets
Independent variables
Liquidity
LQ
Cash and cash equivalents to total deposits
Ratio.
+
Leverage
LEV
Equity to Total Assets Ratio
+
Non-performing Loans
NPLs
Nonperforming loans as a percentage of the
gross loans
-
4. Results
4.1 Descriptive Analysis
The chapter presents the findings of the studies including the measures of dispersal, central tendency and regression. The
regression was based on the OLS model that assumes no significant differences in the cross-sectional units. The chapter also
presents the discussions of the study findings. The descriptive analysis was based on measures of central tendency and
32 Kitheka: Effect of Financial Distress Factors on Profitability of Microfinance Banks Licensed by Central Bank of Kenya
dispersal including maximum, minimum, mean and standard deviation. The findings are presented in Table 2.
Table 2: Summary of Descriptive Analysis
NPL
Mean
0.477705
SD
0.994452
MIN
0
MAX
8
Note: ROA- Return on assets, LEV-Financial Leverage, LQ- Liquidity, NPL- Non-performing loans
Profitability was measured by Return on assets. The mean ROA for MFBs was -0.10938 meaning that most firms were in
loss making. The standard deviation of 0.150465 implies that individual licensed MFBs had their profitability spread around
the mean by about 15%. The minimum ROA was -0.60204 capturing the licensed MFB with the lowest ROA in the study
period. The maximum was 0.039409 capturing the licensed MFB with the highest ROA in the study period. Financial leverage
was measured by the ratio of equity to total assets. The mean financial leverage was 0.217095 implying that on average, equity
as a percentage of total assets was about 21.7%. The standard deviation of 0.29798 around the mean that individual MFB had
financial leverage spread around the mean by about 29.7%. The minimum financial leverage was -1.2037 capturing the
licensed MFB with the lowest financial leverage level. The maximum financial leverage was 0.836449 capturing the licensed
MFB with the highest financial leverage. Liquidity was measured by the ratio of cash& cash equivalents to total customer
deposits. “The mean liquidity was 0.503426 implying that on average, cash and cash equivalents as a percentage of customer
deposits of licensed MFBs was about 50.3%. The standard deviation of 0.369369 showed that the liquidity of individual firms
was about 36.9 % around the mean. The minimum liquidity was 0.061224 implying the individual MFB with the lowest
liquidity had a liquidity of 6.12% as a percentage of total deposits. While the maximum liquidity of 1.625 implies that the
MFB with the highest liquidity had liquid assets of about 1.625 times the customer deposits. Nonperforming loans were
measured as a ratio of non-performing loans to gross loans. The mean non-performing loans was 0.477705 with a standard
deviation of 0.994452 around the mean. The minimum non-performing loans was 0 capturing the licensed MFB with the
lowest NPL.” The maximum non-performing loans was 8 capturing the licensed MFB with the highest non-performing loans.
4.2 Regression Analysis
The study adopted OLS multivariate regression analysis to examine the effect of financial distress factors (leverage, liquidity
and non-performing loans) on profitability licensed MFB in Kenya. The dependent variable was profitability licensed MFB in
Kenya while the independent variables were distress factors including leverage, liquidity and non-performing loans. The
findings are presented in Tables [3 -5].
Table 3: Model Summary
Regression Statistics
Multiple R
0.422194348
R Square
0.387125807
Adjusted R Square
0.338485877
Standard Error
0.140728199
Observations
66
The overall Pearson correlation coefficient (Multiple R= 0.422) in Table 4.2 shows that the independent variable financial
distress factors (financial leverage, liquidity and non-performing loans) were positively and moderately correlated with the
dependent variable profitability. Further, the coefficient of determination (R2 = 0.38712) reveals that financial distress factors
(financial leverage, liquidity and non-performing loans) explained 38.71% of the total variation in profitability. The remaining
variation in profitability of 61.28% is captured by other variables affecting profitability but not studied in this research.
Table 4: Analysis of Variances (ANOVA)
df
SS
MS
F
Significance F
Regression
3
0.266341
0.08878
4.482853
0.006521
Residual
62
1.227874
0.019804
Total
65
1.494215
The analysis of variances given in Table 4 revealed that financial distress factors (financial leverage, liquidity and non-
Journal of Economics, Finance and Business Analytics 2023; 1(2): 26 - 35 33
performing loans) have a significant impact on profitability captured by ROA. This is evidenced by a p-value lower than 0.05
level of significance (F=4.482853, p-value = 0.006521<0.05). The study thus concluded that distress factors (financial
leverage, liquidity and non-performing loans) have a major effect on profitability among the licensed MFBs in Kenya.”
Table 5: Regression Coefficients
Coefficients
Standard
Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-0.107259624
0.03108
-3.45104
0.001011
-0.16939
-0.04513
LEV
0.187728322
0.072695
2.58241
0.012186
0.04243
0.333043
LQ
0.057219866
0.026162
2.18713
0.031224
0.03949
0.095047
NPL
-0.029459026
0.011351
-2.59528
0.010507
-0.06614
0.007224
Note: ROA- Return on assets, LEV-Financial Leverage, LQ- Liquidity, NPL- Non-performing loans
Table 5 presents the regression coefficients associated with the explanatory variables. The effect of liquidity on profitability
was inverse and statistically significant (α1= 0.05721, t = 2.18713, p = 0.031224 < 0.05). Financial leverage had a direct and
statistically significant effect on profitability (α2= 0.1877, t= 2.582414, p= 0.012186<0.05). The study also revealed that the
impact of non-performing loans was inverse and statistically significant (α3= -0.029459, t= -1.6053, p= 0.010507< 0.05).
5. Discussion
The analysis of variances given revealed that financial distress factors (financial leverage, liquidity and non-performing
loans) have a significant impact on profitability captured by ROA. This is evidenced by a p-value lower than 0.05 level of
significance (F=4.482853, p-value = 0.006521<0.05). The study thus concluded that distress factors (financial leverage,
liquidity and non-performing loans) have a major effect on profitability among the licensed MFBs in Kenya. Further, the effect
of liquidity on profitability was inverse but not statistically significant (α1= 0.05721, t = 2.18713, p = 0.031224 < 0.05). A
one-unit increase in liquidity results in 0.05721 units increase in profitability of licensed MFBs in Kenya. The positive effect
implies that when MFBs have adequate liquidity, they can utilise the excess liquidity to invest in financial assets like
government bills and bonds to get additional revenues. MFBs can also use the excess liquidity to offer more loans to borrowers
hence earning the firms additional interest revenues. The finding agrees with Banafa (2016) who established that Liquidity
influences the financial performance of listed non-financial firms at the Nairobi Securities Exchange positively. Further,
Caliskana and Lecuna (2020) noted that liquidity is more crucial for profitability. Ramadhanti, Marlina and Hidayati (2019)
revealed that liquidity has a positive and significant effect on profitability credit risk has a negative effect and is significant on
profitability. The study also revealed that financial leverage had a direct and statistically significant effect on profitability (α2=
0.1877, t= 2.582414, p= 0.012186<0.05). A one-unit increase in financial leverage leads to a 0.1877 unit increase in
profitability. The finding implies that MFBs that were more leveraged also tended to perform better. Increasing Financial
leverage implies that more and more percentage of the profitability is exempt from taxation hence more profitability is
available for distribution to shareholders of the MFBs. The finding agrees with Kirimi, Simiyu and Dennis (2017) who
established a positive relationship was revealed between debt debt-equity ratio and ROE respectively. Chesang (2016) also
showed that debt to equity ratio has a statistically significant effect on the profitability of agricultural firms listed at the Nairobi
Securities Exchange. Finally, the study revealed that the impact of non-performing loans on profitability was inverse and
statistically significant (α3= -0.029459, t= -1.6053, p= p= 0.010507<0.05). A unit increase in non-performing loans led to a
reduction in profitability by 0.02945 units. The findings imply that increasing non-performing loans as a ratio of the total loans
leads to increasing loan loss expenses that eat into the profits of the MFBs in Kenya. Increasing NPLs hence leads to reducing
profitability of the licensed MFBs in Kenya. The finding is in congruence with Ekinci and Poyraz (2019) who established that
there is a negative relationship between credit risk and ROA as well as between credit risk and ROE. Chabachib, Yudha, and
Udin (2020) showed that NPL has a negative effect on ROA.
6. Conclusions
The study through analysis of variances revealed that financial distress factors (financial leverage, liquidity and non-
performing loans) have a significant impact on profitability captured by ROA. The study thus concluded that distress factors
(financial leverage, liquidity and non-performing loans) have a major effect on profitability among the licensed MFBs in
Kenya." Further, the effect of liquidity on profitability was inverse. The finding implies that when MFBs have adequate
liquidity, they can utilise the excess liquidity to invest in financial assets like government bills and bonds to get additional
revenues. MFBs can also use the excess liquidity to offer more loans to borrowers hence earning the firms additional interest
34 Kitheka: Effect of Financial Distress Factors on Profitability of Microfinance Banks Licensed by Central Bank of Kenya
revenues. The study also revealed that financial leverage had a direct effect on profitability. The study thus concluded that
MFBs that were more leveraged also tended to perform better. Increasing Financial leverage implies that more and more
percentage of the profitability is exempt from taxation hence more profitability is available for distribution to shareholders of
the MFBs. Finally, the study revealed that the impact of non-performing loans on profitability was inverse. The findings imply
that increasing non-performing loans as a ratio of the total loans leads to increasing loan loss expenses that eat into the profits
of the MFBs in Kenya. Increasing NPLs hence leads to reducing profitability of the licensed MFBs in Kenya.
Given the direct effect of liquidity on the profitability of licensed MFBs in Kenya, the study suggests to management of
licensed MFBs in Kenya to hold adequate liquidity. The MFBs should hold liquidity in the form of cash and cash equivalents
such as treasury bills and bonds that can be easily converted into cash when the need arises. The CBK as the regulator should
also continue ensuring that licensed MFBs have adequate liquidity above the statutory levels to ensure their stability. The study
also showed that the effect of financial leverage on the profitability of licensed MFBs in Kenya was positive. The study thus
suggests that management of the licensed MFBs continue leveraging through accepting more deposits form the public. The
deposits generated can thus be used for lending purposes to earn the MFBs more interest income which leads to increased
profitability. The study also suggests to CBK to continue ensuring that licensed MFBs do not take on leverage beyond their
capacity to protect the depositors of funds in the said firms. Finally, based on the negative effect of non-performing loans on
the profitability of licensed MFBs in Kenya, the study suggests that management to advance high-quality loans. The firms
should have stringent credit analysis policies that ensure that only credit worthy customer is given loans to protect the firms
against non-performing loans. The study also recommends that the CBK continue requiring licensed MFBs to provide for loan
loss provisions commensurate with the level of non-performing loans to protect the MFBs against loan losses. The current
study was on the effect of financial distress factors on the profitability of licensed MFBs in Kenya. The study suggests to
future researchers to introduce control variables in the model to improve the robustness of the model. The studies can also
introduce more proxies of financial distress factors. The study also suggests that future studies should be based on other
financial firms including Deposit taking Sacco's and commercial banks to establish whether findings hold across the board.
.
Funding
The study was financed by the personal resources of the author.
Acknowledgement
I would wish to recognize the input and insights of my lecturer. Dr. Jacqueline Muiruri
Conflicts of Interest
“The authors declare no conflicts of interest.”
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