Thesis (1) (2)AYE.doc

DEPARTMENT OF ACCOUNTING AND FINANCE
MSc. IN ACCOUNTING & FINANCE PROGRAM
DETERMINANATS OF LIQUIDITY RISK ON PRIVATE
COMMERCIAL BANKS IN ETHIOPIA
A Thesis Submitted to the Accounting and Finance
Department of Ethiopian Civil Service University in Partial
Fulfilment of the Requirements for the Masters of Science in
Accounting and Finance.
By: Mesfin Abera
Supervisor: Giday G. (Ph.D.)
Addis Ababa, Ethiopia
June-2022

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Declaration
I, Mesfin Abera, declare that this thesis entitled: Determinants of Liquidity risks of
private Commercial Banks In Ethiopia and submitted in partial fulfillment of the
requirements for the Degree of Master of Science in Accounting and Finance, is
outcome of my own effort & study and that all sources of materials used for the
study have been duly acknowledged. I have produced it independently with only
guidance and suggestion of my thesis Advisor Gidey G (PhD). The study complies
with the regulations of the University and meets the accepted standards with
respect to originality and quality.
Declared by
Name:___________________________
Signature: ____________________
Department: ____________________
Date: __________________________

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Certification
This is to certify that the thesis prepared by Mesfin Abera, entitled: Determinants of
liquidity risks of private Commercial Banks in Ethiopia and submitted in partial
fulfillment of the requirements for the degree of Master of Science in Accounting and
Finance comply with the regulations of the University and complies the accepted
standards with respect to originality and quality.
Name of Candidate: _____________; Signature: _______________Date: _____________.
Name of Advisor: _____________. Signature: _______________Date: _____________.
Signature of Board of Examiner`s:
External examiner: ____________________Signature: ____________Date: _____________.
Internal examiner: ____________________ Signature: ____________Date: _____________.
Dean, SGS: __________________________ Signature: ____________Date: _____________.

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Acknowledgements
I praise the name of Almighty God who gave me power and patience in every
endeavor throughout my life. Next to that, I would like to express my appreciation
to all who have helped me in conducting this study. First of all; I would like to
express my genuine thank to my advisor, Gidey G. (PhD), for his comments, advice
and inspiration.
I am very grateful to my friends (Teshale, Emawayih & Others) for your
unreserved support during all stage of my learning and study. I am also indebted to
all friends who helped me during data collection.
My heartfelt thanks go to my lovely son Amen Mesfin.
I would like to give my most profound gratitude to my mother, Father, sisters,
brothers and relatives for their unconditional love and steadfast support at all times.
Finally I’m grateful to my friends who gave me an idea and support throughout this
study. This study was impossible without the support of all.

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Table of Content
Acknowledgements ...........................................................................................................................i
Table of Content...................................................................................................................ii
Abstract .......................................................................................................................................v
List of Table and Figures ............................................................................................................vi
List of Table ................................................................................................................................vi
List of Figures ............................................................................................................................vii
List of Acronyms and Abbreviations ........................................................................................ viii
CHAPTER ONE ............................................................................................................................. 1
1. INTRODUCTION .................................................................................................................. 1
1. 1 Background of the Study...................................................................................................... 1
1.2 Statement of the problem ...................................................................................................... 2
1.3 Objective of the study ........................................................................................................... 4
1.3.1 General Objective........................................................................................................... 4
1.3.2 Specific Objective .......................................................................................................... 4
1.4 Research hypotheses ............................................................................................................. 4
1.5 Scope of the study................................................................................................................. 5
1.6 Significance of the study....................................................................................................... 5
1.7 Limitation of the study.......................................................................................................... 5
1.8 Organization of the Study ..................................................................................................... 5
CHAPTER TWO............................................................................................................................. 6
2. REVIEW OF RELATED LITERATURES ................................................................................ 6
2.1 Theoretical Literature............................................................................................................ 6

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2.1.1. Determinants of Bank Liquidity........................................................................................ 7
2.1.1.1 Bank Specific Characteristics.................................................................................. 7
2.1.1.1.1 Profitability........................................................................................................7
2.1.1.1.2 Non-performing loans .......................................................................................8
2.1.1.1.3 Capital Adequacy..............................................................................................9
2.1.1.1.4 Bank Size...........................................................................................................9
2.1.1.3.5 Loan Growth ...................................................................................................10
2.1.1.3.6 Liquid Assets Ratio .........................................................................................10
2.1.1.2 Macroeconomic Fundamentals ............................................................................. 11
2.1.1.2.1 GDP Growth....................................................................................................11
2.1.1.2.2 The Rate of Inflation.......................................................................................11
2.1.1.2.3 Short Term Interest Rate .................................................................................12
2.1.1.2.4 Reserve requirement........................................................................................12
2.2 Review of Related Empirical Studies.................................................................................. 13
2.2.1 Related Empirical Studies in Advanced Countries ...................................................... 13
2.2.2 Related Empirical Studies in Emerging Economies .................................................... 15
2.2.3 Related Empirical Studies in African Countries .......................................................... 16
2.3.4 Related Empirical Studies in Ethiopia ......................................................................... 18
2.3 Conceptual Framework ....................................................................................................... 20
CHAPTER THREE....................................................................................................................... 21
3. RESEARCH METHODOLOGY.............................................................................................. 21
3.1 Research Design.................................................................................................................. 21
3.2 Research Approach ............................................................................................................. 21
3.3 Data Type, Source and Methods of Data Collection........................................................... 22
3.4 Population of the Study....................................................................................................... 22
3.5 Sampling Technique & Sample Size................................................................................... 23
3.6 Methods of Data Analysis................................................................................................... 24
3.7 Variable Description ........................................................................................................... 24

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3.7.1 Dependent variable:...................................................................................................... 25
3.8 Model Specification ............................................................................................................ 28
CHAPTER FOUR......................................................................................................................... 30
4. DATA PRESENTATION AND ANALYSIS........................................................................... 30
4.1 Descriptive statistics.................................................................................................... 30
4.2. Testing the Classical Linear Regression Model (CLRM) Assumptions ............................ 32
4.2.1. Testing for the Normality of error term distribution................................................... 32
4.2.2. Testing for the variance of the error-term is constant/homoscedasticity ................ 32
4.2.3. Test for Normality................................................................................................... 33
4.2.4. Testing for the covariance between the error-terms are zero-(no autocorrelation). 34
4.2.5. Test for Multicollinearity........................................................................................ 35
4.3 Fixed Effect versus Random Effect Model......................................................................... 36
4.4. Discussion of the Regression Result .................................................................................. 36
4.3.2. Discussion of Results of the regression analysis..................................................... 37
CHAPTER FIVE........................................................................................................................... 41
5. SUMMARY, CONCLUSION AND RECOMMENDATION ................................................. 41
5.1. Summary ............................................................................................................................ 41
5.2. Conclusion.......................................................................................................................... 41
5.3. Recommendations .............................................................................................................. 42
5.4 Further research direction.................................................................................................... 43
Reference........................................................................................................................44

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Abstract
While liquidity risk is one of the primary bank risks that can have an impact on the
financial system's development, determining the determinants of this risk is critical
for the financial sector's soundness. The main goal of this research is to determine
the determinants of liquidity risk in Ethiopian private commercial banks over a ten-
year period (2012-2021) using a quantitative research approach on thirteen sample
private commercial banks. This study looked at indicators of financial risk in terms
of liquidity risk using a variety of variables in an objective manner. The fixed panel
fixed effect regression model is used to model liquidity risk. Return on asset, capital
adequacy ratio, and short term interest rate all have a significant positive impact
on liquidity risk, whilst bank size and loan growth rate have a significant negative
impact. Finally, the results show that inflation rate and GDP have insignificant
negative and positive relationships with liquidity risk, respectively.
Key words: Financial risk, Liquidity risk, Ethiopian Private Commercial Bank

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List of Table and Figures
List of Table
Table 3.1 List of Private Commercial Banks in Ethiopia ........................................................................................................23
Table 4.1 Descriptive statistics of study Variables.................................................................................................................30
Table 4.2 Hetroskedasticity test ................................................................................................................................................33
Table: 4.3 VIF Test for the model...............................................................................................................................................35
Table 4.4: Fixed effect regression results.................................................................................................................................37
Table 4.5 Summary of actual and expected signs of explanatory variables on the dependent variables.................40

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List of Figures
Figure 1 Conceptual Framework ................................................................................................................................................20
Figure 2 Rejection and non-rejection regions for DW test...................................................................................................34

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List of Acronyms and Abbreviations
AIB: Awash International Bank
BIS: Bank for International Settlement
BLUE: Best Linear Unbiased Estimator
BOA: Bank of Abyssinia
BIB: Berhan International Bank
BuIB: Bunna International Bank
CLRM: Classical Linear Regression Model
DB: Dashen Bank
CBO: Cooperative bank of Oromia
FEM: Fixed Effect Model
LIB: Lion International Bank
MoFED: Ministry of Finance and Economic Development
NBE: National Bank of Ethiopia
NIB: Nib International Bank S.C
OIB: Oromia International Bank
OLS: Ordinary Least Square
ROA: Return on Assets
ROE: Return on Equity
UB: United Bank S.C
WB: Wegagen Bank S.C
ZB: Zemen Bank

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CHAPTER ONE
1. INTRODUCTION
1. 1 Background of the Study
Banks are financial institutions that act as an intermediary between surplus and deficit economic
units in the economy by directing financial resources from one to the other. The function of the
capital market is negligible, especially in emerging nations like Ethiopia, and as a result,
commercial banks have become the most powerful financial institutions in the financial system.
Banks must be in good health in order to properly fulfill their responsibility of providing monies
to consumers. As it was pointed out by (Diamond, 1984) , the function of banks in altering
maturity and providing protection to depositors' possible liquidity demands is one of the primary
reasons why they may not be in good shape.
Following the 1994 financial reform program, Ethiopia's banking system has seen a significant
expansion. Private Banks were encouraged to enter and expand in the industry as a result of the
reform. As of 2021, Ethiopia had 22 banks regulated under the direct supervision of the National
Bank of Ethiopia, with two of them being government-owned. Despite this, Ethiopia's banking
sector is immature, with operational inefficiencies, little and insufficient competition and market
concentration toward a large government-owned commercial bank (Lelissa 2007).
To improve the banking sector efficiency it is worthwhile to identify the main factors that affect
the performance of a bank. The financial performance failure signifies financial risk. In the
process of providing financial service, commercial banks confront a number of risks like:
operational risks, credit risk, liquidity risk, market risk (foreign exchange risk & interest rate
risk) along with other risk, which may possible, intimidate the survival and success of the bank’s
(Muhammad , Khizer , & Shama , 2011). Hence, the banks should have risk detection
mechanisms to sustain in the present dynamic environment.
According to Bank for International Settlements (2008), liquidity is defined as “the ability of a
bank to fund increases in assets and meet obligations as they come due, without incurring
unacceptable losses”.

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Liquidity risk is the risk of being unable to liquidate a position timely at a reasonable price
(Muranaga & Ohsawa, 2002). The Basel committee on banking supervision (1997) also defines
liquidity risk as the inability of the bank to accommodate decrease in liabilities or to fund
increases in asset. There are various ways of looking at liquidity risk. From market perspective,
liquidity risk is the risk that an asset owner will not be able to realize the full value of the asset at
the time a sale is desired. From banking perspective, liquidity risk relates to the inability to meet
obligations at a reasonable cost when they come due. (Desalegn and Veni, 2019)
Thus, the study will focuses on pure financial risk determinants from the perspective of liquidity
risk in Ethiopian private commercial banks. Basically, there are two classes of bank’s financial
risk determinants: Bank specific variables which are unsystematic and macroeconomic variable
which are systematic risks which affect all banks.
1.2 Statement of the problem
The fundamental role of a bank is to channel funds from surplus economic unit to deficit
economic units. They also provide a channel for policy makers to conduct monetary policies that
control the price and foreign exchange stability. However, the activity of the bank is not without
problems, since banks have fundamental role in the maturity transformation of short-term
deposits into long term loans that inherently exposed for liquidity risk. In such circumstance,
banks will be exposed to liquidity problem and may frustrate their customers and may affect the
financial sector as a whole. On the other hand, when banks hold excess liquid asset which are
non-earning assets such as cash and non-interest-bearing deposits, the bank’s profitability will be
affected. Hence, every bank has to ensure that it operates to satisfy its profitability target and at
the same time to meet the financial demands of its customers by maintaining optimum level of
liquidity. In recent days, following the financial crisis of 2007, liquidity risk has become one of
the major concerns of financial institutions throughout the world. The financial crisis revealed
that, liquidity becomes one of the top priorities of a bank’s management to ensure the availability
of sufficient funds to meet future demands at reasonable costs. Therefore, identifying the
determinants of banks liquidity buffer has become the major concern of all banks and their
regulators so as to mitigate liquidity risk.

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In order to evaluate the determinants of liquidity risk in developed markets, a number of
researches have been done. These studies offer some insight into the factors that influence
liquidity risk. Capital adequacy, capitalization, and size, according to Vodova (2011), all have a
considerable positive link with liquidity risk. Doriana (2013) discovered that larger banks have
greater liquidity risk, but asset quality has only a short-term influence on liquidity risk.
According to Bunda and Desquilbet (2008), bank size has a favorable impact on liquidity risk,
whereas the equity to assets ratio has a negative impact.
In similar vein various studies have been done to evaluate determinants of liquidity risk in
developing markets. Njeri (2013) discovered a link between microfinance institutions' liquidity
and their financial performance. A study Kamau.et.al(2013) by Credit ratings, monetary policies,
government spending, and the state of the balance of payments all have an impact on commercial
bank liquidity. Bank profitability was found to be harmed by liquidity risk, according to Maaka
(2013).In Ethiopian case similar study have also conducted to identify determinants of liquidity
risk in banking sector.
Worku (2006) "liquidity and its impact on the performance of commercial banks in Ethiopia"
and Semu (2010) "the impact of lowering or restricting loan disbursement on the performance of
banks in Ethiopia" are two studies worth highlighting among these attempts. Tseganesh (2012)
attempted to research determinants of banks liquidity and their impact on financial performance
in her paper titled "determinants of banks liquidity and their impact on financial performance."
The researcher, on the other hand, only employed liquidity ratios to assess liquidity risk.
A study by Nigist (2015) conducted study on ‘Determinants of Banks Liquidity: Empirical
Evidence on Ethiopian Commercial Banks’ found that banks liquidity was highly affected by
firm specific variables if compared to macroeconomic variables. Similar study by Samuel (2019)
found, among the bank specific variables; capital adequacy and bank size had statistically
significant effect on the determination liquidity of Ethiopian private commercial banks. And
among the macroeconomic variables GDP, inflation and short interest rate had statistically
significant effect on liquidity of Ethiopian private commercial banks.Whereas loan growth, non-
performing loans and profitability had statistically insignificant effect on the determination of
liquidity of Ethiopian private commercial banks.

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Despite the fact that the two studies were on the same issue, there were numerous differences
between the study periods. It is clear that significant macroeconomic changes have been made
over the last three years, and at least five new banks have entered the financial market. As a
result, the purpose of this study is to fill up the gaps by providing data on macroeconomic
adjustments and bank-specific factors that affect the liquidity of Ethiopian private commercial
banks.
General comments on statement of the problem
1.3 Objective of the study
1.3.1 General Objective
The general objective of this study is to explore the determinants of liquidity risk on selected
private commercial banks in Ethiopia.
1.3.2 Specific Objective
In line with the general objective, the specific objectives of this study are;
 To identify the bank specific determinants of bank’s liquidity in Ethiopian private
commercial banks
 To identify the macroeconomic determinants of bank’s liquidity in Ethiopian private
commercial banks
1.4 Researchhypotheses
Hypothesis may be defined as a proposition or set of proposition set forth as an explanation for
the occurrence of some specified group of phenomena either asserted merely as a provisional
conjecture to guide some investigation or accepted as highly probable in the light of established
facts (Kothari, 2004). In order to evaluate and identify the determinants and to break down the
specific objectives, the following major hypotheses are tested in the case of Ethiopian private
commercial banks.
H1: Capital adequacy has positive and significant effect on banks liquidity

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H2: Bank size has positive and significant effect on banks liquidity
H3: Loan growth has negative and significant effect on banks liquidity
H4: Profitability has negative and significant effect on Banks liquidity
H5: GDP growth rate has negative and significant effect on banks liquidity
H6: Inflation rate has positive and significant effect on banks liquidity
H7: Short term interest rate has positive and significant effect on banks liquidity
1.5 Scope of the study
The scope of the study was limited to looking at the impact of capital adequacy, bank size, loan
growth, the profitability, Real GDP growth rate, inflation rate, and short term interest rate on
liquidity of Ethiopian private commercial banks between 2012 and 2021. Both descriptive and
regression analysis will be used in the course of this inquiry.
1.6 Significance of the study
This study will expected to have great contribution to the existing knowledge in the area of
factors determining private commercial banks liquidity in the context of Ethiopia. This in turn
contributes to the well-being of the financial sector of the economy and the society at large.
Therefore, the major beneficiaries from this study are supposed to be commercial banks, central
bank, the academic staff of the country and the society as a whole in the country.
1.7 Limitation of the study
The study's first restriction is that the government-owned Commercial Bank of Ethiopia is not
included in the analysis. As a result, the study's conclusions may fail to reflect the country's
broader banking industry. The paper's second drawback is that it does not incorporate NPL data
due to confidentiality concerns.
1.8 Organization of the Study
There are five chapters in this research report. The first chapter provides a basic overview of the
entire study. The review of related literatures is described in Chapter 2, and the technique used in
the research is described in depth in Chapter 3. Data presentation, analysis, and interpretation
will be covered in Chapter 4. Finally, the final chapter summarizes the entire research project
and offers pertinent recommendations based on the findings.

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CHAPTER TWO
2. REVIEW OF RELATED LITERATURES
2.1 Theoretical Literature
The financial economics literature distinguishes between two concepts of liquidity: market
liquidity and funding liquidity (Drehmann and Nikolaou, 2009). Market liquidity describes a
particular characteristic of an asset: a high degree of market liquidity implies the ability to offset
or eliminate a position in a given asset at or close to the current market price. This feature of the
asset may not be constant over time. An asset which is currently market liquid may not
necessarily have been market liquid in the past, nor need it be continuously market liquid in the
future. Factors such as market concentration or the prevalence and distribution of asymmetric
information may affect the degree of market liquidity.
Funding liquidity describes particular characteristics of a financial agent: it refers to its ability to
meet obligations as they come due. Funding liquidity risk is the risk that the bank will not be
able to meet efficiently both expected and unexpected current and future cash flow and collateral
needs without affecting either daily operations or the financial condition of the firm. At any point
in time, a financial institution is either funding liquid or not. Nevertheless, the two concepts are
linked (Brunnermeier, 2009).
Liquidity risk refers to the risk that a financial agent will be unable to meet obligations at a
reasonable cost as they come due. In other words, it reflects the probability that the agent will
become funding illiquid during a given time period. As explained in the previous section, banks‟
core business is to "borrow short and lend long" they are especially prone to liquidity risk. Banks
manage the liquidity risk inherent in their balance sheets by maintaining a buffer of market-
liquid assets - such as cash or government securities which anticipates their depositors‟ liquidity
demands within the relevant timeframe.
As pointed out by Diamond and Dybvig (1983), banks thus benefit from the ability to pool
liquidity risk over a large group of depositors. It would be undesirable for banks to invest only in

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perfectly market-liquid assets at all times as this would effectively eliminate the pooling
advantage banks have compared to the liquidity risk management that could be undertaken by
their individual customers. Yet, it would be equally undesirable for banks not to invest in market-
liquid assets at all, as this would burden depositors with excessive liquidity risks.
Until recently, liquidity risk was not the main focus of banking regulators. The 2007-2009 crisis
showed, however, how rapidly market conditions can change exposing severe liquidity risks in
institutions, many times unrelated to capital levels. Now, there is wide agreement that
insufficient liquidity buffers were a root cause of this crisis and the on-going disruptions of the
world financial system, making the improvement of liquidity risk analysis and supervision a key
issue for the years to come (Brunnermeier, 2009 and BCBS, 2008).
2.1.1. Determinants of Bank Liquidity
Internal (bank-specific factors) and external (macro determinants variables) are the two primary
groups of factors that affect bank liquidity in theory.
2.1.1.1 Bank Specific Characteristics
Internal factors (bank-specific factors) are those that have to do with internal efficiencies and
managerial decisions. Bank profitability, bank capital sufficiency, bank size, asset quality, loan
growth, and other criteria are among them.
2.1.1.1.1 Profitability
The impact of improved financial position on bank risk bearing capacity and liquidity
transformation is measured by profitability.(Rauch et al. 2008 and Shen et al. 2010). A healthy
and successful banking industry can better resist negative shocks and contribute to financial
system stability (Athanasoglou et al. 2005). Loans and advances, which account for the majority
of a bank's operational revenue, are one of its best yielding assets. In this regard, banks face
liquidity risk because loans and advances are funded by customer deposits. The bigger the
amount of loans and advances given to consumers, the higher the interest income and profit
potential for banks, but it reduces the bank's liquidity. As a result, banks must strike a balance
between liquidity and profit.

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The relationship between profitability and liquidity varies among different literatures. According
to Bourke (1989), banks holding more liquid assets benefit from a superior perception in funding
markets, reducing their financing costs and increasing profitability. On the other hand, the
studies made by (Molyneux and Thornton 1992; Goddard et al. 2004) argued that holding liquid
asset imposes an opportunity cost on the bank and has an inverse relationship with profitability.
Further, Myers and Rajan (1998) emphasized the adverse effect of increased liquidity for
financial institutions stating that, “although more liquid assets increase the ability to raise cash on
short notice, they also reduce management’s ability to commit credibly to an investment strategy
that protects investors” which, finally, can result in reduction of the “firm’s capacity to raise
external finance” in some cases. Thus, this indicates the negative relationship between bank
profitability and liquidity. The trade-offs that generally exist between return and liquidity risk are
demonstrated by observing that a shift from short term securities to long term securities or loans
raises a banks‟ return but also increases its liquidity risks. As a result of the two opposing views,
the management of banks faced with the dilemma of liquidity and profitability.
2.1.1.1.2 Non-performing loans
Non-performing loans are loans & advances whose credit quality has deteriorated such that full
collection of principal and/or interest in accordance with the contractual repayment term of the
loan or advance is in question (NBE directive No.SBB/43/2008). According to (Ghafoor, 2009),
non-performing loans are loans that a bank customer fails to meet his/her contractual obligations
on either principal or interest payments exceeding the scheduled repayment dates. Thus, NPLs
are loans that give negative impact to banks in developing the economy. Rise of non-performing
loan portfolios significantly contributed to financial distress in the banking sector.
The increased on non-performing loan reflects deteriorated asset quality, credit risk and its
inefficiency in the allocation of resources. According to Bloem and Gorter (2001), though non-
performing loans may affect all sectors, the most serious impact is on financial institutions which
tend to have large loan portfolios. On the other hand, large volume of nonperforming loans
portfolio will affect the ability of banks to provide credit and leads to loss of confidence and
liquidity problems. Therefore, the amount of non-performing loans has a negative impact on
bank’s liquidity.

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2.1.1.1.3 Capital Adequacy
Capital can be defined as common stock plus surplus fund plus undivided profits plus reserves
for contingencies and other capital reserves. Besides, a bank’s loan loss reserves which serve as a
buffer for absorbing losses can be included as bank’s capital (Patheja 1994). The primary reason
why banks hold capital is to absorb risk including the risk of liquidity crunches, protection
against bank runs, and various other risks. Ayele (2012) points that capital adequacy is a measure
of a bank’s financial strength, in terms of its ability to withstand operational costs and fund
liquidity.
Under the first view, the “financial fragility-crowding out” theories predict that, higher capital
reduces liquidity creation and lower capital tends to favor liquidity creation (Diamond and Rajan,
2000, 2001). They stated that, depositors will be charged a nominal fee for the intermediary
service of loaning out their respective deposits. However, this fee differs according to the
borrowers‟ capability of repayment. For those with higher risk borrowing but are reluctant to
incur higher cost, will provoke depositors to withdraw their funds. Furthermore, Gorton and
Winton (2000) show that a higher capital ratio may reduce liquidity creation through another
effect: “the crowding out of deposits”.
The second view is that; higher capital requirement provides higher liquidity to financial
institutions. Where risk absorption theory is realized for higher capital improves the ability of
banks to create liquidity. This evidence is provided by Diamond and Dybvig (1983) and Allen
and Gale (2004) stating that liquidity creation exposes banks to risk. The greater liquidity needs
of banks, incur higher losses due to the disposal of illiquid assets at available market prices rather
than the desired prices to meet the customers‟ obligations. Al-Khouri (2012) has also found that,
bank capital increases bank liquidity through its ability to absorb risk. Thus, under the second
view, the higher is the bank's capital ratio, the higher is its liquidity creation
2.1.1.1.4 Bank Size
Shen et al. (2009) considered bank size as one of the major determinants of bank liquidity risk.
They suggest that bank size measured by the bank's total assets contributes to its liquidity levels
since it has an effect on its ability to mobilize funds from different sources as well as the cost
associated with it. As banks grow in size, they acquire the inherent capacity to mobilize many

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deposits with less difficulty and for that matter are able to grant more loans at any point in
time.Further they noted that the huge financial commitments associated with several branch
openings increases vulnerability to liquidity risk. Bunda and Desquilbet (2008) included the size
of a bank in the determinants of liquidity risk of banks from emerging economies. Their result
showed that the size of a bank had a positive effect on liquidity risk.
2.1.1.3.5 Loan Growth
The loans & advances portfolio is the largest asset and the predominate source of revenue of
banks. According to Diamond & Rajan (2002), lending is the principal business activity for
banks. Since loans are illiquid assets, increase in the amount of loans means increase in illiquid
assets in the asset portfolio of a bank. The amount of liquidity held by banks is heavily
influenced by loan demand and it is the base for loan growth (Pilbeam 2005). If demand for
loans is weak, then the bank tends to hold more liquid assets whereas, if demand for loans is high
they tend to hold less liquid assets since long term loans are generally more profitable. Therefore,
loan growth has negative relationship with bank liquidity.
2.1.1.3.6 Liquid Assets Ratio
Jasienei, Jonas Filomena and Grazina (2012) indicated that the nature of banks assets in terms of the
propensity to transforming them into cash or very liquid assets affects its liquidity risk. Because a bank
could sell or collateralize its liquid assets to obtain liquid funds, holding liquid assets can reduce
a bank's liquidity risk. However, this may not be the case for all the banks due to the difficulty in
selling or collateralizing their liquid assets. As a result, in order to ascertain the degrees of
liquidity of each bank's assets, the liquid assets are classified into either risky liquid assets or less
risky liquid assets after which each is divided by the bank's total assets for standardization. Less
risky liquid assets include liquid assets such as cash and balances with central bank, treasury
bills, monies due from other banks and other short term government securities which could be
sold with little price risk and low transaction cost and easily passes for collateral as well.

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2.1.1.2 Macroeconomic Fundamentals
External or macro determinants are variables that are unrelated to bank management but reflect
the economic and legal environment that has an impact on institutions' operations and liquidity
levels. GDP, interest rate margin, inflation rate, reserve requirement, and other macroeconomic
factors can all affect bank liquidity.
2.1.1.2.1 GDP Growth
Gross Domestic Product (GDP) is one of the macroeconomic factors that affect liquidity of
banks.
A major recession or crises in business operations reduces borrowers‟ capability to service
obligations which increases banks‟ NPLs and eventually banks insolvency (Gavin & Hausmann,
1998). During economic boom, the demand for differentiated financial products is higher and
may improve bank’s ability to expand its loans and securities at higher rate and thus reduce
liquidity. The other study made by Painceira (2010) stated that, banks liquidity fondness is low
in the course of economic boom where banks confidentiality expects to profit by expanding
loanable fund to sustain economic boom while restricted loanable fund during economic
downturn to prioritize liquidity. In line with this argument the loanable fund theory of interest
states that, the supply for loan increases when the economy is at boom or going out of recession
(Pilbeam 2005).
Aspachs, et al (2005) has also inferred that, banks prioritize liquidity when the economy
plummets, during risk lending opportunities, while neglecting liquidity during economic boom
when lending opportunities may be favorable. On the other hand, the studies made by Bordo et
al. (2001) suggested that during recession, it is likely for an increase in the number of loan
default. This causes depositors to perceive high solvency risk and immediately tend to withdraw
deposits held at financial institutions.
2.1.1.2.2 The Rate of Inflation
Inflation reflects a situation where the demand for goods and services exceeds their supply in the
economy. Existing monetary theories agree that, inflation increases the opportunity cost of
holding liquidity and thus distorts the allocation of resources which require liquidity in
transaction. Recent theories emphasize the importance of informational asymmetries in credit
markets and demonstrate how increases in the rate of inflation adversely affect credit market

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frictions with negative repercussions for financial sector performance and therefore long-run real
activity (Huybens and Smith 1998, 1999).
According to Huybens and Smith (1999), the implied reduction in real returns worse the credit
market frictions which leads to the rationing of credit, hence credit rationing becomes more
severe as inflation rises. As a result, the financial sector makes fewer loans, resource allocation is
less efficient, and intermediary activity diminishes with adverse implications for capital/long
term investment. Further, the amount of liquid assets held banks will rise with the rise in
inflation. High inflation rate and sudden changes of inflation have a negative impact on real
interest rates and bank's capital. In this respect, the bank's nonperforming loans will expand,
collateral security values deteriorate and value of loan repayments on banks loans declines. This
way, it has been found that inflation rate significantly determines bank liquidity (Heffernan;
2005).
2.1.1.2.3 Short Term Interest Rate
Short term interest rate is the rate paid on money market instruments. Money market instruments
are securities that have a year or less to maturity, which includes Treasury bills, commercial
papers banker’s acceptances, certificates of deposit, repurchase agreements. Treasury bills are
the most important since they provide the basis for all other domestic short term interest rates.
The money market is important because many of these instruments are held by banks as part of
their eligible reserves, that is, they may be used as collateral if bank wishes to raise funds from
central bank because they are short maturing and have less default risk. The higher short term
interest rate induces banks to invest more in the short term instruments and enhance their
liquidity position Pilbeam, (2005). Therefore, short term interest rate has positive relationship
with liquidity.
2.1.1.2.4 Reserve requirement
The proportion of required reserves placed in the national bank to total assets will be used to
determine these charges in our situation. Because a higher level of reserves (remunerated in
lower interest rates) affects the banks' behavior in setting higher loan rates to compensate for the
missing profit from investing these funds, a positive correlation with the dependent variable is
expected. Only a few studies have looked at the impact of funding costs and sources on bank
liquidity (Bunda and Desquilbet, 2008). According to Alger and Alger (1999), and Munteanu

13
(2012), as refinancing costs rose, banks tended to invest more in liquid assets. This means that as
liability costs rise, banks are more likely to rely on liquid assets as a source of liquidity rather
than the interbank market.
2.2 Review of Related Empirical Studies
This section gives a brief review of the previous studies made on the determinants of bank’s
liquidity from both developed and developing nations. Moreover, most of the studies undertaken
on bank liquidity consider both bank specific and macroeconomic factors to examine the
determinants of liquidity of banks. So, the studies conducted in related to bank’s liquidity are
reviewed as follows.
2.2.1 RelatedEmpirical Studies in Advanced Countries
Bank specific and macroeconomic determinants of liquidity of English banks were studied by
(Aspachs et al, 2005). The researchers used unconsolidated balance sheet and profit and loss data
for a panel of 57 UK-resident banks, on a quarterly basis, over the period 1985 to 2003. They
assumed that the liquidity ratio as a measure of the liquidity was dependent on the following
factors: Probability of obtaining the support from LOLR, which should lower the incentive for
holding liquid assets, interest rate margin as a measure of opportunity costs of holding liquid
assets expected to have negative impact, bank profitability which is according to finance theory
negatively correlated with liquidity, loan growth, where higher loan growth signals increase in
illiquid assets, size of the bank expected to have positive or negative impact, gross domestic
product growth as an indicator of business cycle negatively correlated with bank liquidity, and
short term interest rate, which should capture the monetary policy effect with expected negative
impact on liquidity.
The study made on bank specific determinants of liquidity on English banks studied (Valla et
al.2006) and assumed that, the liquidity ratio as a measure of the liquidity should be dependent
on the following factors: bank profitability and loan growth had negatively correlated with
liquidity while size of the bank is ambiguous. Liquidity created by Germany’s state-owned
savings banks and its determinants has been analyzed by (Rauch et al. 2009). In the first step
they attempted to measure the liquidity creation of all 457 state owned savings banks in Germany
over the period 1997 to 2006 and they analyzed the influence of monetary policy on bank

14
liquidity creation. To measure the monetary policy influence, the study developed a dynamic
panel regression model. According to this study, the following factors determine bank liquidity:
monetary policy interest rate, where tightening monetary policy expected to reduces bank
liquidity, level of unemployment, which is connected with demand for loans having negative
impact on liquidity, savings quota affect banks liquidity positively, size of the bank measured by
total number of bank customers have negative impact, and bank profitability expected to reduce
banks liquidity.
Vodova (2011) examined the determinants of liquidity of commercial banks in Czech Republic
through four liquidity ratios and related them with bank specific and macroeconomic data over a
period from 2001 to 2010. This study observed drop of banks‟ liquidity as a result of the Global
Financial Crisis. The study reveals that the share of liquid assets in total assets and liquid asset in
deposits and short term funding decreases with bank profitability, higher capital adequacy and
bigger size of banks. In their opinion big banks rely on the interbank market and on liquidity
assistance of Lender of Last Resort (LOLR). Liquidity measured by share of loans in total assets
and in deposits and short term borrowings increases with growth of domestic product. They did
not find any significant relationship between interest rates on loans, interest rate on interbank
transactions or monetary policy interest rates, interest rate margins, the share of non-performing
loans and the rate of inflation with liquidity.
The study made by Lucchetta (2007) on the hypothesis that “interest rates affect banks‟ risk
taking and the decision to hold liquidity across European countries”. The liquidity measured by
different liquidity ratios should be influenced by: behavior of the bank on the interbank market.
The more liquid the bank is, the more it lends in the interbank market. The results of the study
revealed that the risk-free interest rate negatively affects the liquidity retained by banks and the
decision of a bank to be a lender in the inter-bank market. Conversely, the inter-bank interest rate
has a positive effect on such decisions. Typically, it is the smaller, risk-averse banks that lend in
the inter-bank markets. Meanwhile, the risk-free interest rate is positively correlated with loans
investment and bank risk-taking behavior
Vodova (2013) had also studied on the determinants of liquidity of Polish commercial banks. The
data cover the period from 2001 to 2010. The results of panel data regression analysis showed

15
that bank liquidity is strongly determined by overall economic conditions and dropped as a result
of financial crisis, economic downturn and increase in unemployment. Bank liquidity decreases
also with higher bank profitability, higher interest rate margin and bigger size of banks. On
contrary, bank liquidity increases with higher capital adequacy, inflation, share of nonperforming
loans and interest rates on loans and interbank transaction
2.2.2 RelatedEmpirical Studies in Emerging Economies
Moore (2010) investigated the effects of the financial crisis on the liquidity of commercial banks
in Latin America and Caribbean countries and specifically addresses the behavior of commercial
bank liquidity during crises in Latin America and the Caribbean. They identify the key
determinants of liquidity, and to provide an assessment of whether commercial bank liquidity
during crises is higher or lower than what is consistent with economic fundamentals. The
regression model was estimated by using ordinary least squares. The result of the study showed
that the volatility of cash to-deposit ratio and money market interest rate have negative and
significant effect on liquidity. Whereas, liquidity tends to be inversely related to the business
cycle in half of the countries studied, suggesting that commercial banks tend to error on the side
of caution by holding relatively more excess reserves during downturns.
Karlee et al. (2013) studied the determinants of liquidity of 15 commercial banks in Malaysia in
period (2003-2012). They used bank specific factors; size of bank, capital adequacy,
profitability, credit and macroeconomic factors such as GDP, interbank rate, financial crisis. The
empirical results show that all factors included are significant except interbank rate. The factors
with positive influence on bank liquidity are Non-Performing Loan, Profitability and Gross
Domestic Product.
On the other hand, factors to bring negative effect to bank’s liquidity are Bank Size, Capital
Adequacy, and Financial Crisis. While Interbank Rate turned out insignificant
The other study made by Vodová (2012) aimed to identify the determinants of liquidity of
commercial banks in Slovakia. In order to meet its objective, the researcher considered the data
for bank specific factors over the period from 2001 to 2009. The data was analyzed with panel
data regression analysis by using an econometric package Eviews7and the findings of the study
revealed that bank liquidity decreases mainly as a result of higher bank profitability, higher

16
capital adequacy and with the size of bank. The level of non-performing loans has no statistically
significant effect on the liquidity of Slovakia commercial banks.
In another study from Pakistan, Malik and Rafique (2013) examines bank specific and
macroeconomic determinants of commercial bank liquidity in Pakistan. Their study period
covers from 2007 to 2011. They have used two models of liquidity. The first model L1 is based
on cash and cash equivalents to total assets. The second model L2 is based on advances net of
provisions to total assets. Their results suggest that, Non-Performing Loan (NPL) and Return on
Equity (ROE) have a negative and significant effect with L1. Capital adequacy (CAP) and
inflation (INF) are negatively and significantly correlated with L1, additionally there is a
significant and positive impact of financial crisis on the liquidity of commercial banks. The
central bank regulations greatly affect the liquidity of commercial banks which means tight
monetary policy can regulate the undesirable effect of inflation on liquidity.
The study made by Vodová (2013) with the aim of identifying the determinants of liquidity of
Hungarian commercial banks which cover the period from 2001 to 2010 and used panel data
regression analysis. The result of the study showed that bank liquidity is positively related to
capital adequacy of banks, interest rate on loans and bank profitability and negatively related to
the size of the bank, interest rate margin, monetary policy interest rate and interest rate on
interbank transaction.
Sushil et al (2013) had made a study on the relationship between liquidity of selected Nepalese
commercial banks and their impact on financial performance and found that capital adequacy,
share of non-performing loans in the total volume of loans had negative and statistically
significant impact on banks liquidity whereas loan growth, growth rate of gross domestic product
on the basis price level, liquidity premium paid by borrowers and short term interest rate had
negative and statistically insignificant impact on banks liquidity. Bank size had positive and
significant impact and inflation rate had positive and insignificant impact on banks liquidity.
2.2.3 RelatedEmpirical Studies in African Countries
Chagwiza (2011) made a study on Zimbabwe, regarding the commercial banks liquidity and its
determinants. The main objective of his study was to identify the determinants of liquidity in

17
Zimbabwean commercial banks. The result of his study revealed that, there is a positive link
between bank liquidity and capital adequacy, total assets, gross domestic product and bank rate.
While the adoption of multi-currency, inflation rate and business cycle have a negative impact on
liquidity. The other studies made by Laurine (2013) in Zimbabwe regarding Zimbabwean
Commercial Banks Liquidity Risk Determinants after dollarization. The aim of his paper was
that empirically investigating the determinants of Zimbabwean commercial banks liquidity risk
after the country adopted the use of multiple currencies exchange rate system. To attain the
intended objective, panel data regression analysis was used on monthly data from the period of
March 2009 to December 2012. The result of the study revealed that, capital adequacy and size
have negative and significant influence on liquidity risk whereas spread and non-performing
loans have a positive and significant relationship with liquidity risk. Reserve requirement ratios
and inflation were also significant in explaining liquidity during the studied period.
Agbada and Osuji (2013) studied the efficacy of liquidity management and banking performance
in Nigeria using survey research methodology. Data obtained were first presented in tables of
percentages and pie charts. The data were empirically analyzed by Pearson product-moment
correlation coefficient. Findings from the empirical analysis were quite robust and clearly
indicate that there is significant relationship between efficient liquidity management and banking
performance and that efficient liquidity management enhances the soundness of a bank.
A study made by Fadare (2011), on the banking sector liquidity and financial crisis in Nigeria
with the aim of identifying the key determinants of banking liquidity and assessing the
relationship between determinants of banking liquidity and financial frictions within the
economy. It was employed a linear least square model and time series data from 1980 to 2009.
The study found that monetary policy rate and lagged loan-to-deposit ratio were significant for
predicting banking sector liquidity. It also showed that a decrease in monetary policy rate,
volatility of output in relation to trend output, and the demand for cash, leads to an increase in
current loan-to-deposit ratios; while a decrease in currency in circulation in proportion to
banking sector deposits; and lagged loan-to deposit ratios leads to a decline in current loan-to-
deposit ratios.

18
The other study made by Mohamed (2015) on Tunisia banks shows that, financial performance,
capital / total assets, operating costs/ total assets, growth rate of GDP, inflation rate, delayed
liquidity have significant impact on bank liquidity while size, total loans / total assets, financial
costs/ total credits, total deposits / total assets does not have a significant impact on bank
liquidity.
2.3.4 RelatedEmpirical Studies in Ethiopia
Some related studies were conducted by different researchers in Ethiopia. Specifically, Berhanu
(2004) studied financial performance of Ethiopian commercial banks and found the following
results. The banking system in general increased their assets position, private banks increased
their market share, and liquidity condition of commercial banks was reliable. Finally,
commercial banks were operating at profit. Berhanu (2004) used profitability ratios and liquidity
ratios to evaluate financial performance of commercial banks in Ethiopia.
Ayalew (2005) used ratio analysis with the help of DEA model and the ratios were capital ratio,
liquidity ratio and loan loss provision to total assets when studied the financial performance of
private banks in Ethiopia. The study revealed that banks were becoming leveraged, the growth of
deposits from depositors increased, efficiency was also increased from year to year. Generally,
Ayalew (2005) concluded that the growth rate was positively related to efficiency scores.
Seyoum (2005) revealed that private banks performance in terms of managerial earning and
operating efficiency was an average and less than that of the biggest government bank i.e.
Commercial Bank of Ethiopia (CBE). Seyoum (2005) also noted that in Ethiopia the banking
sector was still dominated by state owned banks especially CBE, no stiff competition and
compared performance of banks using managerial earning and operating efficiency.
Worku (2006) argued that liquidity has an impact on the performance of commercial banks in
Ethiopia and there was an inverse relation between deposit/net loan and ROE. And the
coefficient of liquid asset to total asset was positive and directly related with ROE. Worku
(2006) also studied capital adequacy and found that the capital adequacy of all commercial banks
in Ethiopia were above threshold, means there was sufficient capital that can cover the risk-
weighted assets. Depositors who deposit their money in all banks were safe because all the
studied banks fulfilled NBE requirement (Worku, 2006). Worku used different ratios when

19
analyzing liquidity effect on banks performance and these ratios were liquid asset/net profit,
liquid asset/total assets, net loans/net deposits, interest income/net deposit and interest
income/interest expense (Worku, 2006).
The study conducted by Semu (2010) intended to assess the impact of reducing or restricting loan
disbursement on the performance of banks in Ethiopia. It also attempts to examine the possible
factors that compel the banks to reduce or restrict lending. Quantitative method particularly
survey design approach was adopted for the study. The findings of the study showed that deposit
and capital have statistically significant relationship with banks‟ performance measured in terms
of return on equity (ROE). New loan and liquidity have relationship with banks‟ performance
measured in terms of both return on asset (ROA) and ROE.
As to the author’s knowledge, the first study was conducted by Tseganesh (2012). She studied
the determinants of banks liquidity and their impact on financial performance on commercial
banks in
Ethiopia including both public and private banks. Her study focused on two steps; first, to
identify determinants of commercial banks liquidity in Ethiopia and then to see the impact of
banks liquidity up on financial performance through the significant variables explaining
liquidity. The data was analyzed by using balanced fixed effect panel regression model for
twelve commercial banks in the sample covered the period from 2000 to 2001 and the result of
her study indicate that capital adequacy, bank size, share of non-performing loans in the total
volume of loans, interest rate margin, inflation rate and short term interest rate had positive and
statistically significant impact on banks liquidity. Whereas, Real GDP growth rate and loan
growth had statistically insignificant impact on banks liquidity.
Abera, (2012) studied Factors Affecting Profitability on Ethiopian Banking Industry. This study
examined the bank-specific, industry-specific and macro-economic factors affecting bank
profitability for a total of eight commercial banks in Ethiopia, covering the period of 2000-2011
using a mixed methods research approach by combining documentary analysis and in-depth
interviews. The result of the interview revealed that the liquidity of banks was one of the major
determinants of Ethiopian banks profitability. But, the output of the regression analysis and the
interview were in agreement in relation to the direction of the effect of liquidity as far as both of
them proved the existence of negative or inverse relationship between liquidity and profitability

20
of Ethiopian banks. The study concluded that the impact of Ethiopian banks’ liquidity on their
performance remains ambiguous and further research is required.
2.3 Conceptual Framework
On the basis of the hypotheses that developed from the literature part and the regression model of
the study, the following conceptual frame work was developed.
Figure 1 Conceptual Framework
Bank Specific Factors Macroeconomic Factors
Dependent Variables
Loan Growth
Profitability
Annual Real Gross
Domestic Product
Bank Size
Inflation
Bank Liquidity
1)
(L
Short-term interest
rate
CapitalAdequacy
Liquid Asset
Ratio

21
CHAPTER THREE
3. RESEARCH METHODOLOGY
General comment of methodology part
The research process normally begins with the identification of a problem, followed by the
formulation of hypothetical assertions, the collection of relevant data, and the analysis of the data
using relevant and appropriate statistical methods. This section describes the research strategy
and methodology, as well as the demographic, sample and sampling process, research tools used
to collect data for the study, and data collection and analysis methodologies. It also goes through
the model and its various components, such as the dependent and independent variables.
3.1 ResearchDesign
According to (Creswell, 2002), the research design to be adopted depends on the nature of the
research problem, personal experiences, and the audiences for whom the researcher seeks to
convey own ideas, opinions, and findings by means of scholarly communication. As a result,
adequately defining and evaluating the research design is critical prior to starting the study.
Explanatory study design, as defined by Bhattacherjee (2012), aims to discover causal causes
and outcomes of the target event. As a result, in order to accomplish the research purpose, the
research design for this study has been explanatory one in order to meet the research objective.
3.2 ResearchApproach
After extensive review of literatures (Hassan, 2011), has classified the research method in to four
broad categories. The first one is Quantitative research method which incorporates numerical
analysis of the data collected from the topic or entity under investigation. It gives Special
emphasis has on the measurement and analysis of causal relationships between the variables
concerned between two states that of the population sample of interest and the survey conditions
under control. The second is Qualitative research method which is an array of interpretative
techniques, which aims to describe, decode, translate, the phenomena taking place in the social
world. The third one is Case study approach which can be defined as an empirical inquiry that
investigates a contemporary phenomenon in its real-life context, especially when the boundaries
between phenomenon and context are not clearly evident. The last is mixed research method

22
which does not generally undertake qualitative and quantitative research at the same time.
However, it is possible for a study to be divided into various phases, in which either a qualitative
or a quantitative approach is applied. As a result, a quantitative research approach will be used in
this study to establish a causal relationship between the liquidity of private commercial banks
and the bank specific and macroeconomic factors affecting bank liquidity in Ethiopia.
3.3 Data Type, Source and Methods of Data Collection
A panel of data, according to Brooks (2008), encapsulates information across time and space and
measures a quantity about them over time. Panel data has the advantage of addressing a broader
range of topics and tackling more difficult problems than pure time-series or pure cross-sectional
data alone. Panel data also has the advantage of providing more meaningful data because it
includes both cross-sectional and time series information, which captures individual variability
and dynamic adjustment (Brooks 2008 pp 488). As a result, this study is expected to employ
panel data because it looked at bank liquidity across a number of private commercial banks
across time.
The data for this study came from secondary sources. Bank-specific data was gathered from
audited financial statements (i.e. Balance Sheet and Profit & Loss Statement) of each selected
commercial bank in the sample, as well as macroeconomic data from the NBE and MoFEC. The
data were collected on a yearly basis from 2012 to 2021, as stated in the scope, and the statistics
for the variables were taken on the 30th of June of each annual year under inquiry.
3.4 Population of the Study
The study population includes all private commercial banks in Ethiopia. According to NBE
report, at the end of June 30, 2021 there are 21 privately owned commercial banks. Therefore
The Following banks will be considered as population for the study.

23
Table 3.1 List of Private Commercial Banks in Ethiopia
No Bank Name Year of Establishment
1 Awash International Bank 1994
2 Dashen Bank 1995
3 Bank of Abyssinia 1996
4 Wegagen Bank 1997
5 Hibret Bank 1998
6 Nib International Bank 1999
7 Cooperative Bank of Oromia 2005
8 Lion International Bank 2006
9 Oromia International Bank 2008
10 Bunna International Bank 2009
11 Zemen Bank 2009
12 Abay Bank S.C. 2010
13 Berhan International Bank 2010
14 Addis International Bank 2011
15 Debub Global Bank 2012
16 Enat Bank 2013
17 ZamZam Bank 2021
18 Hijra Bank 2021
19 Siinqee Bank 2021
20 Shabelle Bank 2021
21 Goh Betoch Bank 2021
3.5 Sampling Technique & Sample Size
For some researches, it is possible to collect data for the entire population as it can be
manageable and data is available, while for some other researches data is collected on sample
base. Sampling provides a valid alternative when it is impractical to survey the entire population
and when there is budget and time constraint to surveying the entire population (Saunders et al,
2009). There are two types of sampling techniques; probability or representative sampling and
non-probability or judgmental sampling. In the probability sampling, the chance or probability,
of each case being selected from the population is known and is usually equal for all cases while
in the non-probability sampling, the probability of each case being selected from the total
population is not known (Saunders. et al, 2009). According to Bhattacherjee (2012), non-
probability sampling is sampling technique in which some units of the population have zero

24
chance of selection or where the probability of selection cannot be accurately determined rather
samples are selected based on certain non-random criteria, such as quota or convenience.
The sampling technique is used in this research is a non-probability sampling and among the non
probability sampling methods, this research uses purposive sampling. As stated by Saunders et al
(2009), purposive sampling is often used when working with small samples and when we wish to
select cases that are particularly informative. Thus, the researcher used purposive sampling by
considering the availability of full data for the selected time period. In Ethiopia, there are 22
commercial banks of which two of them are publicly owned and 21 of them are privately owned.
Among the 21 private commercial banks, 14 of them have more than ten years of service
experience in the industry.
3.6 Methods of Data Analysis
The quantitative method of analysis was used to achieve the research's goal. Two forms of
statistical analysis will be employed to examine the proposed hypotheses to this end. To see the
influence (connection) of explanatory or independent factors on the dependent variable, use
descriptive statistics and inferential statistics/multiple regression analysis. Over the sampling
periods, descriptive statistics for both dependent and independent variables were produced. This
help in the conversion of raw data into a more meaningful form, allowing the researcher to grasp
the concepts more clearly. Then, using statistical descriptions such as standard deviation, mean,
and lowest and maximum values, analyze the data. Then, to establish the relative importance of
each independent variable in impacting liquidity of Ethiopian private commercial banks,
correlation studies between dependent and independent variables were conducted, followed by a
multiple linear regression and t-test analysis.
3.7 Variable Description
In the case of selected private commercial banks in Ethiopia, this research attempted to see the
relationship between the dependent and independent variables by testing hypotheses regarding
the relationships between bank liquidity and firm specific and macroeconomic factors affecting
it.

25
3.7.1 Dependent variable:
Liquidity of banks: The ability of banks to fund increases in assets and decreases in liabilities
without disrupting their day-to-day operations or incurring intolerable losses is referred to as
liquidity. Liquidity ratios are one of the most used methods of assessing a bank's liquidity. This
method employs a variety of balance sheet ratios and is simple to calculate. Because data to
measure bank liquidity is readily available, liquidity ratios chosen for this investigation. The
following proportions were employed:
3.7.2 Independent variables:
Capital adequacy of banks: Common stocks, surplus money, undivided profit, reserve for
contingencies, and other capital reserves make up a bank's capital. There are two contrasting
theoretical interpretations on the relationship between bank liquidity and capital adequacy, as
detailed in the literature review section. The financial fragility-deposit crowding hypothesis and
the risk absorption hypothesis are the two hypotheses. The first argument argues that capital
adequacy and bank liquidity have a negative relationship, whereas the second argument
contradicts this. The second hypothesis was taken into consideration because it has been
employed in a number of empirical researches analyzed in this study. The ratio of equity to total
assets employed as a proxy for capital adequacy in this study.
Size of banks: The capacity of a bank to perform its intermediary function is measured by its
size. As indicated in the literature review section, there are two contradictory theoretical and
empirical explanations concerning the relationship between bank liquidity and size. The classic
transformation view proposes a positive association between size and liquidity, whereas the first
approach posits a negative relationship between size and liquidity. According to the second
premise, this study projected a favorable influence of bank size on liquidity. The natural
logarithm of total assets was used as a proxy for bank size.
Loan growth of banks: The provision of loans is one of the most important functions of banks,
since it allows them to produce liquidity for the general public. Loans are generally seen as
illiquid assets that provide higher revenue for banks. As a result, as loan increases, illiquid assets
increase while short-term/liquid assets decline. The analysis expected a negative link between

26
bank loan growth and liquidity, as suggested by several empirical studies and the above
rationale. The yearly growth rate of gross loans and advances to clients was used as a proxy for
loan growth.
Non-performing loans: Non-performing loans are loans that have been outstanding for a long
period in terms of both principle and interest, in violation of the loan contract's terms and
conditions. This metric assesses the asset quality of a bank. Unlike other businesses, banks'
assets are made up of a huge number of loans. If this loan is deemed uncollectible, the bank's
profitability suffers, and a substantial number of depositors get fearful and flee the institution. As
a result, a negative link between bank liquidity and the amount of non-performing loans was
projected. The percentage of non-performing loans in the total amount of bank loans was
employed as a proxy for non-performing loans.
Profitability of the Banks: A bank's liquidity requirements prevent it from investing all of its
available funds. Banks must be both profitable and liquid, which creates inherent tensions and
the need to strike a balance. Profitability will rise when more liquid assets are invested in earning
assets such as loans and advances, despite the cost of liquidity. As a result, banks must always
strike a balance between liquidity and profitability in order to meet both shareholders' wealth
goals and liquidity needs.
ROA
Accordingly, the following hypothesis is drawn,
Gross domestic products (GDP): The gross domestic product (GDP) measures a country's
overall economic health. According to the theory of bank liquidity and financial fragility, when
the economy is growing or exiting a recession, economic units, including banks, are optimistic
and increase their long-term investments while decreasing their liquid asset holdings, whereas
when the economy is in a slump, the opposite is true. As a result, the analysis predicted that bank

27
liquidity and the economic cycle would have a negative connection. The real gross domestic
product/GDP growth rate was used as a proxy for the economic cycle.
Liquid assets to total assets ratio (L1): Liquid assets to total assets ratio should give us
information about the general liquidity shock absorption capacity of a bank. As a general rule,
the higher the share of liquid assets in total assets, the higher the capacity to absorb liquidity
shock, given that market liquidity is the same for all banks in the sample. Nevertheless, high
value of this ratio may be also interpreted as inefficiency.
Inflation rate: According to the recent theory of information asymmetry in the credit market an
increase in the rate of inflation drives down the real rate of return not just on money, but on
assets in general. The implied reduction in real returns exacerbates credit market frictions. Since
these market frictions lead to the rationing of credit, credit rationing becomes more severe as
inflation rises. As a result, the financial sector makes fewer loans, resource allocation is less
efficient, and intermediary activity diminishes with adverse implications for capital/long term
investment. In turn, the amount of liquid or short term assets held by economic agents including
banks rise with the rise in inflation. To proxy inflation the annual gross inflation rate was used.
Short term/money market interest rates short-term interest rates rise, banks seek to invest
more in Treasury bills and other short-term securities to improve their liquidity position because
they carry less risk of default. According to the NBE, Treasury bills are a liquid asset. The only
regular primary market where securities are traded on a biweekly basis is the Treasury bill
market. As a result, the weighted average yield on all types of Treasury bills was used as a proxy
for the short term/money market interest rate in this study (28 days, 91 days and 182 days).
Because of the type of data used in this investigation, the yearly rate was used (i.e. annual base).
Reserve requirement: These costs in this case will be calculated as the proportion of required
reserves put in the national bank to total assets. A negative correlation with the dependent
variable is expected, because a higher level of reserves will affect a decrease in banks liquidity.
(Total RR at NBE / Total Asset)

28
3.8 Model Specification
The nature of data will be a balanced panel data, which will be regarded to have advantages over
basic cross sectional and time series data, as mentioned in the research design part of this study.
The pooling of cross-sectional observations over various time periods is known as panel data
(Brooks 2008). The panel data, also known as longitudinal data, contains both cross-sectional
and time-series aspects; the cross-sectional element is represented by a sample of Ethiopian
private commercial banks, while the time-series element is represented by the study period
(2010-2019). The purpose of this study was to see if using a specific variable made economic
sense in the setting of Ethiopian private commercial banks. The regression model will used for
this study was adopted from Vodova (2011,2012, 2013), Tseganesh(2012), Rafique & Malik
(2013) and Mekbib (2016). Thus, the following equation indicated the general model for this
study.
Lit =α + βXit +δi +εit where Lit is one of the liquidity ratios for bank i in time t, Xit is a vector of
explanatory variables for bank i in time t, α is constant, β are coefficient which represents the
slope of variables, δi denotes fixed effects in bank i and εit is the error term. The subscript i
denote the cross-section and t representing the time-series dimension.
Therefore, the general models which incorporate all of the variables to test the determinants of
bank’s liquidity is:
Where:
L1it: represents the bank’s liquidity measured by liquid asset to total asset ratio of 𝑖𝑡ℎ bank on
year “t”
CAPit: is capital adequacy ratio of 𝑖𝑡ℎ bank on the year “t”
SIZEit: is the size of 𝑖𝑡ℎ bank on the year “t”
LGit: is the loan growth rate of 𝑖𝑡ℎ bank on the year “t”.
ROA it: is the return on asset of 𝑖𝑡ℎ bank on the year “t”.

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L1it is the ratio of liquid assets to total assets
GDPt: is the real gross domestic product growth of Ethiopia on the year “t”.
INFt: is the inflation rate in Ethiopia on the year “t”.
STIRt: is the short term (monetary) interest rate on the year t. The proxy was the
weighted average annual Ethiopian government Treasury bill rate. δi: denotes
fixed effects in bank
“i” εit: is a random error term
RRt is the proportion of required reserves put in the national bank to total assets.
The bank-specific variables are cross-sectional and time-variable, but the macroeconomic
variables are merely time-variable but are converted to panel data type by incorporating
macroeconomic variables for each cross-sectional unit.
Among the above models, the model, in which liquidity is measured by liquid asset to total asset
ratio (L1) was used by the National Bank of Ethiopia in which the liquidity requirement directive
is issued based on this ratio.

30
CHAPTER FOUR
4. DATA PRESENTATION AND ANALYSIS
This section gives a statistical descriptive analysis of the dependent and independent variables as
well as a summary of the data utilized in the regression model. The descriptive analysis is critical
for gaining insight into the data distribution by bank and over time, as well as their averages.
4.1 Descriptive statistics
The descriptive statistics for variables in the econometrics model of this study are presented in
Table 4.1. There were 130 observations overall for variable (i.e., data for 13 banks for the
period 2012 to 2021). The mean, median, maximum, minimum, and standard deviation are all
reported in the table below. This is made to provide an overview of the data utilized in the
model and to serve as a data screening tool for detecting erroneous figures.
Table 4.1 Descriptive statistics of study Variables
Variables L1 BS ROA CAR LG STIR INFLN GDPG
Mean 0.25156 10.1617 0.03668 0.16381 0.35858 0.12867 0.14141 0.08881
Median 0.21792 10.1893 0.02588 0.13256 0.35059 0.1275 0.12255 0.09065
Maximum 0.68678 11.1096 0.16268 0.8312 1.05044 0.143 0.2678 0.1058
Minimum 0.04272 9.10892 0.00159 0.00127 -0.1088 0.1188 0.0663 0.0606
Std. Dev. 0.13852 0.43069 0.03554 0.1203 0.22799 0.01009 0.06905 0.01434
Skewness 1.42011 -0.31239 2.7494 3.59002 0.59467 0.49047 0.55669 -0.6842
Kurtosis 4.58265 2.67169 9.35028 17.7369 3.3764 1.61287 1.91273 2.37829
Observations 130 130 130 130 130 130 130 130
Source: E-view results of sample private commercial banks
Liquidity is a metric that evaluates a bank's capacity to support asset growth and meet
8commitments when they become due without suffering unacceptable losses. The average value
of L1 was 25.15%. For private commercial banks in Ethiopia, the standard deviations of 13.85
percent suggest little variation in the liquid assets to total assets ratio from the mean. L1 had the
highest and lowest values of 68.678 percent and 4.272 percent, respectively.

31
Among the independent variables specific to the bank, the size of banks varied greatly from the
mean 10.16171 with a standard deviation of 0.43069. The highest and lowest values were
11.10956 and respectively. The average loan growth rate (LG) was 3.668 percent. In the case of
selected private commercial banks in Ethiopia, the value of standard deviation (i.e. 22.7991
percent) indicates moderate dispersion from the mean value of LG. The highest and lowest LG
values were 101 percent and -10.88 percent, respectively.
In terms of size selected private commercial banks in Ethiopia outweigh some banks more than
100%. The mean value of capital adequacy was 16.38% with standard deviation of 0. 0.12. The
maximum and minimum values of capital adequacy were 0.831196 and 0.001267 respectively. In
terms of loan growth private commercial banks in Ethiopia were highly different with the
standard deviation of 0.227991. The other bank specific factor affecting liquidity of commercial
banks was ROA that measures net income after tax/total asset of banks.
The remaining independent variables were the macroeconomic indicators that can affect banks
liquidity position over time. The mean value of real GDP growth rate was 0.08881 indicating the
average real growth rate of the country’s economy over the past 10 years. The maximum growth
of the economy was recorded in the year 2013 (i.e. 10.60%) and the minimum was in the year
2020 (i.e. 6.1%) this may be due to pandemic effect (COVID-19). Since the year 2011 the
country has been recording double digit growth rate with little dispersion towards the average
over the period under study with the standard deviation of 1.4335 percent.
The general inflation rate (i.e. 14.141%) of the country on average over the past ten years was
more than the average GDP. The maximum inflation was recorded in the past year (i.e. 26.78%)
and the minimum was in the year 2015 (i.e. 6.63%). The rate of inflation was highly dispersed
over the periods under study towards with standard deviation of 6.95%.
The other macroeconomic factors were related with interest rate that are short term interest rate
(the annual weighted average interest rate on Treasury bill). On average the rate on government
Treasury bill was 12.8% with maximum rate of 14.30% from the period beginning 2019 up 2021
and the minimum rate of 11.88% starting from 2012 till 2015.

32
4.2. Testing the Classical Linear RegressionModel (CLRM) Assumptions
Regression is a technique for determining and quantifying cause-and-effect relationships.
Regression analysis is a statistical technique used to determine the magnitude and direction of a
possible causal relationship between an observed pattern and the variables that are assumed to
influence the observed pattern.
In this section, the researcher performed relevant diagnostic testing in this section to identify
any violations in the underlining assumption of the classical linear regression model (CLRM).
Five assumptions were made to ensure that the estimation technique, ordinary least squares
(OLS), has a number of desirable properties and that hypothesis tests on the coefficient
estimates can be conducted validly. It was assumed, specifically, that the average error-term
value is zero, the variance of the errors is constant (homoscedastic), the error-terms are
normally distributed (normality), the covariance between the error-terms is zero (no
autocorrelation), and explanatory variables are not correlated (absence of multicollinearity).
4.2.1. Testing for the Normality of error term distribution
According to the first CLRM assumption, the average value of the errors term should be zero.
This assumption will not be violated, according to Brooks (2008), if a constant term is included
in the regression equation. As a result of the inclusion of the constant term in the regression
equation, this assumption will not be violated. All of this means the same thing: residuals (error)
must be random, normally distributed, and have a mean of zero, so that the difference between
our model and the observed data is close to zero. Not only must residuals be normally
distributed, but they must also be normally distributed at all values of the dependent variable,
whereas predictors do not need to be normally distributed (Steyn, 2019).
4.2.2. Testing for the variance of the error-term is constant/homoscedasticity
According to Brooks (2008), the assumption of homoscedasticity (meaning “same variance”) is
central to linear regression models. Homoscedasticity refers to a situation in which the error
term (the "noise" or random disturbance in the relationship between the independent variables
and the dependent variable) is the same for all independent variable values. Heteroscedasticity
(the violation of homoscedasticity) occurs when the size of the error term varies across
independent variable values. The impact of violating the assumption of homoscedasticity is
proportional to the degree of heteroscedasticity. Heteroscedasticity means that the variance of

33
error terms is not constant. When there is heteroscedasticity, the estimators of the ordinary least
square method are inefficient, and hypothesis testing is no longer reliable or valid because the
variances and standard errors are underestimated.
As shown in tables 4.3 and LR Test yielded there is no proof of heteroscedasticity. In this study,
the p-values for the model were significantly greater than 0.05. As a result, the null hypothesis
that the error variance is constant (homoscedasticity) should not be rejected.
Table 4.2 Hetroskedasticity test
Panel Period Heteroskedasticity LR Test
Null hypothesis: Residuals are homoscedastic
Equation: UNTITLED
Specification: L1 BS ROA CAR LG STIR INFLN GDPG C
Value df Probability
Likelihood ratio 7.818921 13 0.8552
LR test summary:
Value df
Restricted LogL 135.9108 122
Unrestricted LogL 139.8203 122
Source: E-view results of sample private commercial banks
4.2.3. Test for Normality
Normality tests are used to determine whether or not a data set is well-represented by a normal
distribution. Ordinary least square estimation can be easily derived with the normality
assumption and is much more valid and straightforward. The popular Bera-Jarque test statistic
was used in this study to ensure that the data was normal (Brooks 2008). The Bera-Jarque test
statistic indicates that normally distributed data is not skewed and has a kurtosis coefficient
close to 3. As shown in Figures 3 the coefficient kurtosis for the credit risk model is (2.76) with
a P-value of 0.242062. Because the p-value is greater than 0.05, we can conclude that there was
no evidence of abnormality in the data. As a result, the null hypothesis that the data is normally
distributed should not be rejected because the p-value was significantly greater than 0.05 and
the coefficient of kurtosis was closer to 3.

34
4.2.4. Testing for the covariance between the error-terms are zero-(no autocorrelation)
The degree of correlation between the values of the same variables across different observations
in the data is referred to as autocorrelation. The concept of autocorrelation is most commonly
discussed in the context of time series data, where observations occur at various points in time.
According to Brooks (2008), when the error term for one observation is related to the error term
of another, the model has an autocorrelation problem. The estimated parameters can still be
unbiased and consistent when there is an autocorrelation problem, but it is inefficient. Two
models were used in this study to identify the determinants of financial risks in Ethiopian private
commercial banks. The Durbin-Watson test statics were used to identify the Autocorrelation
problem.
The model was tested with 130 observations and seven regrersors along with an intercept term.
The relevant critical values for 130 observations and Seven regressors in DurbinWatson test
statistic table have shown an upper critical value (dU) of 1.687 and a lower critical value (dL) of
1.360 and 4 - dU = 2.313; 4 - dL =2.64. As shown on table 4.6, the Durbin-Watson test statistic
of this study is 0.552219which is clearly between the upper limit (du) which is 1.687 and 4-du
which is 2.313. The figure 4.4 below shows the result 1.531104 falls on region of no evidence of
autocorrelation. Thus, it is clear that there is no autocorrelation problem in the liquidity risk
model. The rejection, non-rejection, and inconclusive regions are shown on the number line in
figure 2.
Figure 2 Rejection and non-rejection regions for DW test
Source: Brooks (2008)

35
4.2.5. Test for Multicollinearity
According to Brooks (2008), multicollinearity occurs when some or all of the independent
variables are highly correlated with one another. It shows that the regression model has difficulty
in explaining which independent variables are affecting the dependent variable. If
multicollinearity problem is too serious in a model, unimportant independent variable should be
dropped. However, the maximum level of correlation causes multicollinearity, is not clearly
defined. The Variance Inflation Factor (VIF) measures the severity of multicollinearity in
regression analysis. It is a statistical concept that indicates the increase in the variance of a
regression coefficient as a result of collinearity. The condition of multicollinearity test using VIF
(variance Inflation factor). A variance inflation factor (VIF) provides a measure of
multicollinearity among the independent variables in a multiple regression model. When VIF is
higher than 10 or tolerance is lower than 0.1, there is significant multicollinearity that needs to
be corrected. A VIF result of regression analysis found between 1to6, there is no correlation
between the independent variable and the other variables. Summary of multicollinearity test
were presented above on table 4.6.
Table: 4.3 VIF Test for the model
Coefficient Uncentered Centered
Variable Variance VIF VIF
BS 0.000868 1514.587 2.695049
ROA 0.077526 3.397305 1.638543
CAR 0.006870 4.772169 1.663557
LG 0.001353 4.111117 1.177026
STIR 3.550763 997.3169 6.046194
INFLN 0.033729 14.06424 2.690995
GDPG 0.531746 72.54869 1.828357
C 0.073512 1239.571 NA

36
4.3 Fixed Effect versus Random Effect Model
There are two classes of panel estimator approaches that can be employed in financial research:
fixed effect and random effect models.
The values of the parameters estimated by the fixed effect model and the random effect model
are expected to differ slightly if the number of time series data is big and the number of cross-
sectional units is small, according to Gujarati (2004). As a result, this study has nine cross
section units and ten time series data, which is higher than the number of cross section units, and
the fixed effect model, is more suited than the random effect model.
4.4. Discussion of the RegressionResult
The results of the fixed effect regression model were presented in this section. The regression
results have their own implications, and thus beta indicates the level of influence of each variable
on the dependent variable, which can have a negative or positive coefficient. P-values indicate
what percentage or precession level of each variable is significant, and R2 values indicate the
model's explanatory power; in this study, adjusted R2 values that account for the loss of degrees
of freedom associated with adding extra variables were inferred to assess the models'
explanatory powers. As a result, the results of the fixed effect regression model in this study are
shown in table 4.4 below. The operational panel regression model was used to identify
statistically significant determinants of commercial bank liquidity as measured by the loan to
total deposit ratio was.

37
Table 4.4: Fixed effect regression results
Method: Panel Least Squares
Date:06/02/22 Time: 22:45
Sample: 2012 2021
Periods included: 10
Cross-sections included: 13
Total panel (balanced) observations: 130
Variable Coefficient Std. Error t-Statistic Prob.
BS -0.080869 0.029467 -2.744394 0.0070
ROA 1.548553 0.278435 5.561634 0.0000
CAR 0.316778 0.082887 3.821808 0.0002
LG -0.150640 0.036787 -4.094918 0.0001
STIR 5.659416 1.884347 3.003383 0.0032
INFLN -0.307876 0.183656 -1.676375 0.0962
GDPG 0.307862 0.729209 0.422186 0.6736
C 0.306654 0.271130 1.131021 0.2603
R-squared 0.620016 Mean dependent var 0.251563
Adjusted R-squared 0.598214 S.D. dependent var 0.138521
S.E. of regression 0.087804 Akaike info criterion -1.967859
Sum squared resid 0.940561 Schwarz criterion -1.791395
Log likelihood 135.9108 Hannan-Quinn criter. -1.896156
F-statistic 28.43808 Durbin-Watson stat 1.531104
Source: E-views output from financial statements of sampled banks and own computation
4.3.2. Discussion of Results of the regression analysis
H1: Bank Size has a significant positive influence on liquidity risk

38
According to L1, there is a negative and statistically significant effect of bank size on liquidity,
consistent with the assumption that small banks focus on traditional intermediation and
transformation activities and hold fewer liquid assets. This means that small banks have few cash
and cash equivalent reserves in other banks (central banks and other commercial banks) because
they deal with few other types of investment instruments besides loans. In the case of L1, the
coefficient was -0.080869, indicating that size had a lower impact on the liquidity position of
Ethiopian private commercial banks. In other words, a 1birr increase or decrease in total assets
results in a -0.080869 birr increase or decrease in liquid assets. In general, the findings in this
case show that larger banks. Generally, the result in this case reveal that higher banks have high
amount of liquid assets. And also we reject the hypothesis saying bank size has positive and
significant effect on banks liquidity. The negative coefficient is similar to a study conducted in
Bahrain by Shamas et al (2018), who found that non-performing loans, capital adequacy ratio,
bank size, and financial crises all have a negative impact on liquidity. This study's hypothesis
that profitability has a positive and significant influence on liquidity risk should be rejected.
H2: Return on asset has a significant positive influence on liquidity risk
Return on asset as a measure of profitability found to be significant variable with a high p value
0.0000 through positive coefficient. The result is consistent with the finding of Shamas et al
(2018) from who used financial reports of 7 Islamic banks which shows that liquidity risk
measured by cash to total asset is positively affected by return on asset. Profitability was
measured by return on asset (ROA) for Ethiopia commercial banks in the sampled period and
found to be significant at 5% level of significance with the p-value of 0.000. The coefficient of
1.548553 showed that a 1% rises in banks liquidity leads to 154.85 % increase in the ratio of
total loan to total deposit, holding other variables constant and it was in line with the hypotheses
of this study (H2). The hypothesis in this study stating profitability has positive and significant
influence on liquidity risk should be accepted.
H3: Capital Adequacy Ratio has a significant positive influence on liquidity risk
With a significant level of 5%, the regression findings of the liquidity risk model revealed that
capital adequacy ratio had a significant positive impact on liquidity risk of Ethiopian private
commercial banks, as expected. The findings are backed up by Ali et alresearch. .'s (2011). With
all other variables held constant, a one percent rise in capital adequacy ratio will result in a
31.67 percent increase in liquidity risk for Ethiopian private commercial banks during the

39
sample period 2012-2021. The rationale behind this result is that when bank managers have too
much equity capital, they are more likely to participate in hazardous operations, reducing their
liquidity position, because shareholders demand a high return on equity capital, which is more
expensive than debt (1997). As a result, the idea that capital adequacy and liquidity risk have a
positive and significant relationship should not be ruled out.
H4: Loan Growth has a significant positive influence on liquidity risk
Annual growth rate of gross loans and advances to customers was used as a proxy for loan
growth and which has a negative coefficient of -0.150640. The negative impact of loan growth on
liquidity of Ethiopian commercial banks was opposite to the hypotheses of this study (H14). The
coefficient signs of loan growth in LG show negative effect of loan growth on banks liquidity
position. The negative effect of loan growth on banks liquidity was in line with the hypothesis
(H14) which is based on the argument of taking loans as illiquid assets of banks. According to
this argument when the amount of loans provided by banks increase, the amount of illiquid
assets in the total assets portfolio of banks increase and lead to the reduction in the level of liquid
assets held by banks.
H5: Short term interest rate has a significant negative influence on liquidity risk
The short term interest rate employed in this study is the average bank nominal loan rate, which
typically fits the borrowers' short and medium term financing demands. The findings of this
research reveal that lending interest rate is a substantial determinant of liquidity risk, with a 5%
level of significance and positive coefficient of determination (0.0032). Holding all other
variables fixed, a 1% increase in the lending interest rate results in a 3.2% increase in the
liquidity risk of private commercial banks. This finding is align with the findings of Vodová
(2011 & 2010); Bunda and Desquilbet (2008). As a result, the assumptions that a positive and
significant link exists between lending interest rate and liquidity risk should be accepted.
H6: Inflation rate has a significant positive impact on liquidity risk
Inflation was the other macroeconomic variables of this study and found to be statistically
insignificant factor in explaining liquidity of Ethiopian commercial banks with the p-value of
0.0962. The % change in CPI has a negative coefficient of (i.e. -0.307876); which means that one
percent increase in %change in CPI leads to 30.78% decreases in liquidity of Ethiopian
commercial banks, holding other variables constant. The finding of this study was consistent

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