ASSESSMENT ON RELATIONSHIP BETWEEN ’S LIQUIDITY AND ITS PROFITABILITY: A LONGITUDINAL STUDY OF THE SELECTED IN .

Author Kapanga Isack Student ID MFI004819 Course Title Masters of Science in Finance and Investment Date November 2020

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Masters of Science in Finance and Investments (MSc.FI) of Insitute of Accountancy Arusha

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CERTIFICATION I, the undersigned certify that I have read and hereby recommend for acceptance by the Institute of Accountancy Arusha in Tanzania the dissertation entitled “Assessment on the relationship between bank‟s liquidity and its profitability in Tanzania” In fulfillment of the requirements for the award of the Master of Science in Finance and Investment.

Name: ______(Supervisor) Signature: ______Date: ______

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DECLARATION

I, Isack kapanga, declare that research entitled “Assessment on the relationship between bank‟s liquidity and its profitability in Tanzania” is my original work and has not been submitted and was not presented to any college, institution or university other than the Institute of Accountancy Arusha for academic credit.

SIGNATURE ______DATE______Isack Kapanga (Reg. No Msc.FI/0048/2019)

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COPYRIGHT This is a copyright material protected under the Berne Convention, the copyright act 2002 and other national and international enactments, on that behalf, on intellectual property. therefore, no part of this thesis may be reproduced, stored in any retrieval system or transmitted in any form by any means, electronic, mechanical, photocopying, recording or otherwise without prior written permission of the author or Institute of Accountancy in Arusha (IAA) in that behalf.

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ACKNOWLEDGMENT This research was done with the unlimited support received from different individuals who helped me in one way or another, I thank the Almighty God for giving me good health and strength to undertake the Msc. Finance and Investment (MSc. FI) program and for enabling me to prepare the research report. Time is not enough to mention everyone but let me just say thank you to everyone who played a role in one way or another to make me complete this task successfully. God bless you all..

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LIST OF ABBREVIATIONS AND ACRONYMS ADF Augmented Dickey-Fuller Test ALCO Asset-Liability Committee BFIA Banking and Financial Institutions Act BIS Bank for International Settlements BOT CCC Cash Conversion Cycle CCP Cash Conversion Period CRDB Cooperate and Rural Development Bank GDP Gross Domestic Product GMM Generalized Method of Moments IAA Institute of Accountancy Arusha IT Information Technology LADR Liquid Assets to Deposits Ratio LDR to Deposits Ratio NBC - NBC National Bank of Commerce NIM Net Interest Margin NMB National Microfinance Bank OLS Ordinary Least Squares ROA Return on Assets ROE Return on Equity ROI Return on Investment VIF Variance Inflation Factor

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ABSTRACT This study focused on determining the association between the bank‟s liquidity and performance of commercial banks in Tanzania and the association examined by different scholars in Tanzania and outside the country in different aspects but this study intended to put more light through ratio analysis between liquidity and profitability of the Tanzania commercial banks. These two components are the essential elements for commercial banks operate with the following specific objective; to determine the relationship between liquidity ratios ( LDR and LADR) with net interest margin which is a one profitability measures, to study the association between liquidity ratios (LDR and LADR) with return on asset, to study the relationship between liquidity ratios (LDRand LADR) with return on equity. The bank's liquidity ratios were treated as independent variables and the profitability ratios were treated as dependent variables, and data were analyzed through regression equation analysis.The study was conducted in Tanzania commercial banks whereby five commercial banks were taken as samples (NMB bank, CRDB bank, NBC bank, Tanzania, and Barclays bank Tanzania) were taken into consideration for the time from 2012 to 2019. It was quantitative nature, longitudinal study whereby a researcher used non-probability sampling which was a purposive sampling method to select the sample of five banks from thirty-six (36) licensed commercial banks by Bank of Tanzania at the period of study. The studies used secondary data from the annual report of selected banks and were analyzed by the econometrics test and statistical software.All models revealed that there is weak relationship evidence between the bank‟s liquidity and its profitability, thus banks can concentrate on rising profitability without affecting its liquidity. Consequently, the banks can focus on raising their profitability without upsetting their liquidity, although this is not guaranteed because the situation might change. The researcher recommends that banks should be careful with their profitability, to create consistency in conducting business by proceeding to do a deep analysis of risk and portfolio mixture. Also, it is recommended that the banks should optimally utilize the deposits towards lending to customers, and this is because there are some few cases whereby the banks had very low loans to deposits ratio and other times extremely high loans to deposits ratio, which is not a good sign for the bank that wants to utilize optimally the deposits to be profitable and at the same time liquid. Generally, the study was conducted successfully although the research thinks that the results would be more robust if it was possible to include more banks in the sample and taking a long time frame, something that was not possible in this study.

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LIST OF TABLES Table 4.1: Suitability of panel data ...... 21 Table 4.2: Hausman Test ...... 22 Table 4.3: Liquidity-Profitability Relationship Based on Net Interest Margin (NIM) as a ...... 24 Table 4.4: Liquidity-Profitability Relationship Based on Return on Assets (ROA) as a ...... 25 Table 4.5:Liquidity-Profitability Relationship Based on Return on Equity (ROE) as a ...... 27

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LIST OF FIGURES

Figure 2:1 Conceptual framework ...... 15

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TABLE OF CONTENTS CERTIFICATION ...... ii DECLARATION ...... iii COPYRIGHT ...... iv ACKNOWLEDGMENT ...... v LIST OF ABBREVIATIONS AND ACRONYMS ...... vi ABSTRACT ...... vii LIST OF TABLES ...... viii LIST OF FIGURES ...... ix CHAPTER ONE ...... 1 INTRODUCTION AND PROBLEM SETTING ...... 1 1.0 Introduction ...... 1 1.1 Background to the study ...... 1 1.2 Statement of the problem ...... 2 1.3 Scope of the Study ...... 3 1.4 The objective of the study...... 3 1.4.1 General objective; ...... 3 1.4.2 Specific objectives; ...... 3 1.5 Research hypothesis; ...... 3 1.7 Justification of the study ...... 4 CHAPTER TWO ...... 5 LITERATURE REVIEW ...... 5 2.0 Introduction ...... 5 2.1 Theoretical literature review ...... 5 2.1.1 Bank‟s profitability ...... 5 2.1.1.2 Anticipated income theory...... 6 2.1.1.3 Key Ratios for examining the Bank‟s profitability...... 6 2.1.2 Bank‟s liquidity ...... 7 2.1.2.1 Liability management theory ...... 9 2.1.2.2 Key ratios for examining liquidity ...... 10 2.2 Empirical literature review ...... 10 2.2.1 Evidence from other industries other than bank ...... 10 2.2.2 Evidence from the Banking Industry ...... 13 2.2.3 Conception framework ...... 15

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CHAPTER THREE ...... 16 RESEARCH METHODOLOGY ...... 16 3.0 Introduction ...... 16 3.1 Research Paradigm ...... 16 3. 2 Research Design ...... 16 3.3 Research Type ...... 16 3.4 The Study Area ...... 17 3.5 Study Population...... 17 3.6 Variables and their Measurements ...... 17 3.7 Sampling Design...... 18 3.8 Data Collection Process ...... 18 3.8.1 Type of Data Collected ...... 18 3.8.2 Data Collection Method ...... 18 3.8.3 Reliability and Validity of Data ...... 19 3.8.4 Data analysis ...... 19 3.9 Ethical Issues in Research ...... 19 CHAPTER FOUR ...... 20 PRESENTATION AND DISCUSSION OF FINDINGS ...... 20 4.0 Introduction ...... 20 4.1 Data Coding and Calculation of Profitability and Liquidity Ratios ...... 20 4.1.1 Preliminary test and the panel dataset ...... 21 4.2 The Relationship between Liquidity and Banks‟ Profitability in Tanzania ...... 22 4.2.1 Results Based on Net Interest Margin (NIM) as a Dependent Variable ...... 23 4.2.3 Results Based on Return on Equity ( ROE) as a Dependent Variable ...... 25 CHAPTER FIVE ...... 28 5.0 Introduction ...... 28 5.1 Summary, Conclusion, and Policy Implication ...... 28 5.2 Critical Evaluation of the Study ...... 29 5.3 General Experience during the Study ...... 29 5.3.1 What Went Well During the Study ...... 30 5.3.2 Limitations of the Study ...... 30 5.3.3 Alternative Ways of Undertaking the Study...... 30 5.4 Areas for Further Research ...... 31 APPENDIX ...... 36

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Appendix 1: Profitability and Liquidity Ratios for Five Banks from 2012 to 2019 ...... 36 Appendix 2: Research Budget ...... 38 Appemdix 3: Reseach Schedule ...... 39

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CHAPTER ONE INTRODUCTION AND PROBLEM SETTING 1.0 Introduction This chapter covers the introduction that highlights the background to the study, statement of the research problem, research hypotheses, research objectives, scope of the research, and the significance of the research, and justification of the study.

1.1 Background to the study Liquidity and profitability are the key parameters to measure bank financial performance. To attain long term survival and healthy growth of any business venture, both liquidity and profitability should go directly (Ahmad, 2016). They are five factors in the CAMEL model – the most used tool for measuring capital adequacy, asset quality, management capacity, earnings ability, and liquidity of the financial institutions by the principal regulators all around the world (Kabir, 2012). Calomiris, Heidery, and Hoerovaz (2012) thrash out the importance of a bank‟s liquidity by developing a theory of bank liquidity requirements which considers the substitutability of cash requirements and capital requirements for prudential regulation. They consider the three motives for bank cash holdings: maintaining cash in advance saves on liquidation costs, cash is observable and verifiable that is, it does not require a valuation of the loan portfolio as measuring capital requires, and greater cash holdings improve incentives to manage risk in the non-cash asset portfolio of risky assets held by the bank. On the other hand, the anticipated income theory explains that through proper arrangement and structuring of the loan commitment made by a bank to its customer can effectively manage its liquidity ( Odunayo and Oluwafeyisayo, 2015).

Consequently, in a meeting of expected and contingent liquidity demand, banks must manage the adequate level of perspective borrowing line, liquid asset, and cash. The further point of a is aiming at making a profit because profit is the essential requirement for doing any business. The meaning derived from different authors is that even the reason for banks to seek a knowledgeable management team is to make sure that they maintain good profitability for them to remain competitive in the industry. In addition to that, according to Bobáková, 2003, profits are the least source of funds that a bank earns and can use for capital growth.

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In Tanzania, the profitability and liquidity of banks are amongst the important factors considered during performance analysis. Regulations relating to banks‟ operation and performance are controlled by the Banking and Financial Institutions Act (BFIA, 2006), and the Bank of Tanzania (BOT) as the supervisor of all banks and financial institutions in the country. Look upon the liquidity, regulatory requirements such as minimum cash or liquid assets ratio have been imposed by the BOT to ensure that banks were always capable of meeting the average cash withdraws at short notice, and in the occasion of long or short positions, banks have the opportunity either to an interbank money market or the liquidity windows of the BOT (Bank of Tanzania, 2010). However, when it comes to returns there is no criterion set by the laws, which means that it is upon the banks to ensure that they utilize well their assets to attain good returns. This prescribes an empirical study to find out whether a bank‟s liquidity has an influence on its profitability for the bank operating in Tanzania and how the banks can balance both liquidity and profitability.

1.2 Statement of the problem Competent management of working capital is a vital aspect of the overall corporate strategy for creating shareholder value (Makori and Jagongo, 2013). The tactic that an entity uses in the supervision of working capital can contain a significant impact on both its liquidity and profitability (Shin and Soenen, 1998). Even though the main objective of many firms is to raise profit, and also maintaining liquidity is one of the essential objectives for the entity. This suggests that it is valuable for a firm to strike an equilibrium between liquidity and its profitability to create a shareholder's value. Consequently, it is important to recognize the relationship between liquidity and profitability of a firm in a specified industry to be able to formulate proper decisions for working capital management.

Several studies have revealed there is an association between liquidity and the firm‟s profitability in different industries and different countries. For instance, Abuzar (2004) found a significant inverse relationship between the firm‟s profitability and its liquidity level in joint- stock companies in Saudi Arabia, Niresh (2012) found that there is weak relationship evidence between liquidity and profitability for the sample of the listed manufacturing firms in Sri Lanka, Shahchera (2012) found an inverse influence of liquid asset holdings on bank profitability for a sample of Iranian banks, while Lartey, Antwi, and Boadi (2013) found a weak direct relationship between liquidity and profitability of the listed banks in . Maina (2017) revealed that there was no bidirectional relationship between liquidity and profitability of

2 commercial banks in Kenya. Dickson and Mutaju (2011) use the CAMEL model to examine the financial performance level of the banking system in Tanzania. Since there were different methods used to find out results on the subject matter of the study; this study intended to find out some more light on liquidity-profitability impact in the bank‟s operation in Tanzanian banks by using ratios.

1.3 Scope of the Study According to the CAMELS model, there are six factors involved in the examination of banks‟ performance that was capital adequacy, asset quality, management capacity, earnings ability, liquidity, and sensitivity to market risk; but this study just focused on two factors that were profitability and liquidity. Also, the investigation was conducted only on the banking industry through the same parameters used in other studies and in other sectors such as agriculture, tourism, or mining which also contributing significantly to the country‟s Gross Domestic Product (GDP). Consequently, the financial sector is a huge and growing sector in the country but the researcher chooses to focus just on the bank industry that were five commercial banks as a case study for the period of 2012 to 2019.

1.4 The objective of the study. 1.4.1 General objective; To examine the influence of liquidity on bank‟s profitability, in banks operating in Tanzania. 1.4.2 Specific objectives; i. To study the association between liquidity ratios (loans to deposits ratio and liquid assets to deposits ratio) with net interest margin as a measure of profitability ii. To study the relationship between liquidity ratios (loans to deposits ratio and liquid assets to deposits ratio) and return on assets as a measure of profitability. iii. To assess the relationship between liquidity ratios which are loans to deposits ratio and liquid assets to deposits ratio with return on equity as a measure of profitability.

1.5 Research hypothesis

i. H01: There is no association between liquidity ratios and net interest margin.

ii. H02: There is no relationship between liquidity ratios and return on assets.

iii. H03: There is no association between liquidity ratios and return on equity.

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1.6 Significance of the Study The following are the factors that give an explanation of this study in the modern world of accounting, finance, and investment: i. Satisfying the knowledge gap: As it has been shown in the research problem setting, previous researchers came out with different results on the relationship between liquidity and banks‟ profitability. This study is expected to insert new value to the body of knowledge by coming out with empirical evidence from a new environment. ii. Contribution towards policy formulation and implementation: this study intends to provide clear knowledge to policymaker on how to amend their policy and enforce the implementation to all banks operating in Tanzania, especially when it comes to regulatory requirements such as minimum cash requirement or liquid asset ratio of the bank, use of interbank money market or the liquid window of BOT. iii. Contribution towards management practice: The results of this study allow the management of the bank to suitably controlling their assets and liabilities. This is vital especially once it comes to the management of loans and deposit mobilization, which facilitate banks significantly to attain the bank‟s liquidity and profitability. iv. Finally, this study contributes to the author‟s partial realization of the Masters of Science in Finance and Investments (MSc.FI).

1.7 Justification of the study The researcher had come across the study that was conducted in Tanzania concerning liquidity to a company‟s profitability by a few researchers, especially in the banking industry. Thus, this topic is very essential for finance managers, especially when concentrating on working capital management; hence empirical evidence from Tanzania is very important especially in the banking sector. Even though bypassing across mixed and variety results of the researches conducted before from different countries and industries on the subject matter, the researcher find out it was important to imitate the study to come out with more evidence on the subject matter from the Tanzania environment.

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CHAPTER TWO LITERATURE REVIEW 2.0 Introduction This chapter covers the theoretical literature review and empirical literature review and detects the knowledge gap which is going to be covered by this research.

2.1 Theoretical literature review 2.1.1 Bank’s profitability Profitability is one of the key determinants of the company‟s performance, the earnings attained by the company can either used to uplift capital for the next financial year as retained earnings, support the future growth of assets, disburse all loan losses (bad debt) or provide the return to investors as a dividend. The earning obtained from different sources but interest earnings are the key determinant of the bank‟s profitability, however other sources contribute to the profitability of the bank such as foreign exchange, commission and transaction fees, income from investing activities, and trust operation are also a considerable source of income. (Credit and Finance Risk Analysis, 2012).

It has experienced that new businesses especially banks are not profitable for the first year up to three years, this is because at a starting time they develop their core business operation which is not known to the public, hire employees, branch extension and forced to pay a higher interest rate to facilitate deposits that they will you use to issue credits. However when you analyze the profitability of the bank either a new or the one that operating for years, the following issues are generally considered (Credit and Finance Risk analysis): i. The concentration of the business; whether it is on retail customers, corporate, trade finance, mortgage, investment, leasing, advisory, nominee and custodial services, executor, and trustee. ii. Whether the bank's core returns generated from the home market only. iii. The ratio between interest and non-interest income sources. iv. Whether the operating return has been falling compared to previous periods due to inadequate revenue, higher operating expense, or due to non-accrual loans. v. Whether there is an increase in revenue that is associated with extraordinary or non- recurring items. vi. Whether the increase in returns is derived from the application of new accounting standards.

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It is supposed to be well-known that the need to charge provisions for loan and lease losses against returns can as well diminish the bank‟s profitability. Consequently, the management of the bank is accountable to look at the type of loans in the portfolio, the performance of the portfolio, and more significantly the national, regional, and local economic circumstances. Analyzing the external business environment is important because situations such as recession, increase in unemployment, increase in bankruptcies, local corporate layoffs and plant shutdown, drought, and things of the sort suggest rising numbers of risky loans, something that will have an impact on bank‟s earnings (Credit and Finance Risk Analysis, 2012).

2.1.1.2 Anticipated income theory. The theory proposed that the greatest guarantee ensures adequate liquidity by emphasis on the worthness credit provision and the earning potential of the borrower (Odunayo&Oluwafeyisayo, 2015). Also, the theory suggests that the expected earning can be used to manage a bank‟s liquidity, and it's done through bank loan provided to the potential borrower and the premium in repayment of loan allow the bank to ensure relatively high liquidity when the cash inflow is regularly and can be anticipated ( Koratengi, 2015).

2.1.1.3 Key Ratios for examining the Bank’s profitability. There are several ratios for examining the profitability of the banks but according to (Credit and Finance Risky analysis 2012) there are five ratios that measure the bank‟s profitability elaborated as follows;

Net Interest Margin (NIM); This ratio is computed by dividing Net Interest Income by Average Interest Earning Assets, and it is presented in percentage. Net interest income attained by subtracting interest expense from interest return. This ratio displays how well management employed the earning asset base; therefore the lower the net interest margin is generally a reflection of a bank with a large volume of nonearning or low-yielding assets. On the other hand, high or increasing margins might be the result of a favorable interest rate environment, or the decision of the bank to hold higher-risk, higher-yielding, and less liquid loans or investment securities (Credit and Finance Risk Analysis, 2012).

Return on Assets (ROA); This is calculated by taking Net operating income after interest and taxes including realized gain or loss on investment securities if it is available to divide by Total

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Average Assets (assets at the previous period plus assets at the current period divided by 2). The actual net income should be examined for the addition of extraordinary earnings (which may be excluded). This ratio measures how well assets are utilized to generate profit, or indicates the profitability of the assets base or asset mix (Credit and Finance Risk Analysis, 2012).

Return on Equity (ROE); This is calculated by taking Net operating income after interest and taxes including realized gain or loss on investment securities divide by Total or average equity (common stock) that is the average of the equity of two consecutive periods. This ratio is affected by the strength of capitalization in the financial institution and it measures the ability of the institution to increase its net worth internally and pay dividends (Credit and Finance Risk Analysis, 2012).

Operating Profit Margin; This ratio is calculated by dividing operating profits by net operating revenues. Operating profits are the profits before the loan loss provision and exclude gains or losses from asset sales and amortization expense of intangible assets. Net operating revenue is obtained by taking interest income less interest expense plus non-interest income. This ratio measures the percent of net operating revenues consumed by operating expenses, hence providing the remaining operating profit. The higher the operating profit margin the more efficient the bank (Credit and Finance Risk Analysis, 2012).

2.1.2 Bank’s liquidity Bank‟s liquidity refers to reserves of short term securities that mature either less than or in one year period, cash, bank's ability to convert an asset into cash, and idle bank position of credit. This indicates that a quick conversion of assets the more liquid the asset. A bank is said to be not liquid if its assets are not easily convertible into cash. It should be noted that the time case is a considerable factor while waiting to realize an asset can pretense an additional risk if the price of the asset falls while waiting to liquidate. As a result, if loans or assets are not liquid then liquidity is also limited, mainly if the loans exceed constant deposits and available position of credit. Thus, liquidity must be sufficient to meet all maturing unsecured debt obligations due within a one-year time prospect (Credit and Finance Risk Analysis, 2012). Almost certainly the most critical issue to examine for a bank is liquidity that is its ability to settle obligations. If profitability is underprivileged and liquidity is high, the bank's lending might be too conventional that is a high proportion of returns from deposits is invested in low-

7 yielding liquid assets. However, if profitability is down and liquidity will be down as well, the bank might have an aggressive lending policy accompanied by a large amount of borrowing, which may be unsafe to the bank (Credit and Finance Risk Analysis, 2012). To play down liquidity risk banks often carry out the so-called Liquidity Gap Analysis; this is the attempt to forecast future funding needs of a bank by comparing the number of assets and liabilities maturing above the time. This is usually done by measuring the interest rate sensitivity of assets and liabilities. This results in either a suspicious or aggressive approach to liquidity management depending on the widen between the institution's yields and costs of operations (Credit and Finance Risk Analysis, 2012). Usually, the Asset-Liability Committee (ALCO) of the bank manages different categories of bank assets as follows (Credit and Finance Risk Analysis, 2012): i. Primary reserves: In this, the bank should have to make sure that it has enough cash on hand to compromise customer deposits and withdrawals or payment and collecting payments, and at the same time sustain contemporary reserve requirements. It should be well-known that cash in the vault is non-interest earnings, thus it should not be extreme. ii. Secondary reserves: These comprise a marketable securities portfolio which is short- term ( mature in one year period) and liquid for cash needs and pledging collateral, treasury bills, and short-term securities of states and municipalities. iii. Loans: this is the main product for commercial banks and In most of the banks, total loans tend to comprise nearly or more than 60% of their total assets. This suggests that loans should be issued at an interest rate that is above the cost of funds, and to borrowers with good credit profile such as collaterals, to recognize good returns. However, the interest rates on corporate loans diminish due to stiff competition in the industry. Also, although property loans have the highest rate, they are less liquid with high risk compared to other loan categories, this is something that should be in knowledge of the bankers. With the bank‟s assets, the ALCO can handle liquidity by matching the maturity of assets with liquidity demand. This can be performed by forecasting deposit and loan changes, and establishing commercial loan theory that is keeping within limits of the short term loan, self- liquidating commercial loans, or applying a money market approach that is keeping money market components such as commercial papers bankers‟ acceptances and treasury bills (Credit and Finance Risk Analysis, 2012).

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The ALCO is also accountable for managing sources of funds for the bank not always using funds but they should manage all assets used for the generation of such funds and enables the bank to reach liquidity requirements and raise earning prospective. The following are various sources of funds that the ALCO manages (Credit and Finance Risk Analysis, 2012): customer deposits, non-deposit borrowed funds, federal funds for short-term liquidity, and certificates of deposits if the bank needs funds for a longer period. However, all sources of funds but customer deposits are the cheap sources of funding for the institution. In managing the liquidity from the liability approach, the bank has to look for sources with good interest charges. For instance, the interest rates might be higher when the institution seeks to obtain funds which might force them to take an expensive source, hence requiring the financial condition of the bank to be stable while borrowing.

The banks‟ managers also have goals linked to capital that is to have enough capital that provides the shield to absorb losses, maintain enough equity capital to meet regulatory requirements, and have a financial condition that allows it to borrow funds. For instance, the Bank for International Settlements (BIS) gives a capital procedure that the banks should maintain at least 4% Tier 1 or 8% including Tier 2 capital as a percentage of risk-weighted assets. On the other hand, the BFIA (2006) for Tanzania states that at all times banks ought to maintain core capital of at least 10% of total risk-weighted assets and off-balance sheet exposure; maintain total capital of at least 12% of total risk-weighted assets and off-balance sheet exposure. However, it has been argued that it is poor liquidity, as opposed to poor asset quality or insufficient capital that leads to banks‟ failures (Credit and Finance Risk Analysis, 2012).

2.1.2.1 Liability management theory the theory related to Dodds (1982) and discussed the liability of the bank in the statement of financial performance which is deposits, and the main concern of the theory is that liability could be used to derive the extra liquidity to the bank. Ibe (2013) put more emphasis on the maintenance of the liquid assets and liquid investment by the bank, but the banks focused on the obligation side of its balance sheet. Banks should consider both sides of their balance sheet to attain the proper balance between their profitability and liquidity. (Koratengi, 2015)

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2.1.2.2 Key ratios for examining liquidity Although several approaches can be used to analyze the bank‟s liquidity, the following are the key ratios that can be used to look at the bank‟s liquidity. Loans as a Percentage of Deposits; This ratio is given by dividing loans (gross) by total deposits. It indicates the percentage of a bank's loans funded through deposits. It is recommended that the highest should be 80% to 90% because the higher the ratio the more the institution is relying on borrowed funds for lending purposes. However, it cannot also be too low because loans are considered the highest and better use of bank funds. Thus, between 70% to 80% indicates that the bank still can issue new loans. A high loan-to-deposit ratio indicates that a bank has no enough funds invested in readily marketable assets, which provide a greater margin of liquidity to the bank (Credit and Finance Risk Analysis, 2012).

Liquid Assets to Total Deposits; This ratio is obtained by dividing liquid assets by total deposits. It measures deposits corresponding investments and whether they could be changed quickly to settle redemptions for example guarantees, investment in financial securities like treasury bills, foreign currency exchange market. (Credit and Finance Risk Analysis, 2012).

2.2 Empirical literature review Several empirical studies have been conducted in different parts of the globe concerning the relationship between liquidity and profitability. To give adequate light on the subject matter, this section discussed the studies that were conducted in industries other than the banking industries and then comes to discuss the studies that were conducted in the banking industry.

2.2.1 Evidence from other industries other than bank Masaka (2013) in his study on the assessment of the relationship between liquidity management and companies profitability, a case study of the selected manufacturing companies listed on the Stock Exchange (DSE) in Tanzania found that liquidity management has a significant impact on corporate profitability. Saleem and Rehman (2011) in their study on the association between liquidity and profitability, a case study of the Oil and Gas Companies of , found that there is a significant impact of only liquid ratio on ROA while insignificant to explain ROE and ROI. The results further found that ROE is an insignificant effect by the current ratio, quick ratio, and liquid ratio while ROI is greatly affected by current ratios, quick ratios, and liquid ratios. The authors argued that both liquidity and

10 profitability have a significant effect on the financial positions of enterprises but liquidity ratios are in the first place and pointed out that every stakeholder has an interest in the liquidity position of a company. They gave examples that suppliers of goods normally check the liquidity of the company before selling goods on credit and employees are also concerned about the company‟s liquidity to know whether the company can meet employee-related obligations such as salary, pension, provident fund, etc. Lastly, they concluded by saying that liquidity and profitability are closely related because one increases the other decreases.

Bolek and Wiliński (2012) analyzed the relationship between liquidity and profitability on a group of construction sector companies listed on the Warsaw Stock Exchange. The researchers used quarterly industrial average financial data for 11 years from 2000 to 2010 – thus the examination was comprised of 44 observations. The results indicated that the only statistically significant variable of liquidity that affect profitability is the quick ratio and the probability of its influence on return on assets was 98.24%.

Niresh (2012) studied the relationship between liquidity and profitability by considering 31 listed manufacturing firms in Sri Lanka for a period of 5 years from 2007 to 2011. Correlation analysis and descriptive statistics findings suggested that there is no significant relationship between liquidity and profitability among the listed manufacturing firms. Contrary to that, Priya and Nimalathasan (2013) conducted the same kind of study by this time sampling 10 out of the 31 listed manufacturing companies in Sri Lanka for 5 years from 2008 to 2012. Using correlation and regression analysis they found that Inventory Sales Period (ISP) and Current Ratio (CR) are significantly correlated with Return on Asset (ROA), while Inventory Sales Period (ISP) and Operating Cash Flow Ratio (OCFR) are significantly correlated with Return on Equity (ROE) at 5 percent level of significance. On the other hand Creditors Payment Period (CPP) and OCFR were found to be significantly correlated with ROA, while CPP was also significantly correlated with ROE at a 1 percent level of significance. All the relationships were negative suggesting that there is a significant relationship that exists between liquidity and profitability among the listed manufacturing companies in Sri Lanka!

Zygmunt (2013) examined the liquidity impact on profitability in polish listed IT companies. The data were obtained from the financial statements of the selected companies for the period of 2003-2011. The dependent variables were returned on assets (Y1), return on equity (Y2), and return on sales (Y3) and the preliminary tests showed that there was no evidence of

11 strong random dependence between dependent variables. The independent variables were current ratio (X1), quick ratio (X2), receivable conversion period (X3), inventory conversion period (X4), accounts payables conversion period (X5) and cash conversion period (X6). Preliminary tests showed high multicollinearity between X1 and X2, hence, the independent variables that were considered included: X1, X3, X4, X5 and X6. Using regression analysis, variables X3 and X4 with periods lag showed a statistically significant negative relationship with variable Y1, only variable X5 showed a statistically significant positive relationship with variable Y2, and there was a statistically significant negative relationship between variables X3 and X4 in the relation to dependent variable Y3.

Ben-Caleb, Olubukunola, and Uwuigbe (2013) investigated the relationship between liquidity and profitability based on a sample of 30 manufacturing companies listed on the Nigeria Stock Exchange for the period 2006-2010. The result suggested that the current ratio and liquid ratio were positively related to profitability while the cash conversion period was negatively related to the profitability of the manufacturing companies. However, the relationship was statistically insignificant in all the cases, demonstrating a low degree of influence of liquidity on the profitability of manufacturing companies. The authors recommended that the overall state of liquidity should be improved in the manufacturing companies by establishing a more realistic credit policy which would bring about a shorter cash conversion period, hence have a favorable impact on the profitability of the companies.

Bolek (2013) examined the relationship between profitability, liquidity, and risk in emerging companies listed on the New Connect market in Warsaw. The five main indicators of profitability i.e. Gross Margin (GM), Net Income Margin (NIM), Operating Profit Margin (OPM), ROA, and ROE were considered as dependent variables. Liquidity measures that were used include Current Ratio, Cash Conversion Cycle,

Net Cash Flow to Total Assets ratio and Free Cash Flow to Total Assets ratio, while the companies‟ risk levels were measured by using Debt Ratio, Liability Structure, and Assets Structure. The results showed that GM was positively and significantly related to only current ratio and free cash flow ratio; NIM was positively and significantly affected by free cash flow ratio while there was a negative significant relationship between NIM and cash conversion cycle; there was a significant and positive influence of the free cash flow on OPM and a negative on cash conversion cycle; only ratios based on cash flow significantly positively

12 influence the ROA, and only free cash flow to total assets significantly influence the profitability represented by ROE. Conclusively, different measures of profitability were found to be associated with different indicators of liquidity in different ways.

Addin, Nayebzadeh, and Pour (2013) investigated the relationship between modern liquidity indices and stock return in a sample of 82 active companies listed on the Tehran Stock Exchange, for a period from the year 2001 to 2010. Modern liquidity indices used included a comprehensive liquidity index, net liquidity balance, and cash conversion cycle. A systematic sampling method was employed and multiple regression was used to test the research hypotheses. Results showed a positive significant relationship between comprehensive liquidity index and stock returns while there was no significant relationship between the index of the cash conversion cycle as well as net liquidity balance and stock returns.

2.2.2 Evidence from the Banking Industry Mwizarubi (2013) examines the relationship between banks‟ profitability and liquidity, a case study of Tanzania commercial banks found that there is no statistically significant relationship between banks‟ profitability and liquidity. Bordeleau and Graham (2010) analyzed the impact of liquid asset holdings on bank profitability for a sample of large U.S. and Canadian banks. They found that profitability is generally improved for banks that hold some liquid assets; however, there is a point at which holding further liquid assets reduced banks‟ profitability, assume other factor remains constant. Furthermore, the findings suggested that this relationship varies depending on a bank‟s business model and the state of the economy.

Shahchera (2012) analyzed the impact of liquid asset holdings on bank profitability for a sample of Iranian listed banks using panel data for 2002-2009. Using the liquidity asset and liquidity asset square for estimating liquid asset and profitability relationship, the relationship between liquid assets and bank profitability was found to be negative. The coefficients for the liquid assets ratio, its square, business cycle, and regulation were all statistically significant. There was found evidence of a non‐linear relationship between profitability and liquid asset holdings. Moreover, the business cycle was found to significantly affect bank profits, and the coefficient of regulation was negative and significant. Conclusively, if regulators reduce the constraints imposed on banks, banks obtain profit.

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Lartey, Antwi, and Boadi (2013) studied the relationship between the liquidity and the profitability of seven out of the nine banks listed on the Ghana Stock Exchange. The study was descriptive, used panel data, and document review was the main procedure adopted to collect secondary data for the study. From the financial reports of the banks, the liquidity ratio (liquid assets to total assets ratio) and profitability ratios (Spread Margin, Return on Assets, and Return on Equity) were computed, and time series analysis was used to determine the trend of liquidity and profitability. After regressing the liquidity ratio on the profitability ratios, it was found that both the liquidity and the profitability of the listed banks were declining for the period 2005-2010, and there was a very weak positive relationship between the liquidity and the profitability of the listed banks. Among other reasons, the global financial crisis in the late 2000s was named as a result of liquidity and profitability decline in these banks.

Nimer, Warrad, and Omari (2013) sought to find out whether liquidity (measured by quick ratio) has a significant impact on Jordanian bank's profitability (measured by ROA). They used financial reports of 15 Jordanian banks listed at Amman Stock Exchange (ASE) for the period from 2005-2011. Their study revealed that there is a significant negative impact of the independent variable quick ratio on the dependent variable ROA. Thus they concluded that liquidity has a significant negative influence on the profitability of Jordanian banks. This is because of banks having excessive liquidity instead of investing the money to generate profit. Ibe (2013) investigated the impact of liquidity management on the profitability of banks in Nigeria by randomly taking a sample of three banks: United Bank of Africa, Afribank, and Diamond Bank. The variables for liquidity management included cash and short-term fund, bank balances, and treasury bills and certificates, while profit after tax was the variable for profitability. Elliot Rothenberg Stock (ERS) stationary test was used and thereafter regression analysis was used to test the hypothesis. The result of this study showed that there is a significant relationship between cash and short-term fund and bank profitability, treasury bills and certificates have a significant impact on bank profitability, and bank balance has no significant influence on bank profitability. Thus the author recommended that banks should engage competent and qualified personnel to ensure that the right decision is adopted especially with the optimal level of liquidity and still maximize profit.

Munteanu (2013) presented a model for the optimization of the bank liquidity level, by identifying the marginal impact of the bank liquidity ratio on bank profitability. The relationship between the two variables is validated through the Generalized Method of Moments (GMM)

14 procedure, on a panel of Eastern and Central European commercial banks over the period 2003-2010. The results showed a slight positive and negative impact of liquidity on both ROE and ROA, explaining a non-linear relationship between the variables. The results are consistent with the idea that funding markets will reward banks for holding liquid assets by reducing the funding cost and liquidity risk. When this benefit is outweighed by the opportunity cost of holding low-return assets, maintaining further liquid assets diminish bank profits. Thus, the optimal level of bank liquidity resulted after getting the maximum condition for ROE and ROA, as measures of profitability.

2.2.3 Conception framework The researcher passed through all available literature and found that the study was not being conducted in Tanzania by using ratio analysis on examining the relationship between a bank‟s liquidity and its profitability. Therefore the researcher finds this to be a chance to conduct this research in Tanzania to add something to the body of knowledge.

Figure 2:1 Conceptual framework

Bank’s Liquidity Bank’s profitability

 Loan to deposit ratio . Net Interest Margin (NIM) (LDR) . Return on Asset (ROA)  Liquid assets to deposits . Return on Equity (ROE) ratio (LADR)

Source: Researcher‟s Conceptualization year 2020 In line with the conceptual framework, the research also applied the following three regression model equations.

NIM= β0 + β1LDR + β2LADR + £1 ………………………………………….. (i)

ROA= α0 + α1LDR + α2LADR + £2 ………………………………………… (ii)

ROE= ɣ0 + ɣ1LDR + ɣ2LADR + £3 ……………………………………… (iii)

Where β0, α0 and ɣ0 are the constant terms, and Intercepts β1, α1, and ɣ1 are the coefficients of the LDR and β2, α2, and ɣ2 are the coefficients of the LADR and £1,£2 and £3 are the disturbance error term in the respective models.

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CHAPTER THREE RESEARCH METHODOLOGY 3.0 Introduction This chapter highlights the methods, processes, and steps used by the researcher in investigating the answers for the research hypotheses and the state reasons for using those methods. therefore this chapter covers the research methodology and tools, research design, type of study, the area of the study, study population, units of analysis, variables and their measurements, sample size and sampling techniques, types and sources of data, data collection methods used, and data analysis methods.

3.1 Research Paradigm This study applied a Positivism Research Paradigm. This is because the researcher thought that an objective, the accurate situation exists which is governed by unchangeable natural cause-effect laws, and human beings are rational. Epistemologically the researcher believed that knowledge can be demonstrated systematically, knowledge consists of all valid hypotheses that can be regarded as facts or laws, knowledge holds content for large groups of people or occurs in many situations, knowledge is accurate and certain, and research results are true if the variables can be observed and measured and if the results can be replicated and generalized. Methodologically the researcher was objective and independent from the subject, did an empirical study for the aim of testing the hypothesis by using statistical analysis. All these facts justify that this study applied a Positivism Research Paradigm. (Guba and Lincoln, 1994)

3. 2 Research Design The objective of this study was to examine the relationship between the bank‟s liquidity and its profitability in Tanzania commercial banks to achieve the intended objective the researcher adopts a longitudinal research design to select a sample of banks. This is because the researcher did not need to conclude one bank, Apparently the study could not cover all the banks for a reason of time as well as financial problems. (Kothari, 2004)

3.3 Research Type This study applied quantitative techniques. This is for the reason that almost all the data required for the study are arithmetical and the nature of the study involved hypothesis testing which appropriately is done by using statistical analysis and measurements, including

16 studying trends and associations. therefore the study should be quantitative to meet the research objectives. (Kothari, 2004)

3.4 The Study Area The study was conducted in Tanzania, which covers the five banks which are NMB bank, CRDB bank, NBC bank, Barclay's bank Tanzania and Exim bank Tanzania, to examine the relationship between its liquidity and profitability for the period of 2012 to 2019.

3.5 Study Population In this study, the population includes thirty-six(36) licensed commercial banks in Tanzania. According to BOT website1, the following were the licensed banks in Tanzania; Access Bank (Tanzania) Limited, African Banking Corporation (Tanzania) Limited, Limited, Limited, Limited, (Tanzania) Limited, Bank of Africa (Tanzania) Limited, (Tanzania) Limited, (Tanzania) Limited, Barclays Bank (Tanzania) Limited, Canara Bank (Tanzania), China Dasheng Bank Limited (Tanzania), (Tanzania) Limited, Commercial Bank of Africa (Tanzania) Limited, CRDB Bank Plc, DCB Commercial Bank Plc, Diamond Trust Bank (Tanzania) Limited, (Tanzania) Limited, Exim Bank (Tanzania) Limited, Equity Bank (Tanzania) Limited, First National Bank (Tanzania) Limited, Habib African Bank Limited, I & M Bank (Tanzania) Limited, International Commercial Bank (Tanzania) Limited, KCB Bank (Tanzania), Bank (Tanzania) Limited, Plc, Mwalimu commercial bank Plc, National Microfinance Bank Plc, NBC Bank Limited, NIC Bank (Tanzania) Limited, Peoples‟ Bank of Limited, (Tanzania) Limited, Bank (Tanzania) Limited, TIB Corporate Bank Limited,TPB Bank Plc, (Tanzania) Limited and UBL Bank (Tanzania) Limited.

3.6 Variables and their Measurements This study had two main variables which were the bank‟s liquidity and bank‟s profitability. Bank‟s profitability was treated as regressand while the bank‟s liquidity was treated as a regressor. The variables measured as follows: i. Bank‟s profitability measured by using three financial ratios; Net Interest Margin (NIM), Return on Assets (ROA), and Return on Equity (ROE). ii. Bank‟s liquidity was measured by using two financial ratios; the Loans to deposits ratio (LDR) and the Liquid assets to deposits ratio (LADR).

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3.7 Sampling Design In this study, the researcher used a non-probability sampling technique in the selection of the bank whose profitability trend would be studied against its liquidity. Specifically, the researcher uses a Purposive sampling method. This is for the reason that the licensed banks in Tanzania do differ in various aspects. consequently, the researcher had to choose the banks that fit the study by looking at the banks that were operating in the industry, by taking into account factors such as the value of total assets of the bank, customer deposits, amount of loans provided to clients, annual total revenue, total capital, branch network and the number of employees. The main assumption was that the banks that were leading, based on the factors taken into consideration, need to be more careful about their liquidity and profitability as compared to other banks.

Basing on the sampling technique used, the researcher select five banks: National Microfinance Bank (NMB), CRDB Bank PLC, National Bank of Commerce (NBC), Barclays Bank Tanzania Limited, and Exim Bank (Tanzania). The basis for selecting these banks was because, according to Tanzania Bank Survey (2019), the selected banks were among the top ten banks in the country, in all the named criteria. Another factor that was taken in selecting a sample of banks was the easy accessibility of data.

3.8 Data Collection Process 3.8.1 Type of Data Collected In this study, secondary data were mainly be collected, this is for the reason that available data (secondary data) were enough to answer well all the research hypothesis. The benefit of using secondary data for this study is that its reliability of data, it was easy to get the data hence saves time and it is relevant to the study (Kothari, 2004).

3.8.2 Data Collection Method As pointed above this study used secondary data, hence the researcher review documented data from annual reports or financial statements of the selected banks for the periods from the year 2012 to 2019, making a total of 40 observations. This method is suggested because it provides information to the researcher which is already documented and hence more reliable. (Cooper and Schindler, 2006).

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3.8.3 Reliability and Validity of Data The data collected were considered reliable because it was collected from well-known banks and genuine source, data were prepared and reported under the rules and regulations passed and enforced by the government through the Bank of Tanzania. Hence the data collected is considered to be prepared and audited using the acceptable standards, and hence reliability. To ensure the legitimacy of the data for the study, the researcher decided to use five banks in eight years to get a total of forty observations, which was good for econometrics tests (Gujarati, 2003).

3.8.4 Data analysis The collected data analyzed by using statistical software STATA and the regression analysis was used to summarize the data output to find out the relationship between the bank‟s liquidity and profitability.

3.9 Ethical Issues in Research The first ethical issue observed in this research was consent. The selected banks were given freedom on whether or not to participate in a study, had a complete understanding of the purpose and methods applied in the study, and had the right to withdraw from the study at any time (Jones and Kottler, 2006). The researcher also tried to avoid plagiarizing the work of others and changing data. All the materials used in this study were properly acknowledged, and the researcher used the collected secondary data without modification. (Sinha, Singh, and Kumar, 2009).

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CHAPTER FOUR PRESENTATION AND DISCUSSION OF FINDINGS 4.0 Introduction This chapter discusses the findings that the researcher came out with after data collection and analysis. The findings are proportional to the research objectives and research hypothesis presented in Chapter One and covers the research gap as portrayed in Chapter Three. The discussion in this chapter is divided into three main sections: Data coding and calculation of profitability and liquidity, preliminary tests on the panel dataset, and the relationship between liquidity and profitability of banks in Tanzania.

4.1 Data Coding and Calculation of Profitability and Liquidity Ratios As it is shown in Chapter Three, five banks were involved in this study. It was essential to assign numbers to these banks to ensure it easier to run econometric tests using STATA software because it sometimes disturbs when bank names are used. Therefore, CRDB bank assigned number1, National Microfinance Bank (NMB) was assigned number 2, National Bank of Commerce number 3, and Exim Bank Tanzania number 4, and Barclays Bank Tanzania number 5.

After assigning numbers to the banks, the following exercise was to calculate the financial ratios used in the study. Concerning Chapter Two, three banks‟ profitability ratios and two liquidity ratios were calculated for the named five banks from 2012 to 2019 and the figures are as shown in Appendix 1. The formulae used in calculating the ratios are summarized below (Credit and Finance Risk Analysis, 2012):  Net Interest Margin (NIM): This profitability ratio is computed by dividing net interest income by average interest-earning assets, and it is expressed as a percentage. Net interest income is obtained by subtracting interest expense from interest income.  Return on Assets (ROA): This performance ratio is calculated by taking Net operating income after taxes divided by Total or Average Assets.  Return on Equity (ROE): This profitability ratio is obtained by taking Net operating income after taxes divided by Total or average equity.  Loans as a Percentage of Deposits: This liquidity ratio is calculated by dividing loans (gross) by total deposits. It indicates the percentage of a bank's loans funded through deposits.

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 Liquid Assets to Total Deposits: This liquidity ratio is obtained by dividing liquid assets by total deposits. It measures deposits matching investments and whether they could be converted quickly to cover obligations.

4.1.1 Preliminary test and the panel dataset After inputting data in STATA software, the first step was to command STATA to handle panel data by using the command “xtset”. This command was also essential in testing the suitability of the dataset for longitudinal analysis (analysis of panel data). The results were as shown in the STATA output below Table 4.1: Suitability of panel data

. xtset bank year, yearly panel variable: bank (strongly balanced) time variable: year, 2012 to 2019 delta: 1 year source: STATA output of research data (2020)

In this circumstance “bank” stands for entities or panels (i) and “year” stand for time variable (t). The note “(strongly balanced)” denotes the fact that all banks have data for all years. This recommends that the panel data is appropriate for econometric analysis. Usually, analysis of panel data is done either using fixed-effects or random-effects approach. Hence, it was essential to choose whether a fixed or random-effects approach would be used. This was conducted by using the Hausman test, in which the null hypothesis is that the chosen model is random effects while the alternative hypothesis is that a fixed-effects approach (Greene, 2008). The test was done by running a fixed-effects model and store the estimates, then running a random model and store the estimates, then followed by performing the test. The results are as shown in table 4.2 below.

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Table 4.2: Hausman Test . hausman RE FE

Coefficients (b) (B) (b-B) sqrt(diag(V_b-V_B)) RE FE Difference S.E.

ldr -.2926155 -.3458776 .0532621 .027709 ladr -.0456993 -.1311809 .0854816 .0367585

b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg

Test: Ho: difference in coefficients not systematic

chi2(2) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 14.17 Prob>chi2 = 0.0008 Source: STATA output of research data (2020)

4.2 The Relationship between Liquidity and Banks’ Profitability in Tanzania After recognizing that the model needs fixed effects (fe) approach, the suitable command for examining the relationship between liquidity and profitability was “xtreg y xi, fe”, whereby „y‟ is the dependent variables (in this case profitability) and xi represents explanatory variable(s) which were liquidity variables. In such a model, when STATA output is generated, the following main four aspects are examined before concluding:  Coefficients of the regressors: These show how much Y changes when X increases or decreases by one unit, It can bear a positive or negative value. These coefficients do not tell whether there is a significant relationship or not but it is just a magnitude measure between regressor and regressand coefficients.  t-values: These test the hypothesis individual coefficient if it is different from 0. To reject the null hypothesis that the coefficient equals zero, the t-value should be higher than 1.96 (for a 95% confidence). If this circumstance occurs then it is said that the independent variable has a significant impact on the dependent variable (y). It is said that the higher the t-value the higher the significance of the explanatory variable.  Two-tail p-values: These also test the hypothesis individual coefficient in the model if is different from 0. To reject the null hypothesis that the coefficient equals zero, the p-value has to be lower than 0.05 (for 95% confidence interval). Usually, if this is the case occur it can be said that the explanatory variable has a significant impact on the dependent variable (y).  Prob> F: this is used to test the significance of the whole model, usually if it is not more than 0.05 then the model is said to be significant, which means that the independent

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variables had a significant impact on the dependent variable. This test (F) wants to see whether all the coefficients in the model are different than zero.

To come out with strong findings, the researcher decided to test the relationship in three different ways, as recommended by the research hypotheses in Chapter One; therefore the analysis was first done based on Net Interest Margin (NIM) as a dependent variable, then followed Return on Assets (ROA) and lastly Return on Equity (ROE) as dependent variables as well. The following are three sub-sections elaborating the findings based on the selected dependent variables.

4.2.1 Results Based on Net Interest Margin (NIM) as a Dependent Variable As it is shown in Table 4.3 below, the command used in STATA software was “xtregnimldrladr, fe”. In this command “nim” represents net interest margin (dependent variable) while “ldr” and “ladr” represent loans to deposits ratio and liquid assets to deposits ratio respectively (explanatory variables). It has been seen that the coefficients of the regressor for both explanatory variables were negative, indicates that there is a negative relationship between net interest margin (profitability measure) and the independent variables (liquidity measures i.e. LDR and LADR). Yet, the t-values for both explanatory variables were not more than 1.96 (for a 95% confidence) showing that the explanatory variables had no significant influence on the dependent variable. The t-value for LDR is -0.05 while that of LADR is -0.45 (we usually take the absolute value). Also, it has seen that two-tail p-values for both independent variables are greater than 0.05 or 5% (95% confidence interval) which the value are 0.958 for LDR and 0.655 for LADR, which mean that the independent variables have no significant impact on the dependent variable. Finally, the Prob> F value for the model is greater than 0.05 (it is 0.9033) in this circumstance, which fails to demonstrate that all the coefficients in the model are different from zero. The overall clarification of these results is that there is no statistically significant relationship between banks‟ profitability as measured by net interest margin and banks‟ liquidity as measured by LDR and LADR. Even though the coefficients of the regressors are both negative then it is suggested that a negative relationship between the regressors and the dependent variable, this association not statistically relevant as it is suggested by the t-values, p-values and Prob> F value.

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Table 4.3: Liquidity-Profitability Relationship Based on Net Interest Margin (NIM) as a Measure of Profitability (Dependent Variable) . xtreg nim ldr ladr, fe

Fixed-effects (within) regression Number of obs = 40 Group variable: bank Number of groups = 5

R-sq: within = 0.0061 Obs per group: min = 8 between = 0.0157 avg = 8.0 overall = 0.0004 max = 8

F(2,33) = 0.10 corr(u_i, Xb) = -0.0930 Prob > F = 0.9033

nim Coef. Std. Err. t P>|t| [95% Conf. Interval]

ldr -.0029457 .0550069 -0.05 0.958 -.1148581 .1089667 ladr -.0280439 .0621274 -0.45 0.655 -.154443 .0983552 _cons .0956774 .0416075 2.30 0.028 .0110264 .1803284

sigma_u .03374185 sigma_e .02344229 rho .67445287 (fraction of variance due to u_i)

F test that all u_i=0: F(4, 33) = 16.05 Prob > F = 0.0000

Source: STATA output of research data (2020)

4.2.2 Results Based on Return on Assets (ROA) as a Dependent Variable As it is shown in Table 4.4 below, the command that was used in STATA software was “xtregroaldrladr, fe”. In this command “roa” represents a return on assets (dependent variable) while “ldr” and “ladr” represent loans to deposits ratio and liquid assets to deposits ratio respectively (independent variables). It has been seen that the coefficient of the independent variables is negative for ldr (-0.0309878) and positive for ladr (0.093533), displaying that there is no defined relationship between liquidity (measured by LDR and LADR) and banks‟ profitability measured by ROA. In addition to that, the values of the coefficients are very small showing that the relationship (whether positive or negative) is very weak; but the key point here is that the relationship between liquidity and profitability has weak definition of whether it is positive or negative

Considering other econometric measures, we can see that the t-values for both independent variables are not more than 1.96 (for a 95% confidence) showing that the explanatory variables have no significant impact on the dependent variable. The t-value for LDR is -1.44 while that of LADR is 0.39 (we usually take the absolute value). Also in two-tail p-values for both explanatory variables are more than 0.05 which the value is 0.159 for LDR and 0.702 for LADR, once again as it suggested that the independent variables have no significant influence

24 on the dependent variable. Finally, the Prob> F value for the model is more than 0.05 (it is 0.3241) in this circumstance, demonstrates that the coefficients have weak evidence to prove that all the models are different than zero. The overall interpretation of these results is that there is a weak statistically significant association between banks‟ profitability (as measure through return on assets) and banks‟ liquidity (as measured through LDR and LADR). This began with the contradiction in the coefficients of the regressors, whereby one coefficient is positive though another one is negative, and thereafter comes to be proved by t-values, p- values and Prob> F value that even the suggested relationship (whether positive or negative) is not statistically significant. Tablebelowcontributes more details on this discussion. Even though based on returns on assets, it is not enough to define the relationship between liquidity and banks‟ profitability in Tanzania, it was good to seek the profitability measure to put more light on the study which is Return On Equity (ROE).

Table 4.4: Liquidity-Profitability Relationship Based on Return on Assets (ROA) as a Measure of Profitability (Dependent Variable)

. xtreg roa ldr ladr, fe

Fixed-effects (within) regression Number of obs = 40 Group variable: bank Number of groups = 5

R-sq: within = 0.0660 Obs per group: min = 8 between = 0.2016 avg = 8.0 overall = 0.0066 max = 8

F(2,33) = 1.17 corr(u_i, Xb) = -0.3080 Prob > F = 0.3241

roa Coef. Std. Err. t P>|t| [95% Conf. Interval]

ldr -.0309878 .0214826 -1.44 0.159 -.0746944 .0127189 ladr .0093533 .0242634 0.39 0.702 -.040011 .0587176 _cons .0320742 .0162495 1.97 0.057 -.0009857 .0651341

sigma_u .01406833 sigma_e .00915523 rho .70249392 (fraction of variance due to u_i)

F test that all u_i=0: F(4, 33) = 16.21 Prob > F = 0.0000

Source: STATA output of research data (2020)

4.2.3 Results Based on Return on Equity ( ROE) as a Dependent Variable As it is shown in Table 4.5 below, the command that was used in STATA software was “xtreg roe ldrladr, fe”. In this command “roe” represents a return on equity (dependent variable) while “ldr” and “ladr” represent for loans to deposits ratio and liquid assets to deposits ratio

25 respectively (independent variables). It has been seen that the coefficient of the independent variable s are both negative for ldr (-0.3458776) and ladr (-0.1311809), indicates that there is a negative relationship between return on equity (profitability measure) and the independent variables (liquidity measures i.e. LDR and LADR).

From other econometric measures from the same table, it has been seen that the t-values for both independent variables are less than 1.96 (for a 95% confidence) indicating that the explanatory variables have no significant influence on the dependent variable. The t-value for LDR is -2.34 while that of LADR is -0.79 (we usually take the absolute value). In addition to that, the two-tail probability values (p-value) for one independent variable are more than 0.05 (the value is 0.025 for LDR and 0.437 for LADR), again suggesting that the independent variables do not have a significant influence on the dependent variable. Finally, the Prob> F value for the model is more than 0.05 (it is 0.0674) in this case, which indicates that there is weak evidence for all the coefficients in the model are different than zero.

The overall conclusion drawn from these results is that it is weak statistical evidence on the association between Tanzania banks‟ profitability (as measure through return on equity) and liquidity (as measured through LDR and LADR). This starts with the contradiction in the coefficients of the regressors, and also from the fact that the t-values, p-values and Prob> F value suggested that the relationship (whether positive or negative) is not statistically significant.

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Table 4.5:Liquidity-Profitability Relationship Based on Return on Equity (ROE) as a Measure of Profitability (Dependent Variable) . xtreg roe ldr ladr, fe

Fixed-effects (within) regression Number of obs = 40 Group variable: bank Number of groups = 5

R-sq: within = 0.1508 Obs per group: min = 8 between = 0.2486 avg = 8.0 overall = 0.0024 max = 8

F(2,33) = 2.93 corr(u_i, Xb) = -0.4168 Prob > F = 0.0674

roe Coef. Std. Err. t P>|t| [95% Conf. Interval]

ldr -.3458776 .1475196 -2.34 0.025 -.6460084 -.0457468 ladr -.1311809 .1666155 -0.79 0.437 -.4701627 .207801 _cons .3925308 .1115844 3.52 0.001 .1655105 .619551

sigma_u .09590416 sigma_e .06286841 rho .69943547 (fraction of variance due to u_i)

F test that all u_i=0: F(4, 33) = 13.32 Prob > F = 0.0000

Source: STATA output of research data (2020)It has conducted the econometric tests using three instinct dependent variables against the same independent variables and getting more or less or the same kind of results, the researcher is confident to propose that there is weak statistical significance on the association between banks‟ profitability and liquidity for the banks operating in Tanzania. These results are accurate based on the sample taken, the time period considered and the type of study conducted (longitudinal study). Therefore, there is a chance for other researchers to find a different kind of results if the sample or time factor differ, or if the study will be conducted using a different methodology, but in the meantime the research is confident to say that there is no statistically significant association between banks‟ profitability and liquidity for Tanzanian banks.

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CHAPTER FIVE SUMMARY, CONCLUSION, RECOMMENDATIONS, POLICY IMPLICATIONS AND CRITICAL EVALUATION OF THE STUDY 5.0 Introduction This chapter highlights the findings from the study in short, gives a conclusion and recommendations. It also gives in detail the critical analysis of the study, limitations of the study and what would the researcher do if given another opportunity to repeat the same study. Finally, this chapter suggests the areas for further research in line with the subject matter.

5.1 Summary, Conclusion, and Policy Implication The study intended at studying the association between banks‟ profitability and liquidity for the banks operating in Tanzania. Profitability was treated as a dependent variable while liquidity was treated as the independent variable. Profitability was presented by three key ratios: net interest margin(NIM), return on assets(ROA), and return on equity (ROE) while liquidity was presented by two key ratios: loans to deposits ratio (LDR) and liquid assets to deposits ratio (LADR). The five banks that were taken in the sample include the CRDB Bank, NMB, NBC, Barclays Bank Tanzania (which now is changed to ABSA Bank) and Exim Bank Tanzania. The study applied annual longitudinal or panel data for the time from 2012 to 2019. The econometric tests discovered that there is weak statistical evidence on the relationship between banks‟ profitability and liquidity using all the variables that were taken into consideration. Yet, there is a chance for other researchers to come out with instinct methodology, time or sample. The policy implication of the current findings is that the banks have an opportunity to focus on raising their profitability without fearing too much about liquidity, but they should be very careful since it is not guaranteed that the situation will be the same throughout.

5.2 Recommendations From the results of the study, the researcher provides the following recommendations to put more light on some areas: i. Some of the banks were found to incur losses in some years, and this is mostly associated with a bad loan portfolio. The concept behind this is that these banks were targeting getting supernormal profits so they ended up with non-performing loans due to failure to do a deep analysis of risks. As a result of many clients defaulted made the banks lose a lot of their money and sometimes might fail to recover even the principal of

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the loan and the banks incurred huge losses, the research recommends that the banks should be much more careful while selling loans because this is the main product of the commercial banks. An in-depth risk assessment should be done before disbursing loans to clients. As it is shown in the conclusion, it is not guaranteed that profitability does not influence banks‟ liquidity, therefore there is no chance for the banks that keep on incurring losses to maximize their liquidity and then the insolvent situation will lead to bankruptcy. ii. Even though there are just a few cases, it is good to demonstrate that there are some observed instances whereby the banks did not optimally utilize the deposits as theoretically recommended by the loans to deposits ratio. From the theory, it is suggested that the ratio of between 70% and 80% is good, and the maximum should be around 80% to 90%. However, there is one case whereby the bank used less than 20% of the deposits in lending money while there were two cases whereby another bank exceeded the 90% maximum limit of lending, which is unsafe for the bank. The researcher, therefore, suggests that the banks should keep on utilizing the deposits effectively to lend money to the public.

5.2 Critical Evaluation of the Study As it is shown in the introduction of this chapter, this section discusses what happened when the researcher was conducting the study, what went wrong, what went well, project management techniques applied and what would the researcher do if allowed to repeat the same study. Therefore, this section is an essential portion of the dissertation report because it gives the general picture of what happened and areas for improvement.

5.3 General Experience during the Study In the dissertation state and in the course of conducting the study the researcher experienced that there is a chance to come out with a new focus on the study, compared to the time before undertaking a study. However, at the beginning of this study, the researcher was thinking of undertaking the study basing on one bank as a case study by taking quarterly and sometimes to change the title, but after being advised by the supervisor the study changed to be a longitudinal study, which is a better option to the researcher and has more strong findings. This change was seen as a disturbance at the beginning but in the end, it has been seen to be so meaningful. From this experience, the researcher has learned to be flexible and be ready to learn new things from others. The attitude of “being rigid and sticking to your own ideas” can make someone lose the chance of learning better ways of doing things.

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5.3.1 What Went Well During the Study There are quite a several things that happened during the study, some of them that a researcher would like to mention are as follows: i. The researcher is blissful with the fact that the study elaborate on collecting already documented data hence more reliable. Since most of the data that was required to conduct the study was offered from the websites of the banks, the exercise was a bit easier for the researcher especially in the data collection process. ii. The supervisor showed a very high level of cooperation to the researcher thus enabling the researcher to proceed well with the study. Impressive and constructive comments from the supervisor made the success of this study. iii. The researcher has a finance background hence it was not difficult to conduct this study by using basic econometrics approaches.

5.3.2 Limitations of the Study Even though this study has been conducted successfully, behind all success there are some of the limitations that the research experienced during the study: i. Time was a limiting factor as only about six weeks were available for the researcher to do everything about this study from the scratch. There were a lot of pieces of literatures to be reviewed but the researcher fails to went through all of them due to time constraints, even those which were reviewed could not be thoroughly revised to grasp everything in them. The researcher believes that if more time could be given, a critical review of the literature could be done. ii. The researcher thinks about including more banks in the sample but was not possible since some banks that were targeted were found not to upload their data especially financial reports on their websites. The researcher believes that the results of the study would be made more relevant by including more banks, but this was not possible. iii. The pandemic disease threat (i.e.corona) limit the study in some instance due to social distance requirement made the researcher so difficult to meet his supervisor for comments and clarification of the study.

5.3.3 Alternative Ways of Undertaking the Study If the researcher was to do the same study again, the following is what he could do to make it more robust: i. New literature could be reviewed to broaden the scope of the study.

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ii. The sample would include more banks to increase the power of generalizing research findings. iii. The research could cover enough time to have more number of observations that would increase the chance of making the results more reliable. 5.4 Areas for Further Research After conducting this study, the researcher suggests that the coming researchers can undertake their studies in the following areas as originated from the already researched topic: i. To explore the reasons for the differences in profitability and liquidity trends among the banks in Tanzania, even though they operate in the same macroeconomic environment. ii. The relationship between capital structure and banks‟ profitability or between capital structure and banks‟ liquidity. iii. The impact of macroeconomic variables on banks‟ profitability or liquidity. iv. Assessment of the relationship between liquidity-management and companies profitability, a case listed manufacturing companies.

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APPENDIX Appendix 1: Profitability and Liquidity Ratios for Five Banks from 2012 to 2019 BANK YEAR NIM ROA ROE LDR LADR 1 2012 0.094 0.0375 0.282 0.710 0.176 1 2013 0.097 0.0368 0.244 0.671 0.168 1 2014 0.090 0.0300 0.230 0.739 0.278 1 2015 0.100 0.0300 0.230 0.774 0.320 1 2016 0.101 0.0210 0.100 0.830 0.363 1 2017 0.197 0.0100 0.048 0.717 0.286 1 2018 0.101 0.0160 0.082 0.707 0.398 1 2019 0.109 0.0280 0.147 0.682 0.379 2 2012 0.162 0.0270 0.209 0.605 0.229 2 2013 0.136 0.0410 0.286 0.638 0.280 2 2014 0.146 0.0420 0.257 0.680 0.240 2 2015 0.111 0.0330 0.224 0.699 0.304 2 2016 0.123 0.0320 0.207 0.757 0.296 2 2017 0.118 0.0170 0.120 0.673 0.150 2 2018 0.118 0.0180 0.118 0.782 0.143 2 2019 0.113 0.0230 0.149 0.765 0.266 3 2012 0.058 0.0022 0.024 0.644 0.259 3 2013 0.094 0.0050 0.036 0.452 0.273 3 2014 0.091 0.0050 0.040 0.602 0.248 3 2015 0.090 0.0070 0.047 0.709 0.229 3 2016 0.094 0.0080 0.051 0.633 0.199 3 2017 0.091 0.0090 0.053 0.626 0.159 3 2018 0.085 0.0050 0.039 0.514 0.168 3 2019 0.083 0.0120 0.084 0.523 0.176 4 2012 0.014 0.0037 0.041 0.625 0.219 4 2013 0.014 0.0035 0.037 0.625 0.225 4 2014 0.073 0.0216 0.126 0.734 0.176 4 2015 0.073 0.0213 0.118 0.742 0.168 4 2016 0.075 0.0021 0.014 0.713 0.201 4 2017 0.080 0.0000 0.010 0.768 0.205

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4 2018 0.080 -0.0100 -0.060 0.756 0.196 4 2019 0.070 -0.0100 -0.090 0.756 0.187 5 2012 0.039 0.0020 0.125 0.702 0.284 5 2013 0.060 -0.0050 0.096 0.713 0.196 5 2014 0.050 0.0003 0.139 0.786 0.282 5 2015 0.034 -0.0070 0.217 0.741 0.329 5 2016 0.042 -0.0010 0.183 0.671 0.249 5 2017 0.060 0.0002 0.274 0.550 0.226 5 2018 0.051 0.0219 0.214 0.547 0.463 5 2019 0.053 -0.0040 0.274 0.557 0.292 Source: Annual Reports/Financial Statements of the Banks from 2013 to 2019 Key: Bank 1 – CRBD Bank, Bank 2 – NMB Bank, Bank 3 – NBC, Bank 4 –Barclays, Bank 5 – Exim NIM – Net Interest Margin, ROA – Return on Assets, ROE – Return on Equity, LDR- Loans to Deposits Ratio, LADR – Liquid Assets to Deposits Ratio.

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Appendix 2: Research Budget S/N Items Amount 1 Stationeries 700,000 2 Communication and internet 300,000 3 Upkeeping cost 200,000 4 Emergencies 400,000 Total 1,420,000 Source; researcher 2020

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Appemdix 3: Reseach Schedule Activities Nov 19 Dec19 Feb 20 Mar 20 Apr 20 May 20 Jun 20 Jul 20 Concept Note

Proposal writing Proposal submission Data collection Data analysis

Report writing and submission Source; researcher 2020

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