RELATIONSHIP BETWEEN LIQUIDITY RISK AND FINANCIAL PERFORMANCE OF COMMERCIAL BANKS IN KENYA
RELATIONSHIP BETWEEN LIQUIDITY RISK AND FINANCIAL PERFORMANCE OF COMMERCIAL BANKS IN KENYA
CHAPTER ONE
INTRODUCTION
Besides the Great Depression of the 1930s, the Great Recession of 2008 to 2010, is considered having had one of the most devastating effects on the global economy in recent times. What began as liquidity shocks within a couple of financial institutions in the United States, soon degenerated into a global financial crisis. At the height of the crises in September 2008, Lehman Brothers with a debt of $619 billion against asset net worth of $639 declared bankruptcy (Wiggins, Piontek, & Metrick, 2014), becoming the first single largest bank to become a victim of the crisis. In response to the financial crises, the Basel Committee on Bank Supervision (BCBS) released an additional set of reform measures meant to strengthen existing banking sector regulation, supervision and risk management (Basel III, 2010). Predominantly, liquidity risk management emerged as the cornerstone of the Basel III bank supervision and regulatory guidelines.
In line with Basel II and Basel III deliberations, the Central Bank of Kenya (CBK) in its supervisory and regulatory roles requires all commercial banks in the country to give stringent attention to risk management. With respect to liquidity, CBK (2014) identifies the measure as an important financial stability indicator. To avert situations where liquidity shortfall in one bank causes systematic financial crises in the whole banking sector due to interconnected nature of operations, CBK has instituted statutory minimum liquidity requirements of 20 percent which all banks need to observe (CBK, 2014). With regard to the above requirement, the average liquidity ratio for commercial banks in Kenya as per CBK bank supervision report of 2014 stood at 37.7 percent. Clearly, this is a demonstration of sound liquidity risk management within the Kenyan banking sector.
Post the financial crisis of 2008 which almost brought the global economy into its knees, a number of studies have been conducted to restate the link between liquidity risk management loopholes and the builder-up of the crisis (Scannella, 2016). While some of these studies have focused on establishing determinants of liquidity risk, a good number have sought to establish the impact of liquidity and liquidity risk management on commercial bank performance. Following the financial crisis of 2008, for instance, Ly (2015) studied the relationship between liquidity risk and the performance of commercial banks in Europe where a negative relationship was confirmed. Mamatzakis and Bermpei (2014) in their study of determinants of financial performance of commercial banks in G7 countries and Switzerland concluded a negative relationship between liquidity and bank performance. In Nigeria, Olagunju, David, & Samuel (2012) established a positive relationship between liquidity and bank performance, where they concluded that banks need to improve their liquidity levels if they are to become more efficient.
The issue of liquidity and bank performance has received considerable attention from scholars within Kenyan borders. Vaita (2017) who examined the effect of liquidity on the performance of tier one banks listed in NSE concluded that liquidity had no significant effect on performance, but an increase in liquidity coverage ratio (LCR) is likely to lead to deterioration in performance. In a study to determine the relationship between liquidity and bank failure in Kenya, Ogilo, Omwoyo and Onsomu (2018) found a positive relationship between the dependent and independent variable. Equally, Wekesa (2016) research on the relationship between liquidity and bank performance concluded that liquidity risk has a significant influence on the performance of commercial banks in the country.
In line with the need for continuous assessment of commercial banks in Kenya performance with respect to various financial risks associated with their activities, this research study focuses on establishing the relationship between liquidity risk and performance. Unlike previous studies that majorly focused on liquidity ratio (LR) as mandated by CBK, this study uses more additional liquidity ratios, namely; cash to deposit ratio (CDR), loans to deposits ratio (LDR), cash to assets ratio (CAR) and Deposits to Assets Ratio (DAR)
According to CBK (2014) and BCBS (2010), liquidity risk can be described as the current and future risk that a bank will not be in a position to meet its financial obligations. Scannella (2016) argues that liquidity risk within a commercial bank or financial system primarily arises due to a mismatch between the maturity period of deposits (short) and loans (long). In this regard, the best way to effectively manage liquidity risk is by ensuring that there are adequate levels of liquid assets to cater for customer withdrawals requests among other needs.
A financial institution may lose liquidity or experience heightened level of liquidity risk, if suddenly it experiences large deposit withdrawals or if a large number of depositors decide to call on their deposits (BCSB, 2010). In these circumstances, a bank or group of banks with fewer liquid assets which includes, cash at hand, balances with the central bank, treasury bills and cash balances held by other banks is likely to find itself in a tough operational situation. To avert situations where a bank may be called to task due to a sudden surge in the number of deposit withdrawals, sound liquidity risk management is important.
Since the onset of the financial crises of 2008, where liquidity crises arise from transformation of short-term customer deposits to long terms loans in the form of highly risky (subprime) mortgages in the U.S. financial industry, liquidity risk management continues to receive renewed attention from bank regulators (Mamatzakis, & Bermpei, 2014). The significance of liquidity as an important financial health indicator is further supported by the fact that a liquidity shortfall in one bank may cause systematic crises in a whole financial system due to interconnected operations (Crowe, 2009; Maaka 2013). In severe cases, liquidity crisis within one specific bank has the potential of tearing apart a whole financial system if urgent measures are not undertaken to mitigate liquidity shortfalls (Wiggins, Piontek, & Metrick, 2014). This is attributed to the interconnected nature of the financial system where banks borrow from one another to cover liquidity shortfalls or hold assets in other banks (Diamond & Rajan, 2001). For this reason, banks need to pay keen attention on their ability to meet their obligations when they become due However, in doing so, it is important to note to equally keep a large amount of highly liquid assets in the form of cash in hand and reserves kept with central bank as these assets earn zero to very little income.
According to Alexandru, Genu, & Romanescu (2008) return on assets (ROA) and return on equity are the two fundamental measures of financial performances. Additionally, in the financial industry, the net interest margin (NIM) is another important measure of financial performance (Saksonova, 2014). Primarily, the main purpose of undertaking financial performance assessment is to determine how effective management uses resources at their disposal to generate a return for shareholders. This is in accordance with the agency principle where the main purpose of a firm’s management is to grow shareholders wealth. While profit is an important financial performance measure, most of the times it is disregarded as beyond the absolute figures it does not explain a firm’s level of efficiency in terms of return on assets and equity employed in operations.
While profitability remains the most important measures of financial performance among commercial banks, other performance measures that usually receive attention include the overall financial health of a firm determined using liquidity, and solvency ratios. Growth in deposits and loan portfolios, and a number of accounts being held by customers are other important financial indicators that can be used to assess the performance of a bank (Saksonova, 2014). Besides this, the financial performance of commercial banks can also be assessed in terms of how their competitive position in the market. In particular, the attractiveness of a bank’s stock for those listed in NSE can be a good performance indicator.
While a bank’s management is usually held responsible for banks financial performance as determined by profitability growth, market share growth, stock price growth among other parameters, it is important to note that bank performance is also subject to external factors. Prevailing economic conditions, both inside the country and outside measured in gross domestic terms (GDP), prevailing interest and inflation rates are some of the external factors that might affect bank performance. Besides this, bank regulation policies such as the capping of interest rate and prevailing foreign currency exchange rate are some of the other external factors that might influence bank performance (CBK, 2010). Among these, changes in interest rates have been found to have a significant impact on profitability since commercial banks primarily rely on the interests of their assets to generate positive performance (Rasaiah, 2010).
For the purposes of this study, however, financial performance among commercial banks in Kenya will be limited to key important financial ratios. In line with other studies which have studied the relationship between liquidity risk and financial performance, this study equally restricts itself to ROA as a measure of financial measure. ROA has been found to be a robust financial measure as it compares the extent to which management uses assets availed to them to generate net income.
Crowe (2009) posits that a bank with adequate capital levels, good asset quality, and exemplary earning, is much likely to fail if it does not maintain adequate liquidity and sound liquidity risk management. Moreover, Kumar (2008) argues that the ability of a commercial bank to grow the size of its asset portfolio, and generate a positive return for its shareholders is underscored by its liquidity. For this reason, Scannella (2016) says that it is increasingly important to continue measuring, managing and assessing the impact of liquidity risk on the performance of commercial banks.
A couple of studies both locally and globally have sought to determine the relationship between liquidity risk and bank performance. The outcome from these studies, though mixed in terms of results, offer important insight on the relationship of liquidity risk and bank performance. While Bourke (1989) found a strong relationship between liquidity risk and bank performance, Molyneux and Thornton (1992) in their study concluded that the two variable have a weak relationship. In a more recent and comprehensive study done by Lion and Dragos (2006), liquidity risk management was found to have a contrasting relationship with profitability. The duo concluded that keeping inadequate liquidity assets may force a bank to borrow additional capital at a higher cost to meet liquidity requirement. This is expensive and may actually impact profitability negatively. On the other hand, Bernanke (2008) argues that while keeping the majority of the deposits banks receives as highly liquid assets might be an option, doing this will create opportunity cost. As such, for banks to remain profitable while minimizing risk, requires a balance between liquid assets and total asset portfolio.
Dewatripont, Rochet and Tirole (2010) posit that banks are primarily concerned with creation of liquidity where they have to borrow short and lend long Diamond (1984) posits that banks are inherently exposed to liquidity risks which arise when they are unable to meet customer withdrawal requests due to shortfall of cash and reserve requirements at their disposal, necessitating borrowing at additional cost from the market. Due to these additional costs, banks that constantly rely on borrowing to fulfill liquidity requirements incur higher operational costs that eat into their profits. As a way of mitigating such costs, therefore, banks need to observe sound liquidity risk management measures.
A bank that is constantly to borrow from other banks to meet liquidity requirements or get funds from the capital markets to meet customers’ withdrawal requirements is likely to perform worse than the industry average (Diamond & Dybvig, 1983). Borrowing funds from other banks or from the capital market are more expensive compared to keeping sufficient levels of liquid assets to meet liquidity requirements. However, caution should still prevail when it comes to the handling of liquid assets as too much of the same will result in an opportunity cost in terms of interest income (Bernanke, 2008). Given that liquidity levels and risks are never constant in a market, it is important to evaluate how it impacts on bank financial performance constantly.
With respect to the Banking Act, Cap 488, CBK is the principal institution that is mandated by the law to issue banking regulations and guidelines to which all commercial banks in the country must adhere to (Laws of Kenya, 2015). In its supervisory and regulatory capacity, CBK has the responsibility that the commercial banks in the country meet specific regulatory benchmarks such as liquidity levels, and capital levels with the view of protecting depositor’s reckless use of their savings. In an effort to promote economic growth in the country, commercial banks are encouraged to extend credit to low-income earners.
Like many other countries in the world, the Kenyan financial system is mainly dominated by commercial banks. While the country has a total of 43 banks, locally and foreign-owned, these banks only 6 of these banks are grouped under tier one and only 12 of them are listed in the Nairobi Stock Exchange, NSE. This implies that the majority of the banks in the country are under tier 2 and tier 3 where there are a total of 16 medium banks and 21 small banks in the country. It is important to note that while there were only 6 large banks in the country, their combined market capitalization of 49.9% as of 2014 was more than of medium banks which stood at 41.7% and small banks as 8.4% (CBK, 2014)
In its capacity as principal regulator and supervisor of commercial banks in the country, CBK has over the years relied on the Basel Committee on Bank Supervision guideline measures. Effectively, all banks in the country are required to meet minimum Basel III CAMEL guidelines and additional measures instituted by CBK. Liquidity being a key performance measure receives the greatest attention from CBK where the industry average liquidity ratio of 38% as of 2014 is much higher than the statutory requirement of 20%. While this is the case, however, individual banks have slight variations in terms of liquidity risk. In relation to this, it is important to determine the extent to which liquidity risk among commercial banks in the country is related to their financial performance (Maaka, 2013; Wekesa, 2015).
Commercial banks in Kenya play an important role in the growth and development of the country’s economy (Ogilo, Omwoyo & Onsomu, 2018). Besides acting as an important intermediary between borrowers and savers, these institutions do offer thousands of job opportunities, both directly and indirectly. With respect to this, it is in the best interest of everyone, including the government, shareholders and the public in general that these financial institutions consistently generate positive financial results. In addition to profitability measures such as gross revenues, net income earned, NIM, ROA and ROE; financial risk measures including liquidity risk, credit risk and market risk are usually given keen attention as exposure to excessive risk can hamper the ability of banks to generate profits (CBK, 2014). In line with the importance of performance measurement and financial risk management in the financial industry; the focus of this study is to determine the relationship between liquidity risk and financial performance of commercial banks in the country.
The primary way commercial banks generate a financial return is by transforming short-term liabilities (customer deposits) to long term assets (loans) which inherently exposes them to liquidity risks. With respect to transformation of short term deposits to long term loans, liquidity risk arises when a bank becomes unable to meet customer withdrawal requirements without incurring further additional borrowing cost from other financial institutions or from the capital market (Kumar, 2008; Harker, & Satvros, 1998). To this effect, it is important that commercial banks maintain a certain level of liquidity as a precaution to chances of incurring liquidity risk. Owing to the vulnerability of commercial banks to liquidity risk and in line with the BCSB III (Basel 2010) guidelines on liquidity risk management, CBK requires commercial banks in the country to keep a minimum of 20 percent liquidity levels. In line with this requirement, CBK (2014) attests that the banking industry in Kenya has liquidity levels of 37% as of 2014.
Since risk management is an important activity within the financial industry, Harker and Satvros, (1998) expect a bank’s financial performance to be greatly influenced with the efficiency at which they manage risks associated to their operations. With the growth in the complexity of banking operations where today the Kenyan banking industry is characterized by new innovative operations such as development in online banking, mobile and agent banking, risk management of financial activities continue to receive renewed attention. In line with the objective of this study, Vaita (2017); Muriithi and Waweru (2017) established that liquidity risk measured using LCR had no significant influence on the financial performance of commercial banks in the country. On the other hand, Maaka (2013) concluded that the profitability of commercial banks in the country is negatively affected with an increase in liquidity gap and leverage while the level of customer deposits was positively correlated. While Maaka (2013) found a negative relationship between liquidity risk (measured using liquidity gap and leverage ratio) and financial performance, Wekesa (2016) concluded that liquidity risk has a significant influence on the performance of commercial banks in Kenya.
From the review of the literature, it is clear that there is no particular trend between liquidity risk and financial performance. While some studies both within and outside the country have found no significant relationship between liquidity risk and financial performance, others have successfully established a significant relationship between the two variables. Given the importance of liquidity risk management in the activities of financial institutions, it is important that more and more studies be carried out. In line with this view, the purpose of this study is to determine the relationship between liquidity risk and financial performance of commercial banks in the country. Unlike other previous studies, however, this study will focus more on having more liquidity risk variables and how they individually relate to financial performance.
The objective of this study is to determine the relationship between liquidity risk and financial performance of commercial banks in Kenya.
In a recent couple of years, the commercial banking industry in Kenya has witnessed a number of changes. Some of the most important and recent developments in the industry include the emergence of mobile and online technology as a viable alternative to a physical visit to commercial banks institutions to request for loans or make deposits. While these developments have highly enhanced the capability of commercial banks in the country to grow their customer bases and hence profit base, it has come with an increased level of risks. Together with other developments in the industry, including tightening of liquidity requirements by the Central Bank, it is sensible continuous research on the impact of these developments on the performance of the Kenyan commercial banking industry. Therefore, findings on this study with regard to the relationship between liquidity risk and financial performance of commercial banks in the country are going to be of importance to several parties.
Commercial banks will be the key recipients of the benefits of this study. Findings from the study with regard to liquidity management are going to help management find the best way to balance the number of liquidity assets versus illiquid assets. Recommendations from the study may also be of interest to the Central Bank. Being the primary institution with the mandate to regulate and supervise the banking industry in Kenya, findings on the relationship between liquidity risk and financial performance of commercial banks in the country will be of significant importance to CBK. The findings are likely to inform policymakers of the body with respect on how best to manage liquidity.
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter examines available literature on liquidity risk and bank performance with the view of determining what these scholarly articles and research studies have been able to establish. From the review of the literature, we seek to determine what these previous possible areas our study will prove. Besides this; we seek to familiarize with the existing body of knowledge concerning liquidity risk and financial performance of commercial banks, to provide background information about the research problem we seek to prove. In line with this, we first give a theoretical literature review, then local and international empirical review, and as well as methodological review. Review of theoretical literature will establish relevant theories to our topic of study. On the other hand, review of empirical and methodological literature seeks to establish the relationship that exists between the variables and the various methods of analysis with has been applied before with the view of coming with an alternative method of data analysis.
2.2 Theoretical Framework
2.2.1 Inventory Theory of Capital and Liquidity Buffer
According to Baltensperger (1980) it is extremely costly for commercial banks to keep a large stock of liquid assets in the form of cash on hand or cash held at the central bank. At the same time, banks have to keep certain levels of liquid assets to meet customer withdrawal requirements. As such, there is always a trade-off of holding excessive liquid assets with low returns and holding too little of the same to avert liquidity risk (Bernanke, 2008). In managing liquidity, the inventory theory of capital and liquidity buffer expects the size of liquidity buffer held by a bank to be interpreted in terms the cost of opportunity cost holding liquid assets rather than loans, and as well as the cost of raising capital at short notice (Diamond and Dybvig, 1983). Therefore, informed management of liquidity among commercial banks should be able to address the question of the additional cost of raising funds to meet the volatility of customer withdrawal requests and other obligations that may become due. To lower the maturity gap between long term assets and short term liabilities which is the principal cause of liquidity risk, banks need to hold a buffer of liquid assets (Mugenyah, 2015). The need to keep adequate liquidity is further supported by Diamond and Dybvig (1983) who argue that while keeping buffer stock of liquid assets represent an opportunity cost to a bank to earn return, it is more advantageous given the fact that sufficient liquidity helps to minimize the risk of not being position to meet customer withdrawal request. Therefore, keeping buffer stock of liquid assets helps to protect against incurring high costs of obtaining additional funds to meet liquidity requirements under short notice terms.
Diamond and Rajan, (2001) argue that it is crucial that banks hold sufficient amounts of liquid assets as a precaution against liquidity risk. Given that financial institutions usually use short term liabilities to fund long term assets, a bank may find itself in a difficult situation in cases of unexpected large deposit withdrawals, or when it cannot obtain overnight lending and discount rate loans at short notice. To avoid situations where liquidity problems in one bank do not spread to the whole banking system, banks should be mandated by law to keep a certain amount of liquid assets (Diamond & Rajan, 2001).
The purpose of holding a buffer of liquid assets in the form of cash, Treasury bills and reserves is thus to avoid cases of liquidity crises emanating from unexpected request to withdraw a large number of funds. For commercial banks in Kenya to be in a position to meet their liquidity requirements as per their core function of creating liquidity in the industry through the transformation of short term customer deposits into loans with longer maturity periods, it is important that they pay attention to this theory. This means that they need to keep desirable levels of liquid assets as keeping huge amounts of this stock assets creates an opportunity cost that is likely to affect their profitability.
2.2.2 Theory of Financial Intermediation
Developed in the 1960s, the theory of financial intermediation posits that the essential function of commercial banks in a financial system is the transformation of short term customer deposits into long term loans offered to customers (Diamond, 1984). In other words, the primary work function of commercial banks in a financial system is the creation of liquidity through borrowing short and lending long (Dewatripont, Rochet & Tirole, 2010). Related to the function of creating liquidity, the theory of financial intermediation argues that financial institutions work to eliminate or at least bridge the information asymmetry gaps between lenders and borrowers. Since it is extremely difficult for commercial banks to eliminate information asymmetry between savers and borrowers, the theory of financial intermediation argues for the need for these institutions to keep adequate levels of liquid assets. This is meant to minimize chances of liquidity shortfalls arising when too many customers than unanticipated come to withdraw their deposits.
For purposes of being in a capacity to meet financial obligations when they become due, including customer withdrawal requests it is important that commercial banks maintain a certain level of liquidity. If this is not observed, commercial banks may be forced to liquidate their illiquid assets at a great cost in the event that it is unable to meet liquidity requirements (Diamond & Dybvig, 1983). As seen in the case of recent failure of Chase Bank and Dubai Bank which were occasioned by depositors running to withdraw their deposits at the same time, in the process overwhelming the capacity of these banks to meet their customer withdrawal requirements, it is important that attention be given to the amount of liquidity kept by commercial banks in the country.
2.2.3 The Liability Management Theory
Following the recovery from the recession of 1960-61, Woodworh (1968) avers that large commercial banks in New York City were under intense pressure from the market to make more loan provisions for borrowers. The fact that the economy was starting to grow again meant that banks received far much fewer deposits as the majority of those with surplus income opted to invest rather than make bank deposits. It was under these circumstances that Woodworth (1968) observed in an article published in the Banker’s Magazine, that banks could create additional liquidity by creating additional liabilities in the form of issuing short term notes (certificate of deposits), borrowing from other banks and central banks. In line with the need to meet additional funds, the liability management theory indicates that there is no need for banks to follow the old liquidity norms of maintaining liquid assets and liquid investments. Instead, the liquidity management theory proposes that banks can work really on these instruments to meet their liquidity requirements. This explains why commercial banks facing short term liquidity shortfalls opt for overnight lending from one another and discount lending from the central bank.
While this theory has contributed immensely to the development of the banking sector in terms of ways banks can create additional liquidity and hence additional profits through borrowing or issuing of short term-debt, it has a number of limitations. Since raising additional liquidity through borrowing from other banks, central bank and issue of short term notes attract high-interest levels and is often highly restricted; it cannot be relied upon by banks facing huge liquidity requirements (Diamond & Rajan, 2001).
As noted with the financial crises of 2008, commercial banks may refuse to lend to one another for fear default. On the other hand, banks that opt to raise additional capital through the issue of share might not be able to effectively deal with liquidity requirements as this depends on how the market responds. This is specifically relevant for medium and small commercial banks in Kenya which despite operating in several years, many are finding it difficult to attract additional funding from the capital through the issue of shares due to lack of interest from the public. For this reason, commercial banks in the country cannot entirely rely on the issue of short term debt, borrowing from CBK and other banks to meet their liquidity requirements. This limitation implies the need for these institutions to grow their deposits and keep an adequate level of liquid assets to fund their activities. Despite this, the liquidity management theory is relevant to this study and the Kenyan Banking industry as it continues as it has contributed to the use of overnight lending and discount lending by the central bank can help create liquidity albeit minimally.
2.3 Determinants of Financial Performance
Ongore and Kusa (2013) precisely argue that determinants of financial performance of commercial banks can be categorized into bank-specific internal factors and external macroeconomic factors. Factors that can be influenced by management decisions are referred to as bank specific internal variables and these include capital adequacy, asset quality, management efficiency, Earnings ability and liquidity (CAMEL) factors. Besides this, other internal factors include technology, personnel productivity, bank size and ownership. Unlike internal factors which can be manipulated, management has less or zero control on external factors. Example of important internal factors that have great influence on financial performance includes prevailing economic condition measured in GDP, inflation and interest rates, political stability and social-cultural development.
While determinants of financial of commercial banks are largely grouped into the five CAMEL factors, this study limits itself to a number of liquidity ratios with the aim of determining the relationship between liquidity risk and financial performance of commercial banks in Kenya. From the review of the literature, a bank may have adequate levels of capital, good asset quality and exemplary earnings but still fail if it does not maintain adequate liquid assets. Therefore, the ability of a commercial bank to generate positive financial performance is dependent on how better it manages its liquidity.
2.3.1 Cash to Assets Ratio
Cash at hand, held in the CBK or due from other commercial banks play an important aspect in the activities of commercial banks. While cash as an asset does not earn interest most of the times, it remains an important financial asset for commercial banks whose absence is likely to trigger financial crises. For this reason, it is always important to keep adequate levels of cash in proportion to total assets for purposes of meeting customer withdrawal requests and meeting other obligations when they become due. This is advisable at it will help commercial banks to avoid situations where they have to sell their illiquid assets such as government securities held to maturity at cost. Musiega, Olweny, Mukanzi, and Mutua (2017) established that liquid assets to total assets ratio has a significant positive relationship with profitability. This implies that keeping a good proportion of highly liquid assets, in this case cash may be beneficial to commercial bank activities. However, since cash is a non-earning asset, it is important not to keep excessive amounts of cash in proportion to total assets.
2.3.2 Cash to Deposits Ratio
In order to meet customer withdrawal requirements, it is important that commercial banks in their pursuit of increasing their asset base through transformation short term deposits into long terms loans extended to customers, keep a certain level of these deposits in highly liquid assets form. This explains why banks are advised against lending all the deposits they receive from customers as doing so will likely expose them to tight liquidity position especially if such banks do not have an adequate amount of liquid assets from shareholders capital. In a study to determine the relationship of liquid assets to deposits ration on financial performance of commercial banks in Kenya, Musiega, Olweny, Mukanzi, and Mutua (2017) found a negative relationship. But, since this study focused on liquid assets, which includes government securities which are not entirely liquid as one has to hold them to maturity to earn return, we decided to focus on cash to deposit ratio and see how this ratio is related to financial performance.
2.3.3 Deposits to Assets Ratio
Commercial banks mostly rely on customer deposits to fund increases in their asset base in terms of loans extended to customers. As opposed to other sources of funding such as borrowing from capital markets through issue of short term notes, or borrowing from other commercial banks; use of deposits to fund increases in assets provides banks with the cheapest source of funding. This implies that the more deposits a bank is able to attract in proportion to the total amount of assets it holds the better its financial performance is likely to be. Perhaps, this explains the reason why large commercial banks with huge customer deposit accounts continue to outperform those with relatively fewer deposits. With respect to this, Rasaiah (2010) and Wekesa (2015) argues that the greater the ability of a commercial bank to offer more loans raised through customer deposit, the higher the possibility of generating profits. Okun (2012) studied the relationship between customer deposits to total assets and financial performance and found a significant positive relationship. However, the level of deposits kept by commercial banks should be proportionate with the demand for loans, otherwise, Rasaiah (2010) posits that banks are likely to find themselves in loss-making situation. Therefore, we saw it important to determine the impact of total customer deposits to total assets on performance of commercial banks in the country.
2.3.4 Loans to Deposit Ratio
Universally, loans to total customer deposits is one of the important ratios used to attest liquidity. The importance of this ratio is attested by the fact that high proportion of commercial banks revenues, for the case of Kenyan banks as high as 75 percent comes from difference between interest rates charged on loans and paid for deposits (CBK, 2014). Since loans also form the highest proportion of bank assets, and deposits form the highest proportion of bank liabilities for the case of commercial banks operating in Kenya, it is important to always compute the LDR ratio when testing the relationship between liquidity and financial performance. According to BCSB (2010) banks should strive to maintain loans to deposit ratio of 70 percent to 90 percent. LDR ratio of 100 or more means that all deposits have been converted to loans. This is not advisable as it likely to create liquidity in the event that a bank does not have adequate level of capital or is unable to convert its illiquid assets to cash without incurring a loss.
2.3.5 Liquidity Ratio
While the Basel III recommended commercial banks regulators to adopt the liquidity coverage ratio (LCR) as the most potent liquidity risk measure, CBK, the regulator of Kenya Banking industry is yet to do so. In its place, CBK continues to use the liquidity ratio to measure the level of liquidity and liquidity risk in the Kenyan financial industry. Statutory, commercial banks in Kenya are required to keep a minimum 20 percent liquidity ratio, a threshold which all the banks have been able to meet over the year (CBK, 2014). This liquidity ratio is usually determined by combining cash at hand, plus cash held with the CBK, plus government securities held, plus cash due from other banks less the cash owed to other banks over total assets. With respect to this ratio, Ongore and Kusa (2013) established insignificant relationship between LR and ROA.
2.4 Empirical Review
2.4.1 Local Evidence
Mugenyah (2009) research focused on establishing determinants of liquidity risk for commercial banks in Kenya. A descriptive research design targeting all the 43 commercial banks operating in the country between the period 2010 and 2014 was conducted. To evaluate determinants of liquidity risk, the researcher used multiple regression analysis where the dependent variable, loan to deposit ratio was regressed against the independent variables; capital adequacy ratio, size of bank, ownership type, liquid assets ratio, and leverage. Mugenyah (2015) found that capital adequacy, liquid asset ratio, ownership type, size and leverage as significant indicators of liquidity risk among commercial banks in the country. In a follow-up study, Mugenyah (2015) found that bank size which is interpreted in terms of total assets being held by a bank as having a positive relationship with liquidity levels as it has an impact on the ability of a commercial bank to mobilize funds to meet liquidity requirements. In this same study, Mugenyah (2015) was able to establish direct link between liquidity risk associated with the type of loan assets a bank is holding with financial performance. Last but not least, researcher established a significant relationship capital levels and liquidity risk. However, findings of the study are limited to the quality of the financial statements used, which are subject to managerial discretion. Additionally, the period under study might not have considered periods the financial sector had been under intense liquidity stress.
Maaka (2013) posits that bank with adequate capital levels, strong earnings and good asset level may still fail if does not maintain adequate liquidity levels. Using a correlation research design, Maaka (2013) sought to determine the relationship between liquidity risk and financial performance of commercial banks in Kenya. Employing secondary data covering the period 2008 to 2012, Maaka was able to establish a significant relationship between liquidity and financial performance of commercial banks in the country for the period under study. Specifically, liquidity gap and leverage ratios were found to have a significant but negative relationship with financial performance of the 33 commercial banks whose data was used in the study. Maaka, (2013) concluded that with a significant liquidity risk, banks may be forced to borrow at higher cost, thereby affecting their profitability. The same study was able to establish a significant positive relationship between customer deposits and financial performance of commercial banks in the country.
Ouma (2015), using descriptive survey research design studied the relationship between liquidity risk and profitability of commercial banks in Kenya. The study was carried out in 2015 and covered a period of five years, 2010 to 2015 were using the secondary data obtained from CBK annual bank supervision reports established that there was significant relationship between the dependent variables (liquidity ratios) and financial performs which was measured using net interest income ratio. Specifically, the researcher established that unit increase in current ratio, liquidity risk and customer deposits translates to improvement in net interest income as measure of profitability and financial performance (Ouma, 2015). However, the findings of this researcher are limited to conclusive as financial performance of commercial banks are affected by other factors including capital adequacy.
2.4.2 International Evidence
Ferrouhi (2014) research study sought to determine the relationship between liquidity and financial performance of commercial banks in Morocco. To establish this relationship, Ferrouhi (2014) adopted panel analysis research design where data from four commercial banks operating in the country for the period 2001 to 2012 was regressed. The study employed six liquidity ratios and was able to establish that Moroccan banks financial performance is mainly influenced by 7 determinants to which liquidity ratio is among these ratios. The researcher concluded that the outcome from these type of research studies that seek to establish a relationship highly depends on the model employed to analyze the results.
In North America, Bordeleau, Crawford and Graham (2009) employing 55 U.S. and 10 Canadian commercial banks sought to determine how liquidity affected banks performance. Covering the period 1997 to 2009 and employing a descriptive research design, the study was able to establish a nonlinear relationship between liquidity and profitability. For a certain level of liquidity levels, financial performance was found to be positively correlated but beyond this level, financial performance was found to diminish as liquidity levels increased. Theoretically, the outcome from this research is in line with the idea that funding markets reward a bank for holding a certain level of liquid assets meant to keep liquidity risk on check. Since keeping excessive level of liquid assets represents an opportunity costs, performance diminishes with excessive amounts of liquidity.
2.5 Conceptual Framework
A conceptual framework is an illustration that show the relationship between the predictor variables and dependent variable. For the purpose of this study, the predictor variable entailed liquidity risk measured using Liquidity Ratio (LR), Deposits to Assets Ratio (DAR), Cash to Assets ratio (CAR), Cash to Deposits Ratio (CDR), and Loans to Deposits Ratio LDR). Financial performance, the dependent variable was measured using ROA.
| CAR |
| LDR |
| ROA |
| DAR |
| LR |
| CDR |
| Dependent Variable |
| Independent Variables |
Source (Students, 2019).
2.6 Literature Review Summary
While it is extremely costly for commercial banks to keep a large stock of liquid assets in the form of cash on hand or cash held at the central bank (Baltensperger, 1980); Inventory Theory of Capital and Liquidity Buffer require these institutions to keep a certain level of liquid assets to meet customer withdrawal requirements. The trade-off that is associated with keeping of excessive amounts of liquid assets implies that prudent management of liquidity is important if a bank is to generate positive financial performance without necessarily exposing itself to too much liquidity risk that comes with keeping too little amount of liquid assets. The Theory of Financial Intermediation posits that the primary role of commercial banks is to create liquidity by transforming short term customer deposits into long term loans. In doing so, banks are expected to maintain sufficient amount of liquid assets to cater for customer withdrawal requests. However, this argument is countered by the Liability Management Theory that posits that banks should not be worried about liquidity as they can always borrow from the market to cater for its liquidity requirements.
In the phase of the financial crisis of 2008, a number of studies have been conducted across the globe to establish the relationship between liquidity risk and financial performance. From the review of literature, it can be seen that the relationship between liquidity risk and profitability is non-linear. While some studies have found a positive relationship between liquidity and financial performance, others have established a negative relationship between liquidity and profitability. In line with this, this study employs new set of liquidity ratios with the aim of building the knowledge of the relationship between liquidity risk and financial performance of commercial banks in the country.
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Introduction
This chapter covers the research design the study adopted and why it was chosen. Explanation of population of interest, sampling design, type and sources of data and technique used to analysis of data is also covered in this chapter. At the very end of this chapter, an explanation of analytical model adopted and test of significance is given.
3.2 Research Design
Research design is an overall strategy that researchers use to integrate different components of a study into a coherent and logical manner. To effectively address a research problem, it is important that an appropriate research design to be adopt. In this respect, this study adopts a descriptive research design as the intention of the research was to determine the relationship between the liquidity risk and financial performance. Kothari (2004) postulates that descriptive design suits research studies where the goal is fact-finding, establishing a relationship and describing the same. Creswell (2003) observes that a descriptive research design is usually adopts in cases where the researcher has no control over the variables, meaning that he or she can only make a report of the findings. Therefore, the descriptive design adoptive in this study will explain the extent to which liquidity risk affects financial performance of commercial banks in the country under the period of study.
3.3 Population
According to Mugenda and Mugenda (2003), a population refers to a clusters of characters that share similar characteristics. For purpose of this study, the population of interest was all the commercial banks in Kenya that operated for the period under study (2012-2018). During this period, 2012 to 2018, there were a total of 43 commercial banks. The main reason why the industry was considered for the study is due to availability of secondary data which was obtained from CBK annual bank reports and from financial statements released by banks enrolled in the study.
3.4 Sampling
A sampling refers to a process by which a researcher selects subjects from a population with the objective of drawing conclusions and making inference about the population under study. In other words, sampling can be described as process of selecting a sample from a population to be examined for purposes of making generalized conclusion about the characteristics of the whole population (Thompson, 2012). Therefore, sampling design is a procedure that helps a researcher(s) in collection of relevant data to be used in a study from the sample population. For purposes of this study, a sample of secondary data based on liquidity risk and financial performance variables from all the 43 commercial banks will be employed in the study. The sample size will be collected over an 11 year period analyzed on annual basis (2007-2017). The study adopts a convenience sampling design as data will be obtained at the convenience of the researchers.
3.5 Data Collection
This study makes use of secondary data collected from CBK annual bank reports and from published financial statements of respective commercial banks for the period under study. The specific information that that was collected from these secondary data sources include ROA, CAR, CDR, DAR, LDR and LR ratios. While some of the data was obtained directly from the financial statements, the rest had to be calculated using the data obtained from these financial statements.
The study period is 2007 to 2017. This period was chosen owing to the effect of the financial crisis of 2008 and other minor incidences such as the liquidity issues in the Kenyan Banking sector. The period was also chosen due to coming into force of the Basel III bank supervision guidelines which were introduced as way of dealing with liquidity risks which as identified as one of the key factors that led to escalation of the crisis.
3.6 Data Analysis
The study adopted multiple linear regression analysis where data was first entered into excel before being transferred into SPSS for the final and ultimate analysis. To accomplish the objective of the research, the dependent variable, ROA was regressed against Capital adequacy, current ratios, liquidity ratio, LR, customer deposits to total assets ratio, and total loan to customer deposits ratios. The coefficients from the regression will manifest the extent to which the predictor variables can positively or negatively affect the dependent variable.
3.6.1 Analytical Model
Multiple linear regression model is used and contains liquidity variables and capital adequacy regressed against financial performance measured using return on assets. The regression model is in the form;
Y = α + β1X1 + β2X2 + β3X3 +β4X4 + β5X5 + ε
Where;
Y is financial performance (ROA) = (As obtained from CBK Annual Bank Supervision Reports)
X1 is Cash to Assets Ratio = (Cash at hand and at CBK/total assets)
X2 is Cash to Deposits Ratio = (Cash at hand and at CBK/ Total Customer Deposits)
X3 is Deposits to Assets Ratio = (Total Customer Deposits/ Total Assets)
X4 is Loan to Deposits Ratio = (Total Loans/Total Customer Deposits)
X5 is Liquidity Ratio (As Provided by CBK Annual Bank Supervision Reports)
ε = Error or random term
α= Constant
β1, β2, β3, β4, β5, and β6; Partial coefficients of GDP with respect to X1, X2, X3, X4, and X5, respectively.
3.6.2 Test of Significance
ANOVA analysis is used to test the significance of the multilinear regression model adopted to determine how the financial performance is affected by liquidity risk. Under the analysis variance, the F-test, tests the significance of model used in the study. The P-value under the F-test should be less than 5% significance level (95% confidence level) for a model to be significant (Kothari 2004).
CHAPTER FOUR
DATA ANALYSIS, RESULTS AND DISCUSSION
4.1 Introduction
This chapter presents results of the study on the relationship between liquidity risk and financial performance of commercial banks in Kenya. The secondary data analyzed and whose results are discussed were obtained from CBK annual bank supervision reports from 2007 to 2017. The tools of analysis involved the use descriptive statistics, and inferential statistics. Two inferential statistics; Pearson correlation of coefficients which tested and determined the relationship between the variables employed in the study, and Regression analysis which established the model summary and model of coefficients that test significance of the model and that of the variables used was the other inferential statistics employed. To determine the relationship between the dependent variable (bank profitability) and independent variables (liquidity risk), a total of six variables, one for the former and five for the latter was employed.
4.2 Descriptive Statistics
In descriptive statistics analysis, we sought to establish, from the secondary data employed the mean, minimum and maximum value of the each of the variables employed in the study. Also, descriptive analysis also helped us to establish the standard deviations of all the variables from the mean values. The analysis as presented in TABLE 4.1 below enabled us to understand, interpret and make generalized conclusions about our study population, i.e. commercial banks in Kenya.
Table 4.1 Descriptive Statistics
| Descriptive Statistics | ||||||||
| N | Minimum | Maximum | Mean | Std. Deviation | Kurtosis | |||
| Statistic | Statistic | Statistic | Statistic | Std. Error | Statistic | Statistic | Std. Error | |
| ROA | 11 | 2.54% | 4.70% | 3.5355% | 0.25784% | 0.85517% | -1.763 | 1.279 |
| CAR | 11 | 6.20% | 13.80% | 10.5182% | 0.80992% | 2.68619% | -1.472 | 1.279 |
| CDR | 11 | 8.80% | 19.00% | 14.3909% | 1.06485% | 3.53170% | -1.436 | 1.279 |
| DAR | 11 | 70.50% | 75.60% | 73.0000% | 0.48015% | 1.59248% | -.953 | 1.279 |
| LDR | 11 | 69.90% | 87.20% | 76.8818% | 1.91244% | 6.34284% | -.929 | 1.279 |
| LR | 11 | 37.00% | 45.00% | 40.5273% | 0.88565% | 2.93738% | -1.567 | 1.279 |
| Valid N (listwise) | 11 | |||||||
Table 4.1 above is a summary of variables (both dependent and independent variables) used in the study where the aim is to establish the relationship between liquidity risk as represented by CAR, CDR, DAR, LDR and LR ratios on financial performance of commercial banks in Kenya tested using ROA for the 11 year period. Findings indicate that the mean ROA was 3.5355% (S.D. = 0.85517%) with the lowest performance of 2.54% in 2008 and highest return of 4.70% in 2013. As established from the above table: the mean CAR was 10.5182% (S.D. = 2.68619%) with minimum levels of 6.20% in 2016 and maximum levels of 13.80% in 2008 and 2013; the mean CDR was 14.3909% (S.D =3.53170%) with minimum levels of 8.80% in 2016 and maximum value of 19.00% in 2008; the mean DAR was 73.00%(S.D =1.59248%) with minimum value of 70.50% in 2016 and maximum of 75.60% in 2017; the mean LDR was 76.8818% (S.D=6.34284%) with minimum value of 69.90% in 2007 and maximum value of 87.20%. Last but not least, the mean LR was 40.5273% (S.D = 0.88565%) with minimum value of 37.00% in 2008 and 2011 and maximum liquidity levels of 45.00% in 2010.
From the data, we were able to establish important observations concerning two of the independent variables employed in the study. Arise in the LDR (Loan to Deposit Ratio) resulted to a decrease in the CDR ratio for the years under study except for 2017. These two ratios showed a regular pattern where from 2007 to 2012, CAR ratio ranged from 10.40% lowest in 2009 to 13.80% highest in 2008 and 2012 while in 2013 to 2017 it ranged from 6.20% lowest in 2016 to 9.20% highest in 2014. On the other hand, LDR from 2007 to 2012 ranged from 69.90% lowest in 2007 and 77.40% highest in 2011while in 2013 to 2017 it ranged from 71.30% lowest in 2017 to 87.20% highest in 2016. For the remaining variables, ROA, DAR, LR and CDR, there was no clear pattern. But, it was generally observed that CDR declined from 2007 – 2012 highest to 2013-2017 lowest which can be explained by reduction in cash at hand, cash held at CBK and cash due from other banks in proportion to total assets.
4.3 Pearson’s Correlation Coefficient
The association of variables employed in the study was tested using the Pearson’s Correlation Coefficients. Correlation analysis is used to determine the extent to which different variables considered in a study relate with one another. In a Pearson’s correlation analysis: a value of 0 to 0.3 (0 to -0.3) shows no correlation; 0.3 to 0.5 (-0.3 to -0.5) weak correlation; 0.5 to 0.7 (-0.5 to -0.7) moderate correlation and 0.7 to 1.0 (-0.7 to -1.0) strong correlation between two different variables being compared. The significance of Pearson correlation coefficients are tested at 95% confidence level with a 2-tailed test
Table 4.2 Pearson’s Correlation
| Correlations | |||||||
| ROA | CAR | CDR | DAR | LDR | LR | ||
| ROA | Pearson Correlation | 1 | .271 | .255 | -.143 | .033 | -.038 |
| Sig. (2-tailed) | .419 | .450 | .676 | .923 | .912 | ||
| CAR | Pearson Correlation | .271 | 1 | .999** | .435 | -.636* | .054 |
| Sig. (2-tailed) | .419 | .000 | .182 | .035 | .874 | ||
| CDR | Pearson Correlation | .255 | .999** | 1 | .409 | -.611* | .034 |
| Sig. (2-tailed) | .450 | .000 | .211 | .046 | .922 | ||
| DAR | Pearson Correlation | -.143 | .435 | .409 | 1 | -.904** | .518 |
| Sig. (2-tailed) | .676 | .182 | .211 | .000 | .103 | ||
| LDR | Pearson Correlation | .033 | -.636* | -.611* | -.904** | 1 | -.500 |
| Sig. (2-tailed) | .923 | .035 | .046 | .000 | .117 | ||
| LR | Pearson Correlation | -.038 | .054 | .034 | .518 | -.500 | 1 |
| Sig. (2-tailed) | .912 | .874 | .922 | .103 | .117 | ||
| **. Correlation is significant at the 0.01 level (2-tailed). | |||||||
| *. Correlation is significant at the 0.05 level (2-tailed). | |||||||
| c. Listwise N=11 | |||||||
Results from the TABLE 4.2 above indicates a weak positive relationship between financial performance, ROA with CAR, CAR, and LDR. Equally, ROA has a negative but weak relationship with DAR, and LR.
4.4 Regression Analysis
This study employed multilinear regression model to determine the relationship between liquidity risk and financial performance of commercial banks in Kenya.
4.4.1 Model Summary
Table 4.3 below presents regression model summary results. The R-value indicates the extent the dependent variable varies with respect to the independent variables employed in the study. The R Square value which is the coefficient of determination to which independent variables influence the dependent variable. The Adjusted R valued indicates the reliability of the regression results.
Table 4.3 Model Summary
| Model Summary | ||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | .697a | .486 | -.027 | 0.86673% |
| a. Predictors: (Constant), LR, CDR, DAR, LDR, CAR | ||||
The coefficient of determination which determines the extent to which the predictor variables influence change in the dependent variable is given by the R-Square value. The R-Square value for our study is 0.486 which imply that the predictor variables, LR, CDR, DAR, LDR and CAR explain 48.6% changes in the financial performance of commercial banks in Kenya. The remaining 51.4% change in the ROA could be explained by other variable not covered in this study.
4.4.2 Analysis of Variance
The analysis of variance explain whether there is a statistically significant relationship between the variables covered in the study.
Table 4.4 Analysis of Variance
| ANOVA | ||||||
| Model | Sum of Squares | Df | Mean Square | F | Sig. | |
| 1 | Regression | 3.557 | 5 | .711 | .947 | .523b |
| Residual | 3.756 | 5 | .751 | |||
| Total | 7.313 | 10 | ||||
| a. Dependent Variable: ROA | ||||||
| b. Predictors: (Constant), LR, CDR, DAR, LDR, CAR | ||||||
The results in from the Table 4.4 above indicate that the overall model is not statistically significant. For a model to be significant, it must significance p-value of 0.05 and below. The p-value for our study is 0.523 which is greater than 0.05. This indicates that the model is not a good predictor of financial performance of commercial banks in Kenya.
4.4.3 Model Coefficients
The coefficients of the regression model used in the study is indicated in the table 4.5 below. The Column B section in the table denotes the extent to which the predictor variable contributes to the value of the dependent variable. The last column shows the significance levels of the variable studied.
Table 4.5 Model of Coefficients
| Coefficients | ||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | T | Sig. | ||
| B | Std. Error | Beta | ||||
| 1 | (Constant) | .707 | 43.701 | .016 | .988 | |
| CAR | 4.356 | 2.422 | 13.682 | 1.799 | .132 | |
| CDR | -3.114 | 1.793 | -12.861 | -1.737 | .143 | |
| DAR | -.107 | .447 | -.199 | -.239 | .821 | |
| LDR | .104 | .145 | .769 | .716 | .506 | |
| LR | .040 | .117 | .139 | .344 | .745 | |
| a. Dependent Variable: ROA | ||||||
The regression model for the study is as follows;
ROA= 0.707+4.356X1 – 3.114X2 – 0.107X3 + 0.104X4 + 0.04X5 + ε
Cash to total assets ratio (CAR), Loans to Deposits Ratio (LDR) and Liquidity Ratio (LR) have direct relationship with financial performance of commercial banks in Kenya, measured using the Return on Assets Ratio (ROA). This implies a unit increase in the above variables explain certain positive change in ROA. On the other hand, results from the model of coefficients table above indicate that that Cash to Deposits Ratio (CDR) and Deposits to Total Assets Ratio (DAR) have an inverse relationship with ROA, which means that a unit increase in these variable will result in a unit decrease in financial performance.
4.5 Discussion and Findings
In this section, we discuss the findings of the analysis right from descriptive analysis results to correlation analysis then finally the regression analysis outcome. From the descriptive analysis outcome, commercial banks in the country recorded the best financial performance, ROA of 4.70% in 2013 and lowest return of 2.54% in 2008. Given that the country was not affected seriously by the 2008 global financial crisis, the low performance can be explained by the fact that the banking sector just like the rest of the economy had been affected greatly by the political crisis that followed the outcome of the 2008 general trend. This view is also supported by the fact that that financial performance of commercial banks in the country in the period 2017, a year which was characterized by heightened political temperatures was generally low at 2.60% average ROA.
The outcome of the descriptive analysis also indicate that CAR and CDR had the highest ratio in 2008 of 13.80% and 19.00% respectively. This might an indication of caution among industry players on the possible effect of political activity on the ability of borrowers to repay loans. Slowing economic activities during election may also be explanation to increased level of cash during election years. For the other two ratios, LDR and LR, the outcome of the descriptive analysis does portray a particular trend with the other four ratios discussed above.
The Pearson Correlation analysis was used to test the extent to which the variables correlate or rather show association with one another. The correlation of coefficients results revealed strong correlation between CDR and CAR, and between LDR and DAR, which both had correlation scores of more than 0.9. For ROA, however, the correction scores were within acceptable levels of below 0.7.
For regression analysis, the model of summary shows that the five predictor variables employed contributed to about 48.6% change in the financial performance of commercial banks in the country. The remaining 51.4% is arguably explained by other factors not covered in the study. As seen in the descriptive analysis outcome, political activity seems to have a big influence on the financial performance of commercial banks in the country. During the electioneering years, financial performance of the industry tends to slow, only to pick the following year, after the end of elections.
Test for ANOVA is used to determine the extent to which the model used is can be applied in explaining changes in the dependent variable. F-test is used to assess this significance. For the model to be significant, p-value should be 5% or less. Anything beyond 5% indicates that that the model is not statistically significant. Concerning this, the F-test result of 0.947 was not statistically significant as the p-value of 0.523 was far higher than the acceptable 0.05 value.
The model of coefficients shows the direction of the relationship between the predictor variables and the dependent variables. For our study, CAR, LDR and LR had a direct positive relationship with ROA which indicates that arise in this ratios is likely to result in a better performance. For the LR ratio, however, the relationship was not very strong. On the other hand, CDR and DAR ratios have an inverse relationship with ROA, which means that a unit increase in these variable will result in a unit decrease in financial performance. In line with this findings, it can be concluded that amount of liquid assets, particularly cash kept by commercial banks as precautionary to liquid risk should be derived from other sources especially from shareholders capital and not from deposits. Concerning deposits, banks need to find optimum level of deposits to total asset to keep as having a high proportion of deposits to total assets may affect performance in a negative way.
CHAPTER FIVE
SUMMARY OF THE FINDINGS, CONCLUSIONS AND RECOMMENDATIONS
5.1 Introduction
This chapter comprises of summary of findings drawn from Chapter 4, conclusions, limitations, recommendations, and outlined areas of further research.
5.2 Summary of Findings
The principal objective of this research study was to explore the relationship between liquidity risk and financial performance of commercial banks in Kenya. The research identified a total of five predictor variables, namely; CAR, CDR, DAR, LDR and LR. In relation to financial performance measured using ROA indicator. As evidenced above, the main focus of the research was on non-traditional ratios which previous studies on the relationship between liquidity risk and financial performance had not factored. We focused on cash in relation to total assets, total deposits, in particular, to determine its relationship with financial performance. To establish the extent of this relationship, the analysis of data included correlation and regression analysis.
Coefficient of determination was used to determine the extent to which the independent variables employed in the study affected financial performance of commercial banks in the county. The results showed that the predictor variables explained about 48.6% change in the dependent variable. A unit change in CAR, LDR and LR explained a positive change of 4.356 units, 0.104 units and 0.04 units in the ROA. On the other hand, a unit change in CDR and DAR explained a negative change of 3.144 and 0.107 in ROA respectively.
5.3 Conclusion
The principal objective of the study was to determine the relationship of liquidity risk on financial performance of commercial banks in Kenya for the period 2007 to 2017. Return on Assets, a key financial performance indicator was used as the dependent variable as it is more standardized compared to net income. ROA also gives a better reflection on how management utilizes assets to generate value. The outcome from the regression analysis showed that CAR, LDR and LR had positive relationship with financial performance while DAR and CDR had negative relationship. However, it important to note that none of these relationships was significant at t-test p-value of 0.05 (5%) and below level.
Additionally, we note that the predictor variables only explained 48.6% change in ROA meaning the remaining percentage could be explained by other variables such as political activities, capital adequacy, management efficiency and quality of assets held by commercial banks in the country. Furthermore, weaker performance from middle tier and lower tier banks which are associated with cases of mismanagement can explain the outcome of the study.
5.4 Limitations of the Study
The study covered the period 2007 to 2017 and all commercial banks under the regulation of CBK were part of this study. While data used in the study was easily obtained from the CBK annual bank supervision reports, we noted some inconsistencies with how the data was recorded. First, there was inconsistency with regard to government securities wherein 2007 and 2008 for instance, there was no separation of securities held for sale and those that are held into maturity. Perhaps, this inconsistency affected our calculation of CDR and CAR ratios for the years in question. The second difficulty that we came across and which has potential of affecting the outcome of this study is restatements of the financial statements. Last but not least, dealing with all the commercial banks in Kenya as opposed to a select number of commercial banks as it is the case in other studies (Vaita, 2017), for instance, could have affected the outcome of our analysis.
While the findings of the study could be implemented across the Kenyan banking industry, it is limited to the commercial banks only. The fact that we focused on liquidity ratios alone as opposed to inclusion of other ratios such as those dealing with capital adequacy, asset quality and management quality might affect the generalization of the findings as well.
5.5 Recommendations
Though not significant, results from this study indicate that liquidity level is a good predictor of financial performance. Therefore, it is important for commercial banks in the country to pay attention to liquidity management. Based on the findings, we recommend that the amount of cash kept by commercial banks for liquidity purposes should be derived from shareholders capital as opposed from deposits. Keeping large amount of deposits as cash may be counterproductive as banks are required to pay interest to these deposits. This implies that the amount or level of cash kept by commercial banks to meet customer withdrawal requests and other obligations should primarily come from capital and from interest earned from loans extended to customers.
5.6 Areas of Further Research
Other than liquidity risk, there are many other factors that affect performance of commercial banks. Some of the internal factors include asset quality, management, capital adequacy as well as size of commercial banks. Concerning size, we recommend that further research to tailor their research to include banks within a specific category only. For comparison purposes, it may be prudent carrying a study where the relationship of liquidity to financial performance is compared among tier 1, tier 2 and tier 3 commercial banks in the country. Last but not least, these research studies should try to make use of quarterly and semiannual returns of commercial banks while doing these studies.
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APPENDIX I: LIST OF COMMERCIAL BANKS IN KENYA
- Name Category
1 KCB Bank Kenya Ltd Large
2 Co – operative Bank of Kenya Ltd Large
3 Equity Bank Kenya Ltd Large
4 Barclays Bank of Kenya Ltd Large
5 Standard Chartered Bank (K) Ltd Large
6 Commercial Bank of Africa Ltd Large
7 Diamond Trust Bank (K) Ltd Medium
8 Stanbic Bank Kenya Ltd Medium
9 NIC Bank PLC Medium
10 I & M Bank Ltd Medium
11 National Bank of Kenya Ltd Medium
12 Chase Bank Ltd** Medium
13 Citibank N.A. Kenya Medium
14 Family Bank Ltd Medium
15 Bank of Baroda Ltd Medium
16 Bank of Africa Kenya Ltd Medium
17 Prime Bank Ltd Medium
18 HFC Ltd Medium
19 Ecobank Kenya Ltd Medium
20 Bank of India Medium
21 Imperial Bank Ltd** Medium
22 Guaranty Trust Bank (Kenya) Ltd Small
23 Gulf African Bank Ltd Small
24 African Banking Corporation Ltd Small
25 Victoria Commercial Bank Ltd Small
26 Mayfair Bank Ltd Small
27 Sidian Bank Ltd Small
28 SBM Bank (Kenya) Ltd Small
29 Development Bank of Kenya Ltd Small
30 Jamii Bora Bank Ltd Small
31 Spire Bank Ltd Small
32 First Community Bank Ltd Small
33 DIB Bank Kenya Ltd Small
34 Guardian Bank Ltd Small
35 Consolidated Bank of Kenya Ltd Small
36 Habib Bank A.G. Zurich Small
37 Transnational Bank Ltd Small
38 Paramount Bank Ltd Small
39 M-Oriental Commercial Bank Ltd Small
40 Credit Bank Ltd Small
41 Middle East Bank (K) Ltd Small.
42 UBA Kenya Bank Ltd Small
43 Charterhouse Bank Ltd* Small
*Banks under statutory management
**Banks in receivership
APPENDIX II: DATA COLLECTION FORM
| YEAR/RATIO | ROA=Net Income/Total Assets
|
CAR=Total Cash/Total Assets | CDR =Total Cash/Total Deposits | DAR= Total Deposits/Total Assets | LDR=Loans/ Total Deposits | LR= Liquid Assets/Total Deposits |
| 2007 | ||||||
| 2008 | ||||||
| 2009 | ||||||
| 2010 | ||||||
| 2011 | ||||||
| 2012 | ||||||
| 2013 | ||||||
| 2014 | ||||||
| 2015 | ||||||
| 2016 | ||||||
| 2017 |
APPENDIX III
RAW DATA