. International Journal of Economics and Financial Issues ISSN: 2146-4138 available at http: www.econjournals.com International Journal of Economics and Financial Issues, 2018, 8(2), 287-297. International Journal of Economics and Financial Issues | Vol 8 • Issue 2 • 2018 287 Impact of Credit Risk Management on the Performance of Selected Nigerian Banks E. Chuke Nwude1*, Chinedu Okeke2 1Department of Banking and Finance, Faculty of Business Administration, University of Nigeria Nsukka, Enugu Campus, Nigeria. 2Zenith Bank, Central Business District, Abuja, Nigeria. *Email: chuke.nwude@unn.edu.ng ABSTRACT This study investigated the impact of credit risk management on the performance of deposit money banks in Nigeria using five banks that had highest asset base. Ex-post facto research design was adopted using dataset for the period 2000–2014 collated from the annual reports and financial statement of the selected deposit money banks. Three hypotheses were proposed and tested using ordinary least square regression model. The findings reveal that credit risk management had a positive and significant impact on total loans and advances, the return on asset and return on equity of the deposit money banks. The study recommended that bank managers need to put more efforts to control the non-performing loan by critically evaluating borrowers’ ability to pay back. The regulator should strengthen its monitoring capacity in this regard. Keywords: Credit Risk, Bank Performance, Risk Management, Return on Equity, Return on Asset, Non-performing Loans JEL Classifications: G21, G32 1. INTRODUCTION For every endeavor that involves the mortal being there is risk. Financial institutions are piloted by human beings and therefore incur challenges for a multitude of reasons, some of the major causes of such problems continues to be directly related to lax credit standards for borrowers and counter parties, poor portfolio risk management, lack of attention to changes in economic or other circumstances that can lead to a deterioration in the credit standing of a bank’s counter parties. Banks use customers deposit to generate credit for their borrowers, which in fact is a revenue generating activity for banks. This credit creation process exposes the banks to high default risk which might lead to financial distress including bankruptcy. Banking industry is no doubt the most regulated sector in any economy because of the riskiness of its operation. As a result risk management in banks is a discipline every participants and players in the industry need to align with. This is why the subject of risk occupies a central position in the business decisions of bank management. Investors and the general public to a large extent approach banks for loans and advances in large volumes which constitute risk- assets of banks and necessitate the need for provisioning against them. As long as there is risk there is need for risk management. Risk management is the identification, assessment, and prioritization of risks followed by coordinated and economical application of resources to minimize, monitor and control the probability and impact of unfortunate events. Deposit money banks assume various kinds of risks in the process of providing financial services as lending is the core business activity of banks. The loan portfolio is typically the largest asset and the predominant source of revenue to banks and also one of the greatest sources of risk to a bank’s safety and soundness. At the core of credit extension in the banking industry risk management is seen as the process of identifying risks, assessing their implications, deciding on a course of action, and evaluating the results. Effective risk management seeks to maximize the benefits of a risky situation while minimizing the negative effect of the risk. Adequate management of credit risk in financial institutions is critical for their survival and growth. To accomplish this, the bank management must have a thorough knowledge of each portfolio composition or mix, industry and geographic concentrations of credits, average risk ratings, and other aggregate characteristics. They must be sure that the policies, processes, and practices implemented to control the risks of individual loans and portfolio segments are sound and that lending Nwude and Okeke: Impact of Credit Risk Management on the Performance of Selected Nigerian Banks International Journal of Economics and Financial Issues | Vol 8 • Issue 2 • 2018288 personnel adhere to them. Many Nigerian banks had failed in the past due to inadequate management of their risk exposure. The problem has continued to affect the industry with serious adverse consequences as banks are generally subject to wide array of risks in their business operations. Against this background, the need to empirically examine the impact of credit risk management on the performance of selected deposit money banks in Nigeria becomes necessary. In this regard the main objective of this study is to critically evaluate the effect of credit risk management on the performance of selected Nigerian commercial banks. In line with this, the specific objectives of the study are to ascertain the impact of credit risk management on (i) total loans and advances (ii) return on assets (ROAs) (iii) return on equity (ROE) of the selected deposit money banks in Nigeria. In line with the specific objectives the study will seek to provide solutions to the research questions such as, to what extent has credit risk management affected (i) total loans and advances (ii) ROAs (iii) ROE of the selected deposit money banks in Nigeria? As a follow-up to the research questions raised above, the following hypotheses were tested. H1: Credit risk management has no positive and significant impact on (i) total loans and advances (ii) ROAs (iii) ROE of the selected deposit money banks in Nigeria. The study focused on the impact of credit risk management on some of the profitability indices of selected deposit money banks in Nigeria. The study period covered is 2000–2014. The five banks with highest asset base were selected which are First Bank of Nigeria plc, United Bank for Africa (UBA), Diamond Bank plc, Zenith Bank plc and Guaranty Trust Bank (GTBank) plc. The base year 2000 was chosen because the transition to democratic civilian government came into being on May 29, 1999 with full year effect from 2000. The year 2000 started a new environment for banking practice in Nigeria. This study will be of immense benefit to both the selected banks and other banks’ management in observing the weight credit risk management exerts on the selected performance indices of banks. This is likely to spur them to critical review of their credit risk management styles. The findings will enable investors and general banking public to place judgment on the bank management in terms of leadership capacity having observed the level of non- performing loans (NPLs) in the banks, total asset value, ROAs, and ROE. Essentially, the results of the study can provide an input reference document for further researches in evaluation of risk management. It also provides policy makers more insight into risk management and guide them in policy making, implementation monitoring. 2. LITERATURE REVIEW 2.1. Conceptual Review The perspectives of risk differ and risk definition depends on and affected by the risk observer. Hillson (2002) states that the way risk is perceived influences the manner in which it is handled. To Kannan and Thangavel (2008), risk implies exposure to uncertainty or threat. Risk sometimes entails some economic benefits as firms may derive considerable gains by taking risk. Kaye and Lowe (2010) are of the view that risk is integral to opportunities and threats which may adversely affect an action or expected outcome. Drucker (1977) submits that business grows through greater risk taking. Hillson and Murray-Webster (2011) see risk as ‘uncertainty that matter’ in business enterprise. In support of Drucker (1977) and Hillson and Murray-Webster (2011), Olajide (2013) explains that recent economic volatility gives risk management a new focus and eminence. They are of the opinion that getting rid of risk can undermine the source of value creation which truncates potential opportunities. Bakpo and Kabari (2009) stressed that one of the most important decision problems that require serious attention is granting of loans by a financial institution. According to Idowu and Awoyemi (2012), liquidity risk, market risk, foreign exchange risk and solvency risk are the most applicable risk to the banks. In accordance with Basel II accord, credit risk, market risk, and operational risk are types of risks usually found in the banking organization. (a) Credit risks also known as default risk, is one of the oldest. It is the most vital forms of risk faced by banks as financial intermediaries (Broll et al., 2002). It is the potential loss arising from the failure of a borrower to meet its obligations in accordance with agreed terms. (b) Market risks is risk in volatility in the market that affects the bank’s return. It is the risk of loss from adverse movement in financial market rates (interest and exchange rate, bond, equity or commodity prices). A bank’s market risk exposure is determined by both the volatility of underlying risk factors and the sensitivity of the bank’s portfolio to movements in those risk factors (Zahangir and Masukujj, 2011). (c) Operational risk is the potential financial loss as a result of breakdown in day to day operational processes. It can arise from failure to comply with policies, laws and regulations, from fraud or forgery. These include direct and indirect laws resulting from inadequate or frail internal processes, people and systems or from external event. Moreover, Nigerian Deposit Insurance Corporation (2009) stressed that insider abuse is perhaps the most significant factor that led to bank failure. Many owners and directors abused or misused their privileged positions or breached their fiduciary duties by engaging in self-serving activities. The abuses included granting of unsecured credit facilities to owners, directors and related companies which in some cases were in excess of their banks statutory lending limits in violation of the provisions of the banks and other financial institutions act (BOFIA) of 1991 as amended; granting of interest waivers on non-performing insider-credits without obtaining CBN prior approval as required by BOFIA; diversion or conversion of banks resources to service their other business interests such as allocation of foreign exchange without Naira cover to insiders which later crystallized as hard-core debts; compelling their banks to directly finance trading activities either through the banks or other proxy companies, the benefits of which did not accrue to the banks. Where losses were incurred, they were passed to the banks. Credit risk management provides a clear and structured approach to identifying, measuring and prioritizing risks in order to take Nwude and Okeke: Impact of Credit Risk Management on the Performance of Selected Nigerian Banks International Journal of Economics and Financial Issues | Vol 8 • Issue 2 • 2018 289 appropriate actions to minimize losses. An effective credit risk management (ECRM) practice does not eliminate risks, but minimize risks. The implementation and maintenance of ECRM warrants firm commitment to improve the efficiency of business processes. The efficiency can attracts some benefits like (i) saving resources: Time, assets, income, property and personnel; (ii) Protection of an organization reputation and public image; (iii) prevention or reduction of legal liabilities; (iv) increasing the stability of operations and promoting continuous improvement; (v) protecting people and environment from harm; (vi) avoiding fines for corporate non-compliance with regulations and legislation; (vii) enhancing the ability to prepare for unforeseen and unexpected circumstances; (viii) enhancing competitive advantage through improved decision support and market intelligence based on more accurate risk-adjusted management information; (ix) improved shareholder value and confidence, which is especially valuable in times of crisis when shareholder trust is stressed to its maximum limits; and (x) assisting in clearly defining suitable risk management techniques, including insurance needs (Meulbroek, 2002; Hillson, 2006; Protiviti Inc., 2006). In the case of banks, according to Abdullahi (2013), the issue of credit risk is even of greater concern because of the higher level of perceived risks resulting from some of the characteristics of clients and business conditions that they find themselves in, which needs thorough empirical examinations. Sequel to this, Olalekan and Adeyinka (2013) pointed out that while banks are expected to absorb the losses from the normal earnings, there may be some unanticipated losses which cannot be absorbed by normal earnings. Credit risk management is critical to every business and especially to banks whose stock is cash. The main profitable activity of the bank is lending money and the underlying principle of lending is risk mitigation, which goes with determining a borrower’s ability and propensity to repay the loaned amount. Credit risk management starts with assessing the likelihood of credit risk; determining this risk involves reviewing the borrower’s past credit history and earned income. Someone with a good credit rating poses less of a risk than someone with a poor credit history. It is therefore, inevitably not possible to separate banking with credit risk management; which has to be a continuous process and implemented wherever banking business exist. Effective credit-risk management requires financial managers or risk manager to have in depth understanding of the corporate financial risks and how they interrelate with credit risk. This demands analysis of the business environment in which the bank operate and the assessment of the credit risk in terms of likelihood and impact on the entity`s loan portfolio and profitability. The assessment avails important information to banks necessary to identify, measure, monitor and control credit risk as well as to determine that they hold adequate capital against these risks and that they are adequately compensated for risks incurred. 2.2. Theoretical Review Profitability is one of the ultimate goals of banks and so all strategies and activities are designed to realize this grand objective. As banks increases and maximizes its profits, it must either increases risk or lower its operating cost. Banks make money from a series of activities of borrowing and deposits but the occurrence of NPLs and advances is considered as losses to the bank. Higher NPL means higher losses, which adversely influence the banks available capital for further borrowing and hence the efficiency of banks investment is affected, further influencing the profitability. NPLs can be attributed to both macroeconomic conditions and banks specific factors. Excessive risk taking valued by loans-to assets ratio and growth rate of banks loans can as well lead to high NPLs. Profitability will generally vary directly with riskiness of bank’s portfolio and operations. As a result in order to increase the return, banks need to know which risk factors have greater impact on profitability which eventually leads to bank financial performance. Saunders and Cornett (2005) stated that the pervasive incidence of non-performing loan is one of the prime causes of failure in the banking system. The very nature of the banking business is so sensitive because more than 85% of their liability is deposits from depositors. While banks engage in lending as main source of income, they are at the same time exposed to various risks which if not properly analyzed and managed could threaten its continued existence. Even though the survival of any bank depends largely on its effective risk management strategies, some bank executives do neglect this aspect of bank job purely for selfish motives. Soyemi et al. (2014) emphasized that excessive and poorly managed risk can no doubt lead to losses and thus endanger the safety of a bank’s depositors. Once risks have been identified, they should be measured in order to determine their impact on the banking institution’s profitability and capital. This can be done using various techniques ranging from simple to sophisticated models. Accurate and timely measurement of risk is essential to effective risk management systems. Studies on the influence credit risk management practices on financial performance have been largely conceptual drawing on the theoretical frameworks provided by institutional regulators. Tandelilin et al. (2007), Rivard and Thomas (1997), Li (2007) and Kim and Santomero (1998) opine that a major objective of bank management is to increase shareholders’ return as an index of bank performance. They maintain that this objective often comes at the cost of increasing risk. They detailed bank risks to include interest risk, market risk, credit risk, off-balance risk, technology and operational risk, foreign exchange risk, country risk, liquidity risk, and insolvency risk. The bank’s motivation for risk management comes from those risks which can lead to bank underperformance. Schroeck (2002), Nocco and Stulz (2006) in Ariffin and Kassim (2009) stress the importance of good risks management practices to maximize firms’ value. While the former proposes that ensuring best practices by instituting effective and prudent risk management practices: Increase earnings, the latter specifically posits that effective enterprise risk management have a long-run competitive advantage to the firm (or banks) compared to those that manage and monitor risks individually. In the light of this and as a follow-up, a holistic approach is suggested in managing risk. According to Bikker (1999) and Kosmidou (2008) concept of bank performance can be expressed in multidimensional ways which Nwude and Okeke: Impact of Credit Risk Management on the Performance of Selected Nigerian Banks International Journal of Economics and Financial Issues | Vol 8 • Issue 2 • 2018290 include competition, concentration, efficiency, productivity and profitability. These are the multidimensional nature of the concept of bank performance which calls for wide range of its measures. However the most frequently used measure of banking profitability are ROA, ROE ratio and net interest margin (NIM). Despite the fact that ROE is still the primary performance measure for the most investors and analyst, sometimes ROAs provides a better understanding of a company performance as pointed out by Hagel et al. (2010). Besides some experts consider that a single - ratio cannot be a good proxy for bank performance due to the complex operational environment of banks (Yang, 2012). 2.3. Empirical Review Credit risk is a serious threat to the performance of banks and as a result various researchers have examined the impact of credit risk on banks in varying dimensions. Hakim and Neamie (2001) as documented in Ariffin and Kassim (2009) examine credit risk and bank’s performance in Egypt and Lebanon banks in the 1990s by using data for banks from the two countries over the period 1993–1999, the study estimates an ordinary least squares regression (OLS) model of bank return with varying intercepts and coefficients. The findings show that credit variable is positively related to profitability, while liquidity variable is insignificant across all banks and have no impact on profitability. The study also finds a strong link between capital adequacy and commercial bank return, with high capitalization being the hindrance to return. The study concludes that the capital is a sunk cost with large banks realizing high profits in absolute but not in percentage terms. Burner (2010) observed that a reduction in real risk-free rates of interest to historically low levels led to credit expansion in a ferocious search for yield among investors. Major financial crisis around the world can be attributed to inordinate ambition to return excellent return to their owners by decision makers and the board thereby taking excess risk to boost stock prices. Adeusi et al. (2013) focused on the association of credit risk management practices and bank financial performance in Nigeria. Using a secondary data for 10 banks and for four years reported an inverse relationship between financial performance of banks and doubtful loans, capital asset ratio was found to be positive and significant. Similarly it suggests that the higher the managed funds by banks, the higher the performance. The study concludes a significant relationship between banks performance and risk management. Hence, the need for banks to practice prudent risks management in order to protect the interests of investors. Kargi (2011) evaluated the impact of credit risk on the profitability of Nigerian banks. Financial ratios as measures of bank performance and credit risk were collected from the annual reports and accounts of sampled banks from 2004 to 2008 and analyzed using descriptive, correlation and regression techniques. The findings revealed that credit risk management has a significant impact on the profitability of Nigerian banks. It concluded that banks’ profitability is inversely influenced by the levels of loans and advances, NPLs and deposits thereby exposing them to great risk of illiquidity and distress. Epure and Lafuente (2012) examined bank performance in the presence of risk for Costa-Rican banking industry during 1998–2007. The results showed that performance improvements follow regulatory changes and that risk explains differences in banks and NPLs negatively affect efficiency and ROAs while the capital adequacy ratio has a positive impact on the NIM. Kithinji (2010) assessed the effect of credit risk management on the profitability of commercial banks in Kenya. Data on the amount of credit, level of NPLs and profits were collected for the period 2004–2008. The findings revealed that the bulk of the profits of commercial banks are not influenced by the amount of credit and NPLs, therefore suggesting that other variables other than credit and NPLs impact on profits. Kolapo et al. (2012) investigated the relationship between bank performance and credit risk management using panel data regression for the period 2000–2010. It could be inferred from their findings that ROE and ROAs both measuring profitability were inversely related to the ratio of non-performing loan to total loan of financial institutions thereby leading to a decline in profitability. Ahmad and Ariff (2007) examined the key determinants of credit risk of commercial banks on emerging economy banking systems compared with the developed economies. The study found that regulation is important for banking systems that offer multi-products and services; management quality is critical in the cases of loan- dominant banks in emerging economies. An increase in loan loss provision is also considered to be a significant determinant of potential credit risk. The study further highlighted that credit risk in emerging economy banks is higher than that in developed economies. Ben-Naceur and Omran (2008) in attempt to examine the influence of bank regulations, concentration, financial and institutional development on commercial banks’ margin and profitability in Middle East and North Africa (MENA) countries from 1989 to 2005 found that bank capitalization and credit risk have positive and significant impact on banks’ NIM, cost efficiency and profitability. Al-Khouri (2011) assessed the impact of bank’s specific risk characteristics, and the overall banking environment on the performance of 43 commercial banks operating in 6 of the Gulf Cooperation Council countries over the period 1998–2008. Using OLS regression analysis, results showed that credit risk, liquidity risk and capital risk are the major factors that affect bank performance when profitability is measured by ROAs while the only risk that affects profitability when measured by ROE is liquidity risk. Ahmed et al. (1998) in their study found that loan loss provision has a significant positive influence on NPLs. Therefore, an increase in loan loss provision indicates an increase in credit risk and deterioration in the quality of loans consequently affecting bank performance adversely. Furthermore, Hakim and Neamie (2001) also as documented in Ariffin and Kassim (2009) examine credit risk and bank’s performance in Egypt and Lebanon banks in the 1990s by using data for banks from the two countries over the period 1993–1999, the study estimates a fixed effects model of bank return with varying intercepts and coefficients. The findings show that credit variable is positively related to profitability, while liquidity variable is insignificant across all banks and have no impact on profitability. The study also finds a strong link between capital adequacy and commercial bank return, with high capitalization being the hindrance to return. The study concludes that the capital is a sunk cost with large banks realizing high profits in absolute but not in percentage terms. Nwude and Okeke: Impact of Credit Risk Management on the Performance of Selected Nigerian Banks International Journal of Economics and Financial Issues | Vol 8 • Issue 2 • 2018 291 Another dimension is offered by Bruner (2010) on taking excessive risk to boost performance. Burner (2010) observed that a reduction in real risk-free rates of interest to historically low levels led to credit expansion in a ferocious search for yield among investors. He attributes major financial crisis around the world to inordinate ambition by decision makers and the board of directors to return excellent return to their owners which push them to taking excess risk to achieve this objective. Hosna et al. (2009) reemphasized the effect of credit risk management on profitability level of banks. They concluded that higher capital requirement contributes positively to bank’s profitability. Muhammed et al. (2012) used descriptive, correlation and regression techniques to study whether credit risk affect banks performance in Nigeria from 2004 to 2008. They also found that credit risk management has a significant impact on profitability of Nigerian banks. Boahene et al. (2012) used regression analysis to determine whether there is a significant relationship between credit risk and profitability of Ghanaian banks. They followed the line of Hosna et al. (2009) by using ROE as a measure of bank’s performance and a ratio of NPLs to total asset as proxy for credit risk management. They found empirically that there is an effect of credit risk management on profitability level of Ghanaian banks. The study also suggests that higher capital requirement contributes positively to bank’s profitability. Liyuqi (2007) examined the determinants of bank’s profitability and its implications on risk management practices in the United Kingdom. The study employed regression analysis on a time series data between 1999 and 2006. Six measures of determinants of bank’s profitability were employed. He proxied internal determinants of bank’s performance as liquidity, credit and capital. GDP growth rate, interest rate and inflation rate were used as external determinants of banks profitability. The six variables were combined into one overall composite index of bank’s profitability. ROA was used as an indicator of bank’s performance. It was found that liquidity and credit risk have negative impact on bank’s profitability. Poudel (2012) appraised the impact of the credit risk management in bank’s financial performance in Nepal using time series data from 2001 to 2011. The result of the study indicates that credit risk management is an important predictor of bank’s financial performance. Fredrick (2010) also demonstrated that credit risk management has a strong impact on bank’s financial performance in Kenya. Meanwhile, Jackson (2011) towed the line of Fredrick (2010) by using CAMEL indicators as independent variables and ROE as a proxy for banks performance. His findings were also in line with that of Fredrick who also concluded that CAMEL model can be used as proxy for credit risk management. Musyoki and Kadubo (2011) also found that credit risk management is an important predictor of bank’s financial performance. They concluded that banks success depends on credit risk management. While analyzing the credit risk management efficiency in Nigerian commercial banking sector from 2004 through 2009, Onaolapo (2012) provides some further insight into credit risk management as profit enhancing mechanism. They used regression analysis and found rather an interesting result that there is a minimal causation between deposit exposure and bank’s performance. Muhammed et al. (2012) used descriptive, correlation and regression techniques to study whether credit risk affect banks performance in Nigeria from 2004 to 2008. They also found that credit risk management has a significant impact on profitability of Nigerian banks. 2.4. Review Summary Several works motivated this research though as highlighted in the empirical review. Among such works are Adeusi et al. (2013), Boahene et al. (2012), Epure and Lafuente (2012), Kolapo et al. (2012), Muhammed et al. (2012), Onaolapo (2012), Poudel (2012), Al-Khouri (2011), Jackson (2011), Kargi (2011), Musyoki and Kadubo (2011), Fredrick (2010), Hosna et al. (2009), Ariffin and Kassim (2009), Liyuqi (2007) and Hakim and Neamie (2001) who with regression analysis found significant relationship between credit risk management and profitability of banks. They mainly employed ROE and ROA as measures of bank performance and a ratio of NPLs to total asset as proxy for credit risk management. Even though the survival of many banks depends on its effective risk management strategies, only few studies have actually investigated concepts of risk management in deposit money banks with the aim of statistically determining their impact on bank performance in Nigeria. More so, the relationships that exist between risk management and bank performance have not received the desired attention in finance literatures. The need to fill the aforementioned knowledge gaps justifies the relevance of this study. Against this background, the need to carry out this research becomes necessary in order to objectively and empirically examine the impact of risk management and the performance of Nigerian deposit money banks. 3. METHODOLOGY The research design adopted in this study is the ex post facto and analytical design. The data set is secondary data obtained from the Central Bank of Nigeria statistical bulletins and annual reports of the five selected banks (Diamond, First, GTB, UBA and Zenith). The study borrowed its analytical model from the work of Kaaya and Pastory (2013) which examined Credit Risk and Commercial Banks Performance in Tanzania. The linear regression employed by the authors is of the form: Y=α+β1X+µ (1) Where Y=ROA (as measure of bank profitability), X=Impaired loans, β=Coefficient of the independent variable, µ=Error term. Models for this study will be patterned after the above model but with introduction of a control variable, natural logarithm of total asset. The functional relationships for hypotheses one, two and three are therefore specified as follows: Hypothesis one: TLAR=α+β1NPLR+β2NLTA+µ (2) Hypothesis two: ROA=α+β1NPLR+β2NLTA+µ (3) Hypothesis three: ROE=α+β1NPLR+β2NLTA+µ (4) In writing the model equation, the following symbols were used to denote our respective variables. Return on asset=ROA, return on Nwude and Okeke: Impact of Credit Risk Management on the Performance of Selected Nigerian Banks International Journal of Economics and Financial Issues | Vol 8 • Issue 2 • 2018292 equity=ROE, NPLa/total loans=NPLR, total loans and advances/ total asset=TLAR, natural log of total asset=NLTA. 3.1. Dependent Variables ROE and ROAs are two measures for deposit money banks’ profitability. The data used to calculate these two ratios are retrieved from the annual reports and financial statements of the selected commercial banks between the periods 2000 and 2014. ROA=Earnings before interest and tax/total asset while ROE=Earnings after interest and tax/Shareholders’ funds. Total loans and advances ratio (TLAR) is one of our independent variables and is calculated as the ratio of total loans and advances to total assets. It is an indicator of how loans and advances are covered by the total assets. 3.2. Independent Variable NPLs ratio (NPLR) is an indicator that measures credit risk management. NPLs can be defined as a sum of borrowed money upon which the debtor has not made his or her scheduled payments for at least 90 days. A nonperforming loan is either in default or close to being in default. NPLR=Total NPLs and advances/total Loans and advances. 3.3. Control Variables Natural logarithm of total asset is usually used as proxy for bank size. Since every other variable in our model equations are in ratios, we find it necessary to logarithmize the absolute value of the total assets to bring all the variables to equal base. We transformed to natural log simply because our best results during the preliminary tests are based on that transformation after several trials. 3.4. Techniques of Analysis The objective of this study is to examine the impact of credit risk management on selected deposit money banks in Nigeria for the period 2000–2014. To achieve this objective the OLS regression was used in analysis. The use of linear regression helps to determine the effect of the independent variable(s) on the dependent variable and to what degree. In other words, it determines both direction and magnitude of the relationships. Data collected were presented with the aid of tables. Diagnostic tests such as for normality and unit root were carried out. 4. DATA PRESENTATION AND ANALYSIS 4.1. Data Presentation Table 1 presents the data from annual reports and statement of accounts of selected deposit money banks namely Diamond, First bank, GTBank, UBA and Zenith bank while Table 2 contains the descriptive statistics of the variables. 4.2. Preliminary Tests 4.2.1. Test for normality To confirm if the data set assume a pattern of standard normal distribution we utilized the Jarque-Bera (JB) statistic. Table 2 showed that all the variables are normally distributed as the p-values are greater than the critical value of 0.05 and all right- Bank Year ROA ROE NPLR TLAR TA (N’million) Diamond 2000 0.0092 0.7484 0.0133 0.0199 110,994,950 2001 0.0260 1.2162 0.1664 0.2003 115,047,860 2002 0.0261 1.0854 0.2331 0.1046 118,249,720 2003 0.0269 0.9340 0.0811 0.2133 120,326,540 2004 0.2630 0.9365 0.0633 0.1266 122,004,950 2005 0.0281 0.8482 0.273 0.3173 124,994,957 2006 0.2370 1.0125 0.0659 0.2702 223,047,862 2007 0.0281 1.4746 0.0722 0.382 312,249,721 2008 0.2490 1.7967 0.0132 0.2761 603,326,540 2009 −0.0149 −0.6747 0.0087 0.3074 604,000,914 2010 0.0172 0.9011 0.0751 0.4833 548,402,560 2011 −0.0380 −3.1596 0.0185 0.5005 714,063,959 2012 0.0267 3.1879 0.0166 0.1275 1,059,173,257 2013 0.0245 4.1110 0.021 0.1208 1,354,930,871 2014 0.0139 1.9047 0.0324 0.1332 1,750,270,423 First 2000 0.0293 6.3612 0.02601 0.1427 256,500,000 2001 0.0330 7.9285 0.182 0.2457 275,450,000 2002 0.0387 6.0319 0.0635 0.312 284,650,000 2003 0.0395 6.4648 0.2241 0.255 302,456,000 2004 0.0451 6.3369 0.205 0.0514 312,490,000 2005 0.0401 6.1659 0.3024 0.3038 377,496,000 2006 0.0367 5.3718 0.0718 0.3252 540,129,000 2007 0.0289 3.1254 0.0791 0.2873 762,881,000 2008 0.0326 3.0641 0.0112 0.321 1,165,461,000, 2009 0.0276 2.8212 0.0091 0.3451 1,667,422,000 2010 0.0170 1.9688 0.0882 0.5184 1,962,444,000 2011 0.0213 2.9089 0.0249 0.4582 2,463,543,000 2012 0.0300 4.3603 0.0142 0.1188 2,770,675,000 2013 0.0236 3.6384 0.0231 0.1132 3,246,577,000 2014 0.0197 0.3483 0.1579 0.144 3,586,433,000 GTB 2000 0.0195 0.9364 0.1032 0.2001 102,500,080 2001 0.0264 1.2756 0.2203 0.2309 110,865,000 2002 0.0251 1.5756 0.0913 0.1331 125,400,000 2003 0.0324 1.5416 0.0833 0.2441 132,500,000 2004 0.0394 2.2888 0.1332 0.1503 154,800,000 2005 0.0417 1.7769 0.2909 0.2874 167,897,704 2006 0.0328 2.8925 0.0631 0.306 305,080,565 2007 0.0320 3.2532 0.0802 0.2643 478,369,179 2008 0.0390 0.2364 0.024 0.2896 963,118,828 2009 0.0324 0.2227 0.0122 0.3645 1,078,177,585 2010 0.0407 3.3726 0.0911 0.4963 1,168,052,897 2011 0.0402 3.5101 0.0262 0.5001 1,608,652,646 2012 0.0618 5.7941 0.0295 0.1254 1,620,317,223 2013 0.0527 5.8132 0.0199 0.1097 1,904,365,795 2014 0.0518 6.3491 0.0432 0.217 2,126,608,312 UBA 2000 0.0053 0.9242 0.0344 0.2561 178,500,000 2001 0.0074 1.3352 0.0165 0.2433 195,580,000 2002 0.0092 1.4594 0.0233 0.1772 201,305,000 2003 0.0115 2.0053 0.0732 0.1009 201,150,000 2004 0.0142 2.7202 0.1332 0.2441 225,000,000 2005 0.0253 3.0411 0.2702 0.3306 248,928,000 2006 0.0147 3.2458 0.0511 0.2642 851,241,000 2007 0.0259 3.4500 0.0663 0.2497 1,102,348,000 2008 0.0359 4.6395 0.0202 0.2861 1,520,093,000 2009 0.0113 1.1958 0.0044 0.3222 1,400,879000 2010 0.0060 0.1675 0.0669 0.4638 1,432,632000 2011 −0.0002 −1.013 0.0301 0.3895 1,655,465,000 2012 0.0238 2.8727 0.0115 0.1773 1,933,065,000 2013 0.0233 2.8186 0.0256 0.1241 2,217,714,000 2014 0.0181 2.4305 0.0344 0.1339 2,338,858,000 Zenith 2000 0.0100 0.7871 0.0322 0.1335 186,500,000 2001 0.0122 1.1513 0.1662 0.2633 205,896,000 2002 0.0170 1.1860 0.0144 0.2447 225,980,000 2003 0.0211 1.9880 0.0413 0.3115 265,800,000 Table 1: Computed data of selected deposit money banks (Contd...) Nwude and Okeke: Impact of Credit Risk Management on the Performance of Selected Nigerian Banks International Journal of Economics and Financial Issues | Vol 8 • Issue 2 • 2018 293 tailed with Skewness that ranged between 1.54 and −0.45 and peakedness of between 4.06 and 1.64. 4.2.2. Test for stationarity To ensure that the parameters estimated are stationary (stable over time) we utilized the Augmented Dickey-Fuller (ADF). At lag of one Table 3 indicates that for all our variables, the probability values for our five standard criteria (Levin, Lin and Chu In Pesaran and Shin W-stat, ADF - Fisher Chi-square and PP - Fisher Chi- square) are all <5% critical value. Therefore, we reject the null hypotheses that our variables have unit root (are not stationary), and accept the alternate hypotheses that our variables have no unit root (are stationary). 4.3. Test of Hypothesis Test of hypothesis one (H1) which states that credit risk management has no positive and significant impact on total loans and advances of the selected deposit money banks in Nigeria. From the Table 4, our explanatory variable, NPLR, has positive and non-significant impact on total loans and advances of selected Nigerian deposit money banks. This was explained by the positive coefficient value of our explanatory variable and the corresponding probability value of the t-statistic 0.0658, which is >5% critical value. The R2 is the summary measure that tells us how well the sample regression line fits the data. From the model above, R2 of 0.52 means that 52% variation in TLAR was explained by a change in NPLs, and the remaining 48% was explained by variables not included in the model. The overall regression is confirmed to be significant as indicated by the p-value of the F-statistics. Durbin- Watson (DW) statistic approximate value of 2.39 shows there is a trace of negative serial autocorrelation. The null hypothesis is rejected while the alternate hypothesis accepted. This implies that credit risk management has a positive but non-significant impact on total loans and advances of deposit money banks in Nigeria. Test of hypothesis (H2) two which states that Credit risk management has no positive and significant impact on ROA of the selected deposit money banks in Nigeria. From the Table 5, our independent variable, NPLR has positive and non-significant impact on the ROAs of the selected deposit money banks. This was explained by the positive coefficient value of our independent variable and the corresponding probability value of the t-statistic 0.3601, which is greater than critical value of 0.05. The R2 is the summary measure that tells us how well the sample regression line fits the data. From the model above, R2 of 0.60 means that 60% variation in ROAs was explained by a change in NPLs, and the remaining 40% was explained by variables not included in the model. The overall regression is confirmed to be significant as indicated by the p-value of the F-statistics (P = 0.0117). Durbin Watson statistics (DW) approximate value of 2.33 shows that there is a trace of negative serial autocorrelation. The null hypothesis is not accepted while the alternate hypothesis is not rejected. This implies that credit risk management has a positive and non- significant impact on the ROA of deposit money banks in Nigeria. Test of hypothesis three (H2) which states that Credit risk management has no positive and significant impact on ROE of the selected deposit money banks in Nigeria. From the Table 5, our independent variable, NPLR, has positive and non-significant impact on the profitability of Nigerian deposit money banks. This was explained by the positive coefficient value of our independent variable and the corresponding probability value of the t-statistic 0.61, which is >0.05 critical value. The R2 is the summary measure that tells us how well the sample regression line fits the data. From the model above, R2 of 0.58 means that 58% variation in ROE was explained by a change in NPLs, and the remaining 42% was explained by variables not included in the model. The overall regression is confirmed to be significant as indicated by the p-value of the F-statistics. Durbin- Watson statistics (DW) approximate value of 2.21 shows that there is a trace of negative serial autocorrelation. The null hypothesis is rejected while the alternate hypothesis is accepted. This implies that credit risk management has a positive and non-significant impact on the ROE of deposit money banks in Nigeria. 4.4. Implications of Results This study examined the impact of credit risk management on the performance of Nigeria banks from 2000 to 2014. Bank Year ROA ROE NPLR TLAR TA (N’million) 2004 0.0255 2.2712 0.2113 0.1993 296,500,000 2005 0.0277 2.3853 0.2774 0.2909 329,716,511 2006 0.0249 2.5047 0.0691 0.2541 608,505,175 2007 0.0263 3.7794 0.0555 0.3136 883,940,926 2008 0.0291 5.5569 0.0214 0.288 1,680,302,005 2009 0.0177 1.4622 0.0143 0.3325 1,789,458,000 2010 0.0273 2.1235 0.0754 0.4407 1,573,196,000 2011 0.0263 2.6309 0.0218 0.3891 2,169,073,000 2012 0.0385 6.1028 0.0119 0.1452 2,436,886,000 2013 0.0326 5.3136 0.0185 0.1067 2,878,693,000 2014 0.0355 6.3402 0.02771 0.2331 2,987,600,000 Source: Computations from annual reports and statement of accounts of selected deposit money banks, ROA: Return on asset, ROE: Return on equity, NPLR: Non-performing loan ratio, TLAR: Total loans and advances ratio Table 1: (Continued) Table 2: Descriptive statistics of computed data of selected deposit money banks Indices ROA ROE TLAR NPLR Ln (TA) Mean 0.02652 0.263080 0.19560 0.070561 14.28920 Median 0.02110 0.263300 0.024600 0.032200 14.68214 Maximum 0.031000 0.440700 0.480000 0.277400 15.72666 Minimum 0.012500 0.106700 0.072500 0.011900 10.88358 Standard deviation 0.007375 0.092222 0.124430 0.081687 1.985723 Skewness 0.536578 0.027120 0.348003 1.548395 −0.451650 Kurtosis 1.644040 2.512120 3.166743 4.062090 1.988132 Jarque-Bera 1.551247 0.150606 0.747614 6.698839 1.781938 Probability 0.354441 0.927463 0.516688 0.035105 0.482345 Sum 0.263000 3.946200 3.501100 1.058410 199.8880 Sum square deviation 0.000874 0.119067 0.120716 0.093418 31.90323 Observations 15 15 15 15 15 Source : Researchers computation, ROA: Return on asset, ROE: Return on equity, NPLR: Non-performing loan ratio, TLAR: Total loans and advances ratio Nwude and Okeke: Impact of Credit Risk Management on the Performance of Selected Nigerian Banks International Journal of Economics and Financial Issues | Vol 8 • Issue 2 • 2018294 Following a detailed cross sectional data analysis, the findings revealed plausible results on the economic growth parameter. The implications of these findings are discussed in line with the objectives of this study. As revealed from the finding of this study, credit risk management has a positive and significant impact on total loans and advances of the selected deposit money banks in Nigeria. Also a cursory examination revealed that the rate of Table 3: Pool unit root test Pool unit root test: Summary Series: ROA, TLAR, NPLR, Log (TA), ROE Sample: 2005 2014 Exogenous variables: Individual effects Automatic selection of maximum lags Automatic lag length selection based on SIC: 0–1 Newey–West automatic bandwidth selection and Bartlett kernel Method Obs Cross sections t-stat Prob** Null: Unit root (assumes common unit root process) Levin, Lin and Chu t* 20 3 −6.01165 0.0000 Null: Unit root (assumes individual unit root process) Im, Pesaran and Shin W-stat 20 3 −2.47422 0.0076 ADF - Fisher Chi-square 20 3 18.4432 0.0051 PP - Fisher Chi-square 20 3 28.3339 0.0001 Source: Researcher’s eviews results, ROA: Return on asset, ROE: Return on equity, NPLR: Non-performing loan ratio, TLAR: Total loans and advances ratio Table 4: Regression result of hypothesis one Dependent variable: TLAR Method: Least squares Date: 08/9/16 time: 14:52 Sample: 2000 2014 Included observations: 15 Variable Coefficient Standard error t-statistic Prob. C 0.074144 0.263357 0.281534 0.7831 NPLR 0.062433 0.344571 0.471408 0.0658 NLTA 0.013345 0.018945 0.704397 0.4946 R-squared 0.523424 Mean dependent var 0.263080 Adjusted R-squared 0.466005 S.D. dependent var 0.092222 S.E. of regression 0.097424 Akaike info criterion −1.642634 Sum squared resid 0.113897 Schwarz criterion −1.501024 Log likelihood 15.31976 Hannan-Quinn criter. −1.644143 F-statistic 0.272371 Durbin-Watson stat 2.390369 Prob (F-statistic) 0.026155 Source: Researcher’s Eviews Results, ROA: Return on asset, ROE: Return on equity, NPLR: Non-performing loan ratio, TLAR: Total loans and advances ratio, NLTA: Natural log of total asset Table 5: Regression result of hypothesis two Dependent variable: ROA Method: Least squares Date: 08/9/16 time: 14:52 Sample: 2000 2014 Included observations: 15 Variable Coefficient Standard error t-statistic Prob. C 0.0574127 0.017132 3.031492 0.0025 NPLR 0.019988 0.023425 0.761743 0.3601 NLTA −0.003484 0.001132 −2.816775 0.0143 R-squared 0.6020197 Mean dependent var 0.024267 Adjusted R-squared 0.432179 S.D. dependent var 0.008385 S.E. of regression 0.005318 Akaike info criterion −7.107776 Sum squared resid 0.000348 Schwarz criterion −6.966166 Log likelihood 41.30032 Hannan-Quinn criter. −7.109285 F-statistic 5.202117 Durbin-Watson stat 2.331326 Prob (F-statistic) 0.011721 Source: Researcher’s Eviews results, NPLR: Non-performing loan ratio, NLTA: Natural log of total asset Nwude and Okeke: Impact of Credit Risk Management on the Performance of Selected Nigerian Banks International Journal of Economics and Financial Issues | Vol 8 • Issue 2 • 2018 295 penetration of credit management by financial institutions as selected within the region contributes to 6% of NPLs. This implies that on the average about 6% of the total loans and advances constituted irrecoverable loans which is bad and considered as losses. Several reasons may have been behind the dismal performance. Top among these reasons is high incidence of business failures. Bobakovia (2003) asserts that the profitability of a bank depends on its ability to foresee, avoid and monitor risks, possible to cover losses brought about by risk when arisen. This has the net effect of increasing the ratio of substandard credits in the bank’s credit portfolio and decreasing the bank’s profitability (Mamman and Oluyemi, 1994). The bank supervisors are well aware of this problem, it is however very difficult to persuade bank managers to follow more prudent credit policies during an economic upturn, especially in a highly competitive environment. They claim that even conservative managers might find market pressure for higher profits very difficult to overcome. As revealed from the Tables 5 and 6, credit risk management has a positive and significant impact on profitability of the selected deposit money banks in Nigeria. Also a cursory examination revealed that the rate of penetration of credit risk management by the selected financial institutions within the region contributes to 2% of returns generated from the assets of these banks within the period (2000–2014). This implies that on the average about 2% of the total returns on investment was generated through the assets. Although, the result from the hypothesis tested indicates that Credit risk management had positive and significant impact on profitability, compared to other regions several reasons may have been behind the dismal returns on assets. Top among these reasons is high incidence of NPLs. However, according to IMF (2013), the banking sector time series (including that for the NPLs) have recent and fundamental structural breaks due to the major consolidation since 2005–06, and the significant changes in the structures of the banks’ balance sheets following their surrender of bad assets. As shown on Table 2, the mean of ROE from 2000 to 2014 period of our study is 26% which is relatively fair as yearly returns to owners’ capital. The consolidation challenges and global economic meltdown were part of factors which contributed to low returns on equity. The result of our hypothesis indicated that credit risk had a positive and non-significant impact on ROE. Loans should be made available only to borrowers who are likely to be able to repay and who are unlikely to become insolvent. Credit analysis of potential customers is carried out in order to judge the credit risk with the borrower and to reach lending decisions. Loan payments are monitored and action taken when a customer defaults. Ahmed et al. (1998) in their study found that loan loss provision has a significant positive influence on NPLs. Therefore, an increase in loan loss provision indicates an increase in credit risk and deterioration in the quality of loans consequently affecting bank performance adversely. 5. CONCLUSION The findings from the specific objectives of this study are that credit risk management had a positive and non-significant impact on total loans and advances, the ROA, ROE of the selected deposit money banks in Nigeria. In line with the findings of this study, we recommend bank managers need to put more efforts to the credit risk management by critically evaluating borrowers’ ability to pay back. There is need to strengthened bank lending policy through effective and efficient regulatory supervision and monitoring when facility is given out especially during utilization of the facility by the borrower. 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