FUO Quarterly Journal of Contemporary Research, Vol. 9 No

FUO Quarterly Journal of Contemporary Research, Vol. 9 No.2, June, 2021

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CREDIT RISK CONCENTRATION AND PERFORMANCE OF COMMERCIAL BANKS IN NIGERIA

ABU, IKPONMWOSA NORUWA

Department of Finance Faculty of Management Sciences University of Lagos, Lagos Nigeria

[email protected]

OKOYE, JOHN NONSO Department of Banking & Finance Faculty of Management Sciences University of Lagos, Lagos Nigeria

[email protected]

ABSTRACT This study investigated the effect of Credit Risk Concentration on Performance of commercial Banks in Nigeria. The study used only banks in Nigeria that have consistently published their audited annual financial report between 2009 and 2018. A sample of twelve (12) banks was used for the study to ensure adequate coverage. The aim of this study is to address sectoral credit risk concentration of banks in Nigeria (financial and non financial) using a panel regression technique to examine their effects on performance of Banks in Nigeria. We adopted a panel data analysis to identify the possible bank’s specific patterns of credit concentration. The problem that led to this research work is poor credit risk management which has been unstable and this is a virus that has cause a great devastating effect on the performance of deposit banks in Nigeria. Specifically, the objectives of the study are to; determine the effect of credit risk concentration on Return on Assets, ascertain the extent to which credit risk concentration affect Return on Equity and to verify the extent credit risk concentration affect Net interest Margin. The analytical tools employed are descriptive statistics, correlation matrix and panel regression analysis. Based on the result of the return on asset model, the panel regression reveals that there is a positive but insignificant relationship between oil sector credit concentration and bank performance. This therefore implies that concentrating or diversifying credit portfolios positively influence the return on equity level which proxy bank performance. The study also found that there is a positive but insignificant relationship between financial sector credit concentration and bank performance. The implication of this findings is that granting loan to financial institutions have positively influenced return on equity of deposit money banks but though insignificant. In view of our findings we recommend that the concentration risk management framework and underlying policy should be embedded in the institution’s risk management culture at all levels of the business. It should be subject to regular review, taking into account changes in risk appetite and in the business environment.

Keywords: Credit Risk Concentration, Banks, Nigeria, CBN, Environment

Abu, I. N. & Okoye, J. N.

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INTRODUCTION

Generally, banks and allied financial institutions have been observed to utilize their competitive advantage by specializing on a specific market segment even though there seems to be an unending debate as to the implication of loan portfolio concentration in the sector. Fernando, Emilio, Fabrizio, and Javier, (2013) are among numerous observers who hold that financial institutions flourish when they specialize in market segments where they exercise some degree of competitive advantage. Nevertheless, Diamond (1984) has advocated that loan diversification can be used to minimize the occurrence of the problem of financial distress that are triggered by imperfect correlation of project returns such as the models outlined in traditional portfolio theory. In the case of banks, the issue of credit risk concentration has also specially become of greater concern because the primary business of banks and financial institutions is mainly loan portfolio. Moreover, the risks resulting from some of the characteristics of clients and business conditions can be very significant. Banks that run on the principle of avoiding all risks, or as many of them as possible, will be a stagnant institution and will not adequately serve the legitimate credit needs of its community. On the other hand, a bank that takes higher risks or what is more likely, takes them without recognizing their extent, will surely run into difficulty, particularly in times of expanding business activities.

This committee exhorts financial authorities to supervise and measure the risks of the portfolios of their financial institutions, including credit concentration risk. Credit risk concentration has been traditionally analyzed in relation to credit activities. Credit risk concentration refers not only to risk related to credit granted to individual or interrelated borrowers but to any other significant interrelated asset or liability exposures which, in cases of distress in some markets/ sectors/ countries or areas of activity, may threaten the soundness of an institution (The Committee of European Banking Supervisors (CEBS) Guidelines, 2010). Credit risk concentration is one of the main possible causes of major losses in a credit institution. Evidence of 2008 financial crises has brought to light many examples of losses as a result of credit risk concentrations within institutions (The Committee of European Banking Supervisors (CEBS) Guidelines, 2010). Credit risk concentration in banks’ credit portfolios arises either from an excessive exposure to certain names (often referred to as name concentration or coarse granularity) or from an excessive exposure to a single sector or to several highly correlated sectors (i.e. sector concentration). In the past, financial regulation and previous research have focused mainly on the first aspect of concentration risk (Joint Forum, 1999). The consequences of bank failures are numerous and very unpalatable, not only to the depositors but also the investors, the general banking public and indeed, the entire economy.

In Nigeria, numerous studies have examined the impact of credit risk management but none has focused on Credit Risk Concentration. Such studies include Garuba (2010), which found out that Loans and advances still account for a sizeable proportion of banks’ assets in Nigeria. This revelation fundamentally shows that credit risks remain the major source of risk in the banking industry. In other words, the outcome of the study supports the evidence that poor credit risk management has been the bane of the Nigerian banking industry.


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The review of past empirical literature indicated a lack of consensus in the findings of most researchers. This lack of consensus points to the existence of a research gap and therefore the need for further research on this subject. The problem of bank failures and its consequences, and the lack of consensus in the findings of previous studies are the motivation for this study. The aim of this study is to address sectoral credit risk concentration of banks in Nigeria (financial and non financial) using a panel regression technique to examine their effects on performance of Banks in Nigeria. This is necessary because the issue of credit risk concentration has generated various perspectives and the question of how credit risk concentration would impact on bank performance especially in the area of Return on Assets, Return on Equity, and Net interest Margin remains unanswered. Research Objectives

The objectives of the study are to; determine the effect of credit risk concentration on Return on Assets and ascertain the extent to which credit risk concentration affect Return on Equity Research Design

Since this study is an empirical one, we therefore develop recursive models and methods that will help us to systemically present and analyze the different procedures involved in order to have a more reliable, valid and verifiable results. Some of the issues addressed include research design, population and sample size, and model specifications. It specifies the model, hypotheses to be tested, data sources and the justification for the choices of the estimation techniques.

This study employed survey research design. The methodology adopted is the Panel Regression Techniques. The estimation obtained would help to determine Credit risk concentration and performance of Nigerian bank. Empirical Review

Kobia and Baimwera (2018) examined the influence of credit risk management on the financial performance of commercial banks in Kenya for the period 2007 to 2017. The study adopted ROA (proxy for financial performance and dependent variable), and capital adequacy, cost to loans ratio, non-performing loans and loans to assets ratio as proxies for credit risk management and explanatory variables. Secondary data was collected from the financial reports of banks and the annual reports of the Central Bank of Kenya. Data analysis methods used include descriptive statistics, Pearson correlation model and multiple linear regression tools. The findings revealed mixed results even though credit risk management had significant influence on the profitability of banks: loans to assets ratio and capital adequacy had positive impact on financial performance; while non-performing loans ratio and cost to loans ratio exerted negative influence on financial performance.

Abdrahamane, Alpha, and Kargbo, (2017) examined the effect of government regulation and bank risk on bank performance in Mali using data from 1998 to 2013. The model adopted captured return on assets as the dependent variable and the indicator of bank performance; the ratio of non-performing loans to total loans (credit risk), ratio of total assets to total deposits (liquidity risk) and inflation represented bank risk; and bank guarantee scheme and capital adequacy requirement stood for government regulation. They used descriptive statistics and simple least squares regression technique to analyze data. The study concluded that banks’ appetite for risk is higher leading to better


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performance under government guarantee schemes and lower capital adequacy requirement.

Chenyam and Abderaman (2017) examined the impact of credit risk management on the financial performance of banks in Eritrea using panel data for 18 years from 1998 to 2015. The study variables included ROA (proxy for financial performance), non-performing loans ratio, capital adequacy ratio, loans and advances ratio and loan loss provision ratio (the predictive variables). They employed descriptive statistics, Pearson correlation analysis and multiple regression technique for analyzing data. The findings showed that credit risk management was inversely linked to banks’ financial performance.

Etale, Ayunku and Etale (2016) investigated the link between nonperforming loans and the performance of banks in Nigeria for the period 1994 to 2014. The study adopted substandard loans, doubtful loans and bad loans to represent non-performing loans, while return on capital employed (ROCE) was used as proxy for performance. Using descriptive statistics, ADF unit root test and multiple regression statistics to analyze data obtained from the annual reports of banks, the study found that high level of non-performing loans reduced banks’ performance.

In another study Etale and Ayunku (2016) examined the relationship between the profitability of banks and nonperforming loans in Nigeria for the period 1989 to 2013. This study used ROE and ROA as proxy for profitability and maximum lending rate (a control variable) as the predictor variables; while non-performing loans as responsive variable. The results of analysis showed ROE and MLR had significant negative link with non-performing loans, while ROA had negative but insignificant link with non-performing loans.

Also, Etale and Bingilar (2016) examined the impact of corporate governance on financial performance of banks in Nigeria. They adopted capital adequacy, asset base and liquidity ratio as the predictor variables representing corporate governance, while return on total assets was used as proxy for financial performance. Secondary time series data was collected from the annual reports of listed banks obtained from the Nigerian Stock Exchange (NSE) for the period covering 1995 to 2014. The study employed multiple regression analysis technique based on the windows SPSS 20 version to analyze data. The results of the analysis revealed a significant positive relationship between capital adequacy, asset base and return on total assets, while liquidity ratio showed significant negative effect on return on total assets.

Arif, Ihsan and Hussain, (2016) assess the effect of risk management on the performance of both large banking institutions and small banking institutions from2005-2014. The result of the regression result concluded that capital adequacy ratio, non-performing loans, interest rate risk and liquidity risk are key drivers of profitability in large banks while nonperforming loans and capital adequacy ratio are the only drivers of profitability in small commercial banks of Pakistan.

Samuel, (2015) examined the effect of credit risk on commercial banks performance. The result of the study revealed that the ratio of loan and advances to total deposit is insignificant negatively relate to profitability and that the ratio non-performing loan to loan and advances negatively relate to profitability at 5% level of significant. This study shows that there is a significant relationship between bank performance (in terms of profitability) and credit risk management (in terms of loan performance).


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Amako, (2015) using time series data from 2001 – 2011 appraised the impact of the credit risk management in bank’s financial performance in Nepal. The result of the study indicates that credit risk management is an important predictor of banks’ profitability and financial performance. Karugu and Ntoiti (2015) investigated the effect of credit risk management on profitability of commercial banks listed on the Nairobi Securities Exchange. The predictor variables used include credit risk governance, credit appraisal, credit monitoring, and debt collection practices. The study utilized primary data generated through a structured questionnaire involving 55 employees from 11 listed banks. They made use of descriptive statistics and multiple linear regression models in analyzing the study data. The study found that all the predictor variables had significant positive effect on the profitability of banks in Kenya

Mohammad (2015) examined the relationship between banking risk and banks’ performance using a set of panel data concerning banks in a developing economy (Pakistan) and a developed economy (the USA). The study adopted return on assets as proxy for efficiency and financial performance; while capital adequacy ratio, bank size, liquidity risk, leverage and management quality as proxies for risk management. Time series panel data obtained from banks in Pakistan (LDC) and USA for the period 2004 to 2014, were analyzed using descriptive statistics and pooled linear regression models. The results showed that banking risk management had positive impact on banks’ performance.

Olamide, Uwuigbe and Uwuigbe (2015) examined the effect of risk management on financial performance of banks in Nigeria using data from 2006 to 2012. They adopted return on equity as proxy for financial performance (the dependent variable); and non-performing loans ratio, capital ratio, loan/deposit ratio and risk disclosure as proxies for risk management and the explanatory variables. Time series secondary data was obtained from the annual reports of sampled 14 quoted banks. The study employed the ordinary least squares regression technique for the analysis of data. The results showed that risk management had no causality link with financial performance of banks.

Mutua (2014) conducted a research to investigate the effects of Credit Risk Management on the financial performance of commercial banks in Kenya. The study revealed that Sixty four percent (64%) of the respondents felt that Non-performing loans contribute to the financial performance practices in the commercial banks.

Also, Li, Zou and Lions (2014) investigated the relationship between credit risk management and profitability of commercial banks in Europe, using secondary data from 47 commercial banks covering the period 2007 to 2012. The findings revealed that credit risk management had positive effect on profitability; but more specifically, between the two proxies for credit risk management, non-performing loans ratio had significant effect on both ROE and ROA, while capital adequacy ratio had an insignificant effect on ROE and ROA.

Yousfi, (2014) assessed the impact of risk management practices on Jordanian Islamic banks‟ performance for the period of fifteen years from 1998 to 2012. The fixed effect results reveal that liquidity, credit and operational risk management practices have a negative and significant statistical impact on performance, and market risk management practices have a positive and significant statistical impact on banks‟ performance (ROA and ROE).

Soyemi, (2014) study the risk management practices and financial performance: evidence from the Nigerian deposit money banks (DMBs) in the 2012 financial year. The


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cross sectional data was analyzed using descriptive statistics to depict pattern and robust standard errors OLS regression to estimate significant influence between banks‟ risk management practices (credit, liquidity, operating and capital risk practices) and their financial performance. The findings appear to be largely consistent with previous works as the explanatory variables significantly accounted for variations in the financial performance [ROA-92% (71.78); ROE-84% (46.55)] in both models.

Gakure, Ngugi, Ndwiga and Waithaka (2012) investigated the effect of credit risk management techniques on the banks’ performance of unsecured loans. They concluded that financial risk in a banking organization might result in imposition of constraints on bank’s ability to meet its business objectives.

Boahene, Dasah and Agyei (2012) adopted the regression analysis to evaluate the significant relationship between credit risk and Ghanaian bank profitability. Their research followed Manzura and Juanjuan (2009) by using the ratio of non-performing loans to total assets as an indicator for credit risk management and return on equity as a measure of bank profitability. They highlighted that credit risk management impinges dramatically on bank profitability. The study indicated that higher capital adequacy positively contributes to bank profitability.

Poudel (2012) examined the effect of credit risk management on financial performance of banks in Nepal using data for 11 years from 2001 to 2011. The study adopted ROA as the measure of profitability and the dependent variable, while loan default rate, cost per loan assets and capital adequacy were used as proxies for credit risk management (the explanatory variables). Secondary data was collected from 31 banks operating in Nepal. Descriptive statistics and regression techniques based on SPSS 20 version were employed to analyze data. The results revealed that credit risk management by all parameters used had inverse impact on the financial performance of banks.

Model Specification Model is a mathematical representation of theory. The functional model of the study

expresses the relationship between credit concentration risk and the profitability of the firm which is expressed below as: Functional Model Model One: Return on Assets ROAit = f (OIL_RATIOit, MANF_RATIOit, FC_RATIO it, OTHERS_RATIO) Model Two: Return on Equity ROEit = f ( OIL_RATIO it, MANF_ RATIOit, FC_RATIOit, OTHERS RATIO) Econometrics Model However, this study developed an econometrics model specified to show the relationship between credit risk concentration and bank performance is given as; Model 1: Model one shows the relationship between Return on Asset and the Ratio of credit lending to oil and gas sector Sector to total credit. ROA it = βo + β1 OIL_RATIOit + β2 MANF_RATIOit + β3 FC_RATIOit + β4 OTHER_RATIOit + Ʃt Model 2: Model two shows the relationship between Return on Equity and The Ratio of credit lending to manufacturing sector to Total credit. ROEit = βo + β1 OIL RATIOit + β2 MANF_RATIOit + β3 FC_RATIOit + β4 OTHERS_RATIOit + Ʃt


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Descriptive Statistics The descriptive statistics for Return on Assets (ROA), Return on Equity (ROE), non-

interest margin (NIM), manufacturing ratio (Manf_ratio), Fc_ratio, others_ratio, logof ratio and cash to ratio are presented in the Table 4.1 below: Table 4.3:1 Descriptive Statistics Stats Mean Max Min Standard

Deviation Kurtosis P50 Skewness

ROA 1.229167 9.54 -20.23 3.157779 21.28457 1.35 -3.105474

ROE 5.222583 122.8 -394.32 48.55899 44.51581 10.95 -5.632629

NIM 59.38533 82.73 28.19 11.02353 2.607605 59.24 -.2587999

Oil_Ratio 1.210626 53.36957 0 6.020852 52.73497 .1463605 6.769427

Manf_ratio .2309202 11.60542 .0013269

1.065831 109.952 .0902738 10.28267

Fc_ratio .1335897 1.995434 0 .214933 48.25171 .0658357 5.728924

Others_ratio .5933749 2.227834 .0169667

.3024695 14.225 .5652173 2.771075

SIZE 9.066167 10.77 8.19 .4223706 3.952783 9.07 .3023062

CA 12.945 34.32 0.58 7.590293 2.431644 12.295 .3496073

SOURCE: Researchers’ computation, 2019 The standard deviation is a measure of the amount of variation of a set of data values. Amongst all the variables, ROE and NIM have the highest variability, meaning that they vary significantly in terms of ROE and NIM. Kurtosis is a measure of “peakedness” of a distribution. For ROA, ROE, oil_ratio, manf_ratio, fc_ratio and others_ratio, kurtosis statistics are far higher than 3, implying that the variables are leptokurtic relative to the normal. In examining the association among the variables, we employed the Pearson correlation coefficient (correlation matrix) and the results are presented in table 4. Correlation Coefficient Matrix

The Table 4.2 below gives the result of the correlation coefficient matrix. It indicates the result of the correlation between the dependent variables and independent variables. Also the results indicates the correlation between the independent variables. As shown there is weak and positive correlation between ROA and cash_ratio. Also the correlation between ROE and cash_ratio is weak and positive. Other correlations are shown in the table. Table 4.2: Correlation Coefficient Matrix

ROA ROE NIM Oil_ratio Manf_ratio

Fc_ratio Others_ratio

CA SIZE

ROA 1.0000

ROE 0.1922 1.0000

NIM 0.3486 0.1281 1.0000

Oil_Ratio 0.0036 0.0317 0.0277 1.0000

Manf_Ratio 0.0599 0.0359 0.1122 0.8822 1.0000

FC_Ratio 0.0944 0.0723 0.0593 0.7705 0.8060 1.0000

Others_Ratio

0.0939 -0.030 -0.0822 0.7176 0.4718 0.4067 1.0000

CA 0.2047 0.1738 0.0119 -0.1923 -0.1392 -0.1361 -0.1713 1.0000

SIZE 0.2419 0.2676 0.3658 -0.0456 0.0077 -0.0735 -0.1432 0.4367 1.


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0000

SOURCE: Researchers’ computation, 2019 ROE Model

Table 4.3: Pooled Regression ROE Estimation Model Variables Apriori

Expectations OLS (Coefficients) Robust (Coefficients)

C Oil_Ratio Manf_ratio Others_ratio Fc_ratio SIZE CA R-Squared Adj R-Squared F-statistics VIF Test Heteroskedasticity Observations

+ + + + + +

-255.8122 [0.018] 0.8461457 [0.717] -7.37124 [0.485] -7.999124 [0.743] 38.6243 [0.284] 28.09561 [0.018]*** 0.5082882 [0.437] 0.0906 0.0423 1.88(0.0910) 4.26 121.24(0.00000) 120

-42.50148 [0.009]*** -0.006686 [0.988] 5.302923 [0.459] -11.82789 [0.036]** 2.479771 [0.496] 5.201659 [0.004]*** 0.4341848 [0.000]*** 10.42(0.0000) - - - - 119

Note: (1) bracket [ ] are p-value (2) **, ***, implies statistical significant at 5% and 1% levels respectively.

The Table 4.1 above is the results of the pooled regression estimation model. The result indicates the adjusted R-Squared of 0.04. This mean that 4% of the changes or variations in the dependent variable (ROE) can be explained by all the independent variables combined. While the remaining 96% can be explained by other factors that are not captured in this study because it is outside the scope of this study. The F-statistic value of 1.88 and its p-value of 0.0910 shows that the OLS pooled regression model, on the overall is statistically insignificant at 5% level. However, at the 10% level, it is statistically significant, and this may mean that the OLS pooled regression is valid and can be used to make statistical inference. Also, the above table shows VIF mean value to be 4.26. This value is less than the benchmark value of 10, which thus indicates the absence of multicolinearity. Similarly, from the results of the OLS pooled regression model above, the heteroskedasticity value is 121.24(0.00000). This results implies the presence of heteroskedasticity. This was corrected by running the robust regression. Table 4.4: Panel Regression ROE Model Results

Variables Apriori Expectations Fixed Effect (ROE) Random Effect (ROE)

C Oil_Ratio Manf_ratio FC_ratio Others_ratio CA SIZE

+ + + + + +

-288.5463 [0.183] 1.153331 [0.654] -11.7808 [0.318] 46.5246 [0.287] -2.765225 [0.921] 0.3200896 [0.676] 31.58723 [0.186]

-255.8122[0.016]*** 0.8461457 [0.716] -7.37124 [0.484] 38.6243 [0.281] -7.999124 [0.742] 0.5082885 [0.435] 28.09561 [0.017]***


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R-Squared F-statistics Haussman test Observtions

0.0873 0.56(0.7646) 0.9138 120

0.0906 0.46(0.9252) 0.9138 120


In testing for the cause-effect relationship between the dependent and independent variables in ROE model, the two widely used panel data regression estimation techniques (fixed effect and random effect) were adopted in this study. The Table 4.2 above presents the results of the two panel estimation models. The results show differences in the magnitude of the coefficients, signs as well as the number of insignificant variables. The estimation of of the fixed effect panel regression model was based on the assumption of no correlation between the error term and explanatory variables, while random effect regression model is based on the assumption that there is correlation between the error term and the explanatory variables. In selecting the appropriate technique from the two effects, the Hausman test was conducted which is based on the null hypothesis that the random effect model is preferred to fixed effect model. From the Table 4.2, the value of the Hausman test is 0.9138, implying that null hypothesis should be rejected and alternative hypothesis accepted. Thus, the result indicates that random effect should be accepted. Test of Hypothesis and Discussion of Findings Hypothesis One: There is no significant relationship between oil sector credit concentration and bank performance

Following the above, it should be noted that random effect panel regression models provided the following results; oil sector credit concentration (Oil_RATIO) based on the slope coefficient (0.8461457) appears to have a positive influence on banks Return on equity (ROE) performance but was statistically insignificant at 5 and 1 percent since the probability value of 71.6 percent was greater than the threshold of 5 percent. This result accept Null hypothesis, which suggests that oil sector credit concentration has an insignificantly relationship with Return on equity as a proxy of bank’s performance. We can therefore conclude that there is a positive but insignificant relationship between oil sector credit concentration and bank performance. Hypothesis Two: There is no significant relationship between manufacturing sector credit concentration and bank performance

Based on the result of the random effect panel regression models provided above; manufacturing sector credit concentration (Manu_RATIO) based on the slope coefficient (-7.37124) appears to have a negative influence on banks Return on equity (ROE) performance but was statistically insignificant at 5 and 1 percent since the probability value of 48.4 percent was greater than the threshold of 5 percent. This result accept Null hypothesis, which suggests that manufacturing sector credit concentration has an insignificantly relationship with Return on equity as a proxy of bank’s performance. We can therefore conclude that there is a negative but insignificant relationship between manufacturing sector credit concentration and bank performance Hypothesis Three: There is no significant relationship between financial sector credit concentration and bank performance


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Based on the result of the random effect panel regression models provided above; financial sector credit concentration (FS_RATIO) based on the slope coefficient (38.6243) appears to have a positive influence on banks Return on equity (ROE) performance but was statistically insignificant at 5 and 1 percent since the probability value of 28.1 percent was greater than the threshold of 5 percent. This result accept Null hypothesis, which suggests that financial sector credit concentration has an insignificantly relationship with Return on equity as a proxy of bank’s performance. We can therefore conclude that there is a positive but insignificant relationship between financial sector credit concentration and bank performance Hypothesis Four: There is no significant relationship between other sectors credit concentration and bank performance

Based on the result of the random effect panel regression models provided above; other sectors credit concentration (Other_RATIO) based on the slope coefficient (-7.999124) appears to have a negative influence on banks Return on equity (ROE) performance but was statistically insignificant at 5 and 1 percent since the probability value of 74.2 percent was greater than the threshold of 5 percent. This result accept Null hypothesis, which suggests that other sectors credit concentration has insignificantly relationship with Return on equity as a proxy of bank’s performance. We can therefore conclude that there is a negative but insignificant relationship between other sectors credit concentration and bank performance.

In the case of our control variables it was observed that cash to asset ratio being a proxy of liquidity appears to be consistent with apriori expectation but was statistically insignificant in explaining Return on Equity (ROE) performance in Nigeria Banks .While the total assets (LOG_TA) which proxy bank size had a positively impact on Return on Equity (ROE) that was statistically significant. This means that large bank in Nigerian generate superior Return on Equity (ROE) than smaller banks ROA Model Table 4.5: Pooled Regression ROA Estimation Model

Variables Apriori Expectations

OLS (Coefficients) Robust (Coefficients)

C Oil_Ratio Manf_ratio Others_ratio Fc_ratio SIZE CA R-Squared Adj R-Squared F-statistics VIF Test Heteroskedasticity Observations

+ + + + + +

-18.91162 [0.005]*** -0.4379095 [0.003]*** 1.038733 [0.115] 4.782841 [0.002]*** 4.424604 [0.049]** 1.812018 [0.014]*** 0.0443339 [0.276] 0.1699 0.1258 3.86(0.0015) 4.26 41.00(0.00000) 120

-0.9914973 [0.715] -0.3544054 [0.000]*** 5.119421 [0.000]*** 0.15432 [0.871] -0.5183457 [0.404] 0.1910017 [0.526] 0.0390803 [0.019]*** - - 9.87(0.0000) - - 119



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The Table 4.3 above is the results of the pooled regression estimation model. The result indicates the adjusted R-Squared of 0.1258. This mean that 12.5% of the changes or variations in the dependent variable (ROA) can be explained by all the independent variables combined. While the remaining 87.5% can be explained by other factors that are not captured in this study because it is outside the scope of this study. The F-statistic value of 3.86 and its p-value of 0.0015 shows that the OLS pooled regression model, on the overall is statistically significant at 5% level. This may mean that the OLS pooled regression is valid and can be used to make statistical inference. Also, the above table shows VIF mean value to be 4.26. This value is less than the benchmark value of 10, which thus indicates the absence of multicolinearity. Similarly, from the results of the OLS pooled regression model above, the heteroskedasticity value is 41.00(0.00000). This results implies the presence of heteroskedasticity. This was corrected by running the robust regression. Table 4.6: Panel Regression ROA Model Results

Variables Apriori Expectations

Fixed Effect (ROA) Random Effect (ROA)

C Oil_Ratio Manf_ratio Others_ratio Fc_ratio SIZE CA R-Squared F-statistics Haussman test Observtions

+ + + + + +

-11.70637 [0.358] -0.4289234 [0.005]*** 0.6743486 [0.332] 5.17676 [0.046]** 4.295896 [0.011]** 1.033051 [0.461] 0.0535052 [0.237] 0.1506 2.12(0.0568) 18.34 (0.0054) 120

-18.91162 [0.004]*** -0.4379095 [0.002]*** 1.038733 [0.112] 4.424604 [0.047]** 4.782841 [0.002]*** 1.812018 [0.013]*** 0.0443339 [0.237] 0.1699 23.13(0.0008) 18.34 (0.0054) 120

Note: (1) bracket [ ] are p-value (2) **, ***, implies statistical significant at 5% and 1% levels respectively. ` In testing for the cause-effect relationship between the dependent and independent variables in ROA model, the two widely used panel data regression estimation techniques (fixed effect and random effect) were adopted in this study. The Table 4.4 above presents the results of the two panel estimation models. The results show differences in the magnitude of the coefficients, signs as well as the number of insignificant variables. The estimation of the fixed effect panel regression model was based on the assumption of no correlation between the error term and explanatory variables, while random effect regression model is based on the assumption that there is correlation between the error term and the explanatory variables. In selecting the appropriate technique from the two effects, the Hausman test was conducted which is based on the null hypothesis that the random effect model is preferred to fixed effect model. From the Table 4.4, the value of the Hausman test is 18.34 (0.0054), implying that null hypothesis should be accepted. Thus, the result indicates that fixed effect should be accepted.

Test of Hypothesis Hypothesis One: There is no significant relationship between oil sector credit concentration and bank performance


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Following the above, it should be noted that random effect panel regression models provided the following results; oil sector credit concentration (Oil_RATIO) based on the slope coefficient (-0.4289234) appears to have a negative influence on banks Return on Assets (ROA) performance but was statistically insignificant at 5 and 1 percent since the probability value of 0.5 percent was less than the threshold of 5 percent. This result fails to accept Null hypothesis, which suggests that oil sector credit concentration has an insignificantly relationship with Return on assets as a proxy of bank’s performance. We can therefore conclude that there is a negative but significant relationship between oil sector credit concentration and bank performance. Hypothesis Two: There is no significant relationship between manufacturing sector credit concentration and bank performance

Based on the result of the random effect panel regression models provided above; manufacturing sector credit concentration (Manu_RATIO) based on the slope coefficient (0.6743486) appears to have a positive influence on banks Return on assets (ROA) performance but was statistically insignificant at 5 and 1 percent since the probability value of 33.2 percent was greater than the threshold of 5 percent. This result accept Null hypothesis, which suggests that manufacturing sector credit concentration has an insignificantly relationship with Return on assets as a proxy of bank’s performance. We can therefore conclude that there is a positive but insignificant relationship between manufacturing sector credit concentration and bank performance Hypothesis Three: There is no significant relationship between financial sector credit concentration and bank performance

Based on the result of the random effect panel regression models provided above; financial sector credit concentration (FS_RATIO) based on the slope coefficient (4.295896) appears to have a positive influence on banks Return on assets (ROA) performance but was statistically significant at 5 and 1 percent since the probability value of 1.1 percent was less than the threshold of 5 percent. This result accept Null hypothesis, which suggests that financial sector credit concentration has a significantly relationship with Return on assets as a proxy of bank’s performance. We can therefore conclude that there is a positive but significant relationship between financial sector credit concentration and bank performance Hypothesis Four: There is no significant relationship between other sectors credit concentration and bank performance

Based on the result of the random effect panel regression models provided above; other sectors credit concentration (Other_RATIO) based on the slope coefficient (5.17676) appears to have a negative influence on banks Return on assets (ROA) performance but was statistically insignificant at 5 and 1 percent since the probability value of 4.6 percent was less than the threshold of 5 percent. This result fails to accept Null hypothesis, which suggests that other sectors credit concentration has insignificantly relationship with Return on equity as a proxy of bank’s performance. We can therefore conclude that there is a positive but significant relationship between other sectors credit concentration and bank performance. In the case of our control variables it was observed that cash to asset ratio being a proxy of liquidity appears to be consistent with apriori expectation but was statistically insignificant in explaining Return on Assets (ROA) performance in Nigeria Banks .While the


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total assets (LOG_TA) which proxy bank size had a positively impact on Return on Assets (ROA) that was statistically insignificant. This means that large bank in Nigerian do not generate superior Return on Assets (ROA) than smaller banks NIM Model Table 4.7: Pooled Regression NIM Estimation Model

Variables Apriori Expectations OLS (Coefficients)

C Oil_Ratio Manf_ratio Fc_ratio Others_ratio SIZE CA R-Squared Adj R-Squared F-statistics VIF Test Heteroskedasticity Observations

+ + + + + +

-39.7442 [0.085] -0.4949071 [0.325] 3.027849 [0.183] -0.5044267 [0.923] 2.251278 [0.771] 11.30624 [0.000]*** -0.268537 [0.085] 0.1854 0.1421 4.29(0.0006) 4.26 0.08(0.7739) 120


The Table 4.5 above is the results of the pooled regression NIM estimation model. The result indicates the adjusted R-Squared of 0.1421. This means that 14.2% of the changes or variations in the dependent variable (NIM) can be explained by all the independent variables combined. While the remaining 85.8% can be explained by other factors that are not captured in this study because it is outside the scope of this study. The F-statistic value of 4.29 and its p-value of 0.0006 shows that the OLS pooled regression model, on the overall is statistically significant at 1% level. This may mean that the OLS pooled regression is valid and can be used to make statistical inference. Also, the above table shows VIF mean value to be 4.26. This value is less than the benchmark value of 10, which thus indicates the absence of multicolinearity. Similarly, from the results of the OLS pooled regression model above, the heteroskedasticity value is 0.08(0.00000). This results implies the absence of heteroskedasticity.


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Table 4.8: Panel Regression NIM Model Results Variables Apriori

Expectations Fixed Effect (NIM) Random Effect (NIM)

C Oil_Ratio Manf_ratio Fc_ratio Others_ratio CA SIZE R-Squared F-statistics Haussman test Observtions

+ + + + + +

90.5455 [0.008] -0.7387875 [0.069] 2.246961 [0.225] 4.552286 [0.506] 2.510946 [0.568] -.0713445 [0.553] -3.525101 [0.345] 0.0299 1.03(0.4076) 0.0784 120

45.3738 [0.128] -0.6629958 [0.104] 2.012764 [0.279] 5.525336 [0.415] 2.18718 [0.621] -0.1150186 [0.338] 1.522403 [0.643] 0.0523 4.77(0.5734) 0.0784 120

Note: (1) bracket [ ] are p-value (2) **, ***, implies statistical significant at 5% and 1% levels respectively. In testing for the cause-effect relationship between the dependent and independent variables in NIM model, the two widely used panel data regression estimation techniques (fixed effect and random effect) were adopted in this study. The Table 4.6 above presents the results of the two panel estimation models. The results show differences in the magnitude of the coefficients, signs as well as the number of insignificant variables. The estimation of the fixed effect panel regression model was based on the assumption of no correlation between the error term and explanatory variables, while random effect regression model is based on the assumption that there is correlation between the error term and the explanatory variables. In selecting the appropriate technique from the two effects, the Hausman test was conducted which is based on the null hypothesis that the random effect model is preferred to fixed effect model. From the Table 4.6, the value of the Hausman test is 0.0784, implying that null hypothesis should be rejected. Thus, the result indicates that random effect should be accepted.

Test of Hypothesis Hypothesis One: There is no significant relationship between oil sector credit concentration and bank performance

Following the above, it should be noted that random effect panel regression models provided the following results; oil sector credit concentration (Oil_RATIO) based on the slope coefficient (-0.6629958) appears to have a negative influence on banks Net interest Margin (NIM) performance but was statistically insignificant at 5 and 1 percent since the probability value of 10.4 percent was greater than the threshold of 5 percent. This result accept Null hypothesis, which suggests that oil sector credit concentration has an insignificantly relationship with net income margin as a proxy of bank’s performance. We can therefore conclude that there is a negative but insignificant relationship between oil sector credit concentration and bank performance. Hypothesis Two: There is no significant relationship between manufacturing sector credit concentration and bank performance


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Based on the result of the random effect panel regression models provided above; manufacturing sector credit concentration (Manu_RATIO) based on the slope coefficient (2.012764) appears to have a positive influence on banks net income margin (NIM) performance but was statistically insignificant at 5 and 1 percent since the probability value of 27.9 percent was greater than the threshold of 5 percent. This result accept Null hypothesis, which suggests that manufacturing sector credit concentration has an insignificantly relationship with net income margin as a proxy of bank’s performance. We can therefore conclude that there is a positive but insignificant relationship between manufacturing sector credit concentration and bank performance Hypothesis Three: There is no significant relationship between financial sector credit concentration and bank performance

Based on the result of the random effect panel regression models provided above; financial sector credit concentration (FS_RATIO) based on the slope coefficient (5.525336) appears to have a positive influence on banks net income margin (NIM) performance but was statistically significant at 5 and 1 percent since the probability value of 41.5 percent was greater than the threshold of 5 percent. This result accept Null hypothesis, which suggests that financial sector credit concentration has an insignificantly relationship with net income margin as a proxy of bank’s performance. We can therefore conclude that there is a positive but insignificant relationship between financial sector credit concentration and bank performance Hypothesis Four: There is no significant relationship between other sectors credit concentration and bank performance

Based on the result of the random effect panel regression models provided above; other sectors credit concentration (Other_RATIO) based on the slope coefficient (2.18718) appears to have a positive influence on banks net income margin (NIM) performance but was statistically insignificant at 5 and 1 percent since the probability value of 62.1 percent was greater than the threshold of 5 percent. This result accept Null hypothesis, which suggests that other sectors credit concentration has insignificantly relationship with net income margin as a proxy of bank’s performance. We can therefore conclude that there is a positive but insignificant relationship between other sectors credit concentration and bank performance.

In the case of our control variables it was observed that cash to asset ratio being a proxy of liquidity appears to be inconsistent with apriori expectation but was statistically insignificant in explaining net income margin (NIM) performance in Nigeria Banks. While the total assets (LOG_TA) which proxy bank size had a positively impact on net income margin (NIM) that was statistically insignificant. This means that large bank in Nigerian do not generate superior net income margin (NIM) than smaller banks

CONCLUSION Panel regressions were run to determine the movements in concentration of credit

portfolio of the selected banking sector over 2009 to 2018. Concentration risk in all its forms is a challenge for banks, particularly as they struggle to develop meaningful enterprise risk management. The panel results showed increased concentration of loan portfolios on oil and gas sector of the overall banking sector over the sample period. However, the results from the Panel regressions provide strong evidence of movement towards higher


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concentration of loan portfolios over the review period for all measures of distance and concentration. These results supported the findings from preliminary data analysis indicating that oil and gas sector lending was more concentrated and thus exposes the banks to high level of risk.

Conclusively, for too long concentration risk has been managed in an imprecise, judgmental way that has not optimized bank strategic decisions, nor led to a clear dialogue between banks and their regulators about each bank’s distinct risk profile. This study has filled the gap by examining credit concentration risk on the performance of banks in Nigeria. RECOMMENDATIONS Based on the findings in this study, the following suggestions are recommended: 1. The concentration risk management framework and underlying policy (ies) should be

embedded in the institution’s risk management culture at all levels of the business. It should be subject to regular review, taking into account changes in risk appetite and in the business environment.

2. Institutions should derive a practical definition of what constitutes a material concentration in line with their risk tolerance. Moreover, institutions should determine the level of concentration risk arising from the different exposures they are willing to accept (i. e. determine their concentration risk tolerance), with due regard to (inter-alia) the institution’s business model, size and geographic activity.

3. The management body should understand and review how concentration risk derives from the overall business model of the institution. This should result from the existence of appropriate business strategies and risk management policies.

4. Institutions should carry out regular analyses of their portfolios and exposures, including estimates of their trends, and should take account of the results of these analyses in setting and verifying the adequacy of the processes and limits, thresholds or similar concepts for concentration risk management.

Contribution to Knowledge The study has added to our extant knowledge of banks performance in Nigeria based

on the result of the return on asset model, that there is a positive but insignificant relationship between oil sector credit concentration and bank performance ii) The study has also added to our knowledge of bank performance that there is a positive but insignificant relationship between financial sector credit concentration and bank performance. The implication of this finding is that granting loan to financial institutions would positively influenced return on equity of deposit money banks but though insignificant. This findings was consistent with Al-Khouri (2011), he found that bank capitalization and credit risk have positive and significant impact on banks’ net interest margin, cost efficiency and profitability. iii) The study has also added to our knowledge of the determinant of banks performance that there is a negative but insignificant relationship between manufacturing and other sector credit concentration and bank performance. This finding implies that credit diversification to manufacturing and other sectors have rather increase the risk exposure of bank in Nigeria. iv) The study has also added to our knowledge of bank performance that there is a negative but insignificant relationship between manufacturing and other sector credit concentration


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and bank performance. This finding implies that credit diversification to manufacturing and other sectors have rather increase the risk exposure of bank in Nigeria.

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FUO Quarterly Journal of Contemporary Research, Vol. 9 No