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Asian Finance & Banking Review; Vol. 4, No. 1; 2020 
ISSN 2576-1161    E-ISSN 2576-1188 

Published by Centre for Research on Islamic Banking & Finance and Business, USA 

 

     17 
 

 

Implications of Non-Performing Loans on the Nigerian Deposit Money Banks 
 
 
 

Saliu Hakeem Tomi M.Sc 
Department of Banking and Finance 

 Faculty of Management Sciences 
 Kogi State University 

 P.M.B 1008, Anyigba, Kogi State, Nigeria 
E-mail:tomisaliu@gmail.com 

 
Idih Ogwu Emmanual PhD 

Department of Banking and Finance 
 Faculty of Management Sciences 

  Kogi State University 
 P.M.B 1008, Anyigba, Kogi State, Nigeria 

E-mail: emmite1010@gmail.com  
 

Adewole Joseph Adeyinka PhD 
Department of Banking and Finance 

 Faculty of Social and Management Sciences 
 Adekunle Ajasin University 

 P.M.B 001, Akungba Akoko, Ondo State, Nigeria 
E-mail: princeadeyinkaadewolej@gmail.com 

 
Abstract 
The study examined the arguments and counterarguments within the scientific discussion on the implications of non-performing 
loans on the Nigerian deposit money banks. The main objective is to examine the effect of Non-Performing loan on the 
Performance of Deposit Money Banks in Nigeria. Data were sourced from Central Bank of Nigeria Statistical Bulletin. A 
systematization literary approach for data analysis was Auto Regression distribution lag (ARDL) bound tests. Findings revealed 
that there exist a long run significant relationship between Non performing loan and the Performance of Deposit Money Banks 
in Nigeria. It was revealed that persistence increase in Non-performing loans results in poor Performance of Deposit Money 
Banks in Nigeria. It was also discovered that Non Performing Loan reduces deposit money banks return on asset. The study 
therefore recommends that deposit money banks should employ competent risk managers that always use their skills to reduce 
the incident of non-performing loans in the Nigerian deposit money banks. The study also recommends that deposit money 
banks in Nigeria should always monitor the end-use of funds given to their customers in order to curb the incident of fund 
diversion which may result in non-performing loan. 
 
Keywords: Non-Performing Loan, Performance, Return on Assets, Economic Growth Rate, Credit Risk Management.   
 
1. Introduction 
The increasing documentations of non-payment of either loan principals or loan interest in at least 90 days are alarming and 
most responsible for financial sector problems experienced in Nigeria (Adeyemi, 2011; Benji, 2013; Samayo, 2010). Hamisu 
(2011) posited that the banks and other economic industries drive and played key part in the socio-economic growth in the 
Nigeria Economy where they provide multiple economic supports that contribute positively to the survival of the Nigerian 
Economy. However, many banks in Nigeria today are making huge losses due to the problem of non-performing loans. Clearly, 
this is a negative impact against the banks' intermediate role in economic growth. The rate at which these organizations lend 
credit to companies and certain individuals increases the nation's economic growth rate (Kolapo, Ayeni, Oke, 2012). Nigeria 
banking system is regulated and monitored by the bank of Nigeria with the Banking Act made by the parliament of Nigeria. The 
Acts has regulations which guide the activities of all banks and some other financial institutions in the country.  

The main objective is to examine the effect of Non-Performing loan on the Performance of Deposit Money Bank in 
Nigeria. The specific objectives are to ascertain the existence of Long Run relationship between the Non-Performing Loan and 
Deposit Money Banks Performance in Nigeria, to find out the short run and Long run impact of Non-Performing Loan on 

mailto:Tomisaliu@gmail.com
mailto:emmite1010@gmail.com
mailto:princeadeyinkaadewolej@gmail.com


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Performance of Deposit Money Banks in Nigeria, to identify the speed of adjustment at which Deposit Money banks adjust to 
long run equilibrium as a result as a result of Change in Nonperforming Loan and to evaluate the proportion of variation of the 
Performance of Deposit Money bank explained by Non-Performing Loan. 

The research questions formulated to guide the study are; what is the Long Run relationship between the Non-
Performing Loan and Deposit Money Banks Performance in Nigeria? What is the short run and Long run impact of Non-
Performing Loan on Performance of Deposit Money Banks in Nigeria? To what extent is the speed of adjustment at which 
Deposit Money banks adjust to long run equilibrium as a result of Change in Nonperforming Loan? What is the proportion 
of variation of the Performance of Deposit Money bank explained by Non-Performing Loan? 
 
2. Methodology 
This study employed time series secondary data covering the period from 1986 to 2018. Data was sourced from CBN Statistical 
Bulletin. The study adopted Auto Regression distribution lag (ARDL) bound tests to analyze the Long Run Relationship that 
exist between Non Performing Loan and DMB Performance in Nigeria while the Error Correction Model was used to evaluate 
the proportion of variation of the dependent variable explained by each of the independent variables. The Variance 
decomposition was adopted to capture the proportion of variation of the dependent variable explained by each of the 
independent variables while some diagnostic test was carried to test the reliability and predictability of the model.  
 
2.1 Model Specification 
ROA = F (NPL, BDB, BCRID, TDB) 

ROAt  = B0 + B1lnNPLt + B2lnBCDt + B3lnTDBt + B4BCRIDt  + Ut 

b1<0, b2>0, b3>0, b4>0 and b5>0. 

ROAt  = B0   + ∑ B𝑛
𝑡=𝑖 1 lnNPLt +∑ B𝑛

𝑡=𝑖 2lnBCDt +∑ B𝑛
𝑡=𝑖 3lnTDBt +  ∑ B𝑛

𝑡=𝑖 4BCRIDt + Ut 

The estimated short-run equation and the error correction term are 

ROAt  = B0   + ∑ B𝑛
𝑡=𝑖 1 lnNPLt +∑ B𝑛

𝑡=𝑖 2lnBCDt +∑ B𝑛
𝑡=𝑖 3lnTDBt +  ∑ B𝑛

𝑡=𝑖 4BCRID+ ECMt=i + Ut 

Many authors have used similar method to explain the issue of non-performing loan; Mohammad, Ammara, Abrar and 
Fareeha (2012) examined economic determinants of non-performing loans using correlation and regression analysis to analyze 
the impact of selected independent variables and the result reveals that interest rate, energy crisis, unemployment, inflation and 
exchange rate has a significant positive relationship with the non-performing loans of Pakistan banking sector, while GDP 
growth rate has a significant negative relationship with the non-performing loans of Pakistan banking sector.  

Bofondi and Ropele (2011) investigated the macroeconomic determinants of bad loans of Italian banks for the period 
1990-2010 using quarterly data and found that non-performing loans are positively associated with the unemployment rates, 
lending rates and negatively associated with the GDP growth rate. Similarly, Ekanayake and Azeez (2015) viewed that the 
determinants of non-performing loans in certified commercial banks in Sri Lanka for the period 1999-2012 were explored and 
it was discovered that the level of non-performing loans can be attributed both to macroeconomic situations and to particular 
factors for banks. Results of their study reveal that non-performing loans tend to increase with deteriorating bank efficiency and 
a positive correlation between loan asset ratio and non-performing loans occurred. They also observed that banks with high 
credit growth rates are associated with lower levels of non-performing loans, while larger banks incur loan defaults that are lower 
than smaller banks. However, the study found with regards to the macro economic variables, that non-performing loans vary 
negatively with growth rate of GDP, while inflation was positively related to the prime lending rate. 
 
3. Presentation of Data 

 BCD BCIR NPL ROA TDP 

 Mean  84.01459  65.57019  14.56781  2.495662  12.46906 

 Median  81.84000  66.15500  13.81500  2.332251  10.25000 

 Maximum  106.7700  81.37000  37.25000  4.120000  22.54000 

 Minimum  61.22000  55.69000  2.120000  0.090000  6.070000 

 Std. Dev.  12.65723  6.762784  9.647148  0.777410  4.976759 

 Skewness  0.252767  0.270515  0.563522 -0.366385  0.280608 



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 Kurtosis  2.107124  2.220614  2.678805  4.584726  1.551357 

 Jarque-Bera  1.403723  1.200207  1.831195  4.064411  3.218038 

 Probability  0.495662  0.548755  0.400277  0.131046  0.200084 

 Sum  2688.467  2098.246  466.1700  79.86119  399.0100 

 Sum Sq. Dev.  4966.371  1417.793  2885.092  18.73536  767.8121 

 Observations  32  32  32  32  32 

Source: Authors Computation E-view 9.0 
 

The tables depicts the descriptive statistics of the variables A mean of  84% of the Bank deposit is granted as loan in which 14% 
of such loan turn bad and non performing to achieve a Mean Return on asset of 2.49  The maximum NPL during the period is 
37.25 while the Minimum NPL is 2.12. The NPL varies from its mean by a standard deviation of 9.64.  
 
3.1 Stationary Test 

Source: Authors Computation E-view 9.0 
 

The result above shows the level of stationary of the different variables. The table shows that lnBCIR and lnROA are 
stationary at levels while LnNPL, LnBCD and LnTDP are stationary at first difference. Since the variables are stationary at level 
and first difference we establish the presence of stationary and adopt the autoregressive distributed lag model (ARDL). 
 
3.2 ARDL Bound Test 
The F-statistics calculated for all underlined variables fall outside the critical bounds at the 1 and 5 percent levels of significance. 
The calculated F-statistic is higher than the upper bound critical value at 99% level of significance, so there is need to reject the 
null hypothesis which states that there is no cointegration, which suggests that the variables under consideration are cointegrated 
and they have the long-run relationship hence conclude that there exist the presence of long run relationship among Non-
Performing Loan and the performance of Deposit Money Bank in Nigeria. 
 
 
 
 
 
 
 
 
 
 
 
 
 
Short run and Long Run Estimate  

    Cointeq = LNROA - (-0.0198*LNTDP + 0.0916*LNBCD  -0.8877*LNBCIR +0.2239*LNNPL + 1.5775 ) 
 

Long Run Coefficients 

Variable Coefficient Std. Error t-Statistic Prob. 

LNTDP -0.019837 0.171690 -0.115543 0.9092 

LNBCD 0.091566 0.483005 0.189577 0.8516 

LNBCIR -0.887740 0.997051 -0.890366 0.3839 

Variables ADF 
Statistics 

 1% 5% 10% Probability Decision 

LnROA -5.181152 -3.653730 -2.957110 -2.617434 0.0002 1(0) 

LnNPL -5.325477 -3.724070 -2.986225 -2.632604 0.0001 1(1) 

LnBCD -3.208560 -3.724070 -2.986225 -2.632604 0.0314 1(1) 

LnTDP -3.173502 -3.724070 -2.986225 -2.632604 0.0317 1(1) 

LnBCIR -3.745093 -3.653730 -2.957110 -2.617434 0.0080 1(0) 

Null Hypothesis: No long-run relationships exist 

Test Statistic Value K 

F-statistic  8.587291 4 

Critical Value Bounds 

   Significance I0 Bound I1 Bound 

10% 2.45 3.52 

5% 2.86 4.01 

2.5% 3.25 4.49 

1% 3.74 5.06 



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LNNPL -0.223905 0.077448 -2.891041 0.0090 

C 1.577451 2.177796 0.724334 0.4772 

ECM -1.776328 0.308669 -5.754791 0.0000 

 
`The result above shows the long run estimate of the variables. The tables show that only Non-Performing Loan has 

significant effect on Bank Performance in the Long run. This might be due to the fact that the amount of nonperforming 

loan measures the quality of bank assets (Tseganesh, 2012). The effect of non-payment of due debts on banks‟ profitability can 
be identified with a possible bank failure, barrier to further lending, reduction in profit level and negative economic growth in 
the society. Hence a unit Percentage increase in Non-Performing Loan will lead to 22% reduction in the Return of Asset in the 
Long Run. 

 
Short Run Coefficient Equation: Error Correction Model 

Short  Run Coefficients 

Variable Coefficient Std. Error t-Statistic 

LNTDP 0.024720 0.08119 0.30447 

LNBCD -0.010515 0.02432 -0.4323 

LNBCIR -0.021908 0.04150 -0.52797 

LNNPL -0.597116 0.27759 -2.15106 

C -0.009 0.03255 -0.277 

   R-Squared = 0.471269  Adj. R2 = 0.233 Durbin Watson= 
1.84 

 

Source: Authors Computation from Eview 9.0 
 

The short-run dynamics among the variables are explored by employing error correction mechanism (ECM). Error 
correction model explains the speed of adjustment in restoring the equilibrium in the dynamic model with a negative sign. 
Bannerjee , Dolado, and Mestre (1998) also observe that a significant ECM is evidence that a stable long-term relationship 
exists. Table above shows the result of ECM in terms of changes in Return on asset to change in other variables. The magnitude 
of the ECT coefficient of -1.77 at 5 percent level of significance shows that the speed of adjustment towards long-run 
equilibrium is very high, that is, there is 177 percent increase over the previous year. The short run dynamics further affirm the 
long run relationship that Bank credit ratio to total deposit BCD, total financial system deposit and Bank cost to income Ratio 
has no significant impact on the return on asset in the short run also but only the Non-Performing Loan exhibited a negative 
significant impact on return on asset. 
  
Diagnostic Test 

Test F Statistics Probability 

Breusch-Godfrey serial correlation LM 
test 

3.648037 0.00000 

Heteroscedasticity(ARCH) 17.3412 F 1,28)  0.0096 

Heteroscedasticity(BreuschPagan-
Godfrey 

 
20.84385 

 
0.0468 

Normality Test (Jarque- Bera) 117.58 0.00000 

Source: Authors Computation from Eview 9.0 
 

Diagnostic tests such as Breusch- Godfrey serial correlation LM test, Breusch-Pagan-Godfrey Heteroskedasticity test 
and Jacque-Bera normality test.  These tests show that there is no serial correlation, presence of homoscedastic and normal 
distribution. Hence, the model is line with the econometric properties and the results are suitable for reliable interpretation and 
policy implication.  
 
3.3 Impulse Response Function 
This describes the evolution of a model’s variables in reaction to a shock in one or more variables. This feature allows tracing the 
transmission of a single shock within an otherwise noisy system of equations and, thus, makes them very useful tools in the 
assessment of economic policies. 



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-.4

-.2

.0

.2

.4

2 4 6 8 10

Response of LNROA to LNNPL

Response to Cholesky One S.D. Innovations

 
The result above shows the response of Return on Asset to innovation from Non-Performing Loan. This study shows 

that return on asset respond to Non-Performing Loan although the period and the responds is relatively uniform. 
 
  Variance Decomposition 

       
        VD of 

LNROA:       
 Period S.E. LNROA LNBCD LNBCIR LNTDP LNNPL 

       
        1  0.313488  100.0000  0.000000  0.000000  0.000000  0.000000 

 2  0.350966  86.27910  3.386231  0.998066  5.743668  3.592938 
 3  0.436573  64.60282  4.201521  21.29085  4.921632  4.983175 
 4  0.476380  63.69035  5.752814  20.40146  5.611523  4.543850 
 5  0.500836  65.45655  5.557380  4.253990  5.076896  4.253990 
 6  0.524890  66.61772  6.218811  3.873575  4.625277  19.65519 
 7  0.550445  66.46396  7.945937  4.027573  4.400085  18.66462 
 8  0.575806  67.86450  8.331418  3.816965  4.026012  17.16244 
 9  0.595678  68.71249  8.552364  3.676716  3.768660  15.96110 
 10  0.617050  69.07802  9.030516  3.784593  3.512694  15.28977 

       
       Source: Authors Computation from Eview 9.0 

 
The result of the variance decomposition above shows that total financial system deposit causes the highest variance in 

the return on asset in the 2nd period leading to the ratio of bank credit to total deposit contributing highest in the 3 rd period 
consequently this large bank credit turns bad leading to large contribution of NPL in the 4th to the 10th period. 
 
4. Findings 
Deposit money bank with an ROA of 2.49% implies how profitable a deposit money bank is relative to its total assets. This 
shows that only 2.49% of the total asset of the Deposit Money banks is profitable therefore this low ROA shows that deposit 
Money Banks is not making enough income from the use of its assets.   Hence deposit money bank can achieve a 
high ROA either by boosting its profit margin or, more efficiently, by using its assets to increase sales. 

14% of the total loan in deposit Money banks turn bad and becomes non-performing as indicated in the result of the 
descriptive statistics. The rationale for this high NPL cannot be far from the existence of high-interest rate, Low GDP, Poor 
credit appraisal, Inflation, unemployment and improper lending disbursement to agriculture sector within the Deposit money 
banks. 

However, the study confirms the presence of long run relationship among Non-Performing Loan and the performance 
of Deposit Money Bank in Nigeria and affirms that only Non-Performing Loan has significant effect on Bank Performance in 
the Long run and the Short run as well although the speed of adjustment of the explanatory variables are significant hence the 
speed of adjustment towards long-run equilibrium is very high, that is, there is 177 percent increase over the previous year. 

Nonetheless,  Return on asset respond to Non-Performing Loan although the period and the response is relatively 
uniform as indicated by the impulse response function while the variance decomposition of the study shows that total financial 
system deposit causes the highest variance in the return on asset in the 2nd period leading to the ratio of bank credit to total 
deposit contributing highest in the 3rd period consequently this large bank credit turns bad leading to large contribution of NPL 
in the 4th to the 10th period. 
 



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5. Conclusion 
The study concluded that persistence increase in Non-performing loans results in poor Performance of Deposit Money Banks in 
Nigeria. Also, Non-Performing Loan reduces deposit money banks return on asset. The study therefore recommends that 
deposit money banks should employ competent risk managers that always use their skills to reduce the incident of non-
performing loans in the Nigerian deposit money banks. The study also recommends that deposit money banks in Nigeria should 
always monitor the end-use of funds given to their customers in order to curb the incident of fund diversion which may result in 
non-performing loan. 
 
References 
Abiola, I. & Olausi, A. S. (2014). The Impact of Credit Risk Management on the Commercial Banks Performance in Nigeria. 

International Journal of Management and Sustainability, 3(5), 295-306. 
Ajayi, L. B. & Ajayi, F. I. (2017). Effects of Credit Risk Management on Performance of Deposit Money Banks in Nigeria. 

International Journal of Research in Management & Business Studies, 4(3), 50-55. 
Bofondi, M. & Ropele, T. (2011). Macroeconomic Determinants of Bad Loans: Evidence from Italian Conference, INEAG, 

Samos. 
Ekanayake, E.M. N.N. & Azeez A.A. (2015). Determinants of Non- Performing Loans in Licensed Commercial Banks: 

Evidence from Sri Lank. Asian Economic and Financial Review. 5(6), 868-882 
Hamisu, S.K. (2011). Credit Risk and the Performance of Nigerian Banks, Department of Accounting, Ahmadu Bello 

University, Zaria, Nigeria. 
Iwedi, M. & Onuegbu, O. (2014). Credit Risk and Performance of Selected Deposits Money Banks in Nigeria: An Empirical 

Investigation. European Journal of Humanities and Social Sciences, 31(1), 1684-1694. 
Kolapo, T. F., Ayeni, R. K. & Oke, M. O. (2012).Credit Risk and Commercial Banks’ Performance in Nigeria: A Panel Model 

Approach. Australian Journal of Business and Management Research, 2(2), 31-38. 
Muhammad,  F., Ammara S., Abrar, H.C, & Fareeha, K. (2012). Economic Determinants of Non- Performing Loans: 

Perception of Pakistan Bankers. European Journal of Business and Management. 4(2), 20-29. 
Salas,V. & Sanrina, K (2006)  Credit risk in two international setting : Spainish commercial and savings Bank. Journal of 

finance services Research, 22(3), 203-224. 
Saba, I. Kouser, R. & Azeem, M. (2012). Determinants of Non-Performing Loans: Case of US Banking Sector, International 

Journal of Banking and Finance, 4(4), 479-488. 
Sinkey, H., & Greenwalt  J. (1990). Determinant of non-performing loans in Guyana-Research Gate. 
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a comparative panel data analysis. Advances in Management and Applied Economics, 3(2), 135-164. 
 
Appendix 

Year LnNPL lnBCIR LnBCD lnROA lnTDP 

1986 1.311754 1.85901 1.890086 0.372822 1.352954 

1987 1.334454 1.864371 1.903416 0.370515 1.235528 

1988 1.334454 1.869667 1.859978 0.368194 1.248709 

1989 0.968483 1.874899 1.86976 0.365862 1.135769 

1990 0.977724 1.880068 1.848497 0.363516 0.812245 

1991 0.856729 1.745777 1.786893 0.361158 0.871573 

1992 1.571126 1.786041 1.863025 0.358787 0.895423 

1993 1.304059 1.765147 1.885531 0.356403 0.964731 

1994 0.761176 1.774955 1.877371 0.354006 0.930949 

1995 0.569374 1.745777 1.971461 0.351596 0.826075 

1996 0.5302 1.83512 2.016114 0.349172 0.783189 

1997 0.326336 1.825296 2.028449 0.222716 0.813581 

1998 1.287802 1.745777 2.026656 0.522444 0.843855 

1999 1.40824 1.786041 1.97635 0.164353 0.9154 

2000 1.354108 1.765147 1.915769 0.457882 0.960471 



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2001 1.294466 1.774955 1.911584 0.614897 1.03583 

2002 1.330414 1.767823 1.914343 0.5302 0.984077 

2003 1.311754 1.836007 1.92557 0.401401 0.974972 

2004 1.334454 1.848189 1.956313 0.374748 0.932981 

2005 1.334454 1.815777 1.971137 0.392697 0.940018 

2006 0.968483 1.832253 1.95564 0.403121 0.942008 

2007 0.977724 1.790567 1.978911 0.567026 1.082785 

2008 0.856729 1.761101 2.009366 0.521138 1.227115 

2009 1.571126 1.910464 2.016657 0.581153 1.287802 

2010 1.304059 1.85685 1.977449 0.528917 1.243534 

2011 0.761176 1.842983 1.887617 -1.04576 1.228144 

2012 0.569374 1.791059 1.821382 0.511883 1.240799 

2013 0.5302 1.807535 1.809358 0.361728 1.254064 

2014 0.471292 1.795254 1.857332 0.367356 1.253096 

2015 0.686636 1.834201 1.899711 0.230449 1.247728 

2016 1.107888 1.836546 1.928908 0.260071 1.237292 

2017 1.170555 1.838879 1.886247 0.336932 1.248219 

 
 
 
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