journal of economics, management and social vol 4 - no. 4

© 2018 Federal University Wukari Journal of Economics, Management & Social Science Volume 4 No 4, December 2018, Pg 90 – 102

CREDIT RISK IN MODELS OF DEPOSIT MONEY BANKS PROFITABILITY IN NIGERIA

IBE, R. C. PhD. Department of Finance and Banking University of Port Harcourt Email: [email protected]

NWACHUKWU, U. O. PhD. Port Harcourt Business School Port Harcourt, Rivers State

ABSTRACT This study investigated the effect of credit risk on the profitability of deposit money banks (DMBs) in Nigeria. Credit risk indicators, the independent variables were proxied by liquidity ratio (LIQR), loans and advances (LOAD) and non-performing loans (NPL) while dependent variable bank profitability was proxied by gross earnings to total assets (GETA). All four variables were stationary at I (1) based on the results of the Augmented Dickey-Fuller unit root test. Being integrated, the analysis was pushed further to ascertain whether the variables are cointegrated or not. Thus, the study employed the Johansen Multivariate Cointegration test. There is a long-run relationship between the variables. Loans and Advances ratio (LOAD) coefficient exerts most significant positive effect on the profitability of deposit money banks in Nigeria. Based on our findings, this paper is of the opinion that reforms and deregulation does not necessarily translate to better performance but when combined with other regulatory policies, banks stand a better chance of growth and survival. The regulatory authorities need to ensure that certain policy tools such as liquidity ratio, monetary policy rate are effectively managed to enhance good corporate governance and better performance of the banking industry.

Keywords: Cointegration, Diagnostic, Heteroskedasticity, Serial Correlation, Stability.

INTRODUCTION Deposit money banks (DMBs) by their nature are unique and riskier among financial

institutions such as merchant banks, investment trust, development bank and savings institutions. Part of their riskiness is because they are the only business where the proportion of borrowed funds is far higher than the owners’ equity. They have the capacity to create new credit money, many times in excess of the amount of shareholders’ capital and customer’s deposit, through the action of the bank credit multiplier. In order to perform credit functions, DMBs apply some of the customer’s deposits and the balance invested in low and high risk assets. If the borrowed funds are withdrawn at short notice, for example, due to rumours founded or unfounded, it can precipitate a run on a bank leading to liquidity risk. Where the deposited funds used by the banks to create loans or perform credit functions cannot be recovered from borrowers, the bank faces credit risk. Such bad loans


P a g e | 91

had been known to be a major cause of bank failures in many countries (Central Bank of Nigeria / Nigeria Deposit Insurance Corporation, 1995).

Credit default has been a major source of risk to banks arising from the borrower’s inability or unwillingness to repay loans extended to them. The failure to repay has been attributed to so many factors including failed businesses in which the borrowed funds were invested and outright diversion of the funds to unintended uses. The statistics from the 34 liquidated banks in Nigeria in 2004 clearly showed that the inability to collect loans and advances extended to customers and directors, companies related to directors/managers, was a major contributor to their distress. At the height of the distress in 1995 when 60 of the 115 operating banks were financially not sound, the ratio of the distressed banks non-performing loans and leases to their total loans was 67 percent. That ratio deteriorated to 79 percent in 1996, 82 percent in 1997 and by January 16th, 1998 the licences of 31 of the distressed banks had been revoked. To ensure that depositors’ funds are not impaired by losses sustained, banks are required by law and regulation to hold adequate capital cover to cushion any losses. In exercising their joint supervisory functions over financial institutions, more especially the banking sub-sector, movement of key variables of the sector are keenly watched on regular basis because deviations of those variables from the expectations of the concerned authorities could result in financial crisis. The critical issues, which attract the attention of the supervisory authorities, include capital adequacy, quality of bank assets, liquidity ratio, and loan – loss ratio.

In the face of these startling realities, can we say that the various banking reforms and regulations so far implemented to shore up the Nigerian banking sector have produced their desired effect? This is doubtful! Thus, it is important to make proper analysis of the actual impact of credit risk on the profitability of banks in Nigeria. Granted, some studies have been conducted to determine such relationships, but majority of them focused on the banking – economic growth nexus. Not many of the relevant studies, to the best of the author’s knowledge, covered the effects of credit risk on the profitability of deposit money banks. More so, even the fragmented studies were based on the experiences of developed countries. The experiences of developing countries are yet to be fully documented. That of Nigeria may be better recognized more in its absence than in its dearth, from the author’s point of view. Recognizing this obvious research gap, the authors set out to contribute to the existing body of empirical literature by examining the relationship between credit risk and the profitability of deposit money banks (DMBs) based on the Nigerian evidence. Therein lies the motivation for this study. The remainder of this study is structured as follows: Section two is review of related literature, section three discusses the methodology adopted, and section four is data analysis and interpretation of findings while section five presents the conclusion and recommendations.

LITERATURE REVIEW

Typical Risks Run by Banks The risks faced by deposit money banks (DMBs) are in the main associated with the

nature of banking business, meaning that such risks are endogenous whilst others are exogenous to the banking system. The endogenous risks that bankers should be able to control include amongst others: liquidity risk, credit risk, reputational risk, legal risk, operational risk, customer satisfaction risk, leadership risk and information technology risk. On the other hand, the risks that are exogenous to the banking system of which they have

Ibe, R. C. & Nwachukwu, U. O.

P a g e | 92

little or no control over and tend to pose the greatest control problem include: general operating risks, interest rate risk, exchange rate risk, income risk, regulatory risk, industry risk, government policies risk, political risk, fund placement risk, funds transfer risk, computer-fraud risk, management risk, earnings projection risk, sovereign risk and market risk. It is almost impossible to eliminate all the risks in the course of doing business in the banking sector but what management can do is to use standard quantitative capital ratios for measuring a bank’s profitability as a guide and judgement to avoid as much risks as possible and stay clear of trouble.

Liquidity Ratio (LIQR) The Central Bank of Nigeria (CBN) is authorized by Act to prescribe the statutory

minimum liquidity ratio which a bank must maintain depending on the general liquidity disposition of the economy. It is an important index for gauging the ability of a bank to fund its daily operations and at minimal cost. It measures the capacity of a bank to withstand heavy deposit withdrawals of customers, and to repay promptly all due short-term funds borrowed from other banks or from other non- bank financial intermediaries, by applying bank’s liquid assets only. Liquidity ratio could also influence positively or adversely the quality of the assets portfolio of a bank. The ratios tell a variety of stories, which fundamentally rest on the liquidity position of a bank, and the degree of liquidity risk a bank, may be running in the course of its operation. As the ratio of loans and advances to total deposits increases, banks’ generating the high ratio slides deeper into the zone of liquidity risk. A liquidity ratio much higher than the prescribed ratio often send jitters to monetary authorities, who respond quickly by imposing restrictive credit policy measures. Liquidity may not necessarily mean solvency because a bank may be temporarily liquid with borrowed funds only to collapse shortly after. While excess liquidity generates confidence among depositors, low liquidity causes panic among bank customers, and may trigger a deposit – run off, which may endanger the banking system and the economy. It is in the interests of monetary authorities, bank managers and depositors alike, that neither a situation of excess liquidity nor that of very low liquidity prevails in the banking system.

Loans and Advances (LOAD) The term ‘loan’ refers to the amount borrowed by one person from another. The

amount is in the nature of loan and refers to the sum paid to the borrower. Thus, from the view point of borrower, it is ‘borrowing’ and from the view point of bank, it is ‘lending’. Loans may be regarded as ‘credit’ granted where the money is disbursed and its recovery is made on a later date. It is a debt for the borrower. While granting loans, credit is given for a definite purpose and for a predetermined period. Interest is charged on the loan at agreed rate and intervals of payment. ‘Advance’ on the other hand, is a ‘credit facility’ granted by the bank. Banks grant advances largely for short-term purposes, such as purchase of goods traded in and meeting other short-term trading liabilities. There is a sense of debt in loan, whereas an advance is a facility being availed of by the borrower. However, like loans, advances are also to be repaid. Thus a credit facility- repayable in installments over a period is termed as loan while a credit facility repayable within one year may be known as advances.


P a g e | 93

Non-Performing Loans (NPL) There is no single definition of a nonperforming loan because it varies from country

to country. Some use quantitative criteria such as the number of days overdue, others rely on qualitative norms such as the client’s financial status, management judgment about future payments. What is appropriate in one country may differ from another. However, there is some kind of convergence on the definition based on International Monetary Fund (IMF) Compilation Guide on Financial Soundness Indicators 2004. The Guide recommends that assets other than loans should be classified as nonperforming using the same criteria. A loan is nonperforming when payments of interest and/or principal are past due by 90 days or more, or interest payments equal to 90 days or more have been capitalized, refinanced, or delayed by agreement, or payments are less than 90 days overdue, but there are other good reasons —such as a debtor filing for bankruptcy—to doubt that payments will be made in full. The 90 days overdue criterion is commonly—but not universally—used. The second part of the definition ensures that NPLs cannot be reclassified as “performing” simply by replacing them with new loans. Because the 90-day criterion is not universal, any international comparisons relating to NPLs require metadata relating to national practices. It is important also to recognize what the term nonperforming does and does not mean. It essentially means that the ‘orderly repayment of the debt is in jeopardy and it follows from this that some losses are probable.

Empirical Evidence Ahmed, Takeda and Shawn (1998) postulated that loan loss provision has a

significant positive impact on non-performing loans. This implies that an increase in loan loss provision leads to an increase in credit risk and deterioration in the quality of loans consequently affecting bank profitability negatively.

Ahmad and Ariff (2007) examined credit risk determinants of commercial banks of developing economies compared with the developed economies. Their study indicated the importance of regulation in the banking systems that offered multi-products and services and that the quality of management is critical in the cases of loan-dominant banks in developing economies like Nigeria. Also increase in loan loss provision was a significant determinant of potential credit risk. The study further indicated that credit risk in the banks of developing economies were higher than that in developed economies.

Felix and Claudine (2008) investigated the relationship between bank performance and credit risk management using return on equity (ROE) and return on assets (ROA) to measure profitability. Their results indicated an inverse relationship between the ratio of non-performing loans and total loan of financial institutions which led to a decline in profitability.

Ben-Naceur and Omran (2008) examined 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 1989-2005.They found that there is a positive and significant relationship between bank capitalization and credit risk and they both have significant impact on banks’ net interest margin, cost efficiency and profitability.

Kithinji (2010) investigated the effect of credit risk management on the profitability of commercial banks from 2004 to 2008 in Kenya using data on the amount of credit, level of non-performing loans and profits. The findings showed that the profits of commercial


P a g e | 94

banks were not affected by the amount of credit and non-performing loans, implying that other factors other than credit and non-performing loans impacted on profits.

Al-Khouri (2011) studied the impact of bank’s specific risk characteristics, the banking environment on the performance of 43 commercial banks in 6 Gulf Cooperation Council (GCC) countries from 1998 to 2008 using fixed effect regression analysis. His results showed that credit risk, liquidity risk and capital risk were the major factors that affected bank performance when profitability is measured by return on assets while only liquidity risk affected profitability when measured by return on equity (ROE).

The study of Kargi (2011) investigated the impact of credit risk on the profitability of deposit money banks (DMBs) in Nigeria using financial ratios as proxy for bank performance. Credit risk data was collected from the annual reports and accounts of sampled banks from 2004 to 2008. Data was analyzed using descriptive, correlation and regression techniques. His findings revealed that credit risk management has a significant impact on the profitability of Nigerian banks. He postulated an inverse relationship existed between banks’ profitability, loans and advances, non-performing loans and deposits thereby exposing them to liquidity risk and distress.

Epure and Lafuente (2012) studied bank performance and risk for Costa-Rican banking sector from 1998 to 2007. The results showed that there is a link between performance improvements and regulatory changes and that risk influenced differences in banks and non-performing loans negatively affected efficiency and return on assets while the capital adequacy ratio had a positive impact on net interest margin.

Chen and Pan (2012) examined credit risk efficiency of 34 Taiwanese commercial banks from 2005 to 2008. They used financial ratio in assessing credit risk and based their analyses on Data Envelopment Analysis (DEA). The credit risk parameters were credit risk technical efficiency (CRTE), credit risk allocative efficiency (CR-AE), and credit risk cost efficiency (CR-CE). They concluded that only one bank was efficient during the period and that the DEA results showed relatively low average efficiency levels of CR-TE, CR-AE and CR-CE in 2008.

METHODOLOGY The hypothesized credit risk and deposit money banks (DMBs) profitability relationship can be expressed as:

GETA = f (CR)

And where credit risk (CR) is decomposed into liquidity ratio, loans and advances and non-performing loans, the relation turns multiple in the functional form:

GETA = f (LIQR, LOAD, NPL) ------------ (1)

This can be specifically stated as follows:

GETAt = f ( LIQRt, LOADt, NPLt) --------------(2)

The above model is specified linearly in the form of an econometric equation below as follows: GETAt = αo + α1LIQRt + α2LOADt + α3NPLt + Ut -----(3)

α1 & α3 <0 & α2 > 0

Where


P a g e | 95

GETA = Gross Earnings to Total Assets

LIQR = Liquidity ratio

LOAD = Loans and Advances

NPL = Reserves for depreciation and non-performing loans

t= indicates time series data

α0 = Intercept

α1, α2, α3 = Parameters of the coefficients

U = Error or Disturbance Term

In this study, the entire variables shall be cast into rates of growth over the period of the study to achieve a uniform data base. Thus, we use the growth rate of loans and advances or (LOAD), growth rate of non-performing loans or ( NPL), growth rate of gross earnings to total assets (GETA), and growth rate of liquidity ratio (LIQR) . It is expected that the use of the uniform rates of change or growth rates will help reduce or eliminate obvious econometric problems.

PRESENTATION OF DATA AND ANALYSES

Stationarity Analysis between GETA, LIQR, LOAD, and NPL The relationships between gross earnings to total assets variable (GETA) and credit

risk indicator variables (LIQR, LOAD, and NPL) are estimated starting with the Augmented Dickey-Fuller (ADF) unit root test. This is followed by the Unrestricted Cointegration Rank Tests (Trace and Maximum Eigenvalue) after the order of linear deterministic trend in order to determine the long run effects. Next, the study used the Equation Estimation method to estimate the short-run effects.

Table 4.1: Augmented Dickey-Fuller Unit Root Test Results

Variable ADF Stats Status Remarks

GETA Level 5%

-1.738341 -3.012363

Not Stationary

GETA (1st Diff 5%

-4.483036 -3.020686

I (1)

Stationary

LIQR Level 5%

-2.74688 -3.012363

Not Stationary

LIQR (1st Diff) 5%

-4.459050 -3.020686

I (1)

Stationary

LOAD Level 5%

-3.187666 -3.012363

I (1)

Stationary

LOAD (1st Diff) 5%

-5.422519 -3.020686

I (1)

Stationary

NPL Level 5%

-4.031758 -3.012363

I (1)

Stationary


P a g e | 96

NPL (1st Diff) 5%

-6.431056 -3.020686

I (1)

Stationary

Source: EVIEWS 9 Printout

Table 4.1 depicts the results of the Augmented Dickey-Fuller (ADF) unit root test of the variables: GETA, LIQR, LOAD and NPL. The results show that GETA and LIQR variables were not stationary at level but became stationary after first differencing while the LOAD and NPL variables were both stationary at level and first differencing, implying that they were all integrated or stationary at order one i.e. I (1). Therefore, we reject the hypothesis of no stationarity in all the variables.

Cointegration between GETA, LIQR, LOAD, and NPL The variables being integrated at order I (1), the analysis was pushed further to

ascertain whether they are co-integrated or not. Thus, the study employed the Unrestricted Cointegration Rank Tests (Trace and Maximum Eigenvalue) after the order of linear deterministic trend. The results of the tests are in Tables 4.2a and 4.2b respectively.

Table 4.2a: Unrestricted Cointegration Rank Test (Trace):

Series: GETA, LIQR, LOAD and NPL Trend assumption: Linear deterministic trend

Date: 10/11/18 Time: 14:50

Sample (adjusted): 1994 2017

Included observations: 24 after adjustments

Trend assumption: Linear deterministic trend

Series: GETA LIQR LOAD NPL

Lags interval (in first differences): 1 to 1

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.884550 75.96445 47.85613 0.0000

At most 1 * 0.678731 32.78602 29.79707 0.0220

At most 2 0.312438 10.07650 15.49471 0.2749

At most 3 0.121221 2.584443 3.841466 0.1079

Trace test indicates 2 cointegrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Source: EVIEWS 9 Printout.


P a g e | 97

Table 4.3c: Unrestricted Cointegration Rank Test (Maximum eigenvalue):

Series: GETA, LIQR, LOAD and NPL Trend assumption: Linear deterministic trend

Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.884550 43.17843 27.58434 0.0002

At most 1 * 0.678731 22.70952 21.13162 0.0298 At most 2 0.312438 7.492056 14.26460 0.4327 At most 3 0.121221 2.584443 3.841466 0.1079

Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Source: EVIEWS 9 printout

From Table 4.2a, it can be seen that the Trace Statistic is computed to be 75.96445 and 32.78602 while the critical values at alpha 0.05 are 47.85613 and 29.79707 respectively, which indicates a rejection of the null of none and at most one co-integrating equations. Thus the alternate hypothesis of two cointegrating equations is not rejected. Equally, the Max-eigenvalue test indicates two cointegrating eqn. (s) at the 0.05 level (statistic = 42.17843 and 27.58434; critical values = 27.58434 and 21.13162) respectively. These results indicate the existence of a sustainable long run equilibrium relationship between GETA, and the credit risk indicators trio of LIQR, LOAD, and NPL.

Relative Long Run Relationships between GETA, LIQR, LOAD and NPL Table 4.2c depicts the long run cointegration equation showing the nature and

magnitude of the observed relationships. The equation is normalized for GETA – the dependent variable

Table 4.2c: Normalized cointegrating coefficients (standard error in parentheses) 1 Cointegrating

Equation(s):

Log

likelihood 23.39277

Normalized cointegrating coefficients (standard error in parentheses)

GETA LIQR LOAD NPL

1.000000 0.001264 0.441565 0.248902

(0.00068) (0.05507) (0.02592)

Source: EVIEWS 9 printout

The normalized beta coefficient representing the long run relative statistical relationship between the GETA and LIQR is 0.001264 with a standard error of 0.00068, suggesting a t-statistic of 1.86. This is insignificant at 5% level and by implication; there exist a statistically insignificant long run relationship between the GETA and the LIQR variable. The sign implication suggests a positive relationship which disagrees with a priori expectation.

On the other hand the normalized beta coefficient representing the long run relative statistical relationship between GETA and LOAD is calculated to be 0.441565 with a standard


P a g e | 98

error of 0.05507 (t-statistic = 8.02). The computed t-statistic is significant at 5% significant level. Thus, the relationship between GETA and LOAD is positive as a priori expected and statistically significant at the conventional 5% level in the long run.

The normalized beta coefficient representing the long run relative statistical relationship between GETA and NPL is shown to be 0.248902 and Standard error of 0.02592, suggesting a t-statistic of 9.60. This is significant at 5% level. There exist a statistically significant relationship between GETA and the NPL variable. The sign implication suggests a positive relationship which is in disagreement with a priori expectation.

Relative Short Run Relationship between GETA, LIQR, LOAD and NPL. This study employed the Equation Estimation method to estimate the short-run

relationship between GETA, LIQR, LOAD and NPL. The results are summarized on Table 4.2d.

Table 4.2d: Equation Estimation Regression Result Dependent Variable: GETA

Method: Least Squares

Date: 10/11/18 Time: 11:50

Sample (adjusted): 1993 2017

Included observations: 25 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C -0.011761 0.026209 -0.448747 0.6610

LIQR 0.000783 0.000678 1.154474 0.2691

LOAD 0.021029 0.043363 0.484949 0.6358

NPL -0.037841 0.014067 -2.690037 0.0185

GETA(-1) 0.887737 0.122160 7.267013 0.0000

LIQR(-1) -0.000227 0.000741 -0.305966 0.7645

LOAD(-1) 0.029757 0.032990 0.901981 0.3835

NPL(-1) 0.007681 0.016903 0.454435 0.6570 R-squared 0.882466 Mean dependent var 0.100000

Adjusted R-squared 0.819179 S.D. dependent var 0.038730

S.E. of regression 0.016469 Akaike info criterion -5.092330

Sum squared resid 0.003526 Schwarz criterion -4.694417

Log likelihood 61.46946 Hannan-Quinn criter. -5.005973

F-statistic 13.94382 Durbin-Watson stat 2.435021

Prob(F-statistic) 0.000040

Source: EVIEWS printout (version 9)

As can be seen from the results after adjustments, the beta coefficient representing the relationship between GETA and LIQR is -0.000227, while observed t-statistic is -0.305966 with a probability of 0.7645 which is insignificant at 5% level. We do not reject the null hypothesis of no significant relationship between GETA and LIQR in the short run. More so, the observed relationship is negative, which is in agreement with a priori expectation. The relationship between GETA and LOAD is positive, but not statistically significant at 5% level (Beta = 0.029757; t-stat = 0.901981; prob. = 0.3835). Thus, we do not reject the null hypothesis of no significant relationship between GETA and LOAD in the short run.

The beta coefficient representing the relationship between GETA and NPL is 0.007681, while the observed t-statistic is 0.454435 which is insignificant at 5% level (prob. = 0.6570). Given these, we do not reject the null hypothesis of no significant relationship


P a g e | 99

between GETA and NPL in the short run. Also, the observed relationship is positive, which disagrees with a priori expectation.

The R2 otherwise known as the measure of the “goodness of fit” or the “coefficient of determination” shows the percentage of the total variation of our dependent variable (Y) that can be explained by the independent variables (X1, X2, and X3). Therefore, the R2 is expressed as a percentage, and that part of the variation of the dependent variable (i.e. 100-R2) which is not explained by the regression line is attributed to the existence of the disturbance or error term (Ut). The R2 is calculated to be 0.882468 or 88.2% meaning that the variations in the dependent variable i.e. gross earnings to total assets (GETA) is 88.2% attributable to the changes in the independent variables liquidity ratio (LIQR), Loans and advances (LOAD), and non-performing loans (NPL). Also, the standard deviation of the dependent variable (0.038730) is larger than the standard error of the regression, so this regression has explained most of the variance in GETA. The DW (Durbin-Watson) as shown in the regression is 2.435021 and well above the traditional benchmark of 2.0 which is consistent with no serial correlation.

How far does the influence of the various explanatory variables conform to a priori expectation expressed in section three? This question is warranted since any reliable estimated regression equation is expected to conform to a priori restrictions imposed or determined by the theoretical underpinning of the study in question.

DIAGNOSTIC TESTS

Test for heteroskedasticity This is a Lagrange Multiplier (LM) test for autoregressive conditional

heteroskedasticity (ARCH) in the residuals (Engle, (1982). This was motivated by the observation that in many financial time series, the magnitude of residuals appeared to be related to the magnitude of recent residuals. ARCH in itself does not invalidate standard LS inference. However, ignoring ARCH effects may result in loss of efficiency. To test the null hypothesis that there is no ARCH up to the order q in the residuals, we run the regression

= +

+ . . . + +

where e is the residual. This is a regression of the squared residuals on constant and lagged squared residuals up to order q. The f-statistic is an omitted variables test for the joint significance of all lagged squared residuals. The Obs * R Squared statistic is Engle’s LM test statistic, computed as the number of observations times the R² from the test regression.

Test for Serial Correlation This test belongs to class of (asymptotic) large sample tests known as Lagrange

Multiplier (LM). Unlike the Durbin-Watson statistic the LM test may be used to test for higher order ARMA errors, and it is applicable whether or not there are lagged dependent variables. The null hypothesis of the LM test is that there is no serial correlation. The alternative is ARMA(r, q) errors where the number of lag terms p = max (r, q).

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 2.429486 Prob. F(1,12) 0.1450

Obs*R-squared 3.535760 Prob. Chi-Square(1) 0.0601

Source: EVIEWS 9 Printout


P a g e | 100

The probability (0.0601) of the observed R squared indicates we do not reject the null hypothesis of no serial correlation.

Test for Stability This test examines whether the parameters of the model are stable across various

subsamples of the data. The null and alternative hypotheses of the RESET test are : N (0, I)

: N ( , I), 0

The crucial question in constructing the test is to determine what variables should enter the Z matrix. Note that the Z matrix may, for example, be comprised of variables that are not in the original specification. In testing for incorrect functional form, the nonlinear part of the regression model may be some function of the regressors included in X.

Output from the test reports the test regression and the F-statistic and Log likelihood ratio for testing the hypothesis that the coefficients on the powers of fitted values are all zero. The RESET test could detect specification error in an equation which was known a priori to be misspecified but which nonetheless gave satisfactory values for all the more traditional test criteria—goodness of fit, test for first order serial correlation, high t-ratios. The Ramsey RESET test is applicable only to an equation estimated by least squares.

Ramsey RESET Test

Equation: UNTITLED

Specification: GETA C LIQR LOAD NPL GETA(-1) LIQR(-1) LOAD(-1) NPL(

-1)

Omitted Variables: Squares of fitted values Value Df Probability

t-statistic 0.523906 12 0.6099

F-statistic 0.274477 (1, 12) 0.6099

Likelihood ratio 0.474924 1 0.4907

CONCLUSION AND RECOMMENDATIONS The following conclusions are made from both the multiple regression equation and

Johansen cointegration analysis of the effect of credit risk on deposit money banks profitability measured by gross earnings to total assets (GETA).

In the short run, liquidity ratio has an insignificant and negative relationship with banks profitability while loans and advances and non-performing loans also have insignificant but positive relationship. However, in the long run all the three variables have positive relationship with banks profitability. Liquidity ratio has an insignificant relationship. These three variables jointly affect the profitability of the banks as revealed by the probability of the F – statistics. Loan and Advances ratio (LOAD) coefficient exerts most significant positive effect on the profitability of deposit money banks in Nigeria.

Based on our findings, this paper is of the opinion that reforms and deregulation does not necessarily translate to better performance but when combined with other regulatory policies, banks stand a better chance of growth and survival, so the regulatory authorities need to ensure that certain policy tools such as liquidity ratio, monetary policy rate are effectively managed to enhance good corporate governance and better performance of the banking sector.


P a g e | 101

References Ahmad, N. H., & Ariff, M. (2007). Multi-country Study of Bank Credit Risk Determinants,

International Journal of Banking and Finance, 5(1), pp.135-152.

Ahmed, A. S., Takeda, C., & Shawn, T. (1998). Bank Loan Loss Provision: A Reexamination of Capital Management and Signaling Effects, Working Paper, Department of Accounting, Syracuse University, pp.1-37.

Al-Khouri, R. (2011). Assessing the Risk and Performance of the GCC Banking Sector, International Journal of Finance and Economics, ISSN 1450-2887, Issue 65, pp.72-8.

Basel Committee on Banking Supervision (2001). Risk Management Practices and Regulatory Capital: Cross-Sectional Comparison (available at www.bis.org)

Ben-Naceur, S., & Omran, M. (2008). The Effects of Bank Regulations, Competition and Financial Reforms on MENA Banks’ Profitability, Economic Research Forum Working Paper No. 44.

Chen, K., & Pan, C. (2012). An Empirical Study of Credit Risk Efficiency of Banking Industry in Taiwan, Journal of Chinese Management Review, 15(1), pp.1-16.

Demirguc-Kunt, A., & Huzinga, H. (1999). Determinants of Commercial Bank Interest Margins and Profitability: Some International Evidence, The World Bank Economic Review, 13(2), pp.379-40.

Drehman, M., Sorensen, S. & Stringa, M. (2008). The Integrated Impact of Credit and Interest Rate Risk on Banks: An Economic Value and Capital Adequacy Perspective”, Bank of England Working Paper No.339

Epure, M., & Lafuente, I. (2012). Monitoring Bank Performance in the Presence of Risk, Barcelona GSE Working Paper Series No.61.

Felix, A. T., & Claudine, T. N (2008). Bank Performance and Credit Risk Management, Unpublished Masters Dissertation in Finance, University of Skovde.

Gieseche, K. (2004). “Credit Risk Modeling and Valuation: An Introduction”, Credit Risk: Models and Management, Vol.2, Cornell University, London.

Gujarati, D. N., & Sangeetha, S. (2007). Basic Econometric, 4 th Edition, McGraw-Hill Education Books Ltd., India.

Kargi, H. S. (2011). Credit Risk and the Performance of Nigerian Banks, AhmaduBello University, Zaria.

Kithinji, A. M. (2010). Credit Risk Management and Profitability of Commercial Banks in Kenya, School of Business, University of Nairobi, Nairobi.

Lindgren, H. (1987). Banks, Investment Company, Banking Firms, Stockholm Enskilda Bank (1924-1945), Institute for Research in Economic History, Stockholm School of Economics, Stockholm.

Marsh, I. W. (2008). The Effect of Lenders’ Credit Risk Transfer Activities on Borrowing Firms’ Equity Returns, Cass Business School, London and Bank of Finland.


P a g e | 102

Michalak, T., & Uhde, A. (2009). Credit Risk Securitization and Banking Stability: Evidence from the Micro-Level for Europe”, Draft, University of Bochum, Bochum.

Psillaki, M., Tsolas, I. E., & Margaritis, D. (2010). Evaluation of Credit Risk Based on Firm Performance, European Journal of Operational Research, 201(3), pp.873-888.

Shao, Y., & Yeager, T. J. (2007). The Effects of Credit Derivatives on U.S. Bank Risk and Return, Capital and Lending Structure, Draft, Sam M. Walton College of Business, Arkansas.

journal of economics, management and social vol 4 - no. 4

Documents