Abstract

The study reviews the performances of banks within the context of Non-performing Loans(NPLs) The results show that earnings risk is most prevalent in explaining variations in non-performing loans followed by interest rate risk and monetary policy rate. The results are largely consistent with the results from the study on Non-performing loans (NPLs) conducted on 8ub-$aharan African countries by Fofack (2005). The paper recommends that an Efficient Loan Appraisal Techniques (ELAT) consisting of conventional investment analysis and risk measurements be adopted and credit policy must be in line with the institutional objectives. The Basel accords needs to be reviewed in the light of the current credit crunch.

Keywords: Banks, Non-performing Loane(NPL), Credit Risk, Monetary Policy

Jel Classification: G18, G21, G32, G52

I. INTRODUCTION

The financial institutions generally serve as financial intermediaries. It is their functions to mobilize funds savers by issuing to them their own securities. This form of asset transformation is required to ensure that funds are moved form surplus economic units to deficits economic units within the economy. These institutions, like any other business organization, have some risks to manage before they can successfully achieve their aim and objectives, which are almost always profit oriented. Non-performing Loans (NPLs) generally refer to loans which for a relatively long period of time do not generate income; that is the principal and/or interest on these loans has been lei" unpaid for at least 90 days [Caprio and Klingebiel (1999)]. Non-performing Loans (NPLs) could also occur when the amortization schedules are not realized as at when due resulting in over-bloated loan interest due for payments.

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Non-Performing Loans (NPLs) reduces the liquidity of banks, credit expansion, it slows down the growth of the real sector with direct consequences on the performance of banks, the firm which is in default and the economy as a whole. According to the theory of finance, there are various risks facing financial institutions. They include: credit risk, liquidity risk, market risk, operating risk, reputation risk and legal risk. The system is highly sensitive while the activities of the operators need to be conducted within the laid down and agreed rules and procedures, in order to achieve a reasonable level of efficiency.

Lending involves the creation and management of risk assets and is an important task of bank management. As in liquidity and portfolio management, effective management of the lending

portfolio requires an articulated lending policy. The policy should set out the bank's lending philosophy and objectives including the modalities for implementation, monitoring appraisal and review. Since lending means taking risks and assessing the risks of defaults and movements in interest rates, a written policy would act as a signpost to guide management and lending institutions. Well-conceived lending policies and careful lending practices are essential in facilitating efficient credit system and minimize risk in lending.

It is worthy to clearly point to the fact that risks are major intents of banking business. The degree of success of a bank greatly depends on the ability of management to ensure that the practice of risk management mitigates the impact of risk in such a way, and to such an extent that recorded surplus is not only robust and covers the interests of various stakeholders, but also assures the health integrity of the bank.

One of the major components of bank's assets is loans and advances, and the effective management of such loan portfolio has been a problem. The failure of many banks is not because of their inability to mobilize adequate deposits from the surplus sector to the deficit sector of the economy, but mainly because their lending portfolio have been poorly man{lged. The banking sector is seen to have an important role to play in the economic development of the country. This is mostly pronounced in the realm of financial intermediation. However, previous studies on the sector showed that little success was recorded in this regard. Some banks find it difficult to meet their obligations to their customers and owners due to fault or weakness in managing their lending portfolio and the shortcomings which could render them either illiquid or insolvent.

II. LITERATURE REVIEW

Non-performing Loans(NPLs) have become contemporary issues in credit management and undoubtedly the new frontier in finance The accumulation of Non-performing Loans(NPLs) is generally attributable to a number of factors, including economic down turns and macroeconomic volatility, terms of trade deterioration, high interest rates, excessive reliance on overly high-priced inter-bank borrowings, insider lending and moral hazard (Goldstoin and Turner (1996). deServigny and Renault, (2004) submitted that Non-performing Loans (NPLs) has taken a new dimension in finance just as interest rate and asset and liability management were 15 years ago. Because of mounting pressure of Non-performing Loans (NPLs) on bank's balance sheets and incessant bank failures, the Central Bank of Nigeria's Prudential Guidelines (1990) and subsequent reviews subsume credit facilities into loans, advances, overdrafts, commercial papers, banker's acceptances, bills discounted, leases, guarantees, and other loss contingencies connected with a bank's credit risks. The activities of these credits in terms of frequency of repayment or inability to repay same have further made it possible to group them into performing and non-performing credit facilities. Kassim(2002) suggested some causes of Non-performing Loans(NPLs) as:

* Poor management

* Lack of sound credit policy

* Inadequate credit analysis

* Errors in documentation

* Undue emphasis on profitability at the expense of loan quality

* Fraudulent practices

* Political instability / economic depression

* Abnormal competition

* Policy and regulatory inconsistencies

* Weak real sector

* Political and social influence on bank operators.

From the view of Elaine (2007), Non-performing Loans (NPLs) or credit risk encapsulates the potential loss in the event of credit deterioration or default of a borrower. Thus a sound credit appraisal of loans is very important to the creditor. As argued by Dorfman (1998), bankers required an understanding of credit standards, the process by which credit worthiness and credit structure are analyzed, decision-making techniques, negotiation, follow-up and problem resolution, in order to effectively manage credit risk. Abolo (1999) supported Dorfman's assertion and presented his own principles of lending under three headings, that is, safety, suitability and profitability of credit, which equally compel bankers to follow the lending rules. Although credit depends on good faith, and no matter the amount of confidence that parties have on each other, it does not reduce the importance of scrutiny of these loan portfolios where good faith has been violated either deliberately or inadvertently. Thus, the lenders must search for and avoid dishonest borrowers. This involves sound credit analysis, which Nwankwo (1991) describes as the process of assessing the risk of lending to a business or individual against the benefits to accruable from such investment. The benefits can be direct, such as interest earnings and possibly deposit balances required as a condition of the loan or indirect, such as initiation or maintenance of a relationship with the borrower, which may provide the bank with increased deposits and with demand for a variety of bank services. He argues further that credit risk assessment has two aspects. One is qualitative, and generally the more difficult; and the other is quantitative. To evaluate the qualitative risk, the loan officer has to gather and appraise information on the borrower's record of financial responsibility, determine his true or correct need for borrowing, identify the risks facing the borrower's business under current and prospective economic and political situations, and estimate the degree of his commitment regarding the repayment.

To estimate the financial viability of a portfolio, banks should not only limit their analysis to project evaluation techniques alone, but also by evaluating all credit risks that could become threats to the overall performance of such a portfolio. Schall and Halley (1980) outlined the key indicators for loan analysis as capacity, collateral, capital, condition and character. He concludes that lending involves the creation and management of risk assets and is an important task of bank

management. While being the highest earning asset, the loan portfolio is also the most illiquid and most risky of banks' operation. The fiscal costs of these impaired loans are important as well, and vary with the scope and length of the crisis [Cortavarria Luis, C. Dziobek, A. Kanaya and I. Song (2000)].

Non-performing Loans (NPLs) are the most common causes of bank failures. This has made all regulatory institutions to prescribe minimum standards for credit risk management. The basis of sound credit risk management is the identification of the existing and potential risks inherent in lending activities. Measures to counteract these risks normally comprise clearly defined policies that express the bank's credit risk management philosophy and the parameters within which credit risk is to be controlled. deServigny and Renault (2004) opined that specific credit risk management measures typically include three kinds of policies. One set of policies include those aimed to limit or reduce credit risk, such as policies on concentration and large exposure, adequate diversification, lending to connected parties, or over-exposure. The second set includes policies of asset classification which expose a bank to credit risk. The third set include policies of loss provisioning or the making of allowances at a level adequate to absorb anticipated loss-not only on the loan portfolio, but also on all other assets that are sensitive to losses.

III. A REVIEW OF BASEL ACCORDS ON CREDIT MANAGEMENT

Basel Accords are recommendations on banking laws and regulations issued by the Basel Committee on Banking Supervision. The purpose is basically to create an international standard that banking regulators could use when creating regulations in their respective countries. The regulation is about how much of the bank's capital that are needed to guard against the types of financial and operational risks banks face i.e. credit risk, market risk and operational risk. The accords have done a lot of good work for ensuring an internationally accepted minimum capital standard.

The cornerstone of the first Basel Accord is the Cooke ratio. This ratio is defined as the amount of capital divided by risk-weighted assets. This ratio must be at least 8 percent. Risk-weighted assets are simply the amount lent multiplied by the risk weight. The great novelty with the 1996 amendment is that it has allowed banks to use their own internal model in order to measure market risk. In addition, this amendment has enabled banks to integrate off-balance sheet instruments such as securitization. The simplicity of the Basel I made it vulnerable to some unanticipated credit risk such as we are witnessing now globally. Consequently, a new more risk--sensitive capital adequacy framework was issued in 2002.

The Basel II Accords which made some improvement over the previous focus on three pillars of risks minimization. They are (1) Pillar 1--minimum capital requirement--is expected to determine the amount of capital requirement given the level of credit risk, market risk and operational risk facing banks; (2) Pillar 2--supervisory review-provides a frame work for dealing with all the other risks a bank may face, such as systematic risk, pension risk, concentration risk, strategic risk, reputation risk, liquidity risk and legal risk, which the Accord combines under the title of residual risk and (3) Pillar 3--market discipline-designed to allow the financial community to have a better picture of the overall risk position of the bank and to allow the competitors/counter parties of the banks to price and deal appropriately.

On November 15, 2005, the Committee released a revised version of the Accords, incorporating changes to the calculations for market risk and the treatment of double default effects. This revision, like the previous ones, was expected to reduce bank failures in case of credit crisis. Not done yet, on July 4, 2006, the Committee finally released a comprehensive version of the Accords, with no new elements introduced in this compilation. This version is now the current version. Nevertheless, all nations which have subscribed to Basel Accords have intensified their efforts in introducing new safety valves in risk measurements to safeguard the banking system from collapse. However, deServigny and Renault (2004) suggested that disclosure principles related to sound credit risk should be mandated by regulatory authorities, as recommended by the Basel Committee on Bank Supervision.

As a further step to safeguards banking sector, the Central Bank of Nigeria has made it mandatory that all outstanding balance of unpaid loans or bad debts accruable during the financial year must be fully provided for in the balance sheets at the end that financial year. In addition, all credits must abide by credit policy set by the monetary institution.

However, much as the Basel Accords have been seen to be providing some effective framework for credit management so as to avoid credit crisis, it appears some of the risks measurement have to be strengthened if only to reduce the high incidence of Non-performing Loans (NPLs). These risks are (1) risk of corporate governance(creation of bad credits either by managers or major shareholders), and Special or Uncalculated risk(caused by hurricane, thunder, natural disaster, etc which are beyond financial expectation of risk undertaking institutions) and overhead risk. Somoye (2008) argued that banking consolidation and restructuring are not necessarily sufficient conditions for banking sector stability and sustainability, but efficient and faithfulness of the management of these institutions. For example, banks managers and shareholders consider their overheads more important that the safely of loans and advances. There is the need therefore to shift our focus to weighted values of these risks in evaluating investments.

IV. EMPIRICAL EVIDENCE OF NON-PERFORMING LOANS IN SOME COUNTRIES

Empirical evidences and results from studies show similar trends on the negative effect of non-performing loans on bank performances. Current global financial crisis attests to this direction.

In Turkey, Karabulut and Bilgin (2007) carried out a study with the purpose of examining the impact of the unlimited deposit insurance on Non-performing Loans (NPLs) and market discipline. They argued that deposit insurance program play a crucial role in achieving financial stability. Governments in many advanced and developing economies established deposit insurance schemes for reducing the risk of systemic failure of banks. The report shows that deposit insurance has a beneficial effect of reducing the probability of a bank run. However deposit insurance systems have their own set of problems. Deposit insurance systems create moral hazard incentives that encourage banks to take excessive risk. Turkey established an explicit deposit insurance system in 1960. Until 1994, the coverage was determined by a flat rate but in that date, Turkey experienced a major economic crisis. In April 1994, Turkish government had to establish an unlimited deposit insurance scheme to restore banking system stability. In conclusion, the study shows that unlimited deposit insurance caused a remarkable increase at

Non-performing Loans (NPLs). What this means is that deposit insurance institutions established by monetary authorities must re-examine the current policy of blanket guarantee of deposits in the banking sector.

In Taiwan, Hu, Li and Chu (2004) carried out their own study examining how ownership structure affects Non-performing Loans (NPLs). Their findings revealed that an increase in the government's shareholding facilitates political lobbying. On the other hand, private shareholding induces more Non-performing Loans (NPLs) to be manipulated by corrupt private owners. The results show that the rate of NPLs decreased as the ratio of government shareholding in a bank rose (up to 63.51%), while the rate thereafter increased. The report posits further that joint ownership has the lowest rate of NPLs among Taiwanese public, mixed and private commercial banks. The joint ownership effect on NPLs ratios is negative and its magnitude is sufficiently large in Taiwan's banking industry. Bank size is negatively related to the rate of NPLs, which supports their argument that larger banks have more resources for determining the quality of loans.

In Africa, Fofack (2005) investigated the determinants of non-performing loans in sub-Saharan Africa using correlation and causality analysis. The analysis was based on data drawn from 16 African countries (7 CFA and 9 non-CFA). The sub-panel of CFA countries includes: (1) Benin, (2) Cameroon, (3) Chad, (4) Cote d'Ivoire, (5) Senegal and (7) Togo. The sub-panel of non-CFA countries includes: (8) Botswana, (9) Cape Verde, (10) Ethiopia, (11) Kenya, (12) Malawi, (13) Rwanda, (14) South Africa, (15) Swaziland and (16) Zimbabwe. The sample selection was dictated by the scope of the database and availability of financial information on these countries. The data are provided on an annual basis end-of-period, between 1993 and 2002, included. The minimum length of the panel covers a period of 3 years for the shortest series (Chad and Rwanda), and up to 10 years for the longest series, producing an unbalanced panel. The correlation and causality analysis focuses on a number of macroeconomic and microeconomic (banking-sector) variables.

At the macroeconomic level, the study investigates the correlation between non-performing loans and a subset of economic variables: per capita GDP, inflation, interest rates, changes in the real exchange rate, interest rate spread and broad money supply (M2). At the microeconomic level, it focuses on the association between Non-performing Loans (NPLs) and banking-sector variables. The key banking variables include return on asset and equity, net interest margins and net income, and inter-bank loans. These variables were chosen in the light of theoretical considerations and subject to data availability. Non-performing Loans (NPLs) are adjusted for specific provisions (non-performing loans as a proportion of loans loss provisions) to provide the basis for cross-country comparisons.

In the correlation analysis, the results showed a negative association between real GDP per capita and non-performing loans expressed as a percentage of loans loss provision. This implies that falling per capita income is associated with rising scope of Non-performing Loans (NPLs) to the extent that changes in per capita income is proxy for changes in economic growth. The negative association with non-performing loans may reflect the impact of cyclical output downturns on the banking sector; a result that is expected in the literature (Gonzalez-Hermosillo (1997)). The sign of the coefficient is consistent across state and private banks, though the

magnitude of the correlation is stronger for state banks and financial institutions.

The study also investigates the association between non-performing loans (NPLs) and, domestic credit, broad money supply (M2) and inflation. Though the magnitude of the coefficient of correlation between inflation and Non-performing Loans (NPLs) is low, the sign is negative; unexpected rise in inflation under cyclical downturns is likely to negatively affect the performance of the banking sector and recovery of loans to private operators and investors. In the extreme case, the study shows that hyper-inflation may erode banks assets and equity and weaken banks position through the interest rate channel. However, the magnitude of the coefficient is relatively low, and may reflect the general context of declining inflationary pressures in the nineties, especially in the sub-panel of CFA countries. The results also show a positive association between real exchange rate appreciation and Non-performing Loans (NPLs). The magnitude of this association is particularly strong in the sub-panel of CFA countries, which underwent a devaluation of their currency in the early nineties. This direction is not consistent throughout the sample, however. This relationship is ambiguous for the sub-panel of non-CFA countries. In spite of its magnitude, the coefficient associated with these countries has a negative sign.

At the microeconomic level, the correlation analysis shows a negative association between non-performing loans and most banking variables, including return on asset and equity, total deposit, net interest, margin and net income. This result is consistent for most countries in the sub-panel of CFA and non-CFA countries and between state and privately-owned banks. For instance, the coefficient of correlation between return on asset and Non-performing Loans (NPL) higher in absolute terms for private banks and state banks. A coefficient of correlation suggests that about 60 per cent of variations in the scope of Non-performing Loans (NPLs) are explained by changes in return on assets. However, a correlation analysis does not necessarily imply causation.

In the causality analysis, the Granger-Causality test was applied to the sample of countries. At the macroeconomic level, results revealed that inflation, real interest rate, growth rate of GDP per capita are causal to non-performing loans across most sub-Saharan countries. However, in few countries like Botswana, inflation and real interest rate are not particularly significant and do not appear to cause non-performing loans. For these countries, the dynamics of Non-performing Loans(NPLs) is best explained by the growth rate of GDP.

At the microeconomic level, measures of profitability (net interest margins and returns on assets) play a key role in explaining the causal link between non-performing loans and banking sector variables. In particular, net interest margin is significant across the sub-panel of CFA and non-CFA countries, and Granger-causes Non-performing Loans (NPL) at one and in some cases up to two lags. This variable is significant at i per cent level. Other key microeconomic determinants of Non-performing Loans (NPLs) include "equity over total liquid assets" and inter-bank loans over total assets. These variables are shown to Granger-cause Non-performing Loans(NPLs) in a number of countries, including Botswana, Cameroon, Cote d' Ivorie, Mali and South Africa.

V. REVIEW OF PERFORMANCE OF COMMERCIAL BANKS LOAN PERFORMANCE

We review performances of fifteen (15) selected banks using banking variables such as total

assets, total loans, non-performing loans, equity capital and profit-before-tax as tools. We also consider non-performing loans as a percentage of total loans and return on equity or return on net assets as the basis for the review of the banks' non-performing loans and performance respectively. The review was however on average level basis over 11 years period from 1997 to 2007.

Table 1 indicates that there is fluctuation in the average total assets of the selected banks over the 11-year period being reviewed. With N 59074.5m in 1998, the total assets decreased to N 44698m in 2000 after which it started increasing consistently up to N 498651.8m in 2007. It is equally interesting to note that a significant rapid increment can be observed from 2005 to 2007, a post-consolidation period, which could have been a positive effect of the banking reforms by the Central Bank of Nigeria in 2004.

In addition, table 1 shows further that there is fluctuation in the total loans and non-performing loans of the selected banks over the period being reviewed. With N9411.4m and N1682.8m in 1998, they both respectively increased to N151291.2m and N129844.7m in 2007. However, the increase in non-performing loans was at a diminishing rate when related to total loans. This is revealed by the percentage proportion that the non-performing loans bear to the total loans. With 17.88% in 1998, the non-performing loans percentage initially increased to 42.56% but reduced afterwards consistently to 8.58% in 2007 which is commendable for the industry. This dramatic reduction could be attributed to another positive effect of banks consolidation. However, the one-time increment of 27.81% in 2003 could have been as a result of the banking distress experienced prior to consolidation which necessitated the need for the banking reforms. However, a fluctuation can also be observed in the equity capital and profit-before-tax of the selected banks over the period under review. With N 3572.8m and N 945.9m in 1998, they both increased respectively up to N 76509.5m and N 13272.3m in 2007. On the profitability side, a different situation was observed. The percentage of Profit-before-tax to equity capital or return on equity (ROE) or return on net assets (RONA) revealed an initial increase in ROE from 29.38% in 1998 up to 42.37% in 2001. Afterwards, it started falling consistently to 18.38% in 2005 after which a marginal increase can be noticed in 2006 and 2007 of 16.53% and 19.64% respectively.

From the fore-going, the review implied that there was banking distress and crisis experienced in the sector as revealed by the indicators approximately between the period 2002 and 2004 prior to the banking reforms exercise. However, the situation started to change for better immediately after the consolidation at the end of 2005.

VI. MODEL, DATA, METHODOLOGY

Since the study focuses on variation of risks on Non-performing Loans (NPLs), and its effect on performance in the banking sector, it is believed that only the Non-performing Loans (NPLs) portion of Banks' credit portfolio demands detail investigation. To estimate the impact of the independent variables on Non-performing Loans (NPLs), we consider that risks and monetary policy variables have strong influences on bank performances. Our reasons, among others are (1) While banks may be more interested in achieving high profitability and stock values, the risks inherent in recovering loans and advances as indicated in banks' cash flow is high. For example,

will banking sector responds favourably to interest and exchange rates volatility? Will stock price rise and earnings increase? Will deposit grow as expected? and (2) Most of the banks' balance sheets are loaded with fictitious figures to reflect positive performances only for the same institution to fail. It is for all these reason that banking institutions have focused more on how to mitigate risks arising from financial crisis.

The total number of banks operating in the sector as at date is 24. Out of this, a sample size of fifteen (15) banks which represents approximately 63% is drawn for this study. This sample size represents equal proportion of big, medium and small banks in the banking sector. We extracted our data from the audited statements of accounts (1997-2007) of all the fifteen (15) banks to generate our data to be used for inferential analysis as indicated in Table 2.

To estimate our variations, we adopt a multiple regression model of the Ordinary Least Square (OLS) method. Furthermore, for the purpose of testing the reliability and validity of our study, the relevant correlation coefficient (r) was tested using the student "t" distribution test. Thus, we express Non-performing loans(NPLs) as a function of our independent variables as follows:

NPL = f(MPR, INTR, CR, LR, MR, IRR, ER, SR) (1)

When expressed in a linear form, the function (1) becomes:

NPL = [[beta].sub.0] + [[beta].sub.1] MPR + [[beta].sub.2] Intr + [[beta].sub.3] CR + [[beta].sub.4] LR + [[beta].sub.5] MR + [[beta].sub.6] IRR + [[beta].sub.7] TER + [[beta].sub.8] SR + [??] (2)

where

NPL = Non-Performing Loans (dependent variable)

MPR = Monetary Policy Rate

Intr = Interest Rate

CR = Credit Risk

LR = Liquidity Risk

MR = Market Risk

IRR = Interest Rate Risk

ER = Earning Risk

SR = Solvency Risk

[[beta].sub.0] = The intercept of the regression function

[beta] = The regression coefficient of the independent variables

[??] = Error Term

The result of our estimation in shown in Table 3

VII. RESULT AND ANALYSIS OF FINDINGS

From table 3, we observe that each of the estimates produces a positive correlation coefficient. The values are 0.512 for monetary policy rate, 0.303 for interest rate, 0.416 for credit rate, 0.317 for liquidity rate, 0.352 for market rate, 0.64 for interest rate risk, 0.9 for earnings risk and 0.389 for solvency risk. However, the coefficients of interest rate, liquidity risk, and market risk and solvency risk are low showing that they have weak positive relationship with Non-performing Loans(NPLs). Similarly, the coefficient of monetary policy rate is average and that of credit risk is close to average showing that they have moderate positive relationship with non-performing loans. Conversely, the coefficient of interest rate risk is above average showing that it has strong positive relationship; while that of earnings risk is very high- showing that it has a very strong positive relationship with non-performing loans.

R Square ([R.sup.2]) attempts to measure the extent to which changes in the independent variables can be caused by the independent variables. Thus, about 26.2%, 9.2%, 17.3%, 10.1%, 12.4%, 36.5%, 81.1% and 15.1% changes in Non-performing Loans(NPLs) can be explained by changes in monetary policy rate, interest rate, credit risk, liquidity risk, market risk, interest rate risk, earning risk and solvency risk respectively. Among all the independent variables, only earnings risk has a significant explained variation while that of others are insignificant.

From the above interpretation, the positive relationship established empirically between the dependent variable and all the independent variables indicate that they all move in the same direction. By implication, as each of the independent variables increases, non-performing loans also decreases likewise. However, both the correlation coefficients and the explained variation coefficients ([R.sup.2]) further indicate that earning risk has a greater significant influence on non-performing loans, followed by interest rate risk which has moderate influence than other independent variables being studied.

VIII. CONCLUSION AND RECOMMENDATIONS

This study investigates the variations of credit risk of Non-performing Loans (NPLs) on bank performances in Nigeria. The study shows that (1) there is a positive relationship between Non-performing Loans (NPLs); (2) the coefficients of interest rate, liquidity risk, market risk and solvency risks are low showing that they have weak positive relationship with Non-performing Loans (NPLs); (3) the coefficient of monetary, policy rate is average and that of credit risk is close to average showing that they have moderate positive relationship with Non-performing Loans (NPLs); (4) the coefficient of interest rate risk is above average showing that it has strong positive relationship; while that of earnings risk is very high showing that it has a very strong positive relationship with Non-performing Loans (NPLs); (5) about 26.2%, 9.2%, 17.3%, 10.1%,

12.4%, 36.5%, 81.1% and 15.1% changes in Non-performing Loans (NPLs) can be respectively explained the variations in the changes in monetary policy rate, interest rate, credit risk, liquidity risk, market risk, interest rate risk, earning risk and solvency risk; (6) among all the independent variables, only earnings risk has a significant explained variation while that of others are insignificant. This result posits further that interest rate do not significantly influence the performance of loans. The accumulation of Non-performing Loans (NPLs) is largely driven by earnings risk measured as the standard deviation of profit after tax, followed by interest rate risk and monetary policy rate which have moderate influence on Non-performing Loans (NPLs). Interesting enough, interest rate does not appear to be particularly significant in explaining the variations of Non-performing Loans (NPLs). The positive relationship exhibited by all these variables with non-performing loans is consistent with the results from the study on Non-performing Loans (NPLs) conducted on Sub-Saharan African countries by Fofack (2005). The difference is that inflation, real exchange rate, interest rate and growth rate of GDP per capita are significant casuals to Non-performing Loans (NPLs) except in few countries like Botswana where the variations of Non-performing Loans (NPLs) is best explained by the growth rate of GDP.

The study recommends the strengthening of banking regulation; adequate provisions for Non-performing Loans so as not to distort the true presentation of the banks position in their balance sheets; sound credit analysis; acceptable level of risk-reward and tradeoff for portfolio activities; the institution of bank credit strategy that will take into account the cyclical aspects of economy and shifts in the composition and quality of credit portfolio; and adoption of an Efficient Loan Appraisal Techniques (ELAT) consisting of conventional investment analysis and risk measurements.

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R. O. C. SOMOYE

Olabisi Onabanjo University, Ago-Iwoye, Nigerai Table 1Performance of Commercial Bank Performance (1997-2007) In N' mill.

Average Average Average Non-Performing Total Total Non-Performing % of TotalYear Assets N Loans N Loans N Loans

1997 3903.61998 59074.5 9411.4 1682.8 17.88

1999 48868.1 12045.7 4592.5 38.132000 44698.0 9921.5 4222.8 42.562001 71517.6 14856.5 4789.6 32.242002 83330.0 43643.5 6202.0 14.212003 99911.2 22919.4 6374.7 27.812004 124337.3 32686.2 6326.4 19.352005 165449.8 50756.6 7299.2 14.382006 311574.2 88198.7 11384.7 12.912007 498651.8 151291.2 12984.7 8.58

Equity Profit Capital BeforeYear N Tax N ROE or RONA %

19971998 3572.8 945.9 29.381999 4126.0 1499.3 34.692000 3618.9 1498.4 40.522001 5310.7 2303.8 42.372002 7624.8 2451.8 33.862003 9616.5 3211.2 30.172004 12230.0 3876.6 28.922005 25167.2 5127.8 18.382006 42241.8 8361.6 18.532007 76509.5 13272.3 1964

Sources: Audited Statement of Accounts of selected banks (1998-2007)and Authors computation; Non-Performing loans % = Non-Performingloans/Total loans and ROE/RONA = PBT/Equity Capital

Table 2Data Used for the Study

Non- Performing Monetary Loans Policy InterestYear ('Mill) Rate (%) Rate (%)

1997 3903.6 13.50 18.431998 1682.8 14.30 19.821999 4592.5 18.00 24.262000 4222.7 13.50 19.772001 4789.6 14.31 19.822002 6202.0 19.00 27.052003 6374.7 15.75 21.482004 6326.4 15.00 19.892005 7299.2 13.00 18.662006 11384.7 12.25 17.712007 12984.7 8.00 17.66

Credit Liquidity MarketYear Risk (%) Risk (%) Risk (%)

1997 26.99 32.75 42.711998 12.94 30.69 66.961999 20.88 31.68 77.112000 24.52 27.94 82.80

2001 23.04 26.36 64.622002 20.52 28.98 49.842003 18.07 28.27 47.792004 15.83 31.43 45.222005 14.43 31.75 74.332006 18.66 31.51 62.082007 12.56 32.77 36.15

Interest Rate Earning SolvencyYear Risk (%) Risks Risk (5)

1997 114.76 392.22 16.651998 111.76 608.39 12.211999 112.81 995.31 6.282000 115.03 1263.65 4.332001 111.02 1437.88 13.042002 113.42 1362.22 -0.202003 112.97 1731.01 10.002004 112.97 3081.12 11.862005 127.55 3433.39 25.152006 121.10 4201.85 13.272007 118.68 6107.79 23.52

Source: Extractions from the Audited Accounts of LicensedBanks in the Stock Markets

Table 3Results of Estimation of Non-performing Loans (NPLs)

Independent ConstantVariable [[beta].sub.0] B R [R.sup.2] f*

MPR 13342.067 -505.799 0.512 0.262 3.197 4103.91 * 282.897 * 3.251 ** -1.788 **

Intr 12327.602 -303.068 0.303 0.092 0.912 6538.313 * 317.421 * 1.885 ** -0.955 **

CR 10338.763 -225.385 0.416 0.173 1.886 3168.554 * 164.130 * 3.263 ** -1.373 **

LR -6837.615 427.269 0.317 0.101 1.008 12953.106 * 425.472 * -0.528 ** 1.004 **

MR 9994.51 -65.255 0.352 0.124 1.273 3520.956 * 57.825 * 2.839 ** -1.128 **

IRR -34491.705 351.363 0.64 0.365 5.166 17891.702 * 154.586 * -1.928 ** 2.273 **

ER 2851.258 1.470 0.9 0.811 38.53 663.261 * 0.237 * 4.299 ** 6.207 **

SR 4308.794 148.063 0.389 0.151 1.601 1676.184 * 117.017 * 2.571 ** 1.265 **

R = correlation coefficient

[R.sup.2] = R-Square (explained variation)

f* = f-statistics

[??]* = standard error

** = t-statistics