1

ANALYSIS OF THE RISK TAKING BEHAVIOUR

OF BANKS IN NIGERIA

BY

OKANYA, OGOCHUKWU CHINELO

PG/PhD/2004/38346

DEPARTMENT OF BANKING AND FINANCE

FACULTY OF BUSINESS ADMINISTRATION

UNIVERSITY OF NIGERIA

ENUGU CAMPUS

DECEMBER 2012

ANALYSIS OF THE RISK TAKING BEHAVIOUR

OF BANKS IN NIGERIA

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CHAPTER ONE

INTRODUCTION

1.1 Background of Study

Banks facilitate economic growth in a number of ways, principal among which is the provision

of funds for investment. Indeed the main channel through which banks affect the economy is

through the provision of credit to fund private investment and consumption. In this respect,

Ebong (2006) observes that banks facilitate economic activity primarily by mediating between

the savings surplus units and the savings deficit units. In acting as intermediary between these

two units, banks are able to mobilize funds from the various savings surplus units, pool them

together and then consequently serve as a source from which the various savings deficit units can

acquire funds for investment purposes. In the absence of banks to carry out this very important

task, funds would have been fragmented across the various savings surplus units and this would

hinder investment in an economy.

Without question, translating individual savings into investment through bank lending is an

important variable in any economy. Bank lending provides a bank the opportunity to earn

income which translates to dividend for its owners, interest to the savings surplus units and profit

to the bank, which will in fact inspire further growth. Just like every other human endeavour,

bank lending is fraught with risks. Being highly leveraged institutions as well as the fractional

reserve system which is a key banking principle add up to expose banks to a wide variety of

risks.

Sabato (2010) defines bank risk as the possibility that an adverse outcome could be the result of

an action or event. The adverse outcome could result in a loss of earning or lead to some

constraints on a bank’s capacity to achieve set goals. Bank risks according to Soludo (2006) may

be classified into financial risks (this covers credit, liquidity, interest rate, foreign exchange ,

market prices and solvency risks); operational risk (this is primarily concerned with the quality

of personnel as well as the processes involved in the actual day to day operations / activities of

the bank); business risks ( this relate to risks inherent in a bank’s operating business environment

and usually arise from the larger economy) and event risks (this includes Political crises and

industrial actions).

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Although these risks appear different, the existence of one type of risk may serve as a pre cursor

to another type of risk. For example, a credit risk not handled well may potentially trigger a

liquidity risk. This work intends to focus on credit risk while acknowledging the intertwining

relationship that exists between the various types of risk.

Credit risk is the risk that promised cash flows from loans and securities may not be paid in full.

It often arises due to changes in credit quality and ultimately ends in default. Simply put, credit

risk is the potential that a bank borrower /counterparty will fail to meet the obligations or agreed

terms stated in a loan agreement (Rajan, 1994). Banks are well aware when providing loans, that

there is a risk that a loan may not be paid back fully along the agreed lines as at when the credit

was extended. Kuritzkes and Schuermann (2008) believe credit risk to be the most important risk

type banks face.

Essentially for banks to survive, they must engage in the acquisition of risk assets-loans. If a

bank decides to abstain from acquiring risk assets, even if it had supersized deposits, its inability

to translate deposits (liabilities) into assets (loans) would mean that the bank is not generating

sufficient profits and in fact the bank manager may be sacked for not generating sufficient profit

from his branch. It then follows that the more risk assets are acquired (within reasonable limits),

all things being equal, the better off the bank is. A bank’s risk appetite is a reflection of how

much risk a bank chooses to undertake.

Risk taking behavior refers to the propensity of a bank to undertake activities or actions that

would clearly increase its risk exposure. A general belief is that the riskier an investment is, the

higher the returns to be derived there from. A cursory look at the activities of a bank would often

indicate its risk appetite which may lean towards being either risk averse or risk loving. The

amount of risk that a bank chooses to undertake is a function of several variables. Some are bank

specific while others are linked to factors that lie outside the direct influence of the banks. A key

question then is “what are the factors that could influence a bank’s risk taking behavior?

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In recent years, a number of both theoretical and empirical studies (for example, Gersl et al

(2011), Rochet (1992)) have examined the risk taking behavior of banks. These studies have all

attempted to establish a causal relationship between bank risk taking and several variables which

include but are not limited to competition, interest rates, capital, profit and liquidity. This

research analyzes the risk taking behavior of banks as it relates to three specific variables namely

interest rate, capital and liquidity. To achieve this, there would be need to ascertain the degree of

influence exerted by capital adequacy, liquidity and interest rate on the credit risk profile of

banks in Nigeria.

Bank executives and banking industry analysts readily agree that interest rate is an important

variable to watch out for. This is because interest rate movements affect bank earnings and banks

explicitly acknowledge this impact (of interest rate) on their asset and liability management

practices. As banks grant loans, they (the banks) are not privy to all the information available in

the market as the market is imperfect. The pioneer work by Stiglitz and Weiss (1981) suggest

that the interest rates charged by a credit institution serve a double function of sorting potential

borrowers (leading to adverse selection), and also affecting the actions of borrowers (leading to

the incentive effect). Interest rates thus affect the nature of the transaction (Atieno, 2001).

The double effects are seen as a result of the imperfect information inherent in credit markets.

Adverse selection occurs because lenders like to identify the borrowers most likely to repay their

loans since the banks’ expected returns depend on the probability of repayment. In an attempt to

identify borrowers with high probability of repayment, banks are likely to use the interest rates

that an individual is willing to pay as a screening device. However, borrowers willing to pay high

interest rates may on average be worse risks; thus as the interest rate increases, the riskiness of

those who borrow also increases, reducing the bank’s profitability. As banks are faced with a

higher rate of default, they attempt to screen out borrowers by raising interest rates which leads

to a fresh set of defaults and sparks off a process referred to in literature as “adverse selection

death spiral”. Adverse selection death spiral occurs when a bank in a bid to screen out risky

borrowers increases lending rates. The increase in interest rates pushes the genuine low risk

borrowers out of the market and so the bank is left with a smaller pool of borrowers who are high

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risk and potential defaulters. The end result is a vicious cycle were the banks, faced with a high

incidence of loan default lose out on the long run.

The incentive effect as opined by Stiglitz and Weiss (1981) occurs because as the interest rate

and other terms of the contract change, the behaviour of borrowers is likely to change since it

affects the returns on their projects. Interest rate changes can affect not only the value of

individual assets and liabilities but also the way and manner a bank views its risk exposure.

When interest rates are low, theory (Maddaloni and Peydro, 2010) suggests more loans are

granted and this in turn ignites the formation of asset price bubbles. Lower interest rates decrease

financing costs, thus banks’ motivation to screen borrowers declines, which in turn may result in

their accepting riskier applicants. Another reason why lower interest rates may be linked to

increased risk taking is that in periods of low(er) interest rates, Diamond and Rajan (2006)

observe that there could be a reduced threat of deposit withdrawals. Lower interest rates generate

more liquidity in the banking sector, which provides less of an incentive for depositors to

withdraw and more of an incentive for banks to finance risky projects.

Bank capital is another variable capable of affecting bank risk taking. The Basle Accord

maintains that banks ought to continually meet two capital adequacy ratios; Tier 1and total

capital ratios. Nwankwo (1980) believes adequate capital provides the ultimate protection against

insolvency and liquidation arising from the risks inherent in banking and so bank capital is seen

as a kind of buffer or shock absorber expected to reduce the impact of a shock.

Typically, the theoretical banking literature links a bank’s riskiness with its level of capital.

Keeley (1990) predicts a negative relationship between the two, meaning that insufficiently

capitalized banks may take on more risk. Rochet (1992) equally believes insufficiently

capitalized banks may exhibit risk- loving behavior.

Koehn and Santomero (1980) however provide evidence of a positive relationship between the

level of bank capital and risk taking suggesting that bigger banks (highly capitalized banks)

might easily become “too big to fail” and so they (Koehn and Santomero, 1980) predict that

banks rich in capital may engage in riskier lending. In addition Kahane (1977) and Kim and

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Santomero (1988) show that increased regulatory capital standards may have the opposite effect

of what it intended to achieve. In such a frame work, changes in capital and portfolio risk would

be positively correlated. Blum (1999) comes to similar conclusions in a dynamic frame work,

proving the effect of capital regulation, which may push an under- capitalized bank to increase

risk in period t in order to meet regulatory requirements in period t + 1. The above viewpoints

show that there are opposing views as to the effect of capital regulation on risk taking. These two

streams of research suggest that it is necessary to investigate further how bank risk taking in

emerging market economies are influenced by changes in capital structure.

Liquidity is another determinant of bank risk taking behavior. There is need for banks to

maintain sufficient liquidity. This need is however hindered by the bank’s need to also earn

sufficient income. Banks being commercial interests are obligated to their shareholders to make

maximum profit. These two conflicting obligations (being liquid and/or profitable) present the

banker with a dilemma referred to as the conflict between liquidity and profitability.

When a bank invests its funds in longer term assets, cash balances are reduced and the liquidity

needed to meet deposit withdrawals and other sundry credit needs is significantly challenged.

However when banks emphasize liquidity , it is at the expense of profitability as the bank holds

more cash reserves and invests mostly on short term highly liquid assets which invariably are

less profitable. Some authors (eg Nwankwo, 1980) predict that having a highly liquid position

not only undermines the profits accruing to a bank, but also results in increased insurance and

storage costs. What typically follows is that when banks are excessively liquid, the sensitivity

they usually have, which allows for proper risk assessment becomes compromised and this then

presents as increased risk taking.

Summarily, bank capital serves as a major determinant of liquidity, while interest rate also

determines the amount of liquidity maintained by a bank. There is a clear relationship among

these three variables, what is not known however is the degree and significance of influence that

these three have on bank risk taking behaviour. There have been several theoretical and

empirical studies along this line, but none have explained the collective impact of these three

variables on risk taking. Sanusi (2010) rightly notes that interest rates, liquidity position as well

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as bank capitalization may have individually or collectively affected the risk taking behaviour of

banks in Nigeria. However, studies that clarify our understanding on the relationship between

capital, liquidity and interest rate and bank risk taking behaviour are still lacking in Nigeria.

Would it be appropriate to generalize the findings of Jeitscko and Jeung(2007), Eid (2011),

Altunbas et al (2009), Jimenez et al (2009) without taking into cognizance the institutional

specifics in Nigeria? It is the need to provide concrete answers to these questions that motivates

this study.

1.2 Statement of Research Problem

The history of banking around the world has been punctuated at relatively frequent intervals by

episodes of crisis and so over the past two decades the credit quality of many banks’ lending and

investment decisions has attracted a great deal of attention. Most banking crises (including the

2007/2008 crisis) have a unique characteristic- they are all linked to excessive risk taking.

Sanusi (2010) opines that banks take on these risks amidst several conflicting situations. For

instance, the Central Bank had specifically via the prudential guidelines (1990 and 2010) stated

the limit of exposure to a single obligor or sector, yet it appears that bank loans were

concentrated in certain sectors. Specifically, bank lending patterns showed over exposure to

certain sectors (Capital Market, Oil and Gas sectors). Furthermore annual reports and other

major indicators show excessively high levels of non- performing loans on industry wide basis.

The need for increased loan loss provisioning following higher non-performing loans showed

most banks to be undercapitalized, hence the instruction by the Central Bank for banks to

recapitalize in very quick succession to reflect their true position.

Consequently, there were several directives for banks to recapitalize from the late 1990s to 2005.

These instructions for banks to recapitalize were driven by the need to improve the banking

industry as well as instill confidence since bank capital is seen as providing buffer effect for

banks. However, despite the capitalization, several banks as cited by Sanusi (2010) still appeared

illiquid and were persistently at the Expanded Discount Window of the Central Bank as well as

being major borrowers at the interbank market. Again a number of the banks offered interest

rates that were significantly higher than the industry average and so it is essential to determine if

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banks sought to increase their acquisition of risk assets to generate more revenue and provide the

higher interest rates offered for deposits. Indeed it is important to ascertain if the higher bank

capital provided banks with excess liquidity which may have fuelled the acquisition of risk

assets.

Though several attempts have been made at explaining and measuring the risk-taking behavior of

banks across the world, not much attempt has been made in emerging market economies like

Nigeria, especially the interplay of Capital, Liquidity and Interest rate and how these in turn

determine a bank’s propensity to take on more risk. This research evaluates these variables

(capital, interest rate and liquidity levels) to determine which of them is likely to expose the

banks to more risk.

The situation appears ambiguous and so it is important to assess through empirical means the

degree of relationship between bank risk taking and these variables. The basic question which

this research is concerned with is whether there exists sufficient evidence to prove that the risk

taking behavior of banks is influenced by any or all of these three factors.

1.3 Objectives of Study

The primary objective of this study was to investigate the impact of capital, liquidity and interest

rate on the risk taking behavior of Nigerian banks. The specific objectives are as follows:

1. To determine the nature of the relationship between interest rates and risk taking behavior in

banks.

2. To ascertain the effect of capitalization on the risk taking behavior of banks.

3. To establish whether there is a significant relationship between liquidity levels and risk taking

behavior.

1.4 Research Questions

1. What is the nature of the relationship between interest rate and the risk taking behavior of

banks?

2. In what ways does capitalization determine the risk taking behavior of banks?

3. Does liquidity level significantly affect the risk taking behavior of banks?

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1.5 Research Hypothesis

1. Interest rate has a negative and significant impact on bank risk taking

2. Capitalization does not have a positive and significant impact on the risk taking behaviour of

banks

3. The liquidity level does not significantly affect the risk taking behavior of banks.

1.6 Scope of Study

This study covered a period of thirteen years (i.e. from 1997–2009). The year 1997 has been

chosen as the base year because of the significant developments that occurred shortly after.

Specifically, the spate of bank failures that were recorded in 1998 make it essential that the study

starts from the year before which is 1997 and observe if any of the variables provide an insight

into bank behavior in the periods immediately preceding a crisis. Another notable event that

occurred within the study period was the introduction of the universal banking scheme in 2001,

which served to encourage the branching out of banks into other non bank financial activities

possibly expanding their risk taking activities.

All the events that occurred within the Nigerian banking Industry within the years 1997 to 2009

lie within the scope of this study. The balance sheet as well as other financial statements from

banks and industry analysts were considered and studied.

1.7 Significance of the Study

While there have been very many studies aimed at isolating the characteristics and performance

determinants of banks in developed countries, there are few that focus on developing countries of

Africa, and indeed, on Nigeria. In studying the degree of influence that interest rate, liquidity and

bank capital have on risk taking, the literature tends to look at each one of these three variables

in isolation and so the inextricable intertwined relationship among interest rates, liquidity and

capitalization within the Nigerian banking Industry has not been investigated so far. This study is

of great significance to stakeholders both in Nigeria and in the international intellectual

community for various reasons.

Specifically, this study is important for the following reasons:

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Regulators/ Policy Makers

The findings will be of profound interest to the Central Bank as it will provide a veritable

platform upon which policies on bank risk taking could be taken. Also, supervisory and

prudential authorities will likely key into the outcome of this study as it will provide insights on

early warning signs and when to be particularly vigilant, and on the subjects that could be more

prone to risk-taking behavior. This will give regulators food for thought as they contemplate the

design of optimal intervention and capital regulation policies.

This study will be of immense interest to the Nigerian Government and policy makers given the

propensity of banks to excessively take on more risk relative to what may be acceptable.

Overall, the study will fill the gap that exists in the study of variables determining bank risk –

taking behavior in emerging economies. The challenge lies in being able to identify when risk

taking becomes excessive. Often times the identification of excessive risk is from ex-post data,

this study will hopefully provide regulators and policy makers with a clearer insight into bank

risk taking behavior and specifically will help gauge the degree of responsiveness of bank risk

taking to the three variables under study.

Academia

The study of bank risk taking over an important period of recent economic history complements

already existing literature. In this regard, this study would be useful to the various scholars in the

field of Banking, Finance and Economics as well as future researchers who may wish to advance

further on the study carried out by this research or use this work as source of secondary data for

any future work.

Banks

The empirical investigations concerning the effects of these three variables under review in this

work make reference to the US banking system as well as the EU banking system. This work

therefore provides evidence on the reaction of banks in emerging market economies to the three

variables under study. Understanding the pattern of bank risk taking will undoubtedly provide

bank loan officers with a better appreciation of bank behavior.

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Potential Investors

The findings and recommendations in this thesis will provide substantial guide to the major

investors as it would enable a reduction in the amount of risk they would accept.

1.8 Limitations of study

The major limitation of this study stems from the data collection process. Considering that data

for this study was derived from secondary sources (mostly annual reports), it follows then that if

any of the data had been manipulated to present a false scenario, the findings of this study would

be contested.

1.9 Operational Definition of Terms

Asset Bubble

An asset bubble is formed when the prices of assets are over-inflated due to excess demand.

Bubbles tend to be concentrated in sectors where productivity growth has, or is perceived to

have, risen. It reflects as a spike in asset values within a particular industry, commodity or asset

class and is usually caused by exaggerated expectations.

In Nigeria, the instability brought about by the phenomenal rise in oil prices as well as the

excessive credit creation embarked upon in the aftermath of the consolidation encouraged a

‘financialization’ (Sanusi, 2010) which was far too rapid. Consequently the economy was unable

to absorb the excess liquidity (from oil revenue and the consolidation exercise) and this provided

the enabling environment for the formation of asset bubbles. Indeed the rosy picture that

emerged from the capital market and which served as fodder for the creation of even more asset

bubbles was the increases in market capitalization of the NSE which according to Sanusi (2010)

“increased by 5.3 times between 2004 and its peak in 2007, and the market capitalization of bank

stocks increased by 9 times during the same period.”

Bubbles tend to be fuelled by an explosion of credit, a wave of unwarranted optimism and a

subsequent mispricing of risk. Bubble-induced distortions have medium-term implications for

the economic structure that are more familiar than the short-term effects.

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Credit Crunch

A credit crunch is generally defined as a decline in the supply of credit because, although banks

are less willing to lend, lending rates do not rise. A credit crunch is a situation in which credit-

worthy borrowers cannot obtain credit at all, or cannot get it at reasonable terms, and lenders

show excessive caution, which may or may not be traceable to regulatory distortion, leaving

would-be borrowers unable to fund their investment projects. A credit crunch can have several

causes, such as regulatory pressures and over-reaction to deteriorating bank asset values and

profitability.

In the aftermath of the economic meltdown of 2007 to 2008, credit worthy borrowers in Nigeria

were unable to access funds as banks became cautious in their acquisition of risk assets.

Excess Liquidity

Excess liquidity is defined as total bank liquidity minus the required bank liquidity. The required

liquidity (or reserve) ratio is usually set by the central bank. Excess liquidity is usually non-

remunerated. Following the consolidation exercise of 2004 to 2005 within the Nigerian banking

industry, it is believed bank liquidity essentially became more than was required. Theorists like

Eid(2011) find that increases in liquidity is directly linked to increases in risk taking.

Moral Hazard

When excessive risk taking occurs principally because banks are aware that the cost of a

negative outcome will be borne by someone else, then moral hazard is the underlying reason for

the behavior. Mayers and Smith [1982] refer to moral hazard as any self-interested and voluntary

response to an insurance contract by an insured party. In banking, such behavior can take the

form of excessive risk taking by bank mangers, in response to deposit insurance which is often

underpriced. Excessive risk taking occurs when banks take risks with substantial variances of

possible outcomes, such that there is a significant probability that the cost of negative outcomes

will be borne ultimately by someone else.

In Nigeria just like in other countries, there are arguments that the provision of deposit insurance

as well as bailout packages in the event of a crisis may encourage banks to expect assistance

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when insolvency becomes likely. It is this expectation of unending assistance that we refer to as

“moral hazard” in this study.

Risk Appetite

Risk appetite is the amount of risk a bank is prepared to take on at a given time and is often a

direct reflection of its strategic objectives.

Risk Management

Pyle (1997) defines risk management as the process through which a bank determines suitable

risk /reward ratios by identifying key risks, obtaining consistent, understandable and operational

risk measures, choosing which risks to reduce and which risks to increase and by what means.

Essentially it requires the enthronement of procedures to monitor the resulting risk position

within a bank.

Risk Shifting

Risk shifting occurs when creditors or guarantors are exposed to loss without receiving adequate

compensation. When a bank faces potential insolvency, it will be tempted to reject good loans

and accept bad (riskier) loans thereby shifting the risk onto its creditors.

Bank managers are said to shift risks when the downside of the profit opportunities that the bank

pursues is absorbed in nontransparent fashion by the bank’s creditors and guarantors.

Consequently, it presents as a bank taking excessive risk at the expense of its investors. Risk

shifting is facilitated by information asymmetries that tempt government officials/regulators to

deceive creditors, investors and taxpayers about how (in) effectively they are at measuring and

controlling bank risk.

Special Purpose Vehicle

A special purpose vehicle (SPV) is a limited-purpose organization that serves as a pass through

conduit in creating securities backed by mortgages, credit card and auto loans, leases, and other

financial assets.

Within the Nigerian banking system, commercial banks set up numerous Special Purpose

Vehicles to lend money to themselves for stock price manipulation as well as for the purchase of

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assets. Sanusi (2010) confirms that bank Chief Executive Officers (CEOs) used SPVs to all

intents and purposes. In Sanusi’s words “One bank borrowed money and purchased private jets

which we later discovered were registered in the name of the CEO’s son……., another bank set

up 100 fake companies for the purpose of perpetrating fraud. A lot of the capital supposedly

raised by these so called “mega banks” was fake capital financed from depositors’ funds. 30% of

the share capital of Intercontinental bank was purchased with customer deposits. Afribank used

depositors’ funds to purchase 80% of its IPO. It paid N25 per share when the shares were trading

at N11 on the NSE and these shares later collapsed to under N3. The CEO of Oceanic bank

controlled over 35% of the bank through SPVs borrowing customer deposits.”

SPV’s pave the way for the securitization of problem assets (potential toxic assets) through

complex mechanisms which are then transferred to third parties. This suggest that banks being

prone to waves of enthusiasm and/or deliberate risk taking are likely to buy assets they do not

really understand, are myopic in their risk assessment, or believe that they can get out (sell to a

‘greater fool’) before the market collapses. Securitization doubtlessly facilitates risk transfer, but

also reduces transparency, making it more difficult to track risk. If market participants do not

know which of their counterparties is holding suspect assets (those whose prices are under

downward pressure), it becomes more difficult for them to avoid getting “infected” by the crisis.

Sanusi (2010) concurred and notes that special purpose vehicles were used to hide losses as non-

performing loans into commercial papers and bank acceptances were transformed into off-

balance sheet SPVs.

Toxic Assets

A toxic asset is a debt that is unlikely to be recovered by a bank. In simple terms, it is a non

performing loan. The value of a toxic asset is so uncertain that there is no functioning market for

them. The assets having declined sharply in value are such that no one wants them. A bank

weighed down, if not assisted would be worse off because of the weight of such non performing

assets.

Realizing the impact of toxic assets, an attempt was made to relieve banks by establishing

AMCON, the asset management corporation of Nigeria following the Central Bank stress tests of

2009.

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CHAPTER TWO

REVIEW OF RELATED LITERATURE

2.1 CONCEPTUAL FRAMEWORK

Economies the world over have had to grapple with a spate of bank failures and the resultant

crises that ensues in the aftermath (Caprio and Honohan, 2008). Ogunleye (2003) observes that

in the last two decades “no country was immune to the wave of financial sector crises.” To

mention just a few, Benin lost an estimated 17% of its GDP between 1988 and 1999; Venezuela

between 1980 and 1982 lost an estimated 55. 3% of its GDP and Spain between 1994 and 1995

lost 18% of its GDP as a result of bank failure. There have been many banking crises in the

OECD in the last 35 years, and Hoggarth and Saporta (2001) identify seventeen in Australia,

Canada, Denmark, Finland, France, Hong Kong, Italy, Japan, Korea, New Zealand, Norway,

Spain, Sweden, the UK and the US.

Banking crises refers to the widespread insolvency of banks leading to closures, mergers,

takeovers, or injections of government resources. Caprio and Honohan (2008) note that banking

crisis is virtually as old as banking and go on to provide support for this assertion;

When modern banking emerged ….in 13th Century Europe, bankers faced information

problems more severe than in the least developed countries today. A clients’ trade was

subjected to a variety of shocks – wars, plague, shortage of coins, losses in trade (e.g. ships

sinking or being plundered), defalcation by borrowers, etc. – that made lending hazardous.

And depositors faced the risk that their bankers would not survive these shocks, or would

themselves abscond with funds. Repeated failures led to some drastic remedies: a

Barcelonan banker was executed in front of his failed bank in 1360 – a far cry from the

limited liability that protected bank owners in later times. Sovereigns were less likely to

incur such extreme sanctions when they were the source of the problem, and bankers often

succumbed to the temptation or were required (literally for their survival) to lend to the

monarch. Such famous early Italian banking houses as the Riccardi of Lucca, the Bardi, the

Peruzzi and even the illustrious Medici of Florence, owed their banking downfall in whole

19

or large part to kings and princes that would not or could not repay. Financing the loser in a

war was a sure route to failure, but even winners reneged, leading to a higher interest rate

spread on loans to kings and princes than to the more business-minded town governments.

Banking crisis can be directly traced to the risk decisions that a bank makes. Bouri and Ben

Hmida (2006) define “risk as an exposure to events that may cause economic loss; the risk may

be one bond, a portfolio of assets and liabilities, or an entire firm”. Van Laere et al (2007)

observe that credit risk is a serious threat to bank solvency and defines Credit risk as the risk of a

decrease in value or a loss due to an unexpected deterioration in the credit quality of a borrower.

Consequently, Bank lending is associated with a risk that a borrower might become unable to

repay a loan and so default.

Excessive bank risk-taking is considered as the main cause of the instability that has

characterized the entire global financial landscape including the most recent which started

sometime in 2007. Peydro (2011) notes that this period ( starting from 2007) was marked by

dramatic losses in the banking industry worldwide such that banks that had been performing

well, suddenly and without any previous warning announced large losses due mainly to credit

exposures that had soured. As a direct response, all banking establishments (both the affected

and unaffected) scuttled to upgrade their risk management and control systems. The truth is that

banking and risk taking are like peas in a pod, you cannot have one without the other, but there

are measures that if adhered strictly to will provide an early warning signal which would give

room for an early (or at least earlier) response. Soyibo et al (2004) note that the occurrence of

bank failures could in fact be accurately predicted. Such Prediction of bank failure is based on

the development of early warning systems (EWSs), which provide the tools for proper

classification of banks needing intervention and other forms of regulatory activity. EWS models

used for predicting bank failures grew out of attempts in the literature to use accounting data to

predict corporate failures (e.g., Altman, 1968).

Rojas-Suarez (2001) adds that principal factors known to foster banking crisis include excessive

loans growth, unrealistic exchange rate regime, financial liberalization, an inadequate regulatory

and supervisory regime, as well as a weak legal and institutional framework. Godlewski (2004)

20

agrees and adds that the genesis of excess risks in banks stems from bad risk management and

control. The real challenge lies then in identifying ex ante risk taking and defining an adequate

policy response to deal with it. Guttentag and Herring (1984), Rajan (1994) and Saurina and

Jimenez (2005) all attempt to explain what may appear as the irrational behaviour exhibited by

banks towards risk. Disaster myopia, herd behaviour, agency problems and institutional memory

loss hypothesis are some of the main arguments used to explain the behaviour of banks,

particularly as it relates to their respective credit policies.

Herd behaviour, according to Rajan (1994) is the reason why banks are prepared to finance

negative NPV projects during periods of expansion or boom. A bank manager has a greater

tendency to behave as his peers ( copy cat tendencies) and so can justify a loss since it is most

likely going to be an industry wide issue (i.e. the issue of increased loan defaults arising from

weak credit policy). Indeed, credit mistakes are judged more leniently if they are common to the

whole industry. Moreover, a bank manager who continues to lose market share is most likely to

be sacked. To meet with short term objectives (of maintaining market share and appearing to do

as well as others), managers are most likely going to behave the way a pack of animals (say a

herd of cows) would behave. In agreement, Caprio and Honohan (2008) opine that in a banking

crisis, various forms of contagion or herd effect come into play. Even bank managers, who do

not share the optimism, feel pressured to relax credit approval standards for fear of losing market

share and relevance. In their opinion, experienced bankers who are normally alert to isolated

indications of unsound practices among their peers, in contrast, during the euphoria of the boom

phase, are unlikely to detect even fatal weaknesses. Indeed it does appear that the formation of

banker expectations and strict adherence to proper credit policies can be influenced by peer

observation and a generally pervasive attitude of overconfidence.

Berger and Udell (2003) proposed the institutional memory loss hypothesis. They (Berger and

Udell) proffer that as time passes, bank managers get less skilled in avoiding high risk

borrowers. In their view, when a boom occurs after a recession, the chances of lending to high

risk borrowers are quite high because loan officers tend to forget the key criteria to look out for

in granting loans. In other words, Berger and Udell(2003) believe that because bank failures are

not so frequent, current loan officers are most unlikely to recall what may have transpired

21

previously and so fail to observe what should ordinarily serve as warnings when dealing with

high risk borrowers. This occurs because there is a memory loss or rather loss of learning

experience given that bank failures are sufficiently rare in any one country for learning to be

perfect.

Disaster myopia, another factor thought to be responsible for systemic bank failures is said to

occur when banks are unable to assign a probability to a future event. There are unforeseen

circumstances that occur without any previous expectation. Such an event may be due to political

instability, a natural or man-made disaster, (Guttentag and Herring,1984). Disaster myopia

prevails, with decision makers disregarding the relevance of historical experience at home and

abroad.

Knopf and Teall (1996) link ‘questionable’ investment decisions to the classical principal – agent

problem between bank shareholders and managers. In their view, the decision to finance projects

with negative NPV could also be influenced by bank managers who may choose to focus on their

own immediate rewards rather than maximize value for the shareholders. Summarily, Anderson

and Fraser (2000) as well as Knopf and Teall (1996) find that banks controlled by a majority of

managers –insiders are more risky particularly as managers –insiders would easily attempt to

shift risks. Bank managers are said to shift risks when the losses earned by faulty bank

investments are absorbed by the bank’s creditors and guarantors. Risk shifting is facilitated by

information asymmetries.

A cursory look at the locations of recent banking crises around the world supports the following;

first, more than a few banks have taken potentially ruinous risks. Second, the bills taxpayers have

paid to bail out depositors and deposit-insurance funds in particular crises clarify that a

substantial amount of bank risk may have been shifted to taxpayers. Finally, the repeated failure

of authorities to check bank risk shifting until it surged into an actual or potential taxpayer

disaster suggests that changes in the risk-taking technologies used by banks may have in fact

surpassed the capacity of government regulators whose responsibility it is to control the safety

and soundness of the financial system (Sanusi, 2010).

22

There are several factors that are seen as contributing to poor risk taking decisions of a bank.

Llewellyn (2002) points out five such factors.

(1) An inefficient process of risk analysis, management and control.

(2) Insufficient monitoring of loans

(3) Perverse or weak incentives to managers (which may predispose even the most honest

of managers to engage in excessive risk-taking in a bid to generate returns). Knopf

and Teall (1996) link this behaviour to the agency problem.

(4) Insufficient information transfer (asymmetric)

(5) Inadequate Corporate governance.

The substantial resources devoted to the design of a Capital Adequacy Framework by central

bankers and regulators in the Basel Committee indicate that there is a strong concern about

incentives for excessive risk-taking. Bank managers, on the other hand, tend to deny the extent to

which such incentives influence decisions regarding bank lending, which is the main route

through which risk taking occurs. However, the incentives need not reveal themselves as

deliberate risk taking. There are several reasons why it is important for regulators to preserve the

soundness of a banking system. First, a banking crisis tends to occur without much warning and,

as a result, policy makers must react very quickly to stave off threats to the financial system.

Second, an important function of the banking system is to supply liquidity, and lack of trust in

the banking system can rapidly become very costly. Central banks can provide liquidity

assistance to banks in distress, but the difficulty of distinguishing between liquidity- and

insolvency crises in combination with the fear of contagion tends to compel governments to issue

blanket guarantees of all creditors or to bail-out banks through, for example, rapid

recapitalization. Third, banks are opaque with the implication that one bank’s distress can lead to

runs on healthy banks. Fourth, the failure of one bank can have systemic implications through

inter-bank clearing and settlement systems as bank risk problems have the capacity to transmit

through interbank contagion (Iyer and Peydró, 2010, and Bandt, et al., 2009). A credit crunch

often occurs in the aftermath of any bank crisis ( Jiménez et al., 2010b) and this will in turn

affect the real economy (Ciccarelli et al., 2010). Jimenez et al (2010b) proffer that it is the

23

concurrence of three elements: a strong reliance of banks on short-term liabilities to leverage up,

a weak supervision of bank and a widespread use of financial innovation (notably securitization)

that facilitated the quick spread and evolution of the 2007 banking crisis.

Throughout this research, we use the term “bank risk-taking” to indicate the risk that banks are

taking through their lending activity. There are other ways in which banks may change their risk

exposure, for example by changing the composition of other assets and/or liabilities. Since these

mechanisms are not the subject of this theses, our analysis of bank risk-taking refers exclusively

to the lending activity and the credit risk which emanates thereof.

Credit (default) risk is defined as the risk of losses following a borrower’s default regarding its

obligations or deterioration in its financial soundness (Bolak 2004: 10). Credit Risk Control

refers to the process by which all loans, advances, credit facilities or accommodation granted by

a bank to a customer are administered to ensure that the facilities run satisfactorily and are repaid

by the due date. Basically, the objective of the Credit Risk Control function is to enable a bank

keep abreast of all developments that may affect loans and advances granted by it with a view to

ensuring that the terms governing the loan or credit is adhered to so that there is no default. The

purpose is to encourage banks to be proactive and take appropriate measures to protect Risk

Assets and forestall/ mitigate default. This function is therefore an important aspect of banking

business. A well managed and effective Credit Risk Control process is important to every bank,

as large loan losses could lead to non-profitability and the eventual collapse.

There are, in theory, three key factors that credit risk management focuses on. These are

1. The Probability that a creditor would default,

2. The amount exposed at default, and

3. The actual loss once a default occurs.

The first refers to the riskiness of the borrower or counter-party and therefore relates to the

probability that a borrower will fail to repay by the due date. The probability of default is

analyzed by reviewing environmental/industry risk factors as well as financial and non-financial

indicators of risk such as market share, profitability of core business, cash flow, leverage,

24

ownership structure, management quality, corporate governance and reputation which could

influence it. Every bank ideally sets its own “risk appetite” and defines its target market based on

its determination of acceptable risk levels. It is usual for different banks to have different risk

appetites.

The second factor is the total exposure at risk at the time an obligor defaults, while the third

factor, the actual loss in the event of a default, is the inverse of the recovery rate. In simple

language, when a bank lends money to a counter-party, its credit risk is a function of the

perceived credit-worthiness of the obligor (the higher the credit worthiness, the lower the risk);

the size of the loan (the larger a loan is, the higher the risk); and the amount that can be

recovered through collateral/guarantees in the event of default (the higher the recovery rate, the

lower the risk).

Good credit risk management is, therefore, about lending to good customers, setting prudent

limits and taking adequate collateral. Furthermore, the more diverse the risk assets held by a

bank (by obligor, industry, geography, product, etc), the less risky the bank’s portfolio. This is

because default in an asset potentially leads to delinquency in correlated assets. The more highly

correlated a bank’s risk assets are, therefore, the higher the potential losses from that portfolio.

Hence in this study an attempt is made to account for the relationship that exists between the risk

taking behavior of banks and three bank- specific characteristics and these are liquidity,

capitalization, and interest rates. This research hopes to establish if a link exists between the

propensity to take on excessive risk on the one hand and interest rate, capitalization and liquidity

levels on the other hand. Available literature suggests that increased bank capital provides a

buffer effect to banks making them capable of absorbing more risk and thereby allowing banks

remain liquid and solvent. These may however lead to the creation of more risk assets vis-à-vis

the abundance of liquidity. Abundant liquidity apparently increases the incentives for bank risk-

taking (Allen and Gale, 2007). Because of agency problems, excess liquidity which ought to

have been given to shareholders is used by bank managers to finance projects that may be more

risky but which appear overly attractive because of promised returns. Consequently, banks tend

to “over lend” in times of excess liquidity. Furthermore, the degree of liquidity is directly

25

determined by monetary policy and so a ‘lax’ monetary policy may exacerbate a liquidity

position that was already high. In agreement, Allen and Carletti (2009) and Allen and Gale (2007

and 2004) explain that ample liquidity is not resolved by expansionary interest rate regimes.

In this chapter, we present studies from various strands of literature that relate to this work,

starting from interest rate and risk taking, followed by literature on capitalization and risk taking

and finally literature on liquidity and risk taking.

2.2 INTEREST RATES AND RISK TAKING

Bank executives and industry analysts would readily agree that interest-rate is important to

depository institutions. Research shows that interest rate movements affect bank earnings and

value, and banks explicitly acknowledge this impact in their asset and liability management

practices. Interest rate changes can affect not only the value of individual assets and liabilities,

but also the value of firm strategies, such as banks’ investment programs. From a historical

perspective, easy monetary conditions are a classical ingredient in boom-bust type business

fluctuations (Fisher, 1933; Hayek, 1939; Kindleberger, 1978).

To trace the nature of relationship between interest rate and risk taking, a number of theorists

(Jimenez et al (2008), Maddaloni and Peydro (2010) looked at the relatively low interest rates

that prevailed among OECD countries in the early to mid 2000s and were able to subsequently

provide evidence of a negative relationship between interest rate and risk taking. Taylor and

Keeley (2007) support the existence of a link between interest rates and increased risk taking

behaviour. Simply, they found sufficient reason to state that a low interest-rate environment

encourages banks to raise the level of risk assets in their portfolios as there was a reduction in

risk aversion by bank managers. Borio and Zhu (2008) classify this to as the risk-taking channel

which shows a steady projection of how changes in monetary policy rates affect risk.

In agreement with the above postulations, the Bank for International Settlement (BIS) in its

2008-2009 Annual Report notes:

Low real interest rates had a variety of important effects, some more

predictable than others. On the more predictable side, by making borrowing

26

cheap, low interest rates led to a credit boom in a number of industrial

economies. For instance, credit in the United States and the United Kingdom

rose annually by 7% and 10%, respectively, between 2003 and mid-2007.Thus,

even though it may be difficult to establish clear causal links between interest

rates and risk taking, it seems reasonable to conclude that cheap credit may

have formed the basis for increases in the lending profiles witnessed in the

period leading up to the crises that ensued.

Another effect of the low interest rates was the incentive to banks to take

advantage of the eased monetary policy in the asset management business.

Banks regularly enter into long-term contracts committing them to produce

relatively high nominal rates of return. When interest rates become unusually

low, the returns promised in those contracts can become more difficult to

generate. At that point, the institution responds by taking on more risk in the

hope of generating the returns needed to remain profitable. So, low interest

rates may entail more risk-taking in lending by banks, directly and in

conjunction with weak banking supervision standards.

Ioannidou et al (2009) investigate the impact of changes in interest rates on loan pricing using

Bolivian data over the period 1999–2003. They find that, when interest rates are low, not only do

banks increase the number of new risky loans but they also reduce the rates they charge to riskier

borrowers, relative to what they charge to less risky ones.

Interestingly, the reduction in the corresponding spread (and the extra risk) is higher for banks

with lower capital ratios and more bad loans. Lower interest rates may reduce the incentives to

screen borrowers, thereby effectively encouraging banks to relax their credit standards.

The means through which interest rates affect the risk taking behavior of banks is discussed by

several theorists (Keeley (1990), Borio (2003) and with Zhu (2008), Campbell and Cochrane

(1999), (Shiller, 2000; and Akerlof and Shiller 2009)). First, when interest rates are low, risky

assets appear more attractive. Agents (bank managers) become less risk-averse and no longer

bother to screen borrowers, which is in itself a key factor in the decision to lend to a potential

27

customer. A bank’s screening potential creditors allows a proper assessment of a customer.

Though not foolproof, it still affords a bank the opportunity to adequately study the customers’

request and even possibly assess the credit records of the customer. Therefore, it appears that a

more accommodative monetary policy, by supporting real economic activity, may result in

lowering investors’ risk aversion.

Second, there could be also monetary illusion associated to low levels of interest rates inducing

banks to choose riskier products to boost returns. Riskier assets tend to generate higher returns.

Third, low short-term interest rates may decrease banks’ intermediation margins (profits), thus

reducing banks’ charter value, in turn increasing the incentive for risk-taking.

Fourth, when interest rates are lower, banks may respond by attempting to benefit from a

maturity mismatch. This they can do by increasing the yield curve slope. This act would in turn

induce banks to increase loan supply to exploit the maturity mismatch between assets and

liabilities – since banks finance themselves at short maturity and lend at longer maturities.

Fifth, when a central bank focuses solely on monetary policy, it does so at the detriment of other

policies (fiscal policy for example). If a central bank fails to encourage the adoption of several

policies to run concurrently or in a phased pattern to complement the role of monetary policy in

price stability, the environment becomes conducive for fostering bubbles in asset prices and

credit.

We shall now attempt to take a closer look at these five identified channels.

2.2.1 BOOM-INDUCED LOAN DEFAULTS

This channel suggests that following a boom period, banks are more likely to adopt a more risk-

averse stance. Valencia (2008) agrees with this view, noting that a bank’s risk taking increases

during periods of monetary expansion, but fails to adjust loan terms to fully account for the

additional risk. In his opinion, monetary policy instruments like interest rate have a direct

bearing on the risk taking tendencies of commercial banks. Valencia (2008) opines that

following periods of expansion, agents of monetary policy usually hike up interest rates in a bid

28

to stem inflation. The hike in interest rate sets off an increase in default rates as borrowers faced

with changing credit terms are more likely to default. A hike in interest rates sets off a chain of

adverse selection.

Adverse selection occurs because lenders would like to identify the borrowers most likely to

repay their loans since the banks’ expected returns depend on the probability of repayment. In an

attempt to identify borrowers with high probability of repayment, banks are likely to use the

interest rates that an individual is willing to pay as a screening device. However, borrowers

willing to pay high interest rates may on average be worse risks; thus as the interest rate

increases, the riskiness of those who borrow also increases, reducing the bank’s profitability. The

incentive effect occurs because as the interest rate and other terms of the contract change, the

behaviour of borrowers is likely to change since it affects the returns on their projects. Stiglitz

and Weiss (1981) further show that higher interest rates induce firms to undertake projects with

lower probability of success but higher payoffs and representing an increased risk appetite.

Acharya and Richardson (2010) agree with Valencia’s view and go further to suggest that the

more recent crisis which started in 2007 can be traced to the credit boom and the resulting asset

bubble in the US mortgage market of the early to mid 2000s.

2.2.2 LOW INTEREST RATE-THE PATH TO RISKIER PRODUCTS

This channel states that a low interest rate regime is likely to push a bank in the direction of

investing in riskier products which it probably would have abstained from had the interest rate

been higher and more conducive for safe(r) investments. Furthermore, a low interest rate would

inadvertently facilitate the contagion effect. Rajan (2005) proffers that low interest rates make

riskless assets less attractive and may lead to a search-for-yield by financial intermediaries.

Rajan (2005) suggests that it is this search for a higher yield that translates as excessive risk

taking as low interest rates may increase incentives for asset managers to take on more risks.

Altunbas et al (2009) believe that “In a period of declining interest rates, yields available on

highly-rated government bonds are low and results in a discouraging position for the bank when

it compares the spread on its lending and deposit rates. The resulting gap can lead banks to invest

29

in higher-yielding, higher-risk instruments.” This attitude confirms a negative relationship

between interest rates and risk taking.

Following periods of low(er) interest rates, banks may opt for securitization. Securitization of

loans results in assets yielding attractive returns for investors and also enhances bank lending

capacity, especially when the capacity constraint is binding (in times of high credit growth,

partially stemming from low monetary policy rates). This suggest that banks being prone to

waves of enthusiasm and/or deliberate risk taking are likely to buy assets they do not really

understand, are myopic in their risk assessment, or believe that they can get out (sell to a

‘Greater fool’) before the market collapses. Securitization which is common place in periods of

lower interest rate facilitates risk transfer, while also reducing transparency, thereby making it

more difficult to track risk. If market participants do not know which of their counterparties is

holding suspect assets (those whose prices are under downward pressure), it becomes more

difficult for them to avoid getting “infected” by the crisis (Diamond and Rajan(2006)).

Indeed, low interest rates appear to predispose banks to engage in softer lending standards

through lower screening and monitoring of securitized loans.

2.2.3 DECREASED PROFITS AND THE INCENTIVE FOR QUICK MONEY

A higher interest rate structure is likely to provide banks with a wider profit margin, assuming of

course that the rate at which the fund was obtained from the savings surplus units was lower.

Unfortunately, a reduction in interest rate would contend with a decreased interest rate spread

meaning that banks would make less profit and may even tend towards a loss as new funds are

considerably cheaper than maturing assets which were sourced at a higher rate. The end result of

this interest mis-match is that banks decide to take on more risk, so far as it’s likely to lead to a

more profitable position.

Acharya and Richardson (2010) agree on the impact of low interest rates on increased risk

taking but also attempt to include the possible role played by excess liquidity. In their view, Low

interest rates, directly and also in conjunction with weak banking supervision standards and high

30

securitization activity, may imply more risk taking by banks through several links. One possible

way, through which low interest rates impact upon risk taking is moral hazard theory.

Moral hazard occurs when bank managers exhibit behavior that is less careful than it ought to be,

either because they believe that their carelessness will not be found out, or because they are

encouraged to behave carelessly. Again the knowledge of an insurance cover protecting them

from the adverse effects of their action serves as further incentive for bank managers. Moral

hazard problem implies that banks have incentives to take on risk that can be shifted to a deposit

insurance fund or to tax payers. These incentives are particularly strong if equity capital is low.

Thus, deposit insurance systems can contribute to the very problem (systemic bank failure) they

are designed to reduce. Some theorists (Allen and Gale, 2007) provide evidence that abundant

liquidity increases the incentive for bank risk-taking as banks may “over-lend” the extra-liquidity

and go ahead to finance projects with negative net present value.

Consequently, low levels of both short- and long-term interest rates may induce a search for

yield from financial intermediaries due to moral hazard problems (Rajan, 2005). It does follow

that low rates may reduce adverse selection problems in credit markets and consequently

decrease screening by banks. Rajan (2006) goes on to state explicitly that the source of such

bank behavior could be an environment of low interest rates. For instance, a prolonged period of

low interest rates, and the associated decline in the volatility of these rates, increases the risk

appetites of banks and a subsequent move to higher risk positions. In addition, very low nominal

rates are usually coupled with a reduction in the margin between the lending and the deposit rate

of banks (i.e. bank margins).

2.2.4 LOW RATES AS AN INDUCEMENT TO TAKE ON MORE RISK

Low short-term interest rates also soften lending standards by abating adverse selection problems

in credit markets thereby increasing bank competition; by reducing the threat of deposit

withdrawals; and by improving banks’ net worth thereby increasing leverage (Borio and Zhu,

2008). In addition, current low interest rates may signal low interest rates in the future, and thus

banks fearing a further drop in expected profit or even an outright loss position are likely to

31

further increase their risk profile and engage in lending which they ordinarily would have

abstained from.

The excess liquidity created by loose monetary policy which reflects as lower interest rate and

general expansionary monetary conditions, encourage banks to increase their actual risk

positions in at least two ways. First, low interest rates affect valuations, incomes and cash flows,

which in turn can modify how banks measure estimated risks. Second, low returns on

investments, such as government (risk-free) securities, coupled with the lower cost of obtaining

new debt for borrowers may increase incentives for investors (including banks) and borrowers to

take on more risk. These incentives can be due to behavioral, contractual or institutional reasons,

for example to meet a target nominal rate of return or misconceptions about the actual risk

undertaken.

2.2.5 CENTRAL BANKING POLICIES AND THE INCREASED PROPENSITY TO

TAKE ON RISK

This channel opines that the pursuit of a lower monetary policy standard by the central bank is

really to blame for the increased propensity of commercial banks to take on more risk. This is

because when a central bank decides to focus on price stability alone without considering other

key macroeconomic indicators, a scenario emerges where interest rates are lowered to stimulate

investments, production and demand.

Campbell and Cochrane (1999) opine that following periods of economic expansions (boom

periods) investors tend to exhibit an increased risk appetite. This increased risk appetite gets

transmitted to banks who now assume that it is alright to inch up their risk taking just a little bit

to reflect the actions of their customers who are now driven to sustain the boom period by

whatever means possible (Altunbas et al, 2009). It then follows that an easing of monetary policy

may, by increasing real economic activity, decrease the degree of investors’ risk aversion. This

mechanism is in line with the findings from literature on asset-pricing models, which predict

higher credit spreads in the long run after periods of lower interest rates (Longstaff and

Schwartz, 1995; Dufresne et al., 2001).

32

Maddaloni and Peydro(2010) agree that weak supervision by the central banks is an important

link in the way low interest rates result in increased risk taking and subsequent financial crisis.

Using a unique dataset drawn from the Euro area and the U.S. bank lending standards, they find

that low (monetary policy) short-term interest rates soften standards, for household and corporate

loans. This softening is further complicated by widespread use of financial innovation and weak

supervision standards and securitization activity.

Given these theoretical considerations, this study is keen to ascertain the actual relationship that

exists between interest rate and risk taking. Does a negative relationship exist between interest

rates and bank risk taking? When interest rates are low, are banks more likely to perceive the low

level of interest rates as threatening to their profitability? And if yes, did they (the banks)

increase their risk-taking appetite in search for yield?

Clearly, more research is needed on the interest rates-bank risk nexus. It then becomes necessary

to analyze empirically whether such a negative relationship between the level of interest rates

and bank risk-taking exists.

2.3 CAPITALIZATION AND RISK TAKING

Regulation in banking can be classified into two types: preventive regulation and protective

regulation. Preventive regulation refers to measures taken by the authorities to restrict entry into

banking business. This can be done through licensing; the restriction of the types of business in

which banks can engage; capital adequacy requirements; control of liquidity and other statutory

reserves; the approved limits to which banks can lend or invest; and finally bank examination.

Protective regulation on the other hand, includes measures such as deposit insurance schemes

and the control exerted by central banks on commercial banks. Regulation generally helps to

minimize incidents of banking crisis and in the event of a crisis helps by maximizing ex post

damage mitigation.

Bank capital which falls under preventive regulation has over time proven to be one of the most

used forms of regulation used by regulators worldwide. There are several reasons adduced for

the preference of capital regulation over and above the other forms of prudential regulation.

33

Dewatripont and Tirole (1994) point out that Capitalization is an important component of

reforms in the banking industry, owing to the fact that a bank with a strong capital base has the

ability to absorb losses arising from non-performing liabilities (NPL), consequently capital

serves as a buffer against losses and hence failure. It is the main defence against volatility as it

plays a crucial role in the long-term financing and solvency position of banks. Another reason

they adduced why bank capital should be regulated is the need to avoid the risk-shifting bank

managers are capable of. To this end, Berger et al (1995) in Bouri and BenHmida (2006) proffer

that capital adequacy regulation plays an important role in aligning the incentives of bank owners

with depositors and other bank creditors. The principal objective of capital regulation is the

stability of a national banking system by decreasing the likelihood of bank failures. The need for

such measures can be justified on the grounds that a number of negative externalities exist in

banking practice that cause risk to be deliberately under priced.

Arua (2006) opines that bank capital, which is also referred to as owners’ capital is required to

reduce the risks inherent in banking. Bank capital is able to do this through several means.

Firstly, bank capital serves as a cushion in the event of a loss. However when the losses become

so large, even bank capital is unable to stem the tide of insolvency and Arua observes that “it is

only when a bank’s losses are so large that they overwhelm not only all other defenses but also

owners capital will the bank be forced to shut down.” Again the presence of sufficient capital is

reassuring and will serve to attract even more deposits to the bank as it instills public confidence

which is a key element required for a healthy banking environment. A third reason adduced by

Arua explaining the importance of bank capital is that the presence of a large capital increases

the volume of business that a bank is allowed to undertake. Bearing in mind that a bank cannot

grant loans in excess of 20% of its shareholders’ funds, banks with smaller owners’ capital will

be unable to partake in larger investment options. Therefore bank capital serves to regulate a

banks growth. Ogunleye (2003) recalls the role inadequate bank capital may have caused in the

spate of bank failures that plagued Nigerian banking in the 1930s and 1940s.

Arua (2006) surmises that the use of bank capital is important because it not only enhances the

‘safety and soundness of banks’ but reduces the probability of failure as well as provides much

needed liquidity which is another important factor in the proper functioning of banks.

34

To Adegbaju and Olokoyo (2008), recapitalization literarily means increasing the amount of long

term finances used in financing the organization. Consequently, it entails increasing the debt

stock of the company or issuing additional shares through existing shareholders or new

shareholders or a combination of the two. It may also take the form of a merger or acquisition or

introduction of foreign direct investment. Whichever form it takes the end result is that the long

term capital stock of the organization is increased substantially to sustain the current economy

trend in the global world.

The main justification for capital regulations of banks is often given in terms of the “moral

hazard” problem, where the adoption of a mispriced deposit insurance scheme encourages bank

managers not to do enough to reduce risk by opting for risky projects that are accompanied by

higher returns, which if not stopped in time, may compromise a bank’s solvency in the long run.

Therefore, the theoretical reason for capital adequacy regulations is to counteract the risk-

shifting incentives originating from deposit insurance.

Capital regulation and supervision of the banking system are policy tools essentially designed to

protect banks against failure and to prevent an economic crisis due to contagion and systematic

risk. At any given time, debt-holders and regulators want banks to maintain a certain level of

capital. However, bank management and shareholders have a contrary incentive to minimize

capital as this frees up economic resources that can be used for value creating activities and as

such increase the return on equity. The principal concern of the authorities who set capital

requirements is the protection of the economy against systematic risks. The imposition of

adequate capital regulation ensures to a large extent, the protection of several stakeholders, who

in the event of a crisis would all make considerable losses. These include government, the

Central bank and other regulatory bodies, the deposit holders and also investors.

The theoretical literature on banking provides little insight into many basic questions about the

behavioral implications of capitalization: In what circumstances can banks be relied upon to

behave prudently and choose, of their own accord, adequate levels of capitalization? In what

other circumstances is it necessary to monitor bank capital closely, to ensure that the probability

of failure remains acceptably low? What is the relationship between the effort which regulators

35

make in monitoring bank net worth and incentives to take risks or loot bank assets? What is the

impact of regulatory capital requirements?

Indeed the relationship between banks' capitalization and risk-taking behavior is one of the

central issues in banking, particularly as it has potential implications for regulatory policies. Just

like banks in developed countries, banks in emerging markets have been through several waves

of change particularly within technological and regulatory areas. However the relationship

between capital and risk taking behavior in the banking industry of emerging market economies

has received less attention than in the United States and developed countries.

The minimum capital requirement which currently constitutes the core regulatory instrument for

the banking industry is based on the premise that increased capital enhances bank safety. In

agreement, Sharpe (1978) states “At some level the capital is adequate, implying that the

deposits are safe enough” This quote lends credence to the notion that capital requirements exist

to reduce the probability that banks will fail. However as discussed in Jeitschko and Jeung

(2004), this premise may not hold under all circumstances. Considering such important policy

implications, Jeitschko and Jeung (2007) show that the relationship between capitalization and

risk may differ depending on the relative forces of the three entities that are directly and

indirectly involved in the risk determination of a bank, which are regulatory agencies,

shareholders and management.

One of the most important developments of the banking industry in both developed and

developing countries all over the world, during the past decade or so, has been the

implementation of minimum capital standards for internationally active banks under the Basle

Capital Accord

and under similar national guidelines.

Following the successful implementation of the accord and similar national guidelines in the

OECD countries between 1988-1992, many developing countries also started to implement their

national versions of Basle-like capital regulations in order to: (i) promote the soundness of their

banking system, (ii) to overcome the weaknesses that became apparent during the wave of

financial crisis in several developing countries; and (iii) to counteract the moral hazard problem

of newly introduced deposit insurance programs in several countries, during the 1990s.

36

Indeed, even though the Basle Accord I was designed to apply to the internationally active banks

of mostly OECD countries, its impact was rapidly felt more widely and by 1999 formed part of

the regime of prudential regulation not only for international banks but also for strictly domestic

banks in more than 100 countries, including developing countries. Notwithstanding the debate on

the effectiveness of such rules in reducing credit risk and other unfavorable consequences of

such regulations, such rules have become an important part of national commercial banking

policies worldwide and there are indications that such rules will evolve but remain in place in the

foreseeable future as well.

However, the weaknesses in applying consistent, robust risk asset definitions globally have lead

to distortions of true capital adequacy positions. Banks could become highly leveraged with

insufficient capital to absorb losses in times of crisis. For example, the two largest Swiss banks

were regarded as some of the best capitalized banks in the world based on capital as a percentage

of risk assets (Rime, 2001). However, their capital bases proved to be woefully inadequate

during the crisis and so they required significant capital injections. Likewise some of the

Nigerian banks which had to be bailed out recently had capital in excess of 20% of risk assets,

and yet were found to be short on capital when losses materialized (Sanusi, 2010). Again some

theoretical papers have suggested that capital requirements applied uniformly across a broad

class of assets may induce banks to substitute towards the riskier assets in the class, leading in

some cases to an overall rise in the riskiness of the bank’s portfolio. The broad nature of the

Basle Accord risk classes does give considerable scope for substitution between more and less

risky assets. Owing to the great difficulties in measuring bank risk-taking with available data, the

very limited academic literature in this area is inconclusive.

Some theorists (Godlewski, 2004 and 2005) have suggested that well-capitalized banks are less

inclined to increase asset risk as it is believed that higher capital requirements result in higher

stability of the banking sector whereas poorly capitalized banks have a greater incentive to take

on more risk. With smaller capital, it is more likely that losses will be born ultimately by debt

holders. Stockholders have less exposure to losses when capital is low and, hence, are less

concerned about probable losses resulting from risk-taking. Indeed the critically under-

capitalized bank under immediate threat of closure, even if it is fundamentally profitable, is

37

concerned only with survival, leading to the short-sighted risk-loving behavior suggested by the

basic model of `moral hazard'. On the other hand a slightly under-capitalized bank is concerned

with the future as well as the present and thus, in order to protect future profits (or `charter

value'), is risk-averse. Available literature posits that while there is a positive relationship

between the risk taking behaviors exhibited by banks, it can only be the case in banks that were

compromised ab initio even before a new capital structure is put in place.

While most authors (Godlewski (2005), Boyd & Graham (1986), McManus & Rosen(1991),

Keeley (1990) and Furlong (1988)) find a negative relationship between asset risk and

capitalization, some authors(Peek & Rosengreen(1997), Santomero & Vinso

(1977),Sheldon(1995) and Sheldon (1996b)) find little connection or a positive relationship

between the two.

2.3.1 IS THERE A POSITIVE RELATIONSHIP BETWEEN CAPITALIZATION AND

RISK TAKING?

The key question we examine in this section is whether banks increase the riskiness of their asset

portfolios in response to the imposition of regulatory capital requirements. The motivation for

investigating this issue is a series of theoretical papers, including Asedionlen (2004) who argue

that capitalization may raise liquidity in the short run but will not guaranty a conducive

macroeconomic environment required to ensure high asset quality and good profitability. These

authors debated the possible effects of increased capital requirement on banks’ portfolio choices

and proffer that banks may be induced to shift towards the more risky assets as a direct

consequence of an increase in capital requirements by regulatory authorities.

Early studies that examined the influence of capital requirements on bank solvency, such as the

ones of Kahane (1977), Kareken and Wallace (1978) and Sharpe (1978), show that with a flat

insurance premium in place, banks have an incentive to increase risk-taking. Koehn and

Santomero (1980), and Kim and Santomero (1988) reach similar conclusions, arguing that

uniform capital regulations can increase rather than decrease banks’ risk-taking incentives.

Koehn and Santomero (1980) showed that the presence of higher leverage ratios (which an

increase in capitalization implies) will lead banks to shift their portfolio to riskier assets. In

38

agreement, Bouri and Ben Hmida (2006) believe that the impact of capital requirements on risk

taking by commercial banks is theoretically ambiguous since the capital requirements restrict the

risk-return frontier of a bank, the forced increase in leverage may induce the bank to reconfigure

the composition of its portfolio of risky assets; thereby leading to an increase in risk taking

behaviour.

As a solution to such a situation, Kim and Santomero (1988) suggested that this problem can be

overcome if the regulators use correct measures of risk in the computation of solvency ratio and

not a broad one size fits all approach which is what is often practiced when uniform

capitalization requirements are placed on all banks irrespective of their structure.

Many other studies provide mixed results. For example, Kendall (1992) suggests that higher

capital requirements may cause riskier bank behaviour at some point in time, but this does not

imply a trend toward a riskier banking system. Subsequently, Rochet (1992) extended the work

of Koehn and Santomero (1980) and found that effectiveness of capital regulations depended on

whether the banks were value maximizing or utility maximizing.

Using a dynamic framework (multiple periods), Blum (1999) found that increased capital

regulation may increase banks’ riskiness due to an inter-temporal effect. Using a two-period

model, he showed if banks find it too costly to raise additional equity to meet new capital

requirements tomorrow or are unable to do so, they will increase risk today. He also pointed out

that this second effect will reinforce the well-known risk-shifting incentives due to the reduction

in profits.

Thakor (1996) opines that increases in capital requirements leads to increased risk taking. Thakor

believes that when faced with stringent capital regulations, banks are less willing to screen risky

borrowers leading to a positive relationship between risk taking and capitalization. Sheldon

(1996) also believes in this positive relationship and opines that the implementation of the Basle

Accord with the resulting calls for increased bank capitalization ultimately had a risk- increasing

impact on banks.

39

Shrieves and Dahl(1992) find evidence that, even for banks that are not constrained by

regulation, changes in capital and risk are positively related such that increases in capital would

lead to an increase in the risk taking exhibited by a bank. Jacques and Nigro (l995) extended the

work of Shrieves and Dahl (1992) by using a simultaneous equations model to capture the

relationship between changes in bank capital, portfolio risk and risk-based capital standards.

Their empirical work suggests that the new risk-based capital standards brought about increases

in both bank capital and risk, even for those institutions that were not capital-constrained.

2.3.2 IS THE RELATIONSHIP BETWEEN CAPITALIZATION AND RISK TAKING

NEGATIVE?

Some theorists (including Milne and Whalley (1998), Van Roy (2003)) disagree with the first

group and proffer that increases in capital regulation does not lead to increases in risk and so is

negatively related to asset risk. In their opinion, increases in capital regulation would only lead to

increases in risk taking for banks that are already significantly compromised. Consequently,

since the deposit insurance subsidy decreases with the capital adequacy ratio, relatively well-

capitalized banks will be less inclined to increase asset risk. This suggests that poorly capitalized

banks have a greater incentive to take risks. With smaller capital, it is more likely that losses will

be born ultimately by debt holders. Stockholders have less exposure to losses when capital is low

and, hence, are less concerned about probable losses resulting from risk-taking. Essentially these

theorists argue that increased capitalization is only positively related with increased risk appetite

in banks with a CAR below a critical level.

Furlong and Keeley (1989) and Keeley and Furlong (1990) show that once the possibility of

bank failure and the effects of changes in the value of the deposit insurance put option are

appropriately considered, the bank does not increase its portfolio risk with increased capital

standards when it pays a flat rate deposit insurance premium. This is attributed to the decrease in

the marginal value of the deposit insurance option with respect to asset risk as leverage

decreases. Consequently, an increase in capital standards has an adverse effect on risk-taking.

Furthermore, in a model with information asymmetry and a principal-agent problem between the

bank and the borrowing firm, Santos (1999) shows that an increase in capital standards results in

lower incentives to take risk and therefore results in a lower risk of insolvency.

40

The results of the global study of Barth et al. (2004) indicate that while more stringent capital

requirements are associated with fewer non-performing loans, capital stringency is not robustly

linked with banking crises when controlling for other supervisory regulatory policies. It is likely

to be the case that in some periods banks may find it difficult to maintain the fixed minimum

capital requirements and therefore may be forced to cut back lending. It would in fact be strange

if fixed minimum capital requirements did not bite in some periods, thereby constraining the

banks, given that the purpose of bank capital requirements is to limit the amount of risk that can

be taken relative to capital.

Boissay (2010) studies the micro- and macro- prudential effects of regulatory capital

requirements on bank risk taking and is of the opinion that a rise in capital requirements reduces

risk taking at the bank level.

Van Roy (2003) researched the impact of capital requirement on risk taking by commercial

banks of seven OECD countries using a simultaneous equations framework. He found that

changes in capital and credit risk were negatively related over the period studied, which

supported the argument that increased stringent capital requirements went hand in hand with

greater financial stability in addition to imposing a higher capital buffer against unexpected

credit risk losses. He however found that in the case of undercapitalised banks, the relationship

between capital and risk taking was in fact positive. Van Roy in a later study (2005) suggests

that the 1988 Basel Accord was generally effective in increasing capital buffers thereby

preventing banks from engaging in riskier action. In agreement, Iyer and Peydro (2010) suggest

that a negative relationship exists btw bank capital and risk taking in banks. They opine that

banks with lower capital tend to lend more on average to firms with worse risk thereby

representing a riskier investment option.

Milne and Whalley (1998) studied bank capital and risk-taking in a continuous time model with

a closed-form solution by assuming uncertain cash flow, random regulatory audit and a

constraint on equity issue. They noted that capital reserves are built up towards a desired level as

an insurance against the threat of liquidation and so risk-taking being an irregular function of the

level of capital, would not lead to an increase in the risk appetite. Consequently, it is their view

41

that minimum capital standards have little long-term impact on behavior. Frequent audit is seen

as a major tool for restraining moral hazard.

The findings of Hassan and Hussain (2004) does not support Koehn and Santomero’s conclusion

that banks will try to compensate for the loss of utility due to higher capital ratios by switching to

higher risk. In their view, increases in capital regulations will not lead to an increase in risk.

Indeed, if increased capital induces a bank to increase asset risk (the so-called asset substitution

effect of capital), and this effect supersedes the buffer effect of capital (ie larger capital absorbs

more risk), then it is possible that a highly capitalized bank has a higher probability of failure.

This risk-taking behavior of banks related to capitalization explains why banks often experience

rapid, large declines in their capital-to-asset ratio (CAPASS), and are reclassified by regulators

from well-capitalized to troubled banks in as little as a single reporting period(Jeitsckho &

Jeung, 2004) ). The implication of this positive relationship between risk taking and

capitalization is that capital regulation alone may not be adequate to guarantee the soundness of

the banking business as other factors may in fact be more closely linked to increased risk taking

tendencies of banks. Koehn & Santomero(1980),Kim & Santomero (1988),Gennotte & Pyle

(1991), Besanko &Kanatas (1996),and Park(1997) recognize that risk-determination has several

potential sources, which are not necessarily tied to minimum capital requirements.

Indeed, the evidence of the option models put forward by Furlong and Keeley (1989) and Keeley

and Furlong (1990) was weakened by the findings of Gennotte and Pyle (1991). They relaxed the

assumption that banks invest in zero net present value assets and found that there are now

plausible situations in which an increase in capital requirements results in an increase of asset

risk. Subsequently, Marshal and Prescott (2000) showed that capital requirements directly

reduced the probability of default and portfolio risk and suggested that optimal bank capital

regulations could be made by incorporating state-contingent penalties based on bank’s

performance. At the same time, Vlaar (2000) found that capital requirements acted as a burden

for inefficient banks while increasing the profitability of efficient banks.

42

In short, whether imposing harsher capital requirements leads banks to increase or decrease the

risk structure of their asset portfolio is still a debated question and, at least for now, it seems,

there is no simple answer to this question.

We summarize the finding of the literature discussed in the review section as follows: there is

little consensus about how banks’ risk appetite is influenced capital. In emerging economies,

there is no reliable evidence one way or the other as to whether capital requirements encouraged

banks to increase risk raking, as implied by some theoretical models. This means that the effect

has to be examined in terms of the effect which capital requirements have on the riskiness of the

whole portfolio of the bank.

2.4 LIQUIDITY AND RISK TAKING

Several authors (Bryant, 1980; Diamond and Dybvig, 1983) believe that banks through their role

in liquidity creation are able to influence the financial stability of the economy in which they

operate. Vasquez and Federico (2012) observe that the role of bank liquidity in the global

financial crisis has been subject to substantial attention, noting in particular that the banking

crises in most countries were preceded by periods of abnormal liquidity creation (Berger and

Bouwman, 2008, 2009).

Liquidity creation is one of banks’ raisons d’être and so in this research on bank behavior, it is

essential to study how bank liquidity affects risk taking. Since the creation of liquidity is one of

the key reasons why banks exist, and regulators are concerned about risk taking, these issues are

of first-order importance for bank regulators, policy makers, and researchers. In particular, we

examine the long-run impact of banks’ liquidity creation on risk taking. The aim of this research

is to provide evidence to prove the existence of a significant relationship between risk taking and

liquidity.

The liquidity creation theory, states that liquidity is created when banks transform liquid

liabilities into illiquid assets, whereas liquidity is destroyed when liquid assets are financed by

illiquid liabilities or equity (Berger and Bouwman, 2009a). Indeed, banking illiquidity has been a

main source of bank fragility and Nwankwo(1980) rightly states that “adequate liquidity is a sine

43

qua non of banking” and observing that liquidity is achieved by astutely managing the maturity

structure and liability portfolio at any given time to avoid a situation where a bank is unable to

meet up to its funding needs .

Diamond and Dybvig (1983) developed a model to explain why banks choose to issue deposits

that are more liquid than their assets .They specifically investigated bank liquidity and found out

that a lack of it may lead to a bank run. A bank run is the sudden and unexpected increase in

bank deposit withdrawals. Besides, the model has been widely used to understand bank runs and

other types of financial crises, as well as ways to prevent such crises. The more recent financial

crisis (starting from 2007) raised the additional question of how bank liquidity creation responds

during crises.

Banking crisis originating from liquidity problems are not sudden events that occur without

sufficient warning; rather, they are the endogenous result of a build-up in risk-taking and an

associated overextension in balance-sheets over a prolonged period—what might be termed the

build-up of financial imbalances. Unmistakable signs of such imbalances are the growth of (overt

and hidden) leverage; unusually low risk pricing and volatilities, and buoyant asset prices.

Indeed, the build-up to the crisis is often characterized by ‘artificial liquidity’.

Undoubtedly, there is a need to determine if risk-taking incentives are a function of liquidity. It is

essential to establish whether access to liquidity allows banks to switch to riskier assets, which

eventually fail to materialize into the expected huge profits. The “herd” behavior exhibited by

bank managers, where incentive distortions may have made it hard to withdraw from a lending

boom for fear of loss of market share, thereby resulting in too much risk is also another popular

reason being adduced for the possible link between excess liquidity and an increased risk

appetite.

There are several possible scenarios that may explain the relationship between excess liquidity

and the risk taking behavior of banks. Existent literature suggest the following conditions: (a)

bank managers tend to misprice risk when liquidity is sufficiently high. Consequently, banks are

likely to approve investment in riskier projects; (b) asset price bubbles are formed as a result of

44

excess bank liquidity. Asset bubbles are more likely to be formed for riskier assets; (c) bubbles

are more likely to be formed when the underlying macroeconomic risk is high inducing investors

to save with banks rather than make direct entrepreneurial investments and finally bubbles are

more likely to be formed following loose monetary policies adopted by the central bank.

Another factor which may influence risk taking through the liquidity factor is the financial

innovations that accompanied the era of financial liberalization. This era (starting in Nigeria

starting in the late 1990’s and culminating in the adoption of Universal banking in 2001 (Sanusi,

2010)) encouraged the eradication of traditional lines of difference between the commercial and

merchant banks and allowed financial institutions to initiate any new financial activity, which

was based on the discretion of the banks to dispose of their loan portfolio in accordance with risk

management. In the United States, it was the repeal of the of the US 1933 Glass-Steagall Act in

1999 which now allowed the merging of commercial and investment banking and thereby

enabling financial institutions to separate loan origination from loan portfolio. It is possible that

the adoption of universal banking may have resulted in banks granting facilities without

screening their customers properly. Factors like credit history of a customer, collateral and the

character of the borrower were not well screened. This poor screening aided the situation of

information asymmetry which further compounds the case.

In Eid’s (2011) opinion, liquidity induced risk-taking can be explained by several factors which

include; a search for high yield by customers encourages banks to shift investment to higher

earning assets (which translates to riskier assets); the pro-cyclical valuation of assets, income and

cash flows which may change risk perception and credit decisions and so not truly reflect true

risk positions; the abundance of liquidity at a low cost; and finally the reassuring effect central

bank policies.

All these views of the aforementioned authors suggest two possible means through which excess

liquidity can bring about more risk taking. Essentially, the principal-agent problem becomes

more obvious if bank liquidity is high enough to serve as a precursor providing a suitable

environment for the mispricing of assets and inadvertently leads to the formation of asset price

bubbles. Second, banks are more likely to sanction investment in riskier assets if/when bank

45

liquidity is high enough. We shall in subsequent sections trace how excess liquidity translates to

higher risk.

2.4.1 THE TRANSLATION OF EXCESS LIQUIDITY TO HIGHER RISK

2.4.1.1FACTOR ONE: MIS-PRICING RISK AS A FUNCTION OF EXCESS LIQUIDITY

Myers and Rajan (1998) suggest that when banks are flushed with liquidity, it (excess liquidity)

acts to hedge the managers from the downside of risks they undertake, and this induces risk-

taking incentives. This is because in the presence of excessive liquidity, the manager attaches too

little weight to the scenario where the bank might later face liquidity shortfalls. In other words,

excessive liquidity lowers the probability of liquidity shortfalls and hence encourages managers

to over-invest via the under-pricing of underlying risk. A cycle forms where excessive liquidity

encourages bank managers to increase the volume of credit in the economy via the mis-pricing of

underlying risk.

Financial theorists like Wagner(2005) provide evidence on how an improved ability to sell assets

(through the use of credit derivatives) will make banks less vulnerable to liquidity shocks and

would further reduce the overall level of risks on banks’ balance sheets by facilitating

diversification and the transfer of risk out of the banking sector. In Wagner’s view, an increase in

liquidity in normal times does not affect stability; rather it initially improves stability by

facilitating the transfer of risk via a secondary market. Wagner’s work is to a large extent,

consistent with the empirical work of Cebenoyan and Strahan (2004), who find that better access

to secondary markets increases banks’ profits and lending, but does not necessarily reduce

banking risk.

In contrast, they (Wagner, Cebenoyan and Strahan) all agree, that an increase in asset liquidity in

times of crisis, paradoxically, reduces stability. There is an initial positive impact on stability,

this time because it makes the bank less vulnerable to bank runs. This is counteracted by

increased incentives for taking on risks, first, because the likelihood of a bank run is reduced,

and, second, because the costs of a bank run for the bank are reduced since the losses from

selling loans in a crisis is lowered. The latter leads to an increase in the bank’s optimal

probability of default and as a result the bank takes on an amount of risk that more than offsets

46

the initial impact on stability. Wagner (2005) opines that even though the increased liquidity of

a bank’s assets removes a main cause of banking fragility, stability is not increased. The reason

for this is that any reduction in the bank’s stability reduces the bank’s costs from retaining risk

on its balance sheet and causes an offsetting change in the bank’s behavior in the primary

market. Stability even falls if the losses from selling assets in a crisis are reduced; the reason

being that this undermines the bank’s incentives to limit its risk-taking, while retaining its

incentives for taking on excessive risks due to limited liability.

2.4.1.2 FACTOR TWO: EXCESS LIQUIDITY AND ASSET BUBBLES

Arena (2008) acknowledges that liquidity is one of the driving factors affecting the likelihood of

a bank failure. Acharya and Naqvi(2009) go further to suggest that excess liquidity within the

banking sector may ignite the formation of asset price bubbles . Some theorists have suggested

that the central banks can prevent the emergence of bubbles by adopting a tight monetary policy

at times when macroeconomic risk is increasing in order to offset the flight to quality in the

banking sector.

However, if the central bank acts too late and tightens monetary policy after a bubble has already

been formed (as was the case in the current crisis) this would simply ‘prick the bubble’ and

would be much more costly as opposed to a policy of tightening monetary policy before the

formation of a bubble given that the cost of a bubble bursting is very high.

2.4.1.3 FACTOR THREE: MACRO ECONOMIC FACTORS AND EXCESS LIQUIDITY

There is a self-reinforcing process between liquidity and risk-taking. Gatev and Strahan (2006)

submit that when investors are apprehensive of the risk in the entrepreneurial sector owing to

current macroeconomic factors, they are more likely to deposit their investments in banks rather

than make other direct investments. This “flight to quality” means that there is a movement of

depositors to banks rather than direct investments may be due to the belief that banks possess

greater expertise in screening borrowers during stress times, inducing a natural negative

correlation between the usage of lines of credit and deposit withdrawals.

47

Acharya and Naqvi(2009) agree that in times of heightened macroeconomic risk, investors in the

economy reduce direct investment and hold more bank deposits. This ‘flight to quality’ leaves

banks loaded with excess liquidity, lowering the sensitivity of their profits to the downside risk

of loans and inducing excessive credit growth and asset price bubbles. The seeds of a crisis are

thus sown. A Central Bank, that can detect the macroeconomic risk or the flight to quality effect,

can curb the risk-taking incentives at banks with a contractionary monetary policy that draws out

excess bank liquidity. Conversely, an expansionary monetary policy in such times only enhances

the liquidity insurance enjoyed by banks, further aggravating their risk-taking incentives.

Boissay (2010) uses a general equilibrium model in a theoretical framework and opines that

excess liquidity ultimately leads to increased risk taking.

2.4.1.4 FACTOR FOUR: MONETARY POLICY AND EXCESS LIQUIDITY

Naqvi (2007) observes that the central bank’s lender of last resort operations needs to be

complemented ex ante by an efficient supervisory framework so as to avoid the moral hazard

repercussions later on. Indeed, supervision is even more essential during times when the banking

system is flushed with liquidity. Furthermore, if the central bank does resort to a loose monetary

policy, for instance either to counter deflation or to stimulate the economy, such monetary policy

needs to be accompanied by adequate supervision of the banking system in order to curtail the

risk-taking appetites of banks (Naqvi, 2007).

Acharya and Naqvi (2009) presented a theoretical model, explaining why access to abundant

liquidity aggravates the risk taking moral hazard at banks, giving rise to asset price bubbles that

can be counter-acted by Central Banks with a contractionary monetary policy, but are

exacerbated by expansionary monetary policy. Somewhat perversely, the seeds of crisis are sown

when the macroeconomic risk is high and investors in the economy switch from investments to

savings in the form of bank deposits. Expansionary monetary policy is tempting in such times,

but banks become flush with liquidity and ignite credit and asset bubbles. A precise knowledge

of the underlying macroeconomic risk can enable central banks to formulate a monetary policy

that will avoid the formation of a bubble altogether as it is extremely important to mitigate the

emergence of bubbles.

48

Just as monetary policy is used to target interest rates and employment even though both the

natural interest rate and the natural rate of employment are not perfectly observable, so too can

monetary policy target asset prices by estimating the underlying macroeconomic risk and bank

liquidity. Acharya and Naqvi’s research go on to argue that when a contractionary monetary

policy is adopted by the central bank in periods of increasing macroeconomic risk, then it can

counter the flight to quality effect, draw out the increases in bank liquidity, and hence avoid the

emergence of a bubble. However when an expansionary or loose monetary policy (in this case

the lowering of interest rates) is pursued, then the groundwork is laid for the formation of

bubbles. Without question, an increase in the money supply increases bank liquidity and hence

makes asset prices more vulnerable to the formation of bubbles as shown by the impact of a lax

monetary policy by the Scandinavian Central Banks in the1980’s, by the Bank of Japan during

1986-1987, and by the United States Federal Reserve Bank during most of the Greenspan era

culminated in housing and real estate bubbles in these countries.

On the theoretical front, Allen and Gale (2000) obtain a similar result in a model of risk-shifting

where uncertainty in monetary policy acts to exacerbate the risk-taking incentives ex ante and

fosters an asset price bubble. Diamond and Rajan (2008) explain why lowering interest rates ex

post may be desirable to avoid bank runs and fire sales, but that this can induce a moral hazard

ex ante and encourage banks to hold more illiquid assets. It may thus be desirable for the Central

Bank to commit to raising interest rates when they are low.

A low interest rate structure serves to hedge banks against liquidity shocks and this makes risk-

taking more attractive ex ante. Consequently, bank managers will have an incentive to invest in

the riskier asset if bank liquidity is sufficiently high.

2.4.1.5 FACTOR FIVE: EXCESS LIQUIDITY AS A FACTOR OF CAPITALIZATION

Berger and Bouwman (2009a) show that capital is a key determinant for liquidity creation, while

Berger and Bouwman (2009b) present evidence that banks with higher capital ratios are able to

increase their market shares of liquidity creation with the implied increases in risk taking during

banking crises.

49

Financial crises raise the question of how effectively banks can be disciplined by regulators and

private parties alike in episodes of extraordinary distress. Acharya et al (2007) underscore that

liquidity provision becomes a crucial issue during crises. Banks that experience distress may

suffer even greater declines in liquidity creation in a crisis. From a policy perspective, it is

therefore important to ascertain whether the effects of different forms of interventions are

identical for crisis and non-crisis periods.

Berger and Bouwman (2009a) find that a fragile capital structure encourages the bank to commit

to monitoring its borrowers, and hence allows it to extend loans. Additional equity capital makes

it harder for the less-fragile bank to commit to monitoring, which in turn hampers the bank’s

ability to create liquidity by way of increased risk taking.

Since regulators intervene to reduce undue risk taking, it is expected that risk declines after

regulatory interventions. Capital injections by bankers associations should directly reduce risk

both because higher capital ratios have a greater risk-absorption capacity and because they

reduce moral hazard incentives. It is important to note that injections in capital may come with

explicit or implicit demands on banks to reduce risk, for example through portfolio adjustments,

and these demands may have (possibly inadvertently) reduced liquidity creation.

Summarily, in the aftermath of the latest banking crises which was global in scale, it has indeed

become important to find out what caused the tremendous asset growth and the subsequent

puncture that characterized the years immediately leading up to the crisis. Some economists have

linked the formation of asset bubbles to the lowering of interest rates which was prevalent from

the early 2000. What followed was a period of abundant availability of liquidity to the financial

sector, bank balance-sheets grew two-fold within four years, and as the “bubble burst", a number

of agency problems within banks in those years came to the fore. Such problems were primarily

concentrated in centers that were in charge of undertaking, and in principle managing, large

risks, and manifested as them taking huge payouts based on the volume of assets they created or

traded rather than on (long-term) profits they generated.

50

Liquidity constraints can cause strains on solvency, by precipitating fire sales and a credit

crunch. In addition, difficulties in distinguishing sound from unsound banks, not least owing to

the web of contractual relationships that ties them together, can spread the run across the banking

system. The process has certain self-fulfilling aspects: concerns about being late in withdrawing

funds precipitate their early withdrawal (Diamond and Dybvig 1983). Low liquidity hampers

business and may induce a run on bank deposits.

2.5 BANKING IN NIGERIA

Commercial banking in Nigeria began in 1892 when the African Banking Corporation opened a

branch in Lagos. Not long after, the bank experienced severe operational problems that

necessitated its closure and subsequent take-over in 1894 by the Bank of British West Africa,

now known as first Bank of Nigeria plc. The next bank to open shop was the Barclays Banks

DCO {now union Bank of Nigeria plc} in 1917. These two expatriate banks essentially served as

a means through which the colonial government serviced its commercial interests. Indeed there

was no legislation governing the business of banking in Nigeria at this time. The customs,

principles and practice of banking were essentially at the discretion of the operators.

Furthermore, there was no attempt to encourage the setting up of indigenous banks as these two

banks were clearly capable of serving the interests of government at the time. Consequently the

two banks dominated the banking scene, until 1927 when the first indigenous bank, the industrial

and commercial bank was established.

Several indigenous banks sprang up between 1927 and 1951: most of them failed before even

getting off to a good start. Their chances of survival were in fact slim as several factors militated

against their existence; there was a complete of absence of laws to check the establishment and

management of banks at this time. Also the banks had inadequate manpower. Indeed, it appears

that most of the banks were set up with nationalistic considerations and not economic factors

(CBN/NDIC, 1995). Inadequate capital, bad management and fraudulent practices were but a

few of the challenges facing the banks. The table below shows the names of banks and the years

of establishment and closure.

The colonial Government had not in any way provided for any legislative or supervisory body to

regulate the activities of these banks until the first ever legislation on banking in Nigeria which

51

was the Banking Ordinance of 1952. The Banking ordinance of 1952 prescribed an operating

license and for the first time, emphasis was placed on a minimum equity capital for all banks

(Onoh, 2002). The 1952 ordinance set standards, required reserve funds, established bank

examinations, and provided for assistance to indigenous banks.

TABLE 2.1 BANK FAILURES (1927 TO 1954)

S/NO NAME OF BANK DATE ESTABLISHED REMARKS

1 The Industrial and Commercial Bank 1929 Failed in 1930

2 The Nigerian Mercantile Bank 1931 Failed in 1936

3 The Nigerian Penny Bank 1945 Failed in 1946

4 The Nigerian Farmers and Commercial Bank 1947 Failed in 1953

5 Merchants Bank 1952 Failed in 1960

6 Pan Nigerian Bank 1951 Failed in 1954

7 Standard Bank of Nigeria 1951 Failed in 1954

8 Premier Bank 1951 Failed in 1954

9 Nigeria Trust Bank 1951 Failed in 1954

10 Afroseas Credit Bank 1951 Failed in 1954

11 Onward Bank of Nigeria 1951 Failed in 1954

12 Central Bank of Nigeria * 1951 Failed in 1954

13 Provincial bank of Nigeria 1952 Failed in 1954

14 Metropolitan Bank of Nigeria 1952 Failed in 1954

15 Union Bank of British West Africa 1952 Failed in 1954

16 United commercial Credit Bank 1952 Failed in 1954

17 Cosmopolitan Bank 1952 Failed in 1954

18 Mainland Bank 1952 Failed in 1954

19 Group Credit &Agric Bank 1952 Failed in 1954

20 Industrial Bank 1952 Failed in 1954

21 West African Bank 1952 Failed in 1954

Source: Central Bank of Nigeria Annual Reports, 1968

*This bank is in no way connected with the Central bank Of Nigeria

Umoh (2003) observes that when the banks failed, depositors lost their deposits and some even

their lives. There were no schemes, either implicit or explicit, that were available to protect

depositors, small or big. In fairness to the colonial Government, a haphazard attempt was made

to provide a regulatory body- the West African currency board (WACB). However the board was

over stretched as it was virtually responsible for monitoring financial activities in all of British

west Africa (Ghana, Nigeria, Gambia and Sierra Leone) and of course to expedite the

repatriation of funds to the Motherland – England. The failures of these banks encouraged the

nationalists to press for the establishment of an indigenous central bank. This prompted the

52

Federal Government then, backed by the World Bank Report to institute the Loynes commission

in September 1958. The outcome was the promulgation of the ordinance of 1958, which

established the Central Bank of Nigeria (CBN) although formal operations started on July 1st,

1959. The period (1959–1969) marked the establishment of formal money and capital markets in

Nigeria.

The incidence of bank failures did not however end with the establishment of the Central Bank

of Nigeria (CBN). For a while, there was a reduction in the number of troubled banks as banks

appeared more stable and even more banks were established. Government decided to put

statutory regulations in place to regulate the business of banking. This led to the enactment of the

Banking Act of 1969 – which, subject to some amendments within the period, remained the

primary legislation on banking in Nigeria until the enactment in 1991 of the Central Bank of

Nigeria Act 1991 (as amended) and the Banks & Other Financial Institutions Act of 1991 (as

amended). These two pieces of legislation became the fundamental laws regulating banking in

Nigeria.

In describing the evolution of the Nigerian banking system, Nnanna (2005) and Kama (2006)

observe that Nigerian banking history has passed through four stages of evolution. The first stage

refers to the period between 1930 and 1959. This stage was characterised by the establishment of

several banks, many of which failed in their infancy due primarily to the poor management and

inadequate capitalization. This stage ended with the establishment of the CBN in 1959.

The second phase started in 1960 when Nigeria got her independence and lasted till 1985. This

period was characterized as a period of growth with the oil boom and its attendant good effects.

Kama (1986) goes further to note that in this period, banking licences were more restricted and

the “come one, come all” practice of the first period was absent. This period of ended as Nigeria

tried adjusting to a situation of dwindling oil revenue. The structural adjustment programme

(SAP) commenced as this stage ended and bank reforms at this time paved the way for the entry

into the market of a fresh set of banks that would revolutionise the Nigerian banking system.

53

The third stage also referred to as the post structural adjustment program (SAP) was one that

stretched the liberal maxim of free entry to a limit. The new entrants into the market included

Guaranty Trust bank, Zenith bank, and First city monument bank. The number of banks grew

very quickly and an embargo on bank licensing in 1991 stemmed the floating of new banks but

Sobodu and Akiode (1996) opine that the damage had already been done as they (Sobodu and

Akiode, 1996) note the marked increase in non- performing loans and the emergence of a riskier

banking system. A CBN/NDIC study of Distress revealed a marked downturn in the banking

Industry. This period lasted from 1986 to 2004.

Kama(1986) goes on to add that the fourth period starting in 2004 has been one where hard

lessons have been learnt and focus shifted to risk based bank supervision. “Risk focused

supervision and proactive regulation” can be said to be the main features of this period.

The distress in the system reflected through non –performing loans, insolvency, liquidity

problems and defaults in meeting with depositors and inter- bank obligations. Some authors

(Sanusi, 2009 and Kama, 1986) note factors responsible for the massive bank failures of 1989-

1993 and chief among them were;

(i) Bad loans and advances

(ii) Fraudulent practice

(iii) Gross undercapitalisation (with respect to the volume of activity and the portfolios of

the banks)

(iv) Rapid and inconsistent changes in government policy

(v) Bad and inefficient management

(vi) Inadequate supervision

Indeed, the promulgation of the Banking legislation of 1991 paved the way for the CBN to

effectively enforce compliance with banking laws and to intervene in banks that were seen to be

troubled. Stricter prudential standards were observed and it was not long before pervasive

weaknesses became apparent in the balance sheets of some banks.

54

Although it has been argued that Nigeria has had banking crises spread for a very long time,

1998 was the single year in which twenty six banks were liquidated at once for being technically

insolvent. It may be appropriate to regard this as a period of crises in view of the fact that

banking crises (failure) of this magnitude had not occurred in any particular year in the history of

Nigerian banking (Ezema, 2008)

Furthermore, the CBN as part of a general deregulation embarked upon a universal banking

system in January 2001. The universal banking system allowed for the merging of both merchant

and commercial bank operations in Nigerian banks and so led to the creation of “banking

supermarkets” where all financial services ranging from insurance, mortgage and even actual

banking transactions took place under one roof and brand name. Initially, the universal banking

scheme seemed to provide an answer to all the problems that plagued the Nigerian banking

industry. It did not take too long before several problems became apparent.

The CBN proceeded to make the Nigerian banking system even safer insisting all banks were to

recapitalise. The basic thrust of this was from the Basel Accord which had encouraged that the

capital requirements for banks be truly reflective of their portfolio and so in 2004, the Nigerian

banking industry underwent a recapitalisation exercise. At this time, 89 banks operated in

Nigeria with 3,382 branches nationwide and their capital base was approximately N 2 billion

each. The reform instructed that banks were now to recapitalise to a minimum of N 25 billion.

Twenty five banks emerged from the exercise and many believed the worst was over for

Nigerian banking as the recapitalisation was expected to lead to the emergence of stronger banks

better equipped to stand on their own (Odiawa ,2006). Indeed the consolidation exercise in

Nigeria was reassuring as it was believed that the strong (er) banks were now impermeable to

distress from any quarter. The joy was short lived.

Barely two years after, the global economy started to slowly but surely witness some

catastrophic developments. The global economic crisis that ensued was unprecedented in history

both in terms of scope and severity and there were fresh worries as to the stability of the Nigerian

banking sector.

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2.5.1 MATTERS ARISING FROM THE CONSOLIDATION EXERCISE OF 2004- 2005

Most reforms have occurred against a backdrop of crisis due to several factors including but not

limited to inadequate/gross undercapitalization relative to the volume of deposits and business

transactions; weakness in the regulatory and supervisory framework of banks; weak management

practices; and the tolerance of deficiencies in the corporate governance behaviour of banks

(Uchendu, 2005). There are however instances when a reform is a pre-emptive action taken to

forestall the occurrence of crisis within a banking system and the consolidation exercise was

largely viewed as one of such. Adegbaju and Olokoyo (2008) agree and observe that Bank

consolidation, which is at the core of most banking system reform programs, occurs, some of the

time, independent of any banking crisis.

In embarking upon the consolidation exercise, the main reason was to weed out banks that were

not strong enough to compete favorably in the international market. Other considerations

included the need to positively enhance the surviving banks such that they now seemed

impervious to disaster of any kind or magnitude. A cursory look at the state of health of the

Nigerian banking industry in the years leading up to and immediately after the consolidation

suggest that some banks remained shaky even in the post consolidation era when everyone

seemed to be basking in the post consolidation euphoria. The table below provides evidence to

buttress this assertion.

Table 2.2 NIGERIA: STATE OF THE BANKING INDUSTRY

Category 2001 2002 2003 2004 2005 2006

Sound 10 13 11 10 25 10

Satisfactory 63 54 53 51 - 5

Marginal 8 13 14 16 - 5

Unsound 9 10 9 10 - 5

Sources CBN Publication (2006)

Ogowewo and Uche (2006) provide a detailed account of the frequency of recapitalization

exercises in the Nigerian banking system as shown in the table below.

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TABLE 2.3 BANK CAPITAL REQUIREMENTS (1952-2005)

YEAR REQUIRED CAPITAL

REMARKS

1952 £12,500 17 indigenous banks failed consequently.

1958 £100,000 £200,000

Indigenous banks Foreign banks

1962 £250,000 Both foreign and indigenous banks

1969 £300,000 £750,000

Indigenous banks Foreign banks

1979 N1,000,000 N2,000,000

Merchant banks Commercial banks

1988 N6,000,000 N10,000,000

Merchant banks Commercial banks

1989/1990 N12,000,000 N20,000,000

Merchant banks Commercial banks

1991 N40,000,000 N50,000,000

Merchant banks Commercial banks

1997 N500,000,000 Both merchant and commercial banks

1999(1999-2002)

N1 billion All banks

Jan 2004 N2 billion All banks

July 2004-2005

N25 billion The increase of 1150% came even before the expiration of the N2billion recapitalization exercise

Ezema (2008) adduces that the probable cause of problems in the post consolidation era may in

fact be the result of poor planning. This poor planning manifests in mergers and acquisitions

where sound banks were “forced” into arrangements with unsound banks and so it still remains

the subject of further research to ascertain the true empirical effects of the consolidation exercise.

Berger et al(1999), and Barnes et al (2002) all conclude that voluntary consolidation do not

necessarily enhance the performance of the participating banks and so it is doubtful whether

consolidation exercises brought on through government policy would achieve the purpose of

enhancing performance given the likelihood of mergers and acquisition of sound and unsound

banks which ultimately would not augur well for even the healthiest banking industry.

Somoye (2006) points out that the link between consolidation on one hand and financial sector

stability and growth on the other can be viewed from two perspectives. Some argue for

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consolidation and suggest that consolidation leads to increase in size, bank returns, revenue and

other benefits that accrue in the course of large scale production. This pro-consolidation group

further proffers that consolidation would help in eliminating weak banks through a natural

process which would further improve the environment. The arguments against are by no means

less vocal. This group argues that banks’ propensity to take on risk is actually increased as banks

have too much money on their hands post consolidation and may even start to engage in behavior

they may ordinarily have avoided, in a bid to secure a significant share of the market, or simply

to make more profit.

Somoye (2006) further notes “The implication for bank consolidation within the Nigeria banking

industry is whether the bigger (not yet mega) banks will set good balance between growth and

risk management. However, evidence has shown that consolidation exercise leads to more banks

being established in the long run thereby returning back to the status quo.” Indeed there is

evidence that the pressure on banks to deliver high returns to their shareholders after the rapid

expansion in their capital base post-consolidation contributed to some of the highly risky

behavior that led to the collapse of some of the banks.

Summarily Somoye (2006) observes that the consolidation exercise has not improved the overall

performances of banks significantly and though there have been contributions to the real sector,

it does appear that such contributions may in fact be marginal.

Indeed, the Nigerian banking sector witnessed dramatic growth post-consolidation as evidenced

by the surge in quoted stock prices of banks at the Nigerian Stock Exchange. Apparently nobody

(the banking industry, regulators or even industry watchers) had the interest or motivation to

sustain and monitor the phenomenal growth that occurred post consolidation. Asset bubbles were

the resulting effect of the explosive growth and with the benefit of hindsight, everyone failed to

notice the risks as they built up within the system until the “implosion” that occurred when banks

were “stress tested” in 2009. The results of the tests conducted on the 25 banks were far from

impressive and revealed huge flaws that had been perpetuated by the management of the affected

banks as well as the CBN which had failed to perform its oversight and regulatory functions

satisfactorily. It appeared as though the CBN having “successfully” consolidated the banking

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system had decided that the banks were now ready to self-regulate. The affected banks eight in

number are Union Bank, Finbank, Oceanic bank, Intercontinental Bank , Afribank,

PlatinumHabib Bank, Spring Bank.

Writing on the causes of this latest crisis, Sanusi (2010) attributes the instability that occurred

within the Nigerian banking system to eight independent factors. These factors broadly speaking

were from three distinct categories. Group one was directly attributable to the banks while the

second group were the consumers, the bank customers. Group three referred to the factors linked

to the financial sector regulators.

The eight factors according to Sanusi (2010) include:

Macro-economic instability caused by large and sudden capital inflows

Major failures in corporate governance at banks

Lack of investor and consumer sophistication

Inadequate disclosure and transparency about financial position of banks

Critical gaps in regulatory framework and regulations

Uneven supervision and enforcement

Unstructured governance and management processes at the CBN/Weaknesses within the

CBN

Weaknesses in the business environment.

2.5.1.1 Bank Factors

MACRO-ECONOMIC INSTABILITY

The Nigerian economy to all intents and purposes exudes the characteristics of a mono product

economy () given its dependence on oil revenue. Consequently the Middle East crises and the

attendant fluctuations in oil prices had several ramifications for the Nigerian economy leading to

a persistent transmission of shocks that impacted on the Nigerian economy. Government

spending mirrored the volatility from the oil market and this resulted in instability within the

economy. The banking industry was by no means immune to this shocks and the consolidation

exercise which required banks to increase capital requirements caused banks to have excess

funds available for credit creation (Sanusi, 2010).

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CORPORATE GOVERNANCE

Sanusi (2010) believes that the instability discussed above was aggravated by the poor corporate

governance structure in most banks. He (Sanusi) observes that there were several cases of

unethical and potentially fraudulent business practices by top bank management, who obtained

for themselves un-secured loans at the expense of depositors. The adoption of special purpose

vehicles (SPVs) to serve as conduits for management to move funds undetected was quite

pronounced at this time.

POOR DISCLOSURE

Banks deliberately failed to disclose the full extent of their operations to their customers. Banks

selectively decide which information to provide and which to conceal, full disclosure was not

applied. Consequently customers lacking in expertise had their positions worsened by the

asymmetric information on true stock positions. Sanusi (2009) again notes that some investors

“made ill-advised decisions regarding bank stocks, enticed by a speculative market bubble which

was allegedly partly fuelled by the banks through the practice of margin lending.”

2.5.1.2 Regulator Induced Factors

A number of the factors in the financial crises could be directly attributed to the poor regulatory

and supervisory role performed by the central bank. Sanusi (2010) blames “uneven supervision

and a poor regulatory structure as being serious factors” that contributed to the crises. In his

opinion, Regulators were ineffective in foreseeing and supervising the massive changes in the

industry or in eliminating the pervasive corporate governance failures. Bank supervisors failed to

look out for and measure accurately the factors that traditionally point to a crisis position. For

instance, a number of the banks were consistently at the CBN’s Expanded Discount Window, an

indication of a weak situation which the supervisors failed to assess as a source of potent threat.

The regulator(s) according to Sanusi lacked the capacity (possibly due to an inadequate legal

framework) to stop the diversion of capital by the banks to subsidiaries given that Universal

banking had been adopted in 2001. This diversion by banks made it possible for banks to

“remain under the regulatory radar” since the CBN did not have the capacity (and/or right) to

monitor the activity of bank subsidiaries in the capital market-this fell under the ambit of the

SEC.

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The role of proper bank regulation cannot be over stretched as it is key to proper functioning of a

nation’s financial system. Giddy (1984) and Sheng (1990) as cited in Soyibo et al suggest four

reasons why banks must be regulated. Smooth functioning of monetary policy is the first reason.

The ability to create money is checked by an efficient monetary policy and as such there is need

to monitor the ease with which banks create money. The second reason relates to the need to

monitor banks given that they serve as important channels of credit. The provision of a level

playing ground for all banks helps the avoidance of collusion and the creation of cartels. (The

LIBOR scandal in Britain (2012) has proven the need for even more stringent supervision and

monitoring of bank activities). Finally, a fourth reason is that there is a need to check the

problem of asymmetric information, ensure compliance with laid down procedures and ensure

that banks being depositories of private savings, operators of payments mechanisms are not

vulnerable to collapse.

NIGERIAN BANKING POST STRESS TESTING

In a bid to forestall the loss of consumer confidence, the Central Bank of Nigeria in August

2011, revoked the licenses of three of the eight banks. The revocation of banking licenses fall

outside the scope of this study but are mentioned as an update of notable events that have

occurred within the Nigerian Banking Industry.

2.6 EMPIRICAL FRAMEWORK

2.6.1 INTEREST RATE AND RISK TAKING

There are a number of theorists who provide empirical evidence that directly points to an

existing link between interest rates and increased risk taking behaviour.

Altunbas et al (2010) analyze empirically the relationship between interest rates and risk-taking

by banks. Using a unique database of quarterly balance sheet information and risk measures for

listed banks operating in the European Union (Austria, Belgium, Denmark, Germany, Greece,

Finland, France, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, the

United Kingdom) and the United States, the data taken from Bloomberg over the period 1998-

2008 provides evidence of a negative relationship between risk taking and interest rate.

Specifically, they found that in periods of low interest rate, there equally occurred a matching

61

increase in banks’ risk-taking. The negative relationship between interest rates and risk taking

according to Altunbas et al stem from the reduced incentive to screen borrowers properly during

periods of lower interest which effectively encouraging banks to relax their credit standards.

Jiménez et al (2009) studied data from the Spanish Credit Register and used discrete choice

models to investigate the impact of interest rates on credit risk taking by banks. They study the

evolution of Spanish credits from 1988 to 2006 and confirm the presence of a negative

relationship between interest rate and risk taking behavior. They present robust evidence

confirming previous theoretical predictions (Ioannidou, Ongena and Peydro, 2008) that though

both small and large banks are affected, smaller banks are more likely to exhibit increased risk

appetites as a direct response to low interest rates.

Going further, Jimenez et al proffer that bank risk taking is exacerbated by the “search for yield”

by bank customers who believe that it is better to save with banks in periods of low interest rate.

Their study goes on to provide a much needed link between interest rate and liquidity as one of

their findings attribute increased risk taking to the presence of lower interest rates as well as the

desire of banks not to continue with the higher cost of holding liquid assets given their relative

low yields. They sum up by stating that bank risk appetite is heightened after periods of low(er)

interest rates.

Delis and Kouretas (2011) studied 3,628 European banks from 16 countries over the period 2001

to 2008. Their data yielded over 18000 annual observations and they present empirical evidence

linking low interest rate to increased risk taking by banks. Delis and Kouretas (2011) define bank

risk using two indicators: the ratio of risky assets to total assets and the ratio of non-performing

loans to total loans. Results confirm a strong negative relationship between interest rate and risk

taking. They however observe that the impact of interest rate on risk assets is reduced for banks

with higher equity capital but more obvious and amplified for banks with higher off balance

sheet items.

Maddaloni and Peydro (2009) also studied banks from twelve Euro area countries between 2002

Q4 and Q9 of 2009. They set out to determine the relationship between interest rates and risk

62

taking. Their study applied the Taylor- Rule and notes that when interest rates is low, banks’

appetite for risk increases. This finding is indicative of a negative relationship.

Geršl et al (2012), using data from the Czech banking system set out to establish whether riskier

loans were associated with lower interest rate suggesting a negative relationship. Their findings

imply that at times of low interest rates, banks will seek to finance riskier borrowers.

De Nicolo et al (2010) studied banks in the United States between 1997 and 2008. Using simple

ordinary least squares regression analysis, their results show a strong negative relationship

between interest rate and bank risk taking.

Eid (2012) studied the risk behavior of the main French banks as it relates to interest rates from

1998-2008 and concludes to the existence of a negative relationship between these two variables.

Eid’s research provides evidence that bank risk-appetite and risk-taking behavior are higher

when interest rates are lower. Several theoretical explanations exist to this phenomenon, such as

the managerial compensation schemes linked to fixed objectives, the pro-cyclical valuation

methods of assets, income and cash flows, or the abundant liquidity at a low cost.

Theory (Taylor, 2007 and Keeley, 1990) provides support that lower interest rates tend to boost

asset value and thus provides validation to the negative relationship that exists between interest

rates and risk taking behavior. Indeed when monetary policy is expansive, banks might engage in

lending relations with borrowers that were initially perceived as risky in the past but are now

eligible for credit due to an improvement in their net worth.

2.6.2 CAPITALISATION AND RISK TAKING

The effect of bank capital on a bank’s risk appetite remains contentious as theorists continue to

espouse the various channels through which it may serve as a determining variable in a bank’s

decision to undertake activities which may influence its risk profile.

Milne and Whalley (1998) studied bank capital and risk-taking in a continuous time model with

a closed-form solution by assuming uncertain cash flow, random regulatory audit and a

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constraint on equity issue. They noted that capital reserves were built up towards a desired level

as an insurance against the threat of liquidation. Risk-taking was found to be an irregular

function of the level of capital. In their opinion, minimum capital standards have little long-term

impact on behavior. Frequent audit is seen as a major tool for restraining moral hazard.

In presenting the empirical works on capitalization, we find that there are differences in finding

and so we again classify the empirical studies in to two groups, namely those that find a negative

relationship and those that find the relationship between bank capital and risk taking to be

positive.

2.6.2.1 NEGATIVE RELATIONSHIP BETWEEN CAPITAL AND RISK TAKING

Furlong and and Keeley (1989), Keeley and Furlong (1990) have argued that the mean- variance

framework proposed by Koehn and Santomero (1980), Keeton (1988) and Kim and Santomero

(1988) is inappropriate in the banking context because it ignores the option value of deposit

insurance. Using a contingent claims model, these authors show that increased capital standards

won’t necessarily push banks to increase the risks they take.

Sheldon (1996) using a different approach proffers that increases in capital are not positively

linked to increases in risk taking. Specifically, Sheldon (1996) used an option-pricing framework

to analyze the effects of capital adequacy on bank risk across eleven G-10 countries and found

that increases in capital (as proposed by the Basle Accord) did not have a risk-increasing impact

on banks’ portfolio. The findings of his research are somewhat ambiguous and difficult to

interpret as he did not control for regulatory and non-regulatory influences. In addition the

sample coverage of his study is not sufficient or representative for the eleven countries the study

set out to cover.

Using a framework of simultaneous equations framework, Van Roy (2003) studied seven G 10

countries (Canada, France, Italy, Japan, Sweden, United Kingdom and United States) between

1988 to1995 and shows that in the countries studied capital and credit risk were negatively

related within the study period.

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In a later study, Van Roy (2005) documents the behavior of banks from six G-10 countries

toward capital and risk between 1988 and 1995 by using a modified version of the model

developed by Shrieves and Dahl (1992). He showed that the impact of the 1988 Basel standards

was not uniform across countries. In Canada, Japan, the UK and the US, banks within one

standard deviation of the minimum regulatory capital requirement improved their tier 1 capital to

assets ratio and/or their total capital to assets ratios in order to comply with the new capital

adequacy rules. However, regulatory pressure had no impact on the capital to assets ratios of

French and Italian banks. In addition, changes in capital and risk were unrelated for Canadian,

French, Italian and UK banks, positively related for Japanese banks and negatively related for

US banks. Finally, and overall, the 1988 Basel Accord was generally effective in increasing

capital buffers and preventing banks from engaging in riskier activities.

Gunther and Robinson (1990) examined the behaviour of insured commercial banks in the Dallas

and Houston metropolitan areas between 1983 and 1984 and find a negative relationship between

capital growth and changes in loan-to-asset ratios. They interpret this result as a negative

relationship between capital adequacy and risk-taking.

Godlewski (2004) using a simultaneous equation framework follows the work of Shrieves and

Dahl (1992) and Jacques and Nigro (1997) and is able to present empirical evidence on the

relationship between bank capital and credit risk taking in emerging market economies. Kwan

and Eisenbeis (1995) equally use a simultaneous equation approach in studying the bank risk-

capitalisation nexus and their work appears consistent with that of Godlewski (2004) who agree

on the negative relationship between capital and risk taking.

Kwan and Eisenbeis (1996) used a simultaneous equations approach on a sample of 254 large

banks between 1986 through 1991 in the United States. Results from their study agree with those

of Wei-Tung et al(2009) and confirm that risk levels of banks are different. Kwan and

Eisenbeis(1996) provide evidence less capitalized banks took on more risk. Their work thus

suggests that a negative relationship exists between capital and risk taking.

65

Rime (2001) studies evidence from the Swiss banking industry for the period 1989 to1996. His

study provides the first application of the simultaneous-equations model to non-U.S. banks. His

results indicated that Swiss banks reacted to capital regulations by increasing their capital but

this did not change banks risk-taking. One of the problems with this study might be the fact that

Rime adopted the PCA regulatory classification to measure regulatory pressure on Swiss banks,

which might be inappropriate given that the additional requirements set by PCA have not been

adopted formally by any other country than the United States.

Hassan and Hussain (2004) study a dataset of 10 banks (Argentina, Hungary, Turkey, Venezuela,

Slovenia, India, Brazil, Korea, Malaysia, Thailand and Chile) find that capital regulations and

bank risk are negatively related to each other.

Jacques and Nigro (1997) study 2,570 US commercial banks with assets of more than $100

million over two years 1990-1991. Their study set out to establish the impact of capital

regulation on risk taking and found that Capital and risk were negatively related for adequately

capitalized banks and increased regulation had zero impact on risk for under capitalized banks.

2.6.2.2 POSITIVE RELATIONSHIP BETWEEN CAPITAL AND RISK TAKING

Using the mean – variance framework, Kahane (1977), Koehn and Santomero (1980) and Kim

and Santomero (1988) have shown that increased regulatory capital standards may have the

opposite effect of what it intended to achieve. In such frame work, changes in capital and

portfolio risk would be positively correlated. Blum (1999) comes to similar conclusions in a

dynamic frame work, proving the effect of capital regulation, which may push an under-

capitalized bank to increase risk in period t in order to meet regulatory requirements in period t +

1. In Blum’s opinion, increased capital regulation may increase banks’ riskiness due to an inter-

temporal effect. Using a two-period model, he showed if banks find it too costly to raise

additional equity to meet new capital requirements tomorrow or are unable to do so, they will

increase risk today. He also pointed out that this second effect will reinforce the well-known

risk-shifting incentives due to the reduction in profits.

66

Rochet (1992) extended the work of Koehn and Santomero (1980) and found that effectiveness

of capital regulations depended on whether the banks were value maximizing or utility

maximizing.

Shrieves and Dahl(1992) using a simultaneous equation model find evidence that, even for banks

that are not constrained by regulation, changes in capital and risk are positively related such that

increases in capital would lead to an increase in the risk taking exhibited by a bank. They used

several periods of cross-sectional data on commercial banks in the United States under the

simultaneous equations framework mentioned before and found that the effectiveness of risk-

based capital regulations depended on how well the regulations reflected the true risk exposure

of banks.

Jacques and Nigro (l995) extended the work of Shrieves and Dahl (1992) by also using a

simultaneous equations model to capture the relationship between changes in bank capital,

portfolio risk and risk-based capital standards. Their empirical work suggests that the new risk-

based capital standards brought about increases in both bank capital and risk, even for those

institutions that were not capital-constrained. It is noteworthy to mention the contributions of

Rime (2001). Though he used a simultaneous equation model in testing the reaction of Swiss

banks to increases in capital, his work does not document any corresponding increases in the risk

appetite of banks.

More recently, Jeitschko and Jeung (2004) proposed a united approach to investigate the

relationship between bank capitalization and risk- taking behavior in a model which incorporates

the incentives of the deposit insurer, the shareholder and the manager. They note that risk taking

will either decrease or increase with capitalization depending on the relative forces of these

agents. In a later study, Jeitschko And Jeung (2007) study banks within the Korean banking

system between 2002 and 2004. They found the relationship between capital and risk taking to

be both positive and negative. Specifically for commercial banks, they found a a negative

relationship between capital and risk while for mutual savings banks, the relationship was

positive.

67

Heid et al. (2004) investigates the relation between bank capital and risk levels by looking at a

sample of German savings banks from 1993 to 2000. The main finding of the authors is that

banks with low capital buffers usually attempt to rebuild an appropriate capital buffer by

decreasing risk and increasing capital simultaneously while banks with high capital buffers

attempt to preserve their capital buffer by increasing risk when capital increases. Heid et al

(2004) find a positive relationship between capital and risk taking.

In summary, not all studies find answers suggestive of either a positive or negative relationship.

For instance, Wei-Tung et al (2009) empirically investigated the effects of bank capital on bank

risk-taking. Specifically these authors set to establish the degree /extent of influence that bank

Capital had on risk using a dataset of 54 American commercial banks during the period of 1997

to 2002. Using Quintile regression, results show that banks behaved differently to changes in

capital regulation. Some of the banks assumed more risk with increased regulation while others

became more risk averse. It is their (Wei-Tung et al’s) view that setting the same capital

regulation for all banks is not beneficial in the long run as banks react differently and so capital

standards should be higher for banks identified as high-risk banks than for those seen as low and

middle-risk banks as stringent capital regulation “where one size fits all” would ultimately lead

to more instability in the financial system.

Similarly, Bouri and Benhmida (2006) whose model derives from that of Shrieves and Dahl

(1992) used simultaneous equations systems and studied monthly data from January 1992 and

August 2005 (212 monthly observations) and directly observed or estimated on the basis of

aggregated financial statements in Tunisia. They find that the relationship between capital and

risk taking is not fixed and that risk taking may in fact be aggravated by other factors and so it

would be difficult to categorically state whether the relationship is positive or in fact negative. In

their view, a positive correlation between portfolio risk and capital may occur when leverage and

portfolio risk are substitutes while a negative correlation may result from the miss-pricing of

deposit insurance. This finding suggests an indifferent reaction to capital regulation as banks in

Tunisia as banks do not adjust their risk exposure according to capital level but rather according

to the quality of their assets.

68

The analysis of Wei-Tung et al (2009) as well as that of Bouri and BenHmida(2006) thus

suggests that the monetary authorities need to consider the heterogeneous response of banks in

determining appropriate capital reforms.

2.6.3 LIQUIDITY AND RISK TAKING

Berger et al (2010) studied the German banking system in an attempt to establish the impact

bank liquidity has on bank risk taking. Their finding suggests that banks with insufficient

liquidity are more likely to take excessive risks in a “gamble for resurrection”. Their finding thus

suggests a negative relationship between the two variables.

Apart from the work of Berger et al, almost every other work finds a positive relationship

between liquidity nad risk taking. Altunbas et al (2009), using a sample was drawn from 16

developed countries across Europe and the United States between 1999Q1 to 2008Q4 sets out to

study the link between interest rate and risk taking behavior. Their study established a positive

relationship between liquidity and risk taking.

Vasquez and Federico (2012) studied the effect of liquidity on risk taking behaviour using a data

drawn from 11,000 observations across Europe and the US between 2001 and 2009 find a

positive relationship between risk taking and liquidity. They use a probit model and observe that

that bank risk-taking in the run-up to the crisis was associated with increased financial

vulnerability which suggests that bank decisions regarding liquidity and capital buffers did not

truly reflect the underlying risks.

Linking abundant liquidity to increased risk taking by banks, Alper et al (2012) used a panel

dataset made up of quarterly averages of monthly balance sheets of Turkish banks for the period

2002Q4 to 2011Q1 and find that bank liquidity is positively related to risk taking. Their findings

prove that the more liquid a bank is, the more it lends. They go on to suggest that any monetary

policy that affects liquidity ultimately direct the availability of credit in an economy. What can

be inferred from their findings is that excessive risk taking attributable to liquidity can in fact be

manipulated via monetary policy. They find that the increased asset liquidity of banks tends to

make banks more stable. However, the stability actually gets eroded because the improved

69

possibilities for liquidating assets in a crisis make a crisis less costly for the bank. The bank

therefore takes on an amount of risk that more than offsets the initial positive impact on stability.

Bearing in mind that risk taking usually presents as increased bank lending, Brooks (2007)

provides evidence proving that liquidity is a significant determinant in the lending behaviour of

banks in Turkey. This empirical work reports that in periods of tighter monetary policy which

reflects as a higher liquidity ratio, lending behaviour responds by tightening, the opposite

reaction is observed in periods of loose monetary policy. A clear implication is that reserve

requirement (legal reserve requirement) stipulated by central banks can influence individual bank

lending significantly.

Eid (2011) studied the risk behavior of a number of French banks between 1998 and 2008 and

using a dynamic panel model provides evidence that suggests that liquid banks are more prone to

risk taking. Eid (2011) finds that in all the six regressions run on data from French banks that

more liquid banks have a higher transmission mechanism for risk and are as such considered to

be more risky. Eid stresses that his findings do not suggest that illiquid banks are less risky than

liquid bank, but rather that all things being equal and assuming that there’s no change in bank

behaviour, then liquidity will continue to be positively related to bank risk taking. Eid’s work

therefore finds that liquid banks are more likely to amplify this channel, thus raising a question

on whether the new Basel III liquidity requirements will make banks more inclined to risk taking

in periods where for some reason, there exists abundant liquidity.

Jiménez et al (2009), found the same effect for Spanish banks and surmises that when banks are

faced with higher liquidity positions they become more vulnerable to risk-taking.

Gersl et al (2012) after studying data on individual banks within the Czech banking system,

suggests that more liquid banks tend to grant loans with lower hazard rates. The negative

association between bank risk appetite and liquidity shows that banks accumulating liquid assets

tend to be more prudent and so grant less hazardous loans. Summarily Gersl et al insist that for

Czech banks “larger and more liquid banks extend fewer loans to firms with a recent bad credit

history at times of monetary easing. In the same periods, banks with a worse relative credit risk

70

track record tend to finance fewer companies with a riskier past. Interestingly, we find that less

leveraged banks are less likely to incur credit risk.”

In an empirical work, Gatev and Strahan (2006) proffer that excessive liquidity is the fallout of

general instability in an economy. From their study, the cause of excess liquidity can be traced to

the desire to protect ones asset in an economic downturn where rather than invest in a business

because of the relative insecurities people believe it is safer to leave money in a bank. The

resulting scenario is that because of this flight to quality, banks suddenly find themselves awash

with liquidity and this ignites or rather aggravates the risk appetite of banks (Acharya and Naqvi

(2010)).

2.7 A PRIORI EXPECTATIONS OF STUDY VARIABLES

The a priori expectations of the research variables are as follows;

2.7.1 INTEREST RATE

The interest rate charged by a bank reflects the price a bank is prepared to lend money at. It

represents the cost of capital to the bank customer and is one of the determinants of bank

lending. Just like with the demand for a normal commodity which changes as price changes, the

amount of loans bank customers seek is a function of the prevailing interest rate in the market.

Customers are more likely to borrow when rates are low and banks being keen to make more

profit will lend more. Naturally, in the econometric analysis we expect a negative relationship.

This can be attributed to weaker incentives to screen borrowers when interest rates that

determine banks’ financing costs are low (Dell’Ariccia and Marquez, 2006). The danger is that

banks may not screen potential customers adequately and this will inadvertently lead to a higher

default rate.

The a priori expectation is that when interest rates are lower, bank lending is higher. It then

follows that interest rates will have to be less than zero suggestive of a negative relationship

between interest rate and bank lending.

71

2.7.2 CAPITAL

Bank capital refers to the funds made available for banking business by the owners of a bank. Its

relationship with bank lending is expected to be positive, such that the higher a bank’s capital,

the more loans it can give out. It then follows that the relationship between capital and bank

lending is positive.

Consequently the a priori expectation for capital is positive and so is expected to be greater than

one. A negative a priori expectation suggests that as banks’ capital increases, bank lending

decreases. This is unrealistic as bank lending will no doubt be reflective of the amount available

to lend at the bank and so higher bank capital should translate to increased bank lending.

2.7.3 LIQUIDITY

Liquidity in banking relates to the volume of assets a bank has, from which it is ready to lend to

customers at any given point in time. When bank liquidity is high, the a priori expectation is that

bank lending will also rise reflecting a positive relationship.

The principle is that when a bank has more funds to lend, it seeks to maximise its profits by

lending more rather than keeping cash balances greater than those set by the regulators and so it

follows that the higher the volume of liquidity available to a bank, the higher its capacity to lend.

The a priori expectation is that liquidity will be greater than one.

2.8 SUMMARY OF LITERATURE REVIEW

There have been several attempts to study bank behavior in Nigeria. Perhaps, the most notable

attempt at analyzing the risk behavior of Nigerian banks is that of Sobodu (1998) who studied

risk-taking and distress in Nigerian banks. Using various measures of risks, Sobodu links bank

financial distress in Nigeria to risk behavior of banks.

Although there have been various contributions (Ezema(2008), Sobodu(1998)),much of which

are commendable, having immensely enriched our understanding of bank financial condition in

Nigeria and their behavior. These studies however have not extended to attempt establishing and

explaining the role of three factors namely interest rate structure, capitalization and liquidity and

72

their resulting relationship to the risk taking behavior of bank risk behavior of banks in emerging

market economies, of which Nigeria is one. This is what this present study attempts to achieve.

Indeed there is a need to determine (through empirical means) how interest rates impact on the

risk taking by banks. Furthermore, there is need to assess if recapitalization (which the

consolidation required) was in any way a remote or direct cause of increased risk taking by banks

as theorists all have different views on the link between recapitalization and increased risk

appetite. Finally, there is without question a need to determine the impact of liquidity on the risk

taking behavior of banks. A large number of theorists discussed earlier opine that the excess

liquidity in the aftermath of the consolidation, laid the ground work for the crises that came later

as it encouraged banks to lend outrageously even without proper documentation and which may

also have caused the high number of non performing loans and other toxic assets within the

system.

73

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CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Research Design

This research was designed to carry out an empirical analysis of the risk taking behavior of banks

in Nigeria. This study is an ex post facto research (after the fact research). Ex post facto research

design studies events that have already taken place (Onwumere, 2005). Asika (2005)

underscored the importance of ex post facto research by pointing out that such research provides

a systematic and empirical solution to research problems, by using data which is already in

existence. Again, though the data is not subject to control or manipulation, since it already exists,

yet the researcher can contrive or create a situation that will generate the requisite data for

analysis. Perhaps more importantly the outcome of the analysis can provide useful insight about

future outcomes.

In this study an attempt is made to account for the relationship that exists between the risk taking

behaviour of banks and three bank specific characteristics, namely liquidity, capitalization, and

interest rates. This research seeks to establish if a link exists between the propensity to take on

excessive risk on the one hand and interest rate, capitalization and liquidity levels on the other

hand. Accordingly, the relationship between risk taking behaviour and these identified variables

was assessed using multivariate regressions. Risk Assets to Total Assets was used as the

dependent variable while the independent variables were the bank-specific attributes such as

capitalization, liquidity and interest rates. The study covered a thirteen year period, from 1997 to

2009.

3.2 Nature and Sources of Data

Following the methods adopted by Jimenez et al(2007), Jeitscko and Jeung (2007), Maddaloni

and Peydro (2010), Ioannidou et al (2010), Eid (2011) on the impact of bank capital, interest rate

and liquidity on the risk taking behavior of banks across the world, this study used secondary

data. Specifically, the secondary data were handpicked from the annual reports and statement of

accounts of the banks in the selected sample.

Data gotten from these sources are believed to be reliable as the Companies and Allied Act of

1990 mandates all banks quoted on the stock exchange to avoid misleading potential and old

investors and are specifically required to disclose truthfully within specified intervals the

83

financial position of the banks. Consequently it is our view that the data used for this study is

reliable.

3.3 Population and Sample size

Asika (2005) defines a population as a census of all elements of interest. In his opinion, a

population comprises of “all conceivable elements, subjects or observations relating to a

particular phenomenon of interest”. Consequently it refers to totality of observations with known

and specific characteristics.

The population of the study is the twenty five banks that emerged from the eighty nine banks that

existed pre consolidation, (See Appendix A). However, it is important to note that some of the

banks had missing observations and bearing in mind the possibility that bias may be introduced

because of this, there was a need to be careful and so based on this need to reduce bias, banks

with more than three missing observations were not included in the sample. Finally out of the 25

banks that emerged post consolidation, only fifteen banks constituting 60% of the population

emerged as sample.

A sample needs to be truly representative of the population from which it is drawn. According to

Eboh (2009), Sampling is the selection of a number of study units from a predefined study

population or universe and so a sample is intended to be a representative or microcosm of the

population of study.

The twenty five banks that emerged post consolidation served as the population for this work. In

selecting the sample, a key consideration was the availability of data and so the main factor in

choosing the sample was based on this. Several banks failed to meet these requirements and were

excluded from the study. Data were sourced for the thirteen year period of the study (1997-

2009), from various secondary sources including annual reports and accounts of the sampled

banks, Nigerian Stock Exchange Fact book and various publications of the Central Bank of

Nigeria. A non-probability sampling method was adopted in the sample selection which required

that only banks with complete data sources be included in the study. Accordingly the study was

restricted to fifteen banks for which there is sufficient data (Appendix B). Ten banks failed to

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meet this requirement and were not used, thus the sample excludes banks whose records for the

thirteen year study period were not readily available.

The researcher is of the opinion that the sample is truly representative since the sample

constitutes sixty per cent of the population. There is no reason therefore to believe that sample

selection biases affected the results.

3.4 Description of Research Variables

The following measurements were used for the dependent and independent variables.

3.4.1 Dependent Variable (Risk)

Analyzing the risk taking behavior of banks requires that all activities that predispose a bank

increasing its risk appetite should be studied. Ordinarily, these will involve the study of every

aspect of a bank. However because this study has chosen to focus on credit risk, it is wise to

specifically introduce a set of measures that help in appraising a bank’s exposure to counterparty

default which is what credit risk is all about in the first place. The allocation of a bank’s assets

among risk categories is the major determinant of a bank’s risk (Heid et al (2004) and so it

follows that in assessing the overall riskiness of a bank, it is the bank’s loans and advances that

provide such an insight.

There are several measurements advanced in literature (Altunbas et al (2010), Salas and Saurina

(2003), Godlewski (2004)) which are used in assessing the risk taking behaviour of banks.

Broadly speaking, the measures are classified as ex post or ex ante. The measures are regarded as

ex ante if risk taking has not occurred. They are mostly measures that guide a bank to know

when a limit is being approached. An ex post measure is used in the aftermath, after a bank’s

assets have been exposed. Ex post measures are used to determine the total value of a bank’s

assets that may have been compromised by a risk exposure. Of the several measures of risk

taking that are found in the literature, the most commonly used are the ratio of non- performing

loans to total loans, the ratio of risk assets to total assets, ratio of loan loss provisions to total

loans, the ratio of loans to deposits, even the ratio of equity capital to total assets and the ratio of

risk weighted assets to total assets. Risk assets to total assets ratio, ratio of loan loss provisions to

total loans as well as risk weighted assets to total assets are considered to be ex ante indicators of

85

risk while non- performing loans to total loans and loan to total deposit ratio are ex post

indicators of bank risk taking.

Jeitschko and Jeung (2007) use the ratio of risk-weighted assets to total assets (the RWATA

ratio) as the dependent variable. Their argument for preferring this measure of risk taking stems

from their view that the risk weighted asset ratio is an ex ante measure of risk and thus dominates

the non performing loan (NPL) ratio which they view as an ex post measure of risk. Empirical

studies including that of Godlewski (2004), Jimenez, Lopez and Saurina (2007) as well as

Gonzales (2005) use non performing loans to total loans ratio while Salas and Saurina (2003) use

the proportion of loan losses to total losses.

Delis and Kouretas (2011) as well as Chmielewski (2005) believe bank risk should be proxied by

a bank’s asset structure and so they both study the relation of risk assets (i.e. loans) to total

assets. For this study, following the previous studies mentioned above, three measures of risk

taking are used as proxy for bank risk taking namely risk weighted assets to total assets, risk

assets to total assets and finally loan to deposit ratio.

Bank Risk Taking= Risk Weighted Assets/Total Assets…………………………………..3.1

Bank Risk Taking= Risk Assets/Total Assets………………………… …………………..3.2

Bank Risk Taking=Loans/Total Deposits ……………………………………………….…3.3

3.4.2 Explanatory Variable

The independent variables in this study are capitalization, liquidity and interest rates.

Interest Rate

In this work, we also follow methodology close to that of Altunbas et al. (2009), Eid (2011) and

assess the impact of interest rates on Nigerian banks’ risk during the period 1997-2009. This

study brings an element of novelty. First, our computation of risk-taking behavior is based on

previous studies. Interest rates are derived from the prime lending rate as decided by the Central

Bank and are usually reflective of the state of the economy. In a recession, interest rates are

lowered while in an inflationary situation monetary authorities are keen to raise interest rates to

discourage borrowing. Some literature (Demarguc-Kunt et al, 2003) suggests that the interest

rate spread is related to asset risk and capitalization.

86

Interest rate = Prime lending rate …………………………………………………….3.4

Bank Capitalization

Jeitschko and Jeung (2007) define Capital as the total shareholder's equity reported on the

balance sheet at the end of period. The ultimate objective of the implementation of a capital

requirement on banks is to enhance the stability of the banking system. To measure capitalization

we follow the empirical work of Jeitschko and Jeung (2007) and use the Capital to asset ratio

CAPASS as measure of bank capitalization. This ratio is a superior measure for bank

capitalization relative to other measures of bank capitalization.

Bank Capitalization Ratio = Capital/ Total Asset ……………………………………..3.5

Liquidity

Liquidity ratios measure the capacity of banks to meet up with their liabilities as they mature.

Brunnermeier (2008) differentiates funding liquidity from market liquidity noting that the former

refers to the ease with which liquidity can be obtained by potential investors while market

liquidity is the end result of funding liquidity. When funding liquidity is abundant, then the

market liquidity is said to be excessive

There are essentially two measures that can be used to determine liquidity. One is to determine

the ratio of liquid assets to total assets. Another way is to use the current ratio which is given as

the liquid assets divided by current liabilities. The latter is preferred and so for this study Bank

liquidity is determined by the current ratio.

Bank liquidity =Current Assets /Current Liabilities…………………………….......3.6

3.4.3 Control Variables

Without question, bank risk is driven by the prevailing conditions in a country. These conditions

could range from regulatory, macroeconomic and/or structural conditions (Laeven and Levine,

2009). Failing to control for the regulatory conditions will most likely lead to a serious omitted

variable bias. In selecting control variables for this study, we defer to banking and economic

theory to pinpoint multifarious factors (causative factors) that favor or impinge a policy

reflecting expansionary risk assets profile.

87

While studying the role of capital structure on bank liquidity, Uremadu (2012) observes the link

between liquidity, capital and profitability. Following this line of thought as well as that of Barth

et al (2008), we decide to add the capital adequacy ratio CAR and return on total assets ROTA as

control variables.

3.4.3.1 Capital Adequacy Ratio (CAR)

The decision to use CAR is influenced largely by banking and economic theory. The capital

adequacy ratio, also called capital to Risk weighted assets ratio is used to assess the degree of a

bank’s risk relative to its capital. The Basel Accord promotes this measurement as it is seen as an

important variable in the promotion of banking stability around the world. Two types of capital

are measured: tier one capital, which can absorb losses without a bank being required to cease

trading, and tier two capital, which can absorb losses in the event of a winding-up and so

provides a lesser degree of protection to depositors.

Capital adequacy ratio is defined as

CAR= Tier 1 capital + Tier 2 capital/ Risk weighted Assets……………………3.7

Where

Tier 1 Capital (Core capital) is calculated as follows:

+ Paid-up share capital and common stock

+ Disclosed reserves (including retained earnings)

Less: Goodwill

And

Tier 2 Capital (Supplementary capital) is calculated as follows:

+ General provisions/general loan-loss reserves

+ Asset revaluation reserves

+ Hybrid (debt/equity) capital instruments

+ Subordinated debt

+ Undisclosed reserves

Less: Investments in unconsolidated financial subsidiaries

Less: Investments in the capital of other financial institutions

Total Capital = Tier 1 Capital + Tier 2 Capital

Source: Basel Committee on Banking Supervision 1988.

88

Available at: http://www.bis.org/publ/bcbs04a.pdf.

3.4.3.2 Return on Total Assets (ROTA)

Jeitschko and Jeung (2007) suggest that ROTA is a key variable capable of affecting the value of

bank equity. They go further to note that the decision of how much risk assets to acquire is

influenced by ROTA and from their study on the Korean banking industry find that profitability

as evidenced by ROTA affects the acquisition of risk assets noting that large banks (those with

higher ROTA) tend to have more diversified portfolios, and thus tend to have lower levels of risk

than small banks (those with lower ROTA). That said, Jeitschko and Jeung (2007) believe that

larger banks are still more likely to engage in risky activities, by making more commercial and

industrial (C&I) loans.

Noting the relationship between bank profitability and one of the research variables,

Uremadu(2012) opines that profitability has an inverse relationship with liquidity and any

decision that influences liquidity would ultimately impact on bank profitability.

Bank Profitability= Profit after Tax/ Total Assets…………………………….3.8

Table 3.1: Summary of Operational Definitions of Research Variables

Name of Variable Denotations Operational

Definition

Bank Risk taking RWATA Risk Weighted

Assets to Total

Assets, RWA/TA

Bank Risk taking RATA Risk Assets to Total

Assets, RA/TA

Bank Risk taking LOANDEP Loan to Deposit

Ratio, Total Loans/

Total deposit

Interest Rate INTEREST RATE Weighted Average

Lending Rate

Bank Capital CAPASS Shareholders

Fund/Total Assets

Liquidity LIQ Current Assets/

Current Liabilities

Capital Adequacy

Ratio

CAR Tier 1+Tier 2

Capital/Risk

weighted Assets

Return on Total

Assets

ROTA Profit after tax/Total

Assets

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3.5 Technique for Analysis

This study set out to analyze the risk taking behavior of banks in Nigeria. The data attributes of

this study qualify it as a panel study or cross sectional times series study. In particular the time

element (the thirteen year period) and the cross sectional element (fifteen banks) makes a strong

case for the use of panel study techniques. Panel studies are preferred to cross section or time-

series because they allow for differences in behaviour across individual and /or time periods,

Green (2003). However because panel data have both cross-sectional and time series dimensions,

the application of regression models to fit econometric models are more complex than those for

simple cross-sectional data sets.

Descriptive statistics and multiple regression analytical models will be the analytical tools used

for this study. In estimating the relationship between bank risk taking and the identified

explanatory variables, multiple regression models were used while descriptive statistics will be

used to present bank risk taking in favor of the explanatory and control variables namely interest

rate (proxied by operating maximum lending rate), bank capital (proxied by capital to risk

weighted assets ratio CAPASS) ,Liquidity (proxied by current assets to total assets ratio) as well

as the two control variables (ROTA and CAR). This study applied the pooled ordinary least

squares method.

There are three models in all, with each testing for the relationship between risk taking on one

hand and interest rate, bank capital and liquidity on the other hand. In each of these three models,

we control for bank profitability (ROTA) and capital Adequacy ratio.

The base model below is the one from which the three models for this study are derived from:

Yit = βo + β1X1it + β2X2it + β3X3it+℮ it………………………………………………………………………………..3.9

Where Y is the dependent variable, β is the unknown but fixed parameters of the regression

coefficients, X represents the various explanatory and control variables and ℮ it is the error term.

3.6 Model Specification

Factors such as interest rate, liquidity and capitalization are suggested in the literature as playing

a role in the risk taking behavior exhibited by banks. Consequently, it becomes necessary to

90

specify in clear terms the functional relationship that exists between the dependent and

independent variables.

Model specification involves the determination of the dependent and explanatory variables

which will be included in the models, the theoretical expectations about the sign as well as the

size of the parameters of the function, Koutsoyiannis (2003).

Model specification for this study is related to previous research efforts in the area of study. A

considerable number of previous research efforts on risk taking behavior of banks (Morkoetter et

al (2012),Jeitscko and Jeung (2007), Maddaaloni and Peydro (2010), Delis and Kouretas (2009)

all used regression analysis to prove/disprove the presence of a relationship between the

variables under study and the risk taking behavior of banks. Therefore in this study, risk taking

behavior will be estimated by regressing loans/advances on the independent variables of

capitalization, liquidity, and interest rates. The model specification was based on the hypotheses

earlier outlined in chapter one. Morkoetter et al (2012) considers it important in building any

model on risk taking to include profitability and capital. These two variables can be analyzed in

the context of being control variables. This approach was also used by Wheelock and Wilson

(1995) as mentioned in Morkoetter et al (2012).

This work utilizes multiple linear regressions since it has been used by previous empirical works

on the subject in a bid to assess the relationships among the variables identified in this work.

Consequently, a linear relationship between risk taking behavior and the determinants of risk

taking behavior was estimated in the symbolic form as presented above in equation 3.9

There are in all three models, one for each of the three stated hypotheses. Consequently, a linear

relationship between risk taking behavior and the determinants of risk taking behavior was

estimated and equation 3.9 was modified to reflect the various hypotheses.

Hypotheses One: There is no positive and significant relationship between risk taking behaviour

and interest rates. Stated functionally, we have;

91

Risk Assets/Total Assets= ƒ (INT, CAR, ROTA)…………………………………..3.10

Hypotheses Two: The relationship between capitalization and risk taking behavior is negative

and not significant.Stated functionally, we have;

Risk Assets/Total Assets=ƒ (CAPASS, CAR, ROTA)……………………………..3.11

Hypotheses Three: The liquidity level does not significantly affect the risk taking behavior of

banks. Stated functionally, we have;

Risk Assets/Total Assets=ƒ (LIQ, CAR, ROTA)…………………………………..3.12

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CHAPTER FOUR

DATA PRESENTATION AND ANALYSIS

4.1 Introduction

In this chapter, relevant data for this study following previous studies is gathered, presented and

subsequently analyzed. Again, all three hypotheses formulated earlier on are tested and inference

drawn from there on how bank risk taking is shaped/influenced by Interest Rate, Capitalization

and Liquidity.

The fifteen banks studied in this work constitute sixty two and half percent (62.5%) of deposit

money banks in operation in Nigeria at the time of the study. Of the twenty five banks that

emerged from the consolidation exercise, the sample accounts for almost seventy per cent

(69.74%) of the total capital base which stood at nine hundred and twenty nine billion, eight

hundred million Naira (N929.8b). Furthermore the sample represents approximately eighty six

percent (85.59%) of Total Assets, eighty seven percent (86.79%) of Total Deposits, eighty eight

percent (87.83%) of Risk Assets and eighty two percent (82%) of Total Loans within the

Nigerian banking Industry in 2006.The sample for this study is not only adequate but is truly

representative for a study of this kind as it includes majority of the banks that would eventually

be labeled as under-capitalized in 2009. Of the eight banks (Union, Oceanic, Wema , Skye,

Intercontinental, Finbank, Afribank and Bank Phb) that failed the stress test of 2009, all but two

are part of this sample of fifteen banks; five of the banks were asked to recapitalize or have their

banking licenses revoked; three would eventually be nationalized following their inability/refusal

to recapitalize as directed. Another of the banks, Wema Bank would be advised to apply for a

regional license due to its inability to shore up its capital base to the level required to operate as a

national bank. Indeed there is no doubt that the sample is rich and that data generated from this

study can be generalized.

To properly present all the variables studied in this research, the dependent and independent

variables are presented separately.

95

4.2 DATA PRESENTATION

4.2.1 PRESENTATION OF DEPENDENT VARIABLES

TABLE 4.1 RISK WEIGHTED ASSETS

BANKS/YRS 1997RWA(Nm) 2003RWA(Nm) 2009RWA(Nm)

ACCESS BANK 9515165 6957000 5134005

AFRI BANK 16824673.5 42060

DIAMOND BANK 476723.5 18302794 18302794

ECO BANK 888089 13445138 239635

FIRST BANK 18762493.5 126705 1000548

FCMB 2748467 7007077.5 317715

FIN BANK

13427785.5 88686.5

FIDELITY

9605000 259043831

GTB 79485065 53831000 739557

INTERCONTINENTAL 3970895 68914337

OCEANIC BANK

21568292 766249646

UNION BANK 45179 146194 722980

UBA

47100.5 6980505

WEMA 5807983 27330000 74868.5

ZENITH BANK 4461123.5 28182745.5 984,088.5

AVERAGE 9,532,390.46 17928881.93 70658590.63

SOURCE: COMPUTED FROM ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-

2009)

The Risk Weighted Assets as shown in the table above allows for the presentation of the risk

weighted assets of the sampled banks in the period under study. The figures for most of the

banks appear consistent. What is worrisome though are the figures for Oceanic bank in the last

year of the study as the percentage increase in RWA is very high. Figure 4.1 below presents the

Risk weighted assets. For most of the banks, there is a marked increase in the RWA figures in

2009 the last year of the study period.

96

Figure 4.1 Risk Weighted Assets

TABLE 4.2 TOTAL ASSET

BANKS/YRS 1997TA (Nm) 2003 TA (Nm) 2009 TA (Nm)

ACCESS BANK NA 22,582,000 710,326,000

AFRI BANK 33,435,224 98,055,000 NA

DIAMOND BANK 13,273,445 59,295,392 650,891,836

ECO BANK 8,922,151 27,314,000 355,662,000

FIRST BANK 72,818,807 320,578,000 2,009,914,000

FCMB 6,519,268 15,164,119 515,602,000

FIN BANK NA 20,910,312 157,843,000

FIDELITY BANK NA 22,517,000 506,267,000

GTB 14,746,821 89,496,000 1,066,504,000

INTERCONTINENTAL 10,751,337 96,858,000 NA

OCEANIC BANK NA 64,978,000 869,319,176

UNION BANK 83,324,000 418,728,000 1,238,797

UBA NA 200,995,000 1,400,879,000

WEMA 13,441,691 61,323,000 129,609,000

ZENITH BANK 16,016,557 112,534,638 1,573,196,000

AVERAGE 18,216,620.07 108,755,230.7 663,150,120.6

SOURCE: COMPUTED FROM ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-

2009)

0

200000000

400000000

600000000

800000000

1E+09

1.2E+09

1997 RWA(Nm)

2003 RWA(Nm)

2009RWA(Nm)

97

The total assets presented above when looked at in conjunction with the Risk weighted assets

provide a clearer picture of the state of the assets of the banking Industry.

Guaranty Trust Bank shows a progressive drive and improvement as shown by the growth of its

total assets during the period under study. Diamond Bank as well shows the same phenomenal

growth in assets though not as high as that of GT Bank. The first generation banks are not left

out of the list of banks with high total assets as Wema, UBA and Union banks all show steady

improvements.

TABLE 4.3 RISK WEIGHTED ASSETS TO TOTAL ASSETS RATIO

BANKS/YRS 1997RWA/TA 2003RWA/TA 2009RWA/TA

ACCESS BANK 0.53 0.3 0.72

AFRI BANK 0.63 0.49

DIAMOND BANK 0.04 0.3 0.65

ECO BANK 0.09 0.49 0.67

FIRST BANK 0.26 0.39 0.49

FCMB 0.42 0.46 0.61

FIN BANK

0.55 0.56

FIDELITY

0.43 0.51

GTB 0.54 0.6 0.69

INTERCONTINENTAL 0.37 0.97

OCEANIC BANK

0.33 0.88

UNION BANK 0.54 0.35 0.59

UBA

0.23 0.498

WEMA 0.43 0.45 0.0005

ZENITH 0.27 0.25 0.62

AVERAGE 0.68 0.82 0.49

SOURCE: COMPUTED FROM ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

Loans are the prime risk assets of banks. Table 4.4 presents the risk assets of the sampled banks.

Average risk assets at the start of the study were N15, 276, 427.9 in 1997. This figure rose to

N40, 634,611.73 representing an increase of 165% from 1997. A jump of 1327% was recorded

between 2003 and 2009 as average risk assets rose to N580, 148, 705.30. Corresponding to

common knowledge about the state of banking, Union and Oceanic banks, two of the banks that

would eventually be asked to recapitalize posted extremely above average figures. Although

First bank and Zenith also posted high figures, they had the ability to balance it out with when

matched to their total assets.

98

TABLE 4.4 RISK ASSETS

BANKS/YRS 1997RA(Nm) 2003RA (Nm) 2009RA (Nm)

ACCESS BANK NA 13,720,000 521,759,000

AFRI BANK 20,676,398 57,345,000 NA

DIAMOND BANK 5,892,083 22,093,144 419,495,205

ECO BANK 3,289,964 19,950,000 250,193,000

FIRST BANK 21,380,728 197,364,000 1,022,778,000

FCMB 2,808,084 7,219,428 320,987,000

FIN BANK NA 7,912,562 127,301,000

FIDELITY NA 11,860,000 411,791,000

GTB 9,729,318 55,746,000 758,028,000

INTERCONTINENTAL 6,525,907 54,607,000 NA

OCEANIC BANK NA 47,725,000 463,060,964

UNION BANK 71,310,000 191,002,000 890,818,000

UBA NA 72,301,000 822,636,000

WEMA 6,546,789 26,875,000 67,819,000

ZENITH BANK 4,605,008 29,075,470 1,465,267

AVERAGE 10184285.27 54,319,706.93 405,208,762.4

SOURCE: ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

Table 4.5 below presents the Risk assets to total assets ratio. Average RATA was 0.49 in 1997

and remained consistent in 2003 when the average was 0.48. However in 2009, reflecting an

increasing risk appetite, average RATA rose to 0.67. When put into perspective, the sustained

increase in the ratio of risk assets to total assets reflects an increase in the risk appetite and is

therefore consistent with the results of the 2009 stress test which showed on average that bank

exposure to risk was relatively high. From the table above, GT Bank maintained an above

average figure while First Bank maintained a below average RATA through the years studied.

Oceanic, Union Bank and Intercontinental were consistent in posting higher than above average

Risk assets to Total assets. Average RATA in 1997 stood at 0.49 dipping slightly in 2003 to 0.48

and eventually peaking at 0.67 in 2009.

99

TABLE 4.5 RISK ASSETS TO TOTAL ASSETS (RATA)

BANKS/YRS 1997 RATA 2003RATA 2009 RATA

ACCESS BANK NA 0. 60 0.73

AFRI BANK 0.61 0.58 NA

DIAMOND BANK 0.44 0.37 0.64

ECO BANK 0.36 0.61 0.7

FIRST BANK 0.29 0.41 0.5

FCMB 0.43 0.47 0.62

FIN BANK NA 0.37 0.8

FIDELITY NA 0.52 0.81

GTB 0.65 0.63 0.71

INTERCONTINENTAL 0 .60 0.56 NA

OCEANIC BANK NA 0.73 0.53

UNION BANK 0.85 0.45 0.71

UBA NA 0.35 0.58

WEMA 0.48 0.43 0.52

ZENITH BANK 0.28 0.26 0.94

AVERAGE 0.29 0.45 0.59

SOURCE: COMPUTED FROM ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-

2009)

The graph on the next page is a graphical presentation of the ratio of risk assets to total assets for

the sampled banks from 1997 to 2009.

100

Figure 4.2 Risk Assets to Total Assets Ratio

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1997 RATA

2003 RATA

2009 RATA

101

TABLE 4.6 NON PERFORMING LOANS

BANKS/YRS 1997 (Nm) 2003 (Nm) 2009 (Nm)

ACCESS BANK NA 7,748,740 8,765,935

AFRI BANK 6,535,268 6,305,000 NA

DIAMOND BANK 433,608,000 1,917,712 23,378,125

ECO BANK NA 73,603,000 750,876,000

FIRST BANK 5,614,534 224,184,000 3,701,985

FCMB 132,928,000 136,325,000 243,992,000

FIN BANK NA 3,134,989 155,492,000

FIDELITY BANK NA NA 7,207,519

GTB 3,885,068 41,681,000 411,346,000

INTERCONTINETAL NA 4,179,000 NA

OCEANIC BANK NA NA NA

UNION BANK 8,915,000 18,262,000 92,724,000

UBA NA NA 39,637,000

WEMA NA 2,074,000 10,157,000

ZENITH BANK NA 545,800,000 46,413,000

AVERAGE 39,432,391.33 71,014,296.07 119,579,370.9

SOURCE: COMPUTED FROM ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-

2009)

Table 4.6 above presents the non performing loans for the sampled banks.

TABLE 4.7 TOTAL LOAN

BANKS/YRS 1997 (Nm) 2003 (Nm) 2009 (Nm)

ACCESS BANK NA 71,346,710 405,345,679

AFRI BANK 17,284,302 25,220,000 NA

DIAMOND BANK 3,890,008 15,932,057 314,107,542

ECO BANK 3,081,624 8,270,000 187,719,000

FIRST BANK 5,784,423 56,046,000 740,397,000

FCMB 2,712,812 5,833,979 271,103,000

FIN BANK NA 7,646,315 181,597,000

FIDELITY BANK NA 7,175,000 239,675,948

GTB 5,798,609 50,830,000 563,488,000

INTERCONTINETAL 372,806,000 32,146,000 NA

OCEANIC BANK NA 12,875,000 8,732,278,993

UNION BANK 22,530,000 70,959,000 421,473,000

UBA NA NA 611,847,000

WEMA 5,121,722 20,740,000 46,167,000

ZENITH BANK 4,317,239 27,290,021 669,261,000

AVERAGE 29,555,115.93 27,487,338.8 89,229,734.4

SOURCE: ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

102

Table 4.7 shows the total loans position of the sampled banks during the study period. All the

banks show a marked increase in their loans figures across the years. Worrisome though is the

extremely high figure posted by Oceanic bank in 2009.

TABLE 4.8 NON PERFORMING LOAN TO TOTAL LOAN

BANKS/YRS 1997 NPL/TL 2003 NPL/TL 2009 NPL/TL

ACCESS BANK NA 0. 10 0.02

AFRI BANK 0.37 0.25 NA

DIAMOND BANK 0.11 0.12 0.07

ECO BANK NA 0.89 0.04

FIRST BANK 0.97 0.4 0.05

FCMB 0.49 NA 0.09

FIN BANK NA 0.41 0.85

FIDELITY BANK NA 0.19 0.03

GTB 0.67 0.82 0.73

INTERCONTINETAL NA 0.13 NA

OCEANIC BANK NA 0.06 0.71

UNION BANK 0.39 0.25 0.22

UBA 0.37 NA 0.06

WEMA 0.09 0.1 0.22

ZENITH BANK NA 0.02 0.07

AVERAGE 0.23 0.24 0.21

SOURCE: COMPUTED FROM ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-

2009)

The average non- performing loans to total loans ratio was 0.43 in 1997. It dropped to 0.28 in

2003 and subsequently dropped further to 0.24 by 2009. Diamond, Union and Wema Bank

consistently have below average figures during the study period while GT Bank has higher than

average non-performing ratios. In figure 4.2 below, a bar chart illustrates the position of non-

performing loans to total loans.

103

Figure 4.3 Non Performing Loans to Total Loans Ratio

0

0.2

0.4

0.6

0.8

1

1.2

1997 NPL/TL

2003 NPL/TL

2009 NPL/TL

104

TABLE 4.9 TOTAL DEPOSITS

BANKS/YRS 1997DEP(Nm) 2003DEP(Nm) 2009DEP (Nm)

ACCESS BANK NA 10,666,000 460,280,000

AFRI BANK 37,019,232 72,493,000 NA

DIAMOND BANK 9,543,751 42,147,177 444,815,118

ECO BANK 5,700,407 19,979,000 260,978,000

FIRST BANK 55,497,623 191,088,000 1,364,866,000

FCMB 3,566,396 9,215,514 348,235,000

FIN BANK NA NA 196,429,000

FIDELITY BANK NA 16,888,000 356,137,293

GTB 9,231,401 50,830,000 698,063,000

INTERCONTINETAL 6,646,020 66,387,000 NA

OCEANIC BANK NA 49,366,000 545,915,574

UNION BANK 59,310,000 282,524,000 920,959,000

UBA NA 142,427,000 1,151,086,000

WEMA 9,321,207 43,762,000 108,825,000

ZENITH BANK 7,138,528 61,574,455 1,173,917,000

AVERAGE 13,531,637.67 70,623,143.07 535,367,065.7

SOURCE: ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

Table 4.9 presents the total deposit figures for the sampled banks. First Bank, UBA and Zenith

have the highest deposit rates over the years.

TABLE 4.10 TOTAL LOAN TO DEPOSIT RATIO

BANKS/YRS 1997LOANDEP 2003LOANDEP 2009LOANDEP

ACCESS BANK NA 0. 60 0. 90

AFRI BANK 0. 47 0. 34 NA

DIAMOND BANK 0. 40 0. 37 0. 70

ECO BANK 0. 54 0. 41 0. 70

FIRST BANK 0. 10 0. 29 0. 54

FCMB 0. 76 0. 63 0. 77

FIN BANK NA 0. 53 0. 92

FIDELITY BANK NA 0. 42 0. 67

GTB 0. 62 0. 60 0. 80

INTERCONTINETAL 0. 05 0. 48 NA

OCEANIC BANK NA 0. 26 0. 70

UNION BANK 0. 37 0. 25 0. 45

UBA NA 0. 08 0. 53

WEMA 0. 54 0.47 0. 42

ZENITH BANK 0. 60 0. 44 0. 57

AVERAGE 0.29 0.41 0.58

SOURCE: ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

105

The Loan deposit ratio is extremely important as it served as one of the measures of bank risk

taking behavior. Average loan deposit ratio was 0.445 in 1997, 0.41 in 2003 and 0.66 in 2009.

Figure 4.4 below provides a graphical representation of the loan deposit ratio across the study

period. Very clearly from the diagram there is a marked increase in the loan deposit ratio in

2009.

Figure 4.4 Loan Deposit Ratio

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1997L/D

2003L/D

2009L/D

106

4.2.2 PRESENTATION OF INDEPENDENT VARIABLES

TABLE 4.11 PROFIT AFTER TAX

BANKS/YRS 1997PAT(Nm) 2003PAT (Nm) 2009 PAT (Nm)

ACCESS BANK NA 557,000,000 21,034,000

AFRI BANK 269, 917 988,000 NA

DIAMOND BANK 422, 200 145 113,000 6,931,127

ECO BANK 309, 793 817,000 (4,588,000)

FIRST BANK 996, 866 10,323,000 12,569,000

FCMB 101, 131 51, 301,000 3, 995,000

FIN BANK NA 436, 217,000 (147, 206,000)

FIDELITY BANK NA 857,000 1,833,000

GTB 794, 035 3, 259,000 23,676,000

INTERCONTINETAL 717, 262 3, 409,000 NA

OCEANIC BANK NA 2,819,000 (90,652,690)

UNION BANK 1,209 8,341,000 (72,521,000)

UBA NA 2,989,000 12,889,000

WEMA 2,020,602 1,448,000 (20,455,000)

ZENITH BANK 860,606 4,424,186 18,365,000

AVERAGE 192,161.13 39,333,745.73 -6,061,304.2

SOURCE: ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

Table 4.11 above shows the profit after tax which is a fairly accurate measure of bank

profitability. For most of the banks the profit after tax is lower in 2003 than in 1997. By 2009,

several banks show a loss rather than profit after tax. Indeed it is not surprising that of the four

banks that posted losses after tax, three were eventually labeled as being undercapitalized

following the CBN stress test of 2009.

Table 4.12 below presents the total assets of the entire sample. Guaranty Trust Bank shows a

progressive drive and improvement as shown by the growth of its total assets during the period

under study. Diamond Bank as well shows the same phenomenal growth in assets though not as

high as that of GT Bank. The first generation banks are not left out of the list of banks with high

total assets as Wema, UBA and Union banks all show steady improvements.

107

TABLE 4.12 TOTAL ASSET

BANKS/YRS 1997TA (Nm) 2003 TA (Nm) 2009 TA (Nm)

ACCESS BANK NA 22,582,000 710,326,000

AFRI BANK 33,435,224 98,055,000 NA

DIAMOND BANK 13,273,445 59,295,392 650,891,836

ECO BANK 8,922,151 27,314,000 355,662,000

FIRST BANK 72,818,807 320,578,000 2,009,914,000

FCMB 6,519,268 15,164,119 515,602,000

FIN BANK NA 20,910,312 157,843,000

FIDELITY BANK NA 22,517,000 506,267,000

GTB 14,746,821 89,496,000 1,066,504,000

INTERCONTINENTAL 10,751,337 96,858,000 NA

OCEANIC BANK NA 64,978,000 869,319,176

UNION BANK 83,324,000 418,728,000 1,238,797

UBA NA 200,995,000 1,400,879,000

WEMA 13,441,691 61,323,000 129,609,000

ZENITH BANK 16,016,557 112,534,638 1,573,196,000

AVERAGE 18,216,620.07 108,755,230.7 663,150,120.6

SOURCE: ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

Figure 4.4 below is a diagrammatic presentation of table 4.12 above and depicts the Total assets

of the banks within the study period. Five banks (First Bank, Zenith, UBA, GT Bank and Access

bank) stand out in terms of their asset base. In comparison with their peers, the total asset of

these five banks is indeed outstanding.

108

Figure 4.5: Total Assets of Banks

0

500000000

1E+09

1.5E+09

2E+09

2.5E+09

1997TA (Nm)

2003 TA (Nm)

2009 TA (Nm)

109

TABLE 4.13 RETURN ON TOTAL ASSETS

BANKS/YRS 1997 PAT/TA 2003 PAT/TA 2009 PAT/TA

ACCESS BANK NA 2.46 2.96

AFRI BANK 0.8 1 .00 NA

DIAMOND BANK 3.18 2.44 1.06

ECO BANK 3.47 2.99 1.28

FIRST BANK 1.36 3.22 0.62

FCMB 1.55 0.33 0.77

FIN BANK NA 2.08 (93.26)

FIDELITY BANK NA 3. 80 0.36

GTB 5.38 3.64 2.21

INTERCONTINENTAL 6.67 3.51 NA

OCEANIC BANK NA 4.33 (10.42)

UNION BANK 1.45 1.99 (0.058)

UBA NA 1.48 0.92

WEMA 1 .50 2.36 (15.78)

ZENITH BANK 5.37 3.93 11.67

AVERAGE 1.94 2.31 -6.51

SOURCE: ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

The return on assets (ROTA), presented above in Table 4.13 is a measure of profitability and

serves the purpose of indicating whether there have been improvements. In 1997, the average

ROTA is 3.08%, it then dropped to 2.63% in 2003. It maintains a downward spiral and is in fact

negative by 2009. From the data the negative values for Fin bank, Wema and Union suggest they

are the reason for a negative average ROA in 2009.

The diagram in figure 4.5 below graphically portrays the Return on total assets.

110

Figure 4.6: Return on Total Assets

SOURCE: COMPUTED FROM ANNUAL REPORTS.

-100

-80

-60

-40

-20

0

20

1997PAT/TA

2003 PAT/TA

2009 PAT/TA

111

TABLE 4.14 SHAREHOLDER FUNDS

BANKS/YRS 1997SHF(Nm) 2003SHF (Nm) 2009 SHF (Nm)

ACCESS BANK NA 2,365,000 184,159,000

AFRI BANK 1,625,936 7,383,000 NA

DIAMOND BANK 1,134,260 5,206,636 116,544,920

ECO BANK 888,089,000 3,519,000 73,534,000

FIRST BANK 4,914,589 25,040,000 351,854,000

FCMB 871,321,000 2,558,586 129,056,000

FIN BANK NA 4,040,528 (120,386,000)

FIDELITY BANK NA 2,515,000 129,419,000

GTB 1,537,037 9,753,000 187,103,000

INTERCONTINENTAL 1,662,204 10,181,000 NA

OCEANIC BANK NA 7,073,000 125,597,875

UNION 3,155,000 39,732,000 58,826,000

UBA NA 13,767,000 187,719,000

WEMA 1,264,258 8,039,000 (39,922,000)

ZENITH BANK 1,544,122 12,651,577 328,383,000

AVERAGE 118416493.7 10254955.13 114125853

Table 4.14 presents the shareholders funds available to the banks during the study period. Zenith

bank and First bank have the most impressive records whereas Wema and Finbank post negative

entries.

Table 4.15 RETURN ON EQUITY

BANKS/YRS 1997 ROE 2003 ROE 2009 ROE

ACCESS BANK NA 23.55 11.42

AFRI BANK 16.6 13.38 NA

DIAMOND BANK 37.22 2.78 5.94

ECO BANK 34.88 23.21 6.23

FIRST BANK 24.83 41.22 3.27

FCMB 11.6 2 .00 3.09

FIN BANK NA 14.95 116.96

FIDELITY NA 34.07 1.4

GTB 51.66 33.41 12.65

INTERCONTINENTAL 43.15 33.48 NA

OCEANIC BANK NA 39.85 (72.17)

UNION BANK 38.32 20.99 (123.28)

UBA NA 21.71 6.86

WEMA 16.02 18.01 51.23

ZENITH BANK 55.73 34.96 5.59

AVERAGE 22 23.70 1.94

112

TABLE 4.16 CAPITAL ADEQUACY RATIO CAR

BANKS/YRS 1997 CAR 2003 CAR 2009 CAR

ACCESS BANK NA 33.99 3.58

AFRI BANK 8.28 17.55 NA

DIAMOND BANK 23.79 28.44 27.56

ECO BANK 20.41 24.38 30.68

FIRST BANK 21.39 19.76 35.16

FCMB 31. 70 36.51 40.62

FIN BANK NA 49.59 (135.74)

FIDELITY BANK NA 26.18 69.75

GTB 1.93 18.27 25.29

INTERCONTINETAL 41.85 21.53 NA

OCEANIC BANK NA 19.62 16.39

UNION BANK 6.98 27.17 8.13

UBA NA 29.22 26.89

WEMA 21.76 26.39 (53.32)

ZENITH BANK 34.61 44.89 33.36

AVERAGE 12.06 28.23 8.55

SOURCE: ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

The Table above shows the determined CAR for deposit money banks in Nigeria. On the average

banks exhibited adequate capitalization as indicated by CAR. Average CAR was 19.33 % in

1997. It rose to 28.23% in 2003. In both 1997 and 2003, the CAR remained above the 10% level

recommended by the new capital adequacy framework for internationally active banks, which

has since become the benchmark for assessing capital adequacy of banks in many countries. In

2009, the low CAR figure may be attributed to the negative values for Wema and Fin Bank.

In Figure 4.7, the Capital Adequacy ratio is presented graphically.

113

Figure 4.7 Capital Adequacy ratios

0

20

40

60

80

100

120

140

160

1997 CAR

2003CAR

2009CAR

114

TABLE 4.17 CAPITAL TO TOTAL ASSETS RATIO

BANKS/YRS 97 CAPASS 03 CAPASS 09 CAPASS

ACCESS BANK NA 10.47 25.92

AFRI BANK 4.86 7.52 NA

DIAMOND BANK 8.54 8.78 19. 90

ECO BANK 9.95 12.88 20.67

FIRST BANK 5.51 7.81 20.67

FCMB 13.36 16.87 25.03

FIN BANK NA 19.32 (76.26)

FIDELITY BANK NA 11.16 25.56

GTB 10.42 10.89 17.54

INTERCONTINETAL 15.46 10.51 NA

OCEANIC BANK NA 10.88 14.44

UNION BANK 3.78 9.48 4.74

UBA NA 6.84 13.4

WEMA 9.4 11.76 ( 30. 80 )

ZENITH BANK 9.64 11.24 20.87

AVERAGE 6.06 11.09 7.5

SOURCE: ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

Like the CAR in Table 4.17 above, the CAPASS is used to assess the level of capitalization in a

bank. Ideally the CAPASS should like the CAR also be greater or equal to 10%. The majority of

banks in 2009 have adequate levels of CAPASS with the exception of three banks which

coincidentally are among those advised to recapitalize. This ratio among other things seeks to

determine the capital position vis-à-vis the total assets of a bank with a view to ensuring that the

capital of a bank is indeed able to withstand challenges associated with banking. Although the

CAPASS when looked at in isolation may not give as clear a picture as when looked at alongside

other explanatory variables, it still serves the purpose of indicating the relative state of health of

a bank.

115

Figure 4.8 Capital to Assets Ratio

-100

-80

-60

-40

-20

0

20

40

97 CAPASS

03 CAPASS

09 CAPASS

116

TABLE 4.18 CURRENT ASSETS

BANKS/YRS 1997CA(Nm) 2003 CA (Nm) 2009CA(Nm)

ACCESS BANK NA 20,517,000 584,004,000

AFRI BANK 27,750,197 91,839,000 NA

DIAMOND BANK 11,989,584 53,216,984 592,931,289

ECO BANK 82,843,493 24,366,000 281,849,000

FIRST BANK 68,251,088 286,543,000 1,714,433,000

FCMB 5,948,729 13,347,808 458,297,000

FIN BANK NA 18,586,168 100,930,000

FIDELITY BANK NA 21,263,000 455,448,000

GTB 13,952,189 75,920,000 882,238,000

INTERCONTINETAL 4,776,392 85,699,000 NA

OCEANIC BANK NA 67,642,000 481,373,271

UNION BANK 73,360,000 375,624,000 1,019,022

UBA NA 169,181,000 1,088,211

WEMA 11,123,643 56,883,000 95,932,000

ZENITH BANK 15,028,040 105,151,940 1,401,050,000

AVERAGE 21,001,557 97,718,660 470,039,519.5

SOURCE: ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

Table 4.18 presents the current assets of sampled banks. Diamond bank posted the most

impressive figures while Finbank had the least impressive.

TABLE 4.19 CURRENT LIABILITIES

BANKS/YRS 1997CL (Nm) 2003CL (Nm) 2009 CL (Nm)

ACCESS BANK NA 19,814,000 504,437,000

AFRI BANK 25,198,837 89,474,000 NA

DIAMOND BANK 11,948,166 46,573,215 526,528,976

ECO BANK 5,700,407 19,979,000 260,978,000

FIRST BANK 55,497,623 193,955,000 1,519,824,000

FCMB 3,566,396 12,382,244 370,681,000

FIN BANK NA 14,354,853 201,736,000

FIDELITY BANK NA 19700,000 373,385,000

GTB 12,582,232 52,883,000 788,610,000

INTERCONTINETAL 6,646,020 66,387,000 NA

OCEANIC BANK NA 49,366,000 823,059,517

UNION BANK 59,310,000 365,923,000 1,149,049,000

UBA NA 142,427,000 1,161,166

WEMA 11,491,799 51,349,000 125,904,000

ZENITH BANK 14,472,077 98,387,393 1,274,002,000

AVERAGE 13760903.8 82,863,647 494327910.6

SOURCE: ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

117

Table 4.19 shows the current liabilities of the sampled banks. Current liability is an important

indicator in measuring the liquidity ratio. Of the sampled banks, oceanic bank has the highest

current liability.

TABLE 4.20 LIQUIDITY RATIO

BANKS/YRS 1997 CA/CL 2003 CA/CL 2009 CA/CL

ACCESS BANK NA 1. 03 1. 15

AFRI BANK 1. 10 1. 02 NA

DIAMOND BANK 1. 00 1. 14 1. 12

ECO BANK 1. 45 1. 21 1. 07

FIRST BANK 1. 22 1. 47 1. 12

FCMB 1. 66 1. 07 1. 23

FIN BANK NA 1. 29 0. 5

FIDELITY BANK NA 1. 07 1. 21

GTB 1. 10 1. 43 1. 11

INTERCONTINETAL 0. 71 1. 20 NA

OCEANIC BANK NA 1. 37 0. 58

UNION BANK 1. 23 1. 02 0. 88

UBA NA 1. 18 0. 93

WEMA 0. 96 0. 09 0. 76

ZENITH BANK 1. 03 1. 06 1. 09

AVERAGE 0.764 1.11 0.85

SOURCE: ANNUAL REPORTS OF DEPOSIT MONEY BANKS (1997-2009)

The liquidity ratio serves as an indicator of the ability of a bank to meet with its current

obligations when they arise. In 1997, average Liquidity ratio stood at 0.98 and rose to 1.11 in

2003. It however fell to 0.98 again in 2009. Figure 4.9 below presents the Liquidity ratio

graphically.

118

Figure 4.9 Liquidity Ratio

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

1997 CA/CL

2003 CA/CL

2009 CA/CL

119

Table 4.21 AVERAGE LENDING RATE

BANKS/INTEREST

RATE

1997 INTEREST 2003 INTEREST 2009 INTEREST

ALL BANKS 13.54 20.48 18.36

SOURCE: CBN STATISTICAL BULLETIN 2010

For this study, interest rate was proxied by average lending rates as disclosed by the CBN in

various annual statistical bulletins. The lending rate was 13.54 in 1997 at the start of this study. It

rose to 20.48 in 2003 and by the end of the study period in 2009 was at 18.36.

Figure 4.10 Interest Rate

0

5

10

15

20

25

1997 INTEREST 2003 INTEREST 2009 INTEREST

ALL BANKS

ALL BANKS

120

4.3 SUMMARY OF DESCRIPTIVE STATISTICS

Table 4.22 Summary of Descriptive Variables

Mean Std. Deviation N

RATA .6182 .21386 182

CAR .1877 .17266 182

CAPASS .1071 .10359 182

LIQ 1.1064 .23496 182

INTEREST 11.0586 2.33616 182

ROTA 4.3118 6.42891 182

From the table above, the variation in data for some ratios is insignificant. This suggests that for

most of these variables, the mean is truly representative of the sample. Other ratios with

relatively small and insignificant variations in data are the liquidity, CAR and capital to asset

CAPASS ratios. The Interest rate and ROTA all show a wide variation in data.

4.4 RESULTS OF THE CORRELATION ANALYSIS

The correlation matrix presents the relationship that exists between all the variables that have

been studied in this work. We attempt to distinguish the relationship between dependent and

independent variables.

TABLE 4.23 CORRELATION MATRIX FOR MODEL 1

(THE RELATIONSHIP BETWEEN RISK TAKING AND INTEREST RATE)

RATA INTEREST ROTA CAR

Pearson Correlation

RATA 1

INTEREST -0.222 1

ROTA -0.106 0.11 1

CAR -0.211 0.071 0.165 1

Sig. (1-tailed)

RATA .

INTEREST 0.001 .

ROTA 0.075 0.069 .

CAR 0.002 0.168 0.012 .

N RATA 182 182 182 182

INTEREST 182 182 182 182

ROTA 182 182 182 182

CAR 182 182 182 182

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4.4.1 CORRELATION BETWEEN DEPENDENT AND INDEPENDENT VARIABLES

For Hypothesis One the dependent variable is risk taking behavior which is proxied by the ratio

of risk assets to total assets while the independent variable is interest rate proxied by the average

lending rates (CBN Statistical bulletin, 2010). The relationship between RATA and all three

variables is negative. This depicts an inverse relationship between risk taking and all three

variables. Only the relationship between RATA and ROTA is insignificant.

4.4.2. CORRELATION BETWEEN INDEPENDENT VARIABLES

In studying the relationship between risk taking and interest rate, the relationship between all

three independent variables is positive meaning that as interest rate increases, CAR and ROTA

also increase. All three variables react similarly to bank risk taking. It is noteworthy to mention

that no two variables had a significant relationship.

TABLE 4.24 CORRELATION MATRIX FOR MODEL 2

(THE RELATIONSHIP BETWEEN RISK TAKING AND CAPITAL)

Correlations

RATA CAR ROTA CAPASS

Pearson

Correlation

RATA 1.000

CAR -.213 1.000

ROTA -.103 .164 1.000

CAPASS .101 .804 .063 1.000

Sig. (1-

tailed)

RATA

CAR .002

ROTA .084 .013

CAPASS .088 .000 .199

N RATA 182 182 182 182

CAR 182 182 182 182

ROTA 182 182 182 182

CAPASS 182 182 182 182

4.4.3 CORRELATION BETWEEN DEPENDENT AND INDEPENDENT VARIABLES

The dependent variable used here was again the risk assets to total assets ratio (RATA) while the

independent variables were ROTA, CAR and CAPASS. The relationship between RATA and

CAR as well as that between RATA and ROTA are negative while the relationship between and

between RATA and CAPASS is positive. Of all three sets of relationships, it is only the

relationship between RATA and CAR that is significant, the other two (RATA and ROTA and

RATA and CAPASS) are insignificant.

122

4.4.4 CORRELATION BETWEEN INDEPENDENT VARIABLES

The relationship between the three independent variables varies only in terms of significance as

all three variables are positively related. Only the relationship between CAR and CAPASS is

significant. The other two (CAR AND ROTA, ROTA AND CAPASS) were not significant.

TABLE 4.25 CORRELATION MATRIX FOR MODEL 3

(THE RELATIONSHIP BETWEEN RISK TAKING AND LIQUIDITY)

Correlations

RATA CAR LIQ ROTA

Pearson

Correlation

RATA 1.000

CAR -.213 1.000

LIQ -.014 .246 1.000

ROTA -.103 .164 .039 1.000

Sig. (1-

tailed)

RATA

CAR .002

LIQ .427 .000

ROTA .084 .013 .302

N RATA 182 182 182 182

CAR 182 182 182 182

LIQ 182 182 182 182

ROTA 182 182 182 182

4.4.5 CORRELATION BETWEEN DEPENDENT AND INDEPENDENT VARIABLES

The correlation matrix shows the relationship between the dependent variable RATA and all

three independent variables (ROTA, CAR and LIQ) is negative. The only significant relationship

was observed between RATA and CAR as both the relationships between RATA and

LIQUIDITY on the one hand and RATA and ROTA on the other is insignificant.

4.4.6 CORRELATION BETWEEN INDEPENDENT VARIABLES

Interestingly all three independent variables are positively related. Of the three, only the

relationship between CAR and Liquidity is significant.

4.5 DISCUSSION OF REGRESSION RESULTS

In this section, we discuss the results of the regression tests conducted for this study. A pooled

cross section times-series was used for the fifteen banks for the thirteen year period from 1997 to

2009. Risk assets to total assets ratio (RATA) was used as proxy for risk taking behaviour

following previous studies on risk taking behaviour (Delis and Kouretas 2011, Jeitschko and

123

Jeung 2007 and Chmielewski, 2005). In this work, we also follow methodology close to that of

Altunbas et al. (2009), Eid (2012). This study brings an element of novelty. First, our

computation of risk-taking behavior is based on several previous researches. Again this work

simultaneously studies the impact of three variables on the risk taking behavior of banks in

Nigeria.

Indeed bank risk taking can be proxied by bank asset structure and so the ratio of risk assets (i.e.

loans) to total assets can be interpreted as a good measure of bank risk taking. The models

presented in the preceding sections were estimated and analyzed using Multiple Regression

Analysis (MRA).

Table 4.26 provides a structure that allows for an interpretation of the MRA output. In all, three

sets of regressions were run.The table below presents the regression results of the three models

previously specified.

Table 4.26 Multivariate Regression Results

MODEL ONE MODEL TWO

MODEL THREE

Intercept 0.981 0.643 0.636

Interest rate -0.017 NA NA

Capital to asset NA 1.578 NA

Liquidity NA NA 0.037

Profitability -0.002 -0.001 -0.002

CAR -0.234 -1.021 -0.262

R 0.300 0.504 0.227

R2 0.090 0.254 0.052

Adjusted R2 0.075 0.242 0.036

F-Statistic 5.972 20.227 3.23

Prob (F-stat) 0.001 0.000 0.024

DW Stat 1.020 0.982 0.811

No. of observations 182 182 182

Source: Results of Regression Analysis SPSS Vol. 17 Ed.

We shall in subsequent sections discuss the results from the individual regressions run separately

on each of the predictor variables.

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4.5.1THE RELATIONSHIP BETWEEN BANK RISK TAKING BEHAVIOUR AND

INTEREST RATES

Interest rate has been recognised in the literature as an important variable that influences the risk

taking behaviour of banks. Of interest in the table above is the value of the Multiple Correlation

Coefficient (R). It is the measure of the strength of the impact that interest rate has on bank risk

taking. R was determined as 0.300 which indicates a weak, below average relationship. , the

coefficient of multiple determination allows us measure with more clarity the amount of

explained variation in risk taking caused by a predictor variable on a scale of 0 – 100 %. With an

of 0.090, we deduce that only 9% of the changes in risk taking is accounted for by interest

rate. Indeed this figure is rather low but given that there may be several other variables not

included in this study (for example competition, ownership structure etc) which can all impact on

the risk taking behaviour of banks in Nigeria, it is not out of place. Since is not a test of

statistical significance, what ultimately determines the weight/relevance of each predictor

variable would rather be each predictor’s beta and its corresponding significance as shown in the

coefficients table and also the F- ratio. Consequently we find that the equation depicting the

relationship between interest rate and risk taking represented by the equation, Y=0.981-0.017int-

0.002rota-0.234 car is statistically significant (F= 5.972 P=0.001). Several prior studies on the

subject provide evidence on the negative relationship between interest rates and risk taking.

Because this studies were all in advanced economies, it was essential to study whether the same

principles would hold given the dynamics of emerging market economies of which Nigeria is

one.

The results from the table above show that there is a negative relationship between interest rate

and risk taking (b=-0.017, p= 0.005). The significance of the relationship between interest rate

and bank risk taking was observed at 1%, 5% and 10%. The significance of this result suggests

that when interest rates are low, banks may give more loans to borrowers with either a bad or no

credit history. Consequently, it appears that following a period of monetary expansion banks

appear to take on more credit risk.

This result confirms the view of Delis and Kouretas (2010) who in their study on euro area banks

present strong empirical evidence linking low interest rate to increased risk taking by banks. The

results are also consistent with those of Jimenez et al (2008) who also investigated the impact of

125

interest rates on credit risk taking by banks and were able to present robust evidence confirming

previous theoretical predictions. In a study on the Czech banking industry, Geršl et al (2012)

associate increased risk appetite of banks to lowered interest rates. Specifically they found that

expansionary monetary conditions are more likely to promote risk-taking among banks. Their

results are consistent with the evidence collected by a growing empirical literature on the effects

of monetary policy on risk-taking (see, for example, Maddaloni and Peydró, 2010 and Ioannidou

et al., 2009) which all provide evidence of a negative relationship between bank risk and the

monetary policy rate. When comparisons are drawn, the results of this present study are in fact

consistent with those of other authors and again when another variable, risk weighted assets to

total assets was introduced as dependent variable, our initial findings are confirmed as being

robust.

4.5.2 THE RELATIONSHIP BETWEEN BANK RISK TAKING BEHAVIOUR AND

CAPITALIZATION

Given that this study makes use of a variant of the model adopted by Jeitschko and Jeung (2007),

we also use RATA as dependent variable in testing the strength of the relationship between

capitalization in banks and risk taking. R, the Coefficient of Multiple Correlations is determined

as 0.504 which is indicative of an average relationship. When R is squared and the Coefficient of

Multiple Determinations is derived as 0.254, we can say that 25.4 percent of the variation in (y)

risk taking is accounted for through the linear effects of the predictor variable (capitalization).

The equation Y = 0.643-1.021car-0.001rota+1.578capass is statistically significant (F=20.227;

p=0.000). Furthermore, the regression results on the relationship between capitalization and risk

taking show a positive relationship between the two and this result is very significant at 1%, 5%

and 10% (b=1.578; p=0.000).

A comparison of these results and those of previous researchers yield opposing views. This is

because it appears that the connection between capitalization and risk taking is theoretically

complex and empirically ambiguous. While some theorists proffer a negative relationship, others

are able to provide evidence on the positive relationship between these two variables.

The findings of this study are interesting in the light of the large body of literature dealing with

this topic that finds a negative influence of capitalization on risk taking. Studies by Van Roy

(2003) as well as that of Jacques and Nigro (1997) provide empirical evidence on the negative

126

relationship. These works based on the previous work of Shrieves and Dahl (1992), made use of

simultaneous equations. However this present study based on that of Jeitschko and Jeung

(2007), followed their study and also made use of multivariate regression analysis which yielded

results showing capital to be positively related to risk taking behaviour such that increases in

capital are more likely to be accompanied by an increase in risk taking behaviour. This study

agrees with that of Jeitschko and Jeung (2007). It is essential to point out that the works of Van

Roy (2003) and Jacques and Nigro (1997) are based on studies in advanced G10 countries

whereas that of Jeitschko and Jeung (2007) is based on the Korean banking industry.

4.5.3 THE RELATIONSHIP BETWEEN BANK RISK TAKING BEHAVIOUR AND

LIQUIDITY

This study set out to ascertain the nature of relationship that exists between liquidity and risk

taking having identified it as one of the driving factors that may lead to bank failure (Arena,

2008). From the regression results presented in Table 5.1 above, this study is able to set out the

exact relationship that exists between these two variables. R, the Multiple Correlation Coefficient

was determined to be 0.227 which points to a weak relationship between Y (risk taking) and the

predictor variable-liquidity. The Coefficient of Multiple Determination is 0.052, thus we can

infer from these that only 5.2% of risk taking behaviour can be directly attributed to liquidity. It

then becomes necessary to determine the statistical significance of the model that defined the

relationship between risk taking and liquidity; this is explained by the F ratio contained in the

Anova table and it is from there that that the overall equation Y=0.636-0.262car+0.037liq-

0.002rota is found to be statistically significant (F=3.230, p=0.024). Furthermore, we establish

the relationship between liquidity and risk taking to be low, direct, positive (B=0.037) but not

significant (p= 0.545). What this suggests is that low levels of liquidity are associated with lower

levels of risk taking behaviour while increases in liquidity will spur on the acquisition of more

risk assets reflecting increased risk taking by banks.

The findings of this research do not agree with those of Taylor and William (2007) as well as

that of Vasquez and Federico (2012) who find a negative relationship between liquidity and risk

taking behaviour. A major cause of this difference (in findings) could be related to the time of

study. Taylor and William’s study only looked at the impact of liquidity intra crisis and not as a

127

precursor to the crisis as this study has done. The findings of this study are however consistent

with those of Eid (2011), Berger et al (2010) and Altunbas et al (2009) who all agree that risk

appetite increases in direct proportion to increases in liquidity. As stated previously, this study

found risk taking to be positively related to liquidity. The insignificance of this relationship was

upheld at 1%, 5% as well as 10%.

4.6 ROBUSTNESS TESTS

The essence of running parallel regression is to confirm the robustness of findings. For

robustness checks, two other measures of bank risk other than Risk Assets to Total Assets

(RATA) taking were used. These measures include the Risk weighted assets to total assets ratio

and the loan deposit ratio. The predictor variables remained the same. Table 4.27 below presents

the relationships observed between the variables. More importantly, the data contained therein

corroborates the relationships previously observed in the main models.

Table 4.27: ROBUSTNESS TESTS

MODEL

ONE(using Risk

weighted assets to

total assets Ratio)

MODEL

TWO(Using Loan

deposit Ratio)

MODEL THREE

(Using Loan deposit

Ratio)

Intercept 1.816 0.436 0.0362

Interest rate -0.064 NA NA

Capital to asset NA 0.013 NA

Liquidity NA NA 0.088

Profitability -0.009 -0.005 -0.002

CAR 0.498 -0.005 -0.001

R 0.168 0.462 0.184

R2 0.028 0.214 0.034

Adjusted R2 0.012 0.201 0.018

F-Statistic 1.730 16.414 2.110

Prob (F-stat) 0.163 0.000 0.101

DW Stat 1.998 1.293 1.219

No. of observations 182 182 182

Source: Results of Regression Analysis SPSS Vol. 17 Ed.

Using the risk weighted assets to total assets ratio and the Loan deposit ratio, it is interesting to

note that the linear dynamics between risk taking and these variables remain the same as with

previous regression results and so we find robust evidence confirming our findings about the

128

relationship between bank risk taking on the one hand and interest rate, capitalization and

liquidity on the other.

Just like the previous regression, we find that of the three predictor variables, capitalization and

Interest rate were both found to have significant impact on risk taking behavior (b= .013,

P<.000). Liquidity was not found to have any significant effect on the risk taking behavior.

Following these findings, we conclude that risk taking is directly affected by capitalization and

interest rate more than the other variables and so these findings are in line with recent empirical

research on the relationship between the dependent variable and the three predictors we have

studied in this work.

4.7 CONDITION NUMBERS: TESTING FOR MULTI COLLINEARITY

Table 4.28 shows the condition numbers of the models used in the study.

Table 4.28: Condition numbers for Multicollinearity tests

Eigen Value Model 1 Model 2 Model 3

Largest Eigen

value

3.075 2.960 3.072

Smallest Eigen

value

0.009 0.089 0.021

Condition

number: square

root of (a)/(b)

=18.48

=5.76

=12.09

Note: The condition number is the condition index with the largest value; it equals the square

root of the largest eigen value divided by the smallest eigen value.

According to (Green, 2003) if the condition number is greater than 20, multi-collinearity is a

concern. A condition number over 30 usually suggests that the regression results should be

rejected.

129

4.8 TEST OF HYPOTHESES

The test of Hypotheses allows us to draw conclusions about a given population parameter from

the sample statistics. Because the sample is small with a size of less than 30 and also given that

the variance of the population is unknown, the appropriate test statistics to use is the t statistics

with the level of significance being five per cent. Indeed in econometric research when the

population variance is one of the unknowns of an estimated model, the t- statistics is usually

applied to test the reliability of the estimates.

The decision criterion is that if the calculated value of t is greater than the critical value of t

derived from statistical table, at the 5 % significance level, then the null hypothesis is rejected

leading to the acceptance of the alternative hypothesis. Since the computation about the critical

value has been done by means of statistical software, the details are presented hereunder and

used in the test of the hypotheses.

Hypothesis one

H0 Interest rate has a negative and significant impact on bank risk taking.

Table 4.29 shows that at prob > F value of 0.001 which is less than 0.05 (that is at a 5% level of

significance) the regression was significant and fitted the data appropriately.

Results

In this study we set out to establish the impact of interest rate on bank risk taking. The table

below shows that the coefficient -.017 is negative and significantly related to risk taking at 1%,

5% and 10% level of significance. Furthermore with a calculated t-value of more than 2 (tc < 2)

the null hypothesis is accepted. The basic interpretation is that interest rate is negatively related

to risk taking which means that at lower interest rates, banks tend to acquire more risk assets.

This relationship is significant.

130

Table 4.29 Regression Results of Hypothesis one

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

(Constant) .981 .110 8.893 .000

INTEREST -.017 .006 -.202 -2.834 .005

ROTA -.002 .002 -.053 -.735 .463

CAR -.234 .090 -.188 -2.607 .010

Source: SPSS Analytical software results.

a. Dependent Variable: RATA Prob > F value of 0.001

Hypothesis Two

H0-Capitalization does not have a positive and significant impact on bank risk taking.

Table 4.30 Regression Results of Hypothesis two

Coefficients(a)

Model

Unstandardized

Coefficients

Standardized

Coefficients

T Sig. B

Std.

Error Beta

1 (Constant) .643 .022 29.184 .000

CAR -1.021 .137 -.825 -7.443 .000

ROTA -.001 .002 -.015 -.234 .816

CAPASS 1.578 .226 .764 6.981 .000

Prob > F value of 0.0000

a. Dependent Variable: RATA

Source: SPSS Analytical software results.

131

Table 4.30 shows that at prob > F value of 0.000 is less than 0.05 (that is at a 5% level of

significance) the regression was significant and fitted the data appropriately.

Results

Among other objectives, this study set out to ascertain the exact impact that bank capital has risk

taking. The coefficient, capital to asset ratio (capass) is not only positive but is very significant at

.000. Thus we have insufficient evidence supporting the acceptance of the null hypothesis and so

we accept the alternative. This means that as bank capital increases, the risk appetite of banks

increases and this reflects as increased risk taking behaviour.

Hypothesis Three

H0- The liquidity level does not significantly affect the risk taking behavior of banks.

Table 4.31Regression Results of Hypothesis three

Coefficients(a)

Model

Unstandardized

Coefficients

Standardized

Coefficients

T Sig. B

Std.

Error Beta

1 (Constant) .636 .076

8.418 .000

CAR -.262 .094 -.212 -2.774 .006

LIQ .037 .069 .041 .545 .587

ROTA -.002 .002 -.069 -.939 .349

Prob > F value of 0.024

a. Dependent Variable: RATA

Source: SPSS Analytical software results.

At prob = F value of 0.024which is less than 0.05 (that is at 0.05 level of significance) the

regression was significant and fitted the data appropriately.

132

Results

This study set out to determine that liquidity levels do not significantly affect risk taking

behaviour. The coefficient for liquidity is positive, low (B=.037) but insignificant (P=.587).

Furthermore, a t-calculated value of 0.545 is less than two and so it follows that the null

hypothesis will be accepted. The interpretation of this is that though increases in liquidity are

accompanied by increases in risk taking, the increase in the acquisition of risk assets is not

significant enough for us to directly establish a strong causal relationship between the two.

133

REFERENCES

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Arena, M., (2008), ‘Bank failures and bank fundamentals: A comparative analysis of Latin

America and East Asia during the nineties using bank-level data’, Journal of

Banking & Finance 32, 299-310.

Berger, A. N., Bouwman, C.H.S.,( 2010), “Bank liquidity creation, monetary policy and

financial crises” Working paper.

CBN Statistical bulletin, 2010

Chmielewski, T., (2005) “Bank Risks, Risk Preferences and Lending,” MPRA Paper 5131,

University Library of Munich, Germany, revised 15 Jan 2006

Eid S., (2011), “Monetary policy, Risk-Taking Channel and Income Structure: An empirical

assessment of the French banking system”, Université Paris I – Panthéon Sorbonne,

mimeo

Delis, D. M. and Kouretas, G. P. (2011), “Interest rates and bank risk-taking”, Journal of

Banking& Finance, 35, pp. 840-855

Eid S., (2011), “Monetary policy, Risk-Taking Channel and Income Structure: An empirical

assessment of the French banking system”, Université Paris I – Panthéon Sorbonne,

mimeo

Greene W. H., (2003), Econometric Analysis, Fifth Edition, Prentice Hall

Ioannidou, V. P., Ongena, S. and J.L. Peydró, (2010), “Monetary Policy, Risk-Taking and

Pricing: Evidence from a Quasi Natural Experiment,” ECB WP 2010.

Jacques, K.T., Nigro,P., (1997), “Risk-Based Capital, Portfolio Risk, and BankCapital: A

Simultaneous Equations Approach,” Journal of Economic and Business, 533-47.

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Jeitschiko, T. D., Jeung, S. D., (2007), “Do well Capitalized Banks Take More Risk? Evidence

from the Korean Banking System,” Journal of Banking Regulation 8, Pp.291-315,

doi:10.1057/Palgrave.jbr.2350054

Maddaloni, A., Peydró, J.L., (2010), “Bank Risk-Taking, Securitization, Supervision and Low

Interest Rates: Evidence from the Euro Area and the U.S. lending Standard,” European

Central Bank Working Paper No 1248.

Shrieves, R. E., Dahl, D., (1992), “The Relationship between Risk and Capital in Commercial

Banks,” Journal of Banking and Finance, 16, 439- 457

Taylor, J.B., Williams J.C., (2009), “A Black Swan in the Money Market”, American Economic

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Van Roy, P., (2003), “The Impact of the 1988 Basel Accord on Banks’ Capital Ratios and Credit

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135

CHAPTER FIVE

SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS

This chapter presents the findings of this research in a bid to establish that the objectives set

earlier have been realized. Conclusions, recommendations on possible causes of action and areas

for further research are also discussed.

5.1 Summary of Research Findings

Without question, risk taking remains the driving factor and a major determinant in most bank

failures. This study has among other things provided a framework for risk taking analysis by

identifying the nature of relationships observed between risk taking and the three predictor

variables.

The findings of this research are as follows. First, regarding the relationship between interest rate

and bank risk taking behavior, evidence from descriptive statistics gave the mean value of

interest rate as 11.05%. At the start of the study period (in 1997), interest rate was at its lowest

rate (13.54%). However it started to rise, eventually getting to 18.29% in 1998 and peaking at

24.4% in 2002, which was the highest it would be within the study period. After 2002, interest

rates fluctuated eventually dipping to 15.14% in 2008.

The regression results show that interest rate negatively and significantly affects bank risk taking

behaviour. We find that interest rates are negatively related to risk such that increases in interest

rates lead to reduced risk taking and when interest rates are low; theory suggests bank risk taking

increases. The results are consistent with findings from prior research work as previous

researches like Ioannidou et al (2010) as well as Maddaloni and Peydro (2010) all find a negative

and significant relationship. The significance is surprising given the “higher” interest rate regime

that operated in Nigeria during the years under study. Because whereas the interest rates were

low in more advanced countries, the interest rate in Nigeria was really far from low and was in

the double digits for much of the period covered by this study.

A second finding of this research follows from studying the effect of capitalization on risk taking

behavior. Descriptive statistics shows the mean value of capital to asset ratio used to proxy for

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capitalization was .1071. A cursory look at the data showed that two of the banks that would

subsequently fail the stress test (Finbank and Oceanic bank) posted negative values for their

capital to asset ratio. These negative capass values occur at exceptionally high levels of risk

taking as shown by the Rata ratios.

The regression results show capital to be positively and significantly related to risk taking

behavior. What this means is that the recapitalization exercises which were meant to make banks

even safer by providing new regulations and safety nets, may not have fruitfully controlled bank

risk-taking behavior as intended.

An implication of this result is that when inadequately capitalized, banks exhibit more risk-

aversion than when capital seems adequate as measured by the regulatory authorities. From the

regression results, it can then be seen that a (mandatory) increase in the capital adequacy ratio

does not prevent banks from raising the credit risk of their portfolio. This study has established

that an increase in capital is positively related to risk taking and this finding is consistent with the

findings of Gennottee and Pyle (1991) who found that there are plausible situations in which an

increase in capital requirements results in an increase in risk assets. It also agrees with the

findings of Jeitshko and Jeung (2007). Our findings however do not agree with those of Van Roy

(2003), Jacques and Nigro (1997) as well as that of Shrieves and Dahl (1992). The regression

result therefore challenges the widely held belief that increases in capital serve as a sufficient

buffer and actually work to curb the risk appetite of banks. Indeed the widely held belief has

always been that it is only when faced with solvency issues and inadequate capital that bank

managers are more likely to try to gamble for a resurrection by engaging in even more risky

behavior. This would present as a negative relationship between risk taking and capitalization.

Our findings clearly present a contrary position.

The third finding of this research is drawn from studying the relationship between Liquidity and

risk taking behavior. Descriptive statistics from the study show that the liquidity levels within the

Nigerian banking industry was not just sufficient but was actually excessive. The excessive

liquidity fuelled the acquisition of assets and this in itself further spurred the formation of asset

bubbles.

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The A priori expectation from this research was to establish the existence of a strong positive and

significant relationship between liquidity and risk taking. Regression results contradict this a

priori expectation. Results show a positive relationship between risk taking and liquidity. This

finding is consistent with that the findings of Eid (2011), Berger et al (2010) and Altunbas et al

(2009) who all find that increases in liquidity were positively related to risk taking. Again just

like with interest rate, the difference lies in the degree of significance as the positive relationship

between the two variables is far from significant. What this then means is that we expect that as

bank liquidity gets higher, the risk appetite of banks also increases. The issue however is that

increases in liquidity does not impact on risk taking enough to be considered an altering variable.

It therefore suggests that there are again possibly other variables not included in this study that

impact on risk taking behaviour more than liquidity.

5.2 Comparison of Findings with Objectives of Study

It is essential at this time to confirm if the findings of this study have led to the realization of the

objectives set at the beginning and again to ascertain whether answers have been provided for the

research questions as well.

There is convincing evidence that the findings arising from this study have helped in satisfying

the objectives of the study and providing reliable answers to the questions posed at the beginning

of this study.

5.2.1 Interest Rate and Risk Taking

Objective One: To determine the nature of the relationship between interest rates and risk

taking behavior in banks.

The first objective of this study set out to examine the relationship between interest rate and risk

taking. To study this relationship, this work borrowed from earlier studies by Jimenez et al

(2008) to explain the influence (if any) that Interest rate has on the risk taking behaviour of

banks. The study provided reliable empirical validation of the link between these two variables

and the overall result when RATA (Y), was regressed on interest rates (y1) and other independent

variables is y = βo + β1γ1it + β2γ2 it + β3γ3it +eit. The regression yielded negative and

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significant values (b=-0.017, p= 0.005) which showed a negative relationship between interest

and risk taking.

The accomplishment of this objective provides an answer to the research question “What is the

nature of the relationship between interest rate and the risk taking behavior of banks?” as the

relationship has been clearly identified as negative.

5.2.2 The Impact of Capitalization on Risk Taking

Objective Two: To ascertain the effect of capitalization on the risk taking behavior of

banks.

The second objective of this research was to study the influence of capitalization on the risk

taking behaviour of banks. To achieve this objective, this study used a variant of the model

borrowed from Jeitscko and Jeung (2007) and provided evidence on a positive and very

significant relationship between risk taking and capitalization (b=1.578; p=0.000). The positive

result is indicative of an increased risk appetite when capital also increases. The result remained

robust when another measure of credit risk (loan –deposit ratio) was used.

Prior empirical works yielded both positive and negative results. Works by Van Roy (2003),

Jacques and Nigro (1997) and several others find a negative relationship exists between the two.

However that of Jeitschko and Jeung (2007) provides evidence of a positive relationship.

Achieving this objective provides an answer to the question: “In what ways does capitalization

determine the risk taking behavior of banks?” as this study has shown that capitalization

determines the degree of risk taking given that an increase in capitalization results in an increase

in the risk taking behavior of banks.

5.2.3 Liquidity and Risk Taking

Objective Three: To establish whether there is a significant relationship between liquidity

levels and risk taking behavior.

Establishing the degree of significance liquidity exerts on the risk taking behaviour of banks was

another objective of this study. The study was able to provide reliable empirical validation of the

link between these two variables. The overall result, when liquidity was regressed on risk taking

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yielded Y=0.636-0.262car+0.037liq-0.002rota. Furthermore, the relationship was found to be

positive but insignificant B=0.037, p= 0.545). The result confirming the relationship provides

answers to the associated research question, “How does the liquidity level affect the risk taking

behavior of banks?”

This work followed that of Eid (2011) and Morkoetter (2012) and established that as liquidity

levels increase, banks also increase their acquisition of risk assets. Acharya and Richardson

(2009) note that in the presence of low(er) interest rates, banks tend to grant more loans. The

resulting abundance of liquidity, they theorize, will ignite the formation of asset price bubbles

and that often leaves a destabilizing effect. The finding of this study supports this assertion but

differs in the degree of significance because whereas previous empirical works find a significant

relationship, this study does not. Indeed it does appear that in the face of excessive liquidity,

rather than maintain idle cash balances; banks grant more loans and their ability to carefully

screen out “bad borrowers” is compromised. Diamond and Rajan (2006) also link high levels

with liquidity with the financing of more risky long term projects.

5.3 Policy Implications of the Research Findings and Contributions of Research to

Knowledge

An important aspect of a research work of this kind is how the outcome of the study would

influence policy formulation and implementation. The findings arising from this study have

several important research and policy implications. Indeed this work was an attempt to prove or

disprove the existence of relationships already established by others and it sought to verify

whether findings from other more developed economies would also apply in Nigeria. Therefore,

the primary contribution of this study has been the extension of the empirical literature on the

relationship between risk taking and three variables namely interest rate, capitalization and

liquidity in emerging market economies. Essentially, the major contribution of this work lies in

our adaptation of a model designed by Jeitschko and Jeung (2007) and tested on the Korean

banking system using data from the Nigerian banking system. We were therefore able to

introduce new evidence on the relationship between bank risk taking and the predictor variables

studied in this work.

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Secondly, even though interest rate did not significantly affect risk taking as much as capital did,

the existence of a negative relationship supports the prediction that a low policy rate is associated

with greater risk taking. The implication for the policy makers is that in making changes to

monetary policy, it is essential to take into account the presence of other intervening variables

that could sabotage the early realization of the initial target of macroeconomic stability. Given

that the realization of price and financial stability are both important, deciding which to sacrifice

at any point is difficult. Consequently this work will hopefully stimulate further research on

monetary policy and bank risk taking.

Thirdly, this work has established the presence of an increased risk appetite even with sufficient

capital. Previously the belief was that banks took on more risk when inadequately capitalized and

were less likely to engage in excessive risk taking because of the “skin in the game theory”

which suggests that when banks have more to lose by way of capital, they (the banks) are less

likely to take on excessive risks. For the regulatory authorities, this is an indication that there is

need to actively seek other regulatory measures other than the frequent resort to recapitalization

as a one drug wonder solution for healing all the problems within the banking industry.

Another contribution of this work comes from studying the impact of liquidity on bank risk

taking. Though liquidity was not determined as constituting a significant factor on bank risk

taking, this study has however established a positive relationship between liquidity and bank risk

taking. This means that as bank liquidity increases, risk taking also increases. A key question at

this time would be “Why does access to abundant liquidity aggravate the risk-taking incentives at

banks, giving rise to excess lending and asset price bubbles?” A simple explanation is that easy

access to liquidity gives bankers insurance against future losses and that coupled with disaster

myopia is what essentially drives risk taking at this time. Again echoing the words of Acharya

and Richardson (2010), the seeds of a crisis are sown when banks are awash with liquidity. This

is primarily because banks tend to misprice risk when bank liquidity is high, bubbles are more

likely to be formed; and so even though this work did not establish a significant relationship

between bank risk taking and liquidity, if a priori expectations are to be believed then it is

essential that banks are discouraged from engaging in excessive risk taking even when access to

liquidity appears relatively easier.

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5.4 Conclusion

A key question has been “what makes banks take the risks that they do? It is not that the banks

themselves are not aware of the inherent dangers, but the pull or rather the need to make more

money pushes banks to take on risks failing to draw the line when it becomes excessive. A

critical contribution of this study is that we simultaneously examine the relationship of three

variables on bank risk taking. Indeed attention was paid to interest rates, bank liquidity and

capital and since all these variables influence bank behavior in some way, it was important to

examine them together.

Using a database of fifteen out of the twenty five post consolidation banks, one hundred and

eighty two observations between 1997 and 2009, this work set out to analyze the risk taking

behavior of banks with emphasis on the influence exerted by the three variables. To guide this

study, three research questions were set: (i) what is the nature of the relationship between interest

rate and the risk taking behavior of banks? (ii) In what way does capitalization determine the

risk taking behavior of banks? (iii) How does the liquidity level affect the risk taking behavior of

banks? The key factor was clearly to determine the degree of sensitivity exhibited by risk taking

to the identified predictor variables. Consequently three models were designed following

previous studies to elicit information that would help answer the raised research questions.

Essentially this research work has helped put in to perspective and further shed light on how

bank risk taking is influenced by the three variables mentioned earlier.

Empirical evidence presented in this work has confirmed the negative relationship between

interest rate and risk taking. The negative relationship discovered between interest rates and risk

taking proves that risk taking is highest or rather more likely at low(er) interest rates. It then

follows that banks are more likely to invest in riskier assets at such times to continue to match

the yield on their liabilities. In the event that low interest rates prevail for a long time, banks will

then need to renegotiate (or even default on) long-term commitments. A switch to riskier assets

(and invariably higher yields) may increase the probability that it will be able to match its

obligations.

Regarding the role played by capitalization on the risk taking behavior of banks, results show

there is a strong positive and significant relationship between capitalization and risk taking. The

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results do not agree with the popular view held by earlier researchers who mostly find a negative

relationship between risk taking and capital. We provide robust evidence that validates this

positive relationship and this confirms that armed with more capital, the risk appetite of banks

increases.

The regression results show liquidity to be positively related to risk taking. Descriptive statistics

from this study suggest that banks held excess liquidity. This confirms the view of Saxegard

(2006), Khemraj (2006), Fielding and Shorthand (2005) who all posit that in many developing

countries, banks hold large quantities of excess liquidity. The positive relationship between

liquidity and risk taking suggests that when liquidity is excessive, there is a flood of funds

seeking excess return, creating bubbles and exposing banks to unending risks in every corner of

the world and so it is only a matter of time before one brings about the next crisis.

In Nigeria, the excessive liquidity within the Nigerian Banking industry in the early to mid

2000s was traceable to several factors including but not restricted to frequent capitalization

exercises, financial liberalization and the emergence of several innovative banking products.

This excess liquidity was never fully drained and is fingered as fuelling the asset bubble era after

the recapitalization of 2004 to 2005 when banks became awash with liquidity. The positive

relationship between liquidity and risk taking was not found to be significant which means that

there are other variables that could influence risk taking.

Having identified the nature of relationships observed between risk taking and the three predictor

variables, subsequent studies may decide to focus on these or other areas (ownership structure,

competition, deposit insurance, operating environment, securitization(which encourages risk

shifting) and so many other variables which may singly or collectively influence the risk taking

behavior of banks ).

Finally though our knowledge of bank behavior has broadened and we have a better

understanding of how bank behavior has been influenced (or not) by these three factors, there is

need to constantly reevaluate and study what variables currently influence risk taking because as

times change, the sensitivity to various factors may also change. Yes today, capital and interest

rate are the most significant of the three factors, tomorrow, given different circumstances; the

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degree of sensitivity may change. What is required is a constant study as bank behavior just like

every other human endeavor is one that is in a constant state of evolution.

This research work demonstrates that though bank risk taking is sensitive to several factors, of

the three studied, it is most significantly affected by capital.

5.5 Recommendations

It may indeed be difficult to explain or aptly define exactly what amount of risk is appropriate,

but the proposition of Markowitz (1952) portfolio selection theory seems brilliant and should be

borne in mind. Markowitz suggests that the best way to achieve an efficient investment portfolio

is by finding the right balance between expected returns and volatility of losses and this he

proffers can only be gotten through diversification, which is the best tool to reduce the risk of an

entire portfolio. There is indeed no doubt that this theory can be easily applied to banks

portfolios. Even though there have been calls for banks to restrict their exposure to any particular

industry or obligor, following Markowitz’s proposition, this is the time to reiterate that advice.

Perhaps the best indicator of what is to come in banking is clarified by Kindleberger and Aliber

(2005) who note the alarming frequency with which banking crises occur. Sadly the lessons

earlier generations had learned the hard way is again faced by future generations even when the

thinking is that the lessons of costly crises must have been learned. The truth is that financial

innovation, changing regulation and regulatory avoidance are certain to continue, so future crises

might just appear different from previous ones.

Again there are arguments that the CBN lacks the focus to properly and adequately monitor

monetary policy as well as control risk taking by commercial banks. The argument put forward

by Ogowewo and Uche (2006) that perhaps the time has come for the central bank to concentrate

more on its agent of monetary policy role and less on the key regulator role of the financial

system is one of such views. Indeed while it may be easy to shift all the blame for excessive risk

taking on a defective monetary policy or excessive liquidity or some other possible cause, it is

clear from the findings of this research that capital regulation which required banks to

recapitalize was what provided banks with excessive funds, thereby securing a will and a way for

144

excessive risk taking to go on. In the words of Acharya and Richardson (2010), that was when

the seeds of the crises were sown.

The backlash following what is perceived as excessive bank risk taking is likely to be increasing

calls for strict regulation of the industry, but is that really the best? A better approach than over-

regulation is for the central bank to have a target on asset prices in a way that does not impede

the functioning of free markets and does not prevent ‘good’ financial innovation. The policy

implication is that in asset-led business cycles guiding monetary policy by developments in

inflation alone will not prevent the bubble from becoming bigger than otherwise. Monetary

policy should therefore be formulated with at least two targets: inflation and the output gap.

South Africa and Ghana are among two of the countries that have adopted inflation targeting.

Available evidence (Uremadu, 2012) indicates that bank risk-taking in the run-up to the crisis

was associated with increased financial vulnerability, suggesting that bank decisions regarding

the associated liquidity and capital buffers were not commensurate with the underlying risks,

resulting in excessive hazard to their business continuity.

The findings of this research are of prominent interest to the Central Bank of Nigeria with

respect to the possible long term effect of its policies on bank risk-taking. Also, supervisory and

prudential authorities may find answers on when to be particularly vigilant, and on the areas that

could be more prone to risk-taking behavior.

From a macro-perspective liquidity is in fact a real culprit, because without excessive liquidity

there would have been no bubbles – no credit, no bubble. There is no doubt that central banks the

world over have their work, albeit more difficult cut out for them particularly as there is a

growing need to measure, monitor and control the total liquidity in the economy. New policies

are desperately needed, and targeting the net wealth of the personal sector is one such policy

suggested in this contribution. Above all we should not lose sight of the fact that this crisis is the

result of regulatory failure to guard against excessive risk taking in the financial sector.

One of the recommendations of this work is the need for the development of better forecasting

tools and techniques. This view is expressed by Demirguc kunt et al (2003) who observes that

empirical models are more useful in identifying variables/factors that are likely culprits in the

145

event of banking crises rather than at predicting the occurrence of crises given that these models

are not ab initio designed as forecasting tools.

It is therefore the recommendation of this work that the Central Bank of Nigeria as well as other

stakeholders in the Nigerian banking industries develop as a matter of urgency, suitable and

appropriate early-warning indicators of impeding bank vulnerability. The question then is

whether earlier warning automatically means earlier response within the Nigerian Banking

system.

Summarily, there is no question that a poor regulatory framework based on the belief that banks

could be trusted to regulate themselves is, in my opinion, among the main sources of the crisis. It

is quite obvious that the CBN did not efficiently monitor the risk management functions of most

banks. Indeed the decision to take on more risk was that of the banks, but the CBN had set up a

monitoring body (the FSRCC) with oversight functions to complement that of the Department

for Banking Supervision which met only once and never bothered to follow up on ensuring

adherence by the banks to set risk management procedures. At the same time, risk management

at most banking institutions has failed to enforce the basic rules for a safe business which

requires that they avoid strong concentrations and also minimize the volatility of their returns.

Supervisory resources should be concentrated on identifying and closing down bad banks (those

with negative expected earnings) and monitoring those institutions with low positive expected

earnings relative to the risks of their assets.

5.6 Recommended Areas for Further Research

This research has by no means exhausted all areas pertaining to bank behavior as it relates to

bank risk taking behavior or the variables that have the capacity to influence such. Related areas

that future research could be centered upon include the following areas;

1. Studying how bank risk taking is influenced by the ownership structure. Does the agency

problem significantly influence the risk appetite of banks?

2. The impact of competition and operating environment on bank risk taking behavior

3. An empirical analysis on the impact of deposit insurance on bank risk taking.

4. A variant of the topic above could be to change deposit insurance with bailout and so

determine the long term effect of government bailout packages on bank risk taking.

146

5. It would also be beneficial if future research could focus on establishing through

empirical means the effect of the universal bank policy on risk taking.

6. It would also be interesting to encompass in one single study the combined effects of

competition, monetary policy, corporate governance structure, capital regulation and

liquidity on a single variable- bank risk taking behavior.

147

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APPENDIX A

BANKING CRISES IN SOME SELECTED COUNTRIES

S/NO COUNTRY CRISES

PERIOD

NON

PERFORMING

LOANS as a %

OF TOTAL

LOANS

ESTIMATE OF

TOTAL

LOAN/COST

1 BENIN 1988-1989 80% 17% OF

GDP(CFA

95bn)

2 COTE

D’IVOIRE

1988-1991 90% 52% OF

GDP(CFA

677bn)

3 GHANA 1982-1989 - 6% OF GNP

4 SENEGAL 1988-1991 20 -30% 17%(US$830)

5 TANZANIA 1989-1995 73% 10% OF GNP

6 MEXICO 1981-1987 - 3%OF GDP

(19.1bn Pesos)

7 ARGENTINA 1981-1991 9.3% 12.15% OF

GDP

8 VENEZUELA 1980-1982 51% 55.3% OF GDP

9 MALAYSIA 1994-1995 30% 18% OF GDP

10 SPAIN 1990s OFFICIAL

US$469

UNOFFICIALLY

$1 TRILLION

RESCUE

COSTMORE

THAN $100bn

11 PHILLIPINES 1977-1985 - 16.8 OF GNP

12 JAPAN 1985-1988 7.8% 4.7% OF GNP

13 USA 1981-1991 - 3.2 % OF

GNP($180bn)

14 NIGERIA 1989-1995 40.9% 2% OF

GDP($38bn)

SOURCE: ANNUAL BANK CONFERENCE ON DEVELOPMENT ECONOMICS

A WORLD BANK PUBLICATION; APRIL 1996

162

APPENDIX B FAILED BANKS

S/NO NAME OF BANK DATE ESTABLISHED REMARKS

1 The Industrial and Commercial Bank 1929 Failed in 1930

2 The Nigerian Mercantile Bank 1931 Failed in 1936

3 The Nigerian Penny Bank 1945 Failed in 1946

4 The Nigerian Farmers and Commercial Bank 1947 Failed in 1953

5 Merchants Bank 1952 Failed in 1960

6 Pan Nigerian Bank 1951 Failed in 1954

7 Standard Bank of Nigeria 1951 Failed in 1954

8 Premier Bank 1951 Failed in 1954

9 Nigeria Trust Bank 1951 Failed in 1954

10 Afroseas Credit Bank 1951 Failed in 1954

11 Onward Bank of Nigeria 1951 Failed in 1954

12 Central Bank of Nigeria * 1951 Failed in 1954

13 Provincial bank of Nigeria 1952 Failed in 1954

14 Metropolitan Bank of Nigeria 1952 Failed in 1954

15 Union Bank of British West Africa 1952 Failed in 1954

16 United commercial Credit Bank 1952 Failed in 1954

17 Cosmopolitan Bank 1952 Failed in 1954

18 Mainland Bank 1952 Failed in 1954

19 Group Credit &Agric Bank 1952 Failed in 1954

20 Industrial Bank 1952 Failed in 1954

21 West African Bank 1952 Failed in 1954

Source: Central Bank of Nigeria Annual Reports, 1968

*This bank is in no way connected with the Central bank Of Nigeria

163

APPENDIX C

BANKS THAT MET AND THOSE THAT COULD NOT MEET THE 25 BILLION NAIRA CAPITAL BASE

(A) Banks that met the 25 Billion Naira Capital Base

Access Bank Plc

Afribank Nigeria Plc

Diamond Bank Plc

Eco bank Plc

Equatorial Bank Plc

Fidelity Bank Plc

First Bank of Nigeria Plc

First City Monument Bank Plc

First Inland Bank Plc

Guaranty Trust Bank

IBTC- Chartered Bank Plc

Intercontinental Bank Plc

Nigeria International Bank (Citibank Group)

Oceanic Bank Plc

Platinum Habib Bank Plc

Skye Bank Plc

Spring Bank Plc

Stanbic Bank Plc

Standard Chartered Bank Plc

United Bank for Africa Plc

Sterling Bank Plc

Union Bank of Nigeria Plc

Unity Bank Plc

Wema Bank Plc

Zenith International Bank Plc

(B) The 14 banks that could not meet the recapitalization conditions are:

African Express Bank (Afex)

Allstates Trust Bank

Assurance Bank

City Express Bank

Eagle Bank

Fortune International Bank

Gulf Bank

Hallmark Bank

Lead Bank

Liberty Bank

Metropolitan Bank

Societe Generale Bank

Trade Bank

Triumph Bank

Source: Soludo, 2006

164

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

ACCESS 1997 1 2.32 2.23 0.62 0.65 0.16 0.1 1.67 13.54 5.353

1998 2 1.04 8.39 0.65 0.3 0.18 0.12 1.06 18.29 0.523

1999 3 2.22 13.49 0.61 0.13 0.32 0.26 0.16 1.16 21.32 0.34

2000 4 1.98 19.79 0.55 0.1 0.39 0.17 0.09 1.03 17.98 0.482

2001 5 1.45 12.62 0.64 0.2 0.44 0.17 0.11 1.02 18.29 0.044

2002 6 -0.16 -0.92 0.58 0.14 0.46 0.29 0.17 1.11 24.4 0.433

2003 7 3.59 32.43 0.59 0.1 0.34 0.18 0.11 1.06 20.48 0.308

2004 8 3.04 31.69 0.76 0.07 0.43 0.12 0.09 1.04 19.15 0.6

2005 9 1.12 5.39 0.78 0.09 0.4 0.26 0.21 1.22 17.85 0.715

2006 10 0.64 3.87 0.67 0.13 0.54 0.24 0.16 1.12 17.26 0.274

2007 11 2.45 28.34 0.49 0.09 0.46 0.17 0.08 1.06 16.94 0.424

2008 12 1.8 10.96 0.42 0.03 0.23 0.38 0.16 1.2 15.14 0.371

2009 13 -0.5 2.08 0.87 0.16 0.88 0.3 0.26 1.32 18.36 7.22

165

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

AFRIBANK 1997 14 0.001 0.02 0.67 0.15 0.46 0.13 0.08 1.03 13.54 0.628

1998 15 0.011 0.19 0.59 0.2 0.48 0.07 0.04 1 18.29 0.564

1999 16 2.21 33.41 0.49 0.17 0.45 0.08 0.04 0.99 21.32 0.464

2000 17 -0.798 -15.384 0.51 0.31 0.25 0.06 0.03 0.98 17.98 0.39

2001 18 1.676 28.942 0.51 0.31 0.28 0.07 0.03 0.97 18.29 0.393

2002 19 2.97 39.42 0.58 0.27 0.42 0.1 0.05 1 24.4 0.491

2003 20 1.502 21.14 0.5 0.25 0.39 0.11 0.05 1.01 20.48 0.492

2004 21 2.33 26.09 0.6 0.3 0.34 0.12 0.07 1.02 19.15 0.484

2005 22 0.707 3.38 0.61 0.31 0.38 0.36 0.22 1.23 17.85 0.472

2006 23 2.885 13.69 0.92 0.24 0.34 0.22 0.2 1.2 17.26 0.532

2007 24 3.89 23.68 0.94 0.15 0.45 0.16 0.15 1.12 16.94 0.619

2008 25 3.68 3.54 0.97 0.12 0.42 0.1 0.1 1.09 15.14 0.419

2009 26 -33.8 -50.4 0.95 0.48 0.67 0.07 0.067 1.08 18.36

166

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

DIAMOND 1997 27 3.65 42.75 0.44 0.11 0.27 0.19 0.08 1.02 13.54 0.036

1998 28 4.15 49.3 0.51 0.04 0.33 0.18 0.09 1.03 18.29 0.418

1999 29 3.89 46.27 0.57 0.06 0.32 0.1 0.09 1.04 21.32 0.419

2000 30 4.05 43.1 0.6 0.07 0.39 0.03 0.02 1.05 17.98 0.472

2001 31 4.69 54.45 0.58 0.02 0.44 0.02 0.01 1.07 18.29 0.469

2002 32 3.56 41.05 0.5 0.04 0.46 0.04 0.02 1.17 24.4 0.43

2003 33 0.54 6.648 0.37 0.12 0.34 0.04 0.01 1.2 20.48 0.296

2004 34 1.68 18.03 0.41 0.08 0.43 0.05 0.02 1.2 19.15 0.368

2005 35 2.69 17 0.88 0.06 0.4 0.04 0.02 1.25 17.85 0.476

2006 36 2.39 15.55 0.82 0.05 0.54 0.02 0.01 1.2 17.26 0.52

2007 37 2.81 16.65 0.69 0.07 0.46 0.02 0.02 1.15 16.94 0.427

2008 38 2.49 22.8 0.85 0.73 0.57 0.01 0.01 1.19 15.14 0.648

2009 39 1.28 11.52 0.87 0.61 0.65 0.01 0.01 1.16 18.36 0.649

167

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

ECOBANK 1997 40 0.04 0.48 0.6 0.54 0.16 0.09 1.08 13.54 0.099

1998 41 30.08 3.85 0.59 0.07 0.28 0.21 0.12 1.09 18.29 0.444

1999 42 34.23 3.71 0.76 0.11 0.29 0.14 0.11 1.08 21.32 0.362

2000 43 37.15 2.91 0.45 0.12 0.3 0.25 0.11 1.08 17.98 0.0281

2001 44 36.9 3.9 0.51 0.92 0.31 0.2 0.1 1.06 18.29 0.304

2002 45 24.24 4.25 0.26 0.94 0.33 0.46 0.12 1.07 24.4 0.441

2003 46 31.57 4.15 0.35 0.89 0.41 0.36 0.12 1.09 20.48 0.492

2004 47 29.84 3.96 0.74 0.15 0.37 0.12 0.11 1.08 19.15 0.544

2005 48 8.79 1.48 0.8 0.84 0.59 0.49 0.39 1.62 17.85 0.54

2006 49 17.09 3.79 0.86 0.48 0.62 0.25 0.22 1.23 17.26 0.648

2007 50 28.85 0.32 0.77 0.09 0.52 0.44 0.03 1.07 16.94 0.496

2008 51 -0.002 2.82 0.91 0.41 0.46 0.19 0.17 1.16 15.14 0.081

2009 52 1.67 0.8 0.91 0.4 0.75 0.22 0.2 1.18 18.36 0.673

168

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

FCM BANK 1997 53 0.49 0.76 0.18 0.13 1.12 13.54 0.421

1998 54 7.2 8.1 0.57 0.61 0.21 0.12 1.09 18.29 0.526

1999 55 6.15 7.1 0.38 0.67 0.29 0.11 1.98 21.32 0.344

2000 56 5.89 5.1 0.42 0.75 0.26 0.11 1.07 17.98 0.403

2001 57 4.1 20.1 0.69 18.29

2002 58 3.35 22.5 0.55 0.7 0.26 0.14 1.13 24.4 0.48

2003 59 3.8 2.23 0.75 0.63 0.13 0.1 0.92 20.48 0.462

2004 60 1.12 6.97 0.76 0.09 0.44 0.76 0.58 0.93 19.15 0.561

2005 61 2.13 15.29 0.91 0.08 0.42 0.15 0.14 0.97 17.85 0.557

2006 62 3.64 14.56 0.8 0.31 0.27 0.3 0.24 1.25 17.26 0.5

2007 63 2.88 23.65 0.85 0.03 0.45 0.13 0.11 1.13 16.94 0.615

2008 64 3.96 22.6 0.53 0.1 0.67 0.53 0.28 1.45 15.14 0.497

2009 65 13.81 4.89 0.63 0.09 0.77 0.39 0.25 1.31 18.36 0.616

169

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

FIDELITY 1997 66 13.54

1998 67 3.08 31.08 0.52 0.3 0.32 0.16 1.08 18.29 0.454

1999 68 3.12 32.05 0.52 0.4 0.26 0.13 1.07 21.32 0.042

2000 69 2.66 2.46 0.39 0.38 0.15 0.09 1.03 17.98 0.469

2001 70 3.48 34 0.27 0.19 0.2 0.23 0.1 1.01 18.29 0.313

2002 71 4.05 33.09 0.61 0.21 0.32 0.16 0.12 1.07 24.4 0.442

2003 72 4.82 43.14 0.68 0.19 0.42 0.16 0.11 1.07 20.48 0.426

2004 73 3.91 30.61 0.74 0.18 0.5 0.17 0.12 1.1 19.15 0.385

2005 74 6.95 16.08 0.81 0.11 0.68 0.34 0.27 1.34 17.85 0.561

2006 75 2.99 14.02 0.94 0.16 0.47 0.22 0.21 1.22 17.26 0.505

2007 76 2.03 14.08 0.87 0.08 0.4 0.15 0.13 1.12 16.94 0.302

2008 77 2.96 1.16 0.91 0.03 0.6 0.27 0.25 1.3 15.14 0.259

2009 78 0.9 0.35 0.9 0.19 0.6 0.28 0.25 1.28 18.36 0.514

170

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

FINBANK 1997 79 13.54

1998 80 3.42 3.42 0.16 0.39 1.07 0.29 0.97 18.29

1999 81 3.34 3.42 0.29 0.51 0.85 0.25 1.25 21.32

2000 82 1.29 6.33 0.34 0.54 0.26 0.09 0.96 17.98 0.348

2001 83 2.04 12.52 0.55 0.05 0.58 0.2 0.11 0.94 18.29 0.551

2002 84 3.6 26.02 0.37 0.81 0.69 0.75 0.28 0.94 24.4 0.366

2003 85 1.94 -19.55 0.41 0.79 0.66 0.46 0.19 1.17 20.48 0.546

2004 86 2 -14.5 0.37 0.76 0.69 0.38 0.14 1.11 19.15 0.552

2005 87 -2.69 3.15 0.52 0.69 -0.25 -0.13 0.79 17.85 0.428

2006 88 -9.7 -55.39 0.74 0.53 0.43 0.22 0.17 0.97 17.26 0.504

2007 89 3.54 14.61 0.86 0.46 0.2 0.14 0.12 1.02 16.94 0.425

2008 90 -0.25 -0.284 0.93 0.37 0.39 0.09 0.08 1.04 15.14 0.663

2009 91 -0.68 9 0.85 0.85 0.25 -0.89 -0.76 0.51 18.36 0.561

171

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

FIRST BANK 1997 92 5.32 1.8 0.29 0.25 0.29 0.29 0.09 1.03 13.54 0.258

1998 93 2.77 37.71 0.41 0.18 0.4 0.23 0.09 1.06 18.29 0.408

1999 94 3.11 45.1 0.4 0.22 0.38 0.21 0.09 1.05 21.32 0.397

2000 95 2.96 44.75 0.26 0.34 0.28 0.29 0.08 1.04 17.98 0.256

2001 96 3 39.1 0.33 0.23 0.32 0.29 0.09 3.4 18.29 2.783

2002 97 2.12 36.42 0.27 0.34 0.37 0.24 0.07 1.04 24.4 0.486

2003 98 3.52 58.55 0.25 0.4 0.23 0.08 0.08 1.07 20.48 0.395

2004 99 3.87 39.87 0.34 0.35 0.33 0.36 0.12 1.14 19.15 0.359

2005 100 3.57 36.27 0.88 0.23 0.37 0.13 0.11 1.13 17.85 0.404

2006 101 3.55 36.44 0.88 0.09 0.39 0.12 0.11 1.11 17.26 0.465

2007 102 2.89 31.55 0.88 0.02 0.36 0.12 0.1 1.1 16.94 0.441

2008 103 3.26 11.18 0.89 0.01 0.66 0.32 0.29 1.43 15.14 0.602

2009 104 2.76 13.13 0.89 0.05 0.63 0.23 0.21 1.28 18.36 0.498

172

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

GT BANK 1997 105 7 6.7 0.67 0.05 0.62 0.15 0.1 1.07 13.54 5.4

1998 106 5.01 4.2 0.61 0.03 0.61 0.19 0.1 0.92 18.29 0.55

1999 107 4.71 3.32 0.68 0.03 0.77 0.2 0.14 0.95 21.32 0.598

2000 108 4.08 4.2 0.7 0.05 0.52 0.13 0.09 0.95 17.98 0.537

2001 109 5.01 4.97 0.55 0.04 0.5 0.18 0.1 1.07 18.29 0.487

2002 110 5.24 3.87 0.68 0.02 0.56 0.19 0.13 1.37 24.4 0.573

2003 111 4.5 3.97 0.82 0.03 0.6 0.13 0.19 1.11 20.48 0.601

2004 112 4.2 3.8 0.7 0.03 0.58 0.15 0.1 1.08 19.15 0.613

2005 113 4.17 1.93 0.68 0.02 0.67 0.31 0.21 1.25 17.85 5.93

2006 114 3.3 2.46 0.72 0.03 0.39 0.18 0.13 1.1 17.26 0.554

2007 115 3.2 3.23 0.7 0.15 0.39 0.14 0.09 1.09 16.94 0.536

2008 116 3.8 1.68 0.81 0.02 0.81 0.27 0.22 1.32 15.14 6.9

2009 117 2.62 1.49 0.73 0.12 0.8 0.24 0.18 1.28 18.36 0.693

173

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

INTERCONTINENTAL 1997 118 8.1 5.2 0.6 0.05 0.25 0.15 1.11 13.54 0.369

1998 119 6.25 50.83 0.58 0.57 0.25 0.15 1.1 18.29 0.322

1999 120 4.55 44.25 0.46 0.45 0.29 0.13 1.09 21.32 0.404

2000 121 5.57 48.82 0.47 0.07 0.52 0.24 0.11 1.09 17.98 0.418

2001 122 4.55 46.24 0.39 0.17 0.43 0.24 0.09 1.04 18.29 0.364

2002 123 4.14 28.07 0.83 0.19 0.36 0.18 0.15 1.13 24.4 0.452

2003 124 4.49 42.98 0.78 0.13 0.48 0.15 0.12 1.09 20.48 0.965

2004 125 19.15

2005 126 4 23.5 0.71 0.05 0.51 0.27 0.19 1.22 17.85 0.419

2006 127 2.78 18.84 0.88 0.05 0.64 0.16 0.14 1.14 17.26 0.571

2007 128 3.21 14.43 0.88 0.04 0.54 0.26 0.23 1.27 16.94 0.68

2008 129 3.1 2.1 0.9 0.18 0.16 0.39 0.14 1.14 15.14 0.657

2009 130 18.36

174

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

OCEANIC 1997 131 13.54

1998 132 3.15 40.45 0.36 18.29

1999 133 3.24 51.24 0.39 0.37 0.16 0.06 1.03 21.32 0.254

2000 134 6.39 91.6 0.22 0.25 0.31 0.06 1.04 17.98 0.18

2001 135 7.65 69.41 0.31 0.3 0.33 0.1 1.07 18.29 0.289

2002 136 5.86 56.08 0.92 0.28 0.35 0.01 1.06 24.4 0.344

2003 137 5.06 41.23 0.28 0.06 0.26 0.42 0.12 1 20.48 0.332

2004 138 3.97 33.25 0.37 0.43 0.37 0.31 0.11 1.1 19.15 0.443

2005 139 3.34 23.37 0.42 0.51 0.47 0.33 0.14 1.13 17.85 0.544

2006 140 3.05 30.24 0.9 0.72 0.32 0.11 0.1 1.07 17.26 0.569

2007 141 2.22 10.33 0.78 0.57 0.49 0.22 0.21 1.24 16.94 0.435

2008 142 -2.7 9.89 0.78 0.98 0.44 -0.03 -0.02 0.92 15.14 0.552

2009 143 -1.3 9.3 0.88 0.7 -0.16 -0.14 0.9 18.36 0.881

175

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

UBA 1997 144 1.2 1.7 0.6 0.38 0.27 0.16 0.09 1.06 13.54

1998 145 5.3 25.66 0.51 0.13 0.34 0.13 0.07 1.03 18.29 0.377

1999 146 1.92 36.84 0.49 0.22 0.34 0.1 0.05 1.02 21.32 0.358

2000 147 3.32 2.92 0.51 0.34 0.21 0.11 0.06 1.02 17.98 0.225

2001 148 3.32 59.97 0.5 0.26 0.18 0.05 0.04 1.02 18.29 0.247

2002 149 1.23 29.22 0.45 0.03 0.3 0.01 0.05 1.03 24.4 0.303

2003 150 2.52 35.52 0.51 0.08 0.32 0.14 0.07 1.05 20.48 0.234

2004 151 2.84 29.68 0.54 0.03 0.37 0.16 0.09 1.08 19.15 0.35

2005 152 2.6 29.94 0.53 0.03 0.33 0.14 0.07 1.06 17.85 0.294

2006 153 1.45 13.67 0.87 0.12 0.14 0.06 0.05 1.02 17.26 0.187

2007 154 2.48 17.21 0.86 0.04 0.35 0.16 0.14 1.12 16.94 0.57

2008 155 3.59 2.9 0.91 0.41 0.46 0.08 0.07 1.04 15.14 0.509

2009 156 4.28 3.65 0.92 0.06 0.49 0.14 0.13 1.11 18.36 4.983

176

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

UNION 1997 157 0.01 0.25 0.32 0.35 0.36 0.22 0.07 1 13.54 0.542

1998 158 2.12 25.66 0.87 0.3 0.32 0.06 0.05 1.01 18.29 0.561

1999 159 2.92 36.84 0.87 0.36 0.29 0.06 0.06 1.02 21.32 0.479

2000 160 2.92 2.92 0.87 0.23 0.27 0.06 0.05 1.02 17.98 2.79

2001 161 3.33 59.97 0.88 0.22 0.21 0.07 0.06 1.02 18.29 0.455

2002 162 3 29.22 0.88 0.25 0.22 0.12 0.07 1.08 24.4 0.476

2003 163 3.33 35.52 0.87 0.25 0.24 0.11 0.09 1.07 20.48 0.349

2004 164 2.82 29.68 0.9 0.23 0.32 0.23 0.09 1.07 19.15 0.264

2005 165 2.35 29.94 0.41 0.19 0.39 0.23 0.09 1.07 17.85 0.206

2006 166 2.03 13.67 0.59 0.17 0.45 0.21 0.18 1.18 17.26 0.368

2007 167 2.49 17.21 0.9 0.16 0.35 0.16 0.15 1.14 16.94 0.442

2008 168 0.03 0.26 0.92 0.24 0.37 0.13 0.12 1.11 15.14 0.492

2009 169 -0.07 -1.25 0.88 0.22 0.52 0.05 0.04 1 18.36 0.584

177

BANK YEAR V3 ROTA ROSE RAYA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

WEMA 1997 170 1.96 20.9 0.53 0.09 0.5 0.17 0.09 0.97 13.54 0.432

1998 171 2.03 24.4 0.55 0.07 0.47 0.15 0.08 1.01 18.29 0.507

1999 172 2.73 28.32 0.59 0.08 0.53 0.16 0.09 1.03 21.32 0.557

2000 173 1.34 13.13 0.52 0.27 0.35 0.19 0.1 1.03 17.98 0.446

2001 174 2.06 30.82 0.37 0.2 0.34 0.17 0.06 1.02 18.29 0.361

2002 175 5.2 60.87 0.42 0.14 0.37 0.19 0.08 1.02 24.4 0.393

2003 176 3.73 31.68 0.43 0.1 0.38 0.26 0.11 1.08 20.48 0.446

2004 177 1.99 17.66 0.67 0.17 0.58 0.16 0.11 0.07 19.15 6.163

2005 178 1.02 4.13 0.75 0.28 0.75 0.32 0.24 1.28 17.85 0.634

2006 179 -5.99 -35.05 0.67 0.56 0.62 0.25 0.17 1.1 17.26 0.604

2007 180 1.14 7.45 0.7 0.23 0.55 0.21 0.15 1.08 16.94 0.619

2008 181 3.6 11.28 0.83 0.7 0.36 0.04 0.03 0.78 15.14 0.559

2009 182 2.1 5.62 0.84 0.22 0.42 0.12 0.04 0.9 18.36 0.0005

178

BANK YEAR V3 ROTA ROSE RATA NPL LOANDER CAR CAPASS LIQ INTEREST RWATA

ZENITH 1997 183 6.4 42.2 0.29 0.6 0.05 0.016 1.06 13.54 0.279

1998 184 5.78 41.25 0.31 0.5 0.08 0.02 1.07 18.29 0.306

1999 185 4.49 44.96 0.3 0.07 0.51 0.05 0.02 1.07 21.32 0.303

2000 186 4.57 38.28 0.32 0.18 0.48 0.04 0.01 1.08 17.98 0.309

2001 187 4.66 40.98 0.27 0.15 0.41 0.06 0.01 1.08 18.29 0.228

2002 188 4.32 41.94 0.25 0.13 0.4 0.04 0.01 1.07 24.4 0.231

2003 189 4.83 42.24 0.26 0.02 0.44 0.05 0.13 1.07 20.48 0.25

2004 190 3.31 40.9 0.32 0.1 0.41 0.02 0.08 1.03 19.15 0.307

2005 191 2.78 24.3 0.41 0.16 0.52 0.02 0.09 1.09 17.85 0.397

2006 192 2.49 16.2 0.37 0.11 0.51 0.02 0.07 1.18 17.26 0.36

2007 193 2.63 20.64 0.32 0.18 0.38 0.02 0.05 1.14 16.94 0.528

2008 194 2.91 58.45 0.31 0.02 0.35 0.15 0.04 1.25 15.14 0.414

2009 195 2.02 25.3 0.87 0.06 0.6 0.09 0.07 1.2 18.36 0.626

179