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
57
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
58
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.
64
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
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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.
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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
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financial crises” Working paper.
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Eid S., (2011), “Monetary policy, Risk-Taking Channel and Income Structure: An empirical
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Greene W. H., (2003), Econometric Analysis, Fifth Edition, Prentice Hall
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Jeitschiko, T. D., Jeung, S. D., (2007), “Do well Capitalized Banks Take More Risk? Evidence
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Interest Rates: Evidence from the Euro Area and the U.S. lending Standard,” European
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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.
137
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
138
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.
140
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