ABSTRACT
This paper
unmasks the impact of banks characteristics, financial structure and
macroeconomic indicators on banks’ Capital base in the Nigerian banking
industry. The study does not account for ratio analysis in the computation of
capital adequacy but rather it examines the determinant of Capital adequacy in
Nigeria during the period 1980 – 2008 within an error correction framework.
Co-integration technique revealed that economic indicators such as rate of
inflation, real exchange rate, demand deposits, money supply, political
instability, and return on investment are most robust predictors of the
determinants of capital adequacy in Nigeria.
After the global credit crunch
capital adequacy, being critical for banks, led the study to examine the
relationship between bank capital base and macroeconomics variables. This
implies that political stability may reduce financial distress and bankruptcy
why foreign investment will affect Banks capital in most developing economy in
the period of financial crisis. However, the study also establishes that there
is a negative relationship between inflation and banks capital base as inflation
erode banks capital in most developing economy. This simply means that Nigerian
government should regulate investment policy why banks regulators should strive
to keep inflation rate at a minimum level, if possible below 5% for them to be
more efficient so as to be globally competitive.
INTRODUCTION
One of the biggest achievements
in the financial sector of the Nigeria economy in 2005 was the reform of the
Banking Sector. It was an achievement via the Central bank of Nigeria increase
in bank capital above 1000 percent. It was an exercise that resulted in the
reduction of Nigeria motley group of mainly anaemic 89 banks to 25 bigger,
stronger and more resilient financial institutions. The reforms engineered a
revolution in the financial services industry leading to an increase both in
the quality of service and quantity of financial products available to
Nigerians and to checkmate the capital adequacy of the banks. Capital Adequacy
can be percentage ratio of a financial institution's primary capital to its
assets (loans and investments), used as a measure of its financial strength and
stability. According to the Capital Adequacy Standard set by Bank for
International Settlements (BIS), banks must have a primary capital base equal at
least to eight percent of their assets: a bank that lends 12 dollars for every
dollar of its capital is within the prescribed limits. However, the assessment
of capital adequacy for precautionary purposes is problematic at best due to
rapidly changing economic and financial services industry. Another role of
capital is the fact that the viability of a bank depends to a critical extent
upon public confidence. There is a strong public relation aspect to capital
adequacy also. It is generally recognized that the availability of capital is
neither a perfect indicator of the state of health of a bank nor a sufficient
condition to ensure the maintenance of confidence by depositors and creditors,
but no doubt, it represents a major element in shaping their perception of the
solidity of an institution. Capital level is used by most regulators to
restrict credit expansion. That explains why banks management are inspired to
determine the correlation between variables like Total credit loan, Demand
deposit, Inflation rate, Political instability, Money supply, Liquidity risk,
Investment and Capital etc and hence indicate whether large capital are
negatively or positively compel banks to meet the capital adequacy requirement
or seek additional capital so as to meet their credit expansion target. By
looking at banks role as a financial intermediary, capital adequacy and
macroeconomic variables has become a key indicator of a bank capital whereby
inflation erodes banks capital in most developing countries. Indeed, several studies
have found evidence that the development of the banking sector is related to
economic growth. The importance of capital adequacy in the banking sub-sector
of the Nigeria economic and financial development directs us to investigate
which economic- macro or micro, banks ratios and balance sheet and
institutional factors that give rise to a vibrant capital adequacy.
Therefore, the problem here is to
use co-integration to determine whether there is a linear relationship between
banks capital and macroeconomics variables and if there is, whether the degree
of linearity is such that capital adequacy issues could be largely a matter of
bank failure or business exigencies as opposed to the current flex of legal
muscle by the regulatory authorities. Against this backdrop, the objectives of
the study are to empirically investigate the determinants of capital adequacy
with respect to economics variables. To analyze the various issues involved in
capital adequacy debate. To examine the components of bank capital and bank
consideration in selecting capital mix. To expound the diverse measurements of
capital adequacy particularly, the CAMELS is the acronym for Capital
(adequacy), Assets, Quality, Management, Earnings, Liquidity, and Sensitivity
to market risks. Furthermore, capital adequacy in the banking sector model is
to permit forecasting of capital adequacy pattern, which is useful for both
policy makers and the banking sector in general for formulating informed course
of action.
In spite of the importance of
banks as financial intermediaries, capital adequacy modelling has not been in
the mainstream of econometric research into the financial sector in Nigeria.
Analyses of the banking sector have so far focused on qualitative assessment of
growth trends and sectoral behaviour patterns in the industry. Discussion in
those studies has, for instance, suggested a number of factors that may
influence the failure pattern of banks, bank products and management. There has
been no model designed to determine the relative impact of banks capital and
macroeconomics variables and their possible linkages between the banking sector
and the real sector of the economy. Since independence, no consensus has been
reached by different Scholars as regards the determinants of capital adequacy
with macroeconomics variables in Nigeria.
Opinion differs among experts in
banking and finance as to what constitutes adequate capital but they all agree
that it is an age long issue for which there do not seem to be any consensus in
sight. Thus, as noted by Nwankwo (1990), Adegbite (2010), the issue of what
constitutes an adequate capital for banks has a long history. It is in fact,
almost as old as banking itself.
Sanusi (2010) was even more
satirical in answering the question of how much capital a bank needs to ensure
the confidence of depositors, creditors, investors and regulators in a country
of high inflation rate and economic instability, when he noted “that in banking
and finance literature, this question is noted as the issue of capital
adequacy. Anyone who knows the answer can gain instant notoriety in the
banking, financial and regulatory communities.
The battle line appears drawn
between the regulators and the bankers. Regulators concerned with the safety of
banks, the viability of insurance funds and stability of financial markets
prefer more capital. This reduced the likelihood of failure and increases bank
liquidity. Bankers on the other hand generally prefer to operate with less
capital. The smaller banker equity base the greater the financial leverage and
equity multiplier. High leverage converts a normal return on assets unto a high
return on equity- Koch (2004). The complexity of the problem brings to the fore
the following questions: what is capital? What are its components? What amount
is adequate? Who determines it and what methodology is appropriate in measuring
bank capital? And what factors determine capital adequacy?
However, the battle between the
banks and regulatory authorities is centered after a prolonged period of recession
and macro-economic instability. Hitherto, several studies have emphasized the
importance of capital adequacy and there is need to review related studies in
order to gain more understanding of the subject.
Mpuga (2002) argued that the
inadequacy of minimum capital standards in accounting for risks in banks assets
portfolio could be one of the major factors leading to bank failures. He
studied the 1998-99 banking crisis in Uganda and how the new banking guidelines
in Uganda was to increase bank solvency and capital adequacy by shifting their
portfolio towards lower risk assets, in an effort to meet the new requirements
Marc J Epstein. (2005) studied on capital adequacy failures and concludes that
capital adequacy and ratio analysis (CA&R) are failed strategies. However,
analysis of the causes of failure has often been shallow and the measures of
success weak.
Yu Min-The (2006), defined the
adequate capital for banks as the level at which the deposit insuring agency
would just breakeven in guaranteeing the deposits of individual banks with
premium the banks pay. An option of theoretical framework was employed in his
study for measuring fair capital adequacy holdings for a sample of depository
institutions in Taiwan, during 1985-1992. Except for the 1989, most banks in
their sample proved to be inadequately capitalized so that capital infusion is
required.
George E Halkos & Dimitrios
(2004) applied non-parametric analytic technique (data envelopment analysis,
DEA) in measuring the performances of the Greek banking sector with respect to
capital adequacy. He proved that data envelopment analysis can be used as
either an alternative or complement to ratio analysis for the evaluation of an
organization's performance with attention to macroeconomics indicators.
Morris Knapp, Alan Gart &
Mukesh Chaudhry (2006) research studies examine the tendency for serial
correlation in bank holding company profitability, finding significant evidence
of reversion to the industry mean in profitability. The paper then considers
the impact of mean reversion on the evaluation of post-merger performance of
bank holding companies. The research concludes that when an adjustment is made
for the mean reversion, post-merger results significantly exceed those of the
industry in the first 5 years after the merger.
Ping-wen Lin (2002) findings
proves that there is a negative correlation and statistical significance
existing between cost inefficiency index and bank capital; meaning banks
engaging in low capital tend to improve cost efficiency. However, the data
envelopment analysis empirical analysis found that bank capital did not improve
significantly to cost efficiency of banks. In another study, he found that (1)
generally; banks capital tend to upgrade the technical efficiency, a locative
efficiency, and cost efficiency of banks; however a yearly decline was noted in
allocative efficiency and cost efficiency. (2) In terms of technical efficiency
and allocative efficiency improvement, the effects of banks mergers were
significant; however, in terms of cost efficiency improvement, the effect was
insignificant.
Robert DeYoung (1997) estimated
pre- and post-merger X-inefficiency in 348 mergers approved by the OCC in
1987/1988. Efficiency improved in only a small majority of mergers, and these
gains were unrelated to the acquiring banks efficiency advantage over its
targets. Efficiency gains were concentrated in mergers where acquiring banks
made frequent acquisitions, suggesting the presence of experience effects.
Chol(2000), studied the credit
crunch in the banking sector in Korea in year 1997, found the replacement of an
old capital standards with risk based and macro economics based variables RBC
& MBC 1997, increased banks below the regulatory capital requirements from
0-14, and a number reduced to 7 in 1998 and the bank’s capital deficiency
amounted to 59 percent of the total Korea asset in 1997.
Hassan (2008) mentioned that
banks had been exposed to standby letters of credit (SLC) and off-balance sheet
activities, which has become a major concern to regulators. This means that
macroeconomic variables such as inflation play a greater role in the
determinants of capital adequacy in most developing countries like Nigeria.
Ajayi (2008), the macroeconomic
indicators (i.e. inflation and economic growth) are significant in spread, bank
capital adequacy and profit regressions. This may suggest that banks tend to
not being profitable in inflationary environment. In addition, economic growth
does not reflect any aspects of banking regulations and technology advance in
the banking sector with require pressing attention.
Adegbite (2010), a cursory look
at the Nigerian financial system shows that the system has been performing its
expected functions albeit at less – than – optimal levels. The incidence of
recurring financial systems crisis is testimony to the fact that the financial
system’s performance still leaves much to be desired. The apex
regulatory/supervisory body in the Nigerian financial system is the Central
Bank of Nigeria (CBN) which came into being in 1959 as a result of the CBN Act
of 1958. Over the years the CBN 1958 Act has been amended and in 1969 there was
another CBN Act and another in 1991 and another in 2007. Charged with the
responsibilities of “Promoting monetary stability, emphasis on capital adequacy
and sound financial structure in Nigeria the CBN Act of 2007, requires that the
CBN must be banker to other banks, “and must in cooperation with the other
banks promote and maintain adequate banking services for the public. The CBN according
to the CBN Act 2007 is also expected to ensure high standard of conduct and
management throughout the banking system”.
Ojo J. A. & Adegbite (2010),
Macroeconomic stability as an ingredient of financial stability requires that
macroeconomic policies must be antitypical, dousing excessive trend in any
direction, maintaining stable prices, ensuring that public sector deficits are
minimal and external debt is sustainable. A stable macroeconomic framework is
one where the level of national saving is high enough to prevent undue reliance
on foreign borrowing. For macroeconomic stability needed to maintain financial
stability, macroeconomic policy instruments must be adequate and consistent
with the exchange rate regime if not inflation will erode banks capital. The
framework for maintaining financial stability requires that if the financial
institutions are stable and macroeconomic is stable then nature of regulatory
and supervisory policies should be preventive. If however the institutions are
at the brink or border of stability and many any moment plunges into
instability, then the nature of regulatory/supervisory policies should be
remedial. If however the institutions have become instable already then the
policies should be Resolution policies.
Newman L. (2010), banks capital
may be affected In face of declining foreign exchange earnings the naira
depreciated against the dollar and foreign reserves fell remarkably from $67
bill in June 2008 to $57 billion by December 2008.
Kweme (2003) also noted that
changes in the structure and stability of banks profit have sometimes been
motivated by statutory reserves. In other to maintain confidence in the banking
system, banks which are subject to minimum capital may cause banks to change
their business mix in favour of activities and assets that entail a lower
capital requirement.
Nnanna (2003), the international
financial crisis of the second half of the 1990s provoked more reflection on
ways to strengthen the global financial system. The international community
identified a number of priorities, including the need to enhance its own
ability to monitor the health of the financial system. The ability to monitor
the financial sector soundness presupposes the existence of valid indicators
which can measure the health and stability of financial systems. The general
macro-prudent indicators as developed by the IMF for assessing and supervising
banks are embedded in the CAMELS framework. CAMELS is the acronym for Capital
(adequacy), Assets, Quality, Management, Earnings, Liquidity, and Sensitivity
to market risks.
In 2009 the Central bank of
Nigeria declared 5 banks in Nigeria as insolvent. The banks were Afribank,
Union Bank, Oceanic Bank, Bank PHB and Intercontinental Bank. In 2011 the
Central Bank of Nigeria declares the take-over of Bank PHB, Sterling Bank and
Afribank by investors or in other word call for the nationalization of those
banks. Before the establishment of Central Bank of Nigeria in 1958 there have
been serious cases of Bank failures and unhealthy capital adequacy base
resulting to uncountable reasons of Bank failures. One of the crucial reasons
of bank failure is inappropriate determinants of capital adequacy. The first
bank failure and unhealthy capital adequacy in Nigeria can be traced to 1930s when
21 Banks were identified as bankrupt. The second Bank failure in Nigeria can be
traced to 1989 where 8 Banks were identified to be weak and in the year 1998
total bank distress were up to 31. Third Bank failure in Nigeria was in the
year 2004 where 89 banks were reduced to 25 banks that is to say that 64 banks
were regarded to be in distressed state. The reason behind this is the
inability of regulators to oversee the activities of these Banks. The causes of
Bank failures cannot be underestimated hence proper attention should be given
to this sector.
I.
MATERIALS AND METHODS
This applies to the error correction
methodology to a regression model based on the traditional determinants of
capital adequacy in the banking sub-sector of the Nigeria economy distilled
from the literature. The idea is to subject the variables to stationary test
and subsequently remove the non- stationary trends by differencing before
regressing. This removes the possibility of the so-called spurious regression.
Any previous studies on the determinants of capital adequacy in the banking
sub-sector of the Nigeria economy if there exist any in Nigeria may not have
considered the problem of unit roots in the determinants of capital adequacy
and macroeconomics variables. As a result, the econometric methodology used in
those studies did not account for non-stationarity in the data. The analysis
here is primarily based on Engle and Granger (1987), and Engle and Yoo (1987).
The idea is to determine the order of integration of the variables, that is, we
test whether they are stationary in their levels or whether they have to be
differenced once or more before they become stationary. Testing for unit roots
is carried out by using an Augmented Dickey-Fuller (ADF) test.
MODEL SPECIFICATION
In order to account for the
determinants of capital adequacy in the banking sub-sector of the Nigeria
economy, the model for the study is hereby specified as follows:
CAB =f (TL, MS,
DIR, INFL, DL, POL, ER, LQ, OPEN, INV).
The above model is hereby written
in log —linear form as:
(L) CAB=
bo + b1TCL(L) + b2MS(L) + b3DIR(L) + b4INFL(L) + b5DL(L) + b6POL(L) + b7ER(L) +
b8LQ(L) + b9OPEN + b10INV + μt ……………………………………………………………………….…….E(1)
apriori, b1> 0, b2> 0, b3> 0,
b4<0, b5> 0, b6<0, b7> 0, b8> 0, b9> 0, b10 > 0
Where:
* CAB = CAPITAL ADEQUACY BASE
TL = TOTAL LOANS.
MS = MONEY SUPPLY
DIR = DOMESTIC INTEREST RATE (REAL)
INFL = INFLATION RATE
DL = DEMAND DEPOSIT
POL = POLITICAL INSTABILITY DUMMY = 1 MILITARY REGIME AND TURBULENT
YEARS,
0 OTHERWISE
ER = EXCHANGE RATE
LQR = LIQUIDITY RISK
OPEN = OPENNESS OF THE ECONOMY (TOTAL TRADE /GDP RATIO)
INV = INVESTMENT proxy by long US interest rate
Capital adequacy being the
dependent variable is the total asset of banks deflated by total number of
capitalize banks operating in the economy while the independent variables such
as demand deposit is total deposits including private and public, investment
include both local and foreign direct investment while others variables
includes total loans, money supply and interest rate (real), exchange rate,
inflation rate (nominal), political instability –including civilian and
military regime. Ut = Captures other variable not included in the model and it
takes care of other factors that cannot be observed or computed due to lack of
data. Ut is referred to as error term, residual or stochastic term.
“The Data Analysis technique
consists of an approach designed to capture the long-run relationship between
the dependent and independent variables, while avoiding spurious influences.
This is the co-integration and error correction techniques which have received
prominent attention in literature (see Adam, 1992, Engle and Granger, 1987,
Gilbert, 1986, Hendry and Richard 1983 and Thomas 1993).
The aim of the new framework was
to ascertain the time characteristics of data, overcome the problems of
spurious correlation often associated with none —stationary time series data,
and generated long —run variable relationship simultaneously. Within this
dispensation, an important starting point for research is an assessment of the
degree of integration of the relevant variables and to check whether they are
co-integrated or not. It should be noted that an important issue in
econometrics is the need to integrate short-run dynamics with long-run
equilibrium. The analysis of short-run dynamics is often done by first
eliminating trends in the variables, usually by differencing. The theory of
co-integration development in Granger (1981) and elaborated in Engle and
Granger (1987) addressed this issue of integrating short-run dynamics with
long-run equilibrium.
Similarly, it is important to
note that the usual starting point of ECM modeling is to assess the order of
integration of both the dependent and independent variables in the model. The
order of integration ascertains the number of time a variable will be
differentiated to arrive at stationary. Dickey-fuller (DF), Augmented
Dickey-Fuller (ADF) and Sargan - Rhargava Durban- Watson (SRDW) are the widely
used test for stationary for both individual time series and residual from OLS
regressions. Co-integration is based on the properties of the residuals from
regression analysis when the series are individually non-stationary.
The original co-integration
regression is specified as follows:
……………………. (1)
Where A represents the dependent
variables, stands for the independent
variable, and e is the random error
term. 0 and 1
are intercept and slope coefficients respectively. To include the possibility
of bi-directional causality, the reverse specification of equation 1 is
considered. To provide a more defensive answer to the non-stationarity in each
time series, the Dickey-Fuller (1979) regression is estimated as follows for a
unit root:
…………………………….. (2)
If X Equals zero e is non-stationary. As a result, A and B are not
co-integrated. In other words, if X is significantly different from zero A and
B is found integrated individually.
Given the inherent weakness of
the root test to
distinguish between the
null and the alternative hypothesis, it is desirable that the Augmented
Dickey-Fuller (ADF) (1981) test
be applied. The desirability is warranted because it corrects for any
serial correlation by incorporating logged changes of the residuals. To be
co-integrated, both A and B must have the same order of integration (Eagle and
Granger, 1987 and Granger, 1986).
The ADF regression is specified as
follows:
…………………. (3)
…………………. (3)
Where ∆ is the first different operator and μt is the
new random error term. M is the optimum number of lags needed to obtain “white
noise” This is approximated when the DW value approaches 2.0 numerically. The null hypothesis of non-co-integration is
rejected, if the estimated ADF statistics is found to be larger than its
critical value at 1 or 5 or 10 per cent level of significance.
If At and Bt are found to be co-integrated, then there must exist
an associated error-correlation Model (ECM), according to Engle and Granger
(1987). The usual ECM may take the following form:
……… (4)
Where ∆ denotes the different operator et-1
is the error correction term, T is the number of lags necessary to obtain
white noise and Vt is another
random disturbance term σoet-1 is significantly
different from zero, then A and B have long-Run relationship. The
error-correction term (et-I) depicts the extent of disequilibrium
between A and B The ECM, reveals further that the change in At not
only depends on lagged changes in Bt, but also on its own lagged
changes. It is appealing due to its ability to induce flexibility by combining
the short-run and long-run dynamics in a unified system. Also, the estimates of
the parameters of the ECM are generally consistent and efficient (Ilendry and
Richard, 1983).
DATA PRESENTATION AND
ANALYSIS
TABLE I: STATIONARY TEST.
|
Variables
|
ADF Test
|
Order of
Integration
|
|
Log
CAD
|
0.04925
(-29969)
|
1
(1)
|
|
Log
CAD
|
-3.7333
(-3.0114)
|
1
(0)
|
|
Log
INV
|
-3.6876
(-2.9798)
|
1
(0)
|
|
Log
ER
|
-2.0299
(-2.9798)
|
1
(1)
|
|
Log
ER
|
-3.5063
(-2.9850)
|
1
(0)
|
|
A
Log DIR
|
-4.2833
(-2.9798)
|
1
(0)
|
|
Log
INV
|
-3.3697
(-2.9798)
|
1
(0)
|
|
Log
INFL
|
-1.3068
(-2.9969)
|
1
(1)
|
|
Log
INFL
|
-40706
(-3.0038)
|
1
(0)
|
|
Log
OPEN
|
0.8224
(-2.9798)
|
1
(1)
|
|
A
Log OPEN
|
-4.1436
(-2.9850)
|
1
(0)
|
|
Log
MS
|
-1.1022
(-2.9798)
|
1
(1)
|
|
A
Log MS
|
-3.0994
(-2.9850)
|
1
(0)
|
Source:
Computed
TABLE II:
JOHANSEN CO-INTEGRATION TEST
SAMPLE:
1980- 2008
Series:
log CAB, Log ER, Log INFL, Log OPEN, Log MS
|
Eigen
Value
|
Likelihood
Ratio
|
5%
Critical Value
|
1%
Critical Value
|
Hypothesized
No. of CE (s)
|
|
0.84
|
114.3228
|
94.15
|
103.18
|
None**
|
Note:*
(**) denotes rejection of the hypothesis at 5% (1%) significance level
L.R.
test indicates 2 co-integration equation(s) at 5 % significance level.
Lag
interval: 1to I
Source:
computed.
TABLE III:
Long-run capital Adequacy Determinants Model Estimates:
Modeling Log (CAB) by
OLS
Sample:
1980 – 2008
|
Variable
|
Co-efficient
|
t-value
|
|
Log
ER
|
0.6772
|
3.4397***
|
|
Log
INFL
|
-0.1325
|
-1.2558
|
|
Log
OPEN
|
0.2896
|
5.1303
|
|
Log
MS
|
0.6427
|
30.9551***
|
NOTES:
Adjusted R2 = 0.72 F= 21.327 a= 0.45
R2=0.75
Prob (F—statistic) = 0.00000
DW=1.87
Schwarz information Criterion 1.561
*significant
at 1% level
*significant
at 5% level
***significant
at 100% level
a=
S.E. of regression
Source:
computed
TABLE IV: Short-run over parameterized Capital Adequacy
Determinants Model.
Model
Estimates Log (CAB) of OLS
Sample:
1980-2008
|
Variables
|
Co-efficient
|
t-value
|
|
Constant
|
1.2840
|
2.6798
|
|
Δ
Log CAD (-1)
|
-0.5866
|
-3.9531***
|
|
Δ
Log INFL
|
-0.2160
|
-0.8619
|
|
Δ
Log INFL (-1)
|
0.1434
|
0.7085
|
|
Δ
Log ER
|
0.9177
|
3.5113***
|
|
Δ
Log ER (-1)
|
0.5939
|
0.7142
|
|
Δ
DIR
|
-0.0096
|
-0.8264
|
|
Δ
DIR (-1)
|
-0.0175
|
-1.5620
|
|
Δ
Log INV
|
-0.3253
|
-1.0929
|
|
Δ
Log INV (-1)
|
0.6758
|
1.8781
|
|
Δ
Log OPEN
|
-0.1542
|
-0.5330
|
|
Δ
Log OPEN (-1)
|
-0.1861
|
-0.6258
|
|
Δ
Log MS
|
-0.7079
|
-0.9319
|
|
Δ
Log MS (-1)
|
3.7842
|
4.2348***
|
|
POL
|
-0.0933
|
-0.5043
|
|
TL
|
-0.3155
|
1.1369
|
|
ΔL
|
-1.4232
|
-3.4808
|
|
Δ
Log LQR
|
0.3846
|
3.3403***
|
|
ECM
(-1)
|
-0.5414
|
2.4385***
|
Notes:
R2= 0.97 F=
10.61 a=0.215
Adj R2 = 0.88 Prob(F-statistic) = 0.007975
DW= 146 Schwarz Information Criterion
=0.713
Source: Computed
TABLE V:
Short- run Parsimonious Model Estimates Modeling
Log(CAB) by OLS
Sample:
1980-2008
|
Variables
|
Co-efficient
|
t-value
|
|
constant
|
1.6480
|
3.9047
|
|
Log
CAD (-1)
|
-0.6818
|
-3.816***
|
|
Log
INFL
|
0.0265
|
0.3570
|
|
Log
ER
|
0.8227
|
3.1236***
|
|
Log
DIR (-1)
|
-0.0193
|
-5.0554
|
|
Log
INV
|
-0.1811
|
-0.5548
|
|
Log
OPEN (-1)
|
-0.2630
|
-1.3896**
|
|
Log
MS (-1)
|
2.7025
|
3.3876***
|
|
POL
|
-0.2676
|
-1.4278
|
|
TCL
|
0.44711
|
2.2388**
|
|
ΔL
|
-1.2350
|
-4.7628***
|
|
Log
LQR
|
0.3498
|
3.5534***
|
|
ECM
(-1)
|
-10.5611
|
-2.9942***
|
Notes:
R2= 0.92 F=10.09 a=0.26
***
Significant at 1% Adj R2=
8.83 Prob (F-statistic)=0.0002770
**
Significant 5% DW=2.08 Schwarz
Information Criteria =1.10
*
Significant at 10%
Source:
Computed
III RESULTS AND DISCUSSIONS
The thrust of the study was to investigate empirically the
determinants of Capital Adequacy in the Banking Sub-sector in Nigeria, and to
test for the validity of some conjectures that have been advanced for the
determinant of Capital Adequacy in Nigeria. The study was motivated by its
importance and contribution to economic growth and development in the banking
sub-sector of the Nigeria economy instead of using the usual balance sheet
ratio computation or single equation analysis, an error correction model (ECM )
was adopted. This approach was preferred because according to past studies, an
Error Correction Model yields a better result that are more reliable and unspurious
than those reported for single equation models. The regression model results
obtained from the study are used as the effect of each variable. It was
discovered through this study that, there were long run relationship between
Exchange rate, Inflation rate, Political Instability, and Money Supply. Also,
all the variables employed in this study were all stationary at their first
difference except rate of real domestic interest rate and return on investment
that were stationary at their levels. There are some major findings that this
study has revealed.
These findings include:
1. From the result of the study, one could see that money supply
is an important determinant of capital adequacy base in Nigeria. Its high
coefficient and very strong level of significance even at one percent suggests
that increase in Money leads to an increase in Bank capital base. The increase
in CAB could also have a feedback effect on economic growth.
2. The real domestic interest rate is also an important
determinant of Bank capital adequacy base in Nigeria, since it is statistically
significant at one percent level of significance, although it is inversely
related to CAB which suggests that the rise in real cost of capital, informed
by an increase in real interest rate would tend to dampen CAB especially those
requiring some degrees of domestic capital.
3. The real exchange rate is another significant determinant of
CAB in Nigeria. Although, the coefficient is not as expected, but existing
literature emphasized an inverse relationship which implies that an increase,
in the real exchange rate will reduce the flow of Foreign direct investment and
so reducing CAB in Nigeria and vice versa.
4. The return on
investment in the rest of the world proxied by long- run US interest rate is
not a strong or significant determinant of CAB in Nigeria while Inflation rate
erodes CAB but existing literature has shown that foreign direct investment has
negative impact in developing economy during period of financial crisis.
5. The Deposit liabilities
and liquidity risk variables are not correctly signed and are not statistically
significant but may increase CAB via increase in money supply.
6. As can been seen,
the coefficients that appears on the INV have his theoretically predicted signs
and in general are statistically significant. The, result indicated that
Investment increases CAB via inflow of Foreign direct investments into Nigeria.
7. Lastly, the
political dummy used as proxy for political instability was appropriately
signed indicating that intermittent coup d’etat and incessant political
upheaval may serve to scare away potential foreign investors thereby, reducing
CAB in Nigeria.
CONCLUSION
The
aim of this empirical study is to investigate the determinants of capital
adequacy patterns in the Banking sub-sector with respect to the effect of
increased capitalization on the soundness of financial system in Nigeria. The
study applied the Error Correction Model (ECM) and found empirical support for
some conjectures made in the literatures. Given the importance of Capital
adequacy in any economy and the likely economic effects on banks’ capital on
growth and development, it becomes expedient to examine how Capital adequacy in
Nigeria can better be improved if attention is given to some macroeconomic
variables.