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CBN Deregulation and financial performance of deposit money bank in Nigeria
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DEREGULATION AND PERFORMANCE OF THE DEPOSIT MONEY BANKS IN
NIGERIA.
1
Abstract
This paper seeks to examine the impact of CBN deregulation and financial
performance of deposit money banks over a stipulated time period of 1986 to
2014, using secondary data sourced from the central Bank statistical Bulletin,
the study utilized the ordinary least square, the stationarity test, the
cointegration (johansen) and the granger causality test, it was discovered that
all predictor variables account moderately for changes in the criterion and
there exist no long run relationship while the short run relationship existent in
the model ran from the Return on Equity of the Deposit Money Banks to the
Monetary Policy Ratio (MPR) and Cash Reserve Ratio (CRR). It was
recommended that there should be appropriate planning before the
developments are carried out. There should be the ensuring of macroeconomic
stabilization, which is the ultimate, as the activities in all other sectors affect
this or is affected by it.
Keyword: Deregulation, Performance, Return on Asset, Return on Equity
2
1. Introduction
A solid and stable financial sector is essential to make a well-functioning
national economy and ensure balance liquidity within the economy.
Appropriate liquidity management is essential to foster economic growth.
Though, to achieve economic stability proper uses of fiscal and monetary
policies are required. Despite establishing regulatory agencies and monetary
policy committees, Nigerian banks have actually been deterred in creating
adequate liquidity and additional credit for the sustenance of the entire
economy (Ndugbu and Okere, 2015).
The financial sector has been liberalized in Nigeria. However, despite the
growth record of banks and non-bank financial institutions in Nigeria, and
financial liberalization policy, the Nigeria economic growth is sluggish (Maduka
and Onwuka, 2013).On the other hand, Economic growth could be defined as
the increase in the amount of goods and services in a given country at a
particular time. This of course indicates that when the real per capita income
of a country increases over time, economic growth is taking place. A growing
economy produces goods and services in each successive time period, showing
that the economy’s productive capacity is at increase. Broadly economic
3
growth implies raising the standard of living of the people and reducing
inequalities of income distribution (Jhingan 2004).
To regulate the financial imbalance in the economy, the government employs
the use of Monetary Policies and Fiscal Policy, Monetary policy is used as
inflation is generally considered as purely a monetary phenomenon (Chipote
and Makhetha 2014). One of the major objectives of monetary policy in the
recent years has been the rapid economic growth of any economy. Other
objectives such as full employment, price stability which also include
controlling economic fluctuations and maintaining balance of payments
equilibrium, are also prominent (Okoro 2013).
Despite the lack of consensus among economists on how monetary policy
actually works and on the magnitude of its effect on the economy, there is a
remarkable strong agreement that it has some measure of effects on the
economy (Nkoro, 2005).
Statement of Problem
The Nigerian financial sector, like those of many other less developed
countries, was highly regulated leading to financial disintermediation which
retarded the growth of the economy. The link between the financial sector and
the growth of the economy has been weak. The real sector of the economy,
4
most especially the high priority sectors which are also said to be economic
growth drivers are not effectively and efficiently serviced by the financial
sector. The banks are declaring billions of profit but yet the real sector
continues to weak thereby reducing the productivity level of the economy.
Most of the operators in the productive sector are folding up due to the
inability to get loan from the financial institutions or the cost of borrowing was
too outrageous. The Nigerian banks have concentrated on short term lending
as against the long term investment which should have formed the bedrock of
a virile economic transformation.
Since the adoption of the Structural Adjustment Programme (SAP) in 1986, in
an attempt to quicken the recovery of the economy from its deteriorating
conditions, a great deal of interest has been shown in the activities and
development in the financial sector. This is so because the restructuring of this
sector was a central component of the SAP reform
Therefore, Nigeria needs an effective, efficient, sound and consistent monetary
policy that has a positive effect on interest rate, employment and real output,
so as to minimize the economic problems disturbing Nigeria as a developing
country.
5
Aims and Objective of the study
The broad objective of this study is to evaluate the impact of financial
regulations on the growth of the economy. The specific objectives however
are:
i. To examine the impact of Monetary Policy Rate on Returns on Equity of
Deposit Money Banks in Nigeria.
ii. To empirically examine the impact of Savings Rate on Returns on Equity
of Deposit Money Banks in Nigeria.
iii. To examine the impact of Cash Reserve Ratio on Returns on Equity of
Deposit Money Banks in Nigeria.
iv. To determine the impact of Liquidity Ratio on Returns on Equity of
Deposit Money Banks in Nigeria.
Research Question
This part is aimed at helping the researcher to adequately address the research
problem and also to investigate beyond what is already known, therefore upon
the following research questions, hypothesis where formulated.
I. In what proportion does Monetary Policy Rate impact on Returns on
Equity of Deposit Money Banks in Nigeria?6
II. How does the Savings Rate impact on Returns on Equity of Deposit
Money Banks in Nigeria?
III. To What statistical magnitude does Cash Reserve Ratio impact on
Returns on Equity of Deposit Money Banks in Nigeria?
IV. To What extent does Liquidity Ratio impact upon Returns on Equity of
Deposit Money Banks in Nigeria?
Research hypothesis:
The research work will be guided by the following hypothesis
Ho1 : Monetary Policy Rate has no significant impact on Returns on Equity of
Deposit Money Banks in Nigeria.
Ho2 : Savings Rate has no significant impact on Returns on Equity of Deposit
Money Banks in Nigeria.
Ho3 : Cash Reserve Ratio has no significant effect on Returns on Equity of
Deposit Money Banks in Nigeria.
Ho4 : Liquidity Ratio has no significant impact on Returns on Equity of Deposit
Money Banks in Nigeria.
7
Scope of Study
This Study will be limited to the activities of the financial sector being
regulated by the Central Bank of Nigeria and over the time period from 1985 to
2013, and limited to the Nigerian Economy
Organization of the study
This research work is divided into five sections. The introduction, which
present the background of the study; the statement of problem; the objectives
of the study, the statement of research hypotheses and the organization of the
study which is part one. This is followed by the Literature review, as part two,
the methodology of the research is part three. While part four is the
presentation and analysis of regression results. Part five shows the research
findings and recommendations.
8
2.0 Literature Review
Theoretical Framework
Theory of financial intermediation
Financial intermediation theory was first formalized in the works of McKinnon
(1973) and Shaw (1973) who see financial markets as playing a pivotal role in
economic development, attributing the differences in economic growth across
countries to the quantity and quality of services provided by financial
institutions. This contrasts with Robinson (1952), who argued that financial
markets are essentially handmaidens to domestic industry, and respond
passively to other factors that produced cross-country differences in growth
(Ogege and Shiro, 2013).
“There is a general tendency for the supply of finance to move with the demand
for it. It seems to be the case that where enterprise leads, finance follows. The
same impulses within an economy, which set enterprises on foot, make owners
of wealth venturesome, and when a strong impulse to invest is fettered by lack
of finance, devices are invented to release it… and habits and institutions are
developed”.
The Robinson school of thought therefore believes that economic growth will
lead to the expansion of the financial sector. He attributed the positive
9
correlation between financial development and the level of real per capital GNP
to the positive effect that financial development has on encouraging more
efficient use of the capital stock. In addition, the process of growth has feedback
effects on financial markets by creating incentives for further financial
development.
Financial regulation theory
Theoretical linkages between financial regulation and economic growth as
earlier noted can be traced back to Schumpeter (1911) and, relatively, more
recently, Mckinnon (1973) and Shaw (1973). In their models, government
regulations and restrictions inhibit financial development and thus negate
overall growth of the economy. Similarly, the more recent endogenous growth
hypothesis, in which services provided by financial intermediaries are modelled
have reached similar conclusions (Khan and Senhadji, 2000). These models
suggest a positive relationship between financial intermediation and growth.
King and Levine (1993) constructed an endogenous growth model in which
financial systems evaluate prospective entrepreneurs, mobilize savings to
finance the most promising productivity-enhancing activities, diversify the risks
associated with these innovative activities, and reveal the expected profits
from engaging in innovation rather than the production of existing goods using
existing methods.
10
Overview of Nigerian Financial System
The Nigerian financial system comprises of various institutions, instruments
and regulations. According to Central Bank of Nigeria (1993), the financial
system refers to the set of rules and regulations and the aggregation of
financial arrangements, institutions, agents that interact with each other to
foster economic growth and development of a nation. The financial system
plays a key role in the mobilization and allocation of savings for productive
purposes. It also assists in the reduction of risks faced by firms and businesses
in their production processes, improvement of portfolio diversification, and
insulation of the economy from external shocks (Nzotta, 2004). In addition,
the system provides linkages for different sectors of the economy and
encourages a high level of specialization and economies of scale.
The Nigerian financial system can be divided into two sub-sectors; the formal
and informal sectors. The informal sector has no formalized institutional
framework, no formal structure of rates and comprises the local money
lenders, thrift collectors, savings and loan associations and all forms of ‘Isusu‘
associations (Nzotta and Okereke, 2009). According to Olofin and Afangideh
(2008), this sector is poorly developed and not integrated into the formal
financial system, therefore, its exact size and effect on the economy remain
11
unknown and are a matter of speculation. The formal sector on the other hand
comprises of bank and non-bank financial institutions. Bank financial
institutions are the deposit taking institutions. As financial intermediaries, they
channel funds from surplus economic units to deficit units to facilitate trade
and capital formation.
They include; central bank, commercial banks, development banks, co-
operative and commerce banks, etc. while, the non-banks financial institutions
include; the money markets, capital markets, insurance companies, pension
funds, etc. These institutions are not deposit taking institutions, but some of
them perform intermediation functions of channelling funds from surplus to
deficit units for economic activities, for instance, money and capital markets.
The regulatory institutions in the financial system are; the Federal Ministry of
Finance, Central Bank of Nigeria as the apex institution in the money market,
the Securities and Exchange Commission (SEC) as the apex institution in the
capital market, Nigeria. Deposit Insurance Corporation (NDIC), National
Insurance Commission (NAICOM) and the National Pension Commission
(PENCOM).
12
Empirical Literature
Schumpeter (1911) asserted that a financial system that functions optimally
will bring about efficiency in allocating resource from unproductive sector to
productive sector. This thought remains the first framework for analysing the
finance-led growth hypothesis. Robinson (1952) argued contrarily that the
relationship should run from growth to finance. According to this view,
increase in economic growth leads to increase in demand for a particular
financial instrument thereby creating a well-developed financial sector that will
automatically respond to financial demand in the economy. This thought is
often describe as growth-led finance hypothesis.
Goldsmith (1969), Shaw (1973) and McKinnon (1973) have contributed
significantly to the literature on the relationship between Financial regulation
and economic growth relationship in a more formalized framework. The major
contribution of these studies was the identifying of different channels of
transmission in explaining the link between Financial regulation and growth;
however, all the studies agreed fundamentally that there is a significant and
positive relationship between Financial regulation and economic growth. For
example, Goldsmith (1969) focuses on the investment efficiency link between
Financial regulation and economic growth. On the other hand, Shaw (1973)
13
and McKinnon (1973) show the importance of financial liberalization in
promoting domestic savings which leads to investment and hence economic
growth.
Using annual data from 1975-2005 for Turkey, Ozturk (2008) found that there
was no longrun relationship between Financial regulation and economic
growth and the results show a one-way causality running from economic
growth to Financial regulation.
Odhiambo (2008) in another study on the link between Financial regulation
and economic growth for Kenyan economy revealed that the direction of
causality between these two variables depends on the financial indicator used
as a proxy of Financial regulation. He however concluded that overall real
economic growth would lead to development in the financial sector and not
otherwise.
Acaravci et al., (2009) review the literature on finance-growth nexus and
investigate the causality between Financial regulation and economic growth in
Sub-Saharan Africa for the period of 1975–2005. Using Panel cointegration and
Panel GMM estimation for Causality, the empirical results show a bidirectional
causal relationship between the growth of real ROA per capita and domestic
credit provided by banking sector for the panels of 24 Sub-Saharan African
14
countries. The findings imply that African countries can accelerate their
economic growth by improving their financial systems and vice versa.
Blanco (2009) examined the relationship between Financial regulation for Latin
American countries for the period 1961-2005, shows that finance development
does not have a causal effect on economic growth, but that real economic
growth leads to development in the financial sector. Likewise, in a study, Hurlin
and Venet (2008) used a new panel Granger causality technique test the
direction causality between Financial regulation and economic growth for 63
sampled countries. Their results show that economic growth granger cause
finance and not the reverse.
Ndebbio (2004) used two financial deepening variables namely the degree of
financial intermediation measured by M2 as ratio to ROA, and the growth rate
of per capita real money balances to investigate the link between financial
deepening, economic growth and development for SubSaharan African
countries. The findings of the study reveal that development in the financial
sector of these countries spurs sustainable economic growth.
Azege (2004) established that there exist a moderate positive relationship
between financial deepening and economic growth. He concluded that the
overall economic growth noticed within the period of the study was attributed
15
to the development of financial intermediary institutions in Nigeria.
Consistently with this, La Porta et al. (1998) study suggested that financial
sectors dominated by greater proportion of state-owned banks tend to have
slower growth in the economy.
Odeniran and Udeaja (2010) examined the linkage between financial sector
development and economic growth in Nigeria using Granger causality test.
They find the existence of a bi-directional relationship between some of the
proxies of Financial regulation and economic growth. The authors found that
except the ratio of money supply to ROA measure, all other Financial
regulation proxies granger cause output even at the 1percent level of
significance.
Wadud (2005) employed a cointegrated vector autoregressive model to
examine the long-run causal relationship between Financial regulation and
economic growth for 3 South Asian countries namely Bangladesh, India and
Pakistan. Disaggregating financial system into “bank-based” and “capital
market based” categories, the empirical results of the error correction model
indicate causality that runs from Financial regulation to economic growth.
Abu-Bader and Abu-Qarn (2008) employed four different measures of Financial
regulation and applied Toda and Yomamoto Granger causality test technique
16
to examine the causal link between Financial regulation and economic growth
for six countries namely; Israel, Syria, Egypt, Algeria, Tunisia and Morocco.
Their empirical findings show that causality runs from finance to growth in five
out of the six countries while a weak causality that runs from economic growth
to finance was found in the case of Israel.
Demetriades and Hussein (1996) analysed time series evidence from 16
countries and their findings revealed that finance is a leading factor in the
process of economic growth. They concluded that majority of these countries;
there is evidence of bi-directional causality, while in some countries, Financial
regulation leads to economic growth.
Luintel and Khan (1999) used multivariate VAR for a sample of ten less
developed countries and found that there is bi-directional causality between
Financial regulation and output growth for all the countries in the study.
Hondroyiannis et al. (2004) used two financial indicators namely banking
system and stock market to assess empirically the relationship between the
development and economic performance in Greece over the period 1986-
1999. Their empirical results indicate a bi-directional causality between finance
and growth in the long-run. While the estimation of the short-run dynamic
17
model suggests that both bank and stock market financing promotes economic
growth.
Al-Awad and Harb (2005) used panel co-integration and variance
decomposition to investigate the relationship between Financial regulation and
economic growth in some Middle East countries and found that in the long
run, these two variables are related while in the short-run, the panel causality
results suggest that economic growth brings about noticeable changes in
Financial regulation. However, no clear evidence of direction of causation was
noticed for individual countries’ causality tests.
Khan (2008) used the Autoregressive Distributed Lag (ARDL) framework to
examine the relationship between Financial regulation and economic growth in
Pakistan from 1961-2005. His results reveal that in the short and long run,
Financial regulation and investment impact positively on economic growth.
The result also reveal that in the short-run, real deposit rate impact
significantly on real output while in the long-run real deposit rate and
economic growth have an insignificant positive relationship. Also, Mohammed
and Sidiropoulos (2006) made use of the autoregressive distributed lag (ARDL)
model for co- integration analysis by Pesaran and Shin (1999) to examine the
impact of Financial regulation on economic growth in Sudan from 1970 to
18
2004.Their empirical results suggested a weak relationship between Financial
regulation and economic growth. They concluded that, poor quality of bank
credit allocation, inefficient allocation of resources by banks and absence of an
appropriate investment climate required to foster significant private
investment are the major factors hindering the promotion of economic growth
in Sudan.
Against this backdrop, it pertinent to note that understanding the relationship
between Financial regulation and economic growth is critical to the overall
growth and sustainable development of any country. In addition, the
hypothesis regarding the relationship between Financial regulation and
economic growth has no specific direction of causality in terms of whether the
country is developed or developing. Lastly, the results obtained may be
sensitive to the financial indicator used as a proxy for Financial regulation as
well as the estimation approach.
19
3.0 Methodology
Research Design
The study design used for this paper is the Ex post facto which is a quasi-
experimental design as a pre-existing group is compared on a dependent
variable and the variables data are past events.
Data Collection Technique
In this research, secondary data has been used. Secondary data is collected
from the Central bank Statistical Bulletin and federal bureau of statistics. In
which there are five variables (NGSP) Nominal Returns on Asset of Deposit
Money Banks, (MPR) Monetary Policy Rate, (SVR) Savings Rate, (CRR) Cash
Reserve Ratio, (LQR) Liquidity Ratio.
Sample Size
The study period spans from (1985-2014), a period of 29 years, which is above
the required minimum of 25 observations and selected based on its statistical
relevance and convenience of researcher.
Model Specification:
To carry out an effective analysis on the study, a model was specified which
would aid the regression analysis. The model is given as:20
ROE = f (MPR SVR CRR LQR INR)
Econometric model:
ROE = α0 + α1MPR + α2SVR + α3CRR + α4LQR + µ
Where:
ROE = Returns on Equity
MPR = Monetary Policy Rate
SVR = Savings Rate
CRR = Cash Reserve Ratio
LQR = Liquidity Ratio
α0 = Constant/Intercept
α1-α4 = Coefficient/Slope
µ = Error Term
Apriori Expectation
Based on theoretical and Empirical Underpinnings, the following are the
Apriori expectation for the study at hand.
α1 < 0,α2 < 0,α3 >0,α4 < 0
Operational Measures of Variables
Dependent Variable:
21
Returns on Equity of Deposit Money Banks: The total market value of all final
goods and services produced in a country in a given year, equal to total
consumer, investment and government spending, plus the value of exports,
minus the value of imports.
Independent Variable:
Cash Reserve Requirement: Also known as Cash Reserve Ratio, This represents
the minimum amount of cash deposits to be maintained by banks (Deposit
Money Banks) with the Central Bank.
Liquidity Ratio: This is certain proportion of the banks deposit liabilities kept in
liquid form in order to satisfy the liquidity needs of their customers, sustain the
confidence of the public and to ensure a sound system policy.
Monetary Base: also called High-powered money or Base money, Monetary
Base is simply known as the aggregate of the total currency outside the
banking system and the commercial banks vault cash and cash balances held
with the central bank, other components could be Government borrowing,
Central bank lending to commercial banks and its holding of international
reserves or foreign asset.
22
Lending Rate: this is the bank rate that usually meets the short- and medium-
term financing needs of the private sector. This rate is normally differentiated
according to creditworthiness of borrowers and objectives of financing.
Savings Rate: The amount of money, expressed as a percentage or ratio, that
one deducts from his/her disposable personal income to set aside as a nest egg
or for retirement.
Monetary Policy Rate: formerly called Minimum Rediscount Rate, it is a central
bank business and the rate charged for rediscounting by the CBN.
Statistical Test
The researcher employed the use of Statistical package: E-view 8 to analyse the
data by using the Ordinary Least Square regression Model, stationarity test
using Augmented Dickey Fuller to determine if employed data have a unit root
and the Cointegration to check for long term relationship between variables
with a topping of Granger causality to .
23
4.0 Data Analysis and Results
In this section, the data that were generated for this study was analysed. The
data set is embedded in the Appendix section.
Table 1: Ordinary Least Square regression Output
Dependent Variable: ROEMethod: Least SquaresDate: 01/08/16 Time: 09:04Sample: 1985 2014Included observations: 30
Variable Coefficient Std. Error t-Statistic Prob.
C -178.5710 48.88557 -3.652838 0.0012MPR -1.613044 3.361799 -0.479816 0.6355SVR 8.090160 2.843565 2.845076 0.0087CRR 5.657120 3.753546 1.507140 0.1443LQR 2.958220 1.006903 2.937940 0.0070
R-squared 0.506432 Mean dependent var 36.48137Adjusted R-squared 0.427461 S.D. dependent var 59.63476S.E. of regression 45.12343 Akaike info criterion 10.60769Sum squared resid 50903.11 Schwarz criterion 10.84123Log likelihood -154.1154 Hannan-Quinn criter. 10.68240F-statistic 6.412883 Durbin-Watson stat 1.643101Prob(F-statistic) 0.001076
Source: Researcher’s E-view 8 result.
From the output above, it can be seen that the constant is -178.5710, which
signifies that if all other variables are kept at a constant or zero, nominal
exchange rate will reduce by 178.5710units, all employed variables are
positively related to the Dependent variable with the exception of Monetary
24
Policy Rate which holds a coefficient of -1.613044, SVR is 8.090160, CRR is
5.657120 and LQR is 2.958220.
The coefficient of Determination is 0.506432 which means that the employed
variables explains 50.64% of the model, while the remaining 49.36% is
stochastic and attributed to other variables not captured by the model. The t-
statistics shows that all variables are not significant except for the savings Rate
and Liquidity ratio based on their probability level of 0.0087 and 0.0070
respectively at the 0.05 significance level
The F-statistics shows an overall significance of the Independent variables on
the independent variables with the f-stat score of 6.412883, at a probability
level of 0.001076. The Durbin Watson Score of 1.643101 shows an absence of
Auto or serial Correlation as it lays below 2.
Unitroot Test
The analysis started with a unit root test to determine the stationarity of the
variables employed in the variable. The result of the unit root text is presented
here under:
Table 2. Result of Unit Root Test at Level.
Variable ADF t-statistics Critical Value 5% Order of Integration
ROE -5.633493 -2.971853 I(1)
25
MPR -6.727382 -3.012363 I(1)
SVR -5.417234 -2.971853 I(1)
CRR -4.893640 -2.971853 I(1)
LQR -6.832507 -2.971853 I(1)
Using both 1% and 5% Significant Level
The above result shows that just two of the entire variable included in the
model at level were stationary at 5% critical value, except for inflation rate
(INFL) and Exchange Rate (EXR) who were differentiated at first level to be
stationary. Meanwhile having established stationarity, the author moved on to
conduct co-integration analysis in other to determine if there is a long run
relationship between the variables under consideration.
Johansen Cointegration
Table 3. Result of Johanson Co-integration Test.
Date: 01/08/16 Time: 09:19Sample (adjusted): 1987 2014Included observations: 28 after adjustmentsTrend assumption: Linear deterministic trendSeries: ROE MPR SVR CRR LQR Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.793865 87.20318 69.81889 0.0011At most 1 0.502746 42.98488 47.85613 0.1329
26
At most 2 0.397814 23.42257 29.79707 0.2259At most 3 0.228996 9.221266 15.49471 0.3453At most 4 0.066924 1.939533 3.841466 0.1637
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.793865 44.21830 33.87687 0.0021At most 1 0.502746 19.56231 27.58434 0.3722At most 2 0.397814 14.20130 21.13162 0.3488At most 3 0.228996 7.281733 14.26460 0.4564At most 4 0.066924 1.939533 3.841466 0.1637
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
Source: Researcher’s E-view 8 result.
The result of the co-integration test shows the non-existence of a long run
relationship amongst integrated variable as it signs the none cointegration
equation at a probability level of 0.0011 for the trace test and 0.0021 for the
maximum Eigenvalue output Having established an absence of con-integration
among the variables, There will be no further need to correct for errors the
Author move to the Granger Causality test.
Table 4. The result of the Granger Causality Test.
Pairwise Granger Causality TestsDate: 01/08/16 Time: 09:20Sample: 1985 2014Lags: 2
27
Null Hypothesis: Obs F-Statistic Prob.
MPR does not Granger Cause ROE 28 0.14312 0.8674 ROE does not Granger Cause MPR 3.42191 0.0500
SVR does not Granger Cause ROE 28 0.31375 0.7338 ROE does not Granger Cause SVR 0.89483 0.4224
CRR does not Granger Cause ROE 28 5.73258 0.0095 ROE does not Granger Cause CRR 3.29039 0.0554
LQR does not Granger Cause ROE 28 0.17013 0.8446 ROE does not Granger Cause LQR 0.89668 0.4217
SVR does not Granger Cause MPR 28 1.50398 0.2433 MPR does not Granger Cause SVR 0.69341 0.5100
CRR does not Granger Cause MPR 28 1.12350 0.3423 MPR does not Granger Cause CRR 0.10264 0.9029
LQR does not Granger Cause MPR 28 1.32015 0.2866 MPR does not Granger Cause LQR 3.33093 0.0537
CRR does not Granger Cause SVR 28 1.03916 0.3698 SVR does not Granger Cause CRR 0.89595 0.4220
LQR does not Granger Cause SVR 28 2.87131 0.0771 SVR does not Granger Cause LQR 1.36661 0.2749
LQR does not Granger Cause CRR 28 1.22217 0.3130 CRR does not Granger Cause LQR 3.85125 0.0361
Source: Researcher’s E-view 8 result.
The result of the granger causality test as shown above at lag 2 judging by the
probability level reviles a unidirectional granger causality between Return on
Equity (ROE) and monetary Policy Rate (MPR) and Return on Equity and Cash
Reserve Ratio (CRR), while there exists no form of bidirectional causality or
influence amongst employed variables, it was only notices that on the Return
on Equity seems to be promoting the supported variables.
28
5.0 Conclusion and Recommendation
Conclusion
This study has shown the effect of financial deregulation on financial
performance of Deposit money Banks in Nigeria, Financial performance of
Deposit Money Banks in Nigeria was proxied by (ROE) Returns on Equity of
Deposit Money Banks, (MPR) Monetary Policy Rate, (SVR) Savings Rate, (CRR)
Cash Reserve Ratio, (LQR) Liquidity Ratio,. Over the time period of 1985 to
2014, the study utilized the ordinary least square, the stationarity test, the
cointegration (johansen) and the granger causality test, it was discovered that
all predictor variables account moderately for changes in the criterion and
there exist no long run relationship while the short run relationship existent in
the model ran from the Return on Equity of the Deposit Money Banks to the
Monetary Policy Ratio (MPR) and Cash Reserve Ratio (CRR).
Recommendations
It should be noted that, though the financial regulations affect the financial
sector and economy as a whole, external factors as well do have effect on the
financial sector and economy. Factors such as political unrest, international
influence etc., and all these can be addressed from without the sector. Based
on the findings, it is recommended that:
29
1. There should be appropriate planning before the developments are carried
out.
2. There should be the ensuring of macroeconomic stabilization, which is the
ultimate, as the activities in all other sectors affect this or is affected by it.
3. There should be a body that supervises the reform and ensure a successful
follow up of such developments.
4. There should be the ensuring of political stability as this also affects the
effective operation of the financial sector.
5. Many individuals should be enlightened on the benefits of the financial
reforms so that they would not take opposing actions against the goal of the
reforms.
30
Reference
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35
Appendix
YEAR MPR SVR CRR LQR ROE
1985 10 9.5 1.8 65 87.16
1986 10 9.5 1.7 36.4 86.4
1987 12.75 14 1.4 46.5 80.41
1988 12.75 14.5 2.1 45 84.41
1989 18.5 16.4 2.9 40.3 88.42
1990 18.5 18.8 2.9 44.3 75.51
1991 14.5 14.29 2.9 38.6 53.43
1992 17.5 16.1 4.4 29.1 41.35
1993 26 16.66 6 42.2 19.27
1994 13.5 13.5 5.7 48.5 12.62
1995 13.5 12.61 5.8 33.1 5.27
1996 13.5 11.69 7.5 43.1 56.78
1997 13.5 4.8 7.8 40.2 67.15
1998 14.31 5.49 8.3 46.8 86.08
1999 18 5.33 11.7 61 80.59
2000 13.5 5.29 9.8 64.1 99.45
2001 14.31 5.49 10.8 52.9 114.3
2002 19 4.15 10.6 52.5 41.63
2003 15.75 4.11 8.6 50.9 29.11
2004 15 4.19 10 50.5 27.23
2005 13 3.83 8.6 50.2 11.7
2006 12.25 3.14 9.7 55.7 18.36
2007 8.75 3.55 11.2 48.8 17.836
2008 9.81 2.84 3 44.3 12.56
2009 7.44 2.68 1.3 30.7 -191.7
2010 6.13 2.21 1 30.4 -91.62
2011 9.19 1.41 8 42 13.11
2012 12 1.7 10 48.3 17.84
2013 12 2.17 12 63.2 22.57
2014 12.25 3.38 12.25 38.28 27.3
Source: CBN Statistical Bulletin 2014.
Regression
Dependent Variable: ROEMethod: Least SquaresDate: 01/08/16 Time: 09:04Sample: 1985 2014Included observations: 30
Variable Coefficient Std. Error t-Statistic Prob.
C -178.5710 48.88557 -3.652838 0.0012MPR -1.613044 3.361799 -0.479816 0.6355SVR 8.090160 2.843565 2.845076 0.0087CRR 5.657120 3.753546 1.507140 0.1443LQR 2.958220 1.006903 2.937940 0.0070
R-squared 0.506432 Mean dependent var 36.48137Adjusted R-squared 0.427461 S.D. dependent var 59.63476S.E. of regression 45.12343 Akaike info criterion 10.60769Sum squared resid 50903.11 Schwarz criterion 10.84123Log likelihood -154.1154 Hannan-Quinn criter. 10.68240F-statistic 6.412883 Durbin-Watson stat 1.643101Prob(F-statistic) 0.001076
37
Graphical Output
Null Hypothesis: D(ROE) has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -5.633493 0.0001Test critical values: 1% level -3.689194
5% level -2.97185310% level -2.625121
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test EquationDependent Variable: D(ROE,2)Method: Least SquaresDate: 01/08/16 Time: 09:15Sample (adjusted): 1987 2014Included observations: 28 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(ROE(-1)) -1.099665 0.195201 -5.633493 0.0000C -2.340625 10.01526 -0.233706 0.8170
38
R-squared 0.549676 Mean dependent var 0.196107Adjusted R-squared 0.532356 S.D. dependent var 77.41839S.E. of regression 52.94217 Akaike info criterion 10.84503Sum squared resid 72874.72 Schwarz criterion 10.94018Log likelihood -149.8304 Hannan-Quinn criter. 10.87412F-statistic 31.73624 Durbin-Watson stat 2.057585Prob(F-statistic) 0.000006
Null Hypothesis: D(MPR) has a unit rootExogenous: ConstantLag Length: 7 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -6.727382 0.0000Test critical values: 1% level -3.788030
5% level -3.01236310% level -2.646119
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test EquationDependent Variable: D(MPR,2)Method: Least SquaresDate: 01/08/16 Time: 09:16Sample (adjusted): 1994 2014Included observations: 21 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(MPR(-1)) -4.289009 0.637545 -6.727382 0.0000D(MPR(-1),2) 2.706849 0.578563 4.678569 0.0005D(MPR(-2),2) 2.380235 0.525728 4.527504 0.0007D(MPR(-3),2) 2.045882 0.442424 4.624259 0.0006D(MPR(-4),2) 1.806863 0.372288 4.853404 0.0004D(MPR(-5),2) 1.318826 0.306405 4.304197 0.0010D(MPR(-6),2) 0.966067 0.212965 4.536281 0.0007D(MPR(-7),2) 0.524442 0.134735 3.892403 0.0021
C -1.278025 0.485322 -2.633356 0.0218
R-squared 0.941536 Mean dependent var -0.392857Adjusted R-squared 0.902561 S.D. dependent var 6.534781S.E. of regression 2.039851 Akaike info criterion 4.561158Sum squared resid 49.93190 Schwarz criterion 5.008810Log likelihood -38.89215 Hannan-Quinn criter. 4.658310F-statistic 24.15696 Durbin-Watson stat 0.795254
39
Prob(F-statistic) 0.000003
Null Hypothesis: D(SVR) has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -5.417234 0.0001Test critical values: 1% level -3.689194
5% level -2.97185310% level -2.625121
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test EquationDependent Variable: D(SVR,2)Method: Least SquaresDate: 01/08/16 Time: 09:17Sample (adjusted): 1987 2014Included observations: 28 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(SVR(-1)) -1.069223 0.197374 -5.417234 0.0000C -0.236693 0.404690 -0.584875 0.5637
R-squared 0.530232 Mean dependent var 0.043214Adjusted R-squared 0.512164 S.D. dependent var 3.040853S.E. of regression 2.123892 Akaike info criterion 4.413127Sum squared resid 117.2839 Schwarz criterion 4.508285Log likelihood -59.78378 Hannan-Quinn criter. 4.442218F-statistic 29.34642 Durbin-Watson stat 1.804000Prob(F-statistic) 0.000011
Null Hypothesis: D(CRR) has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -4.893640 0.0005Test critical values: 1% level -3.689194
5% level -2.97185310% level -2.625121
*MacKinnon (1996) one-sided p-values.
40
Augmented Dickey-Fuller Test EquationDependent Variable: D(CRR,2)Method: Least SquaresDate: 01/08/16 Time: 09:18Sample (adjusted): 1987 2014Included observations: 28 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(CRR(-1)) -0.958266 0.195819 -4.893640 0.0000C 0.361583 0.474171 0.762557 0.4526
R-squared 0.479456 Mean dependent var 0.012500Adjusted R-squared 0.459435 S.D. dependent var 3.373800S.E. of regression 2.480524 Akaike info criterion 4.723566Sum squared resid 159.9779 Schwarz criterion 4.818723Log likelihood -64.12992 Hannan-Quinn criter. 4.752656F-statistic 23.94771 Durbin-Watson stat 1.997247Prob(F-statistic) 0.000044
Null Hypothesis: D(LQR) has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -6.832507 0.0000Test critical values: 1% level -3.689194
5% level -2.97185310% level -2.625121
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test EquationDependent Variable: D(LQR,2)Method: Least SquaresDate: 01/08/16 Time: 09:19Sample (adjusted): 1987 2014Included observations: 28 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(LQR(-1)) -1.247027 0.182514 -6.832507 0.0000C 0.051263 1.793100 0.028589 0.9774
R-squared 0.642283 Mean dependent var 0.131429
41
Adjusted R-squared 0.628525 S.D. dependent var 15.56715S.E. of regression 9.487989 Akaike info criterion 7.406680Sum squared resid 2340.570 Schwarz criterion 7.501837Log likelihood -101.6935 Hannan-Quinn criter. 7.435770F-statistic 46.68316 Durbin-Watson stat 1.833477Prob(F-statistic) 0.000000
Date: 01/08/16 Time: 09:19Sample (adjusted): 1987 2014Included observations: 28 after adjustmentsTrend assumption: Linear deterministic trendSeries: ROE MPR SVR CRR LQR Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.793865 87.20318 69.81889 0.0011At most 1 0.502746 42.98488 47.85613 0.1329At most 2 0.397814 23.42257 29.79707 0.2259At most 3 0.228996 9.221266 15.49471 0.3453At most 4 0.066924 1.939533 3.841466 0.1637
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.793865 44.21830 33.87687 0.0021At most 1 0.502746 19.56231 27.58434 0.3722At most 2 0.397814 14.20130 21.13162 0.3488At most 3 0.228996 7.281733 14.26460 0.4564At most 4 0.066924 1.939533 3.841466 0.1637
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
ROE MPR SVR CRR LQR-0.030819 -0.028520 0.231106 -0.290469 0.335092
42
-0.007877 0.485835 -0.293137 -0.380219 -0.031168-0.023635 -0.181835 0.417455 0.564724 0.003157 0.005006 0.381790 -0.178883 -0.048482 0.005112 0.010212 -0.005181 0.080735 -0.144174 -0.015672
Unrestricted Adjustment Coefficients (alpha):
D(ROE) 4.323323 17.20159 0.734299 -13.66993 -4.781512D(MPR) -1.230562 -0.686394 -0.845593 -0.738488 0.254993D(SVR) 0.456871 -0.165564 -1.116579 0.032492 -0.045634D(CRR) -0.164606 1.183556 -0.396859 -0.499671 0.210971D(LQR) -4.246171 3.378255 -0.416849 -0.112564 -0.630311
1 Cointegrating Equation(s): Log likelihood -390.9650
Normalized cointegrating coefficients (standard error in parentheses)ROE MPR SVR CRR LQR
1.000000 0.925409 -7.498913 9.425148 -10.87306 (2.30933) (1.70812) (2.71276) (0.81675)
Adjustment coefficients (standard error in parentheses)D(ROE) -0.133239
(0.28153)D(MPR) 0.037924
(0.01712)D(SVR) -0.014080
(0.01218)D(CRR) 0.005073
(0.01495)D(LQR) 0.130861
(0.03910)
2 Cointegrating Equation(s): Log likelihood -381.1839
Normalized cointegrating coefficients (standard error in parentheses)ROE MPR SVR CRR LQR
1.000000 0.000000 -6.837949 9.999344 -10.65383 (0.81329) (2.14249) (0.80966)
0.000000 1.000000 -0.714240 -0.620479 -0.236897 (0.10089) (0.26578) (0.10044)
Adjustment coefficients (standard error in parentheses)D(ROE) -0.268743 8.233836
(0.26491) (4.05307)D(MPR) 0.043331 -0.298379
(0.01702) (0.26035)D(SVR) -0.012776 -0.093467
(0.01252) (0.19149)D(CRR) -0.004250 0.579708
(0.01307) (0.19992)
43
D(LQR) 0.104249 1.762375 (0.03285) (0.50261)
3 Cointegrating Equation(s): Log likelihood -374.0832
Normalized cointegrating coefficients (standard error in parentheses)ROE MPR SVR CRR LQR
1.000000 0.000000 0.000000 47.36011 -26.49009 (9.25377) (3.88879)
0.000000 1.000000 0.000000 3.281941 -1.891031 (0.98220) (0.41276)
0.000000 0.000000 1.000000 5.463739 -2.315937 (1.24465) (0.52305)
Adjustment coefficients (standard error in parentheses)D(ROE) -0.286098 8.100314 -3.736746
(0.32998) (4.32594) (4.66296)D(MPR) 0.063317 -0.144621 -0.436180
(0.01990) (0.26087) (0.28119)D(SVR) 0.013615 0.109566 -0.312003
(0.01224) (0.16050) (0.17301)D(CRR) 0.005129 0.651870 -0.550657
(0.01591) (0.20862) (0.22488)D(LQR) 0.114101 1.838172 -2.145622
(0.04077) (0.53446) (0.57610)
4 Cointegrating Equation(s): Log likelihood -370.4423
Normalized cointegrating coefficients (standard error in parentheses)ROE MPR SVR CRR LQR
1.000000 0.000000 0.000000 0.000000 11.09820 (5.22235)
0.000000 1.000000 0.000000 0.000000 0.713746 (0.32587)
0.000000 0.000000 1.000000 0.000000 2.020468 (0.58630)
0.000000 0.000000 0.000000 1.000000 -0.793670 (0.13381)
Adjustment coefficients (standard error in parentheses)D(ROE) -0.354532 2.881271 -1.291426 -6.718747
(0.31052) (5.01209) (4.57017) (5.76637)D(MPR) 0.059620 -0.426568 -0.304077 0.176698
(0.01900) (0.30661) (0.27957) (0.35275)D(SVR) 0.013777 0.121972 -0.317816 -0.701890
(0.01234) (0.19913) (0.18157) (0.22910)D(CRR) 0.002628 0.461101 -0.461274 -0.602089
(0.01544) (0.24917) (0.22720) (0.28667)D(LQR) 0.113538 1.795196 -2.125486 -0.281043
(0.04108) (0.66307) (0.60461) (0.76286)
44
45