Calistus Cbn Deregulation and Financial Performance of Deposit Money Banks

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


iv. To determine the impact of Liquidity Ratio on Returns on Equity of


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


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


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 cointegration 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


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


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

Central Bank of Nigeria (CBN) (2011) Annual Statistical Bulletin.

Chipote P. and Makhetha-Kosi .P. (2014), “Impact of Monetary Policy on

Economic Growth: A Case Study of South Africa”, Mediterranean Journal

of Social Sciences, South Africa.

Clarke, J., Mirza, S.A. (2006) “Comparison of some common methods of

detecting Granger noncausality” Journal of Statistical Computation and

Simulation 76, 207-231.

Demetriades, P.O., Hussein, K.A. (1996) “Does Financial Development Cause

Economic Growth? Time-Series Evidence from 16 Countries. Journal of

Development Economics, December, 387- 411.

Demirguc-Kunt, A., Maksimovic, V. (1996) “Financial Constraints, Uses of funds

and firm growth: An international comparison.” World Bank Working

Paper No. 1671.

Fry, M.J. (1978) “Money and Capital or Financial Deepening in Economic

Development”. Journal of Money, Credit and Banking, 10(4), 404-75.

Goldsmith, R.W. (1969), “Financial Structure and Development,” Yale Univ.

Press, New Haven CN.

Guryay, E., Safakli, O.V., Tuzel, B. (2007) “Financial Development and Economic

Growth: Evidence from Northern Cyprus.” International Research Journal

of Finance and Economics, 8, 57-62.

31

Hondroyiannis, G., Lolos, S., Papapetreu, E. (2004) “Financial Markets and

Economic Growth in Greece, 1986-1999” Bank of Greece Working Paper

17.

Hurlin, C., Venet, B. (2008). Financial Development and Growth: A re-

examination using a panel Granger causality test”. Working Paper Halshs-

003199995.

Jayaratne, J., Strahan, P.E. (1996) “The finance-growth nexus: evidence from

bank branch deregulation” The Quarterly Journal of Economics CXI(3),

639-671.

Khan, M.A. (2008) Financial Development and Economic Growth in Pakistan

Evidence Based on Autoregressive Distributed Lag (ARDL) Approach.

South Asia Economic Journal, 9(2), 375-39.

Khan, M.S., Senhadji, A.S. (2000) “Financial Development and Economic

Growth: An Overview” International Monetary Fund (IMF) Working Paper

No. 209.

King, R.G., Levine, R. (1993). “Finance, entrepreneurship and growth”. Journal

of Monetary Economics 32(3), 513–42.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A.,Vishny, R. (1998) “Law and

Finance.” Journal of Political Economy, 106 (6), 1113-1155.

Luintel, B., Khan, M. (1999) “A Quantitative Reassessment of the Finance‐

Growth Nexus: Evidence from a Multivariate VAR”. Journal of

Development Economics, 60, 381 405.‐

32

Maduka, A.C & Onwuka, K.O (2013), Financial Market Structure And Economic

Growth: Evidence From Nigeria Data , Asian Economic and Financial

Review, 2013, 3(1):75-98.

Mankiw, N. G., Romer, D., Weil, D. (1992) “A contribution to the empirics of

economic growth” The Quarterly Journal of Economics 152(2), 407-437.

McKinnon, R.I. (1973) Money and Capital in Economic Development.

Washington, D.C.: The Brookings Institute.

Mohammed, S.E. and Sidiropoulos, M. (2006) “Finance-Growth Nexus in

Sudan: Empirical Assessment Based on an Application of the ARDL

Model.” Available at: economics.soc.uoc.gr /macro/11conf /docs/sufian

%205+final.doc.

Narayan, P. K. (2005) “The investment and savings nexus for China: Evidence

from cointegration tests” Applied Economics, 37, 1979-1990.

National Planning Commission (NPC) (2004) National Economic Empowerment

Development Strategy (NEEDS).

Nkoro, E. (2005) “The study of monetary policy and macroeconomic stability in

Nigeria: 1980-2000” University of Benin. Benin City.

Nzotta, S. and E. Okereke, 2009. Financial deepening and economic

development of nigeria: An empirical investigation. African Journal of

Accounting, Economics, Finance and Banking Research, 5(5).

Okoro A.S. (2013). “Impact of monetary policy on Nigerian economic growth”,

prome journal, issn: 2315-5051.

33

Ndugbu, M.O & Okere, P.A (2015), Monetary Policy and the Performance of

Deposit Money Banks - the Nigerian Experience. European Journal of

Business and Management, 7(17), 65-72.

Olofin, S. and U.J. Afangideh, 2008. Financial structure and economic growth in

Nigeria. Nigeria Journal of Securities and Finance, 1(1): 47-68.

Ndebbio, J.E. (2004) “Financial deepening, economic growth and development:

Evidence from selected sub-Saharan African Countries”. Research Paper

142 African Economic Research Consortium, Nairobi, Kenya, August.

Nnanna, O.J. (2004) “Financial Sector Development and Economic Growth in

Nigeria” Economic and Financial Review, 42(3), 1-19.

Nzotta, S.M., Okereke, E.J. (2009) “Financial deepening and economic

development of Nigeria: An Empirical Investigation” African Journal of

Accounting, Economics, Finance and Banking Research, 5, 52-66.

Odeniran, S.O., Udeaja, E.A. (2010). Financial Sector Development and

Economic Growth: empirical Evidence from Nigeria”. Central Bank of

Nigeria Economic and Financial Review 48(3), 92-124.

Odhiambho, N.M. (2004), “Financial Development and Economic Growth in

South Africa”, Department of Economics, University of Fort Hare, South

Africa.

Odhiambo, N.M. (2008). “Financial depth, savings and economic growth in

Kenya: a dynamic causal linkage.” Economic Modelling 25, 704-713.

34

Oyinlola, M.A. Babatunde, M.A. (2009) “A Bound Testing Analysis of Exchange

Rate Pass-Through to Aggregate Import Prices in Nigeria: 1980-2006.”

Journal of Economic Development, 31 (2), 97-109.

Ozturk, I. (2008) “Financial Development and Economic Growth: Empirical

Evidence from Turkey”, Applied Econometrics and International

Development, 8(1), 85–98

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


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


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


D(ROE(-1)) -1.099665 0.195201 -5.633493 0.0000C -2.340625 10.01526 -0.233706 0.8170

38


Null Hypothesis: D(MPR) has a unit rootExogenous: ConstantLag Length: 7 (Automatic - based on SIC, maxlag=7)

t-Statistic Prob.*


5% level -3.01236310% level -2.646119


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


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.*


5% level -2.97185310% level -2.625121


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


D(SVR(-1)) -1.069223 0.197374 -5.417234 0.0000C -0.236693 0.404690 -0.584875 0.5637


Null Hypothesis: D(CRR) has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=7)

t-Statistic Prob.*


5% level -2.97185310% level -2.625121


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


D(CRR(-1)) -0.958266 0.195819 -4.893640 0.0000C 0.361583 0.474171 0.762557 0.4526


Null Hypothesis: D(LQR) has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=7)

t-Statistic Prob.*


5% level -2.97185310% level -2.625121


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


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.**


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.**


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)



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)



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)



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

Calistus Cbn Deregulation and Financial Performance of Deposit Money Banks

Documents

Transcript of Calistus Cbn Deregulation and Financial Performance of Deposit Money Banks