The Nigerian Journal of Business and Management Sciences, Volume 2, No. 2, September,
2018 ISSN: 2639-7308 Pp…
MACROECONOMIC INDICATORS AND STOCK MARKET PERFORMANCE IN NIGERIA
BY
Ogbebor, Peter Ifeanyi (PhD)1 & Okolie, Onyeisi Romanus.2;
1 Department of Banking & Finance, School of Management Sciences, Babcock University,
Ilisan Remo, Ogun State, Nigeria2 Department of Accounting, Ambrose Alli University, Ekpoma, Edo State, Nigeria
*Corresponding Author:
[email protected] Tel:234-803-772-7142
Abstract
The postulation that the link between financial development and economic growth has gravitated
mainly towards one direction: economic growth and development is not invariant to dis-
equilibrium from the stock market has dominated finance literature. This study, however, defers
by studying the effects of macro-economic factors on the stock market in Nigeria for the period:
1993 – 2017. A time series regression analysis involving the use of modern econometrics
approach were employed to establish the effects of gross domestic product (GDP), inflation
(INF), industrial production index (IPI), unemployment rate (UR), interest rate (IR) and
exchange rate (ER) on all share index (ASI) in the stated period. Results from the findings were
mixed as inflation (INF), industrial production index (IPI), unemployment rate (UR), interest
rate (IR) and exchange rate (ER) had negative relationship with the all share index (ASI). The
effects of Gross domestic product (GDP) on the All Share Index was positive but in the short-
run. The same trend was observed in the long-run except that in addition to gross domestic
product (GDP), exchange rate (ER) had a positive relation. Based on this, we conclude that
equilibrium exists among the variables, thus, economic growth and development had significant
effects on stock market performance in the period covered by this study. We, therefore,
recommend, amongst others, that economic growth and development should be pursued in
Nigeria with more vigour in order to further enhance the performance of the stock market.
Keywords: Financial Development, Economic Growth, Stock Market, Macro-economic Variables,
Asset Prices.
SECTION ONE:
INTRODUCTION
The relationship between macro-economic indicators and stock market is considered important
due to the synergy between them. The stock market plays an important role in the financial
intermediation process, hence, efforts have been made by governments through policy reforms
to expand the capacity of the stock market to make more significant contributions to the
endogenous growth model. This theme forms the major crucible in the International Monetary
Fund (IMF)’s framework for macro-economic development of the least developed countries
(LICs). IMF (2012) asserts that particular concern arises given the fragile nature of the
economies of LICs especially their vulnerability to external shocks and further pointed out that
where the policy space and instruments to mitigate these shocks are constrained, growth and
welfare costs can be large. On his part, Alenoghena (2014) asserts that keen observers are of
the opinion that well-functioning stock markets increase economic efficiency, investment and
growth.
One of the main challenges facing a good number of emerging nations is the availability of
finance to fund innovative projects, hence, industrialization has remained stunted in these
economies and even more pathetic is the issue of under-development in majority of these
economies. Some of these countries with natural resource endowments such as oil have not
achieved the desired lift in economic growth as expected and their economies have remained
rooted to the base of modern industrial and technological development. The argument of
Quixima and Almeida (2014) is that the growth of a natural resource dependent economy is
exogenous, hence, it is unlikely that the development of the domestic financial sector has a
significant effect on overall growth. This pre-supposes that some generally adopted macro-
economic variables used in the literature as explanatory variables to explain stock price
performance may be inappropriate in the case of Nigeria as the economy is dominated by
hydrocarbon revenue. But the stock market has been touted as a veritable source of financial
intermediation through which finance for sustainable development can be mobilized. Citing
extant literature. Ume, Nelson and Onwumere (2014) point out that the depth or shallowness
of the financial intermediation platform is expected to be of essence to any economy because
financial deepening (FD) engenders economic growth through the demand and supply channels
in the economic system either as supply-leading or demand-following.
The overview has made it imperative to expand the scope of current research efforts on the
relation between macro-economic factors (gross domestic product, inflation rate, industrial
production index, unemployment rate, interest rate and exchange rate) and stock market
performance in Nigeria. Also, majority of the studies on the relationship between the stock
market and economic growth in Nigeria has been in one direction: how the stock market has
impacted economic growth {(Odo, Anoke, Onyeisi & Chukwu, (2017), Hassan, Babafemi &
Jakada (2016), Taiwo, Alaka & Afieroho (2016), Aigbovo & Izekor (2015), Njogo & Ogunlowore
(2014) Yadirichukwu & Chigbu (2014), Maduka & Onwuka (2013)}. This study defers by studying
how macro-economic factors have impacted the stock market in Nigeria. This is in line with the
argument by Pagano (1993) which highlights the significance of financial development on
growth and vice versa. Similarly, Quixima and Almeida (2014) argue that economic growth also
leads to increased demand for credit that should support the development of the financial
sector. In the case of asset prices, Chen, Roll and Ross (1986) are of the view that they are
commonly believed to react sensitively to economic news.
The objective of this research, therefore, is to determine whether macro-economic factors have
impacted stock market performance in Nigeria since stock market liberalization in 1993.
Following from the above, the hypothesis tested is: there is no significant relation between
macro-economic factors and stock market performance in Nigeria.
SECTION TWO: LITERATURE REVIEW
CONCEPTUAL ISSUE
The relationship between macro-economic indicators and stock returns is important due to the
fact that macro-economic factors can be used to account for the variation in asset returns.
Generally, IMF (2012) explains that the effectiveness of macroeconomic policy responses in the
LICs is conditioned importantly by features of their financial systems. According to Alenoghena,
Enakali-Osoba and Mesagan (2014), a well-developed capital market is crucial to well-
functioning capital markets, increased economic efficiency, investment and growth through
price discovery, liquidity provision, reduction in transaction costs and risk diversification.
Quixima and Almeida (2014) aver that economic growth also leads to increased demand for
credit that should support the development of the financial sector. This explains why the
relationship between the stock market and economic development is bidirectional in nature.
For instance, Karimo and Ogbonna (2017) contend that the causal relationship between
financial development and economic growth depends on the stage of economic development.
In their view, in the early stages of economic development, the supply-leading view (financial
sector deepening leading to increased economic growth) can stimulate real capital formation
and that this inevitably creates opportunities for savers and investors and causes an increase in
economic growth. The supply-leading view becomes less-important, the authors argue, as
financial and economic development proceed, the demand-pull (a well-developed economy
driving the development of the financial sector) gradually starts to dominate. Another angle to
this perspective is the assertion by Greenwood and Smith (1997) that market formation is
endogenous and that the costs of market formation will typically require that market
development follows some period of real development. What this implies is that financial
market development may not have to wait for foreign capital in order to develop, although,
they acknowledge that economic history is also replete with examples illustrating the
importance of financial markets in facilitating economic growth.
Without equivocation, Ozlen and Ugor (2012) declare that the role of macro-economic variables
in asset pricing theories is accepted to be important and further explained that macro-
economic factors such as inflation rate, exchange rate, interest rate, current account deficit and
unemployment rate as contained in the literature on finance have been shown to be critical in
predicting the variability of stock returns. Ozlen and Ugor, however, pointed out that there is
no standard set of macro-economic variables, despite the clear relationship between stock
market turnover and economic activities.
Historically, efforts have been channeled towards studying the reasons for the variation in stock
prices. Davis and Etheridge (2006) traced the origin of the theory of speculation to Bachelier’s
Brownian motion which they described as arising as a model of the fluctuations in stock prices.
Courtault, Kabanov, Bru, Crepel, Lebon and Marchand (2000) declare the pioneering work of
Bachelier as of exceptional merit; further stating that the theory of Brownian motion, one of
the most important mathematical discoveries of the twentieth century, was initiated and used
for the mathematical modeling of price movements and the evaluation of contingent claims in
financial markets. As a result, the significance of stock price movements and prediction has
attained a historic status and gained currency over time thereby becoming one of the main
features of both mathematical and modern finance theories. In addition to this, Fama (1990)
points out that many authors find that large fractions of annual stock-return variances can be
traced to forecasts of variables such as GNP, industrial production, and investment that are
important determinants of the cash flows to firms. He further explain that measuring the total
return variation explained by a combination of shocks to expected cash flows, time-varying
expected returns, and shocks to expected returns is a logical way to judge the efficiency or
rationality of stock prices. This explains why Adjei, Ossei and Mensah (2016) point out that
various studies have shown that there is a strong and positive relationship between the
financial sector and economic development. For example, Murcia (2014) used exchange rate,
gold reserves, consumer price index, wholesale price index, investments and overseas workers’
remittances in his study of selected Philippines’ stock market indices.
THEORETICAL LITERATURE
In addition to the foregoing, there is a linkage between macroeconomic variables and stock
market returns as several models [arbitrage pricing theory (APT), aggregate demand and
aggregate supply (AD/AS), monetary transmission mechanisms] provide a basis for the long-run
relationship and short-run dynamic interactions among macroeconomic variables and stock
prices (Ibrahim & Aziz, 2003). Similarly, Murcia (2014) succinctly points out that the Arbitrage
Pricing Theory (APT) links macroeconomic indicators and stock market returns. What appears
to be an important crucible on the relationship between macro-economic factors and stock
returns are the theoretical foundations upon which some of the relationships are derived. For
example, Dincergok (2016) while attributing the return on a stock to be determined by future
cash flows and the discount rate and that what affects these variables equally affects returns on
stock prices, points out that the main dis-advantage of this macro-economic factor model is
that there is no theoretical basis for the selection of the macro-economic variables. However,
this was further to the Arbitrage Pricing Theory deployed by Chen, Roll and Ross (1986). In
addition, the opinion of Osisanwo and Atanda (2012) is apt and particularly throws light on
other factors in addition to important macro-economic indicators that affect stock prices such
as seasonal variation of the market, enlightenment of the investing public or general awareness
of the market, political and social crisis, amongst others.
Regarding the relationship between stock performance and economic growth, Olweny and
Kimani (2011) posit that when the stock market mobilizes savings, it simultaneously allocates a
larger portion of the same to firms with relatively high prospects as indicated by their returns
and level of risk. Bastas and Soytas (2014) enthuse that overall, there is not a common view on
the link between macro-economic variables and stock market. Following this line of argument,
Gajdka and Pietraszewski (2016) explain that there is no agreement, however, on the specific
mechanisms underlying the relationships between the real sphere of the economy and the
stock market or on their direction, i.e. whether it is the real economy that influences the capital
market or whether it is the other way round.
Inflation (a major macro-economic variable) according to Uwubanmwen and Eghosa (2015) is
measured by inflation rate, the annualized percentage change in the general price index
(usually the Consumer Price Index) over time. In their effort to clarify the impact of inflation on
the value of investments, Uwubanmwen and Eghosa (2015) state that the essence of
investment is to attain a reasonable return while minimizing risk. To them, minimizing risk and
earning reasonable returns on investment calls for proper attention on the current rate of
inflation otherwise the value of the investment will be eroded overtime. Following the Fisherian
hypothesis, Tripathi and Kumar (2014) point out that there is a positive relationship between
inflation and stock returns whereby nominal stock returns should rise along with inflation
providing investors a hedge against inflation.
There is a causal relationship between stock returns and unemployment rate, hence, Lougani,
Rush and Tave (1990) elaborated on this relationship. They are of the opinion that in a well-
functioning stock market, the industry stock price represents the present value of expected
future industry profits. Continuing, the authors explain that an increase in the dispersion of
stock prices across industries reflects the occurrence of shocks that are expected to have
differential impacts on industrial profits. Farsio and Fazel (2013) while agreeing with the notion
of a causal relationship between unemployment and stock prices, however, advocates that
since both set of variables are endogenous, moreso, when their movements depend on a
variety of exogenous variables, makes it essential to conduct deeper analysis in order to identify
the predominant causes of movements in these variables before assuming any causal
relationships. But the argument of Farmer (2015) on the causal link between the two variables
is straight forward when the author states that given the causal link from the stock market to
unemployment, it should be possible to predict the future history of the unemployment rate
using its own past and the past history of the stock market. Mainstream economic theory
depicts on a strong link between stock market activity and unemployment (Tapa, Tom, Lekoma,
Ebersohn and Phiri, 2016). Gonzalo and Taamouti (2017) on their part inform that
unemployment rate is chosen in their study to represent the real economy because in addition
to its accuracy, it gauges the economy’s growth rate. In addition, they point out that
unemployment rate is one of the important indicators for the Federal Reserve to determine the
health of the economy when setting monetary policy.
Regarding interest rate, Ayopo, Ishola and Olukayode (2016) are of the opinion that the
monetary policy rule specifies the means through which the monetary policy authority controls
economic activities in an economy, the stock market inclusive. According to this hypothesis,
they further averred, interest rate is the key instrument used in determining the direction at
which the economy moves.
In the case of exchange rate, Aydemir and Demirhan (2009) in emphasizing the significance of
exchange rate as a macro-economic variable, posit that the relationship between stock prices
and exchange rates has preoccupied the minds of economists since both play important roles in
influencing the development of a country. Following globalization and increased integration of
international financial markets, exchange rates assume more fundamental importance in
macro-economic analysis. As in all areas, globalization affects the exchange rate and share
prices (Zeren & Mustafa, 2016). From this perspective, Tursoy (2017) aver that changes in local
stock markets, caused by events that occur in other countries or in the international market, as
well as the interaction between exchange rates, merit close attention because the markets are
more open to international investment and the exchange rate regimes are floating. Salisu and
Ndako (2017) on their part, highlight the significance of studies on the link between macro-
economic variables and the stock market in financial literature by pointing out that the search
for evidence in favour of the linkages between exchange rate shocks and real and financial
variables of the economy is a clear motivation for assessing the nexus between exchange rate
and stock market prices. Theoretically, Tursoy (2017), explains that there are two approaches
(traditional and portfolio) as contained in financial literature which explain the connection
between stock prices and exchange rates. The author, however, points out that the direction of
causation differs in the two approaches and that in the traditional approach, exchange rate
changes usually lead to changes in stock prices, and in the portfolio approach, the reverse is the
case.
EMPIRICAL LITERATURE
The empirical test carried out by Kumuda and Simi (2015) on the relationship between
macroeconomic variables (inflation rate, unemployment rate, GDP rate and interest rate) and
stock prices shows that inflation rate and unemployment rate have positive relationship with
stock prices while GDP and interest rate were negatively related with stock prices. In the case of
the United States of America, Sirucek (2012) used a select group of macro-economic variables
to establish the effect, implication, impact and relationship between these variables and the US
indices (S & P 500) and the Dow Jones Industrial Average (DJIA) between 1999 and 2012 and
found that the macroeconomic factors have stronger relationship with the DJIA than the S & P
500. Based on the results obtained, the author concluded that the model used thus confirmed
the economic theory justifying the impact of macro-economic variables on share prices. In the
case of Sri Lanka, Nijam, Ismail and Musthafa (2015) regressed the following macro-economic
variables: Gross Domestic Product, Inflation, Interest Rate, Balance of Payment and Exchange
rate on the All Share Price Index of Colombo stock exchange. The analysis reveals that the
macro-economic variables have significant relationship with the Index. The analysis further
revealed that the stock market index significantly relates to the Gross Domestic Product,
Exchange rate and Interest rate in a positive manner while it has negative relation with inflation
rate. On its part, it was established that the Balance of Payment was found to have insignificant
relationship with the stock price Index. In particular, Gonzalo and Taamouti (2017) found that
only anticipated unemployment rate has a strong impact on stock prices which is against the
general findings in the literature, they added.
Alenoghena (2014) in his study of the relationship between capital market, financial deepening
and economic growth of Nigeria found that stock market capitalization, narrow money
diversification and interest rate significantly impacted the promotion of economic growth of the
country during the period of the study other measures of liquidity were invariant in explaining
the trend of economic growth, although they exhibited very strong coefficients in the process.
Karimo and Ogbonna (2017) adopting the Toda-Yamamoto augumented Granger causality test
and established that the growth-financial deepening nexus in Nigeria follows the supply-leading
hypothesis as against the hypothesis of growth leading financial deepening. On their part,
Alenoghena, Enakali-Osoba and Mesagan (2014) found that financial deepening variables
actually positively impacted on the performance of the Nigerian stock market.
SECTION THREE: RESEARCH METHODS
The model adopted in this study is the intertemporal capital asset pricing model. Low and Wang
(2006) argue that this model is ideal for an empirical link between asset prices and economic
factors. Therefore, this study models the joint behaviour between prices and companies’
fundamental indicators (proxies for macro-economic variables) in Nigeria for the period: 1993 –
2017. The ex post facto research design was adopted and as such, secondary data were used.
The study adopted ordinary least squares method of regression analysis and used Unit Root
Tests (to check the time series properties of the variables prior data estimation) and
Autoregressive Distributed Lag and Diagnostic tests (normality test, serial correlation test and
heteroscedasticity test) to analyze the impact of Macro-economic Indicators on Stock Market
Performance in Nigeria. It also used the Granger Causality Test to establish the direction of
causality of the variables used, viz: All Share Index (ASI), Gross Domestic Product (GDP),
Inflation (INF), Industrial Production Index (IPI), Unemployment Rate (UR), Interest Rate (IR) and
Exchange Rate (ER).
Model Specification
The empirical theory adopted in this study is the Arbitrage Pricing Theory and is specified as
follows:
ASI = ƒ(GDP, INF, IPI, UR, IR and ER) … Equation (1)
Transformed into an econometric model, thus:
LogASI = α0 + β0Log GDP + β1INF. + 21IPI + β3UR + β4IR + β5ER + εt … Equation (2)
Where:
ASI = All Share Index
GDP = Gross Domestic Product growth rate
INF = Inflation
IPI = Industrial Production Index
UR = Unemployment Rate
IR = Interest Rate
ER = Exchange Rate
εt = Error Term
The relationship between the Dependent variable (ASI) and the Explanatory variables – gross
domestic product (GDP), inflation (INF), industrial production index (IPI), unemployment rate
(UR), interest rate (IR) and exchange rate (ER) specifies a simple model in the estimation of the
relationship between stock returns in Nigeria proxied by ASI and GDP, INF, IPI, UR, IR, ER while
α is a constant, β0 - β5 are the slope coefficients that captures the sensitivity of the stock returns
to gross domestic product per capita, inflation, industrial production, unemployment, interest
and exchange rate while εt is the stochastic error term.
TABLE 1: A PRIORI EXPECTATION
Dependent Variable Independent Variable Change in Sign
All Share Index Gross Domestic Product +
All Share Index Inflation -
All Share Index Industrial Production Index +
All Share Index Unemployment Rate +
All Share Index Interest Rate -
All Share Index Exchange Rate -/+
Source: Authors’ Compilation, 2019.
SECTION FOUR: DATA PRESENTATION AND ANALYSIS
Data
The data used in this study (Appendices 1 & 2: DATA SET FOR TREND ANALYSIS AND DATA SET
FOR EMPIRICAL ANALYSIS respectively) were from secondary sources such as Nigerian Stock
Exchange Fact Books (several editions), National Bureau of Statistics and Central Bank of Nigeria
Statistical Bulletins (several Issues). The data are data relating to All Share Index (ASI), Gross
Domestic Product (GDP), Inflation (INF), Industrial Production Index (IPI), Unemployment Rate
(UR), Interest Rate (IR) and Exchange Rate (IR).
Trend Analysis
Market Volume of Securities Traded
Figure 1 shows that the market volume increased through the years to edge up at 193.14billion in
2008 when the market was booming. After that, it experienced a major decline. The volume rose
from 0.47 billion in 1993 to well over 18billion in 2009 but declined sharply thereafter until
2010 – 2017 when it steadied and stood at 100.5billion at the end of 2017.
Market Turnover
As seen in the Figure 2 below, the average values of total turnover during the period of 1993 to
2008 shows an increasing trend just as that of the market volume. The value rose from N0.05
billion in 1993 to a peak of N18.71 billion in the year 2008. However, the Figure shows that the
recent trend from 2010 – 2017 is cyclical is that of an upward trend.
New Issues
In Figure 3 the average values of new issues during the period of 1993 to 2008 shows an
increasing trend. Specifically, the average value which was N3.94 billion in 1993 rose to the
highest point at the end of the year 2008 with a value of N2576.19 billion but declined in 2009 to
N275.24 billion. Thereafter, some fluctuations were noticed in the trend till recent time.
All Share Index on the Nigerian Stock Exchange
In Figure 4, the average values of the all shares index during the period of 1993 to 2008 shows
an increasing trend. The figure depicts that the average value of all share index which was
1229.03 in 1993 rose to 50424.70 at the end of the year 2008 but between 2010 and 2017, this
value shows some cyclicality.
19931996
19992002
20052008
20112014
2017
0.00
50.00
100.00
150.00
200.00
250.00Market Volume
Year
Mar
ket V
olum
e (B
illio
n)
19931996
19992002
20052008
20112014
2017
0.002.004.006.008.00
10.0012.0014.0016.0018.0020.00
Total Turnover
Year
Tota
l Tur
nove
r (N
' Bill
ion)
Figure 1: Market Volume Figure 2: Total Turnover (N' Billion)
19931996
19992002
20052008
20112014
2017
0.00
500.00
1000.00
1500.00
2000.00
2500.00
3000.00New Issues
Year
Val
ue N
ew Is
sues
(N'B
illio
n)
0.00
10000.00
20000.00
30000.00
40000.00
50000.00
60000.00 All Share Index
Year
Inde
x
Figure 3: New Issues Figure 4: All Share Index
Number of Listed Companies
Figure 5 depicts increasing trend between the years 1993 and 2010. Explicitly, the average
number of the listed companies which was 174.00 in 1993 increased to 217.00 at the end of the
year 2010. Thereafter, the number began to decline and was just above 170 at the end of 2017.
The highest number recorded during the period was 217.00 companies that were recorded in
2010. The implication of this is that more companies delisted than those that were newly listed
on the stock exchange.
Number of Listed Securities.
In Figure 6, the average number of listed securities during the period of 1993 to 2017 shows a
declining trend as it fell from 272.00 in 1993 to 247 at the end of year 2017. However, the
highest number (310.00) of listed securities was recorded in 2007 and the trend clearly shows a
declining trend in recent years.
0.00
50.00
100.00
150.00
200.00
250.00Number of Listed Companies
Year
Num
ber
19931996
19992002
20052008
20112014
2017
0.0050.00
100.00150.00200.00250.00300.00350.00
Number of Listed Securities
Year
Num
ber
Figure 5: No of Listed Companies Figure 6: Number of Listed Securities
From the overview, the stock market indices in Nigeria indicated declining trends after the initial
lift in its indices in the boom years of 2004 – 2009. The boom could be attributed to reform of
the financial markets but risky behaviour on the part of market participants and distress
resolution mechanism of the banking sector crisis of that era can be said to have accounted for
this situation. While the expectation is that economic growth can impact the stock market
positively especially when the macro economy witnessed positive growth, the stock market
declined. Can this be attributed to a falling equity culture and/ or that economic growth did not
impact the stock market in Nigeria positively
Descriptive Analysis
The result of the descriptive statistics for variables considered in this study is presented in Table
1. From the table, the average value of all share index (ASI) is 20411.15 with minimum and
maximum values of 1229.03 and 50424.70 respectively. The real gross domestic product growth
rate (GDP) during the period takes its own values between -2.04% and 15.33%, with an average
value of 4.62%. Inflation rate (INF) ranges from 5.38% to 72.84% with a mean value of 18.57%
during the period. The industrial production index (IPI) proxied by manufacturing capacity
utilization has a minimum value of 29.29% and a maximum value of 64.31% with 47.70% as the
average value. The ranges of the values that unemployment (UR) has are 5.10% and 19.70%
with an average value of 11.42%. With respect to interest rate (IR), the minimum and maximum
values of 18.36% and 36.09% respectively are recorded with an average value of 23.56%. The
minimum and maximum values of exchange rate (ER) indicate that the rate of exchange during
the period of this study hovers around N21.89 per USD and N305.79 per USD with an average
value of N120.25 per USD. However, the standard deviation reveals diverse variability in the
series.
Table 1: Summary Statistics
ASI GDP INF IPI UR IR ER Mean 20411.15 4.62 18.57 47.70 11.42 23.56 120.25 Maximum 50424.70 15.33 72.84 64.31 19.70 36.09 305.79 Minimum 1229.03 -2.04 5.38 29.29 5.10 18.36 21.89 Std. Dev. 14306.37 3.99 17.53 11.25 3.74 4.245 71.96 Observations 25 25 25 25 25 25 25Source: Authors’ Computation, 2018; underlying data are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin, 2017 and World Development Indicator (WDI), 2017.Note: ASI represents All Share Index, GDP represents Real Gross Domestic Product Growth Rate, INF represents Inflation Rate, IPI represents Industrial Production Index, UR represents Unemployment Rate, IR represents Interest Rate and ER represents Exchange Rate.
Stationarity Test
The summary of the result of the unit root tests carried out in their level and first difference
forms using Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) approaches are presented
in Table 2. The ADF and PP tests results provide evidence that the null hypothesis that all the
series except gross domestic product growth rate (GDP), unemployment (UR) and interest rates
(IR) have unit roots can be safely accepted at all levels within the 1% and 10% conventional
levels of significance. In other words, the acceptance of null hypothesis indicates that the series
are not stationary at level. Investigating further, the result shows that the series can only be made
stationary by first difference. However, the rejection of the null hypothesis at level within the 1%
to 10% conventional level of significance when gross domestic product growth rate (GDP),
unemployment (UR) and interest rates (IR) series are tested for unit root; strongly indicates that
the series are integrated of order (0). It is, therefore, worth concluding that the series have
different orders of integration, that is, both at I(0) and I(1) and the study proceeds to bounds
testing of ARDL approach to examine the long run relationships among the series.
Table2: Unit Root Test
Variable/t-stat/Critical Value
Augmented Dickey-Fuller Phillips-Perron@Level @1st Diff. Order @Level @1st Diff. Order
ASI
t-Stat -2.176 -4.488***
I(1)
-2.171 -4.692***
I(1)1% -4.394 -4.441 -4.394 -4.4165% -3.612 -3.633 -3.612 -3.62210% -3.243 -3.255 -3.243 -3.249
GDP
t-Stat -2.243 -6.913***
I(1)
-2.096 -9.285***
I(1)1% -4.394 -4.416 -4.394 -4.4165% -3.612 -3.622 -3.612 -3.62210% -3.243 -3.249 -3.243 -3.249
INF
t-Stat -2.033 -4.800***
I(1)
-1.522 -5.277***
I(1)1% -4.394 -4.416 -4.394 -4.4165% -3.612 -3.622 -3.612 -3.62210% -3.243 -3.249 -3.243 -3.249
IPI
t-Stat -1.923 -5.815***
I(1)
-1.959 -5.815***
I(1)1% -4.394 -4.416 -4.394 -4.4165% -3.612 -3.622 -3.612 -3.62210% -3.243 -3.249 -3.243 -3.249
UR
t-Stat -3.431* -4.558***
I(0)
-3.375* -10.081***
I(0)1% -4.394 -4.468 -4.394 -4.4165% -3.612 -3.645 -3.612 -3.62210% 3.243 -3.261 -3.243 -3.249
IRt-Stat
-4.773*** -5.385***
I(0)-4.651*** -20.097***
I(0)1% -4.394 -4.441 -4.394 -4.4165% -3.612 -3.633 -3.612 -3.62210% -3.243 -3.255 -3.243 -3.249
ER
t-Stat -1.608 -3.565**
I(1)
-1.336 -3.565**
I(1)1% -4.416 -4.416 -4.394 -4.4165% -3.622 -3.622 -3.612 -3.62210% -3.249 -3.249 -3.243 -3.249
Source: Authors’ Computation 2018; underlying data are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin, 2017 and World Development Indicator (WDI), 2017.Note: ASI represents All Share Index, GDP represents Real Gross Domestic Product Growth Rate, INF represents Inflation Rate, IPI represents Industrial Production Index, UR represents Unemployment Rate, IR represents Interest Rate and ER represents Exchange Rate.***, **and * represents the level of significance at 1%, 5% and 10% respectively.
Bounds Co-integration Test
Following the unit root test results in the preceding subsection, the study proceeds to ascertain
whether long run relationships exist among the variables and in achieving this, the study
followed the approached developed by Pesaran, Shin and Smith (2001). One of the major
strengths of this approach is that the variables in the cointegrating relationship can be a mixture
of I(0) or I(1). In addition, the approach performs better when it comes to a small sample data.
As in Table 3, the computed F-statistic value of 3.650 is greater than the upper critical bound
value of 3.61, thus indicating the existence of a long-run relationship among the variables.
Alternatively, this suggests the rejection of the null hypothesis of no co-integration at 5% level of
significance. In view of this, the long-run and short-run relationship between the variables is
represented with an error correction model.
Table 3: Bounds Co-Integration Test
Test Statistic Value K
F-statistic 3.667196 6
Critical Value Bounds
Significance I0 Bound I1 Bound
10% 2.12 3.235% 2.45 3.612.5% 2.75 3.991% 3.15 4.43
Source: Authors’ Computation 2018 with underlying data obtained from Central Bank of Nigeria (CBN) Statistical Bulletin, 2017 and World Development Indicator (WDI), 2017.
Short - Run Coefficient
The results from the estimation of the short – run and the long-run models based on the estimated
ARDL (1, 0, 0, 1, 1, 1, 1) using AIC1 are presented in Table 4 below. Focusing on the error
correction term (ECMt-1) of the short – run model, it can be seen that the coefficient has the
1
expected sign and it is statistically significant at 1% level of significance. This further confirms
the existence of a long-run relationship between the dependent variable and the independent
variables.
The result further shows that current value of GDP, INF and ER significantly affect ASI in the
short-run. Specifically, the three variables (GDP, INF and ER) have inverse relationship with
ASI in the short run. On the other hand, the coefficient of IPI, UR, and IR are positive but
statistically insignificant meaning that that all share index (ASI) is not influenced by IPI, UR and IR
significantly during the period of this study in the short – run.
Long Run Coefficient
Long Run Coefficient
The long-run dynamics of the relationship between independent and dependent variables as
displayed in the Table imply that in the long-run; the relationship all share index (ASI) and gross
domestic product growth rate (GDP) remains negative and statistically significant at 5% level. Similarly,
inflation (INF) and interest rate (IR) exhibit negative and significant relationship with all share index
(ASI) in the long – run at 5% level of significance. Conversely, the coefficient industrial production index
(IPI) is positive and statistically significant at 1% level. This indicates that 1.87 percent increase in all
share index (ASI) in the long run is associated with 1 percent increase in industrial production index (IPI).
Finally, it is evident from the F = Stat (Prob.) = 31.90 (0.000) and R2 = 0.967 that the ARDL model
is significant and fit.
Table 4: ARDL Cointegrating Short Run and Long Run Forms
Short Run Coefficients
Variable Coefficient Std. Error t-Statistic Prob.
D(GDP) -0.059211 0.024133 -2.453585 0.0304D(INF) -0.007033 0.003532 -1.991042 0.0697DLOG(IPI) 0.814029 0.542424 1.500725 0.1593D(UR) 0.005229 0.011808 0.442873 0.6657D(IR) 0.013334 0.019250 0.692668 0.5017D(ER) -0.009220 0.003203 -2.878912 0.0139CointEq(-1) -0.774706 0.138864 -5.578885 0.0001
Cointeq = LOG(ASI) - (-0.0764*GDP -0.0091*INF + 1.8683*LOG(IPI) + 0.0424*UR -0.0543*IR + 0.0052*ER + 3.4163 )
Long Run Coefficients
Variable Coefficient Std. Error t-Statistic Prob.
GDP -0.076431 0.026457 -2.888879 0.0136INF -0.009078 0.003708 -2.448536 0.0307LOG(IPI) 1.868325 0.538542 3.469227 0.0046UR 0.042436 0.022820 1.859607 0.0876IR -0.054303 0.022851 -2.376394 0.0350ER 0.005192 0.002102 2.470218 0.0295C 3.416324 2.120445 1.611135 0.1331
Source: Authors’ Computation 2018; underlying data are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin, 2017 and World Development Indicator (WDI), 2017.Note: ASI represents All Share Index, GDP represents Real Gross Domestic Product Growth Rate, INF represents Inflation Rate, IPI represents Industrial Production Index, UR represents Unemployment Rate, IR represents Interest Rate and ER represents Exchange Rate. R2 = 0.967, F = Stat (Prob.) = 31.90 (0.000), Durbin-Watson stat = 1.70
Diagnostic Tests
The Autoregressive Distributed Lag (ARDL) model is validated by conducting normality test,
serial correlation test, and heteroscedasticity test on the residual of the estimated model using
Jarque-Bera for normality test with the null hypothesis of normality, Breusch-Pagan serial
correlation for serial correlation test with the null hypothesis of no serial correlation and
Breusch-Pagan-Godfrey and ARCH effects tests for heteroscedasticity with the null hypothesis
of homoscedastic. Following the tests result presented in Table 5, the model passed all the
diagnostic tests. In other words, the insignificant values of the test results suggest the acceptance
of null hypotheses and indicate that the residual is normally distributed, there is no evidence of
serial correlation and the model is free from heteroscedasticity problem.
Table 5: Diagnostic Test
Normality Test
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 0.519339 Prob. F(1,11) 0.4862Obs*R-squared 1.082018 Prob. Chi-Square(1) 0.2982
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 1.540073 Prob. F(11,12) 0.2345Obs*R-squared 14.04865 Prob. Chi-Square(11) 0.2303Scaled explained SS 1.821402 Prob. Chi-Square(11) 0.9990
Source: Authors’ Computation, 2018; underlying data are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin, 2017 and World Development Indicator (WDI), 2017.
Granger Causality
According to the results in Table 6, it can be seen that there exist a unidirectional short-run
causal relationship among some of the variables. Specifically, at 5% level of significance the
results show that ASI Granger causes IPI (prob. = 0.0214). Also, it shows that ER Granger
causes ASI (prob.=0.0499). The causality between UR and GDP shows that UR Granger causes
GDP (prob. = 0.0256). Furthermore, causality flows from IR to GDP, ER to IPI and ER to IR.
Table 6: Pairwise Granger Causality Tests
Null Hypothesis: Obs F-Statistic Prob.
GDP does not Granger Cause LOG(ASI) 24 0.03064 0.8627 LOG(ASI) does not Granger Cause GDP 0.02798 0.8688
INF does not Granger Cause LOG(ASI) 24 0.46397 0.5032 LOG(ASI) does not Granger Cause INF 1.49065 0.2356
LOG(IPI) does not Granger Cause LOG(ASI) 24 2.52328 0.1271 LOG(ASI) does not Granger Cause LOG(IPI) 6.17898 0.0214
UR does not Granger Cause LOG(ASI) 24 0.74127 0.3990 LOG(ASI) does not Granger Cause UR 2.31218 0.1433
IR does not Granger Cause LOG(ASI) 24 0.03133 0.8612 LOG(ASI) does not Granger Cause IR 1.66793 0.2106
ER does not Granger Cause LOG(ASI) 24 4.32972 0.0499 LOG(ASI) does not Granger Cause ER 0.16839 0.6857
INF does not Granger Cause GDP 24 0.00031 0.9860 GDP does not Granger Cause INF 1.27165 0.2722
LOG(IPI) does not Granger Cause GDP 24 0.23373 0.6338 GDP does not Granger Cause LOG(IPI) 2.23555 0.1497
UR does not Granger Cause GDP 24 5.77570 0.0256 GDP does not Granger Cause UR 0.19169 0.6660
IR does not Granger Cause GDP 24 3.53075 0.0742 GDP does not Granger Cause IR 0.29917 0.5902
ER does not Granger Cause GDP 24 0.00754 0.9316 GDP does not Granger Cause ER 2.42187 0.1346
LOG(IPI) does not Granger Cause INF 24 0.01290 0.9107 INF does not Granger Cause LOG(IPI) 2.63533 0.1194
UR does not Granger Cause INF 24 0.08535 0.7730 INF does not Granger Cause UR 2.64787 0.1186
IR does not Granger Cause INF 24 2.05268 0.1667 INF does not Granger Cause IR 1.34164 0.2597
ER does not Granger Cause INF 24 0.25206 0.6209 INF does not Granger Cause ER 0.30566 0.5862
UR does not Granger Cause LOG(IPI) 24 2.18484 0.1542 LOG(IPI) does not Granger Cause UR 0.29328 0.5938
IR does not Granger Cause LOG(IPI) 24 2.15124 0.1573 LOG(IPI) does not Granger Cause IR 0.11567 0.7372
ER does not Granger Cause LOG(IPI) 24 4.96215 0.0370 LOG(IPI) does not Granger Cause ER 2.20846 0.1521
IR does not Granger Cause UR 24 0.44749 0.5108 UR does not Granger Cause IR 0.07621 0.7852
ER does not Granger Cause UR 24 2.31853 0.1428 UR does not Granger Cause ER 0.30284 0.5879
ER does not Granger Cause IR 24 3.39916 0.0794 IR does not Granger Cause ER 0.58103 0.4544
Source: Authors’ Computation 2018; underlying data are obtained from Central Bank of Nigeria (CBN) Statistical Bulletin, 2017 and World Development Indicator (WDI), 2017.
SECTION 5.0: CONCLUSION AND RECOMMENDATIONS
Based on the findings, the study concludes that equilibrium exists among the variables, thus,
economic growth and development were found to have impacted stock market performance in
Nigeria during the period covered by this study. The study, therefore, recommend, amongst
others, that the economic growth and development of Nigeria should be pursued more vigorously
in order to enhance the performance of the stock market. Also, policy makers should strive to put
in place measures that can boost liquidity of the stock market which can improve its performance
over-time. This can be done through the use of monetary policies such as the lowering of the
monetary policy rates by the Monetary Policy Committee of the Central Bank of Nigeria which
can significantly improve liquidity in the equity market. Furthermore, quick passage of the
already amended Investments and Securities Act should be carried out and provisions of the Act
should be implemented to the letter in order to make the market more competitive
internationally. Finally, transaction costs in the stock market should be reduced drastically in
order to improve the volume of transactions in the market.
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APPENDIX 1: DATA SET USED FOR TREND ANALYSIS
Year ASI Market Volume (Billion)
Total Turnover (N' Billion)
Value of New Issues (N'Billion)
No of Listed Companies
No of Listed Securities
1993 1229.03 0.47 0.05 3.94 174.00 272.00
1994 1913.23 0.52 0.06 2.67 177.00 276.00
1995 3815.12 0.40 0.12 7.08 181.00 276.00
1996 5955.14 0.88 0.16 21.45 183.00 276.00
1997 7638.59 1.25 0.22 9.11 182.00 264.00
1998 5961.88 2.10 0.23 17.28 186.00 264.00
1999 5264.19 3.11 0.27 44.44 196.00 269.00
2000 6701.18 5.00 0.37 35.71 196.00 261.00
2001 10185.08 5.16 0.49 44.71 194.00 261.00
2002 11631.87 6.62 0.95 65.32 195.00 258.00
2003 15559.90 13.31 0.88 184.97 200.00 265.00
2004 24738.65 19.21 2.61 227.38 207.00 276.00
2005 22876.72 26.69 3.13 728.66 214.00 287.00
2006 27647.51 36.66 5.74 1489.63 202.00 288.00
2007 48773.31 138.28 16.18 2296.37 212.00 310.00
2008 50424.70 193.14 18.71 2576.19 213.00 299.00
2009 23091.55 102.85 3.77 275.24 216.00 266.00
2010 24775.51 93.34 4.41 2437.24 217.00 264.00
2011 23393.65 89.58 3.30 1810.39 201.00 277.00
2012 23432.62 89.15 3.33 195.36 198.00 285.00
2013 36207.08 106.54 4.58 286.76 198.00 279.00
2014 39409.82 108.47 6.30 271.05 196.00 280.00
2015 30867.20 92.86 4.23 1386.74 190.00 286.00
2016 26624.08 95.83 2.56 1558.17 170.00 247.00
2017 32161.11 100.52 5.87 2271.36 167.00 247.00
APPENDIX 2: DATA SET USED FOR EMPIRICAL ANALYSIS
Year ASI GDP INF IPI UR IR ER1993 1229.03 -2.04 57.17 37.19 7.68 36.09 22.051994 1913.23 -1.82 57.03 30.40 7.59 21.00 21.891995 3815.12 -0.08 72.84 29.29 7.51 20.79 21.891996 5955.14 4.19 29.27 32.46 7.41 20.86 21.891997 7638.59 2.93 8.53 30.40 10.20 23.32 21.891998 5961.88 2.58 10.00 32.40 10.00 21.34 21.891999 5264.19 0.58 6.62 34.60 12.50 27.19 92.692000 6701.18 5.01 6.93 36.10 13.50 21.55 102.112001 10185.08 5.92 18.87 42.70 13.60 21.34 111.942002 11631.87 15.33 12.88 54.90 12.55 30.19 120.972003 15559.90 7.35 14.03 56.50 11.20 22.88 129.362004 24738.65 9.25 15.00 55.70 11.00 20.82 133.502005 22876.72 6.44 17.86 54.80 12.79 19.49 132.152006 27647.51 6.06 8.24 53.30 13.80 18.70 128.652007 48773.31 6.59 5.38 53.38 14.20 18.36 125.832008 50424.70 6.76 11.58 53.84 13.60 18.70 118.572009 23091.55 8.04 11.54 55.14 19.70 22.62 148.882010 24775.51 9.13 13.72 56.22 5.10 22.51 150.302011 23393.65 5.31 10.84 56.22 6.00 22.42 153.862012 23432.62 4.21 12.22 57.61 10.60 23.79 157.502013 36207.08 5.49 8.48 57.90 15.16 24.69 157.312014 39409.82 6.22 8.06 58.23 6.40 25.74 158.552015 30867.20 2.79 9.02 64.31 10.40 26.71 193.282016 26624.08 -1.58 15.70 44.30 14.20 27.29 253.492017 32161.11 0.83 22.38 54.50 18.80 30.68 305.79