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American Economic & Social Review; Vol. 6, No. 1; 2020 
ISSN 2576-1269   E-ISSN 2576-1277 

Published by Centre for Research on Islamic Banking & Finance and Business, USA 

 

     1 
 

Stock Market Activities and Economic Growth in Nigeria: A Cointegration 
Approach 

 
 

Ashamu Sikiru O.  PhD 
Department of Banking and Finance  

Faculty of Management Sciences 
Lagos State University, Ojo, Nigeria 
E-mail: soyerinde2012@gmail.com 

 
 

Soyebo Yusuf A.  PhD 
Department of Banking and Finance 

Faculty of Management Sciences 
Lagos State University, Ojo, Nigeria 
E-mail: yusuf.soyebo@lasu.edu.ng 

 
 
Abstract 
Stock markets have been the main catalyst of economic growth in most economies. However, there are mix results on the exact 
nature of the influence of key stock market variables on economic growth in developing economies. Thus, this study examined the 
effect of stock market measures on economic growth in Nigeria. This paper employed an ex-post facto research design using 
annualized data obtained from the Central Bank of Nigeria (CBN) Statistical Bulletin and Stock Exchange Fact books for the 
period 1985 to 2017 and they were analysed using the error correction estimate regression technique at 5% level of significance. 

The result ecm (-1) is statistically sign and significant, stock market activities such as market capitalization (𝜆1 = 0.0386, p > 

0.05), number of deals (𝜆2 = 0.0487, p < 0.05) and All share Index (𝜆4= 0.0921, p < 0.05) have positive influence on economic 

growth, while volume of trading (𝜆3 = -0.6110, p < 0.05) has a negative effect on economic growth. These results suggested that 
all the specified variables except market capitalization have significant effect on economic growth in Nigeria. The study concluded 
that stock market activities influence the economic growth in Nigeria. Therefore, it is recommended that investors should take the 
advantage of the numerous opportunities offered by the stock market while market makers and regulators should continue to 
operate according to best market practices in order to ensure confidence and continuous patronage by various economic agents. 

Keywords: Stock Market, Market Capitalization, Economic Growth, Investors, Cointegration.  
 
JEL Classification: G12, G21.  
 
1. Introduction 
Generally, the stock market provides the mechanism for assembly and allocation of medium and long-term financial resources 
between economic units in order to facilitate optimum resource distribution, wealth creation and economic growth. Stock market 
enables financial intermediation as they ensure regular trading in listed financial securities and ensure that quoted firms have 
adequate access to capital. Thus, a functional stock market supports financial system through the provision of alternative finance 
to business and investment outlet to investment (Pan & Mishra, 2016). 

Beck and Levine (2001) reported that a well-functioning stock market nurtures growth and profit incentives and prompt 
in risk management as a developed stock market provide liquidity that lowers the cost of capital essential for development, 
particularly in less developed countries that generate insufficient domestic savings (Bencivenga, Smith & Stair 1996). 

However, in most emerging economies, financial turnover is slow due to long gestation period required in the ownership 
transfer process. Thus, the transaction costs on the stock market activities often influenced production decision and the growth 
rate in the economy. Similarly, transaction costs tend to fall on the stock market as it develops, the liquidity risk decreases and 
more illiquid projects can be funded. The link between stock market activities and economic growth in Nigeria has been puzzling 
due to the peculiar nature of stock market activities as well as the mix results turned in by various scholars due to the measurement 
of variables, data analysis techniques and scope of study.  

Hence, the postulate that stock market activities influence economic growth in Nigeria requires further empirical 
consideration. This study assumes that there is no significant short and long-run relationship between stock market activities and 

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economic growth in Nigeria. The remaining sections of the paper are structured as follows: next are the literature review, methods 
and materials, followed by results, discussion, recommendations and conclusion. 

 
2. Literature Review 
The economic growth of a nation represents the changes in the level of economic activities over a specific period and the stock 
market has the potentials of spurring economic growth via prompt resource allocation and distribution which result in higher 
economic activities (Popoola, 2014). Thus, the role and consequence of an effective capital mobilization mechanism in the 
development of the economy has been observed in literature.  

In developing and developed economies, several authors had reported their views on the influence of stock market on 
economic growth. For example, Osei (2005) examined the relationship between stock market development and economic growth 
in Ghana. The result revealed a unidirectional relationship from stock market performance to economic growth. Similarly, Enisan 
& Olufisayo (2009) studied the influence of stock market performance on economic growth in selected Sub – Saharan Africa 
using the Autoregressive Distributed Lag on annual data such as market capitalization and GDP. The result revealed that stock 
market development has positive and significant long run influence on economic growth in Egypt. South Africa, Cote D’ Ivoire, 
Kenya, Morocco, and Zimbabwe.  

In the same pattern, Nowbutsing & Odit (2009) examined the influence of stock market development on economic 
growth in Mauritius using the Error Correction Approach on annualized data such as market capitalization, volume of trade and 
gross domestic product between 1989 and 2006. The result revealed that stock market development influence economic growth 
in the short run and long run. Also, Odhiambo (2010) examined relationship between stock market development and economic 
growth in South Africa using the Autoregressive Distributed Lag testing on annual data such as market capitalization, value of 
trade, market turnover and real GDP per capita between 1971 and 2007. The results revealed a short and long run causal 
relationship between stock market performance and economic growth.  

Likewise, Oskooe (2010) assessed the relationship between stock market performance and economic growth in Iran 
using Vector Error Correction model on quarterly time series data between 1997 and 2008. The results revealed a short and long 
run interaction between stock performance and economic growth in Iran as economic activities play an important feature in stock 
prices fluctuation in the long run. Mishra, Mishra, Mishra & Mishra (2010) examined the influence of stock market efficiency on 
economic growth in India using multiple regression analysis on quarterly time series data such as market capitalization, market 
turnover and stock price index between 1991 and 2010. The result showed a nexus between stock market and economic growth.  

A study by Koirala (2011) examined the impact of stock exchange on gross domestic product in United Kingdom using 
a multiple regression analysis and the finding disclosed that market capitalization ratio has positive effect on gross domestic 
product. Fynn (2012) examined the effect of the stock market on economic growth in selected countries using the Generalized 
Least Squares techniques on annual panel data between 2005 and 2010. The result showed that the nature of stock market 
influence on growth is country and time specific. Wang and Ajit (2013) examined the influence of stock market development on 
economic growth in China using cointegration approach on quarterly data between 1996 and 2011. The result revealed that stock 
market development has a negative influence on economic growth as its activities are administrative-driven. Ikikii & Nzomoi 
(2013) examined the influence of stock market development on economic growth in Kenya using multiple regression on quarterly 
data such as trade volume, market capitalization and economic growth between 2000 and 2011. The result revealed that stock 
market development has a positive influence on economic growth.  

Similarly, Ishioro (2013) examined the causal linkage existing between stock market development and economic growth 
in Zimbabwe using the long-run non-causality technique on quarterly data such as market capitalization and GDP growth rate 
between 1990 and 2010. The results revealed a bi-directional relationship between economic growth and stock market 
development. Pan and Mishra (2016) examined the relationship between stock market development and economic growth in 
China using the Autoregressive Distributed Lag estimation technique and the results revealed that Global Financial Crises (GFC) 
had a substantial influence on various economic activities as stock market has a negative influence on the economy. Similarly, an 
evidence of a demand driven hypothesis was observed as economic growth influence stock market development.  

In Nigeria, several studies have examined the nature of the interaction between stock market activities and economic 
growth but their measurement of variables and results yield mix reports. For instance, Kolapo & Adaramola (2012) examined the 
influence of stock market on economic growth in Nigeria using Co-integration and Granger Causality test on annual data such as 
gross domestic product, market capitalization, total transactions and listed equities. The results showed that stock market activities 
have positive influence on economic growth.  

Equally, Okodua & Ewetan (2013) examined the influence of stock market performance and sustainable economic 
growth in Nigeria using the Autoregressive Distributed Lag estimation technique and the result revealed that economic growth is 
insignificant to stock market performance. Okoye & Nwisienyi (2013) examined the influence of stock market on economic 
growth in Nigeria using multiple regression analysis on annual data such as all share index, market value, capitalisation and GDP 
between 2000 and 2010. The result revealed that stock market has a significant influence on economic growth. Echekoba, Ezu 
and Egbunike (2013) examined the influence of stock market on economic growth in Nigeria using multivariate regression on 



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annualized data such as all share index, market capitalisation and GDP between 1999 and 2011. The result revealed that market 
capitalization and all share index have positive influence on economic growth. 

Equally, Jibril, Salihi, Wambai, Ibrahim, Muhammad & Ahmad (2015) examined the influence of stock market 
development on economic growth in Nigeria using multiple correlation and regression analysis on annual data such as value of 
stock traded, market size, capitalisation and GDP between 1990 and 2010. The result showed that market capitalization and value 
of stock traded exact a negative influence on economic growth while turnover exacts a positive influence on economic growth. 
Afolabi (2015) assessed the influence of stock market on the Nigerian economy using multiple regression analysis on annual data 
such as gross domestic product, market capitalization, foreign direct investment, inflation rates, new issues, transaction values and 
listed firms between 1992 and 2011. The result of the data analysis revealed an insignificant interaction between stock market and 
the Nigerian economy. 

Popoola, Ejemeyovwi, Alefe, Adu & Onabote (2017) examined the influence of stock market on and economic growth 
in Nigeria using the Error Correction Model on annualized data such as real gross domestic product, gross capital formation, 
market capitalization, all share index and value of transaction between 1980 and 2014. The result revealed that most of the selected 
measure exact a negative insignificant influence on economic growth. 

 
3. Methods and Materials 
The study obtained annual secondary data on stock market activities and economic growth in Nigeria from the Central Bank of 
Nigeria (CBN) Statistical Bulletin for the period between 1986 and 2017. The choice of this period coincides with the 
commencement of the Stock Market All Share Index computation in Nigeria. The study examined the influence of stock market 
activities on economic growth in Nigeria. The mathematical equation for estimating the relationship between stock market 
activities and economic growth is derived from the Efficient Market Hypothesis (EMH) which states that a financial market is 
efficient on prices of dealing financial assets as they are balanced and reflect all appropriate information as well as collective beliefs 
and preference of all market participants about the financial instruments’ market prospect (Fama, 1970). 

Copeland & Weston (1979), observed that the EMH is not much restrictive than perfect stock market as market prices 
fully and instantaneously reflect all available information though it may not be realistic within the concept of asymmetric 
information. Thus, the measurement of market efficiency is contingent on the available information and varies according to nature 
of events in the stock market. The information can be in form of message (m) having diverse values to different market participants 
as the potential desirable outcome from the information relative to the wealth maximization objective function and information 

can encourage decision making. Thus, the value V(η) of information derived from the efficient stock market can be quantified as 
follows: 

   ( ) ( ) ( / ) ( , )
a

m e

V q m MAX p e m U a e =                            (1)  

where, 

V(η)  = Value of the measured information η. 
q(m)  = the marginal probability of receiving message m. 
p(e/m)  = the conditional probability of an event € given a message (m). 
U(a,e) = the utility resulting from a decision (a)if an event (e) occurs.  
This is referred to as a benefit function or payoff function which can be calibrated mathematically with the stock market activities 
as: 

𝐺𝐷𝑃 = 𝑓(𝑀𝐶, 𝑁𝑂𝐷, 𝑉𝑂𝑇, 𝐴𝐿𝑆𝐼)                                                      (2) 
Statistically the equation is represented as: 

𝐺𝐷𝑃𝑇 =  𝜆0 + 𝜆1𝑀𝐶𝑡 + 𝜆2𝑁𝑂𝐷𝑡 + 𝜆3𝑉𝑂𝑇𝑡 + 𝜆4𝐴𝐿𝑆𝐼𝑡 +  𝜖𝑡                  (3) 
Where  
GDP = Gross Domestic Product in Nigeria; 
MC = Market Capitalization of the Stock Exchange 
NOD = Numbers of Deals 
 VOT = Volume of Trade 
ALSI = All Share Index 
However, due to the diverse nature of the selected components of the measures of stock market activities, it is imperative to take 
the logarithm of the model in order to bring the variables into identical base. Thus, the model becomes: 

Log𝐺𝐷𝑃𝑇 =  𝜆0 +  𝜆1𝐿𝑜𝑔𝑀𝐶𝑡 + 𝜆2𝐿𝑜𝑔𝑁𝑂𝐷𝑡 + 𝜆3𝐿𝑜𝑔𝑉𝑂𝑇𝑡 + 𝜆4𝐿𝑜𝑔𝐴𝐿𝑆𝐼𝑡 +  𝜖𝑡           (4) 
Thus, equation (3) is the base line equation for this study.  

Estimation Procedure 



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Unit Root Test 
A major pre-test before a cointegration estimation is the unit root test, a stationary time series test which check if the mean and 
autocovariances of a series time independent. A simple model for nonstationary is the random walk.  

𝑦𝑡 =  𝑦𝑡−1 +  𝜖𝑡                   (5) 

𝑦 is a constant forecast value conditional on t with an increasing variance while 𝜖 is the stochastic term. A difference stationary 
series is integrated and symbolized as I(d) where d is the order of integration which is the number of a series unit roots or number 
of differencing operations required to make the series stationary.  
 
Cointegration Test 
Having established the unit root test of all the variables employed, the study test for cointegration using the VAR-based approach 
developed by Johansen (1991). This cointegration test was designed using a group object based on a VAR of order p: 

𝑦𝑡 = 𝐴1𝑦𝑡−1 + ⋯ + 𝐴𝑝𝑦𝑡−𝑝 +  𝐵𝑥𝑡 +  𝜖𝑡                                              (6) 

Where:  

 𝑦𝑡 represents k-vector of non-stationary I(1) variables,  

𝑥𝑡 represents d-vector of deterministic variables, and  

𝜖𝑡   represents vector of innovations. This model can be modified as: 

∆𝑦𝑡 =  Π𝑦𝑡−1 + ∑ Γ𝑖

𝑝−1

𝑖−1

∆𝑦𝑡−𝑖 + 𝐵𝑥𝑖 +  𝜖𝑖                                                        (7) 

                         
Where:  

Π =  ∑ 𝐴𝑖 − 𝐼,

𝑝

𝑖=1

  Γ𝑖 =  − ∑ 𝐴𝑗

𝑝

𝑗=𝑖+1

                                                        (8) 

 

Granger’s representation theorem states that if the coefficient matrix Π has reduced rank such that  r is less than k, then there exist 

k x r matrices α and β each with a rank of r such that Π is equal to 𝛼𝛽′ while  𝛽′ 𝑦𝑡   is I(0). r represents the number of cointegrating 

relations and each column of β represents the cointegrating vector. Also, the elements of αs are the Error Correction Model 

adjustment parameters. The Johansen’s method estimates the Π matrix from an unrestricted VAR and test if the restrictions created 

by the reduced rank of Π. 

ECM Regression 
Based on the results of the unit root and cointegration tests, the study estimate the ECM regression associated with 

Autoregressive Distributed Lag (ARDL) derived through the lagged levels and first difference of 𝑦𝑡 , 𝑥1𝑡 , 𝑥2𝑡, …, 𝑥𝑘𝑡 , and 𝑤𝑡 . 

𝜙(𝐿, 𝑝)𝑦𝑡 = ∑ 𝛽𝑖

𝑘

𝑖=1

(𝐿, 𝑞𝑖)𝑥𝑖𝑡 + 𝛿′𝑤𝑡 + 𝜇𝑡                                       (9) 

 

Where L represent a lag operator such that 𝐿𝑦𝑡  = 𝑦𝑡−1 

𝑤𝑡  is a s x 1 vector of deterministic variables such as the intercept term, seasonal dummies or (time trends, or exogenous 
variables with fixed lags.  
The model is linked thus: 

𝑦𝑡 =  Δ𝑦𝑡 + 𝑦𝑡−1   (9) 

𝑦𝑡−𝑠 =  𝑦𝑡−1 −  ∑ Δ𝑦𝑡−𝑗,

𝑠−1

𝑗=1

              𝑠 = 1,2, … , 𝑞𝑖                        (10) 

𝑤𝑡 =  Δ𝑤𝑡 +  𝑤𝑡−1                                                    (11) 

𝑥𝑖𝑡 =  Δ𝑥𝑖𝑡 +  𝑥𝑖,𝑡−1                                                                  (12) 

𝑥𝑖,𝑡−𝑠 = 𝑥𝑡−𝑠 −  ∑ Δ𝑥𝑖,   𝑡−𝑗,

𝑠−1

𝑗=1

                𝑠 = 1,2, … , 𝑞𝑖                       (13) 

 
Substituting equations 8-12 into equation 7 and simplify further, the model gives equation 14 thus: 



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∆𝑦𝑡 = −𝜙(1, 𝑝)𝐸𝐶𝑡−1 + ∑ 𝛽𝑖0Δ𝑥𝑖𝑡

𝑘

𝑖=1

+ 𝛿′Δw𝑡 −  ∑ 𝜙𝑗
∗

𝑝−1

𝑗=1

Δ𝑦𝑡−𝑗 − ∑ ∑ 𝛽𝑖𝑗
∗

𝑞𝑖−1

𝑗=1

𝑘

𝑖=1

Δ𝑥𝑖,𝑡−𝑗 + 𝑢𝑡                                       (14) 

where 𝐸𝐶𝑡 is the correction term defined as  

𝐸𝐶𝑡 = 𝑦𝑡 − ∑ �̂�𝑖

𝑘

𝑖=1

𝑥𝑖𝑡 − 𝜓′𝑤𝑡                                                          (15) 

5. Results and Discussions 
Table 1. Augmented Dickey-Fuller test statistic for the variables 

 

* Significant at 5% level of significance 
Source: (Authors’ Computation, 2018) 
 
Table 1 presents the results of Augmented Dickey-Fuller test statistics for all the variables at level and first differences. The ADF 
test allows for heterogeneous coefficients with a null hypothesis that the specified variables follow a unit root process and to reject 
this, the ADF probability value must be less than or equal to 0.05. The ADF test revealed that all variables are I(1) and stationary 
at first difference. Based on the result, this study proceed to the cointegration test and the result is presented in table 2. 

Table 2. Trace and Maximum Eigenvalue Cointegration Rank Test Results 

Hypothesized 
No. of CE(s) 

Trace test 
Statistics 

Prob. Max-Eigen  
Statistics 

Prob. 

None 174.08  0.0000*  94.7510  0.0000* 

At most 1  79.3322  0.0000*  34.6936  0.0052* 

At most 2  44.6386  0.0005*  21.0466  0.0514 

At most 3  23.5920  0.0024*  13.4536  0.0669 

At most 4  10.1383  0.0015*  10.1383  0.0015* 

* Significant at 5% level of significance 
Source: (Authors’ Computation, 2018) 
 
Table 2 revealed that the Trace test statistics established four (4) cointegrating equations while the Max-Eigen statistics established 
two (2) cointegrating equations. This implies that a cointegrating relationship exist among the specified variables and create the 
basis for the computation of the ECM. The result of the ECM is represented in Table 3. 

Table 3. Error Correction Model Result 

Dependent Variable: dLogGDP 

Variable Coefficient Std. Error T-Ratio Prob.   

dLogMC 0.0386 .04116 .93886 0.356 

dLogNOD 0.0487 0.0270 1.8031 0.083 

dLogVOT -0.0611 0.0315 -1.9386 0.063 

dLogALSI 0.0921 0.0301 3.0568 0.005 

ECM (-1) -0.0955 0.0434 -2.2004 0.037 

R-squared 0.5157     Mean dependent var 0.0845 

Adjusted R-squared 0.4439     S.D. dependent var 0.0488 

S.E. of regression 0.0364     Sum squared resid 0.0358 

Equation Log-likelihood 63.3355 Akaike Info. Criterion 58.3355 

F-Stat. F (4,27) 7.1873 (0.00) Schwarz Bayesian Criterion 54.6712 

Durbin-Watson stat 1.8105   

 Level First Difference  

 Statistic Prob. Statistic Prob. Interpretation  

LogGDP 1.153462  0.9999 -4.137249 0.0141* I(1) 

LogMC -0.540636 0.9759 -4.435531 0.0070* I(1) 

LogNOD -1.049364 0.9221 -5.727334 0.0003* I(1) 

LogVOT -1.156397 0.9026 -4.198723 0.0126* I(1) 

LogALSI -0.649602  0.9685 -4.878740  0.0037* I(1) 



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* Significant at 5% level of significance 
Source: (Authors’ Computation, 2018) 

Table 3 shows the ECM result coefficients, standard error, t-statistics and probability values for all the specified model. The result 
reveals the influence of each explanatory variable on economic growth in Nigeria. The ECM result (-0.0955, p < 0.05) is 
statistically sign and significant which implies that the speed of adjustment to equilibrium is 9.55% and offset are adjusted in each 

period. The stock market activities measurement such as market capitalization (𝜆1 = 0.0386, p > 0.05), number of deals (𝜆2 = 

0.0487, p < 0.05) and All Share Index (𝜆4= 0.0921, p < 0.05) have positive influence on economic growth, while the volume of 

trading (𝜆3 = -0.6110, p < 0.05) has a negative effect on economic growth. Similarly, the coefficient of determination (R2) value 
of 0.5157 implies that stock market activities can explain about 51.57% variation in economic growth in Nigeria while the F-
Statistic value indicates that all the estimated coefficients are statistically different from zero at 5% level of significance. These 
results suggested that all the specified variables except market capitalization have significant effect on economic growth in Nigeria 
within the period investigated. 

6. Conclusion and Recommendation 
This study examined the influence of stock market activities on economic growth in Nigeria and the results showed a statistically 
sign and significant ECM with measures such as number of deals, All Share Index and volume of trading have a significant influence 
on economic growth in Nigeria. This implies that the number of deals and All Share Index are the main indicator of stock market 
activities in Nigeria that influence economic growth positively within the period while the volume of trading exacts a negative 
influence due to transaction costs and its ancillary. Similarly, the study revealed that market capitalization has a positive influence 
on economic growth, though the extent of the influence is not significant. The study recommend that investors should take the 
advantage of the various opportunities offered by the stock market in Nigeria while market makers and regulators should continue 
to operate according to acceptable best market practices and global standards in order to ensure increased confidence and 
continuous patronage by various economic agents. 
 
References 
Afolabi, A. A. (2015). Impact of the Nigerian capital market on the economy, European Journal of Accounting Auditing and 

Finance Research, 3(2), 88-96. 
Beck, T. & Levine, R. (2001). Stock Markets, Banks, and Growth: Correlation or Causality? Policy Research Working Paper 

2670, Washington DC: World Bank. 
Bencivenga, V. R., Smith, B. & Starr, M. (1996). Equity Markets, Transaction costs, and Capital Accumulation: An illustration. 

World Bank Economic Review, 10, 643- 65. 
Copeland, T. E. & Weston J. F. (1979).  Financial Theory and Corporate Policy, 3, 32-26.  
Echekoba, F. N., Ezu, G. K. & Egbunike, C. F. (2013). The impact of capital market on the growth of the Nigerian economy 

under democratic rule, Arabian Journal of Business and Management Review, 3(2), 53-62. 
Enisan, A. & Olufisayo, A.O.(2009). Stock market development and economic growth: evidence from seven Sub-Sahara African 

Countries. Journal of Economics and Business, 61(2). 
Fama, E.F. (1970). The behaviour of stock market prices, Journal of Business, 39(1), 31-65. 
Fynn, K. D. (2012). Does the equity market affect economic growth? Macalester Review Journal, 2(2), 1-12. 
Ikikii, S. M. & Nzomoi, J. N. (2013). An analysis of the effects of stock market development on economic growth in Kenya, 

International Journal of Economics and Finance, 5(11), 145-155. 
Ishioro O. B. (2013). Stock market development and economic growth: evidence from Zimbabwe. Ekon. Misao praksa dbk 
Jibril, S. R., Salihi, A. A., Wambai, U. S., Ibrahim, F. B., Muhammad, S. and Ahmad, T. H. (2015). An assessment of Nigerian 

stock exchange market development to economic growth, American International Journal of Social Science, 4(2), 51-
58. 

Johansen, S. (1991). Estimation and hypothesis testing for cointegration vectors in Gaussian Vector Autoregressive Models. 
Econometrica, 59,1551-1580. 

Koirala, J. (2011). The effect of stock market development on economic growth: an empirical analysis of UK, Electronic copy 
available at: http://ssrn.com/abstract=2494640 

Kolapo, F. T. & Adaramola, A. O. (2012). The impact of the Nigerian capital market on economic growth (1990-2010), 
International Journal of Developing Societies, 1(1),11–19. 

Mishra, P.K., U.S., Mishra, U.S., Mishra, B.R., & Mishra. P. (2010). Capital market efficiency and economic growth: The case 
of India. European Journal of Economics, Finance and Administrative Sciences Issue ,27 (18), 130-138. 

Nowbutsing, B. M. & Odit, P. M. (2009). Stock Market Development and Economic Growth:The Case of Mauritius, 
International Business and Economics Research Journal, 8(2), 77- 88. 

http://ssrn.com/abstract=2494640


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Odhiambo, N.M. (2010). Stock market development and economic growth in South Africa: An ARDI Bounds. Department of 
Economics, University of South Africa. 

Okodua, H., & Ewetan, O. O. (2013). Stock market performance and sustainable economic growth in Nigeria: A bounds testing 
co-integration approach. Journal of Sustainable Development, 6(8). 

Okoye, V. O. & Nwisienyi, K. J. (2013). The capital market contributions towards economic growth and development: the 
Nigerian experience, Global Advanced Research Journal of Management and Business Studies, 2(2), 120-125. 

Osei, V. (2005). Does the stock market in Ghana? A granger causality analysis bank of Ghana Working Paper ,05(13). 
Oskooe, S.A.P. (2010). Emerging stock market performance and economic growth. School of Economics, Kingston University. 

American Journal of Applied Sciences, 7(2), 265-269. 
Pan L. & Mishra V. (2016). Stock Market Development and Economic Growth: Empirical Evidence from China. Department 

of Economics Discussion Paper 16/16, ISSN 1441- 5429, Monash Business School. 
Popoola, O. (2014). The effect of stock market on Nigerian economy. Unpublished Manuscript, Department of Economics, 

Landmark University, Kwara State, Nigeria. 
Popoola O.R, Ejemeyovwi O. J, Alege, O.P., Adu O., & Onabote A. A. (2017). Stock market and economic growth in Nigeria. 

International Journal of English Literature and Social Sciences, 2(6), 97-106 
Wang, B. & Ajit D. (2013). Stock Market and Economic Growth in China. Economic Bulletin, Access Econ, 33(1), 95-103. 
 

  
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