Folorunso Sunday AYADI

Folorunso Sunday AYADI (Ph.D) Economics Department , Faculty of Social Sciences, University of Lagos, Akoka-Yaba, Lagos, Nigeria. E-Mail HYPERLINK [email protected] [email protected] Phone Number 08028530208 Abstract Stock markets have been theorized to be an important arbiter of growth. They contribute to growth mostly when they are liquid and large in size. Liquid equity market makes investment less risky and more attractive thereby increasing investors confidence. The beneficial roles of stock market liquidity and size to economic growth has been a subject of controversy worldwide. To this end therefore, this study analyze the impact of the stock market development (as measured by size and liquidity) on economic growth in Nigeria using the Ordinary Least Squares (OLS) and the Generalized Least Squares (GLS) on time series data spanning from 1980 to 2015. The study concluded that stock market contributes to growth via liquidity only. The study recommends sound macroeconomic policies, conducive legal environment, attractive tax structures, good political climate and an urgent need for the re-positioning of the stock exchanges in Nigeria so as to further boost economic growth in Nigeria. Keywords Stock Market Development, Economic Growth, Efficient Markets, Investments, Macroeconomics. Introduction The stock market is an important component of capital market established to promote growth by giving the quantitative as well as the qualitative direction to the flow of funds which brings about rational allocation of resources and economic growth. They do this by converting financial assets into physically productive assets. The logic here is that stock prices determined on the exchange provide information which aids investors in making better investment choices. Better investment decisions facilitate better resources allocation and consequently higher economic growth. Levine (1996) (see also Levine, 1997 and Demirguc-Kunt, 1994) explained the channel through which stock markets may affect economic growth as, the creation of liquidity. According to him, many profitable investments require long-term capital, however, most investors are reluctant to commit their savings to such a long term investments. Liquid equity market therefore makes such an investment less risky and more attractive. This is possible because savers can acquire equity and sell it quickly and at times cheaply if there is an urgent need for them to fall back on their savings or the need to reorganise their portfolios. Companies on their own enjoy permanent capital raised through equity issues. By facilitating longer-term funds, more profitable investments and economic growth is enhanced. The above advantage of stock market was countered because of the effect of liquidity on long-term economic growth. According to this view, very liquid markets encourage investor myopia. In other words, they make it easy for dissatisfied investors to liquidify their investments very quickly thus weakening investors commitment and diminish investors incentives to exert corporate control. To this end, stock market liquidity may disturb growth (Levine, 1996). Levine (1996) stated that most empirical evidence shows that the greater the stock market liquidity boosts, the better the economic growth. He therefore came up with a number of liquidity measures or indicators of boosts. One of which is the total value of shares traded on a countrys stock exchange as a share of GDP. The second is the value of traded shares as a percentage of total market capitalization (the value of stocks listed on the exchange is the turnover ratio measuring trading relative to the size of the stock market). Lastly, the value-traded ratio divided by the stock price volatility. This captures the ability to handle heavy trading without large price swings. Greenwood and Smith (1996) also commented on the beneficial role of larger stock market as lowering the cost of funds mobilizing and consequently facilitating the channelling of investments to their most productive uses and contributing to economic growth. The beneficial roles of stock market liquidity and size to economic growth has been a subject of controversy worldwide. The question here is, when a stock market performs well based on the above indicators of liquidity, is it the same thing with economic growth In other words, is there really a link between stock market liquidity or size and economic growth In providing answer to this therefore, we utilized data spanning from 1983 through 2008 to analyze the impact of stock market development on economic growth in Nigeria using the vector autoregressive (VAR) models. Literature Review Stock market generally gives an indicator about the health of an economys finance. North (1991) posited that the existence of the stock market can boost economic growth by reducing the costs of transference of ownership rights in firms. According to North (1991), this is an important part of economic growth. According to Stiglitz (1985) efficient capital markets promote growth as market prices already reflect all the available information. This reduces efforts and expensive quests for additional information by the prospective investors. Jhingan (2004) observed that in an underdeveloped country where there is scarcity of capital, the absence of a developed capital market is a great hindrance to capital formation and economic growth. Meaning that, a well developed capital market is most essential in a capital-scarce economy. Stock market liquidity is one of the channels through which they contribute to economic growth. Stock market liquidity is the ability to trade stocks easily and it plays some roles in economic development. Greenwood and Smith (1997) also elicit the stock market size advantage when they stated that the size of stock markets is an important determinant of its ability to reduce the cost of mobilizing savings in the economy and facilitating productive investments thus contributing to economic growth. Levine and Zervos (1996) observed that stock markets effect on growth is through the liquidity effect. According to them, many high returning yielding projects require long term funds commitment. However, investors are always unwilling to release their funds for such investments in such a long period time. Without liquidity market, fewer investments will be committed to such a high yielding projects. Studies on the stock market development and economic growth are far from conclusive and results are generally mixed. Some studies have validated the beneficial impact of the liquidity and size advantages to growth. Others have validated either of the two stock market indicators while others have refuted the beneficial roles of the two main indicators. Levine (1996) studied the impact of stock market liquidity which he captured using three indicators total value of shares traded on a countrys stock exchange as a share of GDP. The second is the value of traded shares as a percentage of total market capitalization. Thirdly, the value-traded ratio divided by the stock price volatility. Levines study on 38 countries utilized multiple regression which accounts for other non-financial factors such as inflation, fiscal policy, political stability, education, the efficiency of the legal system, exchange rate policy, and openness to international trade, and stock market liquidity. He concluded that stock market liquidity is a reliable indicator of future long-term growth. This implies that liquidity helps forecast economic growth. Levine and Zervos (1998) in another study also observed that stock market liquidity positively determine the aggregate economic growth. Beck and Levine (2004) investigated the impact of stock markets and banks on economic growth in 40 countries using panel data of 146 observations between 1976 and 1998 using the generalized method of moments. They found that stock markets and banks positively determine economic growth. Shahbaz, Ahmed and Ali (2008) investigated how the charges in financial sector contributed to the overall growth of the economy. They investigated whether there is a relationship between stock market development and economic growth for Pakistan between 1971 and 2006. They applied co-integration and Engle-Granger causality tests and their results showed that there exists a very strong relationship between stock market development and economic growth. The long-run causality results indicate a long-run bi-directional causality between stock market development and economic growth. However, in the short-run, there is only a one-way causality from stock market development to economic growth thus confirming the beneficial role of stock market development in the growth process. Nieuwerburgh, Buelens and Cuyvers (2006) studied the long-run relationship between stock market development (which they captured with the numbers of shares listed on the exchange and the market capitalization) and economic growth (log difference of GDP per capita) in Belgium. They employed the Granger Causality tests and concluded that stock market development determined economic growth in Belgium. Brasoveanu et al (2008) examined the correlation between capital market development (as measured by the market capitalization, numbers of shares listed, trading volume, liquidity and some stock exchanges indices) and economic growth (represented by the GDP, GDP growth rate) in Romania using quarterly data between the first quarter of 2000 and the second quarter of 2006 using the regression function and the VAR models. Their results show that the capital market development is positively correlated with economic growth and that there is a bi-directional relationship between capital market development and economic growth. The strongest link however is from economic growth to capital market. This indicates that capital market development precedes economic growth. Nowbutsing (2009) examined the impact of the stock market development on economic growth in Mauritius by using time series between the period 1989 and 1886 to conduct an error correction model analysis (ECM) to capture both the short-run and the long run impact. Their study also utilized two measures of stock market development (size and liquidity) in the analysis. Liquidity was captured by the volume of shares traded over GDP while size was captured by the share of market capitalization over GDP. The study found that stock market development positively affects economic growth in Mauritius both in the long-run and in the short-run. Ogunmuyiwa (2010) in his study attempted to study the investors sentiment and stock market liquidity as critical ratios for stock market growth and economic development in Nigeria by using data which spans from 1984 to 2005. He utilized the Granger causality test and OLS as the analytical techniques. He therefore concluded that investors sentiments (as measured by the market turnover ratio) and the stock market liquidity (captured as the total value of traded shares as percentage of GDP) granger-cause economic growth. The inherent weaknesses of this study include the paucity of data and the neglect of the size variable in the study. However, other studies came up with mild impacts or no impact of stock market development on economic growth. For instance, Thornton (1995) did a study to validate the hypothesis that financial development can translate to economic growth by making use of data for 22 developing countries. He however found mixed results. While there was evidence that financial deepening promoted economic growth in some countries, in some countries, he found the converse. Spears (1991) also concluded that in the early stages of development, in the studied Sub-Saharan African Countries, financial intermediation contributed to economic growth (this is also similar to the result obtained by Filer, Hanousek and Campos (1999) for India). Akinlo and Akinlo (2009) examines the long run and causal relationship between stock market development and economic growth for seven countries in sub-Saharan Africa using the autoregressive distributed lag (ARDL) bounds test. They concluded that the stock market development is cointegrated with economic growth in Egypt and South Africa indicating a significant positive long run impact on economic growth. Granger causality test based on vector error correction model (VECM) showed that stock market development Granger causes economic growth in Egypt and South Africa. However, Granger causality in the context of VAR shows evidence of bi-directional relationship between stock market development and economic growth for Cote DIvoire, Kenya, Morocco and Zimbabwe. There is a weak evidence of growth-led finance using market size as indicator of stock market development in Nigeria indicating that the contributions of stock development to growth was limited in Nigeria.. Demirguc-Kunt and Levine (1996) however cautioned on the beneficial role of stock market development to growth by saying that excessive liquidity of stock markets may harm growth in three ways. First, through the reduction of uncertainty of investments, greater liquidity can lead to dissavings. Secondly, saving rates may decline through the income and substitution effects. Thirdly, excessive liquidity may lead to investors myopia. All these may hamper growth. Alenoghena (2014) studied the Nigerian capital market, financial deepening and economic growth from 19812012 using time series data on stock market capitalization, narrow money diversification, (that is credit to private sector) and Interest rate. They found that all the above variables significantly impacted on economic growth in Nigeria. Monetization ratio however exhibited an insignificant trend in relation to economic growth. He therefore recommended improvement of the financial market liquidity so as to enhance the overall economic efficiency. Oriavwote and Eshenake (2014) studied the financial sector development and economic growth in Nigeria using time series data from 1990 2011. They employed the Error Correction Model (ECM) for the analysis. They concluded that the financial sector development has significantly improved the level of economic performance in Nigeria. However, the credit to the private sector contributed minimally to economic growth in Nigeria. At times the relationship between one measure of stock market development and economic growth may give an encouraging result while another measure may indicate a negative result. For instance, Garretsen, Lensink and Sterken (2004) found a causal from economic growth to financial market development as measured by the market capitalization/GDP. Yet, market capitalization/GDP is not a significant factor explaining economic growth. Most of the earlier studies suffer from the inherent problem of cross-sectional. Levine and Renelt (1992) argued that some are fraught with misspecification. Evans (1995) stated that some of the analyses based on cross-sectional observations are bound to be misleading since there are country specific characteristics whose estimations are difficult to model. This heterogeneity of slope coefficients among countries makes cross-sectional data inappropriate. To this end therefore, we analyze the impact of the stock market development (as measured by size and liquidity) on economic growth in Nigeria using time series from 1980 to 2015. Theoretical Framework Many of the growth economists confirmed the critical role of capital in the growth process. Solow (1956), Kaldor (1957), Meade (1961) among others. Meades Neo-classical growth model is based on a number of assumptions. The model itself postulates that the net output (Y) in a given economy varies with the following four factors. Availability of land and natural resources, (N) state of technical know-how over time (t), amount of labour force (L) and the net stock of capital available in that economy. He expressed this relationship using a production function as shown below Y F (K, L, N, t) However, the growth rate in the stock of capital and the marginal productivity of capital can be enhanced by an efficient capital market dominated by the stock exchanges. According to Jhingan (2004), The capital market plays an important role in mobilizing savings and channelising them into productive investments for the development of commerce and industry. As such, the capital market helps in capital formation and economic growth of the country. Based on the above therefore, attention must be focused on enhancing the performance of such body that makes long term fund mobilization possible. Methodology To model the relationship between growth and stock market indicators, the study drew from the theory of Solow (1956), Kaldor (1957), Meade (1961) among others. In addition, the study of Levine and Zervos (1996) and Levine (1996) modeled growth as a function of the three stock market indexes used in this study and in addition, other non-financial factors were included in their models. This study differs on some factors to include as determinants of growth. For instance, we could not get a good proxy or data for capturing fiscal policy, political stability, education and efficiency of the legal system. The study however used the active population as a proportion of total population to capture labour, domestic credit and gross capital formation to capture capital. FDI inflow to capture external finance and trade openness to capture internationalization. However, the two measures of liquidity (turnover ratio and liquidity ratio) are highly correlated. Employing them as explanatory variables in the same model may cause multicollinearity. Hence, we broke the models into two. The models are hereunder presented. EMBED Equation.3 EMBED Equation.3 The variables of the models are GDP per capita (current US) (GDPPCC) which is the dependent variable of the two models. Broad money in proportion to the GDP (BMGDP), domestic credit to the private sector as a ratio of GDP (DCPGD), foreign direct investment, net inflows as of GDP (FDIN), Gross capital formation as a of GDP (GCFGDP), and stock market transactions to GDP (LIQ). While Population ages 15-64 as a of total population – a measure of labour (LABPOP), Market capitalization of listed domestic companies as a of GDP (MCAPGDP), trade as a of GDP a measure of trade openness (OPEN) and Stocks traded, turnover ratio of domestic shares () (TURN). To model the relationship between stock market development indicators and economic growth, the study first conducted a unit root test based on the augumented Dickey-Fuller procedure to ascertain the stationarity of the variables. The study further conducted a trace cointegration test to ascertain whether or not there is cointegration or not. Cointegration was confirmed and ordinary least squares (OLS) were employed in the analysis of the models and autocorrelation was present which has the tendency of removing the efficiency of the OLS estimates. To correct the autocorrelation problem the study utilized the CORC adjusted estimates of the generalized least squares method (GLS). Lagrange Multiplier test (Breusch-Godfrey) was conducted and autocorrelation was not present in our estimates. In addition, Ramsey RESET test of model specification error was conducted and the models were found appropriate. Data used for the study were obtained from the World Bank database, Central Bank of Nigeria Statistical Bulletin (various issues) and Nigerian Stock Exchange Annual Reports and Accounts (Various years). Empirical Analysis and Discussion Prior to the estimation of the models, the study conducted the unit root test whose result is presented in table one based on the Augumented Dickey-Fuller (ADF) approach. The t-statistics alongside the 5 critical values as well as the order of integration are presented in the table. Table 1 ADF results of the studys variables VariableT-stat @ level5 CriticalT-stat. @ 1st difference5 CriticalOrder of IntegrationGDPPCC-0.319778 (0.9119)-2.948404-4.928806 (0.0003)-2.951125I(1)BMGDP-3.307504 (0.0224)-2.951125I(0)DCPGD-3.266918 (0.0246)-2.951125I(0)FDIN-3.460885 (0.0155)-2.951125I(0)GCFGDP-4.447056 (0.0012)-2.951125I(0)LABPOP0.140612 (0.9635)-2.967767-6.075950 (0.0000)-2.967767I(1)LIQ-3.057395 (0.0396)-2.951125I(0)MCAPGDP-2.611119 (0.1003)-2.948404-6.838797 (0.0000)-2.951125I(1)OPEN-1.916107 (0.3213)-2.951125-7.999128 (0.0000)-2.954021I(1)TURN-2.539592 (0.1152)-2.948404-6.293144 (0.0000)-3.548490I(1) Table 1 above shows the ADF results of the variables of the study. The broad money in proportion to the GDP (BMGDP), domestic credit to the private sector as a ratio of GDP (DCPGD), foreign direct investment, net inflows as of GDP (FDIN), Gross capital formation as a of GDP (GCFGDP), and stock market transactions to GDP (LIQ) are all integrated of order zero or are stationary at level. While GDP per capita (current US) (GDPPCC), Population ages 15-64 as a of total population – a measure of labour (LABPOP), Market capitalization of listed domestic companies as a of GDP (MCAPGDP), trade as a of GDP a measure of trade openness (OPEN) and Stocks traded, turnover ratio of domestic shares () (TURN) are all integrated of order one or are all stationary at first difference. Running these variables together in a regression model may produce a spurious regression result. So, this study proceeds by verifying their cointegration status using the unrestricted cointegration rank test (Trace) and the result of the test is shown in table 2. Table 2 Result of the cointegration test on the variables of the study Sample (adjusted) 1983 2015Included observations 33 after adjustmentsTrend assumption Linear deterministic trendSeries BMGDP DCPGD FDIN GCFGDP GDPGR LABPOP LIQ MCAPGDP OPEN TURNLags interval (in first differences) 1 to 1Unrestricted Cointegration Rank Test (Trace)HypothesizedTrace0.05No. of CE(s)EigenvalueStatisticCritical ValueProb.None 0.996406518.5913239.23540.0000At most 1 0.925991332.8542197.37090.0000At most 2 0.876455246.9366159.52970.0000At most 3 0.769059177.9288125.61540.0000At most 4 0.708416129.564395.753660.0000At most 5 0.62133688.8941569.818890.0007At most 6 0.58806656.8476647.856130.0057At most 70.38687227.5802229.797070.0882At most 80.27443411.4372215.494710.1860At most 90.0254490.8507043.8414660.3564Trace test indicates 7 cointegrating eqn(s) at the 0.05 level denotes rejection of the hypothesis at the 0.05 levelMacKinnon-Haug-Michelis (1999) p-values Table twopresents the cointegration result, and in the result, trace result shows that there are 7 cointegration equations. All the trace statistics are greater than their 5 percent critical value for at most 6 equations. And the corresponding probabilities are less than 0.05. Cointegration indicates that though each variable of the model may be non-stationary, their combination in a model will yield a stationary and reliable result. Table 3 Half correlation matrix of the variables of the models GDPPCCBMGDPDCPGDFDINGCFGDPLABPOPLIQMCAPGDPOPENTURNGDPPCC1.00000.3716BMGDP-0.11051.00000.3824DCPGD0.11580.79961.00000.7232FDIN-0.3640-0.02430.02211.0000-0.0962GCFGDP0.23930.34080.0629-0.36631.0000-0.1330LABPOP0.3372-0.28590.16790.0143-0.34881.00000.4142LIQ0.19900.32710.66090.0314-0.18270.36071.0000 0.8581MCAPGDP0.0289-0.29100.16200.3257-0.47100.49130.55661.00000.3153OPEN-0.3284-0.24780.00730.4115-0.45160.48150.21470.49541.00000.1281TURN 0.37160.38240.7232-0.0962-0.13300.41420.85810.31530.12811.0000 The half correlation matrix result indicates that per capital growth is positively correlated with the domestic credit to the private sector, gross capital formation, labour force, and all the stock market indicators (namely market capitalization ratio, and the two measures of liquidity- liquidity ratio and stock turnover). Turnover ratio exerts higher level of correlation with the growth measure. Liquidity on its own is moderately correlated with market capitalization and highly correlated with turnover ratio. The correlation between the two ratios is 0.8581. This means that combining the two measures as explanatory variables in the same model is bound to cause a problem of multicollinearity. To avoid this, we used the two variables as separate explanatory variables in two different models. Table 4 OLS results of the two models MODEL 1MODEL 2VariablesCoefficientStd. errort-ratioProb.CoefficientStd. errort-ratioProb.C-24060.1915866.09-1.5164540.1415-26898.2416602.34-1.6201470.1173TURN62.2870530.041372.0733760.0482LIQ110.997295.155341.1664840.2540OPEN-28.495388.874448-3.2109470.0035-28.598269.339757-3.0619920.0051BMGDP-104.580551.74090-2.0212340.0537-129.010653.63744-2.4052340.0236DCPGD53.7571360.061450.8950360.379098.0854656.854821.7251910.0964FDIN12.5355260.547790.2070350.83761.52963363.848500.0239570.9811GCFGDP54.5500322.832852.3891030.024450.5852123.897542.1167540.0440LABPOP510.8904297.00321.7201510.0973573.6046310.28811.8486190.0759MCAPGDP-10.1799615.54768-0.6547580.5184-20.6623818.78966-1.0996680.2816R-squared0.6312660.591669Adjusted R-squared0.5178100.466028F-stat (prob)5.563946 (0.000373)4.709224 (0.001183)Durbin-Watson stat0.6836200.651100Obs.3535 The OLS result of model one of table 4 shows that the coefficient of determination of 63 percent and adjusted coefficient of determination of 52 percent shows that moderately, the independent variables have been able to capture the variability of the dependent variable. The F-statistic of 5.6 with a probability of zero percent indicates the joint contributions of the explanatory variables in explaining the dependent variable at one percent level of significance. With the Durbin-Watson statistic of 0.6836, one can say that this model suffers from the problem of autocorrelation. The consequence of this is that although the OLS estimates may be unbiased, they are no longer efficient as one may erroneously conclude that parameter estimates are more precise than they actually are. Significance test on variables are no longer reliable and another estimation technique has to be applied. In model 2 of table 4, the coefficient of determination is high, the F-statistic with its probability and the Durbin-Watson statistic all behave in the same manner as that of model one. The presence of serial correlation implies that another analytical method that eliminates serial correlation has to be employed. The study employed the Cochrane Orcutt (CORC) method of generalized least squares (GLS) and the results is presented in table 5 below. Table 5 The generalized least squares procedure (CORC adjusted) MODEL 1MODEL 2VariableCoefficientStd. errort-ratioProb.CoefficientStd. errort-ratioProb.C14668.0814713.270.9969290.3287138557.38379980.0.0165340.9869TURN18.454167.0423972.6204370.0150LIQ62.9520627.954832.2519210.0338OPEN-1.9868532.909979-0.6827720.5013-0.8725963.060242-0.2851400.7780BMGDP-9.76392914.36030-0.6799250.5031-11.6005415.07632-0.7694540.4491DCPGD-23.1142915.09248-1.5315100.1387-20.1466415.71266-1.2821920.2120FDIN-11.3323015.36090-0.7377370.4678-14.9641215.99856-0.9353420.3589GCFGDP30.0251511.912712.5204300.018829.5391712.684492.3287640.0286LABPOP-312.3009287.0834-1.0878400.2875-327.9066296.6056-1.1055310.2799MCAPGDP-1.7928944.030239-0.4448610.6604-9.9470065.455653-1.8232470.0807AR(1)1.0231160.04185024.447450.00000.9994100.04127424.214130.0000R-squared0.9681460.965854Adjusted R-squared0.9562010.953049F-stat (prob)81.04922 (0.0000)75.42954 (0.0000)Durbin-Watson stat1.3891721.400221Obs.3434 The GLS result in model one has a coefficient of determination of about 97 percent and adjusted coefficient of about 96 percent showing that the explanatory variables have explained about 97 percent variability of growth. This is a good fit. Also, the F-Statistic of 81 and zero probability has shown the joint significance of the explanatory variables. The other model (model 2) also produces similar results with model one. The GLS result in model two has a coefficient of determination of about 97 percent and adjusted coefficient of about 95 percent showing that the explanatory variables have explained about 97 percent variability of growth. This is also a good fit. In addition, the F-Statistic of 75 and zero probability has shown the joint significance of the explanatory variables in explaining growth. Table 6 presents the Lagrange Multiplier test of the presence of serial correlation and the result is hereunder produced. Table 6 Breusch-Godfrey Serial Correlation LM Test for model 1 and 2 Model 1Model 2F-statistic0.108862Prob. F(2,22)0.8973F-statistic0.365167Prob. F(2,22)0.6982ObsR-squared0.333185Prob. Chi-Square(2)0.8465ObsR-squared1.092402Prob. Chi-Square(2)0.5791 Breusch-Godfrey Serial Correlation LM test for model 1 and 2 is presented above. The F-Statistics as well as the observations R-squared for the two models are shown with their corresponding probabilities. The probabilities of F at the various degrees of freedom are 0.9 and 0.7 respectively. The probabilities of chi squares are also higher than 0.05 indicating the rejection of the presence of serial correlation. The result of table 5 shows that growth in model one is explained significantly and positively by turnover ratio (Stocks traded, turnover ratio of domestic shares in ). A unit increase in turnover ratio, other things being constant can produce 18.5 unit increases in growth per capita. In the same vein, gross capital formation significantly influenced per capita growth positively and significantly. A unit rise in gross capital formation in relation to GDP causes growth to expand by 30.025 dollars. In this model also, the autoregressive root is also highly significant and a point to the fact that autocorrelation has been cured in this model. The results of model 2 in table 5 shows that growth in model 2 is explained significantly and positively by liquidity ratio (stock market transactions to GDP (LIQ)). A unit increase in liquidity ratio, other things being constant can produce 62.952 unit increases in growth per capita. In the same vein, gross capital formation significantly influenced per capita growth positively and significantly. A unit rise in gross capital formation in relation to GDP causes growth to expand by 12.68 dollars. In this model also, the autoregressive root is also highly significant and a points to the fact that autocorrelation has been cured in this model. In addition to the above, the autoregressive roots of the two model are around 1 by approximation and are highly significant. The coefficient of determination and adjusted coefficient of determination are so close validating the GLS estimates. Apart from this, the study therefore went further by applying the Ramsey RESET test to verify the validity of the GLS model. The process and results are hereunder described. Table 6 Ramsey RESET Test Result Specification GDPPCC C LIQ OPEN BMGDP DCPGD FDIN GCFGDPLABPOP MCAPGDP AR(1)Omitted Variables Squares of fitted valuesValuedfProbabilityt-statistic0.575382230.5706F-statistic0.331064(1, 23)0.5706Likelihood ratio0.48591110.4858F-test summarySum of Sq.dfMean SquaresTest SSR13974.28113974.28Restricted SSR984808.12441033.67Unrestricted SSR970833.82342210.17Unrestricted SSR970833.82342210.17LR test summaryValuedfRestricted LogL-222.899224Unrestricted LogL-222.656323 Ramsey (1969) indicates that there are 3 types of specification errors encountered in regression specification error test (RESET). Given a classical linear regression model specified as EMBED Equation.3 If the calculated F value is significant, say at the 5 percent level, one can accept the alternative hypothesis of model misspecification and reject the null hypothesis. Otherwise, we accept the null hypothesis. The result in table 6 indicates that F-statistic value is 0.331064. At degrees of freedom of (1, 23), the probability value is 0.5706 which is greater than the 0.05 needed for the rejection of the null hypothesis, we therefore fail to reject the null hypothesis. We therefore conclude that the GLS model is not misspecified and is good enough for prediction and policy formulation. The main conclusion of this study is that Nigerian stock market significantly propel growth via its liquidity. This finding is in agreement with the findings of Levine and Zervos (1996) who concluded that stock markets effect on growth is through the liquidity effect. According to them, many high returning yielding projects require long term funds commitment. However, investors are always unwilling to release their funds for such investments in such a long period time. Without liquidity market, lower investments will be committed to such a high yielding projects. Levine (1996) also found that liquidity helps forecast economic growth. Levine and Zervos (1998) in another study also observed that stock market liquidity positively determine the aggregate economic growth. The findings of this study is in contrast with the finding of Demirguc-Kunt and Levine (1996) who cautioned on the beneficial role of stock market development to growth by saying that excessive liquidity of stock markets may harm growth in three ways. First, through the reduction of uncertainty of investments, greater liquidity can lead to dissavings. Secondly, saving rates may decline through the income and substitution effects. Thirdly, excessive liquidity may lead to investors myopia. All these may hamper growth. Recommendation The above results showed that stock market can contribute significantly to Nigerias economic growth via the liquidity ground. Given the role played by a functional and efficient market to economic growth in Nigeria and other developing countries, there is a dire need to remove all forms of legal, regulatory, tax and accounting impediments to the efficient flow of resources and efficient performance of this market. This is important because the supervisory systems influence the market size and liquidity. Government must ensure that sound macroeconomic environment is created to ensure that there is liquidity in the market. They can do this by ensuring that a good political climate is created to ensure market liquidity so that stock market can contribute meaningfully to Nigerias economic growth. They must also ensure that there is a smooth integration of Nigerias economy with the rest of the world to ensure a steady inflow of foreign capital to augment the local resources and further boost the economic growth. It must be borne in mind that all forms of impediments to stock market development must be eased to facilitate liquidity which is the ability to trade stocks very easily with lesser risks and more attractively. Since there is a high interdependence of financial markets in any economy, the regulatory bodies must ensure that there is improvement in the financial systems so that practices such as hike in interest rates, excessive tax levies on financial institutions and their customers must be eased off if stock exchanges are to contribute meaningfully to growth in Nigeria. 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(1956). A Contribution to the Theory of Economic Growth. Quarterly Journal of Economics. 70, 65-94. Spears, A. (1991). Financial Development and Economic Growth-Causality tests. Atlantic Economic Journal. 19, 66. Stiglitz, J. E. (1985). Credit Markets and the Control of Capital. Journal of Money, Credit and Banking. 17(2), 133-152. Thornton, J. (1995). Financial Deepening and Economic Growth in Developing Countries. Economia Internazionale. 48(3), 423-430. PAGE MERGEFORMAT 16 Not necessary. Where is the findings of the study and What is the implication of your findings Where are the objectives of the study [email protected] NqYczS/rJlP_WYMKN /CMv FTO7l/XcTeNnUtbEiy G9cwT7TGu2kP2
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