Dynamic Interactions of Nigerian Stock Market and Macroeconomic Variables

This study investigated the Dynamic Interactions of Nigerian Stock Market and Macroeconomic Variables with annual data collected from the Central Bank of Nigeria, Nigerian Stock Exchange Fact Books and National Bureau of Statistics from 1985 to 2018. It was found that economic growth proxied by Growth Domestic product and Interest rate have positive and significant relationship with all share index within the period of study, while inflation exerts negative influence on All Share Index. It was also found that exchange rate has insignificant impact on All Share Index within the scope of the study. Consequently, the researchers are of the opinion among others that government and her regulatory bodies devise adequate measure to curtail inflation in Nigeria. Keywords : Nigeria Stock Market, All Share Index, Macroeconomic Variables, ARDL. DOI: 10.7176/RJFA/11-8-04 Publication date: April 30 th 2020

Meanwhile, in a comprehensive review; Aggarwal (1981) in USA, a developed country, used one macroeconomic variable (dollar exchange rate) to know it variations with stock prices with monthly data of U.S stock prices and exchange rate ranges from period [1974][1975][1976][1977][1978], and analyzed it with simple regression technique, Aggarwal (1981) found a positive outcome between stock prices and exchange rates. The relationship was more robust in short than in long run.
In Japan,  used VECM to evaluate the relationship between Stock Market and macroeconomic variables (exchange rate, inflation, money supply, real economic activity, long-term government bond rate, and call money rate). A co-integrating relationship was found to exist between macroeconomic variables and stock prices. In emerging market, Gay (2016) studied effect of macroeconomic variables on the stock market returns in Brazil, Russia, India and China (BRIC). The study empirically examined the time series relationship between stock market prices and macroeconomic variables (exchange rate and oil price) using Box-Jenkins ARIMA models. The revealed no relationship between exchange rate and oil price on the BRIC countries. No significant relationship was found between present and past stock market returns, suggesting the market of BRIC exhibit the weak form of market efficiency. Also, Hsing (2011) examined the whole BRIC (Brazil, Russia, India, China and South Africa) to know the impact of Macroeconomic variables on stock market index. Hsing (2011) employed the Exponential GARCH model to examine the impact of various economic variables that cause fluctuation in South Africa's stock market index. It was revealed that that index of South Africa stock market has positive relation with growth in real GDP.
In South Asian countries, Aurangzeb (2012) found that Exchange rates have significant positive impact on the performance of stock markets of the three markets of South Asia (Pakistan, India and Sri Lanka). The result was got from descriptive statistic method with monthly data for the period of 1997 to 2010 of the three South Asian countries in a study; examination of the factors affecting the performance of stock markets of South Asian countries.
In Pakistan, Nishat and Mustafa (2007) looked at the stock market and real economy (GDP). The variable were further decomposed into GDP, production growth to represents the liquidity of stock market, real economy, and the size of the stock market represent the stock prices. Nishat and Mustafa (2007) employed error correction model and co-integration to statistically examine the relationship between the stock prices and GDP on annual data . It was found that, in the short run, the stock market movement explains the GDP and output growth, while both short run and long run explained that the growth of stock market variables depends on the overall growth of the economy. Jamil and Ullah (2013) investigated the impact of foreign exchange rates on stock prices using Co-integration Technique and Vector Error Correction Mechanism (VECM) with monthly data from 1998 to 2009. Jamil and Ullah (2013) revealed that relationship exists between exchange rates and stock market returns, both in the short run and long run. The short run period was found to have a positive but significant relationship, while the long run relationship is not significant. The short run sensitivity of stock market returns to exchange rates indicates that the investments in the stock market are short term and most investors liquidate their stock within one year, Jamil and Ullah (2013) added. Later, Hunjra, Chani, Shahzad, Farooq and Khan (2014) included more variables of macroeconomic variables (interest rate, exchange rate, inflation rate and the GDP) to Research Journal of Finance and Accounting www.iiste.org ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online) Vol.11, No.8, 2020 know their impact on stock prices with monthly data spanning from January 01, 2001 to December 31, 2011. After the empirical investigation with Granger Causality and Cointegration tests, it was observed that no relationship exist between interest rate, exchange rate, GDP and inflation rate with stock prices in the short run, but showed a relationship in the long run. This shows that macroeconomic variables are not actually felt in the short, rather in the long run, as affirmed by this study. Also, Ilahi, Ali and Jamil (2015) examined the impact of macroeconomic variables on stock market returns with multiple regression and came out the result that a weak association existed between macroeconomic variables (interest rate, inflation rate and exchange rate) and stock market returns, In Iran, Mehran, Faraheni and Faninam (2016) used VAR to examine the effect of macroeconomic variables on the stock market index of the Tehran stock exchange. After the empirical investigation, it was observed that a positive money stock can increase stock returns. It was observed that macroeconomic variables (inflation rate, exchange rate and GDP) have significant impact on Tehran stock exchange stock index.
In India, Naik (2013) did a work on the relationships between stock market index (BSE Sensex) and five macroeconomic variables(industrial production index, wholesale price index, money supply, treasury bills rates and exchange rates) with monthly data for the period ;1994:04-2011:06. Naik (2013) engaged Johansen's Cointegration and Vector Error Correction Model (VECM) and found that in the long-run, the stock prices are positively related to money supply (M3). It was also established that money supply causes stock prices only in the long-run but no causality from stock price to money supply as found either in the long run or in the short run. Naik (2013) suggested that money supply changes have an indirect effect through their effect on real output which in turn impact the stock prices.
Employing OLS multiple regression, Ullah, Islam, Alam, Khan (2017) examined the significance of macroeconomic variables in effecting stock market performance of SAARC countries using annual data for the period 2005-2015. The result indicated that macroeconomic variables (exchange rate, foreign currency reserve and interest) significantly affect stock market performance of SAARC countries, whereas inflation and money do not have a significant relationship on stock market performance.
In New Zealand, using co-integration and Granger causality test, Gan, Lee, Young and Zhang. (2006) investigated the relationships between stock market index and macroeconomic variables from January 1990 to January 2003. Long run relationship between stock market index and the macroeconomic variables was found. The Granger causality test revealed that stock market index was not a leading indicator for changes in macroeconomic variables. Rather, results suggested that stock market was persistently determined by the interest rate, money supply and real GDP.
In Africa, Ake and Ognaligui (2010) employed Granger's causality test and variance decomposition by Cholesky to investigate the relationship between Doula Stock exchange's Market Capitalization as stock market index and Cameroonian economic growth (GDP) with quarterly time series data from 2006 to 2010. The study showed that market capitalization has positive impact on the GDP. The study also employed mathematical growth function; Gompertz model to estimate the financial variables and found link between these variables. Pearson correlation method also confirmed that financial variables were inter-related. The results revealed a positive growth of market capitalization for another five year period and positive association between macro indicators. In Ghana, Owusu-Nantwi and Kuwornu (2011) applied Ordinary Linear Squares method to study the impact of interest rates on stock market returns. The results showed that Interest rate as captured by 91-Treasury bill rate exhibited a negative relationship with the stock market return. In Kenya, Ouma and Muriu (2014) studied the impact of macroeconomic variables on the stock market returns using Arbitrage Pricing Theory (APT) and Capital Asset Pricing Model (CAPM) framework for monthly data (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013). The study used Ordinary Least Square (OLS) and found money supply, inflation rate are significant determinants of the return at NSE, while exchange rates have a negative impact on stock returns. Interest rate on the other hand is not important in determining long run returns in the NSE.
Down to Nigeria, Emmanuel and Samuel (2009) investigated the impact of real GDP, inflation rate and interest rates (macroeconomic variables) on stock market returns (stock market index). With multiple regression analysis technique, study suggested that there is significant relationship among these variables. Increase in inflation and interest rates adversely affect the stock market returns while there is positive relation between real GDP and stock market returns. Employing Various econometric analyses; Augumented Dickey Fuller (ADF) test, Granger causality test, Johansen Co-integration test and Error Correction method (ECM) were Asaolu and Ogunmuyiwa (2010) to examine the impact of macroeconomic variables on Average share price (ASP) with time series data from 1986 to 2007. The results revealed that a weak relationship exists between ASP and macroeconomic variables. Osamuonyi and Evbayiro-Osagie (2012) employed VECM to unravel the relationship between macroeconomic variables and capital market index with annual data comprised of interest rates, inflation rates, exchange rates, fiscal deficit, GDP and money supply from 1975 to 2005 inclusive. It was observed that short run and long run relationship existed between stock market index and the macroeconomic variables adopted. It was also found that the macroeconomic variables selected in this study significantly influence the capital market index. Ogbulu(2010) employed ECM, cointegration and granger causality to examine the Research Journal of Finance and Accounting www.iiste.org ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online) Vol.11, No.8, 2020 38 relationship between inflation, interest rates and stock return. The study found that there is a positive and significant long run relationship between inflation and stock returns and negative a negative long run relationship between interest rates and stock returns. The granger causality test results suggested unidirectional causality running from inflation and interest rates respectively to the stock returns. Abu-Libdeh and Harasheh (2011) (2011) investigated the performance stock exchange on macroeconomic variables (inflation, exchange rate and market capitalization) relying on arbitrage pricing theory (APT) with monthly data from 2000 to 2004 using the ordinary least square (OLS) method. It was found that there is no significant effect of these macroeconomic variables on stock returns. Abraham (2011) investigated the relationship between the stock market (all share index) and selected macroeconomic variables (inflation, interest and exchange rates). Using Error Correction Model, it revealed that a significant negative relationship exist between the stock market and the MRR in the short run. That means a decrease in MRR, will lead to increase in the performance of the stock market index, whereas Treasury bill and inflation rate were seen to be insignificant. An indication that they were negatively related to the stock market in the short run, hence achieving low inflation rate and keeping the Treasury bill rate low could improve the performance of the stock market. Applying adopted Error correction modeling techniques Adeleke and Gbadebo (2012) also looked at the connection of macroeconomic policy and stock returns. The study revealed that macroeconomic policy associated with aggregate economic activity (measured by GDP), broad money supply (M2), interest rate (INT) and consumer price index (CPI) are the most important macroeconomic factors explaining stock market returns. Arodoye (2012) made an investigation into the impact of macroeconomic variables on stock prices with VAR model and found that there is long run relationship between stock prices and inflation rate and real gross domestic product for the period of 25 years. It was also revealed that variations in stock market prices are majorly caused by inflation rate, growth of domestic product, interest rate and own shocks. Adarmola(2012) employed Johansen"s Cointegration Technique and Error correction mechanism to investigate the impact of macroeconomic variables on stock prices using quarterly data for the period of 1985 to 2009 and found that exchange rate exerts significant impact on stock market both in the short and in the long run. The study also revealed that in the short run, exchange rate had a positive significant impact on stock market performance; while in the long run, the relationship is significantly negative. Onasanya and Ayoola (2012) used VECM model with annual time series data for the period 1985-2008 to unravel that the stock macroeconomic variables do not significantly influence the return at the stock market. Izedonmi and Mgbame and Chijoke-Mgbame (2013) employed Engle and granger two stage error correction methodology to investigate the relationship between macroeconomic factors(money supply, inflation and exchange rate) on stock returns with emphasis on arbitrage pricing theory framework with data 2000:Q1 to 2010:Q4. The results indicated that all the macroeconomic variables selected insignificantly affect stock market both in the long run and the short run. Aigbovo and Izekor (2015) employed six macroeconomic variables (exchange rates, inflation rates, interest rates, money supply, industrial production index and international oil price) on stock market Index with monthly data from January 2000 to December 2010. The study applied multivariate Ordinary Least Square (OLS) and the Error Correction Model (ECM). Prior to that Johansen cointegration test indicate that macroeconomic variables and stock market index are co-integrated. This implies that a long run relationship exists between the specified macroeconomic variables and stock market index in Nigeria. The multivariate Ordinary Least Square (OLS) and the Error Correction Model (ECM) also revealed that inflation rate, interest rate, money supply, industrial production index and oil price do influence stock market index either in the short-run or the long-run. Chude, Ifurueze and Chude (2015) employed ECM and other econometric tools to investigate the impact of some macroeconomic variables on stock market returns. The macroeconomic variables were proxied by GDP, inflation rate, and monetary policy rate. The result revealed that economic growth proxied by GDP exert a positive and significant impact on stock market returns over the years, whereas inflation rate and monetary policy rate exhibited negative and significant influence on stock market returns. It implies that a decrease in inflation and monetary policy will enhance the performance of NSE both in the short run and long run. Udi and Ohwofasa (2018) examined the macroeconomic determinants of stock performance from 1986 to 2016. The study employed ECM and found that interest rate, inflation rate and previous level of market capitalization were the major determining factors for trading activities at the NSE. It was also discovered that a negative relationship exist between stock market performance and inflation rate, interest rate and per capita income. Hunjra, Chani, Shahzad Farooq and Khan (2014) in a study; macroeconomic variables affect the performance of the stock market consider interest rate, exchange rate, inflation rate and GDP as important among macroeconomic variables which affect the performance of the stock market. Therefore study agreed with Hunjra et al (2014) to use the following; Stock return as performance index proxied by All Share Index (ASI) as dependent variables, while the independent variable (macroeconomic variables) are represented Economic Growth (GDP), Exchange Rate

Techniques
To evaluate the stationarity of the variables employed, the Augmented Dickey Fuller (ADF) unit root test is used.
To determine presence of multicollinearity, the correlation matrix is used in this study and other relevant technique to examine and determine the global utility of the specified model. Because of the dynamic nature of the variables under study, Autoregressive Distributive Lags (ARDL) is engaged estimating the models.

Results and Analysis 4.1: Trend Analysis of Data
This estimation of the model specified in this study will commence with trend analysis of data. The time series plot of the data is shown in figure I below. The figures below revealed that all the variables recorded period of peaks and trough as well exhibited undulating movement except GDP that trended smoothly upwards and EXCR that also recorded upward movement, though not smooth, suggesting non-stationarity of the variables as expected.

Authors' computation output using E-view 10.
Next is descriptive statistical analysis;

4.3: Global Utility Examination and Determination
In the macroeconomic analysis, determination of global utility or usefulness of the specified models gives a research confidence to making inference that can be referred for policy making. To achieve this, the researchers employed correlation matrix and Ordinary Least Square (OLS) as shown below;  Table 3 shows the Ordinary Least Square (OLS) estimated model for the relationship between macroeconomic variables and stock market. From the table Durbin-Watson statistics is 0.636529, showing presence of autocorrelation. This is unreliable and cannot be used for further analysis and policy formulation. The researchers proceeded to testing the stationarity of the variables. This procedure is normal in macroeconomic time series analysis to know the most suitable technique for estimating the model. Here, the researchers employed Augumented Dickey Fuller (ADF) unit root test as depicted below;

4.4: Stationarity/Unit Root Test
Stationarity is statistical proven procedure in macroeconomic time series to ascertain a suitable method for data analysis. Table 4 below depicts the stationary test for both level and first difference data. The results show ASI, GDP, EXCR and INFLR are difference once to be stationary or integrated at order one, while INTR is stationary at level. The variables have different orders of integration, justifying the earlier adopted ARDL model.

Authors' computation output using E-view 10.
The researchers therefore proceed to estimating the models with ARDL, aimed at proffering dynamic solution to the static problem of time series. This is shown in table 5 below.

4.6: Model Estimation and Results
Having satisfied with all previous tests, the researchers confidently proceeded to estimating the relationship between stock market performance (ASI) and macroeconomic variables (GDP, EXCR, INFLR and INTR) in Nigeria with ARDL framework.
Table 5 below reveals that ASI has p-value of 0.0000 suggesting that ASI is autoregressive. It is statistically confirmed evidence suggesting that ASI in the past can predict future events in the stock market in Nigeria. It conspicuously revealed that GDP has coefficient of 2.186996 with p-value of 0.0026 and INTR at lag 1 has coefficient of 0.670474 with p-value of 0.0279, suggesting GDP and INTR have positive and significant relationship with ASI at 5% significant level, but GDP at lag 1 has coefficient of -1.993335 and p-value of 0.0031 indicating GDP negatively and significantly impacted ASI afterwards. INFLR has coefficient of -0.173645 with p-value of 0.0462 suggesting that IINFLR has negative and significant impacted on ASI. Whereas EXCR has coefficient of 0.009608 and p-value of 0.9441, showing INFLR insignificantly impact ASI. The adjusted R-square is 0.984499 suggesting that the estimated ARDL (1, 1, 0, 0, 1) model is moderately fitted, with the explanatory variable jointly accounting for 98.4% of total variation of ASI. The probability of F-Statistic is 0.000000, an indication that the estimated model is highly significant. Durbin-Watson Statistics (Dw) is 2.093378 telling the researchers not to border about autocorrelation. The researchers can boldly say that the model did a good job to describe the relationship between macroeconomic variables and stock market in Nigeria. The autonomous component or constant (C) has coefficient of 0.266447 with p-value of 0.7848, which is insignificant. This shows that interest rate, exchange rate, inflation rate and GDP are the most important among macroeconomic variables which affect the performance of the stock market. 0.000000 Authors' computation output using E-view 10. Table 6 below depicts ARDL Bound cointegration Test examining if there is long run relationship in the model. From the bound test, it can be seen that the F-Statistics is 4.731908 which is greater than all the critical values at 1(0) and 1(1) bounds at1% to 10%. These reject the null hypothesis of no levels of relationship. With this result the researchers have enough evidence to pronounce a long run relationship between Stock market performance proxied by All Share Index (ASI) and macroeconomic variables (Economic Growth (GDP), Exchange Rate (EXCR), Inflation Rate (INFLR) and Interest Rate (INTR)) in Nigeria.  Vol.11, No.8, 2020

4.6.3: Correction Short Run Error Test
As shown in the output in Table 8 below, error correction equation, CointEq(-1) has expected negative sign of -0.285707 and p-value of 0.0001 indicating the model is statistically significant. It can also be seen that 28.5% of errors from the equilibrium can be corrected in the next period, and speed of adjustment is 28.5%. Having concluded and satisfied with estimation of the model, the researchers resorted to run some residual diagnostic test; Normality Test, Serial Correlation Test and Heteroscedasticity Test as seen table 9 and 10 below; 4.7: Residual Diagnostic Test 4.7.1: Normality Test From Table 9 below, it is seen that Jarque-Bera Statistic is 1.64058 with P-value of 0.5588763 which not significant at both 5% and 10% and Kurtosis of 3 approximately, clear evidence of normal distribution.

5: Concluding Remarks and Recommendations
The researchers in this study; Dynamic Interactions of Nigerian Stock Market and Macroeconomic Variables, one the most researched areas in Finance made the following empirical observations; that all share index is reliable in predict future activities in the stock market in Nigeria. This is in agreement with the assertions of Musílek (1997) of Stock prices are considered to be one of the best indicators of changes in economic activities by empirical studies and economic theories. If an investor wants more return on investments, they must focus mainly on the  Vol.11, No.8, 2020 46 macroeconomic variables that affect the stock prices. It was also found that economic growth proxied by Growth Domestic product and Interest rate have positive and significant relationship with all share index within the period of study, while inflation exerts negative influence on All Share Index. It was also found that exchange rate has insignificant impact on All Share Index within the scope of the study. The above findings agree with the apriori expextation of this study. It observed that the autonomous component is insignificant, validating the assertion of Hunjra et al (2014) that interest rate, exchange rate, inflation rate and GDP are the most important among macroeconomic variables which affect the performance of the stock market. The output of the analysis also revealed that long run relationship between All Share Index and macroeconomic variables (Economic Growth, Exchange Rate, Inflation Rate and Interest Rate in Nigeria. The findings in this study majorly collaborate with the Arbitrage Pricing Theory that opined that the actual return is influenced by a numbers of market wide variables or factors such as interest rate, the exchange rate, change in inflation, change in output etc. Consequently, the researchers are of the opinion that government and her regulatory bodies devise adequate measures to curtail in inflation in Nigeria. This is because the finding in this study is a plausible affirmation that inflation is an economic crippler that destroys the economic power of investors. Again, investor are encourage channel their fund to the stock market despite the volatility in exchange rate in Nigeria since exchange rate has insignificant impact on All Share Index.

Suggestion for Further Study and Limitation of Study
The researchers suggest further studies should have global perspective by looking at least one stock market of developed, emerging, developing and underdeveloped nations. This will help to validate possible inferences, theories and policy making. The study is limited to Nigeria Stock Market. The researchers had wished it was extended to the study to Stock Market outside Nigeria but was hindered by unavailability of data to the researchers.