Macroeconomic Determinants of Stock Market Development: Evidence from a Panel of Ten Most Capitalized African Stock Markets.

Macroeconomic Determinants of Stock Market Development: Evidence from a Panel of Ten Most Capitalized African Stock Markets

Jeremiah Sunday Musa¹, Mika‟ilu Abubakar²

¹Jeremiah Sunday Musa is a former postgraduate student at the Department of Economics, Usmanu Danfodiyo University, Sokoto

²Mika’ilu Abubakar is a lecturer in the Department of Economics, Usmanu Danfodiyo University, Sokoto

DOI: https://dx.doi.org/10.47772/IJRISS.2025.908000470

Received: 12 August 2025; Accepted: 18 August 2025; Published: 17 September 2025

ABSTRACT

The study investigated the macroeconomic determinants of stock market development (SMD) in Africa’s ten most capitalized stock markets using yearly data from 1986 – 2021. The empirical work is based on the panel unit root test using both Levin, Lin and Chu panel unit root test and Im, Pesaran and Shin panel unit root test, Pedroni co-integration, Pooled Mean Group Estimator (PMGE) and Dumitrescu and Hurlin panel causality test. Both unit root test results showed that the series are stationary at level and first difference. Pedroni co-integration result showed a long-run relationship between SMD and its determinants namely interest rate, exchange rate, FDI, economic growth, inflation and banking sector development, while the Pooled Mean Group results indicated that there is evidence of co-integration between SMD and all the macroeconomic variables at all levels of significance for the combined panel of selected countries. Specifically, there is a positive relationship between SMD and interest rate, exchange rate and banking sector development on the one hand, and a negative relationship between SMD and foreign direct investment, inflation rate and economic growth. Moreso, co-integration exists between SMD and all the macroeconomic variables in each country studied. The result also indicated that the nexus between SMD and economic growth is statistically significant across the country with exception of South Africa and Egypt. Dumitrescu and Hurlin panel causality test results showed a bidirectional causality between SMD and the duo of FDI and BSD and interest rate granger cause SMD. The result recommends policy makers to maintain fiscal and monetary policies to increase demand for funds to the private sector and subsequently enhance stock market development. More so, business cycle stabilization policies that would enhance foreign exchange market should be embarked on, as an appreciating exchange market may boost the stock market.

Keywords: Stock market, exchange rare, interest rate, banking sector development, foreign direct investment, inflation, economic growth, Pedroni co-integration and pooled mean group estimator.

INTRODUCTION

Stock market is a key channel for the mobilization of long-term capital in an economy. Unfortunately, there are mixed and inconclusive results from studies on the determinants of stock market development in African countries. The role of a well-functioning stock market is critical to the performance of economic activities as documented in the literature (see for instance Osamwonyi & Evbayiro-Osagie, 2012; Kemboi & Tarus, 2012; Sirucek, 2012; Issahaku, Ustarz & Domanban, 2013; Sukruoglu & Nalin, 2014; Hsing, 2014; Hunjra, Chani, Shahzad, Farooq & Khan, 2014; Peter & Akujuobi, 2014; Omorokunwa & Ikponmwosa, 2014; Kpanie, Esumanba & Sare, 2014; Wan Yusoff & Guima, 2015; Zhou, Zhao, Belinga & Gahe, 2015; Nijam, Ismail & Musthafa 2015; Kulathunga, 2015; Ernest, David Jnr, & Kofi, 2016; Gay, Jr., 2016; Nduka, Anigbogu & Nyiputen, 2016). A sound and developed stock market accelerates economic growth through mobilization of domestic and foreign financial resources that facilitate investment and economic activities, through access to low-cost capital by quoted companies.

The expected benefits of sound stock market have spurred the interest of developing economies around the globe on appropriate strategies of stock market framework and policies that are suitable in achieving well-developed stock markets. However, majority of stock markets in developing economies are yet to achieve the desired aspirations for a developed stock market due to some existing challenges, such as weak corporate governance, regulatory and legal institutions, political risk, and unethical practices in the stock market especially in the developing economies of Africa (Abdullahi & Fakunmoju, 2019).

There exists an array of studies (both country specific and panel studies) on the nexus between stock market development and various macroeconomic factors. There is, however, no consensus on the nature and causal linkage between stock market development and such macroeconomics factors (see for instance Owiredu, Oppong & Asomaning, 2016; Matadeen, 2017; Tsaurai, 2018). The reason for divergence on these outcomes ranges from types of models applied in empirical analysis, variable measurements, frequency of data employed (whether weekly, monthly, quarterly or annually), economy involved (under-developed, developing and developed) and so on.

It is interesting to note that while major studies on the factors of stock market development emphasized on macroeconomic factors, a handful of studies survey the institutional factors that spur stock market development (see for instance North, 1991; La Porta, Lopez-de-Silanes & Shleifer, 1997; Levine & Zervos, 1998a; Henry, 2000a; Bekaert & Harvey, 2000; Svaleryd & Vlachos, 2002; Ho & Iyke, 2017; Egbulonu & Ezeocha, 2018). Most of these studies employed time series co-integration analysis and institutional determinants such as corporate governance, legal origin, stock market integration, legal protection, financial market liberalization, political risk, bureaucracy quality and trade openness as factors of stock market development. The main result from these studies reveals that macroeconomic factors and institutional factors both influences stock market development.

Notwithstanding the importance of stock market to the development of any economy, and while time series studies on the determinants of stock market development abound (see for instance Adam & Tweneboah, 2008; Kemboi & Tarus, 2012; Peter & Akujuobi, 2014; Zhou et al., 2015; Kulathunga, 2015; Demir, 2019; Abdulrahman, Sidek & Tafri, 2009), there is dearth of literature on panel studies in respect of stock market development especially on Africa countries. The few panel studies include (Thanh et al., 2016; Wan Yusoff & Guima, 2015; Naceur, Ghazouani & Omran, 2007; and Matadeen, 2017).

It is against this background that this study empirically investigates the macroeconomic determinants namely (inflation, exchange rate, foreign direct investment, economic growth, banking sector development and interest rate) of stock market development in ten most capitalized African stock markets namely Algeria, Botswana, Egypt, Kenya, Ghana, Mauritius, Morocco, Namibia, Nigeria and South Africa. Sequel to the above, this study seeks to answer the following research questions: i) to what extent do the following macroeconomic factors – inflation, exchange rate, foreign direct investment, economic growth, banking sector development and interest rate determine stock market development in each of the ten selected countries? ii) to what extent do the following macroeconomic factors – inflation, exchange rate, foreign direct investment, economic growth, banking sector development and interest rate determine stock market development for the combined panel of selected countries? iii) what is the nature and direction of causality between stock market development and each of its determinants for the combined panel of selected countries?

To achieve our objective, the paper is structured into five sections: the next section provides a review of the literature on macroeconomic determinants of stock market development. Section three presents data and methodology. While the fourth section presents the results of the analysis, the fifth section concludes the paper.

LITERATURE REVIEW

This section presents the theoretical framework and a review of empirical studies on the macroeconomic determinants of stock market development.

Theoretical Framework

The theoretical foundation on the macroeconomic determinants of stock market development is majorly two folds. The Capital Asset Pricing Model describes the relationship between the risk inherent to the market and the expected gain of securities. The CAPM methodology has been utilized thoroughly in finance field to measure the execution of portfolios, computation of the expense of capital, mispricing stock, portfolio determination, and measuring the level of business sector reconciliation by means of beta convergence (Bruner, Eades, Harris & Higgins, 1998 and Graham & Harvey, 2001). The Theory of Stock Market Interdependence shows how the stock markets are interlinked over time. The first argument provides reason for co-movement between different stock markets across the world. This relates to the contagion effect which explains a situation where a shock in a particular economy or region spreads out and affects others by way of, say, price movements. The second argument provides explanation on the performance of economies and their linkages via exports and imports of goods and services. This relates to economic integration among the countries (Pretorius, 2002).

REVIEW OF EMPIRICAL LITERATURE

Although considerable attention has been devoted to linking the stock markets to economic growth, there is little study on the determinants of stock market development in developing economies.

The growing nexus between stock prices movement and macroeconomic factors for the developed as well as developing countries have well been documented in the literature over the last several years.

Garcia and Liu (1999) examined the macroeconomic determinants (saving rate, real income, financial intermediary efficiency, stock market liquidity and inflation) of stock market development by conducting panel analysis on pooled data of fifteen industrial and developing countries (Brazil, Argentina, Chile, Colombia, Indonesia, Japan, Korea, Malaysia, Mexico, Peru, Taiwan, Philippines, Thailand, United States and Venezuela) from 1980 through 1995. They showed that saving rate, real income level, financial intermediary efficiency and stock market liquidity are effective indicators of stock market development.

Jung, Shambora and Choi (2007) investigated the effects of expected and unexpected inflation on real stock returns for Germany, France, UK and Italy using quarterly data period Q1 1975 to Q1 2001 sourced from international financial statistics of IMF and Organization for Economic Corporation and Development data CD-Rom and employing Full Information Maximum Likelihood. The findings showed that unexpected inflation affects stock returns in France, Italy and the UK, but that expected inflation does not. In Germany, none of the factors significantly effect on the real stock return which may be attributable to the reunification process in the 1990s.

McGowan (2008) investigated the impact of economic development and the size of the stock market on the total economic output for developed and less developed countries (sample size ranges from 78 countries to 1002 countries) using yearly data sourced from the Standard and Poor’s Emerging Markets Factbook 2004 and the International Finance Corporation Emerging Stock Markets Factbook for 1996 spanning 1994 to 2004. The study used linear regressions of the cross-sectional data to test the hypothesis that the ratio of the value of the stock market capitalization as a proportion of GNI will be higher in more developed economies and lower in less developed economies. The result showed a statistically positive relationship between gross national income per capita and total stock market capitalization to gross national income for each year from 1994 to 2003 for all the countries investigated.

Yeh and Chi (2009) investigated the co-movement of inflation and real stock returns using quarterly data from 12 OECD countries spanning from Q1 1957 to Q1 2003. Co-movement between inflation and real stock returns is described using the correlation coefficient of VAR forecast errors, while co-integration between the variables is done using Autoregressive Distributive Lag framework. The result showed that large portion of the sample of 12 OECD countries displayed negative co-movement between inflation and stock return. Inflation in Australia, France and Ireland are inversely related to real stock return regardless of whether variables are in the co-movement or long run equilibrium relationship. Also, only Japan and Spain exhibited co-movement relationship.

Billmeier and Massa (2009) explored various macroeconomic variables (institutions, remittances, income, investment, inflation change, domestic credit, stocks traded value, oil price index, U.S. federal funds rate) impacting the development of stock market in 17 Middle East emerging stock markets and Central Asia by using fixed-effect panel regression. After examining the relationship on 17 countries using annual data from 1995–2005, their results showed that remittances, income, investment, oil price, Heritage Foundation’s index and stocks traded value influence stock market development.

Subeniotis, Papadopoulos, Tampakoudis and Tampakoudi. (2011) examined how inflation, market capitalization, industrial production and the economic sentiment indicator affect the EU-12 Stock Markets. The data under examination is derived from the EU-12 economies covering the period 2000 to 2005. After applying the Arellano-Bond dynamic panel-data analysis, they concluded that the twelve European countries converge to steady state with a remarkable speed of adjustment, indicating a parallel behaviour and a low diversification effect. Consequently, it could be argued that the selected variables demonstrate a similar impact on all the European capital markets, while at the same time the economies of the EU-12 countries significantly converge.

Pradhan, Filho and Hall (2013) investigated the causal relations between stock market development and the duo of inflation, and economic growth in Hong Kong SAR, China, India, Israel, Jordan, Korea, Pakistan, Sri Lanka, Bangladesh, Indonesia, Japan, Kuwait, Malaysia, Philippines, Thailand, Singapore, and Turkey using yearly data spanning 1988- 2012 sourced from the World Development Indicators database. Employing panel VAR, the study revealed existence of co-integration between inflation and economic growth in the panel of sixteen Asian economies; existence of long run relationship between economic growth and the duo of stock market development and inflation in the sixteen Asian countries; existence of one-way causality running from stock market development to inflation; and that stock market development and inflation may accelerate the pace of economic performance of the countries. Based on this result, they argue that the development of stock market is important in determining macro policies of a developing country, including its optimal money supply which obviously also affects inflation.

Sukruoglu and Nalin (2014) examined impacts of macroeconomic factors (Gross Domestic product, Liquid liabilities,  liquidity ratio,  turnover ratio, Inflation, Cash surplus and Gross domestic saving) stock market development in selected European countries (Belgium, Austria, Croatia, Bulgaria, Czech Republic, Finland, Denmark, Germany, France, Hungary, Greece, Portugal, Italy, Latvia, Netherlands, Slovenia, Spain, Sweden and United Kingdom) by using data covering 1995–2011 obtained from the World Development Indicators (2008) database website. They found that income, liquid liabilities, saving rate, inflation and liquidity ratio impact the development of stock market. While inflation and liquid liabilities have inverse impacts; saving rate, liquidity ration and income have positive impact on the development of stock market.

Tripathi and Kumar (2014) examined long term relationship between stock returns and inflation in BRICS markets using quarterly panel figures spanning March 2000 to September 2013. The study employed Pedroni panel co-integration test and found a negative linkage between inflation rate and stock returns for Russia and a significantly positive relationship for India & China. The result also indicated no long term co-integrating linkage between stock index values and inflation rates for Russia, India and South Africa, while there was a significant co-integrating nexus between stock return and inflation for Brazil and China using Pedroni panel co integration test.

Pradhan, Arvin, Hall and Bahmani (2014) examined the relationship between economic growth, stock market development, banking sector development together with 4 other macroeconomic factors for ASEAN regional forum –ARF-26) comprising: i) ARF-Member Countries, ii) ARF-Dialogue Partner Countries and iii) ARF-Observer Countries using annual data covering a period of 1961–2012. The study employed VECM to determine the nexus between the variables and panel VAR model for testing Granger causality. The results amongst other things showed a long run linkage among the variables when economic growth serves as dependent variable. Also, one way causality from stock market development to economic growth was noted.

Abiy and Chi (2014) examined the linkage between economic growth and stock market development using panel data for 17 emerging market economies and 10 developed market economies for a 12 years’ period spanning from 2000 to 2011 and employing the GMM for dynamic panel data.  The results of the study revealed existence of a statistically significant positive linkage between economic growth and stock market development. The results also indicated that stock market development is an important vehicle for economic growth.

Naik and Padhi (2015) examined the impact of stock market indicators on the economic growth for a group of 27 countries (Argentina, Brazil, Bulgaria, Chile, China, Colombia, Egypt, Estonia, Hungary, India, Indonesia, Latvia, Lithuania, Malaysia, Peru, Pakistan, Mexico, Philippines, South Africa, Romania, Russia, Poland, South Korea, Venezuela, Turkey, Ukraine and Thailand) using annual data over the period from 1995 to 2012. Stock market proxies include market capitalization ratio, total value of shares traded and turnover ratio. Employing system Generalized Method of Moments and heterogeneous panel causality test to examine the direction of causality among the variables, the results indicated that stock market development significantly contributes to economic growth. Further, there existed a unidirectional causality running from stock market development to economic growth.

Pradhan, Arvin and Bahmani (2015) examined the linkages between economic growth, inflation, and stock market development in 34 Organization for Economic Cooperation and Development (OECD) countries over a period of 1960-2012. The study employed Pedroni’s panel co-integration test to reveal whether there is a long-run equilibrium relationship among the variables; panel Granger causality test to present evidence on the nature of the short-run and long-run causal relationship between the variables and panel vector Autoregressive model to detect the direction of causality. The study used three indicators of stock market development (stock market capitalization, turnover ratio, and volume of traded stocks) to investigate the nexus between the three set of variables. The results suggested, among other things, the presence of a long-run equilibrium between economic growth, inflation and the stock market factors. Additionally, there was one way causality from market capitalization to inflation.

Wan Yusoff and Guima (2015) investigated the determinants of development in capital markets in the MENA region from the perspective of three countries Egypt, Saudi Arabia and Tunisia. Secondary data for ten years period of 1992-2012 were used to model the factors influencing the development of their stock markets. Using Pearson Correlation analysis, the study showed that macroeconomic factors (exchange rate, oil rent, income per capita, inflation, domestic savings and interest rate) can be used to study development of capital markets.

Ernest et al. (2016) examined stock market performance and macroeconomic factors (exchange rate, money supply and inflation) using panel data of 41 emerging countries for the period spanning from 1996 to 2011. The study employed robust Ordinary Least Squares, Feasible Generalized Least Squares, Dynamic Ordinary Least Squares and Newey-West to investigate the empirical relationship between stock market performance and the selected macroeconomic variables. The study showed that inflation, policy rate, stock market liquidity and exchange rate all have significant impact on stock market development, while money supply does not have a significant relationship.

Thanh et al. (2016) used cross-sections series to accommodate the unbalanced panel data of 36 developing countries (20 in Asia, 10 in Latin America and 6 in Africa) over the period of 2003–2014 to investigate the determinants of stock market development in Vietnam and other developing countries. Applying two-way GMM, the study showed that stock market liquidity, domestic credit and economic growth positively impact stock market development in developing countries. More so, the result for stock market development in Vietnam indicated a significant inverse relationship with inflation, domestic investment, economic growth, foreign direct investment, domestic credit, stock market liquidity and broad money supply.

Sulaiman et al. (2016) evaluated the impact of inflation on stock market development for Pakistan, India, Sri Lanka, Nepal and Bangladesh. The study used yearly time series data spanning 1989 to 2012 and employs panel co-integration to determine the relationship between (consumer price index) as proxy for inflation and (market capitalization, market turnover and total value of traded stocks) as proxies for stock market development. The results suggested that there is a significant long run relationship between the variables as market capitalization is decreasing inflation, total value of stock traded is increasing inflation and turnover ratio is decreasing inflation. The study also showed a weak positive correlation between consumer price index and market capitalization, stock traded, and turnover ratio.

Lazarov, Kacarski and Nikoloski (2016) investigated the influence of stock market development on economic growth for a group of 14 transition countries (Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Macedonia, Moldova, Montenegro, Romania, Serbia, Slovenia, Hungary, Slovak Republic, Poland and the Czech Republic) for the period 2002-2012 on one hand, and analyse the main characteristics and specificities of stock market for the Republic of Macedonia on another hand. The study applied panel regression models (Fixed and Random Effects) and a dynamic panel model (GMM) to determine the influence of stock market development on economic growth, and a single country approach and comparative analysis to examine the main characteristics of the Macedonian stock market. The results indicated that stock market development is positive and significantly correlated with economic growth. More so, the comparative analysis of the stock market in the Republic of Macedonia suggests that the Macedonian stock market is still underdeveloped and faces a few challenges namely capital market regional integration and the harmonization of legal and institutional frameworks such as bankruptcy procedures, accounting and reporting standards, public sector regulatory bodies, corporate governance and a liberalized trade regime.

Exploring the macroeconomic determinants of stock market development in 21 emerging economies and employing pooled Ordinary Least Squares and fixed and random effects approaches with data ranging from 1994 to 2014, Tsaurai (2018) used the following macroeconomic factors FDI, economic growth, infrastructural development, savings, inflation, trade openness, exchange rates, banking sector development and stock market liquidity to examine their impact on stock market development. FDI, savings, economic growth, trade openness, exchange rates, banking sector development and stock market liquidity to a larger extent were found to have a positive impact on stock market development in emerging markets.

Ogbeide and Akanji (2018) assessed the relationship between economic growth and stock market development in BRICS nations. Using quarterly data spanning 1994 to 2014 sourced from World Bank Indicators and employing Panel Generalized method based on the fixed effect estimation to determine the relationship between stock market development and economic growth of BRICS nations. The results showed that stock market development significantly impacted economic growth. The study also indicated a positive link between stock market development indicators and economic growth of BRICS nations.

Agbloyora, Abora, Adjasi and Yawson (2013) explored the causal linkage between financial markets and FDI in Africa. The study used proxies for the banking sector and stock market to capture financial market development. Using data for 16 countries covering the period 1990 to 2007 and employing 2SLS panel instrumental variable approach to obviate simultaneous causality bias, the results suggested that countries with standard stock markets are likely to attract more FDI. Furthermore, the results showed that FDI flows can accentuate the development of a country’s stock market.

Ngare, Nyamongo and Misati (2014) analysed the nexus between stock market development and economic growth in Africa. The study used annual data from a panel of 36 countries, of which 18 have stock markets in Africa over the period 1980–2010. The study formulated a standard growth model and augment its with stock market dummy to investigate the presence of stock market in any given country under study. The study found  existence of a positive association between stock market and economic growth; and that stock market granger causes economic growth.

Utilizing Generalized Method of Moments and annual data of 11 developing economies from Africa over the period 1995-2014, Fantessi (2016) investigated linkages between economic growth and stock market development. The results revealed that stock market capitalization and values traded inversely impacted economic growth in Africa; and that stock market turnover contributed positively to economic growth, but it is statistically insignificant.

Matadeen (2017) investigated the macroeconomic variables influencing stock market by using a dynamic Panel Vector Error Correction Model within a sample of the 14 Sub-Saharan African countries selected mostly based on availability of data spanning 28 years (1989-2016). The Panel VECM results indicated that the main long run drivers of stock market development in the region are banking sector, economic growth, stock market liquidity, and investment. The study supported the assertion that banking sector complements stock market, and that savings has a significant and detrimental impact on stock market development.

Iddrisu and Abdul -Malik (2017) investigated the interaction between economic growth and stock market developments in a sample of 12 African countries (Ghana, South Africa, Namibia, Nigeria, Morocco, Mauritius, Kenya, Egypt, Botswana, Ivory Coast, Zambia and Zimbabwe) using a panel VAR approach with annual time series data from 1979-2013. The study found no evidence of contemporaneous relationship between stock market and economic growth in Africa. In the long run, the study finds evidence of bidirectional causality economic growth and stock market developments.

Abdullahi and Fakunmoju (2019) examined the influence of stock market development on the economic performance among selected West Africa economies (The Gambia, Ghana, Ivory Coast, Liberia, and Nigeria) covering the period 2011 to 2017. Employing correlation matrix and Variance Inflation Factor test to determine correlation amongst the variables, and Hausman Test determine the choice of panel regression analysis (fixed effect or random effect analysis), the result revealed that stock market development indicators have positive effect on economic performance while corruption perception index have negative effect on economic performance.

From the  literature reviewed, following gaps were noted. Most studies on the African countries adopted limited number of macroeconomic variables. Specifically, the use of economic growth as sole variable for determining stock market development in these studies cannot be overemphasized (see for instance Ngare et.al., 2014; Iddrisu & Abdul-Malik, 2017; Fantessi, 2016). This gap was addressed by employing several macroeconomic variables namely inflation, exchange rate, foreign direct investment, economic growth, banking sector development and interest rate to determine stock market development in Africa.

Gap also existed in the model used in testing stationarity, co-integration and correlation between variables. While existing studies used panel VAR, Panel VECM, Generalized Method of Moments, correlation matrix test; this study used a more robust technique for analysis. Specifically, Levin, Liu and Chu (2002) panel unit root test and Im, Pesaran and Shin (2003) panel unit root test were used for stationarity test, while Pedroni co-integration tests, mean group estimator and pooled mean group estimator were used for estimating long run and short run relationship.

Geographical gap was also noted in the literature reviewed. Most studies focused on a specific geographical cluster. For instance, while Garcia and Liu (1999) focused on advanced and developing countries in America and Asia, Wan Yusoff and Guima (2015) focused on MENA region, Sukruoglu and Nalin (2014) fixated on European countries and Jung et.al, (2007) studied France, Germany, Italy and United Kingdom. This study adds to the few literature on macroeconomic determinants of stock market development in Africa.

Finally, study period for most of the literature reviewed lapsed in 2014 (see for instance Ngare et.al, 2014; Agbloyora et.al, 2013; Yeh & Chi, 2009; Subeniotis et.al, 2011). Only Abdullahi and Fakunmoju (2019) whose study was on stock market development – economic growth nexus extended their study period to 2017. This study bridged the gap by extending the period to 2021.

DATA AND METHODOLOGY

This paper uses ex-post facto research design by employing secondary sources of data. Cross-sectional time series was employed to accommodate yearly unbalanced panel data of ten African countries (South Africa, Nigeria, Namibia, Morocco, Mauritius, Kenya, Ghana, Egypt, Botswana and Algeria) over the period of 1986–2021 from World Development Indicators of World Bank and African Development Indicators. The choice of the study period stemmed from unavailability of data at uniform period for the selected countries under study.

This study considers a multivariate framework conventionally employed in existing studies (see for instance Adam and Tweneboah, 2008; Billmeier and Massa, 2009; Rad, 2011; Osamwonyi and Evbayiro-Osagie, 2012). Stock market development is measured by market capitalization as a percent of GDP (Billmeier & Massa, 2009). While, macroeconomic factors include inflation, exchange rate, foreign direct investment, interest rate, banking sector development and economic growth. Most of the studies in this area employed the standard panel series procedure in exploring the determinants of stock market development. This study is not an exception. First, we check the data for stationarity and non-stationarity of the data using Levin, Lin and Chu (2002) panel unit root test and Im, Pesaran and Shin (2003) panel unit root test. If the series are found to be stationary, we test for co-integration among the variables using Pedroni co-integration tests. If the series are co-integrated, then an error correction term is required and hence Mean Group Estimator (MGE) and Pooled Mean Group Estimator (PMGE) would be applied to find the co-integration between the two variables as it can capture both long-run and short-run relationships. Hausman test shall be used to determine the best estimator to use between Mean Group Estimator (MGE) and Pooled Mean Group Estimator (PMGE). If there is no evidence of co-integration; panel Granger causality test shall be employed to establish the causal relationship among the variables.

The variables in this study are functionally modelled as follows:

STOCK =f (FDI, INTR, INFL, ECON, EXR, BNK) ………………………. (3.1)

The econometric model of the functional specification is as follows:

STOCK_i,t = β₀ + β₁FDI_i,t + β₂INTR_i,t + β₃INFL_i,t + β₄ECON_i,t + β₅EXR_i,t +

 β₆BNK_i,t + e_i  …………………………………………..……………………. (3.2)

Where:

STOCK = Stock market development,

FDI = Foreign Direct Investment,

INTR = Interest Rate,

INFL = Inflation,

ECON = Economic Growth,

EXR = Exchange rate,

BNK = Banking Sector Development,

e_i  = Error term,

i = Sampled Countries,

t = Time Period,

β₀ – β₆ = Coefficients of explanatory variables.

The main hypothesis is:

…………………………… (3.3)

However, since p_i is unknown, we therefore, suggest a three-step procedure to implement our test. In step 1 we carry out separate ADF regressions for individual in the panel and generate two orthogonalized residuals. Step 2 requires estimating the ratio of long run to short run innovation standard deviation for individual.  In the final step we compute the pooled t-statistics. The steps are explained in detail below:

Step 1: Perform ADF regressions and generate orthogalized residuals.

For each individual i, we implement the ADF regression

…………………… (3.4)

The lag order p_i is permitted to vary across individuals.

Having determined autoregression order p_i in (3.4), we run two auxiliary regressions to generate orthogalized residuals. Regress ∆y_it and y_it−1 against ∆y_it−L (L=1,., p_i)  and  the  appropriate  deterministic  variables, d_mt,  then save the residuals  ê_it and  ṽ_it−1 from these regressions. Specifically,

and ……… (3.5)

To control for heterogeneity across individuals, we further normalize ê_it and ṽ_it−1 by the regression standard error from equation (3.4).

…………………………… (3.6)

Where σ_Ɛi is the regression standard error in (3.4). Equivalently, it can also be calculated from the regression of ê_it against ṽ_it−1

 …………………………… (3.7)

Step 2: Estimate the ratio of long-run to short-run standard deviations.

Under the null hypothesis of a unit root, the long-run variance for Model 3.3 can be estimated as follows:

……………… (3.8)

If the data include a time trend (Model 3.5), then the trend should be removed before estimating the long-run variance.

Step 3: Compute the panel test statistics

Pool all cross sectional and time series observations to estimate

………………………. (3.9)

based on a total of NŤ observations, where Ť=T−ṕ−1 is the average number of observations per individual in the panel, and  is the average lag order for the individual ADF regressions.

For Pedroni co-integration test, we compute the regression residuals from the hypothesized co-integration regression. In the most general case, this may take the form:

y_i,t = α_i+δ_it + β_1ix_1i,t + β_2ix_2i,t +…+ β_mix_mi,t + e_i   t = 1,…T; i = 1,…N ……………… (3.10)

where T refers to the number of observations over time, N refers to the number of the individual members in the panel, and M refers to the number of regression variables. Here x and y are assumed to be integrated of order one. The slope coefficients β_1i, β_2i,…, β_mi  and specific intercept α_i vary across individual member of the panel.

To investigate the determinants of stock market development in selected African countries, we also applied the panel ARDL approach.

The model for estimating the panel ARDL is given below:

……………………………. (3.11)

Where X_i,t represent explanatory variables for group, ʎ_ij is the coefficient of the lagged dependent variables, δ_ij represent coefficient of x and i,u equals fixed effects.

For convenience, the above model can be re-parameterized as

……………… (3.12)

Where φ_i is the coefficient of the error term and β_i is the intercept and coefficient of the estimated long run parameters of the independent variables while ʎ_ij and δ_ij are the short run coefficient of the lagged dependent and independent variables.

RESULTS

This section covers descriptive analysis of data on the one hand, and statistical analyses of stationarity tests, panel co-integration and panel causality results on the other hand.

Table 4.1 Summary Statistics (1986 – 2021)

Source: Result Printout (Appendix B)

Table 4.1 presents a summary statistic for the selected variables namely stock, foreign direct investment, interest rate, inflation, economic growth, exchange rate and banking sector development. 360 yearly observations of all the variables were examined to estimate the following statistics. Mean describes the average value in the series. Thus, stock market capitalization as a percentage of GDP  have an average of 37.59, foreign direct investment as a percentage of GDP have an average of 2.01, interest rate have an average of 9.70, inflation indices have an average of 80.11, economic growth represented by annual percentage of GDP per capita have an average of 1.84, exchange rate have an average of 31.77, while domestic credit to private sector as a percentage of GDP which measured banking sector development have an average of 33.86. Standard deviation measures the dispersion or spread of the series around the mean. The standard deviation for stock is 63.06 which is higher than the mean indicating a high volatility in the variable, foreign direct investments have a moderate standard deviation of 2.17, interest rate have a standard deviation of 6.05 indicating that the variable is less volatile, same goes for inflation which have a standard deviation of 63.98 and less than the mean. Economic growth is relatively normal with a standard deviation of 3.39, exchange rate is however highly volatile with a standard deviation of 51.90, while banking sector development have a moderate standard deviation of 23.39. The skewness measures whether the distribution of the data is symmetrical or asymmetrical. A positive skewness value as in all the variables except economic growth indicates that the distribution of the data has long right tail. Aside economic growth (-0.41) and banking sector (0.71) that is normally distributed, the rest variables (stock (2.79), foreign direct investment (1.38), interest rate (1.60), inflation (1.39) and exchange rate (3.13)) have long right tail. Kurtosis estimates in Table 4.1 indicates that all the variables are relatively peaked compared to normal (leptokurtic).

To begin the statistical analysis, all series are in their natural logarithm form and tested for unit roots at both level and first difference values. The Levin, Lin and Chu (2002) panel unit root test and IM, Pesaran and Shin (2003) panel unit root test were used. The optimal number of lags was chosen according to Akaike Information Criterion (AIC) and result of the tests tabulated in Table 4.2.1 and Table 4.2.2

Table 4.2.1 Levin, Lin and Chu Unit Root Test Result

Source: Result Printout (Appendix B).

Table 4.2.1 shows the result of the Levin, Lin and Chu unit root test both at level and first difference. Considering stock market capitalization, foreign direct investment, interest rate, economic growth and banking sector development, the null hypothesis of a unit root cannot be accepted at both level values and first differences at all levels of significance. Inflation and exchange rate are non-stationary at level, stationary at first difference.

Table 4.2.2 Im, Pesaran and Shin Unit Root Test Result

Source: Result Printout (Appendix B).

Table 4.2.2 shows the result of the Im, Pesaran and Shin unit root test both at level and first difference. Considering stock market capitalization, foreign direct investment and economic growth, the null hypothesis of a unit root cannot be accepted at both level and first difference. Taking into consideration banking sector development, inflation rate, interest rate and exchange rate; the null hypothesis of a unit root can be accepted at level. However, the null hypothesis cannot be accepted for all the variables considering the first differences at all levels of significance.

Table 4.2.3 Pedroni Residual Co-integration Test Result

Source: Result Printout (Appendix B).

Table 4.2.3 shows the Pedroni co-integration test result. The result is divided into two components namely, common autoregressive coefficients (within-dimension) which measure the panel v-statistic, panel rho-statistic, panel PP-statistic and panel ADF-statistic. The second component is individual autoregressive coefficients (between-dimension) which measure group rho-statistic, group PP-statistic and group ADF-statistic. From table 4.2.3 the null hypothesis of no co-integration cannot be accepted for panel v-statistic, panel rho-statistic, panel PP-statistic and panel ADF-statistic at 10% level of significance. Also, we cannot accept the null hypothesis of no co-integration for group ADF-statistic at 10% level of significance indicating the presence of co-integration in all the panels and part of the groups listed above. However, we can accept the null hypothesis of no co-integration for the group rho-statistic and group PP-statistic at all level of significance. Since majority of the test results rejected the null hypothesis, the general conclusion is that all the variables under investigation are co-integrated.

Table 4.2.4 Hausman Test Result (MG and PMG Selector)

Table 4.2.4 shows the result of the Hausman test to choose the best estimator between mean group and pooled mean group that would be appropriate to run our model. The result indicated that the null hypothesis can be rejected since the probability is above 5%. Hence, pooled mean group estimator was selected for running the model.

Table 4.2.5 Pooled Mean Group Results (combined panel of selected countries)

Source: Result Printout (Appendix B)

Table 4.2.5 shows the result of the pooled mean group estimator. The result indicates the nature of relationship that exists between stock market development and the various macroeconomic variables for the combined panel of selected countries. From the table 4.2.5, there is evidence of co-integration between stock market development and all the macroeconomic variables at all levels of significance for the combined panel of selected countries. Specifically, there existed a negative relationship between stock market development and foreign direct investment at 5% level of significance. However, the relationship between stock market development and interest rate is positive and insignificant. There existed an insignificant negative relationship between stock market development and inflation at all levels of significance. More so, the relationship between stock market development and economic growth is negative and insignificant at all levels of significance. Exchange rate has a positive non-significant relationship with stock market development while banking sector development and stock market development has a significant positive relationship at 1% level of significance.

The country specific results of the pooled mean group estimation for all the countries showed a statistically significant relationship between stock market development and its determinants. While stock market development indicator showed statistically significant relationship with all the macroeconomic variables in Algeria, no significant relationship existed between stock market development and all the macroeconomic variables in South Africa. The result also indicated that the nexus between stock market development and economic growth is statistically significant across the country with exception of South Africa and Egypt.

CONCLUSION AND RECOMMENDATION

This paper investigated the macroeconomic determinants of stock market development in ten most capitalized stock markets in Africa namely Algeria, Botswana, Egypt, Ghana, Kenya, Mauritius, Morocco, Namibia, Nigeria and South Africa. Stock market capitalization as a share of GDP was regressed against inflation, exchange rate, foreign direct investment, economic growth, banking sector development and interest rate for the period covering 1986 to 2021. The study employed panel unit root test using both Levin, Lin and Chu panel unit root test and I’m, Pesaran and Shin panel unit root test, Pedroni co-integration, Pooled Mean Group Estimator (PMGE) and Dumitrescu and Hurlin panel causality test.

Both unit root test results showed that the series are stationary at level and first difference. Pedroni co-integration result showed a long-run relationship between SMD and its determinants namely interest rate, exchange rate, FDI, economic growth, inflation and banking sector development, while the Pooled Mean Group results indicated that there is evidence of co-integration between SMD and all the macroeconomic variables at all levels of significance for the combined panel of selected countries. Specifically, there is a positive relationship between SMD and interest rate, exchange rate and banking sector development on the one hand. On the other hand, there exist a negative relationship between SMD and foreign direct investment, inflation rate and economic growth. This is not in consonant with results from developed economy and may likely be due to weak institutional frameworks, financial maturity and corporate governance issues. Moreso, co-integration exists between SMD and all the macroeconomic variables in each country studied. The result also indicated that the nexus between SMD and economic growth is statistically significant across the country with exception of South Africa and Egypt. Dumitrescu and Hurlin panel causality test results showed a bidirectional causality between SMD and the duo of FDI and BSD and interest rate granger cause SMD. Bi-directional causality between banking sector development and stock market on one hand, and between foreign direct investment and stock market development on the other hand, is an indication of a contagion effect which explain a situation where an improvement or shock in a particular sector or economy spreads out and affect others by way of price movement. This supports the stock market interdependence theory.

The result recommends policy makers to maintain sound monetary and fiscal policies to increase demand for funds to the private sector in the short-run to stabilize inflation, exchange rate and development of banking sector. These would subsequently enhance stock market development in Africa. More so, business cycle stabilization policies that would enhance foreign exchange market should be embarked on, as an appreciating exchange market may boost the stock market. Further study may identify more macroeconomic variables and incorporate regulatory and institutional factors in the study.

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