Data construction and variables used

This section details how Principal Component Analysis (PCA) based indices are used to construct a composite index of stock market development. The objective of using a PCA methodology is to aggregate three different ranges of traditional measures of stock market development to derive a uni-dimensional measure of stock market development. This is because each variable used individually, may not be sufficient to represent the attributes of stock markets42 in Africa. The approach found in existing literature is the one of summing the

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three measures of stock market development, for example Demirguc-Kunt and Levine (1996), but this assumes that these measures are of equal importance.43

The dependent variable, which measures the stock market development, is proxied by three indicators (i) Market size as measured by the stock market capitalization scaled by GDP (SM). The assumption underlying the use of this variable as an indicator of stock market development is that the size of the stock market is positively correlated with the ability to mobilize capital and diversify risk (Levine and Zervos, 1996). (ii) Market liquidity, which is measured by the total value traded scaled by GDP, is one for which Demirguc-Kunt and Levine (1996) argue that stock market liquidity or the ability to trade equity easily is very important for economic growth. This measure complements the measure of stock market size because markets may be large but inactive (iii) the other measure of relative activity of stock markets used in this study is the turnover ratio. Turnover ratio is defined as the total value of shares traded during the period divided by the average market capitalization for the period. The three variables are used to construct a composite indicator (index 1) named stock market index (SMINDEX). This indicator is useful for analysis since market capitalization, value of share traded and turn-over ratio variables do not reflect, when considered separately, the whole components of the development of stock markets.

The size of the stock market does not provide any indication of its liquidity since some markets can be large but have low levels of trading. The composite index of stock market development (SMINDEX) is obtained using a formula which is similar to the algorithm developed by Demirguc-Kunt and Levine (1996). The construction of the indicator follows a two step procedure. First for each country i and each time t, we compute the means removed market capitalization, total value traded/GDP and turnover ratios. The first step is to compute means-value of stock market capitalization, total value traded and turnover ratio as follows:

43 _{The initial results of PCA show that value of share traded contributes 0.61 of variation in overall measure of}

stock market development. Turn-over ratio is 0.56 and market capitalization is 0.56. Intuitively, the earlier evidence signals that two measures of liquidity are more important to variation of stock market development overtime than market depth for the selected African countries. In this paper our results are based on the PCA method approach which is different from the approach by Demirguc-Kunt and Levine (1996); i.e., the approach of equal weighting. We compare our results from our composite indexes to the one based on Demirguc-Kunt and Levine (1996) method. Levine, 2002 and King and Levine, 1992 and 1993 also used this methodology of aggregate indicator.

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The second step, we compute the weighted average of the transformed values of market capitalization, total value traded and turnover ratios obtained by the expression above to provide the overall stock market index1, namely SMINDEX1.

We also compute additional two composite indexes for stock market development using the methodology described above and PCA approach. The index 2 (SMINDEX11) comprises of stock value traded, stock market capitalization, stock turnover ratio and number of listed companies. The number of listed companies is included as an additional measure of stock market size. The index 3 (SMINDEX111) comprises of stock value traded, stock market capitalization, and stock turnover ratio, a measure of number of listed companies relative to size of banking industry and relative size of stock market capitalization to banking credits provided to private sector.

Banking sector development (BSD) is measured by domestic credit to private sector which refers to financial resources provided to the private sector, such as through loans, purchases of non-equity securities, and trade credits and other accounts payable that establish a claim for repayment. For some countries these claims include credit to public enterprises.

Control variables

Our control variables include GDP per capita, private capital flows, FDI, remittances flows, foreign currency exchange rate, domestic investment and savings, economic openness, inflation rate and interest rate, regulatory quality, bureaucratic quality, corruption perception and government effectiveness.

(i) GDP per capita growth

It is used as a measure of economic development. High incomes are usually accompanied by better property rights, better education, good business environment and more investable

, is the average value of variable SM for each individual country in a panel over the period for each one of these three components.

it it SM SM SM SM SM  

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resources (Andrianaivo and Yartey, 2010). It is anticipated that GDP per capita will have positive impact on the measures of stock market and banking sector development.

(ii)Private capital flows

Participation of external investors in the domestic market will improve the appeal of the stock market to local investors and thus increases investable resources. Therefore, we expect a positive relationship between private capital flows and stock market development measure. Private capital flows consist of net foreign direct investment and portfolio investment.

(iii)Foreign Direct Investment (FDI) and remittance flows

This study also utilises FDI to proxy for importance of external funds in the process of financial sector development. We also utilises workers’ remittances these are compensation of employees that comprises of current transfers by migrant workers and wages and salaries earned by non-resident workers. Remittance funds are supply of “loanable” funds to the banking sector and they can theoretically improve banks’ liquidity position and hence its ability to extend credit to the productive sectors of the economy. We therefore expect a positive relationship between these measures of external finance with the banking sector development in Africa.

iv) Domestic Credit to Private Sector

Consistent with the literature (e.g., see Yartey 2008; and Cherif and Gazdar, 2010), we will incorporate domestic credit to private sector as a share of GDP to proxy for the role of financial intermediaries in promoting stock market development. Financial intermediaries can play a substitute or complementary role with stock markets in providing long-term finance. Therefore, the expected influence of financial intermediaries on stock market development can be either negative or positive.

v) Foreign currency exchange rate

The fluctuation (depreciation) has been found to have adverse impact on the performance of African stock markets (Senbet and Otchere, 2005). High currency volatility creates uncertainty to the real value of the stocks and thus creates an impediment to foreign investments. There is also expectation that stock market development with significant foreign business should be impacted by relative changes in the values of currencies of the foreign

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countries in which business is undertaken. Consistent with literature, we expect foreign currency exchange rate to have negative impact on measures of stock market development.

vi) Domestic saving rate and investment

Savings is calculated as a ratio of gross saving to GDP. We expect it to have a positive effect on stock market development. Investment rate is considered as an important determinant of stock market development. We use the ratio of gross fixed capital formation to GDP as a measure of investment. To avoid causality problem, we simply use last year’s domestic investment and savings and income per capita.

vii)Economic openness

The degree of openness is measured by total trade (imports plus exports) as a ratio of GDP. Openness can be linked to financial development because openness may influence the demand for external finance. Foreign markets’ influence brings opportunities and competition to domestic markets. More openness increases the fraction of producers exposed to international competition and the share of imports in consumption. The implication is that a larger trade share (greater openness) means that the effect of an external shock will be broader and deeper.

viii) Inflation and real interest rate

Macroeconomic environment is important for stock market and banking sector development. In this study we use inflation and real interest rate as measures of macroeconomic stability. High real interest rate and inflation discourages investment by increasing the cost of doing business, so we expect a negative relationship between these variables and the overall measure of stock market development. We expect real interest rate to have a positive effect on the banking sector development and inflation is expected to discourage banking sector lending.

ix) Institutional quality measures

This study utilises individual political risk indexes, namely; regulatory quality, bureaucratic quality and corruption as a measure for the institutional quality of the environment in which the stock market is operating. Institutionally developed markets with strong information disclosure laws, international accounting standards, and unrestricted capital flows, are larger

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and more liquid. Therefore, we expect these measures to have positive relationships with the index of stock market development.

Political risk rating provides means of assessing political stability of the countries covered by the International Country Risk Guide (ICRG) on a comparable basis. This is done by assigning risk points to a pre-set group of factors, termed political risk components. The minimum number of points that can be assigned to each component is zero, while the maximum number of points depends on the fixed weight that component is given in the overall political risk assessment. In every case the lower the risk point total, the higher the risk, and the higher the risk point total, the lower the risk.