1. INTRODUCTION
Investment can be recounted as one of the essential pillars of every country’s economy. There is no doubt that the production increase, as one of the first steps taken parallel towards development, entails elevating the investment level and, due to the same reason, there are theories proposed in economics that pinpoint investment and capital shortages as the major cause of some countries’ underdevelopment and know the vicious circle of production shortcomings as being the consequence of lack of investment. Besides being accompanied by macroeconomic investment outcomes, the issue is also considered as auspicious by the investors because in addition to the preservation of money purchase power against inflation, it causes the temporal value of the money and the reward stemming from consumption delay to be taken into consideration. For the same reason, investment is the necessary issue and the vital prerequisite of progress from both capital demand and supply respects (Islami and Bigdelu, 2006). In the countries of the region, as well, a considerable part of the investor’s assets will be made available within the format of the shares of the companies accepted to stock market with the expansion and development of capital market atop of the apex of which securities exchange market is positioned. The nature of the commercial and investment activities is in such a way that the attainment of return necessitates acceptance of risk. Risk plays a key role in the financial markets so it has to be recognized, measured and forecasted.
On the other hand, the importance of investment for economic and social growth and development is to the extent that it is reminded as a strong lever for development; but, it has to be remembered that the same way that attending to the issue can bring about growth and development through being aligned in a positive circuit, neglecting it can cause economic drop and being relayed in a descending trend. Thus, it has to be stated that longterm economic growth and public welfare elevation cannot come about if investment and the factors extant in investment environment and influencing it are disregarded. Investment risk is one of the important and effective factors influencing decision making regarding investment inside a country. Therefore, the investors pay a great deal of attention to risk levels in making decisions in this regard. Thus, the recognition of the substantial factors that influence the investment risk is of a great importance so that investment can take these factors and their impact rates into consideration and make better plans in connection with investment thereby to achieve a favorable investment risk.
An investor needs to gain a subtle insight about risk and return so as to be able to take right steps in the process of investment and transaction. On the other side, the unexpected events happen rather quickly and become pervasive in the today’s world and, considering some elements, the future will be deeply different from now. Hence, the organizational objectives and policies should be set in consistence with these conditions and more effective strategies have to be selected in order to be faced with new challenges.
The current research paper studies D8 (developing eight) countries comprised of eight developing Islamic countries and the group is in fact including Iran, Turkey, Pakistan, Bangladesh, Indonesia, Malaysia, Egypt and Nigeria as the developing Muslim countries; these eight countries have entered a regional pact that is established for the purpose of creating robust economic relationships between the developing Islamic countries and enhancement of these countries’ influence in the world markets. The present article deals with the study of social and economic variables (GDP, growth rate, formation of gross domestic fixed capital and foreign direct investment) as well as evaluating the stock return of the intended countries for a period of time between 1990 and 2015 through taking advantage of generalized method of moments and dynamic econometric panel data. The study is organized as follows: the second part is the theoretical framework; the third part reviews the empirical research; the forth part offers an empirical model and explains variables; the fifth part estimates the model and the sixth part summarizes the study, concludes and provides suggestions.
2. STUDY THEORETICAL FOUNDATIONS AND BACKGROUND
It can be understood through having a look at the macroeconomic structure of every country and the various markets existing in every country that capital markets are the most essential markets in every economy. The securities exchange market is a constituent of the capital market and it, as a part of the economy system, is a function thereof. In developing countries, the impacts incurred by economy as originated from the stock exchange market are more subtle than in developed countries because the worries regarding the capital value drop are intensified by concerns regarding the evident instabilities of the economy. The change in investment risk output originating from the macroeconomic variables’ undulations can have a large effect on investment options (Pira’ei and Shahsawar, 2009).
Ozun and Cifter (2009) investigated the causality between stock return and gross domestic production in emerging markets based on evidences from Turkish markets for the time span between 1968 and 2002 using Johansen method and vector error correction model. The results demonstrated that there is a bilateral relationship between stock return and GDP in the short run while the relation reverses from GDP and stock return in the long run.
Cheng and Chancham (2010) dealt in their study with the causal relationship between GDP, foreign direct investment and stock return in Germany and England for the period between 1971 and 2010 using vector autoregression (VAR) for Germany and vector error correction method for England and performed granger causality test. The results showed that there is a unilateral causality from GDP too stock return and from GDP to foreign direct investment for Germany. Also, a unilateral causality was evidenced from stock return to foreign direct investment and stock return to GDP as well as a bilateral causality from foreign direct investment to GDP for England.
the study by Flannery and Protopapadakis (2002) that investigated the longterm relationship and causality between per capita energy consumption and per capita GDP in 19 European countries suggest that there is a longterm relationship and causality between the aforesaid variables only in 8 countries and 10 countries, respectively.
Amri (2016) dealt in a study with the relationship between such variables as stock return, per capita GDP and foreign direct investment for developed and developing countries and a combination of the two. The results indicated that there is a bilateral relationship between FDI and GDP and that there is a significant relationship between renewable and nonrenewable energy stock return with GDP in all three groups. Moreover, a bilateral relationship was documented between renewable energy stock return and FDI.
Erdal et al. (2006) showed the effect of macroeconomic variables on longterm movements of the stock market. To accomplish this goal, they offered a cointegration analysis to explain the factors influencing the longterm stock market movements in US and Japan for the time span from 19601 to 20045. The results are suggestive of a positive relationship between industrial productions, consumer price index and shortterm interest rate with stock market as well as a negative relationship between longterm interest rate and stock market.
Feng et al. (2009) dealt with the effect of monetary policy on stock return using singleequation OLS method and vector autoregression for 13 OECD countries for a 30year period between 1972 and 2002. Generally, based on the obtained results, the monetary policy variations exert a considerable effect on stock return. Such a conclusion confirms the mechanism of monetary policy transition via stock market.
Chowdhury et al. (2006) dealt with the impact of macroeconomic variables on the stock market fluctuations in Bangladesh. In this study, they made use of GARCH (1, 1) model and combined monthly index, industrial production index, foreign exchange rate and consumer price index. The results indicated that the relationship between stock market fluctuations and macroeconomic variables is not strong. The absence of a relationship between the foreign exchange rate fluctuations and stock market can be explained through a governing fixed currency regime.
Worthington and West (2010) investigated the role of macroeconomic risk factors in assets’ return of Australia’s financial markets. The study applied MGARCH model for the industrial asset, 3. The study variables incorporated the returns on assets under administration and retail assets, market return, longterm interest rate and capital assets, listed shortterm and longterm credit assets, expected and unexpected inflation rates, construction works and employment and industrial production. The results are reflective of the significance of the macroeconomic factors’ effect on the financial assets’ return.
Yildirtan (2007) dealt with the effect of macroeconomy’s dynamicity on Istanbul’s capital market (ISE). The dependent variable was the index for measuring the papers’ prices (in dollars) and the independent variables encompassed the international reserves (in dollars), trade banks’ capital deposits, return index, imports, exports, balance of trade, actual residual (M1), money coefficient (M2), the ratio of the domestic credits to GDP, real interest rate on deposits, ratio of monetary base (converted to foreign currency) to gross international reserve (monetary base in dollars, exchange rate in dollars), the mean deviation from real exchange rate, differential of the real foreign interest rate (US) and real domestic interest rate over deposits and deviation of the real exchange rate from the other currencies. The obtained results are suggestive of the relationship between the variables and stock return index (bearing different signs) in the model.
Islamlou’eiyan and Zare’e (2006) examined the effect of macroeconomic variables and alternative assets on stock price in Iran and an autocorrelation pattern with ARDL distributed lag model as well as by taking advantage of Lucas’s capital assets pricing model for a time span between 1993 and 2003. The results indicated that industrial production index has no effect on the stock price index. The ratio of internal to external prices, oil price, housing price and coin price were found having a positive effect on the index and the exchange rate and money volume were documented with negative effects thereon.
Ja’afary (2015) used simultaneous equations estimation model and GMM methods based on panel data to investigate the relationships between the GDP, stock return and foreign investment in countries featuring income rates ranging from medium to high. The results of the study showed that the stock return positively and significantly influences the foreign direct investment in countries with intermediate levels of income for the studied period. Also, the economic growth was found having a positive and significant effect on foreign direct investment in these countries and this is reflective of the idea that the more the economic growth in these countries the more the foreign direct investment will be adsorbed thereto. Also, the effect of stock return on economic growth, considering the positive sign of the coefficient, is suggestive of the effect of stock return on economic growth in the aforementioned countries.
3. INTRODUCING THE DYNAMIC PANEL DATA APPROACH
The dynamic panel data method is an estimation method commonly used for the limited and small time frames. The approach is based on generalized method of moment (GMM) estimator and I can make use of instrumental variables to resolve the endogeneity problem of the explanatory variables. In GMM, it is necessary to implement the method in two stages in order to keep the estimated coefficients consistent in such a manner that, firstly, the validity of the instrumental variables specified for the model will be tested for which purpose Sargan Test will be employed. Then, in a second stage, the autocorrelation order of the error terms will be evaluated because the first order differentiation method will not be appropriate for the elimination of the fixed and individual effects of countries in case that the autocorrelation of the error terms is of second order (Ashrafzadeh and Mehregan, 2008).
The following dynamic model is considered for the algebraic and mathematical expression of GMM:
where, y is the dependent variable, X is the explanatory variables’ vector, η denotes the fixed or individual effects of countries, ϕ is the fixed effects of time, ɛ is the error term and i and t designate the countries and the time span, respectively. It is assumed in explicating Model (1) that the error terms are not correlated with the fixed or individual effects of the countries as well as with the lagged values of the dependent variable. The error terms also follow the model with combined error component as shown below:(2)
In the above relation, η_{i} possesses an independent distribution identical to zero mean and a variance equal to, ϕ_{t} denotes the time effects with zero mean and a variance equal to and V_{it} are the error terms. It can be observed in equations (7) and (8) that y_{it} is a function of η_{i} thus y_{it1}, as well, is a function of η_{i} and y_{it1} on the right side of equation (1) is correlated with the error component; therefore, under such conditions, the use of ordinary least square estimator leads to bias and inconsistency of the estimators and it becomes necessary to make use of another estimator like GMM. The estimator is based on lagged values of the endogenous variable (y_{it}) as well as the exogenous variables of the model as the instrumental variables. Furthermore, in case that there is found correlation between η with some explanatory variables, one of the appropriate methods for the elimination of the fixed and individual effects of the countries is the use of first order differentiation method because, in such a state, the use of the method with fixed effects results in biased coefficient estimators. So, it is necessary to subject relation (1) to first order differentiation in which case the relation (7) will be converted to the following relation:
where, the dependent variable’s lagged differentiation (Δy_{it1}) is correlated with the firstorder differentiation of the error term (Δɛ_{it}) and also there is some endogeneity pertaining to some explanatory variables and it has not been taken into account. Therefore, it is necessary make use of instrumental variables to overcome the problem. Thus, the following moment conditions hold for relation (3):(4)(5)
To estimate the parameters in relation (3), instrumental variables’ matrix is applied as below:(6)
Hence, the generalized method of moment (GMM) estimators, denoted by $\widehat{\delta}$, are defined as below:
Next, after estimating the required coefficients, Sargan Test will be applied to investigate the validity of the instrumental variables defined in the model and the model’s being extremely specific. Moreover, the autoregression rank of the error terms has to also be tested because the first order differentiation method for the elimination of the fixed effects is appropriate in case that the error terms’ autocorrelation is not of a second order. Sargan Test features asymptotic χ^{2} distribution that is defined as below (Ashrafzadeh and Mehregan, 2008):
In this test, $\widehat{\epsilon}=YX\widehat{\delta},\u200a\hspace{0.17em}\widehat{\delta}$ is the k ×1 matrix of the estimated coefficients, z is the instrumental variables’ matrix and H is the square matrix featuring a (Tq1) dimension wherein T is the number of observations and q is the number of the model’s explanatory variables. In this test, the instrumental variables defined in the model will be valid if the null hypothesis is not rejected hence satisfying the model’s need for the specification of more instrumental variables; in case that the null hypothesis is rejected, the defined instrumental variables will be inadequate and inappropriate hence it would be necessary to specify more appropriate instrumental variables for the model. Furthermore, Arellano and Bod (1991) suggest a test statistic for the investigation of the error terms’ autocorrelation order that features asymptotic normal distribution and is defined as below:(9)
In this test, the ${\widehat{\epsilon}}_{2}$ part of the error terms features two temporal lags and ${\epsilon}^{*}$ of the error term’s vector ${\sum}_{i=1}^{N}}({T}_{i}4)\times 1$ is consistent with ${\widehat{\epsilon}}_{2}$. The autocorrelation of the error terms is of a second order if the null hypothesis is found rejected and if it is not rejected the error terms feature a first order autocorrelation. Under such conditions, the use of first order differentiation for the elimination of the fixed effects in respect to orthogonal deviations method is an appropriate and favorable method (Baltagi, 2005).
The present study makes use of this approach for the elaboration of the dynamic relationships between the model variables considering the study objective that is the investigation of the relationship between the stock return growth, investment risk, gross domestic capital formation growth, GDP growth and foreign direct investment growth in eight developing countries, known as D8 Countries, and according to the limitations in access to the statistics and information of this group of the countries (for the time span from 1990 to 2015).
3.1. Study Model
Based on the theoretical foundations of the topic and being inspired by the empirical research like the one conducted by Amri (2016), the model proposed within a dynamic form for the investigation of the relationship between the stock return, investment risk, foreign direct investment growth and GDP growth in the studied eight developing countries (D8 member states) takes the following form:(10)
In the above relation, gY is the growth rate of per capita GDP for the fixed price pertinent to 2010, gFDI is the net growth rate of the foreign direct investment inflow, gK is the growth rate of gross domestic fixed capital formation, RET is the growth rate of stock return and IR is the investment risk; i denotes each of the studied temporal crosssections (for the D8 countries) and t designates the study period (19902015).
According to the materials pointed out in the section on introducing the dynamic panel data approach, it can be stated that the use of first order differentiation for the elimination of the fixed and individual effects has been proposed for the panel data featuring a limited temporal interval and the aforesaid model has also been proposed based thereon. On the other hand, the most important advantage of this model form is that the lagged value of the dependent variable is inserted into the model as the independent variable and the dynamic effects of the dependent variable are taken into consideration. It has to be mentioned that the information and statistics existent for the study model variables have been extracted from the data pertaining to World Bank’s development indices, downloadable from www.wdi.org for the time period between 1990 and 2015; STATA14.2 Software was employed to analyze he findings. But, the following measures were taken to obtain the stock return and investment risk of the companies accepted to D8 countries:
3.2. Stock Return
It is the ratio of total gains obtained from making investments during a specified term to the amount of money invested (Khodadady and Kargarpour, 2009).
3.3. Risk Calculation
In a general definition, Weston and Brigham call risk the volatility of investment return. Risk has been expressed as below by Markowitz (1952):(11)
where, R_{i} is the real daily stock return, E(R_{i}) is the expected return of stock i and n is the number of period. It has to be explained that the above formula makes use of daily information to compute risk.
3.4. Real Stock Return
The ratio of the total gains obtained from making investment for a specified period of time to the amount of money invested. Generally, stock return is calculated as below:(12)
In the above formula, R_{t} is the ordinary stock return during period t, P_{t} is the ordinary stock price during period t, P_{t1} is the stock price for the t1 period and DPS_{t} is the cash profit of the ordinary stock for the period t. If the capital is increase through cash contributions and outstanding claims or reserves (bonus shares) then P_{t} cannot be compared with P_{t1} due to the difference in the number of shares before and after capital contributions; so, P_{t} has to be moderated. Finally, the ordinary stock return of a company can be computed as below:(13)
where, a is the percentage of capital increase (through reserves or cash contribution and outstanding claims) and C is the cash contribution upon capital increase (Khodadady and Kargarpour, 2009).
3.5. Expected Return
The expected return of an investment is the geometric mean of the returns from several periods before the given investment and it can be calculated as below:(14)
5. EXPERIMENTAL RESULTS
In this section, generalized method of moments (GMM) estimation has been used within the framework of the dynamic panel data to perform model estimation. Before estimating the model and in order to ascertain the estimated regression’s not being a pseudo one, it is necessary to test the reliability of the study variables. The following tables gives the unit root of the study variables using such tests as IM, generalized FisherDickey Fuller and generalized PhilipsPerron Dickey Fuller: Table 1
The unit root results of IM, Pesaran and Shin, generalized Dickey Fuller and generalized FisherPhillipsPerron tests are indicative of the idea that all of the model variables are in a reliable level and that they possess a reliability rank equal to zero; in other words, for all the variables, namely growth rate of per capita GDP, foreign direct investment growth rate, growth rate of per capita gross domestic fixed capital, stock return growth rate and investment risk, the null hypothesis indicating the unreliability of the variables is rejected in 1%, 5% and 10% significance level and the variables are I (0). So, according to the zero reliability rank of the variables, the experimental model can be estimated based on dynamic panel data method and generalized method of moment estimator with no worry about the regression model’s being a pseudoone.
Five models have been estimated based on GMM in the following section for each of the variables, namely growth rate of the per capita GDP, capital formation, growth rate of the foreign direct investment and stock return. The results of the model estimation for the mode wherein the growth rate of the per capita GDP is the dependent variable of the study have been summarized in the table below.
Based on the results presented in Table 2, it can be stated that the entire estimated coefficients are significant in 5% level. As shown by the results, the lagged variable “the growth rate of the per capita GDP” has a positive and significant effect on the growth rate of the current year production and it is equal in coefficient to 0.42. the variable “foreign direct investment growth rate” also was found having a positive and significant effect on the economic growth rate for the current period and that 1% increase in it brings about an increase by 0.003% in the economic growth rate; in other words, it is expected that the increase in foreign direct investment and technology transfer cause elevation of production capacity in D8 countries followed by the improvement of the economic growth. The growth rate of per capita gross domestic capital formation, as well, exerts a positive and significant effect on the per capita growth rate for a coefficient equal to 0.15. Thus, the increase in investment brings about an increase in the total demand following which the production capacity and national income will be augmented. Stock return rate, as well, has a positive and significant effect on the production growth rate for a coefficient equal to 0.01. It was also figured out that investment risk has a negative effect on economic growth for a coefficient equal to 0.02 which was found significant in a 1% level.
It is noteworthy that the lagged second order differentiation value of gross domestic fixed capital formation has been used as an instrumental variable in estimating the model and Sargan statistic, 1.77, with a probability value equal to 0.99 implies that the null hypothesis is not rejected and the instrumental variables defined in the model are valid because Sargan statistic shows that the defined instrumental variables are not correlated with the error terms subsequent to which the instrumental variables are rendered valid. Next section makes use of Arellano and Bond (1991) test to determine the autocorrelation order of the error terms.
The results of model estimation for the state that the stock return rate is the dependent variable have been summarized in the following table.
Based on the results presented in Table 3, it can be stated that the entire estimated coefficients are significant in a 5% level. As it can be understood from the results, the lagged value of the stock return has a positive and significant effect and it is found influencing the current year’s stock return by 0.52. Foreign direct investment growth rate, as well, exerts a positive and significant effect on the stock return of the current year and an increase in it by 1% causes an increase in stock return by 0.21. per capita gross domestic fixed capital formation was also found having a positive and significant effect on the stock return for a coefficient equal to 0.43. Investment risk positively and significantly influences the economic growth for a value equal to 1.47 which is found statistically significant in a 1% level.
It can be stated according to the above results that the autocorrelation between the error terms is neither of the first nor of the second order type and, resultantly, ArellanoBond method is appropriate for eliminating the model’s fixed effects. In other words, there is no autocorrelation between the differentiated error terms.
The model estimation results have been presented in the table below for the estate that the foreign direct investment growth rate is the dependent variable.
It can be stated based on the results of the above table that the entire estimated coefficients are significant in a 5% level. The lagged value of the foreign direct investment growth rate was found having a significant and positive effect on the current year’s growth rate of the foreign direct investment inflow for a coefficient equal to 0.04. Per capita GDP positively and significantly influences the current year’s foreign direct investment and any one percent increase in it causes the foreign direct investment growth rate to be increased by 28.84%; in other words, it is expected that the increase in production capacity and per capita production growth in D8 countries, the investment motivation will be elevated following which the economic growth will be enhanced. The per capita gross domestic fixe capital formation, as well, was found having a positive and significant effect on the foreign direct investment growth rate in both of the estimated states for a coefficient equal to 14.1; therefore, the increase in investment causes an increase in the total demand following which there will come about an elevation in output levels and eventually the foreign investment absorption rates will be augmented.
It is worth mentioning that the second order lagged differentiation value of the gross domestic fixed capital formation growth rate has been used as the instrumental variable for the model estimation and Sargan statistic reaching in the first state to a value equal to 1.94 with a probability value equal to 0.99 implies that the null hypothesis is not rejected hence the instrumental variables specified for the model are valid because the test statistic indicates that the defined instrumental variables are not correlated with the error terms as a result of which the defined instrumental variables are valid. Next, Arellano and Bond (1991) statistic is applied to determine the autocorrelation order of the differentiated error terms the results of which have been given in Table 4. It can be stated based on the results of the above table that the autocorrelation between the error terms are not either of the first or of the second order type hence ArellanoBond method is found appropriate for the omission of the model’s fixed effects. In other words, there is no autocorrelation between the differentiated error terms.
The results of the model estimation for the mode wherein the investment risk is the dependent variable have been reported in the following table.
It can be expressed based on the results presented in Table 5 that the majority of the estimated coefficients are significant in a 5% level. As it can be perceived from the results, the lagged value of the investment risk has a positive and significant effect on the current year’s production growth rate for a coefficient equal to 0.24. Foreign direct investment growth rate has a negative and significant effect on the current year’s investments and the increase in it by 1% brings about a reduction by 0.01% in the investment risk. Per capita gross domestic fixed capital formation was found having no significant effect on investment risk. The economic growth, as well, negatively and significantly influences the total impact coefficient of the stock return and it also has a positive and significant effect on investment risk for a coefficient equal to 0.013. Finally, the stock return was found having a positive and significant effect on investment risk for an impact coefficient equal to 0.02 so it can be asserted that the any percent increase in stock return causes the investment risk to be increased by 0.02.
The results of the model estimation for the state that gross domestic fixed capital formation is the dependent variable have been summarized in the following table.
Based on the results presented in Table 6, it can be stated that the majority of the estimated coefficients are significant in 5% level. As it can be comprehended from the results, the lagged value of the capital formation rate has a positive and significant effect on the current year’s capital formation for a coefficient by 0.99. The foreign direct investment growth rate was also found having a positive and significant effect on capital formation rate and any one percent increase in the former causes the latter to be increased by 0.06%. Investment risk was found having no effect on capital formation rate. The economic growth rate and stock return rate were found having a positive and significant effect on capital formation rate for coefficients equal to 0.12 and 0.11, respectively.
It has to be pointed out that the second order differentiated lagged value of gross domestic fixed capital formation growth rate has been used as the instrumental variable in the model estimation and Sargan Statistic, 1.78, with a probability value equal to 0.99 implies that the null hypothesis is not rejected hence the instrumental variables specified for the model are valid. Arellano and Bond (1991) test was employed to determine the autocorrelation order of the differentiated error terms. It can be asserted according to the results of the above table that the error terms’ autocorrelation is neither of the first order nor of the second order type and ArellanoBond method is consequently is appropriate for the elimination of the model’s fixed effects. To put it differently, there is no autocorrelation between the differentiated error terms.
6. SUMMARIZATION AND CONCLUSION
The present study was conducted aiming at investigation of the relationships between stock return, investment risk, per capita GDP, capital formation growth and net growth of the foreign direct investment inflow in eight developing countries known as D8 countries for the time span between 1990 and 2015. To do so, the dynamic panel data approach and generalized method of moments (GMM) estimator were first of all utilized to investigate the durability and it was shown that all five variables are stationary. It is worth mentioning that five models were estimated for each of the aforementioned variables. The model estimation results demonstrated that:
Cheng and Chancham (2010) argued that there is a unilateral causality from GDP to stock return and from GDP to foreign direct investment in Germany. Such a unilateral causality was also evident from stock return to foreign direct investment and stock return to GDP as well as a bilateral causality from foreign direct investment to GDP in England.
The first model estimation results for the state wherein the per capita GDP growth rate is the dependent variable indicated that the entire estimated coefficients are significant in 5% level and that all the variables, except investment risk, have a positive effect. The second model estimation results for the mode wherein the stock return rate was the dependent variable showed that the entire estimated coefficients are significant in a 5% level and all of the macroeconomic variables plus investment risk exert positive and significant effects.
Ja’afary (2015) found that the stock return and the economic growth positively and significantly influence the foreign direct investment in countries with intermediate levels of income.
The results of the third model estimation for the state that the foreign direct investment growth rate is the dependent variable demonstrated that the entire estimated coefficients are significant in a 5% level and that all of the variables, except investment risk, have a positive effect.
Pira’ei and Shahsawar (2009) argued that the change in investment risk output originating from the macroeconomic variables’ undulations can have a large effect on investment options in developing countries.
The results of the fourth model estimation for the mode that the investment risk was the dependent variable showed that the per capita gross domestic fixed capital formation has no significant effect on investment risk and stock return, as well, was found with a positive and significant effect and the rest of variables were scored with negative and significant effects and, finally, the results of the fifth model estimation for the state wherein the gross domestic fixed capital formation was the dependent variable are suggestive of the idea that all of the other variables, except capital risk, have positive and significant effects.
Based on the study results, the following suggestions are made to D8 countries’ policymakers:

Since foreign direct investment positively and significantly influences the per capita GDP growth rate, the economy policymakers of this group of countries are suggested to increase the investment opportunities and investment output so that the foreign direct investors could be sufficiently motivated thereby to elevate the production capacity and production rate.

According to the positive and significant effect of the per capita gross domestic fixed capital formation growth rate on the per capita GDP, the economy policymakers are advised to increase the share of investment by various investors, especially the private sector investors, in gross domestic production so as to enhance the production level.

Based on the fact that the nonrenewable and renewable energy stock returns were found exerting a positive and significant effect on the per capita GDP, the economy policymakers are recommended to increase the stock shares of the renewable energy so that the energy output could be enhanced following which production capacity can be elevated.

Considering the positive and significant effect of per capita GDP on the stock return, the economy policymakers are recommended to increase the production share of the domestic goods so that an increasingly higher rate of increase can be brought about in per capita GDP.